I had a great time in Sweden this past summer, at ISMB 2009 (ISMB/ECCB 2009 FriendFeed room). I listened to a lot of interesting talks, reconnected with old friends and met new ones. I went to an ice bar, explored a 17th-century ship that had been dragged from the bottom of the sea, and visited the building where the Nobel Prizes are handed out.
While there, many of us took notes and provided commentary through live blogging either on our own blogs or via FriendFeed and Twitter. The ISCB were very helpful, having announced and advertised the live blogging possibilities prior to the event. Once at the conference, they provided internet access, and even provided extension cords where necessary so that we could continue blogging on mains power.
Those of us who spent a large proportion of our time live blogging were asked to write a paper about our experiences. This quickly became two papers, as there were two clear subjects on our minds: firstly, how the live blogging went in the context of ISMB 2009 specifically; and secondly, how our experiences (and that of the organisers) might form the basis of a set of guidelines to conference organisers trying to create live blogging policies. The first paper became the conference report, a Message from ISCB published today in PLoS Computational Biology. This was published in conjunction with the second paper, a Perspective published jointly today in PLoS Computational Biology, that aims to help organisers create policies of their own. Particularly, it provides “top ten”(-ish) lists for organisers, bloggers and presenters.
If you have any questions or comments about either of these articles, please comment on the PLoS articles themselves, so there can be a record of the discussion.
Lister, A., Datta, R., Hofmann, O., Krause, R., Kuhn, M., Roth, B., & Schneider, R. (2010). Live Coverage of Scientific Conferences Using Web Technologies PLoS Computational Biology, 6 (1) DOI: 10.1371/journal.pcbi.1000563
Lister, A., Datta, R., Hofmann, O., Krause, R., Kuhn, M., Roth, B., & Schneider, R. (2010). Live Coverage of Intelligent Systems for Molecular Biology/European Conference on Computational Biology (ISMB/ECCB) 2009 PLoS Computational Biology, 6 (1) DOI: 10.1371/journal.pcbi.1000640
“It’s not information overload, it’s filter failure.“ (Clay Shirky)
Bonetta (2009) gave an excellent introduction to the micro-blogging service Twitter and its uses and limitations for scientific communication. We believe that other social networkingtoolsmerit a similar introduction, especiallythose that provide more effective filtering of scientifically relevant information than Twitter. We find that FriendFeed (already mentioned in the first online comment on the article, by Jo Badge) shares all of the features of Twitter but few of its limitations and provides many additional features valuable for scientists. Bonetta quotes Jonathan Weissman, a Howard Hughes Medical Institute investigator at the University of California, San Francisco: “I could see something similar to Twitter might be useful as a way for a group of scientists to share information. To ask questions like ‘Does anyone have a good antibody?’ ‘How much does everyone pay for oligos?’ ‘Does anyone have experience with this technique?'” It isprecisely for such and many more purposes that scientists use FriendFeed, which allows thecollection ofmany kinds of contributions, not just short text messages.
Also in contrast to Twitter, comments to each contribution are archived in that context (and without a time limit), providing a solid base for fruitful, threaded discussions. In your user profile, you can choose to aggregate any number of individual RSS or Atom ‘feeds‘, including scientific publications you bookmark in your online reference manager (e.g. CiteULike or Connotea),your blog entries, social bookmarks (Google Reader, del.icio.us, etc.), and Tweets;and any otheritems you wish topostdirectly to your feed. You then look for other users whose profile is relevant to your work and subscribe to them. Every individual item posted in your subscriptions will then appear on your personalized FriendFeed homepage, plus optionally a configurable subset of the feeds you subscribed to. You can choose to bookmark (‘like‘) any of these items(Facebook copied this ‘like’ functionality just before it bought FriendFeed), comment on them, and share discussion threads in various ways.
At first, this aggregation of information and threaded discussions might seem daunting. However, the stream of information can be channeled by organizing it into separate sub-channels (‘lists’; similar to but more versatile than ‘folders’ in email), according to your personal preferences (e.g. one for search alerts). In addition to individual users, you can also subscribe to ‘rooms‘ that revolve around particular topics. For example, the “The Life Scientists” room currently has 1,267 members and imports one feed.
