Some of the best advice on the internet: several years of links

I started off the New Year with a much-needed bookmark reorganization and deletion, which also gave me a chance to re-read some of the links I've held onto (sometimes for years). There's an ever-increasing amount of useful content on the internet, but these have proven some of the most helpful, concrete, and lasting guides for navigating a scientific life.

I thought I'd collate the list here with the hope others might find some of these useful.

How to make it as early career researcher and new faculty: 

• There's no one way to be successful (in fact there are many ways), but hearing what not to do can be as important as all the other advice you will get: Mistakes I've made as an early career researcher (Times Higher Education)
• Lots of what-to-do and what-not-to-do advice here! How to be a new professor (Eco-Evo Evo-Eco)
• It's hard enough that Berkley's career centre has written pages on the topic: Transitioning from PhD to Prof (Careers at Berkley)

Identity and academia:

• I think most of us took different and often interesting routes to science (for example, I grew up in an evangelical Christian family, took a number of years to finally start my undergrad, and had no particular knowledge of ecology when I started my BSc. I wanted to be a vet, but now I'm an ecologist. Close enough :) ) and so I like to hear the many different routes by which scientists found science (SEAS).
• Overcoming imposter syndrome - there are many websites devoted to the topic, but this one provides particularly concrete steps to overcoming this common problem. 
• No one is perfect, and feedback can hurt - why feedback hurts and how to over come that. And no, it isn't enough to say, 'grow a thicker skin' (The Thesis Whisperer).
• Diversify EEB - a useful list of women and minorities working in EEB, worth keeping in mind when making nominations, selecting reviewers, and making various invitations. 
• And it's worth remembering that there is a dark side (one slightly bitter take on it). (Fear and Loathing in Academia)

Mentoring and leadership:

• Mentoring plans - this is super-useful whether you are faculty or a student. In a lot of places there may not be a tradition of having written plans and this provides examples of how to put them into practice. (Meg Duffy, Dynamic Ecology)
• Running successful lab meetings from all perspectives. I like the idea of explicitly deciding what you value and then modelling that when leading lab meetings. (Neuromechanics Lab at Emory)
• Discussing and leading successful journal clubs especially for graduate students.  (Tulane University, Dept of EEB) 
• Running successful scientific working groups and workshops (Conservation Bytes) 
• Preparing PhD students for non-academic careers (The Versatile PhD)

Computing/Data management:

• How to organize your computational projects (PlosBiol)
• Ten commandments for good data management (Brian McGill, Dynamic Ecology)
• Proselytizing about version control (Jennifer Bryan, Peer J)
• A list of lists of links for R statistical resources on Google Documents  
• Data sources worth checking out at The ecological data wiki

Data visualization:

• There are some really beautiful infographics about science from Eleanor Lutz here (Tabletop Whale).
• Information is Beautiful - infographics for inspiration
• Show me Shiny - some great examples of how R Shiny has transformed data visualization and interaction.
• If you are familiar with Edward Tufte's influential work on data visualization, you can use R to produce similar plots here. (Lukasz Piwek)

Teaching:

• The Eco-Evo Evo-Eco blog has a great how-to series, but this one especially has really concrete examples and suggestions for EEB lecturers: How to Teach (Eco-Evo Evo-Eco). 
• An article on how to use new learning methods for quantitative biology and models at Active learning and quantitative biology (Integrative and Comparative Biology)  
• The National Centre for Case Study Teaching in Science for already developed case studies and activities. 
• Common mistakes using active learning - nice to see legit critiques and ways to improve.
• Smart advice on avoiding bad teaching reviews (Terry McGlynn, Small Pond Science)

Miscellaneous links:

• Now part of the PhD canon: The infamous Snake Fight for your thesis to distribute to your PhD students (McSweeney's Internet Tendency).
• Wall of the Dead - poignant memorial list of ecologists lost while working  (Richard Conniff).
• The original text for Gause's The Struggle for Existence and plots. Ecology has changed so much (and so little).
• Visualize the tree of life beautifully and interactively (and get your name on it (sort of)!) (One Zoom)

Fear of missing out

There are so many tools, techniques, communication platforms and methods out there for ecologists that it can be hard to keep track of them all. Whether it be LaTeX, Linux, or Twitter, someone has probably suggested that your research life will improve if you use their favourite tool.

