So I'm really trying to defend and graduate this semester, hence the lack of blog posts. And now, here it is, 3:30 am on Saturday morning, and I can't sleep. Why not? My brain won't stop. What's going through my running brain? What will I say if my committee asks me "What's next?" (I know, that should really be what I'm worrying about when I'm still analyzing data and my thesis is due in 1.5 weeks). But in any case, here's what I've come up with.
I'm seriously considering two completely different routes. I like research, so I'm definitely considering continuing in research. I've also very much enjoyed any teaching I've been able to do. However, I think I would rather choose one or the other. I think trying to teach and do high-level research, i.e. being a research professor at a research university, would not be right for me.
So, for research, I'm interested in national labs and industry positions. (I could name several national labs that do interesting research, including 2-3 in the area where I live. I could name several (2,3,4?) biotech companies I've been researching. I could even forward my resume to a contact I have in the area where I live, and then I could say I'd done that.)
For teaching, I'm interested in undergraduate-level teaching positions where I could focus on teaching, and potentially do some undergrad-friendly research. However, I have very little teaching experience, and these types of positions are few and far between and hard to get. My plan is to continue looking for these types of positions while also applying for national lab and industry research positions. I'm considering anything from a teaching postdoc (or any postdoc where I could spend a good amount of time teaching), to any kind of undergrad-level teaching position (tenure-track, visiting or temporary faculty). Lectureships are also a possibility.
Not that I have to tell my committee this, but in thinking about my plans, I realized two things: 1) with my seemingly undecided teaching vs. research interests, most people have advised me to do a typical postdoc, and 2) a typical post doc is not what I want or need. My problem is not that I need more time to decide which path to take or more research experience to make me a good job candidate, but that I either need experience teaching to determine if I like it and get on the path toward a good teaching position, or if I'm not going to be getting teaching experience, I need to plunge into the type of research position I might have long term to see if I can be happy there. Not to mention that if I'm not getting teaching experience, I should at least get a research position that pays well. A typical low-paid post doc would get me no closer to a potential teaching position, and it would be a low-paid means of getting more research experience.
So, what's next? My goals are an undergrad-level teaching position or a national lab/industry (i.e. well-paid) research position.
Hopefully now I can sleep.
Saturday, November 20, 2010
Thursday, September 16, 2010
The perfect pair of heels - and other thoughts on shoes
So, I'm back on fashion again. In my previous post about my dream wardrobe, I determined I only needed 6 pairs of shoes: ballet flats, running shoes, flip flops, hiking boots, snow-boarding boots, and the "perfect" pair of heels. Maybe for guys, 6 pairs of shoes sounds like plenty, but for girls, that's really extreme. Especially when 1 pair of the 6 is ski boots. With only those 6 pairs of shoes, the shoes listed have to be mega-versatile. The ballet flats have to work with jeans and casual t-shirts, as well as with black pants and dressy shirts. And they have to be mega-comfortable for walking. And those "perfect" heels, well, that's the topic of this post.
I dream of the "perfect" heels. Heels that are sexy as hell, that you can also run a mile in. I know that doesn't exist, but I really *want* it to. So instead, the closest to the single pair of perfect heels would be a classic leather pump, not too round of a toe, but not too pointy either; not too high of a heel (for comfort), but not too low (for attractiveness); and as comfortable as is physically possible. That heel could be worn with jeans and dressy tops, with black pants, with a suit, and with black and non-black dresses. That heel could be worn year-round, fall-winter-spring-summer.
And yet, I still want other shoes. I want a wedge summer sandal, to wear in the summer and spring with a dress or pants. Because summer sandals feel so much fresher in spring and summer than closed-toe shoes, and wedges are sooo much more comfortable than other styles of heels. And I want a wedge fall-winter shoe, to wear when I want to wear a heel, but I want be more comfortable than is possible in the classic pumps. And I want a strappy summer version of the perfect heel, because it's so much sexier and summer appropriate. And I want a city-sneaker, because it looks better with casual t-shirts and is still more comfortable than any ballet flats I've tried.
And I want boots. Oh, how I want boots. I used to just want heeled boots, but now I want more. I want heeled, knee high boots, to tuck my jeans into and wear with dressy tops. I want flat, knee high boots, again to tuck my jeans into and wear with casual and dressy tops. Now I even want heeled booties, to be super-sexy and trendy, to wear with jeans and dressy tops, black pants, dresses.
Oh, and did I mention that I want all of the above in both black and brown?
I really must rein myself in. Really I must.
First, I must say no to the black and brown. I need to limit myself to clothes that go with black. If I buy brown, taupe, or some other color I need to be okay with it going with black. Sometimes that can work really nicely.
Second, I must say no to the boots craziness. Partly because boots are *expensive*! But I like the idea of one pair of boots. Should it be a heeled or a flat boot? Heeled is more dressy, flat is more comfy. But really, for the comfort of flats, I should just wear the ballet flat. So the one pair of boots should be heeled. Should it be a knee-high boot or a bootie? Well, knee high is so cute over jeans, but that's a trend that will come and go. In truth, either length looks the same if it's underneath pants. But if pants stay slim, it's hard to pull them over a tall boot. So maybe it should be a bootie, which just looks like a classic boot if the pants cover the top. So the one pair of boots should be a heeled bootie, as comfortable as possible. And I could wear them with jeans, black pants, and even a dress as the current trend stands.
As for the other shoes, maybe it wouldn't be so bad to just say yes. So I increase my ideal list of shoes from 6 (ballet flats, running shoes, flip flops, hiking boots, ski boots, and the "perfect" pair of heels) to 11 (strappy summer heels, wedge summer sandals, wedge fall-winter shoes, city-sneakers, and heeled booties). That's really not so bad, is it?
I dream of the "perfect" heels. Heels that are sexy as hell, that you can also run a mile in. I know that doesn't exist, but I really *want* it to. So instead, the closest to the single pair of perfect heels would be a classic leather pump, not too round of a toe, but not too pointy either; not too high of a heel (for comfort), but not too low (for attractiveness); and as comfortable as is physically possible. That heel could be worn with jeans and dressy tops, with black pants, with a suit, and with black and non-black dresses. That heel could be worn year-round, fall-winter-spring-summer.
And yet, I still want other shoes. I want a wedge summer sandal, to wear in the summer and spring with a dress or pants. Because summer sandals feel so much fresher in spring and summer than closed-toe shoes, and wedges are sooo much more comfortable than other styles of heels. And I want a wedge fall-winter shoe, to wear when I want to wear a heel, but I want be more comfortable than is possible in the classic pumps. And I want a strappy summer version of the perfect heel, because it's so much sexier and summer appropriate. And I want a city-sneaker, because it looks better with casual t-shirts and is still more comfortable than any ballet flats I've tried.
And I want boots. Oh, how I want boots. I used to just want heeled boots, but now I want more. I want heeled, knee high boots, to tuck my jeans into and wear with dressy tops. I want flat, knee high boots, again to tuck my jeans into and wear with casual and dressy tops. Now I even want heeled booties, to be super-sexy and trendy, to wear with jeans and dressy tops, black pants, dresses.
Oh, and did I mention that I want all of the above in both black and brown?
I really must rein myself in. Really I must.
First, I must say no to the black and brown. I need to limit myself to clothes that go with black. If I buy brown, taupe, or some other color I need to be okay with it going with black. Sometimes that can work really nicely.
Second, I must say no to the boots craziness. Partly because boots are *expensive*! But I like the idea of one pair of boots. Should it be a heeled or a flat boot? Heeled is more dressy, flat is more comfy. But really, for the comfort of flats, I should just wear the ballet flat. So the one pair of boots should be heeled. Should it be a knee-high boot or a bootie? Well, knee high is so cute over jeans, but that's a trend that will come and go. In truth, either length looks the same if it's underneath pants. But if pants stay slim, it's hard to pull them over a tall boot. So maybe it should be a bootie, which just looks like a classic boot if the pants cover the top. So the one pair of boots should be a heeled bootie, as comfortable as possible. And I could wear them with jeans, black pants, and even a dress as the current trend stands.
As for the other shoes, maybe it wouldn't be so bad to just say yes. So I increase my ideal list of shoes from 6 (ballet flats, running shoes, flip flops, hiking boots, ski boots, and the "perfect" pair of heels) to 11 (strappy summer heels, wedge summer sandals, wedge fall-winter shoes, city-sneakers, and heeled booties). That's really not so bad, is it?
Tuesday, August 31, 2010
Why do I suck?
I'm trying to write my thesis. I really am. I just keep getting stuck, so it's going super slowly, and I'm plagued with guilt about being slow. But every time I try to push myself, to give myself deadlines, to increase the pressure on myself, it just backfires, and I get less done.
I think about what I want to do next. I imagine when I defend my thesis, my committee members may ask me what I plan to do next, and I dream about various answers. I think about telling them that I've realized research is just not for me, and I imagine telling them reasons why I think that. I imagine they might be able to offer some insight into whether I'm right, or whether they might tell me how I could learn to be good at and like research.
But the thing is, my committee, my mentors, anyone who might actually be able to offer me insiders' insight when I say I don't think I'm cut out to deal with failing 99.9% of the time, I don't think I have the vision to seek out the paths that lead to good results, and I don't know how to gain that vision, all the people who might be able to say something to help me are also the people who I need to be references for my future jobs. So I need them to think I've never had a doubt in my life about doing anything, and I certainly don't need to help them come up with a list of my weaknesses. They need to think I'm confident, brilliant, passionate about my career (whatever I eventually decide to pursue).
I've wanted to be good at this. I've wanted to bolster my strengths and improve where I have weaknesses. I've just never really figured out what they are. When everything you do is met with negative feedback, you just change random variables all the time. All you know is, that didn't work; I'll try something else.
I did eventually gain some insight over the years, but I feel like I had to learn everything in the hardest way possible: by doing it wrong in a million different ways. But I continued to evaluate myself, and examine what I could see working (or not working) for other people, and I did eventually improve. But now I just feel too tired of it all, and like it's too late.
I started this game as a smart girl, capable of working very hard. As we all did.
But I was naive, and my whole life, all that had been demanded of me was to do what I was told, the way I was told to do it. And my advisor was a micromanager, so he told me what to do, and how to do it, and how to think about it, and I tried to do it.
But lab work doesn't work that way. What my advisor really wanted was publishable results. And with research, no one knows a priori how to get there.
And anyway, a Ph.D. isn't about that. It's not about just doing what someone else wants you to do. It's yours, your work, your thesis, your Ph.D. But damn if standing up to my advisor doesn't result in insults and threats and extreme unpleasantness. But it's what had to be done to make progress, rather than circling the same microproblem forever. So, difficult Lesson 1 - You can't be a people-pleaser, and Lesson 2 - You may have to endure unpleasantness, insults, and threats and you have to try not to let it get to you.
And then there's Lesson 3, that I'm still trying to wrap my head around. How to choose the right paths to pursue. You see, I'm a plodder. I like to do things once, correctly, and double-check my work along the way. It seemed to work for me in school. But it doesn't work for me in the lab.
I used to do homework with a friend, and I used to say we were perfectly matched as homework partners. He was a racer, and I'm a plodder. He could take our ideas, race through the steps, and see that we were on the right path. I could then plod along and make sure we got all the steps just right. I was not good at the half-assedly racing through to see that it was going to work; he wasn't as good at the details of to plodding along to get all the steps right. It was a great partnership.
But it turns out that the skill he had is much more important to getting stuff done in the lab. And I still suck at it. I've tried. But I feel like I race through the wrong way, choose the wrong steps to gloss over, the wrong results to follow. I race through, choose the path, and then the path is wrong. I do this over and over. How the fuck can I get better at it??? Still don't know.
Over and out.
I think about what I want to do next. I imagine when I defend my thesis, my committee members may ask me what I plan to do next, and I dream about various answers. I think about telling them that I've realized research is just not for me, and I imagine telling them reasons why I think that. I imagine they might be able to offer some insight into whether I'm right, or whether they might tell me how I could learn to be good at and like research.
But the thing is, my committee, my mentors, anyone who might actually be able to offer me insiders' insight when I say I don't think I'm cut out to deal with failing 99.9% of the time, I don't think I have the vision to seek out the paths that lead to good results, and I don't know how to gain that vision, all the people who might be able to say something to help me are also the people who I need to be references for my future jobs. So I need them to think I've never had a doubt in my life about doing anything, and I certainly don't need to help them come up with a list of my weaknesses. They need to think I'm confident, brilliant, passionate about my career (whatever I eventually decide to pursue).
I've wanted to be good at this. I've wanted to bolster my strengths and improve where I have weaknesses. I've just never really figured out what they are. When everything you do is met with negative feedback, you just change random variables all the time. All you know is, that didn't work; I'll try something else.
I did eventually gain some insight over the years, but I feel like I had to learn everything in the hardest way possible: by doing it wrong in a million different ways. But I continued to evaluate myself, and examine what I could see working (or not working) for other people, and I did eventually improve. But now I just feel too tired of it all, and like it's too late.
I started this game as a smart girl, capable of working very hard. As we all did.
But I was naive, and my whole life, all that had been demanded of me was to do what I was told, the way I was told to do it. And my advisor was a micromanager, so he told me what to do, and how to do it, and how to think about it, and I tried to do it.
But lab work doesn't work that way. What my advisor really wanted was publishable results. And with research, no one knows a priori how to get there.
And anyway, a Ph.D. isn't about that. It's not about just doing what someone else wants you to do. It's yours, your work, your thesis, your Ph.D. But damn if standing up to my advisor doesn't result in insults and threats and extreme unpleasantness. But it's what had to be done to make progress, rather than circling the same microproblem forever. So, difficult Lesson 1 - You can't be a people-pleaser, and Lesson 2 - You may have to endure unpleasantness, insults, and threats and you have to try not to let it get to you.
And then there's Lesson 3, that I'm still trying to wrap my head around. How to choose the right paths to pursue. You see, I'm a plodder. I like to do things once, correctly, and double-check my work along the way. It seemed to work for me in school. But it doesn't work for me in the lab.
I used to do homework with a friend, and I used to say we were perfectly matched as homework partners. He was a racer, and I'm a plodder. He could take our ideas, race through the steps, and see that we were on the right path. I could then plod along and make sure we got all the steps just right. I was not good at the half-assedly racing through to see that it was going to work; he wasn't as good at the details of to plodding along to get all the steps right. It was a great partnership.
But it turns out that the skill he had is much more important to getting stuff done in the lab. And I still suck at it. I've tried. But I feel like I race through the wrong way, choose the wrong steps to gloss over, the wrong results to follow. I race through, choose the path, and then the path is wrong. I do this over and over. How the fuck can I get better at it??? Still don't know.
Over and out.
Wednesday, August 25, 2010
The catch-22 of the unsupportive advisor
Some advisors are universally unsupportive. Others are more selective. The result seems to generally be the same: failure of the unsupported. So the question is:
Do we fail due to lack of support?
Or do we not get support because we are failures?
Do we fail due to lack of support?
Or do we not get support because we are failures?
Wednesday, August 18, 2010
My dream wardrobe
Ok, I know that wardrobes aren't exactly science-talk, but hear me out - this is a minimization problem.
I've never had a lot of money to spend on clothing. I generally don't have a lot of closet space to devote to clothing. And I seem to travel and move around fairly often. I always thought these three conditions were what drove me towards a minimal wardrobe (i.e. a wardrobe that would keep me reasonably clothed while being as small as possible). I realized recently that there's another factor driving me towards this minimal wardrobe: I don't like to have to think too hard about what I wear each day. I don't want an entire department store worth of clothing and accessories to choose from each morning. I want a small, carefully selected number of items. I want my wardrobe to be small so I have fewer choices, and it's easier to get dressed in the morning.
Now, don't get me wrong. I like clothes. And accessories. And shoes. Especially shoes. I like to play around with outfits and looks sometimes, either in my own closet or in clothing stores. But for the everyday grind, I just want go-to outfits that are comfortable and make me feel and look good.
So, my minimization problem is this: what is the minimum amount of clothing a person can own and clothe themselves nicely for a year? That means they have enough clothing in quantity and style to wear for 1-2 typical weeks (time between laundry sessions), clothing for all seasons, and clothing to cover the usual special occasions.
First, I need to define some terms. What kinds of clothing do I need in 1-2 typical weeks? I need clothing for work, for weekends, for work-outs, and for sleeping. For work, I must have comfortable clothing - comfortable closed-toe shoes, outfits I can bend and move around in, some layers for buildings with poor climate control, and nothing that's going to dangle into the dangerous elements of my experiments. For weekends, I do a lot of walking, a lot of indoor and outdoor activities, so I need to be cute and comfortable. For work-outs, I sweat! I need good clothes to sweat in and do tough work-outs. For sleeping, I must have loose shorts and comfy tops. So there we go - my needs for 1-2 typical weeks.
Next, I need to define the typical special occasions. Let's see...We have weddings and funerals, job interviews and presentations. dressy parties and social outings. I also like to swim, ski, and hike. I think that covers the special occasions category for me.
So we have the requirements for typical 1-2 weeks, the usual special occasions. The requirements for four seasons varies by region, but I'd say appropriate shoes, coat, hat, scarf, gloves, light layers and short sleeves for summer, heavier layers including sweaters and long-sleeves for winter. Those seasonal requirements can be fairly universal. So here we go, my minimal wardrobe, below.
