Keeping Up with the Scientific Literature using Twitterbots: The FlyPapers Experiment

A year ago I created a simple “twitterbot” to stay on top of the Drosophila literature called FlyPapers, which tweets links to new abstracts in Pubmed and preprints in arXiv from a dedicated twitter account (@fly_papers). While most ‘bots on Twitter post spam or creative nonsense, an increasing number of people are exploring the use of twitterbots for more productive academic purposes. For example, Rod Page set up the @evoldir twitterbot way back in 2009 as an alternative to receiving email posts to the Evoldir mailing list, and likewise Gordon McNickle developed the @EcoLog_L twitterbot for the Ecolog-L mailing list. Similar to FlyPapers, others have established twitterbots for domain-specific literature feeds, such as the @BioPapers  for Quantitative Biology preprints on arXiv, @EcoEvoJournals for publications in the areas of Ecology & Evolution and @PlantEcologyBot for papers on Plant Ecology. More recently, Alberto Acerbi developed the @CultEvoBot to post links to blogs and new articles on the topic of cultural evolution. (I recommend reading posts by Rod, Gordon and Alberto for further insight into how and why they established these twitterbots.) One year in, I thought I’d summarize my thoughts on the FlyPapers experiment, and to make good on a promise I made to describe my set-up in case others are interested.

First, a few words on my motivation for creating FlyPapers. I have been receiving a daily update of all papers in the area of Drosophila in one form or another for nearly 10 years. My philosophy is that it is relatively easy to keep up on a daily basis with what is being published, but it’s virtually impossible to catch up when you let the river of information flow for too long. I first started receiving daily email updates from NCBI, which cluttered up my inbox and often got buried. Then I migrated to using RSS on Google Reader, which led to a similar problem of many unread posts accumulating that needed to be marked as “read”. Ultimately, I realized what I want from a personalized publication feed — a flow of links to articles that can be quickly scanned and clicked, but which requires no other action and can be ignored when I’m busy — was better suited to a Twitter client than a RSS reader. Moreover, in the spirit of “maximizing the value of your keystrokes“, it seemed that a feed that was useful for me might also be useful for others, and that Twitter was the natural medium to try sharing this feed since many scientists are already using twitter to post links to papers. Thus FlyPapers was born.

Setting up FlyPapers was straightforward and required no specialist know-how. I first created a dedicated Twitter account with a “catchy” name. Next, I created an account with dlvr.it, which takes a RSS/Twitter/email feed as input and routes the output to the FlyPapers Twitter account. I then set up an RSS feed from NCBI based on a search for the term “Drosophila” and add this as a source to the dlvr.it route. Shortly thereafter, I added a RSS feed for preprints in Arxiv using the search term “Drosophila” and added this to the same dlvr.it route. (Unfortunately, neither PeerJ Preprints nor bioRxiv currently have the ability to set up custom RSS feeds, and thus are not included in the FlyPapers stream.) NCBI and Arxiv only push new articles once a day, and each article is posted automatically as a distinct tweet for ease of viewing, bookmarking and sharing. The only gotcha I experienced in setting the system up was making sure when creating the Pubmed RSS feed to set the “number of items displayed” high enough (=100). If the number of articles posted in one RSS update exceeds the limit you set when you create the Pubmed RSS feed, Pubmed will post a URL to a Pubmed query for the entire set of papers as one RSS item, rather than post links to each individual paper. (For Gordon’s take on how he set up his Twitterbots, see this thread.) [UPDATE 25/2/14: Rob Lanfear has posted detailed instructions for setting up a twitterbot using the strategy I describe above at https://github.com/roblanf/phypapers. See his comment below for more information.]

So, has the experiment worked? Personally, I am finding FlyPapers a much more convenient way to stay on top of the Drosophila literature than any previous method I have used. Apparently others are finding this feed useful as well.

One year in, FlyPapers now has 333 followers in 16 countries, which is a far bigger and wider following than I would have ever imagined. Some of the followers are researchers I am familiar with in the Drosophila world, but most are students or post-docs I don’t know, which suggests the feed is finding relevant target audiences via natural processes on Twitter. The account has now posted 3,877 tweets, or ~10-11 tweets per day on average, which gives a rough scale for the amount of research being published annually on Drosophila. Around 10% of tweeted papers are getting retweeted (n=386) or favorited (n=444) by at least one person, and the breadth of topics being favorited/retweeted spans virtually all of Drosophila biology. These facts suggest that developing a twitterbot for domain-specific literature can indeed attract substantial numbers of like-minded individuals, and that automatically tweeting links to articles enables a significant proportion of papers in a field to easily be seen, bookmarked and shared.

