For all those incoming freshman.

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Summer’s here.  I capped off my semester with graduation. Even though I defended my dissertation last June, I was just over the deadline to graduate last Spring, so I got to graduate with two of my lab buddies, instead. Totally worth the wait.

That's me on the right, with another PhD (left) and master's candidate (center) from my grad lab.

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The last few weeks of the Spring semester leading up to graduation were rough, trying to meet deadlines for coursework and other projects.  This semester I finished:

• Intro to Epidemiology (two exams, four quizzes, a final presentation and a paper)
• Grant Writing (a draft potential R03 grant)
• Biostats II (a final project)

and submitted:

• one project to IRB
• one grant proposal to a local foundation
• one application for a summer workshop
• one poster for an international conference
• one poster for the national meetings
• one poster for the departmental program annual symposium

My semester, especially towards the end, felt a bit like this

Except without the tutu.

For the last several weeks, I was jumping from one deadline to another, having just a few days between to work on the next project on the list.  Not surprisingly, I’ve felt the need to have a way to keep more on top of things: projects, due dates, meetings. While I was writing my dissertation, I had used LifeBalance, but decided against upgrading because their iPhone app apparently has issues, and I had stopped using the desktop version over a year ago because it just wasn’t working for me and I didn’t want to pay for the update.  I downloaded the trial version of Things, but decided it didn’t fit my current (lack of) workflow, and required adaptations that didn’t really work for me.  Same for Midnight Inbox. I settled on OmniFocus instead, personal task management software based on David Allen’s Getting Things Done.  ProfHacker has a nice series of posts on applying the concept as an academic, including an Introduction, Contexts and Academic Work, Mind-sweeping, and Managing Project Files.

I’ve slowly been adding projects, setting contexts and due dates. I’ve been using OmniFocus for about three weeks, but I haven’t completed a mind-sweep yet, partly due to lack of time (cleaning up from the end of semester crazy), partly because I’m still in the process of learning the system (reviewing podcasts, screencasts, and blogs about implementation), and partly because I’m a little afraid to have all of my commitments down on paper. I’m still figuring out exactly what should go on the list. Everything? Or just those out of the ordinary things that I might forget if I don’t write them down? Most of what I have set up so far are work projects, and I feel already like I have a better idea of where things are and what needs to be done next.

That’s good, because my summer is shaping up to be very busy, even without taking classes.  Right now, the project list looks like this:

• resubmit proposal to IRB
• write three articles from dissertation
• write/contribute to other articles as assigned
• write book chapter
• help with PI R01 submission
• present research idea to local partner organization
• meet with biostatistics re. R03 proposal
• develop syllabus/course description for potential class
• plan analysis of samples from PI’s current R01
• create a career development plan

It feels really good to be finishing up the first year of my postdoc, despite feeling like I’m eating an apple while juggling knives balanced on a rickety table sometimes.  I’m hoping GTD and OmniFocus can alleviate some of that end of semester panic in the future.

For the other postdocs out there, what strategies/tips do you have for task management?

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Image credit: Graduation photo courtesy of lab buddies. Knife juggler photo Creative Commons licensed by peter.bryant via Flickr.

Several of the items I’ve run across while surfing this week involve genomics (not surprising, given my job).

• First, from the National Human Genome Research Institute GenomicsCareers: Find Your Future, a website detailing the myriad career choices in the emerging field of genomics. With interactive videos and career profiles, a nice resource for the budding scientist. This one is on my list to share with my oldest, who has expressed an interest in forensics and neuroscience.
• Next, HuGE Navigator, “An integrated, searchable knowledge base of genetic associations and human genome epidemiology.”  BiteSizeBio has a nice review of the high points here and here. I realize these posts are from over a year ago, but I was trapped in dissertation purgatory at the time.
• Finally, Genomicus, a genome browser that lets users compare genomes across species.

