Gold Standard Records

Starting on a new project always brings a certain level of excitement. Often what is a "new" project for a trainee is actually a continuation or a spin-off of a predecessor's work. If you're lucky, the predecessor is still around to give you all the highlights, provide protocols, show you the results and where to find the reagents, etc. If you're not so lucky, then your excitement can soon turn to frustration as you wade through stacks of notebooks and data files, trying to figure out what exactly your predecessor did and where s/he stored key reagents. Sometimes you learn that the "representative" results presented to the PI were actually the best results, a handful out of a virtual mountain of data.

This is just one situation that illustrates the importance of fastidious data management in research laboratories, an issue that might be one of the biggest weaknesses of academic research labs.

Back to basics
Let's start with the lab book. This is where most of us (and I include myself here) need to go back to the first day of gen chem. Anything that goes into the book should be legible and coherent. And we should be writing down everything--well, at least everything pertinent to the experiment (your successors don't really need to know what you had for breakfast or how hungover you are). This includes:

• why you're doing the experiment (a.k.a. the objective)
• the experimental setup and procedure including pesky things like recording concentrations of reagents, volumes for injections, the solvent or buffer used for dilutions, instrument and settings used... You catch my drift.
• raw data (or reference to its location)
  
• locations of data files, including physical location, directory, folder, file names
• how data was processed
• final results (i.e. the pretty graph or table)
• conclusions and/or notes for future experiments
  

Writing all this can become extraordinarily tedious, especially when we're doing similar experiments on a weekly or even daily basis. In some cases, it is sufficient to reference a page in the lab book where the protocol was first described, making note of alterations. Alternatively write up the standard protocol in a word processing document, make appropriate changes for a given experiment, and print and paste it into the lab book. If something changes during the course of an experiments, make a note of it. It doesn't really matter what approach we use, so long as we are being thorough. There should be sufficient detail for someone to repeat the experiment without ever talking to us.

We should also be writing in the book as we work, whenever possible. Too often, we place faith in our memory or our complex system of notes on post-its, paper towels, and gloves. We become slack in maintaining our books, updating them every few days, or maybe even once a week... or less. Then as we're updating our books, we realize we're a little fuzzy on the details... or that we mistakenly tossed that glove in the trash because we thought it was rubbish... so we end up guessing or trying to back-calculate how much of X we added. Not good.

Finally, don't forget to index it! Those wonderfully detailed, coherent notes won't do anyone much good if they can't find it. Chances are, you don't need me to tell you how much of a PITA it is to dig through years of data and notebooks with no idea where you should be looking.

Data in the digital age
The thing about gen chem, at least when I took it, it was beautifully simplistic. I think there was maybe one lab in the entire year that used a probe connected to a computer. The same goes for every chemistry and most biology lab courses that I took as an undergrad. It was simple enough to put everything in a notebook then. As we advance to higher level research, though, the game changes. There's proteomics, FACS, real-time intravital imaging, and a myriad of other techniques that generate massive amounts of data. While working on this post, I was collecting about 5 GB of data... for a one replicate in one group of one experiment. Raw data from such experiments do not lend themselves to hard copy production. They only exist in the digital world. So we must be as fastidious in organizing and maintaining digital records as we are in maintaining our lab books.

Backup plan
I think we have lived in the digital age long enough to realize that sometimes computers die, and despite IT's best efforts, cannot be resuscitated. This is why we should be backing up all of our data files on a regular basis. Both Bear's and Guru's labs keep external hard drives around for this purpose. Some labs may have access to network storage through their institutes. Generally space is fairly limited, but this is fine, if you're not generating gigabytes of data on a daily basis.

When it comes to backups, though, one thing we don't think about so much is our physical lab books and data. However, there is the possibility of fire or flood in the lab destroying our research records. Or they might just sort of wander off. I have yet to see a lab that uses duplicator notebooks or that photocopies or scans notebook pages, but it's probably not a bad idea. Lab books, after all, are the primary record of everything that's been done in the lab.