Overall, I’m very pleased with the way FlyPapers is developing. I had hoped that one of the outcomes of this experiment would be to help promote Drosophila research, and this appears to be working. I had not expected it would act as a general hub for attracting Drosophila researchers who are active on Twitter, which is a nice surprise. One issue I hadn’t considered a year ago was the potential that ‘bots like FlyPapers might have to “game” Altmetics scores. Frankly, any metric that would be so easily gamed by a primitive bot like FlyPapers probably has no real intrisic value. However, it is true that this bot does add +1 to the twitter count for all Drosophila papers. My thoughts on this are that any attempt to correct the potential influence of ‘bots on Altmetrics scores should unduly not penalize the real human engagement bots can facilitate, so I’d say it is fair to -1 the orginal FlyPapers tweets in an Altmetrics calculation, but retain the retweets created by humans.

One final consequence of putting all new Drosophila literature onto Twitter that I would not have anticipated is that some tweets have been picked up by other social media outlets, including disease-advocacy accounts that quickly pushed basic research findings out to their target audience:

This final point suggests that there may be wider impacts from having more research articles automatically injected into the Twitter ecosystem. Maybe those pesky twitterbots aren’t always so bad after all.

Battling Administrivia Using an Intramural Question & Answer Forum

The life of a modern academic involves juggling many disparate tasks, and like a computer using more physical memory than it has, swapping between various tasks leads to inefficiency and low performance in our jobs. Personally, the time fragmentation and friction induced by transitioning from task to task seems to be one of the main sources of stress in my work life.  The main reason for this is that many daily tasks on my to-do list are essential but fiddly and time-consuming administrivia (placing orders, filling in forms, entering marks into a database) that prevent me from getting to the things that I enjoy about being an academic: doing research, interacting with students, reading papers, etc.

I would go so far as to say that the mismatch between the desires of most academics and the reality of their jobs is the main source of academic “burnout” and low morale in what otherwise should be an awesome profession. I would also venture that administrivia is one of the major sources of the long hours we endure, since after wading through the “chaff”, we will (dammit!) put in the time on nights and weekends for the things we are most passionate about to sustain our souls. And based on the frequency of sentiments relating to this topic flowing through my Twitter feed, I’d say the negative impact of adminsitrivia is a pervasive problem in modern academic life, not restricted to any one institute.

While it is tempting to propose ameliorating the administrivia problem by simply eliminating bureaucracy, the growth of the administrative sector in higher education makes this solution a virtual impossibility. I have ultimately become resigned to the fact that the fundamentally inefficient nature of university bureaucracy cannot be structurally reformed and begun to seek other solutions to make my work life better. In doing so, I believe I’ve hit on a simple solution to the adminstrivia problem that I’m hoping might help others as well. In fact, I’m now convinced this solution is simple and powerful enough to actually be effective.

Accepting that it cannot be fully eliminated, my view is that the key to reducing the time and morale burden of administrivia is to realize that most routine tasks in University life are just protocols that require some amount of tacit knowledge about policies or procedures. Thus, all that is needed to reduce the negative impact of administrivia to its lowest possible level is to develop a system whereby accurate and relevant protocols can be placed at one’s fingertips so that they can be completed as fast as possible. The problem is that such protocols either don’t exist, don’t exist in a written form, or exist as scattered documents across various filesystems and offices that you have to expend substantial time finding. So how do we develop such protocols without generating more bureaucracy and exacerbating the problem we are attempting to solve?

My source of inspiration for ameliorating administrivia with minimal overhead comes from the positive experiences I have had using online Question and Answering (Q & A) forums based on the Stack Exchange model (principally the BioStars site for answering questions about bioinformatics).  For those not familiar with such systems, the Q & A model popularized by the Stack Exchange platform (and its clones) is a system that allows questions to be asked and answers to be voted on, moderated, edited and commented on in a very intuitive and user-friendly manner. For some reason I am not able to fully explain, the engineering behind the Q & A model naturally facilitates both knowledge exchange and community building in a way that is on the whole extremely positive, and seems to prevent the worst aspects of human nature commonly found on older internet forums and commenting systems.

So here is my proposal to battling the impact of academic administrivia: implement an intramural, University-specific Q & A forum for academic and administrative staff to pose and answer each other’s practical questions, converting tacit knowledge stored in people’s heads, inboxes and intranets into a single knowledge-bank that can be efficiently used and re-used by others who have the same queries. The need for an “intramural” solution and the reason this strategy cannot be applied globally, as it has for Linux administration, Poker or Biblical Hermeneutics, is that Universities (for better or worse) have their own local policies and procedures that can’t be easily shared or benefit from general worldwide input.