Here’s the Genomicus output from the FOXP2 gene,with the human gene at the bottom of the figure, and a handy demo video.

A subject that’s been on my mind quite a bit this week, and coincidentally, several related things have popped up on my radar. First, a video from SciVee, intended for high school girls who might be considering a career in science.

The best advice? Just do it. If that’s what your passion is, go for it.

When I was a very little girl, I remember my father coming to pick me up from daycare one day. I was in tears, and when he asked me what was wrong, I told him that my teacher had asked all of us to tell what we wanted to be when we grew up. When I said that I wanted to be an astronaut, my teacher said I couldn’t because I was a girl (this was pre-Sally Ride). My father confronted the teacher, and informed her that his daughter could be anything she wanted to be. Knowing that a trusted adult believed in me gave me the confidence to pursue my dreams. Just one of the reasons I adore my dad.

Sally Ride - First American Woman in Space

My high school biology class turned my attention from outer space to DNA. After those Punnett squares, I never looked back. But that confidence in my abilities never wavered.

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Also, the National Postdoctoral Association posted their 6 Core Competencies for postdocs:

to offer guidance to individual postdoctoral scholars who must seek out relevant training experiences, in collaboration with mentors, institutions, and other advisors who provide this training…These competencies are meant to serve primarily as: (1) a basis for self-evaluation by postdoctoral scholars and (2) a basis for developing training opportunities that can be evaluated by mentors, institutions, and other advisors.

Postdocs can use the checklist to identify areas for future development, and as a springboard for discussion with their mentors.

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Finally, an article from Science Careers – Reaching gender equity in science: The importance of role models and mentors.

One of the factors that has inspired more women to pursue scientific careers has been having examples of successful women who have done the same.

I feel very fortunate to have a team of mentors, one of whom is a very prolific and successful scientist, woman, and mother.  When I started graduate school, my concern was mostly with the academics. Completing my courses, making sure I had enough time in the lab to get my research done, writing.  I hadn’t really considered the importance of mentoring in establishing my career. I can see now that it is essential, and especially beneficial to have a group of senior researchers interested in your professional development, even if their advice seems sometimes contradictory.  And while this may not be the case for some postdocs, I know that they all genuinely have my best interests in mind.  I’m beginning to see my graduate training, and by extension my postdoc, as an apprenticeship.  A position where I learn how to be a scientist, with all the core competencies that entails.

We need to do a better job advertising this, because I’m a scientist and this is the first I’ve heard of it. In the spirit of science literacy, here are a few links to explore.

• Scitable – “a free science library and personal learning tool” from Nature. The current focus is genetics (my favorite topic).
• Women in Science – from the Smithsonian Institution Archives, a collection of “images documenting women scientists and engineers from around the world, most of whom were pioneers in their respective fields, or were the first women to receive advanced graduate degrees in their discipline.” The photo below is of Dr. Mary N. Crawford, who discovered the rare Lutheran a-b- blood type.
  

  Mary N. Crawford

  The entire photo collection is an inspiration to women in science.

• Lifeworks E-Mentoring – from the National Institutes of Health Office of Science Education, “a free e-mentoring program that helps high school and college students who are interested in behavioral and social science, biomedical science, dental, and healthcare careers find a mentor.” Sounds like a great place to volunteer if you’re so inclined. What better way to improve science literacy than to help mentor the next generation of scientists?

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Speaking of science literacy: On the book club front, I’m about 2/3 of the way through January’s book.  It’s interesting but dense reading, and I haven’t had as much time to read while nursing as I thought. Baby keeps grabbing the book.  It’s easier to read while pumping, but that gives me roughly 30 minutes a day. I have had a couple of long waits at the car shop over the last week, so I’m making good progress.  Maybe I’ll be finished by this weekend.

There’s an interesting piece in the most recent issue of Nature.  Corie Lok discusses how researchers are dealing with the information firehose that is scientific literature.  How big is that firehose?