For work and weekends:
-1 pair Cute, comfy ballet flats
-1 pair of Dark, straight leg jeans
-1 pair of black pants
-7 casual tops for summer - camisoles, ribbed tanks, short-sleeved T's, graphic T's
-7 dressier tops for summer - dressier short-sleeved and sleeveless blouses
-7 casual tops for winter - long-sleeved t's
-7 dressier tops for winter - thick sweaters, thin sweaters, button downs, blouses
-1 cardigan
-1 blazer/jacket
-1 coat
-scarf, hat, gloves
-14 pairs comfy, no show undies
-2-3 good bras (1 black, 1 nude)
-socks for ballet flats
-belt
-handbag
-sunglasses
For workouts
-Running shoes
-Fleece
-1 pair each of work-out pants: shorts, capris, and long
-1 short-sleeved and 1 long sleeved work-out shirt
-1 great sports bra
-1 pair of great socks
-If these items are the right fabrics, I can rinse them after each work-out and hang dry. If I want to be less gross, I could up the numbers to 3-5 of each item.
For sleeping
-2 pairs of loose fitting shorts
-2 comfy tank tops
-1-2 robes (preferably 1 light-weight for summer, 1 heavy-weight for winter)
For special occasions - dressy
-1 little black dress - sexy but classy
-1 pair of perfect heels
-1 non-black dress
-1-2 wraps/shrugs to go with dresses
-1 suit (can be black pants and blazer from work-wear)
For special occasions - sporty
-flip-flops, swimsuit, swim cover-up, sun hat
-can wear work-out tops for skiing and hiking
-ski boots, socks, thermals, pants, fleece, waterproof shell, gloves, helmet, muffler
-hiking boots, pants (can wear same socks, fleece, shell as for skiing)
There you have it, my minimal wardrobe. So, is this wardrobe what I own? Of course not. But a girl can have dreams, can't she?
I've never had a lot of money to spend on clothing. I generally don't have a lot of closet space to devote to clothing. And I seem to travel and move around fairly often. I always thought these three conditions were what drove me towards a minimal wardrobe (i.e. a wardrobe that would keep me reasonably clothed while being as small as possible). I realized recently that there's another factor driving me towards this minimal wardrobe: I don't like to have to think too hard about what I wear each day. I don't want an entire department store worth of clothing and accessories to choose from each morning. I want a small, carefully selected number of items. I want my wardrobe to be small so I have fewer choices, and it's easier to get dressed in the morning.
Now, don't get me wrong. I like clothes. And accessories. And shoes. Especially shoes. I like to play around with outfits and looks sometimes, either in my own closet or in clothing stores. But for the everyday grind, I just want go-to outfits that are comfortable and make me feel and look good.
So, my minimization problem is this: what is the minimum amount of clothing a person can own and clothe themselves nicely for a year? That means they have enough clothing in quantity and style to wear for 1-2 typical weeks (time between laundry sessions), clothing for all seasons, and clothing to cover the usual special occasions.
First, I need to define some terms. What kinds of clothing do I need in 1-2 typical weeks? I need clothing for work, for weekends, for work-outs, and for sleeping. For work, I must have comfortable clothing - comfortable closed-toe shoes, outfits I can bend and move around in, some layers for buildings with poor climate control, and nothing that's going to dangle into the dangerous elements of my experiments. For weekends, I do a lot of walking, a lot of indoor and outdoor activities, so I need to be cute and comfortable. For work-outs, I sweat! I need good clothes to sweat in and do tough work-outs. For sleeping, I must have loose shorts and comfy tops. So there we go - my needs for 1-2 typical weeks.
Next, I need to define the typical special occasions. Let's see...We have weddings and funerals, job interviews and presentations. dressy parties and social outings. I also like to swim, ski, and hike. I think that covers the special occasions category for me.
So we have the requirements for typical 1-2 weeks, the usual special occasions. The requirements for four seasons varies by region, but I'd say appropriate shoes, coat, hat, scarf, gloves, light layers and short sleeves for summer, heavier layers including sweaters and long-sleeves for winter. Those seasonal requirements can be fairly universal. So here we go, my minimal wardrobe, below.
For work and weekends:
-1 pair Cute, comfy ballet flats
-1 pair of Dark, straight leg jeans
-1 pair of black pants
-7 casual tops for summer - camisoles, ribbed tanks, short-sleeved T's, graphic T's
-7 dressier tops for summer - dressier short-sleeved and sleeveless blouses
-7 casual tops for winter - long-sleeved t's
-7 dressier tops for winter - thick sweaters, thin sweaters, button downs, blouses
-1 cardigan
-1 blazer/jacket
-1 coat
-scarf, hat, gloves
-14 pairs comfy, no show undies
-2-3 good bras (1 black, 1 nude)
-socks for ballet flats
-belt
-handbag
-sunglasses
For workouts
-Running shoes
-Fleece
-1 pair each of work-out pants: shorts, capris, and long
-1 short-sleeved and 1 long sleeved work-out shirt
-1 great sports bra
-1 pair of great socks
-If these items are the right fabrics, I can rinse them after each work-out and hang dry. If I want to be less gross, I could up the numbers to 3-5 of each item.
For sleeping
-2 pairs of loose fitting shorts
-2 comfy tank tops
-1-2 robes (preferably 1 light-weight for summer, 1 heavy-weight for winter)
For special occasions - dressy
-1 little black dress - sexy but classy
-1 pair of perfect heels
-1 non-black dress
-1-2 wraps/shrugs to go with dresses
-1 suit (can be black pants and blazer from work-wear)
For special occasions - sporty
-flip-flops, swimsuit, swim cover-up, sun hat
-can wear work-out tops for skiing and hiking
-ski boots, socks, thermals, pants, fleece, waterproof shell, gloves, helmet, muffler
-hiking boots, pants (can wear same socks, fleece, shell as for skiing)
There you have it, my minimal wardrobe. So, is this wardrobe what I own? Of course not. But a girl can have dreams, can't she?
Tuesday, August 10, 2010
Wednesday, August 4, 2010
Notes, organization, and reproducibility
How do you keep notes and records on your experimental methods, data, analysis, and journal articles? What do you keep on file, and where, and how do you organize it? As scientists, we all face the record-keeping problem, and I'm interested in how others deal with it. Over the course of grad school, I've developed my own system in fits and starts, and I'll describe what's working for me.
What to keep, what to keep? There's essentially one reason for keeping things on file: because you might need it later! I know, obvious. But I'd like to break it down a little further, to four reasons you might need it later: 1) it's a result, 2) it's information needed to reproduce a result, 3) it's information that may help with interpretation of a result, or 4) it's information that may lead to a future result. So, as for what I keep on file, I try to keep everything on file that falls into one of those four categories. That's a lot of stuff. How the heck to you organize this stuff so you can find it later when you need to?
How to organize? In the beginning of a project, starting a system of organization is difficult because you don't have a feeling yet for the number of different sub-projects, the scale of those projects, and all of the variables that will change along the way. But, there are categories that will be important when it comes time for publishing and ensuring that you have all the information needed to reproduce your work. For me, those categories are experimental methods, data, analysis, and references.
Where to keep it? I have 3 places. I have lab notebooks - the daily record of what I've done. I have a physical filing cabinet - keeper of everything from written notes and hand-drawings to printouts of journal articles, hardware specifications, and data. And I have a computer (and backup!) - keeper of essentially everything that's not handwritten and a few things that were scanned in. The computer can really get you in trouble with it's infinite space and the myriad bad ways of organizing and naming files. But what I've outlined below works for me, and perhaps could work for others.
References/Journal Articles
I like to keep lots of pdfs of journal articles, and print lots, but not all. I generally make file folders (both physical and on the computer) that represent some general topic and file lots of papers in each folder. For computer files, I like to name the files "(first author's last name)_(Journal)_(pub year)_(2-3 key words)". This system works...okay. I can easily find the papers again when I need to, and it's nice to have hard copies with notes and soft copies for easy access when I'm away from my filing cabinet. However, now that I'm writing, I've started using EndNote, which may cause me to completely overhaul my system. We'll see.
Experimental methods
Experimental methods has been one of the most troublesome categories for me to organize. Methods can be a very diverse category, particularly in biophysics where methods may range from biochemistry to instrumentation to automation. Developing the methods may be a major component of a research project, or even if it's not, methods may change over time. Figuring out how to deal with diverse, evolving methods has been challenging.
Eventually, I determined two keys to organizing my notes and files on experimental methods: subcategorizing, and organizing by date. Over time, I found I generally had three categories of methods: sample preparation, instrumentation, and data acquisition.
For sample preparation, I found it best to type up a protocol, date it, and paste it in my lab notebook on that date. Then I could refer back to that protocol by date and lab notebook page number each time I performed the protocol. As I modified the protocol, I would still refer back to the same protocol, but note the modifications. Eventually, enough modifications would be added that I'd type a new copy, date it, and paste it in my notebook. Eventually, the protocols were fairly settled and each day I prepared a sample, I could just refer back to that date and page. And it worked well for some items I made and stored. Typically we write the date on anything bio-y that we store, and when I used it, or ran out and needed to make more, I could just refer back to that date in my lab notebook. Convenient. Plus, if a labmate wanted to do something similar, they could also look it up and not need me to "remember" exactly how I'd done or made something.
For instrumentation, I had several subcategories that required various forms of organization. I had designs, part specifications, optimization and characterization techniques, calibrations techniques, automation software. I wasn't the best about organizing these things as I went, but hindsight being 20/20, I've learned what I should have done.
1) Keep an accordion of files and a notebook dedicated entirely to your instrument.
2) Design involves a lot of notes, hand-drawings, calculations, references to articles. Date these things and keep them in a folder.
3) Part specifications become impossible to find later. Keep them on file, hard and soft copies if at all possible. (Also, determining what parts are in an instrument is really hard after the fact. Keep records of what you put in your instrument!) 4) Optimization and characterization techniques evolve, and the instrument evolves with them. Write down and date what you do in a notebook dedicated to the instrument (or at least make notes in the instrument notebook referring to where it is written down).
5)Calibrations are very important. Keep good notes on them, by date, in that instrument notebook.
6)Automation software is still a toughy for me. Ideally, it would change rarely, you would carefully track changes by date, and you would keep old copies. Our automation software is all custom written, in a very large suite of software that all works together. I tried to keep notes on major changes, and I tried to keep old copies, but ultimately, I didn't do it that well.
With hindsight, I eventually found the above guidelines to be a good means of organizing instrumentation techniques.
Data acquisition, was generally a combination of the above sample preparation and instrumentation techniques. Generally I prepared a sample and used the instrument in it's present status with the current calibrations to acquire data. And I used some of the automation software I had written to acquire that data. I usually made notes to this affect in my daily record in my lab notebook, with any modifications or variables noted. Those variables are one of the places where things can get tricky, as you often don't even know what the important variables are until you start changing them. But nevertheless, the lab notebook was generally where I noted the details of data acquisition.
Data
Data was actually the simplest thing for me to organize. Our lab had already established a system of organizing computer files by date and time stamp, with a descriptive base file name and different file extensions representing different types of data. I followed that system and found it works pretty well, as long as you follow it. Keeping notes in my lab notebook about the individual files was also key to knowing which files were what. Also helpful: a description somewhere of what's in the different files, and how to load the files for analysis.
Analysis
Analysis was another doozy of an organizational challenge. My raw data files were all nicely organized by date and time stamp, but for analysis, it took awhile to decide how to organize. Did I organize by date, like the data? But then, what about when I wanted to analyze data from several dates together? Did I organize by type of data? By date I performed the analysis? By type of analysis? And how did I organize the analysis programming itself? And what, if anything, did I write about it in my notebook? Or print anything out? Analysis organization definitely presented it's own set of challenges.
Eventually I decided to organize the analysis by date of the data and purpose of the analysis. If I analyzed several days of data together, I just titled it by the multiple dates and grouped it in the applicable month or year. I generally didn't write notes in my lab notebook about analysis, though I wish I had. I often printed plots, and put them in a 3-ring binder organized by data acquisition date and type of data. I wish I had more notes about my data analysis, and eventually I discovered notes about the analysis itself were very important (see below).
Even more important than the top-level organization of the analysis files was putting notes and organization into the analysis files themselves. My analysis software allows data folders, so that I could arrange data within the file by date, timestamp, and type. I also could make notes within the analysis files. I found keeping notes in the form of "date of analysis, goal of analysis, methods of analysis, and results of analysis" were very useful to making the analysis useful to myself later.
A couple more key points
Following the general rules above, I established a pretty decent filing system for myself. I have a lot of files, hard copy in my filing cabinet, soft copy on my computer, and lots of lab notebooks. In keeping this stuff useful, I have a few more key points:
1)Back up your computer files! In at least 2 places, 3 places ideally.
2)Organize your hard copy files. Filing cabinet, tabbed files.
3)Index your lab notebooks. Our labnotebooks had pages at the front for indexing. I liked to list a major category (sample prep, instrumentation, data, analysis, notes), then the specific task of the day.
Also, I found that the more information that was available on computer, the better. Lugging around lab notebooks and file folders sucks. I like to have soft copies whenever possible. Short of scanning in your lab notebooks (or typing all the entries), I found it useful to make an Excel spread sheet, organized by date, that listed what I did on each day that I acquired data. On this spread sheet, I also added in all the important changes made in the instrumentation, sample prep, and data acquisition. I color-coded it in subcategories. It's made finding data sooooo much easier.
Some final notes on file organization for publication
As I write up my work, I've found it very helpful to make a folder for each paper, and within that folder, to have subfolders for each figure or significant result. Each figure or significant result has it's own 1)analysis file, 2)final figure file, 3)data caption file, and 4)data source file. The analysis file has all the relevant data, from raw to fully analyzed. The source file has where the data came from, how and when it was acquired, how it was analyzed, and any important quantifications. The final figure file is the .jpg, and the caption is a caption that would be appropriate for the figure. With this information, I have everything necessary to include the figure or significant result in the the paper.
And that's it. That's my system. What's your system?
What to keep, what to keep? There's essentially one reason for keeping things on file: because you might need it later! I know, obvious. But I'd like to break it down a little further, to four reasons you might need it later: 1) it's a result, 2) it's information needed to reproduce a result, 3) it's information that may help with interpretation of a result, or 4) it's information that may lead to a future result. So, as for what I keep on file, I try to keep everything on file that falls into one of those four categories. That's a lot of stuff. How the heck to you organize this stuff so you can find it later when you need to?
How to organize? In the beginning of a project, starting a system of organization is difficult because you don't have a feeling yet for the number of different sub-projects, the scale of those projects, and all of the variables that will change along the way. But, there are categories that will be important when it comes time for publishing and ensuring that you have all the information needed to reproduce your work. For me, those categories are experimental methods, data, analysis, and references.
Where to keep it? I have 3 places. I have lab notebooks - the daily record of what I've done. I have a physical filing cabinet - keeper of everything from written notes and hand-drawings to printouts of journal articles, hardware specifications, and data. And I have a computer (and backup!) - keeper of essentially everything that's not handwritten and a few things that were scanned in. The computer can really get you in trouble with it's infinite space and the myriad bad ways of organizing and naming files. But what I've outlined below works for me, and perhaps could work for others.
References/Journal Articles
I like to keep lots of pdfs of journal articles, and print lots, but not all. I generally make file folders (both physical and on the computer) that represent some general topic and file lots of papers in each folder. For computer files, I like to name the files "(first author's last name)_(Journal)_(pub year)_(2-3 key words)". This system works...okay. I can easily find the papers again when I need to, and it's nice to have hard copies with notes and soft copies for easy access when I'm away from my filing cabinet. However, now that I'm writing, I've started using EndNote, which may cause me to completely overhaul my system. We'll see.
Experimental methods
Experimental methods has been one of the most troublesome categories for me to organize. Methods can be a very diverse category, particularly in biophysics where methods may range from biochemistry to instrumentation to automation. Developing the methods may be a major component of a research project, or even if it's not, methods may change over time. Figuring out how to deal with diverse, evolving methods has been challenging.
Eventually, I determined two keys to organizing my notes and files on experimental methods: subcategorizing, and organizing by date. Over time, I found I generally had three categories of methods: sample preparation, instrumentation, and data acquisition.
For sample preparation, I found it best to type up a protocol, date it, and paste it in my lab notebook on that date. Then I could refer back to that protocol by date and lab notebook page number each time I performed the protocol. As I modified the protocol, I would still refer back to the same protocol, but note the modifications. Eventually, enough modifications would be added that I'd type a new copy, date it, and paste it in my notebook. Eventually, the protocols were fairly settled and each day I prepared a sample, I could just refer back to that date and page. And it worked well for some items I made and stored. Typically we write the date on anything bio-y that we store, and when I used it, or ran out and needed to make more, I could just refer back to that date in my lab notebook. Convenient. Plus, if a labmate wanted to do something similar, they could also look it up and not need me to "remember" exactly how I'd done or made something.
For instrumentation, I had several subcategories that required various forms of organization. I had designs, part specifications, optimization and characterization techniques, calibrations techniques, automation software. I wasn't the best about organizing these things as I went, but hindsight being 20/20, I've learned what I should have done.