Safeguard
A peculiarity of data management is that many PIs don't talk about it. In my graduate and postdoc labs, on my first day, someone showed me where the new notebooks were kept. That was it. When I left my graduate lab, I just told the lab manager where my lab books were stored. It seems PIs assume that scientists--whether students or postdocs or research associates--know how to fill out a lab book and keep data organized. Perhaps PIs anticipate that the lab manager or other colleagues will provide direction as necessary. Of course, because this is a day-to-day task, it is not feasible or reasonable for a PI to constantly check lab books. And some people won't make long-term change without constant reminders.

So what's a PI to do? How is s/he to monitory and maintain the integrity of data and records without randomly inspecting lab books? Does anyone actually do the "understood and witnessed by" thing outside of industry?

Guru is a fan of seeing all data--the good, the bad, the ugly, the inconclusive... He periodically meets with individuals to discuss projects and experiments. As a trainee, it's a necessity to bring your notebook to these meetings because Guru might ask you about results from days, weeks, or months ago. In so doing, Guru sees our notebooks. This could offer a solution. Yet I have encountered some of the same problems locating information from previous trainees.

Some might argue (rightfully) that PIs have better things to do and shouldn't bother. Trivial as it is, proper data management is a crux for an efficient and productive laboratory. Researchers must be vigilant in keeping good records, but PIs should ensure that records are clear and consistent.

Socializing scientists

I recently attended a roundtable discussion that was supposedly aimed at telling faculty and postdocs how to use social media to develop networks for collaboration and career development. The concept is a great (although in this case, the execution fell short). Although I blog anonymously, I find a surprising sense of community here, and I am intrigued about how scientists are using social media to connect and collaborate.

There has been explosion of networking tools over the past few years. If you are reading this blog, chances are you have a good idea of what these tools are. So first, a poll:




One point that emerged in the roundtable discussion was that each media outlet serves its own unique purpose. You choose the ones that suit your style, your personality, and the amount of time and effort you want to commit. Facebook has been a way for me to keep tabs on family and friends that I rarely see, but given its casual nature, has never moved beyond that. Twitter and blogging have become my primary connections to the online science community. Twitter is stream of almost constant chatter. It has become a place to exchange snippets from everyday life or share links to interesting articles or blog posts--the sort of things that might be of interest to other people but not needing a full blog post. Blogging allows me to share my views and experiences or to solicit opinions on a given topic. Thus far it has largely been an outlet for discussing the culture and politics of being an early career scientist. It also provides a place for me to develop ideas about mentoring and research issues and philosophies. Plus blogging gives me a chance to write with no limitations, which is necessary to developing writing skills (I might take up some research blogging to hone my science writing skills, as well). I'm very interested in hearing ways others are using social media.

The reason blogging and Twitter have worked so well is that there is a sense of community. We talk about science, but we also throw in personal tidbits along the way. Even if I don't know your real names or where in the world you are, I do feel like I'm talking with "real" people. There is a refreshing level of honesty and personality. And this is where professional social networking sites have thus far failed, in my opinion. I have an account with one or two of these science networking sites. I can't even remember my logins for them. One I would look at maybe every one to six months. Although professional networks will always be different from more casual ones, such as Twitter, Facebook, etc., they suffer from a lack of engagement. (As an aside, the people involved in setting up the NIH-funded $12 million network for scientists would do well to take note of what has and hasn't worked for both professional and open social networks.)

This is where we run into a major issue with convincing other scientists to get into social media. With open networks, anything goes. With restricted networks, nothing is going on. What to do? How do you get skeptics involved? Marketing people and techies are not going to convince scientists and physicians that they should be tweeting or Facebooking or blogging. There are many scientists who are doing great things with social media. These are the people who should be in the room telling other scientists of the utility of these networks.

Why I love blogging

The comments on this post (and others) are a perfect example of why blogging rocks. Some might view blogging as an egotistical thing, and perhaps to an extent, it is. Some might even view my opinions as ungrateful bitchfests. But spouting off into a vacuum wouldn't provide the many perspectives that blogging does. Giving you the benefit of the doubt that you are who you say you are (and given the 'insider' knowledge you express, I think that's fair), I would say argue that I could never, in a face-to-face conversation, discuss the topics and get honest commentary from the range of people and positions represented here. We may not see eye-to-eye all, or even most, of the time, but frankly I'd be disappointed if we did.