We have been piloting the use of the Open Source Question Answer (OSQA) platform (a clone of Stack Exchange) among a subset of our faculty for about a year, with good uptake and virtually unanimous endorsement from everyone who has used it. We currently require a real name policy for users, have limited the system to questions of procedure only, and have encouraged users to answer their own questions after solving burdensome tasks. To make things easy to administer technically, we are using an out of the box virtual machine of OSQA provided by Bitnami. The anonymized screenshot below gives a flavor of the banal, yet time-consuming queries that arise repeatedly in our institution that such a system makes easier to accomplish. I trust colleagues at other institutions will find similar tasks frustratingly familiar.

Untitled

The main reason I am posting this idea now is that I am scheduled to give a demo and presentation to my Dean and management team this week to propose rolling this system out to a wider audience. In preparation for this pitch, I’ve been trying to assemble a list of pros and cons that I am sure is incomplete and would benefit from the input of other people familiar with how Universities and Q & A platforms work.

The pros of an intramural Q & A platform for battling administrivia I’ve come up with so far include:

  • Increasing efficiency, leading to higher productivity for both academic and administrative staff;
  • Reducing the sense of frustration about bureaucratic tasks, leading to higher morale;
  • Improving sense of empowerment and community among academic and administrative staff;
  • Providing better documentation of procedures and policies;
  • Serving as an “aide memoire”;
  • Aiding the success of junior academic staff;
  • Ameliorating the effects of administrative turnover;
  • Providing a platform for people who may not speak up in staff meetings to contribute;
  • Allows “best practice” to emerge through crowd-sourcing;
  • Identifying common problems that should be prioritized for improvement;
  • Identifying like-minded problem solvers in a big institution;
  • Integrating easily around existing IT platforms;
  • Ability to be deployed at any scale (lab group, department, faculty, school, etc.)
  • Allows information to be accessible 24/7 when admininstrative offices are closed (H/T @jdenavascues).

I confess struggling to find true cons, but these might include (rejoinders in parentheses):

  • Security risks (can be solved with proper administration and authentication)
  • Inappropriate content (real name policy should minimize, can be solved with proper moderation);
  • Answers might be “impolitic” (real name policy should minimize, can be solved with proper moderation; H/T @DrLabRatOry)
  • Time wasting (unlikely since whole point is to enhance productivity);
  • Lack of uptake (even if the 90-9-1 rule applies, it is an improvement on the status quo);
  • Perceived as threat to administrative staff (far from it, this approach benefits administrative staff as much as academic staff);
  • Information could be come stale (can be solved with proper moderation and periodic updating).

I’d be very interested to get feedback from others about this general strategy (especially by Tues PM 17 Sep 2013), thoughts on related efforts, or how intramural Q & A platforms could be used in other ways in an academic setting beyond battling administrivia in the comments below.

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On the 30th Anniversary of DNA Sequencing in Population Genetics

30 years ago today, the “struggle to measure genetic variation” in natural populations was finally won. In a paper entitled “Nucleotide polymorphism at the alcohol dehydrogenase locus of Drosophila melanogaster” (published on 4 Aug 1983), Martin Kreitman reported the first effort to use DNA sequencing to study genetic variation at the ultimate level of resolution possible. Kreitman (1983) was instantly recognized as a major advance and became a textbook example in population genetics by the end of the 1980s. John Gillespie refers to this paper as “a milestone in evolutionary genetics“. Jeff Powell in his brief history of molecular population genetics goes so far as to say “It would be difficult to overestimate the importance of this paper”.

Arguably, the importance of Kreitman (1983) is greater now than ever, in that it provides both the technical and conceptual foundations for the modern gold rush in population genomics, including important global initiatives such as the 1000 Genomes Project. However, I suspect this paper is less well know to the increasing number of researchers who have come to studying molecular variation from routes other than through a training in population genetics. For those not familiar with this landmark paper, it is worth taking the time to read it or Nathan Pearson‘s excellent summary over on Genomena.

As with other landmark scientific efforts, I am intrigued by how such projects and papers come together. Powell’s “brief history” describes how Kreitman arrived at using DNA to study variation in Adh, including some direct quotes from Kreitman (p. 145). However, this account leaves out an interesting story about the publication of this paper that I had heard bits and pieces of over time. Hard as it may be to imagine in today’s post-genomic sequence-everything world, using DNA sequencing to study genetic variation in natural populations was not immediately recognized as being of fundamental importance, at least by the editors of Nature where it was ultimately published.

To better understand the events of the publication of this work, I recently asked Richard Lewontin, Kreitman’s PhD supervisor, to provide his recollections on this project and the paper. Here is what he had to say by email (12 July 2013):

Dear Casey Bergman

I am delighted that you are commemorating Marty’s 1983 paper that changed the whole face of experimental population genetics. The story of the paper is as follows.