1) Keep an accordion of files and a notebook dedicated entirely to your instrument.
2) Design involves a lot of notes, hand-drawings, calculations, references to articles. Date these things and keep them in a folder.
3) Part specifications become impossible to find later. Keep them on file, hard and soft copies if at all possible. (Also, determining what parts are in an instrument is really hard after the fact. Keep records of what you put in your instrument!) 4) Optimization and characterization techniques evolve, and the instrument evolves with them. Write down and date what you do in a notebook dedicated to the instrument (or at least make notes in the instrument notebook referring to where it is written down).
5)Calibrations are very important. Keep good notes on them, by date, in that instrument notebook.
6)Automation software is still a toughy for me. Ideally, it would change rarely, you would carefully track changes by date, and you would keep old copies. Our automation software is all custom written, in a very large suite of software that all works together. I tried to keep notes on major changes, and I tried to keep old copies, but ultimately, I didn't do it that well.
With hindsight, I eventually found the above guidelines to be a good means of organizing instrumentation techniques.
Data acquisition, was generally a combination of the above sample preparation and instrumentation techniques. Generally I prepared a sample and used the instrument in it's present status with the current calibrations to acquire data. And I used some of the automation software I had written to acquire that data. I usually made notes to this affect in my daily record in my lab notebook, with any modifications or variables noted. Those variables are one of the places where things can get tricky, as you often don't even know what the important variables are until you start changing them. But nevertheless, the lab notebook was generally where I noted the details of data acquisition.
Data
Data was actually the simplest thing for me to organize. Our lab had already established a system of organizing computer files by date and time stamp, with a descriptive base file name and different file extensions representing different types of data. I followed that system and found it works pretty well, as long as you follow it. Keeping notes in my lab notebook about the individual files was also key to knowing which files were what. Also helpful: a description somewhere of what's in the different files, and how to load the files for analysis.
Analysis
Analysis was another doozy of an organizational challenge. My raw data files were all nicely organized by date and time stamp, but for analysis, it took awhile to decide how to organize. Did I organize by date, like the data? But then, what about when I wanted to analyze data from several dates together? Did I organize by type of data? By date I performed the analysis? By type of analysis? And how did I organize the analysis programming itself? And what, if anything, did I write about it in my notebook? Or print anything out? Analysis organization definitely presented it's own set of challenges.
Eventually I decided to organize the analysis by date of the data and purpose of the analysis. If I analyzed several days of data together, I just titled it by the multiple dates and grouped it in the applicable month or year. I generally didn't write notes in my lab notebook about analysis, though I wish I had. I often printed plots, and put them in a 3-ring binder organized by data acquisition date and type of data. I wish I had more notes about my data analysis, and eventually I discovered notes about the analysis itself were very important (see below).
Even more important than the top-level organization of the analysis files was putting notes and organization into the analysis files themselves. My analysis software allows data folders, so that I could arrange data within the file by date, timestamp, and type. I also could make notes within the analysis files. I found keeping notes in the form of "date of analysis, goal of analysis, methods of analysis, and results of analysis" were very useful to making the analysis useful to myself later.
A couple more key points
Following the general rules above, I established a pretty decent filing system for myself. I have a lot of files, hard copy in my filing cabinet, soft copy on my computer, and lots of lab notebooks. In keeping this stuff useful, I have a few more key points:
1)Back up your computer files! In at least 2 places, 3 places ideally.
2)Organize your hard copy files. Filing cabinet, tabbed files.
3)Index your lab notebooks. Our labnotebooks had pages at the front for indexing. I liked to list a major category (sample prep, instrumentation, data, analysis, notes), then the specific task of the day.
Also, I found that the more information that was available on computer, the better. Lugging around lab notebooks and file folders sucks. I like to have soft copies whenever possible. Short of scanning in your lab notebooks (or typing all the entries), I found it useful to make an Excel spread sheet, organized by date, that listed what I did on each day that I acquired data. On this spread sheet, I also added in all the important changes made in the instrumentation, sample prep, and data acquisition. I color-coded it in subcategories. It's made finding data sooooo much easier.
Some final notes on file organization for publication
As I write up my work, I've found it very helpful to make a folder for each paper, and within that folder, to have subfolders for each figure or significant result. Each figure or significant result has it's own 1)analysis file, 2)final figure file, 3)data caption file, and 4)data source file. The analysis file has all the relevant data, from raw to fully analyzed. The source file has where the data came from, how and when it was acquired, how it was analyzed, and any important quantifications. The final figure file is the .jpg, and the caption is a caption that would be appropriate for the figure. With this information, I have everything necessary to include the figure or significant result in the the paper.
And that's it. That's my system. What's your system?
Tuesday, August 3, 2010
Shenanigans!
What a great analogy for grad school. The shenanigans of treating Ph.D. students as students when it's best for the university and professors that we be students, and as employees when it's best for the university and the professors that we be employees.
Am I a student? Being a student implies that I'm being educated and trained. That I have an advisor to advise me on how to best obtain *my* goals. That I'm being evaluated in some way that will help me learn and prove that I've learned. Being a student somehow implies to me that I will get out of it what I put into it. That I'm the one invested in this endeavor. My success or failure matters a great deal to me, but should only matter to anyone else insofar as how much they care about me. I know, very idealistic.
Being a student also implies paying for that education. And I don't pay. The university does get paid. The "educators" do get paid. But not by me. By grants. Sometimes by grants obtained by me, sometimes by grants obtained by my advisor. And I get paid by these grants, as well.
I get paid, so I'm also an employee. Employees perform services for pay. They typically have contracts. I have a contract. It's a yearly renewed contract that can be cancelled at any time by me or my advisor. It says I work 20 hours per week on research. I have no allotted leave. No sick leave. No vacation leave. None. Zero, zip, zilch. All leave is at the discretion of my advisor. Renewal of my contract is at the discretion of my advisor. All work is at the discretion of my advisor.
My advisor is also an employee. He performs services for pay. He also has a contract, and though I don't know the details, I know the gist. He teaches, does service, and performs research. He is reviewed on his performance in these activities. He now has tenure, so there is little to no chance that his contract will not be renewed. But his performance is still reviewed for promotions and raises. And his career, the opinions of his colleagues, awards, future grants; all these depend on reviews of his performance. As his Ph.D. student and employee, I have little to do with his teaching and service. But I have a lot to do with his research performance.
My advisor is also supposed to be the person who advises me on how to best achieve *my* educational/professional goals. What happens when his goals and my goals no longer align? What happens when it's beneficial for him to keep me toiling away as a senior graduate student, highly productive for his research, but it's not beneficial for me? What happens when it's beneficial for me to take two weeks vacation to try to recover from burnout, but he doesn't see it? What happens when it's beneficial for me to take a course on teaching, but he sees no direct benefit? What happens when it's beneficial for me to spend time looking for jobs, taking trips for interviews, writing grants for those jobs, but it's not beneficial for him? Then what?
What happens when it's beneficial for me to get out from under these shenanigans, in which I'm a student so that the university gets paid, so that I won't leave because I've invested years in trying to get this degree, so that I work 40+ hours per week more than my contract states, yet still get no benefits such as retirement or health care paid or even contracted leave? What happens when I need to be done with that, but it's not in my advisor's and employer's best interest?
Am I a student? Being a student implies that I'm being educated and trained. That I have an advisor to advise me on how to best obtain *my* goals. That I'm being evaluated in some way that will help me learn and prove that I've learned. Being a student somehow implies to me that I will get out of it what I put into it. That I'm the one invested in this endeavor. My success or failure matters a great deal to me, but should only matter to anyone else insofar as how much they care about me. I know, very idealistic.
Being a student also implies paying for that education. And I don't pay. The university does get paid. The "educators" do get paid. But not by me. By grants. Sometimes by grants obtained by me, sometimes by grants obtained by my advisor. And I get paid by these grants, as well.
I get paid, so I'm also an employee. Employees perform services for pay. They typically have contracts. I have a contract. It's a yearly renewed contract that can be cancelled at any time by me or my advisor. It says I work 20 hours per week on research. I have no allotted leave. No sick leave. No vacation leave. None. Zero, zip, zilch. All leave is at the discretion of my advisor. Renewal of my contract is at the discretion of my advisor. All work is at the discretion of my advisor.
My advisor is also an employee. He performs services for pay. He also has a contract, and though I don't know the details, I know the gist. He teaches, does service, and performs research. He is reviewed on his performance in these activities. He now has tenure, so there is little to no chance that his contract will not be renewed. But his performance is still reviewed for promotions and raises. And his career, the opinions of his colleagues, awards, future grants; all these depend on reviews of his performance. As his Ph.D. student and employee, I have little to do with his teaching and service. But I have a lot to do with his research performance.
My advisor is also supposed to be the person who advises me on how to best achieve *my* educational/professional goals. What happens when his goals and my goals no longer align? What happens when it's beneficial for him to keep me toiling away as a senior graduate student, highly productive for his research, but it's not beneficial for me? What happens when it's beneficial for me to take two weeks vacation to try to recover from burnout, but he doesn't see it? What happens when it's beneficial for me to take a course on teaching, but he sees no direct benefit? What happens when it's beneficial for me to spend time looking for jobs, taking trips for interviews, writing grants for those jobs, but it's not beneficial for him? Then what?
What happens when it's beneficial for me to get out from under these shenanigans, in which I'm a student so that the university gets paid, so that I won't leave because I've invested years in trying to get this degree, so that I work 40+ hours per week more than my contract states, yet still get no benefits such as retirement or health care paid or even contracted leave? What happens when I need to be done with that, but it's not in my advisor's and employer's best interest?
Thursday, July 29, 2010
"Library" research
A post over at Uncertain Principles got me thinking about "library" research. What I mean by library research is really literature research, but it's called library research in the post, so I figured I'd keep the same nomenclature. "Library" is in quotes because somehow that seems to convey the sentiment that, in doing literature research, one does everything possible to avoid having to actually, physically, go to the library. And when one finds that the physical library is the only way to access certain information, one often starts to seriously reconsider if that information is all that essential. (Even worse if one finds the information must be ordered from another library, thus creating a delay in information access, in addition to having to physically go to the library.) But I digress.
What I really wanted to say about library research is that I don't understand why I was never taught how to do it correctly. I mean, how hard is it to explain that there are these things called "peer-reviewed journals" and they contain the acceptable articles to reference? And even more explicitly, when opening yourself up to a new research topic, there are things called "review articles" that are usually the best way to get started. There you go, library research, taught in two sentences.
But wait, there are some potential problems with that simplification. Firstly, there are books, which are usually not formally peer-reviewed. And I suspect books play a much more important role in humanities and social science research than in STEM research. Secondly, how does one find these peer-reviewed journals and indentify them from the rest of the vast internet of information? Is there a generally applicable solution to these problems?
For books, the answer seems to be, if it's accessible in the library, it's probably legit enough to reference. Obviously, that answer is more for undergrads doing class projects rather than researchers looking for answers. When you're really looking for answers (rather than just trying to make an A), it seems you intuitively acquire a more refined b-s detector. Ideally, you use the information that appears legitimate and useful, you reject information only if you have a legitimate reason to do so, and you file away questionable information for later. Non-ideally, you just believe what supports your thesis and reject what does not. Or cite the crap and cite what's wrong with it. Whichever.
As for how you find and identify peer-reviewed journals? Well, I do my literature searches in Pub-Med and on ISI Web of Knowledge/Science, and that works for me. And I look up articles cited by other articles in peer-reviewed journals. And that keeps me on track. But what of other fields? I've heard Google Scholar is nice, but I've never used it myself. Are there databases for humanities and social science? I also wonder if there is a statement on peer-reviewed journals' websites or journal hard-copies that states that these journals are peer reviewed? Maybe the best bet is to ask someone in the general field how they do their literature searches.
At least, that's how I learned about this stuff. I learned about the databases I use in graduate school, simply as offhand information offered in converstaion with profs and other students. That's also how I learned about the distinction between peer-reviewed articles and the general internet-information rif-raf. And when I typed some keywords from my thesis topic into those databases and was overwhelmed with the 1000+ articles, I asked my advisor, how the heck do I get a handle on all this literature? And he told me the common sense thing, which was start with the most recent review article and work backwards into the interesting references. But why didn't I learn any of this stuff earlier?
I thought part of the answer to why I didn't learn good literature research techniques earlier was that the internet was still debuting when I was an undergrad (1998-2002). But turns out PubMed has been around since 1996. ISI Web of Knowledge did launch in 2002, so at least my excuse is valid there. (What did non-bio-x scientists do before ISI?) So in reality, online access to journals was still developing when I was an undergrad, and I was being taught cutting edge research techniques in my 1998 college orientation class when I was taught to use the computer-based card-catalog at my college library. I guess the internet really changed things.
Another way the internet changed things? Pre-internet, it was a lot harder to even find information in any source other than peer-reviewed journals and library accessible books. So, I guess I can't place too much blame on my college educators for why I didn't learn to do "library" research the way it should be done in 2010.
The moral of the story? If you're trying to do legitimate research in a field, ask someone in the field how they get started. In science, ISI Web of Knowledge and PubMed (for bio-anything) are the best starting points. Start with the most recent review articles if you can find them, and work backwards into the references. Once you have a handle on the subject, you can generate more specific search terms and can scan other titles to find interesting articles. And that's how "library" research is done. At least in 2010.
What I really wanted to say about library research is that I don't understand why I was never taught how to do it correctly. I mean, how hard is it to explain that there are these things called "peer-reviewed journals" and they contain the acceptable articles to reference? And even more explicitly, when opening yourself up to a new research topic, there are things called "review articles" that are usually the best way to get started. There you go, library research, taught in two sentences.
But wait, there are some potential problems with that simplification. Firstly, there are books, which are usually not formally peer-reviewed. And I suspect books play a much more important role in humanities and social science research than in STEM research. Secondly, how does one find these peer-reviewed journals and indentify them from the rest of the vast internet of information? Is there a generally applicable solution to these problems?
For books, the answer seems to be, if it's accessible in the library, it's probably legit enough to reference. Obviously, that answer is more for undergrads doing class projects rather than researchers looking for answers. When you're really looking for answers (rather than just trying to make an A), it seems you intuitively acquire a more refined b-s detector. Ideally, you use the information that appears legitimate and useful, you reject information only if you have a legitimate reason to do so, and you file away questionable information for later. Non-ideally, you just believe what supports your thesis and reject what does not. Or cite the crap and cite what's wrong with it. Whichever.
As for how you find and identify peer-reviewed journals? Well, I do my literature searches in Pub-Med and on ISI Web of Knowledge/Science, and that works for me. And I look up articles cited by other articles in peer-reviewed journals. And that keeps me on track. But what of other fields? I've heard Google Scholar is nice, but I've never used it myself. Are there databases for humanities and social science? I also wonder if there is a statement on peer-reviewed journals' websites or journal hard-copies that states that these journals are peer reviewed? Maybe the best bet is to ask someone in the general field how they do their literature searches.
At least, that's how I learned about this stuff. I learned about the databases I use in graduate school, simply as offhand information offered in converstaion with profs and other students. That's also how I learned about the distinction between peer-reviewed articles and the general internet-information rif-raf. And when I typed some keywords from my thesis topic into those databases and was overwhelmed with the 1000+ articles, I asked my advisor, how the heck do I get a handle on all this literature? And he told me the common sense thing, which was start with the most recent review article and work backwards into the interesting references. But why didn't I learn any of this stuff earlier?
I thought part of the answer to why I didn't learn good literature research techniques earlier was that the internet was still debuting when I was an undergrad (1998-2002). But turns out PubMed has been around since 1996. ISI Web of Knowledge did launch in 2002, so at least my excuse is valid there. (What did non-bio-x scientists do before ISI?) So in reality, online access to journals was still developing when I was an undergrad, and I was being taught cutting edge research techniques in my 1998 college orientation class when I was taught to use the computer-based card-catalog at my college library. I guess the internet really changed things.
Another way the internet changed things? Pre-internet, it was a lot harder to even find information in any source other than peer-reviewed journals and library accessible books. So, I guess I can't place too much blame on my college educators for why I didn't learn to do "library" research the way it should be done in 2010.
The moral of the story? If you're trying to do legitimate research in a field, ask someone in the field how they get started. In science, ISI Web of Knowledge and PubMed (for bio-anything) are the best starting points. Start with the most recent review articles if you can find them, and work backwards into the references. Once you have a handle on the subject, you can generate more specific search terms and can scan other titles to find interesting articles. And that's how "library" research is done. At least in 2010.
Tuesday, July 6, 2010
Fair's fair?
I just followed a trail of bread crumbs about science and graduate school, beginning with Ph.D. training as a Ponzi scheme, meandering through The Real Science Gap, what's socially wrong with science, Something Deeply Wrong with Chemistry, something's wrong with this lab and it's not atypical, and finally jumping off that miserable, depressing trail to end with the upbeat, (and totally naive) Drawings of Scientists by 7th graders. By the end, when I read what the 7th graders wrote about the scientists being normal, happy people who lead normal, happy lives, I really wanted to cry (and also, tell those poor 7th graders "Don't believe it!").
Sigh...
What does it mean?
First, it's depressing.
But, then, I also note in the comments a few statements like "Sounds like the video game industry," or "Sounds like computer programming."