In short, you guys rock. Keep on bringing it!

How much am I worth?

Professor in Training recently initiated a discussion about the realities of the tenure track. One of the subplots of the discussion regarded paying postdocs "what they're worth". PhysioProf suggests that the NIH/NRSA payscale is a reasonable approximation of what a postdoc is worth. PiT asks, "What is a postdoc really 'worth'? Is $40K/yr sufficient renumeration for someone who has >10 years of college education behind them?"

As of 2009, the NIH set the pre-tax salary of a first year postdoc at $37,368. The pay level increases with each year of completed experience; the increase, which averages out to approximately $2,000 per year, ranges from about $1,600 to $2,800 (evidently the NIH feels that postdocs gain the most worth during their second year). The NIH periodically re-evaluate and increase paylines for "cost of living", usually on the order of $500/yr.

Many institutions use the NIH payscale to set their own postdoc salaries. (At some point, I was under the impression that any institute receiving NIH funds was more or less required to pay the NIH/NRSA salary as a minimum, but I may be wrong; feel free to enlighten me in the comments.) This provides some advantage to postdocs by setting a minimum expectation. Some institutes, however, take the NIH payline as absolute truth and do not consider for cost-of-living or taxation rates (my own institute falls into this category). Many of the prestigious universities and medical schools in the U.S. are located in cities with much higher than average cost-of-living. Cost-of-living in my current city is about 30% higher than the national average (and my previous city), and the state income tax rate jumped substantially upon my move to BRI. By the time I pay out taxes and benefits, my net income is only marginally higher than a grad student at PSU. Some postdocs end up having to take out loans or use credit cards to supplement their living expenses because of the mismatch between salary and cost-of-living. Of course, when a PI is applying for a grant, s/he can only request up to the NIH/NRSA payline to cover a postdoc's salary. Anything over that payline must come (I assume) from discretionary funds that then, of course, cannot be used for other costs like supplies or travel.

I really don't know what, if any, solution there is. But in wage debates, sometimes we neglect to mention or lose sight of the fact that $40k in one state is not the same in another. This was a consideration that influenced my choice of graduate schools. It is also going to play a big role in our next move. I can't help but wonder if some institutions are missing out on some talented postdocs and grad students for this reason.

Home?

I am back from my unintended blog vacation. I thought I would have more time to blog while I was traveling to and staying in my hometown. No such luck.

Paramed and I have been far away from hometown for nearly 7 years now. Somehow, though, holiday visits just seem to get stranger every year. This year time spent with my dad was unusually quiet, strained, and awkward. Time spent with Paramed's family--which is usually full of drama--was surprisingly calm; everyone was on their best behavior for some reason. One night we went to a couple of clubs with Paramed's older cousins--Paramed and I never go to clubs. Most of the week felt too much like wandering around in some parallel reality. I tried to figure the cause(s) for these peculiarities this year. I have a few hypotheses, but honestly, after a few days, I have found myself not particularly caring why things were so different because it really has no impact on me and what I'm doing in the next six months.

Not everything was different. We still got to contend with Paramed's mother putting in requests for a grandchild. It doesn't seem to really matter that, even if we did have a kid at this point, she wouldn't be seeing it often, given the distance between here and there. Or maybe she thinks that we'd move closer if we had kids. Or that I would stop working. Or she would move in with us. I don't know. We also got to deal with the continuous commentary from some of our family about how we needed to finish up and move closer home. At least this commentary has become less guilt-ridden in the past few years. While in grad school, there was usually inclusion of statements about the poor health of family members and that they might not be here next year... I have since grown quite apathetic toward such statements.

This is part of the life of the vagrant academic. "Going home" isn't really going home at all. Paramed and I take our vacation days and money to have a few days of awkward visitation with family a couple of times a year. Maybe one of these decades, we will get to take a real vacation--you know, where you don't know anyone and you're just fine with that, where you spend a week (or more) doing things that make you happy. It's a nice dream anyway.