It was always the policy in our lab group that graduate students invented their own theses. My view was (and still is) that someone who cannot come up with an idea for a research program and a plan for carrying it out should not be a graduate student. Marty is a wonderful example of what a graduate student can do without being told what to do by his or her professor. Marty came to us from a zoology background and one day not very long after he became a member of the group he came to me and asked how I would feel about his investigating the genetic variation in Drosophila populations by looking at DNA sequence variation rather than the usual molecular method of looking at proteins which then occupied our lab. My sole contribution to Marty’s proposal was to say “It sounds like a great idea.”  I had never thought of the idea before but it became immediately obvious to me that it was a marvelous idea.  So Marty went over on his own initiative, to Wally Gilbert’s lab and learned all the methodology from George Church who was then in the Gilbert lab.

After Marty’s work was finished and he was to get his degree, he wrote a paper based on his thesis and, with my encouragement, sent the paper to Nature. He offered to make me a co-author, but I refused on long-standing principle. Since the idea and the work were entirely his, he was the sole author, a policy that was general in our group. I had no doubt that it was the most important work done in experimental population genetics  in many years and Nature was an obvious choice for this pathbreaking work.

The paper was soon returned by the Editor saying that they were not interested because they already had so many papers that gave the DNA sequence of various genes that they really did not want yet another one! Obviously they missed the point. My immediate reaction was to have Marty send the paper to a leading influential British Drosophila geneticist who would obviously understand its importance, asking him to retransmit the paper to Nature with his recommendation. He did so and the Editor of Nature then accepted it for publication. The rest is history.

Our own lab very quickly converted from protein electrophoresis to DNA sequencing, and I spent a lot of time using and updating the computer interface with the gel reading process, starting from  Marty’s original programs for reading gels and outputting sequences. We never went back to protein electrophoresis. While protein gel electrophoresis certainly revolutionized population genetics, Marty’s introduction of DNA sequencing as the method for evolutionary genetic investigation of population genetic issues was a much more powerful one and made possible the testing of a  variety of evolutionary questions for which protein gel electrophoresis was inadequate. Marty deserves to be considered as one of the major developers of evolutionary and population genetics studies.

Yours ,

Dick Lewontin

Some may argue that Kreitman (1983) did not reveal all forms of genetic variation at the molecular level (e.g. large-scale structural variants) and therefore does not truly represent the “end” of the struggle to measure variation. What is clear, however, is that Kreitman (1983) does indeed represent the beginning of the “struggle to interpret genetic variation” at the fundamental genetic level, a struggle that may ultimately take longer then measuring variation itself. According to Maynard Olson interpreting (human) genomic variation will be a multi-generational effort “like building the European cathedrals“. 30 years in, Olson’s assessment is proving to be remarkably accurate. Here’s to Kreitman (1983) for laying the first stone!

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Calvin Bridges, Automotive Pioneer

Calvin Bridges in 1935 (Photo Credit: Smithsonian Institution Collections SIA Acc. 90-105 [SIA2008-0022])

Calvin Bridges (1889-1938) is perhaps best known as one of the original Drosophila geneticists in world. As an original member of Thomas Hunt Morgan’s Fly Room at Columbia University, Bridges made fundamental contributions to classical genetics, notably contributing the first paper ever published in the journal Genetics. The historical record on Bridges is scant, since Morgan and Alfred Sturtevant destroyed Bridges’ papers after his death to preserve the name of their dear friend whose politics and attitudes to free love were radical in many ways. Morgan’s biographical memoir of Bridges presented to the National Academy of Sciences in 1940 contains very little detail on Bridges’ life, and this historical black hole has piqued my curiosity for some time.

Recently, I stumbled across a listing in the New York Times for an exhibit in Brooklyn recreating the original Columbia Fly Room, which will be used as a set in an upcoming film of the same name directed by Alexis Gambis. Gambis’ film approaches the Fly Room from the perspective of a visit to the lab by one of Bridges children, Betsy Bridges. I recommend other Drosophila enthusiasts to check out The Fly Room website and follow @theflyroom and @alexisgambis on Twitter for updates about the project.

In digging around more about this project, I found a link to the Kickstarter page that was used to raise funds for the film. This page includes an amazing story about Bridges that I had never heard about previously. Apparently, after Morgan and his group moved to Caltech in 1928, Bridges built from scratch a futuristic car of his own design called “The Lightening Bug”. This initially came a big surprise to me, but on reflection it is in keeping with Bridge’s role as the main technical innovator for the original Drosophila group. For example, Bridges introduced the binocular dissecting scope, the etherizer, the controlled temperature incubator, and agar-based fly food into the Drosophilist’s toolkit.

Here is a clipping from Modern Mechanix from Aug 1936 describing the Lightening Bug:

Coverage of Calvin Bridge’s Lightning Bug in Modern Mechanix (Aug 1936).