Is this just the way it is? The way it has to be? Is this as fair as it's ever going to get? What's fair? And for that matter, what's best?
Is it fair (or best) that young people (20-40 years old) work very hard for low pay in order to keep technology chugging along at a reasonable pace? Our hard work and low pay makes far more discovery and productivity possible than if we worked fewer hours and demanded higher pay. Right?
Is it fair (or best) that only a small percentage of us can make it to slightly cushier jobs, with some amount of financial payoff, job security and benefits? Surely that's the only way to ensure that only the cream-of-the-crop rise to the top and make the big, high-level decisions about what lines of research to pursue and how to spend the precious research dollars. Right?
And what of the remaining bright, inquisitive, scientific minds that refuse to live that life for long enough to rise to the top, or get a little unlucky somewhere along the way, or just don't make it into the top few percent? Is it right (or best) that they end up un- or under-employed, or working their entire lives like the 20-40 yr old "youngsters" with low pay, no security, few benefits, and ridiculous hours?
Or is it possible that a better way could be found? Could there be a system where bright scientific minds could find financial reward, job security, benefits and reasonable hours as well as intellecutally stimulating, rewarding jobs that also better maximize their contributions, and maybe that they could find these jobs at as early of an age as, say, 22? Is that what you get when you skip the Ph.D. and just join industry right out of college? Or do you get more of the same - high expectations for low pay and some distant possibility of moving up to financial reward, security, and the possibility of intellectual contribution?
Sigh...
What does it mean?
First, it's depressing.
But, then, I also note in the comments a few statements like "Sounds like the video game industry," or "Sounds like computer programming."
Is this just the way it is? The way it has to be? Is this as fair as it's ever going to get? What's fair? And for that matter, what's best?
Is it fair (or best) that young people (20-40 years old) work very hard for low pay in order to keep technology chugging along at a reasonable pace? Our hard work and low pay makes far more discovery and productivity possible than if we worked fewer hours and demanded higher pay. Right?
Is it fair (or best) that only a small percentage of us can make it to slightly cushier jobs, with some amount of financial payoff, job security and benefits? Surely that's the only way to ensure that only the cream-of-the-crop rise to the top and make the big, high-level decisions about what lines of research to pursue and how to spend the precious research dollars. Right?
And what of the remaining bright, inquisitive, scientific minds that refuse to live that life for long enough to rise to the top, or get a little unlucky somewhere along the way, or just don't make it into the top few percent? Is it right (or best) that they end up un- or under-employed, or working their entire lives like the 20-40 yr old "youngsters" with low pay, no security, few benefits, and ridiculous hours?
Or is it possible that a better way could be found? Could there be a system where bright scientific minds could find financial reward, job security, benefits and reasonable hours as well as intellecutally stimulating, rewarding jobs that also better maximize their contributions, and maybe that they could find these jobs at as early of an age as, say, 22? Is that what you get when you skip the Ph.D. and just join industry right out of college? Or do you get more of the same - high expectations for low pay and some distant possibility of moving up to financial reward, security, and the possibility of intellectual contribution?
Saturday, June 26, 2010
Can I achieve a balanced life and work 60 hrs/week?
During my Ph.D., like most people, I've worked a lot of long hours. At first, maybe I worked long hours because I felt pressure from my advisor. Later, I worked long hours because I wanted to get things done. In any case, I worked long hours. And I did not succeed at achieving the kind of balance I'd like in my life.
For a long time, I worked, played hard, and took reasonably good care of myself and my personal priorities. All, of course, at the expense of my sleep. As time passed and I got older, sleep became a bigger and bigger priority; 4-6 hours a night wasn't cutting it. Pretty soon, things started dropping out of my life so that I could get more sleep and continue to work those long hours. Soon, I was barely "playing" at all, I was never exercising, I was making quick, unhealthy food choices, and I was neglecting other personal priorities (family obligations, friends, hobbies). I started to physically and emotionally feel weak, fragile, tired, and eventually, depressed. The depression began to affect my work, and I realized I needed to back off, recover, and take better care of myself. Recently, I've done that, but definitely at the expense of those work hours.
As I look to the future and think about what kind of job to look for next, I think about those long hours, and I wonder, can I work those kind of hours and achieve balance? If I feel pressure to work 60 hours a week, that means 12 hours a day Monday thru Friday, or 10 hours a day Monday thru Saturday. I've always thought I preferred the 12 hours a day Monday thru Friday, so that Saturday can be a fun day, and Sunday can be a day to get stuff done (laundry, cleaning, grocery shopping).
So, if I work 12 hours a day, sleep 8 hours, eat 2 hours, spend 1 hour on personal hygiene, exercise 1 hour, and commute 1 hour, that adds up to 25 hours. Crap, I'm already over 24 hours. And that's without even adding a single moment for family and friends, hobbies, winding down before bed. And it's lumping all the daily chores into the eating and personal hygiene category. And still, I couldn't do it, not even theoretically. And I really don't want to cut any of that out. I could sleep 7 hours, or I could eat every meal but dinner at my desk (including breakfast). But you know what? I don't want to. I don't want to sleep less; I don't want to wolf down my breakfast and lunch at my desk. And I don't want to cut out every moment of possible time to spend on family, friends, hobbies, or winding down. So, working 12 hours a day is not something I want to do.
What about trying the 10 hours 6 days a week? Then I could actually fit my whole list into my day, with 1 hour to spare for family, friends, hobbies, or winding down. And I'd have 1 day left in my week to do everything else - anything fun, all cleaning, laundry, shopping, any family, friend, or hobby time that wouldn't fit into that spare hour I had during my week. You know what? That's shitty.
So do I want to work 60 hours a week? No. No matter how awesomely I am in love with my work, I don't want to do it at the expense of my health. And I've learned that my health depends on having that list of activities in my life. I need sleep; I need time to eat healthily; I need time to exercise; I need time for personal hygiene; and I need personal time for family, friends, fun, and chores. Does that make me a bad person? Does that make me uncommitted to my work? No. No, it doesn't.
It does make me question the supervisors demanding 60 hours a week. I honestly think they're demanding their employees live an unhealthy, unbalanced, unsustainable lifestyle. But hey, if people will do it, why not?
And it makes me wonder, how the heck do parents even begin to do everything I'm doing, plus take care of their kids? Obvious answer: they get no sleep. Sigh. My desire for kids is getting more and more hypothetical.
For a long time, I worked, played hard, and took reasonably good care of myself and my personal priorities. All, of course, at the expense of my sleep. As time passed and I got older, sleep became a bigger and bigger priority; 4-6 hours a night wasn't cutting it. Pretty soon, things started dropping out of my life so that I could get more sleep and continue to work those long hours. Soon, I was barely "playing" at all, I was never exercising, I was making quick, unhealthy food choices, and I was neglecting other personal priorities (family obligations, friends, hobbies). I started to physically and emotionally feel weak, fragile, tired, and eventually, depressed. The depression began to affect my work, and I realized I needed to back off, recover, and take better care of myself. Recently, I've done that, but definitely at the expense of those work hours.
As I look to the future and think about what kind of job to look for next, I think about those long hours, and I wonder, can I work those kind of hours and achieve balance? If I feel pressure to work 60 hours a week, that means 12 hours a day Monday thru Friday, or 10 hours a day Monday thru Saturday. I've always thought I preferred the 12 hours a day Monday thru Friday, so that Saturday can be a fun day, and Sunday can be a day to get stuff done (laundry, cleaning, grocery shopping).
So, if I work 12 hours a day, sleep 8 hours, eat 2 hours, spend 1 hour on personal hygiene, exercise 1 hour, and commute 1 hour, that adds up to 25 hours. Crap, I'm already over 24 hours. And that's without even adding a single moment for family and friends, hobbies, winding down before bed. And it's lumping all the daily chores into the eating and personal hygiene category. And still, I couldn't do it, not even theoretically. And I really don't want to cut any of that out. I could sleep 7 hours, or I could eat every meal but dinner at my desk (including breakfast). But you know what? I don't want to. I don't want to sleep less; I don't want to wolf down my breakfast and lunch at my desk. And I don't want to cut out every moment of possible time to spend on family, friends, hobbies, or winding down. So, working 12 hours a day is not something I want to do.
What about trying the 10 hours 6 days a week? Then I could actually fit my whole list into my day, with 1 hour to spare for family, friends, hobbies, or winding down. And I'd have 1 day left in my week to do everything else - anything fun, all cleaning, laundry, shopping, any family, friend, or hobby time that wouldn't fit into that spare hour I had during my week. You know what? That's shitty.
So do I want to work 60 hours a week? No. No matter how awesomely I am in love with my work, I don't want to do it at the expense of my health. And I've learned that my health depends on having that list of activities in my life. I need sleep; I need time to eat healthily; I need time to exercise; I need time for personal hygiene; and I need personal time for family, friends, fun, and chores. Does that make me a bad person? Does that make me uncommitted to my work? No. No, it doesn't.
It does make me question the supervisors demanding 60 hours a week. I honestly think they're demanding their employees live an unhealthy, unbalanced, unsustainable lifestyle. But hey, if people will do it, why not?
And it makes me wonder, how the heck do parents even begin to do everything I'm doing, plus take care of their kids? Obvious answer: they get no sleep. Sigh. My desire for kids is getting more and more hypothetical.
Monday, June 14, 2010
Student = Customer? Not really
My comment on a post at FSP.
Let's not forget that professors and universities are not only about teaching students, they are also about *evaluating* students. A few other commenters have touched on this point. Sure, students can go learn calculus on the internet and put a line on their resume that they can do calculus like a mo-fo, but do we just want to trust them on that? This discussion reminds me of the line in "Good Will Hunting" where Will makes fun of the Harvard guy for paying a half million dollars for an education he could've gotten for $1.99 at the public library.
And, as has been mentioned, the community and networking developed during college is perhaps just as important as the classroom education. The stats for getting a job definitely support the whole "it's who you know" adage. Not that you get hired by your buddies, but that people like to hire people who come with a recommendation they can trust.
True, online courses are a different beast from simply teaching yourself from a book or the internet. An online course does have exams and means of evaluating the student. And I think it's wonderful that these courses have become available for people who may need a more flexible learning environment because they're working and/or dealing with family commitments. But, I don't think you can replace the learning community and networking opportunities available at a real, physical college campus.
Let's not forget that professors and universities are not only about teaching students, they are also about *evaluating* students. A few other commenters have touched on this point. Sure, students can go learn calculus on the internet and put a line on their resume that they can do calculus like a mo-fo, but do we just want to trust them on that? This discussion reminds me of the line in "Good Will Hunting" where Will makes fun of the Harvard guy for paying a half million dollars for an education he could've gotten for $1.99 at the public library.
And, as has been mentioned, the community and networking developed during college is perhaps just as important as the classroom education. The stats for getting a job definitely support the whole "it's who you know" adage. Not that you get hired by your buddies, but that people like to hire people who come with a recommendation they can trust.
True, online courses are a different beast from simply teaching yourself from a book or the internet. An online course does have exams and means of evaluating the student. And I think it's wonderful that these courses have become available for people who may need a more flexible learning environment because they're working and/or dealing with family commitments. But, I don't think you can replace the learning community and networking opportunities available at a real, physical college campus.
Tuesday, June 8, 2010
Hilarity. And an excellent point.
This is too awesome to not repost:
A comment from steph on a post at FSP
I can't tell you how hard I was laughing by the end of the "aspergers" paragraph. Awesome.
A comment from steph on a post at FSP
steph said...
Instead of obsessing about this kind of stuff, science should be thinking about how it can KEEP the women who can compete with the oh so wonderfully smart men, but leave science for OTHER reasons. Yeah, we should do something at all levels, but there are tons of women who have proven their skills at the BS or PhD level who still end up leaving science. Why not focus more on keeping them?
Oh, because "real" scientists if devote 80 hours a week to it and give up on the idea of having a family and a life outside of science. So, women are just "choosing" to not be scientists....AS SCIENTISTS ARE CURRENTLY DEFINED. If you broadened your definitions and made the scientific culture more accepting of all people who love science and are good at it and put in an effort to succeed, I bet you'd catch more flies/women/minorities/men who aren't crazy aspergers.
Sure that is just my opinion from my experience, but tell me you all haven't seen the same thing?
I can't tell you how hard I was laughing by the end of the "aspergers" paragraph. Awesome.
What should I be when I grow up?
When I fall asleep at night, I like to fantasize about my future, usually at least 3 years down the road. It has to be multiple years into the future, or else I get stressed about something I should be doing right now to make that future happen. But I find 3 years is far enough to feel like lots can change, and I can feel free to get a good nights sleep.
In undergrad, I remember fantasizing about grad school. I fantasized about teaching a classroom full of cool students who all loved me (haha :) and I fantasized about brief periods of staying up all night to push through an important aspect of a project. The fantasy included the super-tired but accomplished feeling I would have afterwards as I finally got to fall into bed, exhausted. Turned out, I never taught in grad school, and I'm still waiting for that final accomplishment of a submitted 1st author paper.
As a young grad student, I fantasized about my talents finally being recognized and my hard work finally paying off. I fantasized about giving talks and publishing papers that wowed people, and finally made my advisor think I was smart and good.
As a slightly older grad student, I gave up that dream and fantasized about going to be a post doc somewhere, where my new advisor would appreciate me and my talents, and my hard work would pay off with fantastic papers and talks. And my new advisor would run into my old advisor at a conference, and talk about how amazing I was, and my old advisor wouldn't know what to say.
I also like to fantasize about further down the road, sometimes with more fantastical scenarios. The most common fantasy involves me being an award-winning, best-selling author. In this fantasy, I write amazing fiction novels as well as non-fiction pieces about science and education. I write in my home office with my 2 golden retrievers loyally at my side. And I make boatloads of money because I'm an award-winning and best-selling author, obviously. Ahhh....Lately I've been combining the author fantasy with a teaching fantasy where I also teach at a respected undergrad institution. Sometimes I can get excited about doing a little experimental research on the side in this fantasy, but sometimes I can't.
One of my more hilarious fantasies involved me being recruited as a spy after my Ph.D. graduation. In this fantasy, I'm whipped into shape by government gurus who recognize my potential and somehow my biophysicsy background is perfect for their spying needs. I also get paid boatloads of money, with tons of benefits such as excellent health-care, paid travel, a huge wardrobe budget, and amazing housing provided via the government. That was a very fun and hilarious fantasy.
But in seriousness, I'm trying to think about these fantasies and what they might mean in regards to what I should actually try to do after I graduate. I recognize the common thread of wanting boatloads of money, but I think that's more about security than anything else. So I really just need to make enough money to be relatively financially secure. I recognize a teaching thread, and I really think I should pursue that. Trouble is, teaching is one of the worst paid jobs out there. The writing fantasy is great, but unless it's combined with an actual paid job, it does not grant financial security. (The whole best-selling, award-winning thing is a little hard to guarantee.) And I wonder about the experimental research aversion. Is it just because it's been soooooo hard in grad school? I keep thinking maybe it will be better in a different lab with a different group and different project. I do like problem solving...But I worry any pull I feel toward experimental research is more because of expectations. I'm expected to go do experimental research because that's what I've been trained in for the last several years. I'm expected to do experimental research because that's much higher paid than teaching. I'm expected to do experimental research because that's what people do after a science Ph.D.
But what if I hate banging my head against a wall?
In undergrad, I remember fantasizing about grad school. I fantasized about teaching a classroom full of cool students who all loved me (haha :) and I fantasized about brief periods of staying up all night to push through an important aspect of a project. The fantasy included the super-tired but accomplished feeling I would have afterwards as I finally got to fall into bed, exhausted. Turned out, I never taught in grad school, and I'm still waiting for that final accomplishment of a submitted 1st author paper.
As a young grad student, I fantasized about my talents finally being recognized and my hard work finally paying off. I fantasized about giving talks and publishing papers that wowed people, and finally made my advisor think I was smart and good.
As a slightly older grad student, I gave up that dream and fantasized about going to be a post doc somewhere, where my new advisor would appreciate me and my talents, and my hard work would pay off with fantastic papers and talks. And my new advisor would run into my old advisor at a conference, and talk about how amazing I was, and my old advisor wouldn't know what to say.
I also like to fantasize about further down the road, sometimes with more fantastical scenarios. The most common fantasy involves me being an award-winning, best-selling author. In this fantasy, I write amazing fiction novels as well as non-fiction pieces about science and education. I write in my home office with my 2 golden retrievers loyally at my side. And I make boatloads of money because I'm an award-winning and best-selling author, obviously. Ahhh....Lately I've been combining the author fantasy with a teaching fantasy where I also teach at a respected undergrad institution. Sometimes I can get excited about doing a little experimental research on the side in this fantasy, but sometimes I can't.
One of my more hilarious fantasies involved me being recruited as a spy after my Ph.D. graduation. In this fantasy, I'm whipped into shape by government gurus who recognize my potential and somehow my biophysicsy background is perfect for their spying needs. I also get paid boatloads of money, with tons of benefits such as excellent health-care, paid travel, a huge wardrobe budget, and amazing housing provided via the government. That was a very fun and hilarious fantasy.