The feature that makes FriendFeed truly useful is its social filtering system. Active discussions move to the top of your FriendFeed homepage with each new addition, which automatically brings them to the attention of you and everyone else who readsthose feeds. In a sense, the most current and the most popular entries compete for attention at the top, making notifications unnecessary. This means that your choice of both rooms andsubscriptions affects and filters the content you see. In that way, for instance, you could set your preferences such that you would only see papers with a certain minimum number of ‘likes’ among your colleagues.Alternatively, you can opt to hideitems with zero likes or comments, ensuringthat only those that someone found interesting will reach you. Thanks to a very fine-grained search functionality, threads also remain easily retrievable.
Some of the synergistic effects of themany scientists interacting on FriendFeed are already apparent at this early stage of adoption.FriendFeed provides a convenient way to microblog from conferences by means of dedicated threads or discussion rooms created for the event, thus allowing to share comments within and across sessions, or even with people not physically present at the meeting. Such conference coverage has even received direct (e.g. ISMB09, BioSysBio09) or indirect (e.g. ISMB08) support from the conference organizers.
Above and beyond conference coverage,scientists use FriendFeed to share papers, experiences on laboratory equipment, resources for teaching, or anything else commonly asked at mailing lists.A number of real-world scientific collaborations have already been sparked from such interactions. Collaborative grant proposals have been initiated, submitted and some of them approved after the idea was passed around and discussed on FriendFeed. Several bioinformatics problems have been solved by code-sharing and advice. Articles in scientific journals have been published by FriendFeed users after meeting and discussing on the platform [1-5].
Of course, since FriendFeed was not designed forscientists, there is room for improvement in terms of usability for scientific purposes. For instance, files can only be uploaded upon starting a thread, not while commenting on it, and there is currently no functionality which infers a measure of reputation to a user from his/her contributions (though the wide-spread use of real names somewhat allows that to be imported). As with all online contributions, citability and long-term archivingare unresolved issues, as is the permanence of services whose source code is not public. Fortunately, the development of social networks tailored to the needs of scientists is actively being pursued from various angles. ThePolymath projects, in which researchers collaborate online to solve mathematical problems,provide a number ofexamples.The recent award of two NIH grantsof over $US10M each for exactly such purposes is another. Ultimately, the continued enthusiastic adoptionof the sophisticated variants of social filtering tools by a broad community of researchers interested in sharing their science will only increase the usefulness for and thus the capabilities of the online scientific community.
1 Lister, A., Charoensawan, V., De, S., James, K., Janga, S. C. C., Huppert, J., 2009. Interfacing systems biology and synthetic biology. Genome biology. 10 (6), 309+. http://genomebiology.com/2009/10/6/309
Recently, more and more biologists, bioinformaticians, and scientists in general have been discovering the usefulness of social networking, microblogging, and blogging for their work. Increasingly, social networking applications such as FriendFeed and Twitter are becoming popular for the discovery of new research in a timely manner, for interactions and possible collaborations with like-minded researchers, and for announcing work that you’re doing. Sharing data and knowledge in biology should not just be limited to formal publications and databases. “Biosharing” can also be informal, and social networking is an important tool in informally conveying scientific knowledge. But how should you get started in this new world? Here are my experiences with it, together with some links to and thoughts to help you get started.
I created my FriendFeed account in Fall 2008 and my Twitter account last month. Why did I start using these social networking sites? Well, with FriendFeed, I had noticed many of my work colleagues starting to use it, but had no real understanding as to why they were so evangelical about it. With Twitter, I held out longer but eventually realised it was a really quick and easy way to get my messages across. The reason I did it and why they are useful to me comes down to a simple answer.
I am interested in sharing knowledge. Social networking promotes an informalsharing of knowledge in a way complementary to more traditional, formal methods of knowledge sharing.