Bridge’s Lightening Bug was notable enough to be written up in Time Magazine in May 1936, which described his car as follows:

It is almost perfectly streamlined, even the license plates and tail-lamp being recessed into the body and covered with Pyralin windows flush with the streamlining. There are no door handles; the doors must be opened with special keys. Dr. Bridges pronounced the Lightning Bug crash-proof and carbon-monoxide-proof. “My whole aim,” said he, “was to show what could be done to attain safety, economy and readability in a small car.”

Newshawks discovered that for months, when he got tired of looking at fruit flies, the geneticist had retired to a garage, put on a greasy jumper and worked on his car far into the night, hammering, welding, machining parts on a lathe. Now & then, the foreman reported, Dr. Bridges hit his thumb with a hammer. Once he had to visit a hospital to have removed some tiny bits of steel which flew into his eyes. It was Calvin Bridges’ splendid eyesight which first attracted Dr. Morgan’s interest in him when Bridges was a shaggy, enthusiastic student at Columbia.

Calvin Bridges next to the Lightening Bug (Time Magazine, 4 May 1936).

Gambis has also posted a video of the Lightening Bug being driven by Bridges taken by Pathé News. Gambis estimates this clip was from around 1938, but it is probably from 1936/7 since Bridges died in Dec 1938 and by the time Ed Novitski started graduate school at CalTech in the autumn of 1938 Bridges was terminally ill, but appears fit in this clip.  This clip clearly shows the design of Bridges’ Lightening Bug was years ahead of its time in comparison to the other cars in the background. I also would wager this is the only video footage in existence of Calvin Bridges.

The only other information I could find on the web about the Lightening Bug was a small news clipping that was making the rounds in local new April/May 1936:

LighteningBug

Interestingly, the only mention I can find of this story in historical accounts of the Drosophila group is one parenthetical note by Shine and Wrobel in their 1976 biography of Morgan that had previously escaped my notice. On page 120, they discuss how Morgan handled the receipt of his 1933 Nobel Prize in Physiology or Medicine (emphasis mine):

…Morgan was very modest about the honor. He frequently pointed out that it was a tribute to experimental biology than to any one man….As Morgan acknowledged the joint nature of the work, he divided the tax-free $40,000 award equally among his own children and those of Bridges and Sturtevant (but not of Muller’s). He gave no reason; in the letter to Sturtevant for example, he said merely I’m enclosing some money for your children. (Bridges, however, is said to have used his to build a new car.)

So there you have it: Calvin Bridges, Drosophila geneticist, was also an unsung automotive pioneer whose foray into designing futuristic cars was likely funded in part by the proceeds of the 1933 Nobel Prize!

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Twitter Tips for Scientific Journals

The growing influence of social media in the lives of Scientists has come to the forefront again recently with a couple of new papers that provide An Introduction to Social Media for Scientists and a more focussed discussion of The Role of Twitter in the Life Cycle of a Scientific Publication. Bringing these discussions into traditional journal article format is important for spreading the word about social media in Science outside the echo chamber of social media itself. But perhaps more importantly, in my view, is that these motivating papers reflect a desire for Scientists to participate, and urge others to participate, in shaping a new space for scientific exchange in the 21st century.

Just as Scientists themselves are adopting social media, many scientific journals/magazines are as well. However, most discussions about the role of social media in scientific exchange overlook the issue of how we Scientists believe traditional media outlets, like scientific journals, should engage in this new forum. For example in the Darling et al. paper on the The Role of Twitter in the Life Cycle of a Scientific Publication, little is said about the role of journal Twitter accounts in the life cycle of publications beyond noting:

…to encourage fruitful post-publication critique and interactions, scientific journals could appoint dedicated online tweet editors who can storify and post tweets related to their papers.

This oversight is particularly noteworthy for several reasons. First, it is fact that many journals, and journal staff, play active roles in engaging with the scientific debate on social media and are not simply passive players in the new scientific landscape.  Second, Scientists need to be aware that journals extensively monitor our discussions and activity on social media in ways that were not previously possible, and we need to consider how this affects the future of scientific publishing. Third, Scientists should see social media represents an opportunity to establish new working relationships with journals that break down the old models that increasingly seem to harm both Science and Scientists.

In the same way that we Scientists are offering tips/advice to each other for how to participate in the new media, I feel that this conversation should also be extended to what we feel are best practices for journals to engage in the scientific process through social media. To kick this off, I’d like to list some do’s and don’ts for how I think journals should handle their presence on Twitter, based on my experiences following, watching and interacting with journals on Twitter over the last couple of years.