But in seriousness, I'm trying to think about these fantasies and what they might mean in regards to what I should actually try to do after I graduate. I recognize the common thread of wanting boatloads of money, but I think that's more about security than anything else. So I really just need to make enough money to be relatively financially secure. I recognize a teaching thread, and I really think I should pursue that. Trouble is, teaching is one of the worst paid jobs out there. The writing fantasy is great, but unless it's combined with an actual paid job, it does not grant financial security. (The whole best-selling, award-winning thing is a little hard to guarantee.) And I wonder about the experimental research aversion. Is it just because it's been soooooo hard in grad school? I keep thinking maybe it will be better in a different lab with a different group and different project. I do like problem solving...But I worry any pull I feel toward experimental research is more because of expectations. I'm expected to go do experimental research because that's what I've been trained in for the last several years. I'm expected to do experimental research because that's much higher paid than teaching. I'm expected to do experimental research because that's what people do after a science Ph.D.
But what if I hate banging my head against a wall?
Thursday, June 3, 2010
Some things I love about my job
I love sitting down to hash out, clean up, modify, or troubleshoot code. I do a lot of coding in Labview and Igor, and I really enjoy it. I love coming up with the framework for how to accomplish my goal; I love sticking in all the odds and ends needed to do the calculations; I love testing to see if it works. I love it, until I don't. I suppose I get fed up when I realize something is far more complicated that I thought, when I think I might need to scrap days of work and start over, or when I start to feel like the solution is soooo un-elegant. But in general, I really enjoy coding.
I love the days when I'm working on an experiment that will really reveal something. Even if I'm still trouble-shooting, and it's not yet "real" science, I love when I've devised an experiment that will give me an answer, one way or another. Either I will see a statistically significant change in this measurable due to this variable, or I won't, and it will mean something. It will mean something because the important signal is somehow guaranteed to be above the noise level. I wish all my experiments had been this way, but they weren't, and they drove me crazy when they weren't. So often in my grad school experience, I did experiments with one desirable outcome, that depended on so many assumptions, and with a million alternative outcomes that would only leave me clueless about which assumption was incorrect.
I love the days when I've reached the experiments that are actually scientifically relevant. These days can only happen once I know I've fixed all the annoying, time-sucking problems that are merely obstacles along the path to real science. I wish more days had been like this in my grad school experience. I would have been happier, and I would have accomplished so much more.
I love instrumentation design. Sitting down to devise the relevant specs, and seeking out the components and composing a design that will satisfy those specifications. I love ordering those components and assembling them into a working instrument. I love testing that instrument. And I suppose I hate troubleshooting that instrument. Because that means the design or a component is flawed. Because I did so much frustrating troubleshooting as a grad student, and it was such a barrier to doing real, publishable work.
I love when I find a problem and realize the solution is easier than I feared. So many of my testing and troubleshooting days were filled with dread at what mysterious new problem I might find, and how many months it might take to fix. I loved when I found a super-obvious culprit, with a super-easy fix.
I love teaching other people the stuff that I know. I love sitting down to a problem I already know how to solve, telling and showing and leading an interested person through the process, and always learning and solidifying my knowledge along the way.
I love discussing science with interested, knowledgeable parties, who aren't combative or judging me. I love arguing out complicated problems, being right or wrong at the start, and settling it in the end (or even not settling it, but keeping it as a problem in progress).
I enjoy giving talks, although I'm still working on being less nervous in front of larger groups.
I enjoy quantifying my results, in plots, in tables, in text. Except when I feel too much pressure for the answer to fit some preconceived notion.
I don't mind doing repetitive, mind-disengaged tasks every-so-often, when I know the product is necessary and useful, and it will be used to make progress.
I enjoy speculating about the biological meaning behind experiments. I like thinking about how the experiments I'm doing now may mean this or that, and may allow other experiments to be done later that could be a window into this process or that process.
I enjoy reading relevant literature, going to relevant talks.
I love feeling like I'm contributing to progress. I love helping other people, as long as they appreciate it and it doesn't get me into trouble for lack of my own progress.
So much to love about my job. Now, to take these things, and try to figure out what I want my next job to be. Post doc? Industry? Teaching? Journalism? Science museum? Educational other?
I love the days when I'm working on an experiment that will really reveal something. Even if I'm still trouble-shooting, and it's not yet "real" science, I love when I've devised an experiment that will give me an answer, one way or another. Either I will see a statistically significant change in this measurable due to this variable, or I won't, and it will mean something. It will mean something because the important signal is somehow guaranteed to be above the noise level. I wish all my experiments had been this way, but they weren't, and they drove me crazy when they weren't. So often in my grad school experience, I did experiments with one desirable outcome, that depended on so many assumptions, and with a million alternative outcomes that would only leave me clueless about which assumption was incorrect.
I love the days when I've reached the experiments that are actually scientifically relevant. These days can only happen once I know I've fixed all the annoying, time-sucking problems that are merely obstacles along the path to real science. I wish more days had been like this in my grad school experience. I would have been happier, and I would have accomplished so much more.
I love instrumentation design. Sitting down to devise the relevant specs, and seeking out the components and composing a design that will satisfy those specifications. I love ordering those components and assembling them into a working instrument. I love testing that instrument. And I suppose I hate troubleshooting that instrument. Because that means the design or a component is flawed. Because I did so much frustrating troubleshooting as a grad student, and it was such a barrier to doing real, publishable work.
I love when I find a problem and realize the solution is easier than I feared. So many of my testing and troubleshooting days were filled with dread at what mysterious new problem I might find, and how many months it might take to fix. I loved when I found a super-obvious culprit, with a super-easy fix.
I love teaching other people the stuff that I know. I love sitting down to a problem I already know how to solve, telling and showing and leading an interested person through the process, and always learning and solidifying my knowledge along the way.
I love discussing science with interested, knowledgeable parties, who aren't combative or judging me. I love arguing out complicated problems, being right or wrong at the start, and settling it in the end (or even not settling it, but keeping it as a problem in progress).
I enjoy giving talks, although I'm still working on being less nervous in front of larger groups.
I enjoy quantifying my results, in plots, in tables, in text. Except when I feel too much pressure for the answer to fit some preconceived notion.
I don't mind doing repetitive, mind-disengaged tasks every-so-often, when I know the product is necessary and useful, and it will be used to make progress.
I enjoy speculating about the biological meaning behind experiments. I like thinking about how the experiments I'm doing now may mean this or that, and may allow other experiments to be done later that could be a window into this process or that process.
I enjoy reading relevant literature, going to relevant talks.
I love feeling like I'm contributing to progress. I love helping other people, as long as they appreciate it and it doesn't get me into trouble for lack of my own progress.
So much to love about my job. Now, to take these things, and try to figure out what I want my next job to be. Post doc? Industry? Teaching? Journalism? Science museum? Educational other?
Wednesday, June 2, 2010
On advice...take it or leave it?
my comment on a cool post over at candid engineer
I still struggle with this very issue. How do you know when to take advice, and when to ignore it? I do think it's an essential part of the learning process to at least have the decision be up to you, and to learn that there is no single answer. Sometimes other people are right, and sometimes they are wrong. Some people are more likely to be right than others, and if you can figure out who the more-often-right people are, and get some insight on their decision making process, then you're golden.
I still struggle with this very issue. How do you know when to take advice, and when to ignore it? I do think it's an essential part of the learning process to at least have the decision be up to you, and to learn that there is no single answer. Sometimes other people are right, and sometimes they are wrong. Some people are more likely to be right than others, and if you can figure out who the more-often-right people are, and get some insight on their decision making process, then you're golden.
Thursday, May 20, 2010
Thursday, May 13, 2010
Responsibilities of a PI
The previous post reminded me of a document I once wrote about what I viewed as a PI's responsibilities. I just reread it, and it's quite a long list of responsibilities. It reminds me of why even thinking about trying to be a good PI is very intimidating. I've copied and pasted the document below.
Note: This list is from my point-of-view as a grad student. I realize it's student--centric. And I realize I've left off many professor responsibilities, such as teaching courses, university service, and grant and paper reviews. Even without those responsibilities listed, the list is looooooong.
What a PI should do
Get grants
Manage money ethically and fairly
Hire good students/postdocs/people
Have good ideas for projects
Manage the lab and people in the lab
-Mediate conflicts of interest/personality between lab members
-Minimize said conflicts by having good and fair policies in place
Ensure safety of lab and lab members
Make sure people get adequate training
-In the essential skills needed to do research
-In the essential skills in their field
-In the essential skills for their desired career
Give people a reasonable amount of intellectual freedom
Not abuse power
-Not ask students/postdocs to do PI's personal errands
Fairly divide non-research tasks (e.g. equipment maintenance, group meeting scheduling)
-e.g. make a list of non-research duties with approximate time commitments
-assign lab members duties so time commitments are reasonably fair
-if can't be fair, draw from a hat and trade every year or six months
-maybe senior students can have a break in responsibility, but otherwise, no favoritism
Not overwhelm lab members with trainees
-Have a plan for each additional lab trainee
-what project will they work on?
-who will train them with what they need to learn?
-ideally, they will receive training by joining a project they can help with
then their training is targeted, and their trainer gets something in return (help)
Give students and postdocs projects that will further their career
-*publication quality projects*
-not repeats of stuff some other lab did that may or may not be hard and time consuming
-not anything that isn't linearly related to their individual publishable project
-not favors for other people/projects they will not receive credit for
-if necessary, should be an explicit trade of favors between individuals
-not technician work (other than above mentioned non-research tasks, fairly divided)
Ensure people get credit when credit is due
-Manage projects so authorship is as clear as possible
-who will be first author?
-what is required to earn authorship?
-ensure people know if their contribution is a favor, not a contribution yielding
authorship
Introduce lab members to people in the field
-Talk up the lab members.
-Their career will impact your career. (i.e. their career good = kudos for you)
Send students and postdocs to present at conferences
-They need the exposure and network opportunities
-You also need exposure of their projects
-Again, their career good = kudos for you
-Help them network at these conferences
Give trainees expectations
-Vacation time expectations (grad school is long, they will need vacation)
-Hours and/or productivity expectations
-if you're happy with their productivity, you won't care about hours
-if their project is not producing, of course you'll want to see their commitment via hours
-years/publications required for leaving/graduating
Give trainees feedback
-What are their strengths?
-What are things they need to work on?
-What are their career goals? What do they need to work on for those goals?
Get trainees feedback
-What can the PI do to help trainees be productive?
Stay high level and big picture as much as possible
-You want good science getting done, they almost definitely want that, too
-Give them suggestions.
-Kindly argue with them about science (don't insult them, don't deride them, don't threaten them)
-Let them try their own way and make mistakes.
-Only insist on your way as a last resort.
-Amount of independence will change as lab members mature scientifically
-And/or senior lab members may direct junior lab members at a more specific level
Advise trainees on what is needed for publications, thesis and conference presentations
-The work will always be "in progress" (the beauty of science...)
-The work can always be better
-You can always figure out more stuff
-When is it enough to write up or present?
Note: This list is from my point-of-view as a grad student. I realize it's student--centric. And I realize I've left off many professor responsibilities, such as teaching courses, university service, and grant and paper reviews. Even without those responsibilities listed, the list is looooooong.
What a PI should do
Get grants
Manage money ethically and fairly
Hire good students/postdocs/people
Have good ideas for projects
Manage the lab and people in the lab
-Mediate conflicts of interest/personality between lab members
-Minimize said conflicts by having good and fair policies in place
Ensure safety of lab and lab members
Make sure people get adequate training
-In the essential skills needed to do research
-In the essential skills in their field
-In the essential skills for their desired career
Give people a reasonable amount of intellectual freedom
Not abuse power
-Not ask students/postdocs to do PI's personal errands
Fairly divide non-research tasks (e.g. equipment maintenance, group meeting scheduling)
-e.g. make a list of non-research duties with approximate time commitments
-assign lab members duties so time commitments are reasonably fair
-if can't be fair, draw from a hat and trade every year or six months
-maybe senior students can have a break in responsibility, but otherwise, no favoritism
Not overwhelm lab members with trainees
-Have a plan for each additional lab trainee
-what project will they work on?
-who will train them with what they need to learn?
-ideally, they will receive training by joining a project they can help with
then their training is targeted, and their trainer gets something in return (help)
Give students and postdocs projects that will further their career
-*publication quality projects*
-not repeats of stuff some other lab did that may or may not be hard and time consuming
-not anything that isn't linearly related to their individual publishable project
-not favors for other people/projects they will not receive credit for
-if necessary, should be an explicit trade of favors between individuals
-not technician work (other than above mentioned non-research tasks, fairly divided)
Ensure people get credit when credit is due
-Manage projects so authorship is as clear as possible
-who will be first author?
-what is required to earn authorship?
-ensure people know if their contribution is a favor, not a contribution yielding
authorship
Introduce lab members to people in the field
-Talk up the lab members.
-Their career will impact your career. (i.e. their career good = kudos for you)
Send students and postdocs to present at conferences
-They need the exposure and network opportunities
-You also need exposure of their projects
-Again, their career good = kudos for you
-Help them network at these conferences
Give trainees expectations
-Vacation time expectations (grad school is long, they will need vacation)
-Hours and/or productivity expectations
-if you're happy with their productivity, you won't care about hours
-if their project is not producing, of course you'll want to see their commitment via hours
-years/publications required for leaving/graduating
Give trainees feedback
-What are their strengths?
-What are things they need to work on?
-What are their career goals? What do they need to work on for those goals?
Get trainees feedback
-What can the PI do to help trainees be productive?
Stay high level and big picture as much as possible
-You want good science getting done, they almost definitely want that, too
-Give them suggestions.
-Kindly argue with them about science (don't insult them, don't deride them, don't threaten them)
-Let them try their own way and make mistakes.
-Only insist on your way as a last resort.
-Amount of independence will change as lab members mature scientifically
-And/or senior lab members may direct junior lab members at a more specific level
Advise trainees on what is needed for publications, thesis and conference presentations
-The work will always be "in progress" (the beauty of science...)
-The work can always be better
-You can always figure out more stuff
-When is it enough to write up or present?
To micromanage, or not to micromanage? That is the question.
Love this discussion over at drug monkey.
And ok, I realize the question isn't exactly about micromanaging. It's about how to run a lab effectively. Should the PI know the nitty-gritty details of the science and techniques? Should the PI actually be in the trenches doing experiments? Or is the PI mainly a manager and fund raiser?
I wonder how, historically, professorships have developed into their current state? I've read some historical accounts of science. One I recall is an account of Millikan's oil-drop experiment. From the story, it sounded like Millikan at most had 1-2 trainees at any given time. In fact, Millikan is the sole author on the most famous of the oil-drop papers. (Although from what I read, this sole-authorship may have been somewhat shady.) Anyway, I wonder how the modern lab came to be, with several trainees per professor, and professors with more responsibilities than seem humanly possible to really handle well.
And ok, I realize the question isn't exactly about micromanaging. It's about how to run a lab effectively. Should the PI know the nitty-gritty details of the science and techniques? Should the PI actually be in the trenches doing experiments? Or is the PI mainly a manager and fund raiser?
I wonder how, historically, professorships have developed into their current state? I've read some historical accounts of science. One I recall is an account of Millikan's oil-drop experiment. From the story, it sounded like Millikan at most had 1-2 trainees at any given time. In fact, Millikan is the sole author on the most famous of the oil-drop papers. (Although from what I read, this sole-authorship may have been somewhat shady.) Anyway, I wonder how the modern lab came to be, with several trainees per professor, and professors with more responsibilities than seem humanly possible to really handle well.
Wednesday, May 12, 2010
Paranoia
Isis has made me paranoid about my pseudonymity. So, I removed a bunch of posts that were concerning me. Unfortunately, it seemed like the posts that would be most concerning if I were outed were also the most likely to have gotten comments. Hence they were probably the interesting ones. Oh well. Now I'm going to think about if/how I want this paranoia to change my future blogging. It's definitely true that science is an extraordinarily small world.
Thursday, May 6, 2010
Teaching and Researching - not necessarily better together
I've written before about the problems I see with lumping teaching and researching into the job description of all college level professors. I know some professors can make it work, but I think many students lose and many scientists lose because excellent teachers must also spend significant time on research, and excellent researchers must also teach.
So, of course I was pleased to find this Nature article that mirrors my opinion. Of course I'm not pleased that research funding is highly likely to decrease. But I do think the article is correct that newer faculty at undergraduate institutions are suffering when saddled with a research load in addition to their heavy teaching load. And their students may suffer, too, if the professors in question can't work 80 hours a week. (Or even if they can.)
So, of course I was pleased to find this Nature article that mirrors my opinion. Of course I'm not pleased that research funding is highly likely to decrease. But I do think the article is correct that newer faculty at undergraduate institutions are suffering when saddled with a research load in addition to their heavy teaching load. And their students may suffer, too, if the professors in question can't work 80 hours a week. (Or even if they can.)
Monday, April 26, 2010
Need more scientists? I call bullshit
The CEO of Xerox says the state of math and science education in the US is "very, very, very poor." And she says that we need to graduate more scientists.
Bullshit.
First, the US has the best science and math education system in the world at the graduate level, as judged by *science productivity. Efficient? Maybe not. Ideally suited for training scientists for what they'll actually do for the rest of their lives? Maybe not. But producing scientific publications? Hell yes.