Online science has allowed fantastic networking and information exchange. However, one side effect of the increased usage of social media can be the feeling that you can’t keep up. The phrase ‘Fear of Missing Out’ in social psychology was coined to describe the anxiety arising from looking at social media and fearing that you have chosen how to spend our time poorly. But this can extend to academic working life too – that nagging fear that your colleagues are doing cooler things (going to more conferences, using more cutting edge techniques, publishing more papers) than you. It can be disconcerting to see that there’s a cool R package you know nothing about, or to receive yet another invite to a work-based social media platform you’ve never heard of.

There are people who are the early adopters and the technological trendsetters; people who derive their success from their methodological/technological skill sets. These people are fantastic, since they provide the examples for others in the field to follow. But for most researchers, it is practical to recognize that knowing every new technique and tool doesn’t promise success.

Graduate students may suffer this fear of falling behind most intensely, given how closely it relates to imposter syndrome and the general skills gap that grad students have to overcome. But I’d argue for most students, that balance is key. Otherwise it can be a slippery slope: maybe you learn R, then find that people recommend knitR and R Markdown, then you learn Shiny to show your results beautifully, then someone suggests things would be faster in a lower level language – and maybe months have gone by. That’s not necessarily bad, intellectual freedom and self-teaching are some of the best parts of graduate school, and if you are going to learn a bunch of new techniques, that is the perfect time. But aficionados will make arguments for everything from Python, Linux, Emacs (apparently the one true operating system), Github, to LaTeX/BibTeX, etc, etc. And every one of these is a useful tool, but maybe not always a necessary tool.

Researchers can never learn everything, and some things will fall out of fashion as quickly as they arose. Sure, it is less than ideal to be a researcher who hasn’t learned a new approach since the advent of SAS. But we need to balance necessity (is it fundamental to my research?), the time investment, and the utility of these skills for future endeavours (e.g. if you are applying for a data analyst job after grad school, learning a few languages might be smart - but if you will rarely use it again, perhaps it is not so important). The danger for many students is that either the time investment in learning a less-than-fundamental tool is very high, or else they learn a number of tools but never master any of them. This isn't to say you can shirk on quantitative skills - on the contrary, having those skills is far more important than mastering any specific program or language. I can think of fantastic ecologists who don’t know what Twitter is, who still use SAS, who have never learned R, and who produce brilliant work. The questions, the ideas, and the knowledge matter too, after all.

(*I should note that I'm a quantitative/theoretical ecologist, and I think every ecologist should have a strong quantitative education! Just that you can do that using lots of different methods and doing it well should always be the first priority.)

Gendered assumptions and science: still a problem

Sometimes I feel like covering sexism and science has the potential to trigger a weary response, a feeling that this is well-travelled territory. And generally, academia is fairly self-aware about the causes and consequences of its current gender gap (see the special issue in Nature). But then I hear or read something that disappointingly reminds me that society as a whole still has a ways to go.

The first was a minor story. The curator of “I f--king love science”, a widely-followed Facebook page on things scientific and otherwise, happened to reveal that they were Elise Andrew--a female. While this seemed to be a non-event, apparently young men everywhere (i.e. on the internet) were shocked that their mental picture of a male scientist was untrue. Many comments fell along the lines of “you’re a girl?!” and “all that time picturing a man!”. Even more frustrating was that commenters also mentioned Elise’s appearance – attractive and female and a scientist--apparently this was so surprising as to be worthy of comment. And while I wanted to dismiss this as being limited to problems with Internet culture and hardly indicative of larger societal trends, something else happened – Yvonne Brill, a brilliant American rocket scientist passed away. Her work on propulsion systems now helps keep communications satellites in orbit, and she was a successful engineer with an interesting career. She clearly deserved a national obituary, and she got one in the New York Times. It started:

“She made a mean beef stroganoff, followed her husband from job to job and took eight years off from work to raise three children. “The world’s best mom,” her son Matthew said.

But Yvonne Brill, who died on Wednesday at 88 in Princeton, N.J., was also a brilliant rocket scientist, who in the early 1970s invented a propulsion system to help keep communications satellites from slipping out of their orbits.”