  • Do engage with (and have a presence on) social media. Twitter is rapidly on the uptake with scientists, and is the perfect forum to quickly transmit/receive information to/from your author pool/readership. I find it a little strange in fact if a journal doesn’t have a Twitter account these days.
  • Do establish a social media policy for your official Twitter account. Better yet, make it public, so Scientists know the scope of what we should expect from your account.
  • Don’t use information from Twitter to influence editorial or production processes, such as the acceptance/rejection of papers or choice of reviewers.  This should be an explicit part of your social media policy. Information on social media could be incorrect and by using unverified information from Twitter you could allow competitors/allies to block/promote each other’s work.
  • Don’t use a journal Twitter account as a table of contents for your journal. Email TOCs or RSS feeds exist for this purpose already.
  • Do tweet highlights from your journal or other journals. This is actually what I am looking for in a journal Twitter account, just as I am from the accounts of other Scientists.
  • Do use journal accounts to retweet unmodified comments from Scientists or other media outlets about papers in your journal. This is a good way for Scientists to find other researchers interested in a topic and know what is being said about work in your journal. But leave the original tweet intact, so we can trace it to the originator and so it doesn’t look like you have edited the sentiment to suit your interests.
  • Don’t use journal account to express personal opinions. I find it totally inappropriate that individuals at some journals hide behind the journal name and avatar to use journal twitter accounts as a soapbox to express their personal opinions. This is a really dangerous thing for a journal to do since it reinforces stereotypes about the fickleness of editors that love to wield the power that their journal provides them. It’s also a bad idea since the opinions of one or a few people may unintentionally affect a journal or publisher.
  • Do encourage your staff to create personal accounts and be active on social media. Editors and other journal staff should be encouraged to express personal opinions about science, tweet their own highlights, etc. This is a great way for Scientists to get to know your staff (for better or worse) and build an opinion about who is handling our work at your journal. But it should go without saying that personal opinions should be made through personal accounts, so we can follow/unfollow these people like any other member of the community and so their opinions do not leverage the imprimatur of your journal.
  • Do use journal Twitter accounts to respond to feedback/complaints/queries. Directly replying to comments from the community on Twitter is a great way to build trust in your journal.  If you can’t or don’t want to reply to a query in the open, just reply by asking the person to email your helpdesk. Either way shows good faith that you are listening to our concerns and want to engage. Ignoring comments from Scientists is bad PR and can allow issues to amplify beyond your control, with possible negative impacts on your journal (image) in the long run.
  • Don’t use journal Twitter accounts to tweet from meetings. To me this is a form of expressing personal opinion that looks like you are endorsing certain Scientists/fields/meetings or, worse yet, that you are looking to solicit them to submit their work to your journal, which smacks of desperation and favoritism. Use personal accounts instead to tweet from meetings, since after all what is reported is a personal assessment.

These are just my first thoughts on this issue (anonymised to protect the guilty), which I hope will act as a springboard for others to comment below on how they think journals should manage their presence on Twitter for the benefit of the Scientific community.

Launch of the PLOS Text Mining Collection

Just a quick post to announce that the PLOS Text Mining Collection is now live!

This PLOS Collection arose out of a twitter conversation with Theo Bloom last year, and has come together through the hard work of the authors of the papers in the Collection, the PLOS Collections team (in particular Sam Moore and Jennifer Horsely), and my co-organizers Larry Hunter and Andrey Rzhetsky. Many thanks to all for seeing this effort to completion.

Because of the large body of work in the area of text mining published in PLOS, we struggled with how best to present all these papers in the collection without diluting the experience for the reader. In the end, we decided only to highlight new work from the last two years and major reviews/tutorials at the time of launch. However, as this is a living collection, new articles will be included in the future, and the aim is to include previously published work as well. We hope to see many more papers in the area of text mining published in the PLOS family of journals in the future.

An overview of the PLOS Text Mining Collection is below (cross-posted at the PLOS EveryONE blog) and a commentary on Collection is available at the Official PLOS Blog entitled “A mine of information – the PLOS Text Mining Collection“.

Background to the PLOS Text Mining Collection

Text Mining is an interdisciplinary field combining techniques from linguistics, computer science and statistics to build tools that can efficiently retrieve and extract information from digital text. Over the last few decades, there has been increasing interest in text mining research because of the potential commercial and academic benefits this technology might enable. However, as with the promises of many new technologies, the benefits of text mining are still not clear to most academic researchers.

This situation is now poised to change for several reasons. First, the rate of growth of the scientific literature has now outstripped the ability of individuals to keep pace with new publications, even in a restricted field of study. Second, text-mining tools have steadily increased in accuracy and sophistication to the point where they are now suitable for widespread application. Finally, the rapid increase in availability of digital text in an Open Access format now permits text-mining tools to be applied more freely than ever before.