The 19 million citations and abstracts covered by the US National Library of Medicine’s PubMed search engine include nearly 830,000 articles published in 2009, up from some 814,000 in 2008 and around 772,000 in 2007. That growth rate shows no signs of abating, especially as emerging countries such as China and Brazil continue to ratchet up their research.

With that amount of data overload, how is an established researcher going to keep up with relevant work in their field? Nevermind how a new investigator can get a handle on establishing a research focus, or a midcareer scientist switch tracks.  That’s where literature mining comes in.  There are several start-up services on the web to help scientists find relevant research, make connections, and generate hypotheses. A few of these are discussed in Lok’s article, but there are many others.

PubMed – My default search engine for research papers, this is the first stop for many scientists.  So let’s try a little experiment.  Searching “alcoholism” generates the following – Results: 1 to 20 of 66827. PubMed does have tools to help you narrow your search.  Say I’m interested in the genetics of alcoholism. Searching “alcoholism genetics” returns – Results: 1 to 20 of 5940. A lot of research to go through. Restricting that search to only publications in the last 5 years, in English, and about humans gives Results: 1 to 20 of 1283. Still a lot of literature, even if you’re only reading the abstracts.

That’s where literature mining can really make a difference.

Scientific Literature Mining Services

• pubget – One of the most time consuming tasks in research is going through the literature and trying to stay current. Add to that most institution’s clunky access to online resources, and the process can be painfully slow.  I can search for articles from my library homepage, but have to go through several extra steps to actually get my hands on the pdf. The “find full text” function in the newest version of EndNote has been a tremendous help in accessing content, but for those researchers at institutions that don’t provide that service (and even those that do but want an additional resource), pubget is a handy tool.  When you create an account, pubget signs in to your institution and allows you to search the subscribed resources. When you find a reference you want, just click the pdf icon and there it is. No clicking through to content provider websites. You can tag references as “keepers” to come back to them later, or search for the newest articles from a particular journal. Unfortunately, it doesn’t allow you to annotate the pdfs. Not yet anyway.

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• GoPubMed – A search engine where “your keywords are submitted to PubMed and the resulting abstracts are classified using Gene Ontology and Medical Subject Headings (MeSH).” So using our example search “alcoholism genetics” pulls 5,940 abstracts from PubMed. What’s different about GoPubMed’s results is the navigation panel.

GoPubMed top terms

You can click the check boxes next to the relevant terms. Restricting the search to “genetic predisposition to disease” gives 720 references. Under the Knowledge Base > Named Groups category, you could restrict it further to only studies using adult subjects, or to exclude studies in children. GoPubMed provides additional information on your search topic as well, including top authors (who’s doing the research) and top journals (who’s publishing the research). There’s even a network of top authors, so you can see how they collaborate on your search topic.

Author network for genetic predisposition to alcoholism.

Philanthropologist might recognize a name toward the top of that network. A useful tool for finding potential collaborators.

• NextBio – A freemium service (basic is free, pro is subscription) that allows researchers to set up a profile, but also has a database for lit mining.  Searching for “alcoholism” in NextBio brings up several relevant sources, including associated genes, literature, researchers, news, even clinical trials.
  

  NextBio genes associated with alcoholism

• EbiMed – analyzes PubMed results “to offer a complete overview on associations between UniProt protein/gene names, GO annotations, Drugs and Species.” Searching “alcoholism” here turns up a table of links to various sources, including proteins, genes, and biological processes related to the search term.
• PubGene – specifically for finding genes, PubGene draws a network of genes associated with a particular keyword.
  

  PubGene network for alcoholism

This network is searchable. Clicking on a gene allows you to browse the literature associated with that gene, in addition to highlighting associations with other genes.  As a geneticist, this tool has considerable utility.

• PubAnatomy – “integrates [the] Allen Brain Atlas gene expression data, relationships between brain regions and diseases for more efficient exploration of Medline database and gene expression data.” A keyword search for “alcoholism” lights up regions on the brain map associated with the disease.