And does the US need more scientists? Maybe my perspective is skewed, being a biophysics Ph.D. student with a physicist for a husband, and currently living in Silicon Valley. But I know too many excellent scientists without jobs to lend any credence whatsoever to the statement that we need more scientists. My husband's company puts out job ads looking for an extremely specific type of physicist, and gets 500+ applications. Who wants more scientists? CEO's of large tech-based companies want more scientist so more people compete for their job openings, so the companies can hire better scientists for worse working conditions. Personally, that's not what I want.
Xerox's CEO says we graduate lots of lawyers in the US, but not enough scientists. Well, maybe we could try paying scientist better. We're in school for several years longer than lawyers, and we pull similarly long hours, but our salaries tend to be **<2/3of lawyers salaries. And I know our graduate education is paid for and lawyers' educations are not, but the fact that they graduate and start making 6 figures at age 25 and we graduate at age 28+ more than makes up for their education costs.
More scientists? No thank you. Better valued and better treated scientists? Yes, please.
*ISI Web of Knowledge search of the Science Citation Index Expanded,
Articles only, Year to date 2010 publications
1. USA - 83,356
2. Peoples R China - 39,613
3. Germany - 23,403
**Total compensation in US, base salary + bonuses + benefits (from salary.com)
Attorney I - $138k
Attorney II - 173k
Attorney III - 218k
Scientist I Biotech - $120k
Scientist II Biotech - 143k
Scientist III Biotech - 163k
Postdoctoral Scientist - $63k
Assistant Professor - Chemistry - 74k
Associate Professor - Chemistry - 88k
Average attorney salary = $176k
Average scientist salary = $108.5k
Bullshit.
First, the US has the best science and math education system in the world at the graduate level, as judged by *science productivity. Efficient? Maybe not. Ideally suited for training scientists for what they'll actually do for the rest of their lives? Maybe not. But producing scientific publications? Hell yes.
And does the US need more scientists? Maybe my perspective is skewed, being a biophysics Ph.D. student with a physicist for a husband, and currently living in Silicon Valley. But I know too many excellent scientists without jobs to lend any credence whatsoever to the statement that we need more scientists. My husband's company puts out job ads looking for an extremely specific type of physicist, and gets 500+ applications. Who wants more scientists? CEO's of large tech-based companies want more scientist so more people compete for their job openings, so the companies can hire better scientists for worse working conditions. Personally, that's not what I want.
Xerox's CEO says we graduate lots of lawyers in the US, but not enough scientists. Well, maybe we could try paying scientist better. We're in school for several years longer than lawyers, and we pull similarly long hours, but our salaries tend to be **<2/3of lawyers salaries. And I know our graduate education is paid for and lawyers' educations are not, but the fact that they graduate and start making 6 figures at age 25 and we graduate at age 28+ more than makes up for their education costs.
More scientists? No thank you. Better valued and better treated scientists? Yes, please.
*ISI Web of Knowledge search of the Science Citation Index Expanded,
Articles only, Year to date 2010 publications
1. USA - 83,356
2. Peoples R China - 39,613
3. Germany - 23,403
**Total compensation in US, base salary + bonuses + benefits (from salary.com)
Attorney I - $138k
Attorney II - 173k
Attorney III - 218k
Scientist I Biotech - $120k
Scientist II Biotech - 143k
Scientist III Biotech - 163k
Postdoctoral Scientist - $63k
Assistant Professor - Chemistry - 74k
Associate Professor - Chemistry - 88k
Average attorney salary = $176k
Average scientist salary = $108.5k
Friday, April 9, 2010
Stupid, Stupid Graduate School
If only it weren't way too late for me, I think I would read this:
http://www.insidehighered.com/news/2010/04/08/ruben
http://www.insidehighered.com/news/2010/04/08/ruben
Thursday, April 8, 2010
Wednesday, March 17, 2010
Lucky?
(Comment by me on a post at Women in Wetlands.)
The original post is interesting and thought provoking, and I encourage you to read it. It dealt with "...the idea that successful scientists are somehow luckier than everyone else." The post makes the assertion that "This is rarely true..." The text below is my response.
Interesting post. I want to completely agree with you, but recent life experiences (graduate school) make me disagree.
It's not that I think successful scientists don't work hard. I know many very successful scientists, and they definitely work very hard. No question. And they're smart. No question. The problem is that I also know many scientists who are also very smart and work very hard but just haven't obtained success. So, what's the difference?
The difference must either be "luck" or some je ne sais quoi traits the successful have and the unsuccessful do not. And, I think it's both.
I'd like to give a concrete example to illustrate. Let's say a smart, hard-working student starts graduate school. The student chooses to do research in a brand new lab with a brand new PI. This choice is risky, since the lab and the PI are unknowns, but with risk often comes the possiblity of high reward. The reward might be learning to build an experiment and a lab from scratch with an enthusiastic, rising-star PI.
But, sometimes a new PI will flounder, and it's impossible for this floundering to not greatly affect the students. Perhaps the PI can't obtain funding after start-up funds run out. And students are left to try to join a new lab 5+ years in, or try to graduate with no publications. Or perhaps the PI's experiments, which sound amazing enough to be funded by brilliant, experienced professors sitting on NIH panels, turn out to be impossible to interpret. And students are left with uninterpretable results, no publications, and really no results that would even make a reasonable thesis. Should the students have been smart enough to avoid a new PI? Or smart enough to avoid a bad new PI? Even the talented and experienced professors who chose to hire the new PI weren't smart enough to avoid the bad PI, so it seems impossible to expect students to be able to discern. So, the student who chose this lab is highly unsuccessful by the standards used for judging success in science. The student possibly receives no Ph.D., or maybe a Ph.D. with no publications. Was that student less talented? Less hard working? No. That student was unlucky.
Could that student have possessed some traits that would have prevented this "unlucky" situation? Yes. The student could have not been a risk-taker, and therefore not chosen a new lab and new PI. Or the student could be non-persistent, and could have chosen to switch labs after 2-3 years of frustration. So, this situation selected for non-risk-taking quitters. Just what we want in science, no?
I suppose the biggest argument would be, could that student still go on to achieve success? Maybe. Maybe that student can try to do a brand new Ph.D. using everything they know now. Or maybe the student can do multiple post docs. But maybe now that student is 28-30 years old, and doesn't feel they have the time or energy to do it all again. Plus, now they know it's not all about hard work and talent. Now they know there's luck involved, and no guarantee that they won't be unlucky again, perhaps in a different way this time.
I actually think this last issue is the worst negative impact of the whole situation. As you say, believing luck has anything to do with success is self-defeating. But isn't it also self-defeating to just decide you're not cut out for this profession because your hardest work wasn't good enough in this particular situation?
The original post is interesting and thought provoking, and I encourage you to read it. It dealt with "...the idea that successful scientists are somehow luckier than everyone else." The post makes the assertion that "This is rarely true..." The text below is my response.
Interesting post. I want to completely agree with you, but recent life experiences (graduate school) make me disagree.
It's not that I think successful scientists don't work hard. I know many very successful scientists, and they definitely work very hard. No question. And they're smart. No question. The problem is that I also know many scientists who are also very smart and work very hard but just haven't obtained success. So, what's the difference?
The difference must either be "luck" or some je ne sais quoi traits the successful have and the unsuccessful do not. And, I think it's both.
I'd like to give a concrete example to illustrate. Let's say a smart, hard-working student starts graduate school. The student chooses to do research in a brand new lab with a brand new PI. This choice is risky, since the lab and the PI are unknowns, but with risk often comes the possiblity of high reward. The reward might be learning to build an experiment and a lab from scratch with an enthusiastic, rising-star PI.
But, sometimes a new PI will flounder, and it's impossible for this floundering to not greatly affect the students. Perhaps the PI can't obtain funding after start-up funds run out. And students are left to try to join a new lab 5+ years in, or try to graduate with no publications. Or perhaps the PI's experiments, which sound amazing enough to be funded by brilliant, experienced professors sitting on NIH panels, turn out to be impossible to interpret. And students are left with uninterpretable results, no publications, and really no results that would even make a reasonable thesis. Should the students have been smart enough to avoid a new PI? Or smart enough to avoid a bad new PI? Even the talented and experienced professors who chose to hire the new PI weren't smart enough to avoid the bad PI, so it seems impossible to expect students to be able to discern. So, the student who chose this lab is highly unsuccessful by the standards used for judging success in science. The student possibly receives no Ph.D., or maybe a Ph.D. with no publications. Was that student less talented? Less hard working? No. That student was unlucky.
Could that student have possessed some traits that would have prevented this "unlucky" situation? Yes. The student could have not been a risk-taker, and therefore not chosen a new lab and new PI. Or the student could be non-persistent, and could have chosen to switch labs after 2-3 years of frustration. So, this situation selected for non-risk-taking quitters. Just what we want in science, no?
I suppose the biggest argument would be, could that student still go on to achieve success? Maybe. Maybe that student can try to do a brand new Ph.D. using everything they know now. Or maybe the student can do multiple post docs. But maybe now that student is 28-30 years old, and doesn't feel they have the time or energy to do it all again. Plus, now they know it's not all about hard work and talent. Now they know there's luck involved, and no guarantee that they won't be unlucky again, perhaps in a different way this time.
I actually think this last issue is the worst negative impact of the whole situation. As you say, believing luck has anything to do with success is self-defeating. But isn't it also self-defeating to just decide you're not cut out for this profession because your hardest work wasn't good enough in this particular situation?
Tuesday, March 16, 2010
How to Get a PhD (in science, anyway) - Step 9
Step 9: Write the thesis.
If you have written one or more publications, with minor modifications you likely already have one or more thesis chapters. Go back to your rough thesis outline and fill it in with the methods you used and the results you have now. Write it similarly to the papers, starting with the rough outline, filling in rough drawn versions of figures, replacing drawn figures with preliminary versions of real figures, writing subsection outlines. Decide on any gaps that you'd like to fill. Fill in text - figure captions, introduction, abstract, text, references, acknowledgements, conclusions. Edit and reedit. Keep track of methods, data, and analysis used in the thesis. Submit to committee, defend, and graduate.
If you have written one or more publications, with minor modifications you likely already have one or more thesis chapters. Go back to your rough thesis outline and fill it in with the methods you used and the results you have now. Write it similarly to the papers, starting with the rough outline, filling in rough drawn versions of figures, replacing drawn figures with preliminary versions of real figures, writing subsection outlines. Decide on any gaps that you'd like to fill. Fill in text - figure captions, introduction, abstract, text, references, acknowledgements, conclusions. Edit and reedit. Keep track of methods, data, and analysis used in the thesis. Submit to committee, defend, and graduate.
How to Get a PhD (in science, anyway) - Step 8
Step 8: Decide. Write the thesis or keep going?
Do you have enough for a thesis? Do you have as much as you want/need before you move on to your next job? Can you convince your advisor to let you go or stay, as you wish? Make a decision and repeat previous steps as necessary.
Do you have enough for a thesis? Do you have as much as you want/need before you move on to your next job? Can you convince your advisor to let you go or stay, as you wish? Make a decision and repeat previous steps as necessary.
How to Get a PhD (in science, anyway) - Step 7
Step 7: Publish.
Once you have an interesting set of results, write it up and publish it. Think about the appropriate journal for the paper. Look at where many of your references are published, and consider the novelty of your work. Then, start with your drawn up picture outline and modify it to fit into the journal's format. Fill in the drawn figures with preliminary versions of real figures. Make a real paper outline with an introduction, methods, results, and conclusions. Do you notice any gaps as you go? Any logical gaps? Any control experiments that need to be performed? Any tests of accuracy or precision that need to be performed? Any uncertainties to be quantified? Plan and perform more experiments as needed. Fill in figure captions, the abstract, and other text, including references. Get feedback from your advisor and coauthors. Edit and reedit and reedit. Perfect figures. Carefully keep track of methods, data, and analysis used in the paper. Submit and wait for your response!
Once you have an interesting set of results, write it up and publish it. Think about the appropriate journal for the paper. Look at where many of your references are published, and consider the novelty of your work. Then, start with your drawn up picture outline and modify it to fit into the journal's format. Fill in the drawn figures with preliminary versions of real figures. Make a real paper outline with an introduction, methods, results, and conclusions. Do you notice any gaps as you go? Any logical gaps? Any control experiments that need to be performed? Any tests of accuracy or precision that need to be performed? Any uncertainties to be quantified? Plan and perform more experiments as needed. Fill in figure captions, the abstract, and other text, including references. Get feedback from your advisor and coauthors. Edit and reedit and reedit. Perfect figures. Carefully keep track of methods, data, and analysis used in the paper. Submit and wait for your response!
How to Get a PhD (in science, anyway) - Step 6
Step 6: Keep going.
Keep reading, discussing, critiquing, planning. Update the picture outline for your paper on a monthly basis, or as your results make a new outline necessary. Keep conducting your experiments, collecting and analyzing your data, and interpreting your results. And keep writing in your lab notebook.
Keep reading, discussing, critiquing, planning. Update the picture outline for your paper on a monthly basis, or as your results make a new outline necessary. Keep conducting your experiments, collecting and analyzing your data, and interpreting your results. And keep writing in your lab notebook.
How to Get a PhD (in science, anyway) - Step 5
Step 5: Start your research and finish up coursework and exams as you can.
*Read.
Get acquainted with your specific research topic and techniques. Start with the most recent review articles on your topic, and work into more specific literature. Read grant proposals if available from your advisor.
*Discuss and critique the literature.
Ideally, discuss literature with colleagues and your advisor. A journal club where someone presents an article and everyone reads and critiques the article can be great for learning an article in depth and learning to critique the literature.
*Plan your research.
From the literature you've read, you should have been able to form some interesting and unproven hypotheses. Plan experiments to test your hypotheses. Plan experiments that will have interpretable results. Avoid experiments that will only be interpretable if your hypothesis is true. Also, ideally design experiments that utilize techniques that are already working in your lab. Don't underestimate the difficulty of developing a technique from the literature into a working technique in your lab. And remember that redoing something from the literature will get you no credit towards a thesis or a publication. Get as much feedback as possible during these planning stages. A good advisor or colleague can provide excellent perspective and ideas on what hypotheses are interesting to pursue, what experiments are more interpretable, and information about existing techniques and their difficulty.
*Make a super-rough, super-early thesis outline.
This outline is really just to focus your work. The thesis will generally consist of an Introduction, Methods Chapter, 2 or more Results Chapters, and Conclusions and References. Coming up with basic topics for your 2 results chapters will help you be focused and driven towards the ultimate Ph.D. requirement - the dissertation. And getting feedback from your advisor on this outline will help the two of you be on the same page from the beginning. Don't be too attached to this thesis outline, since you are only beginning to test your hypothesis, and want to be led to the most interesting possible research by your experiments.
*Make a super-rough picture outline for a paper.
Simply sit down for an hour and rough out drawings of figures you envision being in a paper. Figures often start with an experimental geometry figure, followed by the key results from your experiments. These results may be in the form of photographs, tables, graphs, or other forms of presenting results. Sometimes papers also include figures explaining key interpretations or models drawn from the results. Roughly draw figures you envision for your paper. Again, don't be too attached to this picture outline! Your experiments will yield the results, and these may lead you to different conclusions and down different paths than you expected. Again, feedback on this picture outline helps to get you and your advisor on the same page for expectations.
*Conduct your experiments, collect and analyze your data, and interpret your results.Plan on the long, intermediate, and short time scales. Make a very rough long-term plan that includes time frames for your planned experiments, analysis, and writing up the results as a paper. This long-term plan must be flexible to incorporate the findings of your day-to-day experiments. Plan on the intermediate-scale, e.g. the weekly time-scale. Choose which days to do which experiments, how much time must be set-aside for analysis, and remember to include time for keeping up with the literature, going to interesting talks, and planning. Plan on the short-time scale, i.e. for the experiment for the day.
*Write.
In your lab notebook, each day (or the previous evening), write down a goal or goals for the day, and an explanation of the planned methods. Write down and record as much as possible during the day, and close each day with some conclusions drawn from the day. For repetitive tasks with minimal changes, reference a previous page in the notebook, and/or type a template that can be printed and pasted into the notebook. Computer files are excellent resources, but be very careful about noting in your lab notebook where the files can be found. Also be very careful about backing up files, and remember that computer files are very easily changed. This easy changing is good and bad. The easy changes are good for updating files as new information is available, but bad if you need to reference what existed at a certain time. For example, a computer file holding a Methods procedure may be referenced in your lab notebook. Later, you may improve the procedure and update the computer file. But you may still need to know exactly how you performed the procedure for the data in your lab notebook. So, be careful with computer files and develop a system for updating and keeping old copies, and for backing up your files.
*Read.
Get acquainted with your specific research topic and techniques. Start with the most recent review articles on your topic, and work into more specific literature. Read grant proposals if available from your advisor.
*Discuss and critique the literature.
Ideally, discuss literature with colleagues and your advisor. A journal club where someone presents an article and everyone reads and critiques the article can be great for learning an article in depth and learning to critique the literature.
*Plan your research.
From the literature you've read, you should have been able to form some interesting and unproven hypotheses. Plan experiments to test your hypotheses. Plan experiments that will have interpretable results. Avoid experiments that will only be interpretable if your hypothesis is true. Also, ideally design experiments that utilize techniques that are already working in your lab. Don't underestimate the difficulty of developing a technique from the literature into a working technique in your lab. And remember that redoing something from the literature will get you no credit towards a thesis or a publication. Get as much feedback as possible during these planning stages. A good advisor or colleague can provide excellent perspective and ideas on what hypotheses are interesting to pursue, what experiments are more interpretable, and information about existing techniques and their difficulty.