To acknowledge these changes and the growing body of work in the area of text mining research, today PLOS launches the Text Mining Collection, a compendium of major reviews and recent highlights published in the PLOS family of journals on the topic of text mining. As one of the major publishers of the Open Access scientific literature, it is perhaps no coincidence that research in text mining in PLOS journals is flourishing. As noted above, the widespread application and societal benefits of text mining is most easily achieved under an Open Access model of publishing, where the barriers to obtaining published articles are minimized and the ability to remix and redistribute data extracted from text is explicitly permitted. Furthermore, PLOS is one of the few publishers who is actively promoting text mining research by providing an open Application Programming Interface to mine their journal content.

Text Mining in PLOS

Since virtually the beginning of its history [1], PLOS has actively promoted the field of text mining by publishing reviews, opinions, tutorials and dozens of primary research articles in this area in PLOS Biology, PLOS Computational Biology and, increasingly, PLOS ONE. Because of the large number of text mining papers in PLOS journals, we are only able to highlight a subset of these works in the first instance of the PLOS Text Mining Collection. These include major reviews and tutorials published over the last decade [1][2][3][4][5][6], plus a selection of research papers from the last two years [7][8][9][10][11][12][13][14][15][16][17][18][19] and three new papers arising from the call for papers for this collection [20][21][22].
The research papers included in the collection at launch provide important overviews of the field and reflect many exciting contemporary areas of research in text mining, such as:

  • methods to extract textual information from figures [7];
  • methods to cluster [8] and navigate [15] the burgeoning biomedical literature;
  • integration of text-mining tools into bioinformatics workflow systems [9];
  • use of text-mined data in the construction of biological networks [10];
  • application of text-mining tools to non-traditional textual sources such as electronic patient records [11] and social media [12];
  • generating links between the biomedical literature and genomic databases [13];
  • application of text-mining approaches in new areas such as the Environmental Sciences [14] and Humanities [16][17];
  • named entity recognition [18];
  • assisting the development of ontologies [19];
  • extraction of biomolecular interactions and events [20][21]; and
  • assisting database curation [22].

Looking Forward

As this is a living collection, it is worth discussing two issues we hope to see addressed in articles that are added to the PLOS text mining collection in the future: scaling up and opening up. While application of text mining tools to abstracts of all biomedical papers in the MEDLINE database is increasingly common, there have been remarkably few efforts that have applied text mining to the entirety of the full text articles in a given domain, even in the biomedical sciences [4][23]. Therefore, we hope to see more text mining applications scaled up to use the full text of all Open Access articles. Scaling up will maximize the utility of text-mining technologies and the uptake by end users, but also demonstrate that demand for access to full text articles exists by the text mining and wider academic communities.

Likewise, we hope to see more text-mining software systems made freely or openly available in the future. As an example of the state of affairs in the field, only 25% of the research articles highlighted in the PLOS text mining collection at launch provide source code or executable software of any kind [13][16][19][21]. The lack of availability of software or source code accompanying published research articles is, of course, not unique to the field of text mining. It is a general problem limiting progress and reproducibility in many fields of science, which authors, reviewers and editors have a duty to address. Making release of open source software the rule, rather than the exception, should further catalyze advances in text mining, as it has in other fields of computational research that have made extremely rapid progress in the last decades (such as genome bioinformatics).

By opening up the code base in text mining research, and deploying text-mining tools at scale on the rapidly growing corpus of full-text Open Access articles, we are confident this powerful technology will make good on its promise to catalyze scholarly endeavors in the digital age.