By way of comparison, not one of Steve Jobs’ obituaries started with a mention of his hobbies or personal accomplishments, or his status as a father. The only other recently (2012) deceased female scientist I could think of, astronaut Sally Ride, similarly received an obituary in the NYT that emphasized her gender - "American Woman Who Shattered Space Ceiling".

The need of society, reporters, and popular culture to reconcile a female scientist’s gender with their occupation appears to still be common. So much so that the one science writer came up with the Finkbeiner Test (Columbia Journalism Review) to point out articles which rely on the “she’s a woman AND a scientist” trope. Such articles tend to mention:

• The fact that she’s a woman 
• Her husband’s job 
• Her child-care arrangements 
• How she nurtures her underlings 
• How she was taken aback by the competitiveness in her field 
• How she’s such a role model for other women 
• How she’s the “first woman to…” 

The point is not that it is always unacceptable to include such things in articles, but that unless the article is about sexism or balancing work-life balance, these facts are irrelevant when reporting on a scientist's professional accomplishments. Gender shouldn't be the default position when we consider scientists who happen to be women. And apparently this message still needs to be repeated. Some people have suggested that one equalizer is to simply to also ask male scientists about their personal lives more often. However writer Finkbeiner notes that these questions rarely improve science journalism: "They’re [scientists] all normal human beings and the thing that makes them so interesting is the science. So, if you want to humanize them, talk about their motivations. Talk about how they got interested in their field. Talk about the part of their life that led them to become such an interesting scientist—because childcare is not interesting."

Note: while problems with gendered assumptions is a very general societal issue, academia isn't totally blameless. Having served on a number of lecture organizing committees, I've noticed that if the email for speaker nominations doesn't explicitly say that we wish to nominate male and female scientists at the top of their careers, female scientists are rarely nominated. Students' mental image of a top scientist tends to skew male. If that simple note is included though, nominations begin to approach gender ratios for professors at that career stage.

Science 2.0 - science comes of age on the Internet

by Marc Cadotte, Nicholas Mirotchnick and Caroline Tucker

The Internet is not just for lolcats and porn anymore, scientists have begun using it in constructive ways. The past few weeks’ controversy about the ability (or lack thereof) of bacteria to incorporate arsenic exemplifies how the relationship between science and the Internet is changing. If you’ve missed the debate over the recent Science paper, researchers funded by NASA’s exobiology/evolutionary biology program published experimental results suggesting that a Halomonas species could incorporate arsenic into its DNA in the absence of available phosphorus. This paper received extensive attention in the mainstream media, but also vocal criticism, which was expressed primarily through postings and comments on scientific blogs. Until recently, for scientific communication the Internet has functioned primarily as an electronic source of published journal articles. Earlier attempts to take advantage of the Internet’s potential (immediacy, accessibility, and ability to connect individuals, organizations, and ideas) in scientific discourse have been mixed (e.g. Nature Precedings versus PLoS ONE). The use of blogs as a forum for scientific debate suggests that this is changing: posters tended to be active scientists and the comments were similarly knowledgeable. In contrast to this online approach, the authors of the Science paper stated that they would only respond to peer-reviewed critiques and would not engage in discussions on the blogosphere.

The story of the arsenic-utilizing bacteria highlights an emergent tension in the transition to internet-based scientific discourse. Traditional communication in science has been primarily unidirectional, from the authors of a study to the readership of a journal. Any discourse transpired on the pages of a journal, regulated by editorial and peer review. This gatekeeping meant that this discourse was technically sound and kept personal grudges and tangential discussions to a minimum. This also meant, however, that only a few voices were heard, the discussion was slow (occurring over months) and only happened for one back and forth (journals will not devote precious page space to on-going discussions and debates).