PubAnatomy brain map

And clicking one of those highlighted regions displays the relevant references for that brain structure, as well as genes that are active in that region.

• Neuroscience Information Framework – “An initiative of the NIH Blueprint for Neuroscience Research, the Neuroscience Information Framework advances neuroscience research by enabling discovery and access to public research data and tools worldwide through an open source, networked environment.” This tool can search the full text of articles (at least those that are open source), rather than just the abstract. But it does much more.

Second from the bottom, NIF diplays grants related to your search. You can read the abstract, see who is doing research on your topic, and which granting agencies are funding that research. Useful info when preparing your own proposals.

All of these tools help scientists sip from that firehose, giving different ways to access and interact with the data. What an exciting time to be starting a career in science.

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Lok, C. (2010). Literature mining: Speed reading Nature, 463 (7280), 416-418 DOI: 10.1038/463416a

Science Magazine has a YouTube channel!

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Click here to view the embedded video.

If you haven’t seen it, you should.

I’ve had a few colleagues ask me if Biostats I was a useful class, given my statistics background in grad school.  It’s a requirement for the master’s degree program I’m pursuing, so I have to take it, but I have found it to be a nice refresher of the Biometry course  I had years ago.  Maybe I just know more about statistics now, so it makes more sense; or maybe it’s just explained better in this course, so I have a better grasp of the material. When I started grad school, statistics felt like Farsi. But not now.

Take Type I and Type II error, for example.  In study design, you have to try to minimize both. Type I error is the probability of rejecting the null hypothesis when it is true. The acceptable Type I error rate is determined by alpha, which is generally fixed at 0.05 or lower in the analysis phase of a study.  Type II error, or beta, is the probability of failing to reject the null hypothesis when the alternative hypothesis is true.  While I understood these concepts empirically, the relationship between them had never been explained.  What I had were random facts, with no framework to pin them on.

The relationship between alpha and beta.

This plot represents a one-tailed Student’s t-test of the difference in means between two independent samples, both with a sample size of 75  and with alpha set to 0.05. The probability of accepting the null hypothesis is represented by the red line, while the probability of accepting the alternative hypothesis is in blue.  Notice that the null hypothesis distribution is centered at 0, meaning that you’re testing the hypothesis of no difference between means, and that the two distributions overlap.  The area under the red curve which overlaps the blue curve is alpha, the chance of rejecting the null hypothesis when it is true.  The area under the blue curve which overlaps the red curve is beta, the chance of failing to reject the null hypothesis when the alternative hypothesis is true.

Notice also, that you can’t change the value of alpha without affecting the value of beta. Here’s the same t-test with alpha set to 0.01.

Changing alpha affects beta.

Reducing alpha increases the critical value for rejecting the null hypothesis (from t=1.6552 to t=2.3518), thus increasing the likelihood of failing to reject the null when the alternative hypothesis is true. And the rest of the blue curve, which equals 1 – beta?  That’s power, or the probability of rejecting the null hypothesis when the alternative hypothesis is true.

That’s the framework I was missing. The biostatistics course was worth that alone.

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Images generated using G*Power 3.

Part of President Obama’s “Educate to Innovate” initiative, National Lab Day puts scientists and engineers in the classroom for hands-on learning opportunities. The project runs year round, with a special week of activities in early May. Best of all, everyone can volunteer. If you’re a scientist or engineer, you can help out by designing and implementing hands-on activities at your local schools. Teachers can request specific projects for their students.

The website will automatically match volunteers to requests from educators to participate on the basis of geography and interests. The website also provides resources and ideas for hands-on learning experiments and invites the public to suggest new materials.

The hubby and I have already signed up. I know some of my other science colleagues (looking at you, Philanthropologist), have been searching for volunteer opportunities. For those with a science/technology bent, National Lab Day may be the perfect fit.