References

1. Dickman S (2003) Tough mining: the challenges of searching the scientific literature. PLoS biology 1: e48. doi:10.1371/journal.pbio.0000048.
2. Rebholz-Schuhmann D, Kirsch H, Couto F (2005) Facts from Text—Is Text Mining Ready to Deliver? PLoS Biol 3: e65. doi:10.1371/journal.pbio.0030065.
3. Cohen B, Hunter L (2008) Getting started in text mining. PLoS computational biology 4: e20. doi:10.1371/journal.pcbi.0040020.
4. Bourne PE, Fink JL, Gerstein M (2008) Open access: taking full advantage of the content. PLoS computational biology 4: e1000037+. doi:10.1371/journal.pcbi.1000037.
5. Rzhetsky A, Seringhaus M, Gerstein M (2009) Getting Started in Text Mining: Part Two. PLoS Comput Biol 5: e1000411. doi:10.1371/journal.pcbi.1000411.
6. Rodriguez-Esteban R (2009) Biomedical Text Mining and Its Applications. PLoS Comput Biol 5: e1000597. doi:10.1371/journal.pcbi.1000597.
7. Kim D, Yu H (2011) Figure text extraction in biomedical literature. PloS one 6: e15338. doi:10.1371/journal.pone.0015338.
8. Boyack K, Newman D, Duhon R, Klavans R, Patek M, et al. (2011) Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches. PLoS ONE 6: e18029. doi:10.1371/journal.pone.0018029.
9. Kolluru B, Hawizy L, Murray-Rust P, Tsujii J, Ananiadou S (2011) Using workflows to explore and optimise named entity recognition for chemistry. PloS one 6: e20181. doi:10.1371/journal.pone.0020181.
10. Hayasaka S, Hugenschmidt C, Laurienti P (2011) A network of genes, genetic disorders, and brain areas. PloS one 6: e20907. doi:10.1371/journal.pone.0020907.
11. Roque F, Jensen P, Schmock H, Dalgaard M, Andreatta M, et al. (2011) Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS computational biology 7: e1002141. doi:10.1371/journal.pcbi.1002141.
12. Salathé M, Khandelwal S (2011) Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control. PLoS Comput Biol 7: e1002199. doi:10.1371/journal.pcbi.1002199.
13. Baran J, Gerner M, Haeussler M, Nenadic G, Bergman C (2011) pubmed2ensembl: a resource for mining the biological literature on genes. PloS one 6: e24716. doi:10.1371/journal.pone.0024716.
14. Fisher R, Knowlton N, Brainard R, Caley J (2011) Differences among major taxa in the extent of ecological knowledge across four major ecosystems. PloS one 6: e26556. doi:10.1371/journal.pone.0026556.
15. Hossain S, Gresock J, Edmonds Y, Helm R, Potts M, et al. (2012) Connecting the dots between PubMed abstracts. PloS one 7: e29509. doi:10.1371/journal.pone.0029509.
16. Ebrahimpour M, Putniņš TJ, Berryman MJ, Allison A, Ng BW-H, et al. (2013) Automated authorship attribution using advanced signal classification techniques. PLoS ONE 8: e54998. doi:10.1371/journal.pone.0054998.
17. Acerbi A, Lampos V, Garnett P, Bentley RA (2013) The Expression of Emotions in 20th Century Books. PLoS ONE 8: e59030. doi:10.1371/journal.pone.0059030.
18. Groza T, Hunter J, Zankl A (2013) Mining Skeletal Phenotype Descriptions from Scientific Literature. PLoS ONE 8: e55656. doi:10.1371/journal.pone.0055656.
19. Seltmann KC, Pénzes Z, Yoder MJ, Bertone MA, Deans AR (2013) Utilizing Descriptive Statements from the Biodiversity Heritage Library to Expand the Hymenoptera Anatomy Ontology. PLoS ONE 8: e55674. doi:10.1371/journal.pone.0055674.
20. Van Landeghem S, Bjorne J, Wei C-H, Hakala K, Pyysal S, et al. (2013) Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization. PLOS ONE 8: e55814. doi:10.1371/journal.pone.0055814
21. Liu H, Hunter L, Keselj V, Verspoor K (2013) Approximate Subgraph Matching-based Literature Mining for Biomedical Events and Relations. PLoS ONE 8(4): e60954. doi:10.1371/journal.pone.0060954
22. Davis A, Weigers T, Johnson R, Lay J, Lennon-Hopkins K, et al. (2013) Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the Comparative Toxicogenomics Database. PLOS ONE 8: e58201. doi:10.1371/journal.pone.0058201
23. Bergman CM (2012) Why Are There So Few Efforts to Text Mine the Open Access Subset of PubMed Central? http://caseybergman.wordpress.com/2012/03/02/why-are-there-so-few-efforts-to-text-mine-the-open-access-subset-of-pubmed-central/.

Directed Genome Sequencing: the Key to Deciphering the Fabric of Life in 1993

Seeing the #AAASmtg hashtag flowing on my twitter stream over the last few days reminded my that my former post-doc advisor Sue Celniker must be enjoying her well-deserved election to the American Association for the Advancement of Science (AAAS). Sue has made a number of major contributions to Drosophila genomics, and I personally owe her for the chance to spend my journeyman years with her and so many other talented people in the Berkeley Drosophila Genome Project. I even would go so far as to say that it was Sue’s 1995 paper with Ed Lewis on the “Complete sequence of the bithorax complex of Drosophila” that first got me interested in “genomics.” I remember being completely in awe of the Genbank accession from this paper which was over 300,000 bp long! Man, this had to be the future. (In fact the accession number for the BX-C region, U31961, is etched in my brain like some telephone numbers from my childhood.) By the time I arrived at BDGP in 2001, the sequencing of the BX-C was already ancient history, as was the directed sequencing strategy used for this project.  These rapid changes made discovery of a set of discarded propaganda posters collecting dust in Reed George’s office that were made at the time (circa 1993) extolling the virtues of “Directed Genome Sequencing” as the key to “Deciphering the Fabric of Life” all the more poignant. I dug a photo I took of one of these posters today to commemerate the recognition of this pioneering effort (below). Here’s to a bygone era, and hats off to pioneers like Sue who paved the road for the rest of us in (Drosophila) genomics!

Directed-genome-sequencing