This method of discourse is changing. Journals have experimented with online discussion or commenting features on their websites. Methods in Ecology and Evolution, for example, has a correspondence page with discussion threads for each paper they publish, and PloS ONE allows for comments to be posted to every paper they publish. While, in concept, these are positive developments for scientific communication, commenting features are seldom, if ever, used. The main obstacle to their success is that they are only available on the publishers’ websites, but scientists access articles in many different ways, from database searches to library links. Few scientists actually go to individual journal websites to access papers. This is not to say that there are not discussions about scientific papers occurring online. As highlighted by the arsenic bacterial episode, blogs are an important avenue for discussing and disseminating new ideas in science. Blogs may not, however, actually foster conversations very well. One person or a few people usually run them and there is little discussion among blogs (a comment on a blog post at blog X will not be part of the discussion of the same story at blog Y). Rather, the greatest potential to foster discourse is through virtual networks where people are linked together either through friendships or professional self-identification (e.g., as fisheries biologists), with Google Reader being a particularly powerful communication tool.

It’s exciting to think about what the future of science will look like, given the changes that we’ve already started to see. The major upside of new channels of communication is that they give us the potential to quickly reach thousands of readers, instead of the handful that usually read any given journal article. They also let us communicate in both directions, and in real time. The pitfall, of course, is that they’re free-for-alls; anyone can blog about science.

But here’s what’s unexpected: these free-for-alls have been amazingly reliable at filtering out the bad and promoting the good. Inaccuracies are pulled from Wikipedia faster than anyone had predicted, the social news site Reddit is “astonishingly” altruistic, with users eliminating offensive or erroneous comments from the site and promoting other users’ questions and problems, and the reputations of blogs are shattered if their content becomes unreliable. Social networking has revolutionized the way we consume news, with sites like Facebook and Twitter launching the best articles into viral webspace. The open-access world has evolved self-regulating mechanisms that work surprisingly well so far and if these media are to continue to grow, we will have to ensure that these mechanisms remain built-in.

Seems like an easy task, right? Apparently not. For some reason, academics are slow and conservative when it comes to adopting new media. A letter to Nature two weeks ago scolded scientists for not contributing their share to Wikipedia pages. Various facebooks for academics, like Mendeley and ResearchGATE have emerged, but last week, another Nature article complained that researchers aren’t jumping on the bandwagon. These sites are potential collaborative goldmines, but we seem to be incapable mastering what tweens can do with two thumbs.

It’s not so hard to imagine a world where anyone with a broadband connection can contribute creative ideas to science, the good ideas get automatically filtered to the top and the information is all free to anyone. In this world, children count ants (or bees!) in their backyards and upload their data to global networks. Revolutionary discoveries are published instantly on blogs and thousands of scientists get to decide if they’re valid. Every gene ever sequenced and every tree height ever measured can be readily downloaded in an Excel (or OpenOffice) spreadsheet. In this world, the report on our little arsenophilic friends might never have been published in Science, because instead of being reviewed by two referees, the thousands of readers on the blogosphere would have filtered it out, if was in fact porous.

Academics should be the first, not the last, to adopt new communication tools. We are no longer limited by the postal service, email or PDFs; the web has gone 2.0 and we should follow suit. So go forth, young researchers, and blog, edit and share. And then go tweet about it all so your eight year-old kid knows how hip you are.

People value rare species; at least from their computers

Do people value rare species more than common ones? This is an important question for conservation because not only does valuation justify public funds being spent conserving rare species, but valuation can have negative implications as well. In what is called the ‘anthropogenic Allee effect’, increased valuation can increase species desirability –thus enhancing monetary value for exotic pets, building ecotourism lodges in sensitive habitats, or exotic tasty dishes (ah, The Freshman). In what is probably the most unique approach to assessing whether behavior is affected by the notion of species rarity, Angula and Courchamp, at the Université Paris Sud, used a web-based slideshow measure the amount of time people would wait to see a slideshow of rare versus common species.

Cleverly, they created a French website where visitors could select to view either a slideshow of common or rare species (and the links randomly changed positions on the site). The trick was that a download status bar appears and freezes near the end, and so Angula and Courchamp were able to measure how many visitors selected the rare species show and how long they waited until they gave up. Visitors were much more likely to select the rare species and to wait longer to see them.

I think that this study is extremely neat for two reasons. First it offers a novel way to quantify valuation, and second, it shows how the internet can be used to assess conservation issues in an efficient low-cost way.

Now will they please just show us the pictures of the cute, endangered species!

Angulo, E., & Courchamp, F. (2009). Rare Species Are Valued Big Time PLoS ONE, 4 (4) DOI: 10.1371/journal.pone.0005215