*Make a super-rough, super-early thesis outline.
This outline is really just to focus your work. The thesis will generally consist of an Introduction, Methods Chapter, 2 or more Results Chapters, and Conclusions and References. Coming up with basic topics for your 2 results chapters will help you be focused and driven towards the ultimate Ph.D. requirement - the dissertation. And getting feedback from your advisor on this outline will help the two of you be on the same page from the beginning. Don't be too attached to this thesis outline, since you are only beginning to test your hypothesis, and want to be led to the most interesting possible research by your experiments.
*Make a super-rough picture outline for a paper.
Simply sit down for an hour and rough out drawings of figures you envision being in a paper. Figures often start with an experimental geometry figure, followed by the key results from your experiments. These results may be in the form of photographs, tables, graphs, or other forms of presenting results. Sometimes papers also include figures explaining key interpretations or models drawn from the results. Roughly draw figures you envision for your paper. Again, don't be too attached to this picture outline! Your experiments will yield the results, and these may lead you to different conclusions and down different paths than you expected. Again, feedback on this picture outline helps to get you and your advisor on the same page for expectations.
*Conduct your experiments, collect and analyze your data, and interpret your results.Plan on the long, intermediate, and short time scales. Make a very rough long-term plan that includes time frames for your planned experiments, analysis, and writing up the results as a paper. This long-term plan must be flexible to incorporate the findings of your day-to-day experiments. Plan on the intermediate-scale, e.g. the weekly time-scale. Choose which days to do which experiments, how much time must be set-aside for analysis, and remember to include time for keeping up with the literature, going to interesting talks, and planning. Plan on the short-time scale, i.e. for the experiment for the day.
*Write.
In your lab notebook, each day (or the previous evening), write down a goal or goals for the day, and an explanation of the planned methods. Write down and record as much as possible during the day, and close each day with some conclusions drawn from the day. For repetitive tasks with minimal changes, reference a previous page in the notebook, and/or type a template that can be printed and pasted into the notebook. Computer files are excellent resources, but be very careful about noting in your lab notebook where the files can be found. Also be very careful about backing up files, and remember that computer files are very easily changed. This easy changing is good and bad. The easy changes are good for updating files as new information is available, but bad if you need to reference what existed at a certain time. For example, a computer file holding a Methods procedure may be referenced in your lab notebook. Later, you may improve the procedure and update the computer file. But you may still need to know exactly how you performed the procedure for the data in your lab notebook. So, be careful with computer files and develop a system for updating and keeping old copies, and for backing up your files.
How to Get a PhD (in science, anyway) - Step 4
Step 4: Begin to complete requirements (coursework, exams) and look for a research group, topic and advisor.
Complete general requirements first, unless courses may help you choose your research topic.
Your advisor matters even more than the topic. Choose carefully. An amazing research subject and project can be ruined by a terrible advisor. The support of your advisor will be huge deciding factor on your Ph.D. and future career. Ideally, choose someone who truly wants to and knows how to support Ph.D. students through a successful Ph.D.
Complete general requirements first, unless courses may help you choose your research topic.
Your advisor matters even more than the topic. Choose carefully. An amazing research subject and project can be ruined by a terrible advisor. The support of your advisor will be huge deciding factor on your Ph.D. and future career. Ideally, choose someone who truly wants to and knows how to support Ph.D. students through a successful Ph.D.
How to Get a PhD (in science, anyway) - Step 3
Step 3: Start!
The curriculum - print it out when you start and put it in a file. That way, you have all the requirements in one place when you need to look it up again, and you have all the rules that existed when you started in case requirements change during your degree. You may also want to print requirements for Masters degrees. Why? What if you love your coursework and hate research? Do you really want to spend 5-10 years working on research you hate to perfectly prepare you to do more research you hate? No. Or what if you realize a completely different calling 2 years into your degree? Or what if you get a fantastic job offer, or want to/need to move across the country? You want to know your options if you realize the Ph.D. is not for you.
Requirements to print and keep on file:
Required coursework - specific classes, possible electives
Required exams - Qualifying, Comprehensive, Oral, Written, Proposals, Defense
Other requirements - publications, registration, thesis requirements
Committee requirements, time limits
The curriculum - print it out when you start and put it in a file. That way, you have all the requirements in one place when you need to look it up again, and you have all the rules that existed when you started in case requirements change during your degree. You may also want to print requirements for Masters degrees. Why? What if you love your coursework and hate research? Do you really want to spend 5-10 years working on research you hate to perfectly prepare you to do more research you hate? No. Or what if you realize a completely different calling 2 years into your degree? Or what if you get a fantastic job offer, or want to/need to move across the country? You want to know your options if you realize the Ph.D. is not for you.
Requirements to print and keep on file:
Required coursework - specific classes, possible electives
Required exams - Qualifying, Comprehensive, Oral, Written, Proposals, Defense
Other requirements - publications, registration, thesis requirements
Committee requirements, time limits
How to Get a PhD (in science, anyway) - Step 2
Step 2: Apply
Exams - General and Subject GRE's
Personal Statement
Recommendations
Exams - General and Subject GRE's
Personal Statement
Recommendations
How to Get a PhD (in science, anyway) - Step 1
Step 1: Choose a program
Location, School, Department, Faculty, Available research possibilities
Location, School, Department, Faculty, Available research possibilities
Tuesday, March 9, 2010
Humanities vs Science
(A comment by me on an FSP post)
...From discussions with academics in humanities, the demands, pressures, and everyday lives of humanities and science academics are very different. For one thing, it seems much more difficult to get a paying job in the humanities (as a grad student, as a professor, or as anything else.)
The student-professor relationship also seems very different. Students who work as research assistants in humanities seem to get paid, but get no credit on publications for which they assist? And students' research may have very, very little to do with their advisors' research. In contrast, Ph.D. advisors in the sciences seem to have huge responsibility to their students, often providing funding, lab space and equipment, the big idea behind the projects, and various degrees of involvement in their students' research process. All that responsibility is in exchange for co-authorship on publications, which seems to be the main determining factor on tenure decisions for all academics.
It seems like science professors are more managers and project leaders, while humanities professors continue more directly doing research and writing. That difference is much larger than just the disparity of the fields.
And there's also the difference that in science, researchers have to conceive of an idea that is interesting and not already published, design and build instrumentation to test said idea, acquire data (which may or may not provide interestingly interpretable results, and then analyze, interpret and publish said results. In the humanities, researchers get to skip right from conceive of an idea to the analysis. And they never have to worry that it just won't work. Effing science.
...From discussions with academics in humanities, the demands, pressures, and everyday lives of humanities and science academics are very different. For one thing, it seems much more difficult to get a paying job in the humanities (as a grad student, as a professor, or as anything else.)
The student-professor relationship also seems very different. Students who work as research assistants in humanities seem to get paid, but get no credit on publications for which they assist? And students' research may have very, very little to do with their advisors' research. In contrast, Ph.D. advisors in the sciences seem to have huge responsibility to their students, often providing funding, lab space and equipment, the big idea behind the projects, and various degrees of involvement in their students' research process. All that responsibility is in exchange for co-authorship on publications, which seems to be the main determining factor on tenure decisions for all academics.
It seems like science professors are more managers and project leaders, while humanities professors continue more directly doing research and writing. That difference is much larger than just the disparity of the fields.
And there's also the difference that in science, researchers have to conceive of an idea that is interesting and not already published, design and build instrumentation to test said idea, acquire data (which may or may not provide interestingly interpretable results, and then analyze, interpret and publish said results. In the humanities, researchers get to skip right from conceive of an idea to the analysis. And they never have to worry that it just won't work. Effing science.
Monday, March 8, 2010
"Success" in research training - NIGMS Question 1
1. What constitutes "success" in biomedical research training from the perspectives of an individual trainee, an institution, and society?
From the perspective of the individual, institution, and society, successful research training at the Ph.D. and postdoctoral level should prepare trainees to successfully lead research projects. Thus, trainees should develop and practice all aspects of the research process: project conception, design, and funding (including grant writing); experimental technique (ideally multiple techniques), data collection, and analysis; and interpretation, publication, and presentation of results. Many of these steps in the research process require critical evaluation of the relevant literature. In practice, research training is not complete without safety training, an emphasis on ethics, and knowledge of the rules governing research (such as government and institutional regulations). Trainees should be mentored in all of these skills, and should be given the opportunity to practice and develop these skills during their training.
The requirements for training at the Ph.D. and postdoctoral level are distinct from training for undergraduates or technicians. Undergraduates and technicians may only need training in the technical aspects of research, e.g. experimental techniques, data collection, and analysis. Many undergraduates and technicians may also benefit from the other aspects of training, but Ph.D. and postdoctoral level training demand the other aspects in order to educate trainees to the level of research project leadership.
Having research training completed in the most efficient manner is highly desirable. Efficient training makes a trainee capable of better work faster, which makes for more efficient use of funding, and more efficient use of the trainees time and opportunity costs incurred while training. Efficient training requires a careful and customized balance between guidance and independence. Too much guidance and a trainee may not learn to perform tasks independently; not enough guidance and a trainee may flounder unnecessarily. The efficiency of training is critical to creating excellent scientists without wasting time and money.
Finally, successful training also should include guidance for obtaining a post-training position. Guidance should give the trainee awareness of the types of positions for which they qualify, should help the trainee establish a professional network during training, and should provide evaluations of the trainee to help them choose the right post-graduate positions to pursue. Training should also ideally yield fair metrics for employers to use in judging trainees fit for post-training positions.
In summary, successful research training for Ph.D. and postdoctoral trainees should efficiently guide them through the practice of all aspects of the research process, and should also guide them in choosing their ideal post-training position.
From the perspective of the individual, institution, and society, successful research training at the Ph.D. and postdoctoral level should prepare trainees to successfully lead research projects. Thus, trainees should develop and practice all aspects of the research process: project conception, design, and funding (including grant writing); experimental technique (ideally multiple techniques), data collection, and analysis; and interpretation, publication, and presentation of results. Many of these steps in the research process require critical evaluation of the relevant literature. In practice, research training is not complete without safety training, an emphasis on ethics, and knowledge of the rules governing research (such as government and institutional regulations). Trainees should be mentored in all of these skills, and should be given the opportunity to practice and develop these skills during their training.
The requirements for training at the Ph.D. and postdoctoral level are distinct from training for undergraduates or technicians. Undergraduates and technicians may only need training in the technical aspects of research, e.g. experimental techniques, data collection, and analysis. Many undergraduates and technicians may also benefit from the other aspects of training, but Ph.D. and postdoctoral level training demand the other aspects in order to educate trainees to the level of research project leadership.
Having research training completed in the most efficient manner is highly desirable. Efficient training makes a trainee capable of better work faster, which makes for more efficient use of funding, and more efficient use of the trainees time and opportunity costs incurred while training. Efficient training requires a careful and customized balance between guidance and independence. Too much guidance and a trainee may not learn to perform tasks independently; not enough guidance and a trainee may flounder unnecessarily. The efficiency of training is critical to creating excellent scientists without wasting time and money.
Finally, successful training also should include guidance for obtaining a post-training position. Guidance should give the trainee awareness of the types of positions for which they qualify, should help the trainee establish a professional network during training, and should provide evaluations of the trainee to help them choose the right post-graduate positions to pursue. Training should also ideally yield fair metrics for employers to use in judging trainees fit for post-training positions.
In summary, successful research training for Ph.D. and postdoctoral trainees should efficiently guide them through the practice of all aspects of the research process, and should also guide them in choosing their ideal post-training position.
Thursday, March 4, 2010
NIGMS Strategic Plan for Training and Career Development
Ahhh...a chance to provide some feedback that may actually make a difference. Fellow scientists, let's help make things better for the next generation of trainees.
NIGMS Strategic Plan for Training and Career Development
They would like input to answer the following 7 questions:
1. What constitutes "success" in biomedical research training from the perspectives of an individual trainee, an institution, and society?
2. What can NIGMS do to encourage an optimal balance of breadth and depth in research training?
3. What can NIGMS do to encourage an appropriate balance between research productivity and successful outcomes for the mentor’s trainees?
4. What can NIGMS do through its training programs to promote and encourage greater diversity in the biomedical research workforce?
5. Recognizing that students have different career goals and interests, should NIGMS encourage greater flexibility in training, and if so, how?
6. What should NIGMS do to ensure that institutions monitor, measure, and continuously improve the quality of their training efforts?
7. Do you have other comments or recommendations regarding NIGMS-sponsored training?
My plan: Use my blog to write some thoughtful answers over the next few days, and then submit my input to their website. Anybody else want to play?
NIGMS Strategic Plan for Training and Career Development
They would like input to answer the following 7 questions:
1. What constitutes "success" in biomedical research training from the perspectives of an individual trainee, an institution, and society?
2. What can NIGMS do to encourage an optimal balance of breadth and depth in research training?
3. What can NIGMS do to encourage an appropriate balance between research productivity and successful outcomes for the mentor’s trainees?
4. What can NIGMS do through its training programs to promote and encourage greater diversity in the biomedical research workforce?
5. Recognizing that students have different career goals and interests, should NIGMS encourage greater flexibility in training, and if so, how?
6. What should NIGMS do to ensure that institutions monitor, measure, and continuously improve the quality of their training efforts?
7. Do you have other comments or recommendations regarding NIGMS-sponsored training?
My plan: Use my blog to write some thoughtful answers over the next few days, and then submit my input to their website. Anybody else want to play?
Wednesday, March 3, 2010
Politics
In case I haven't mentioned it previously, I'm from the Southern United States. In my experience, a white person in/from the Southern US is almost guaranteed to be politically conservative. Very conservative. And for my family, my Southern friends, and my family's friends, this guarantee rings very true, meaning my family and my Southern friends are all very conservative. And everyone they know is also very conservative. So everyone I interact with when I go home is very, very politically conservative. And, they kind of assume I am, too.
In my professional life, I'm an academic (even if I am just a lowly grad student). In my experience, academics are almost guaranteed to be politically liberal. Even the academics I knew in the South were politically liberal, so there you go. I guess profession trumps location when it comes to political leanings. And, most of my academic associates also assume I'm politically liberal.
What's fun about this collection of quasi-facts? Well, it means I get to (have to) hear very candid views from both ends of the political spectrum. Sure, anyone can turn on the TV or radio and hear candid views from the people on TV (although in my experience, most people only listen to people on the same side as themselves). But I get to hear it from regular people, all of whom are assuming I agree and are therefore not censoring themselves to avoid argument or hurt feelings. The experience is interesting.
It's really interesting, because I can tell that most people don't ever hear both sides this way. I'm so amazed at how the two sides see each other. How my conservative people see liberal people as so out of touch with reality, so trapped in a bubble where everyone is smart and good-willed, and either has so much they can afford to give it away, or has nothing and wants to get as much for doing nothing as other people get for working hard. And there is a perception that liberals don't understand that many of the policies they support would give government control over everything, and that this control is government's main goal. On the other side, the liberal people I know seem to see conservative people as either really stupid and backwards, or so rich and powerful that they will do anything to keep their wealth and power. And both sides see each other as blind followers who just believe what they're told, because really, why else would anyone believe in or support the politics of the "other" side?
Have I gleaned anything useful from my uncommon perspective? I think I have. I've found there are very, very smart people, with very, very good hearts on both sides, who have really thought things through and made a decision about which way to lean. Of course, on both sides there are also plenty of followers who have gone with the surrounding flow. But for those who have thought about it and made a decision, it really breaks down to different philosophies on people, human nature, and motivation. My conservative people have a more pessimistic view, assuming that people are pretty selfish by nature, and need real consequences for their actions in order to be motivated to be productive members of society. Meaning, if you work hard, you get good pay and live the good life. If you don't work hard, you don't get good pay, and maybe you'll be motivated to work a little harder when you get hungry. My liberal people are perhaps more optimistic, and seem to assume that given the right circumstances, everyone will rise to the occasion of being productive members of society.
Obviously this view is oversimplified, but it seems to be how things break down for many policies. Health care: conservatives think people should work hard to get and pay for their own good health care, liberals want a plan to fall back on for people who end up in bad circumstances. Economics: conservatives want minimal intervention so that natural consequences motivate people, assuming that risk will be rewarded; good, hard work will be rewarded; laziness will not be rewarded. Liberals want more intervention to ensure that uncontrolled circumstances don't dominate the course of people's lives, such as trying to institute policies to give women and minorities a fair chance at education and jobs. National defense: conservatives want to carry the big stick, because the fear of that big stick is the motivation for other countries to leave us alone; liberals want our diplomacy and good deeds to influence good will from other countries.
What do I think? I think there are great ideas on both sides. I also think ideas from either side break down in the extreme, because I think most people do need some consequences in order to be motivated, but I also know life hasn't presented a fair opportunity to each of us. So, here's to being an independent, and hoping that multi-party politics can result in good things getting done, rather than preventing anything from getting done.
In my professional life, I'm an academic (even if I am just a lowly grad student). In my experience, academics are almost guaranteed to be politically liberal. Even the academics I knew in the South were politically liberal, so there you go. I guess profession trumps location when it comes to political leanings. And, most of my academic associates also assume I'm politically liberal.
What's fun about this collection of quasi-facts? Well, it means I get to (have to) hear very candid views from both ends of the political spectrum. Sure, anyone can turn on the TV or radio and hear candid views from the people on TV (although in my experience, most people only listen to people on the same side as themselves). But I get to hear it from regular people, all of whom are assuming I agree and are therefore not censoring themselves to avoid argument or hurt feelings. The experience is interesting.
It's really interesting, because I can tell that most people don't ever hear both sides this way. I'm so amazed at how the two sides see each other. How my conservative people see liberal people as so out of touch with reality, so trapped in a bubble where everyone is smart and good-willed, and either has so much they can afford to give it away, or has nothing and wants to get as much for doing nothing as other people get for working hard. And there is a perception that liberals don't understand that many of the policies they support would give government control over everything, and that this control is government's main goal. On the other side, the liberal people I know seem to see conservative people as either really stupid and backwards, or so rich and powerful that they will do anything to keep their wealth and power. And both sides see each other as blind followers who just believe what they're told, because really, why else would anyone believe in or support the politics of the "other" side?
Have I gleaned anything useful from my uncommon perspective? I think I have. I've found there are very, very smart people, with very, very good hearts on both sides, who have really thought things through and made a decision about which way to lean. Of course, on both sides there are also plenty of followers who have gone with the surrounding flow. But for those who have thought about it and made a decision, it really breaks down to different philosophies on people, human nature, and motivation. My conservative people have a more pessimistic view, assuming that people are pretty selfish by nature, and need real consequences for their actions in order to be motivated to be productive members of society. Meaning, if you work hard, you get good pay and live the good life. If you don't work hard, you don't get good pay, and maybe you'll be motivated to work a little harder when you get hungry. My liberal people are perhaps more optimistic, and seem to assume that given the right circumstances, everyone will rise to the occasion of being productive members of society.
Obviously this view is oversimplified, but it seems to be how things break down for many policies. Health care: conservatives think people should work hard to get and pay for their own good health care, liberals want a plan to fall back on for people who end up in bad circumstances. Economics: conservatives want minimal intervention so that natural consequences motivate people, assuming that risk will be rewarded; good, hard work will be rewarded; laziness will not be rewarded. Liberals want more intervention to ensure that uncontrolled circumstances don't dominate the course of people's lives, such as trying to institute policies to give women and minorities a fair chance at education and jobs. National defense: conservatives want to carry the big stick, because the fear of that big stick is the motivation for other countries to leave us alone; liberals want our diplomacy and good deeds to influence good will from other countries.
What do I think? I think there are great ideas on both sides. I also think ideas from either side break down in the extreme, because I think most people do need some consequences in order to be motivated, but I also know life hasn't presented a fair opportunity to each of us. So, here's to being an independent, and hoping that multi-party politics can result in good things getting done, rather than preventing anything from getting done.
Tuesday, February 16, 2010
Procrastination, or incubation?
I think I'm an incubator, which apparently means I procrastinate, but in a good way. That is, I perform well under pressure, so I put stuff off until the last minute, then become highly engaged and complete the task with high quality.
But I don't think it works well for me as a scientist, since I've had almost no hard deadlines in 8 years.
Crap.
But I don't think it works well for me as a scientist, since I've had almost no hard deadlines in 8 years.
Crap.
Tuesday, January 26, 2010
Start-up package and salary negotations - what the seminars have taught me
(Comment by me in response to post at FSP)
I've seen several seminars about negotiating start-up packages, and they have all advised candidates to do the money negotiations once the offer is made.
I've also talked to several post docs on the job market, and know that interviewers usually want some start-up estimate from the candidate before making an offer, which makes sense. If the university can't offer the kind of money necessary to do the research, the university and the candidate are not a good match.
The seminars I've seen also advise the candidate to *definitely* negotiate salary. I always thought it would be much easier (and much more admired) to negotiate for all the start-up package stuff, other than the salary. All the other stuff is all about getting great science done. The salary seems just selfish. But, it was explained to me this way: 1) the university *expects* you to negotiate the salary, so the offer is lower than they are prepared to offer, and they're surprised when you don't negotiate, and 2) negotiating to a salary that is fair and comparable makes the candidate/employee feel valued, loyal to the organization, and allows the candidate/employee to not waste time/energy worrying about salary.
I've seen several seminars about negotiating start-up packages, and they have all advised candidates to do the money negotiations once the offer is made.
I've also talked to several post docs on the job market, and know that interviewers usually want some start-up estimate from the candidate before making an offer, which makes sense. If the university can't offer the kind of money necessary to do the research, the university and the candidate are not a good match.
The seminars I've seen also advise the candidate to *definitely* negotiate salary. I always thought it would be much easier (and much more admired) to negotiate for all the start-up package stuff, other than the salary. All the other stuff is all about getting great science done. The salary seems just selfish. But, it was explained to me this way: 1) the university *expects* you to negotiate the salary, so the offer is lower than they are prepared to offer, and they're surprised when you don't negotiate, and 2) negotiating to a salary that is fair and comparable makes the candidate/employee feel valued, loyal to the organization, and allows the candidate/employee to not waste time/energy worrying about salary.
Salary negotiations - a link
This salary negotiation tactic sounds like it might actually work. Anybody ever tried it?
(From an anonymous comment on a post over at FSP.)
(From an anonymous comment on a post over at FSP.)
Monday, January 25, 2010
Physics Subject GRE, Part 2
So, what do the Physics Subject GRE scores mean? I would love to see a good study evaluating correlations with PhD completion and assorted other measures of academic success. If anyone knows of one, please leave a comment. In the meantime, I wanted to brainstorm some scenarios that would result in high or low scores.
Qualities required to score highly on the Physics Subject GRE
1. The tester has encountered all or almost all of the material on the exam.
2. The tester has excellent recall of equations.
(I'd say successfully completing the test had less to do with recalling basic concepts and basic equations, and more to do with remembering specific equations that exactly equate the variables given.)
3. The tester is pretty darn quick.
(The test is 100 questions in 170 minutes.)
4. The tester can solve word problems.
5. The tester can afford to take the test.
($140-160, plus fees for score distribution)
6. The tester isn't suffering some disabling condition while taking test.
Scenarios that yield a lower score on the Physics Subject GRE
Basically, the opposite of any of the items listed that would yield a high score.
So, let's evaluate some of the list items, considering how the items might predict future grad school success, and how the items might be artificially skewed. Obviously, items 1-4 should be highly correlated with grad school success. I certainly would predict a grad student who has encountered all relevant material, has excellent recall, is quick, and can problem solve would have 4 qualities that would help with successful PhD completion. Are these 4 qualities enough by themselves to predict success? Absolutely not. But, they all help.
Conversely, would missing one or more of these qualities (1-4) ensure failure? I'd say not having encountered the relevant material (1) or not being able to problem solve (4) would put a person at significant disadvantage. To me, the excellent equation recall (2) and the quickness (3) required for this exam are disproportionate to how much those qualities affect PhD course work and research. Basic concepts matter so much more (and knowing how/where to look up specifics), and the ability to think through a long, multi-step problem matters so much more. Being able to do each step quickly and accurately without having to look anything up certainly would help, though. And maybe one would argue that excellent recall without understanding basic concepts would result in bad test scores anyway, so maybe the test does test depth of understanding more than I'm giving credit.
One might think items 5 and 6 don't matter, and maybe they don't matter much. But the test is expensive, even for a college student in the US. The expense is a major deterrent to taking it more than once, for sure, as are scheduling issues. Since the test is covering what's being covered in classes, people try to take it as late as possible in hopes that they will have actually covered the relevant material in class by then. The expense and the scheduling issues mean many people have one shot, so I'm sure a number of people take it even if they're sick as a dog or their Grandpa just died or they have a concussion or whatever. I'm sure this number is statistically insignificant, but I think grad committees would do well to remember this possibility and take a score with a grain of salt if it looks like an outlier compared to other application components.
Finally, what could artificially skew items 1-4 for a person or a segment of the population? Item 1 is the easiest for me to rip on. A good portion of the material covered on the exam is not covered until the senior year at many universities in the US. The exam must be taken near the beginning of the senior year for the applicant that wants to go straight from undergrad to grad, which is the vast majority of students. Meaning students in the US are getting killed on one of the most important requirements for getting a good score on the exam; we've never even encountered a lot of the material we're being tested on. (Instead we've been taking the required English and history and psychology credits and so forth, but I digress...) But these same students would likely score much higher by the time they actually enter grad school, which is when grad schools actually want them to have covered the material. Luckily, many graduate committees recognize this issue and evaluate domestic and international students separately for GRE scoring.
Another example of a subset of population that gets killed by a skew in item 1: students from institutes with a small physics department. Why? I'm sure there are multiple reasons a small department would have some disadvantages, but in several cases I know, a big disadvantage comes from the frequency of basic class offerings. In a good, but small department, students may take all the relevant courses, but they may have to take the courses very much out of the usual order. For example, the student may have to take their first real Mechanics and E&M (beyond intro level) as a senior. Meaning the student would take the exam without having any mechanics or E&M other than intro level. Mechanics and E&M together make up 38% of the exam. That student is screwed.
Ok, I'm really almost done here. I have a couple other skews to discuss.
One skew people talk about a lot is the test taking traditions in various countries. In the US, most students take tests in their physics classes that take 1-3 hours to solve a few (2-6?) problems. The physics GRE is *100* problems in less than 3 hours. This shift in test taking style is enormous, and I can't imagine that it wouldn't artificially affect scores. I've been told that in Asia and India, the test-taking culture is more similar to the GRE, and that continuity helps when taking the GRE.
The final skew I'd like to discuss: studying. Normally, I'd say studying is not an artificial skew, and that a person that studies is a person willing to work hard is a person who is likely to succeed. But in the case of the GRE, I feel like the studying correlation gets messed up. Why? From what I gather, studying for the GRE breaks into 3-4 categories: 1) studying 1 week or less, 2) studying a few months in the evenings while working or taking classes, generally on your own, 3) taking test prep classes, 4) taking months off of work/school to study, very systematically, probably with lots of commercial test aids.
For students in category 1, maybe they don't study and score badly because they are overly-confidant, or lazy, or don't care, or think it doesn't matter much. If they are any of the first 3, it's a good indication they won't perform well in grad school. If it's the 4th reason, possibly having been told this information by professors they trust, then damn, that sucks for them.
For students in category 2 that still score poorly, maybe they don't have the money or resources to take a test prep class, or one is not available in their area. Or they suck at studying or are lacking in a key quality. First few reasons, it's hard to say it would correlate with poor grad school performance. Last 2 reasons, they'd probably suck at grad school.
For category 3 students, I think they learn a lot of good tricks. But they have to be able to afford those classes, and those classes have to be available in their area.
For category 4 students, I've mostly heard that this type of studying is something some international students do because they know/think they have no chance without practically perfect GREs. So I certainly think high scores achieved this way indicate a large amount of drive, which is a huge must in a PhD program. I don't know how much the scores still depend on innate ability at this level of studying, so I don't know how much correlation is left with other abilities. If anyone else knows, pipe up.
So, in conclusion? Excellent scores on the physics GRE's indicate some very desirable qualities for grad school success. The testable qualities, by themselves, don't ensure grad school success, but would certainly help. Some of the testable qualities can be skewed for individuals or segments of the population, so be careful when interpreting results, and know that a poor score definitely does not perfectly correlate with poor performance in grad school.
Qualities required to score highly on the Physics Subject GRE
1. The tester has encountered all or almost all of the material on the exam.
2. The tester has excellent recall of equations.
(I'd say successfully completing the test had less to do with recalling basic concepts and basic equations, and more to do with remembering specific equations that exactly equate the variables given.)
3. The tester is pretty darn quick.
(The test is 100 questions in 170 minutes.)
4. The tester can solve word problems.
5. The tester can afford to take the test.
($140-160, plus fees for score distribution)
6. The tester isn't suffering some disabling condition while taking test.
Scenarios that yield a lower score on the Physics Subject GRE
Basically, the opposite of any of the items listed that would yield a high score.
So, let's evaluate some of the list items, considering how the items might predict future grad school success, and how the items might be artificially skewed. Obviously, items 1-4 should be highly correlated with grad school success. I certainly would predict a grad student who has encountered all relevant material, has excellent recall, is quick, and can problem solve would have 4 qualities that would help with successful PhD completion. Are these 4 qualities enough by themselves to predict success? Absolutely not. But, they all help.
Conversely, would missing one or more of these qualities (1-4) ensure failure? I'd say not having encountered the relevant material (1) or not being able to problem solve (4) would put a person at significant disadvantage. To me, the excellent equation recall (2) and the quickness (3) required for this exam are disproportionate to how much those qualities affect PhD course work and research. Basic concepts matter so much more (and knowing how/where to look up specifics), and the ability to think through a long, multi-step problem matters so much more. Being able to do each step quickly and accurately without having to look anything up certainly would help, though. And maybe one would argue that excellent recall without understanding basic concepts would result in bad test scores anyway, so maybe the test does test depth of understanding more than I'm giving credit.
One might think items 5 and 6 don't matter, and maybe they don't matter much. But the test is expensive, even for a college student in the US. The expense is a major deterrent to taking it more than once, for sure, as are scheduling issues. Since the test is covering what's being covered in classes, people try to take it as late as possible in hopes that they will have actually covered the relevant material in class by then. The expense and the scheduling issues mean many people have one shot, so I'm sure a number of people take it even if they're sick as a dog or their Grandpa just died or they have a concussion or whatever. I'm sure this number is statistically insignificant, but I think grad committees would do well to remember this possibility and take a score with a grain of salt if it looks like an outlier compared to other application components.
Finally, what could artificially skew items 1-4 for a person or a segment of the population? Item 1 is the easiest for me to rip on. A good portion of the material covered on the exam is not covered until the senior year at many universities in the US. The exam must be taken near the beginning of the senior year for the applicant that wants to go straight from undergrad to grad, which is the vast majority of students. Meaning students in the US are getting killed on one of the most important requirements for getting a good score on the exam; we've never even encountered a lot of the material we're being tested on. (Instead we've been taking the required English and history and psychology credits and so forth, but I digress...) But these same students would likely score much higher by the time they actually enter grad school, which is when grad schools actually want them to have covered the material. Luckily, many graduate committees recognize this issue and evaluate domestic and international students separately for GRE scoring.
Another example of a subset of population that gets killed by a skew in item 1: students from institutes with a small physics department. Why? I'm sure there are multiple reasons a small department would have some disadvantages, but in several cases I know, a big disadvantage comes from the frequency of basic class offerings. In a good, but small department, students may take all the relevant courses, but they may have to take the courses very much out of the usual order. For example, the student may have to take their first real Mechanics and E&M (beyond intro level) as a senior. Meaning the student would take the exam without having any mechanics or E&M other than intro level. Mechanics and E&M together make up 38% of the exam. That student is screwed.
Ok, I'm really almost done here. I have a couple other skews to discuss.
One skew people talk about a lot is the test taking traditions in various countries. In the US, most students take tests in their physics classes that take 1-3 hours to solve a few (2-6?) problems. The physics GRE is *100* problems in less than 3 hours. This shift in test taking style is enormous, and I can't imagine that it wouldn't artificially affect scores. I've been told that in Asia and India, the test-taking culture is more similar to the GRE, and that continuity helps when taking the GRE.
The final skew I'd like to discuss: studying. Normally, I'd say studying is not an artificial skew, and that a person that studies is a person willing to work hard is a person who is likely to succeed. But in the case of the GRE, I feel like the studying correlation gets messed up. Why? From what I gather, studying for the GRE breaks into 3-4 categories: 1) studying 1 week or less, 2) studying a few months in the evenings while working or taking classes, generally on your own, 3) taking test prep classes, 4) taking months off of work/school to study, very systematically, probably with lots of commercial test aids.
For students in category 1, maybe they don't study and score badly because they are overly-confidant, or lazy, or don't care, or think it doesn't matter much. If they are any of the first 3, it's a good indication they won't perform well in grad school. If it's the 4th reason, possibly having been told this information by professors they trust, then damn, that sucks for them.
For students in category 2 that still score poorly, maybe they don't have the money or resources to take a test prep class, or one is not available in their area. Or they suck at studying or are lacking in a key quality. First few reasons, it's hard to say it would correlate with poor grad school performance. Last 2 reasons, they'd probably suck at grad school.
For category 3 students, I think they learn a lot of good tricks. But they have to be able to afford those classes, and those classes have to be available in their area.
For category 4 students, I've mostly heard that this type of studying is something some international students do because they know/think they have no chance without practically perfect GREs. So I certainly think high scores achieved this way indicate a large amount of drive, which is a huge must in a PhD program. I don't know how much the scores still depend on innate ability at this level of studying, so I don't know how much correlation is left with other abilities. If anyone else knows, pipe up.
So, in conclusion? Excellent scores on the physics GRE's indicate some very desirable qualities for grad school success. The testable qualities, by themselves, don't ensure grad school success, but would certainly help. Some of the testable qualities can be skewed for individuals or segments of the population, so be careful when interpreting results, and know that a poor score definitely does not perfectly correlate with poor performance in grad school.
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