And if you’re interested in knowledge sharing, then you should look into social networking. My research focuses on semantic data integration. I have a further interest in common data formats to enable data / knowledge sharing. As I am quite vocal about getting people interested in formal methods of knowledge sharing such as the triumvirate of MIBBI, OBI, and FuGE / ISA-TAB for experimental data1 (and many, many more), it behooved me to learn about the informal methods.
Social Networking: Day-To-Day
But what convinced me that social networking for science was useful? By December I had a realisation: this social networking stuff was giving me more information directly useful to my research than any other resource I had used in the past. Period. You can see my happiness in this blog post from December, where I showed how, these days, I get more useful citations of papers I’m interested in via my friends’ citeulike feeds on FriendFeed than I ever have managed from the PubMed email alerts. What convinced me is not a what, but a who.
Social networking for science is an informal data integrator because of the people that are in that network.
It’s all about the people. I have met loads of new friends that have similar research interests via the “big 2” (FriendFeed and Twitter). I get knowledge and stay up to date on what’s happening in my area of the research world. I make connections.
What is FriendFeed? At its most basic definition, it is an “personal” RSS Aggregator that allows comments on each item that is aggregated. For instance, I’ve added slides, citations, my blogs, my SourceForge activity and more to FriendFeed:
There are loads of other RSS feeds you can add to FriendFeed. Then, when people add your feed to their accounts, they can see your activity and comment on each item. You gradually build up a network of like-minded people. Additionally, you can post questions and statements directly to FriendFeed. This is useful as a form of microblogging, or posting short pieces of useful information quickly.
What is Twitter? It’s a bit like instant messaging to the world. You can say whatever you like in 140-characters or less, and it is published on your page (here’s mine). Just like with FriendFeed, you can follow anyone else’s Twitter feed. You can even put your Twitter feed into FriendFeed. People have a tendency to over-tweet, and write loads of stuff. I use it, but only for work, and only for things that I think might be relevant for quick announcements. If Doug Kell tweets, shouldn’t you? 😉
Other people have posted on how FriendFeed is useful to them in their scientific work, such as Cameron Neylon (who has some practical advice too), DeepakSingh and Neil Saunders who talk about specific examples, and Simon Cockell who has written about his experiences with FriendFeed and Twitter. I encourage you to have a read of their posts.
You don’t have to spend ages on FriendFeed and Twitter to get useful information out of it. Start simply and don’t get social networking burnout.
Ask questions about science you can’t answer in your own physical network at the office (Andrew Clegg did it, and have a look at the resulting discussion on FriendFeed and blog summary from Frank Gibson!). Post interesting articles. Ignore it for a week or more if you want: interesting stuff will be “liked” by other people in your network and will stay at the top of the feed. Trust the people in your network, and make use of their expertise in sending the best stuff to the top, if you don’t have the time to read everything. Don’t be afraid to read everything, or to read just the top two or three items in your feed.
Social Networking: Conferences and Workshops
These “big 2” social networking apps are really useful when it comes to conferences, where they are used to microblog talks, discussions, and breakout sessions. For detailed information on how they are used in such situations, see the conference report for ISMB 2008 in PLoS Computational Biology by Saunders et al. BioSysBio 2009 also used these techniques (conference report, FriendFeed room, Twitter hashtag).
Social Networking: What should I use?
Other social networking sites, billed as “especially for scientists”, have been cropping up left, right and centre in the past year or two. There are “Facebooks for Scientists”2 (there are more than 20 listed here, just to get you started, and other efforts more directed at linking data workflows such as myExperiment). So, should we be using these scientist-specific sites? I certainly haven’t tried them all, so I cannot give you anything other than my personal experience.
As you can see from my FriendFeed screenshot, I belong to “Rooms” in FriendFeed as well as connecting directly with people’s feeds. Rooms such as The Life Scientists, with over 800 subscribers, gets me answers to sticky questions I wouldn’t otherwise know how or where to ask (see here for an example). These, and the people I choose to link with directly, give me all of the science-specific discussions I could want.
The more general the social networking application is and the larger the user-base it has, the more likely it is to be around next year.
Right now, I don’t need any of the specialty features I’d get with a scientist-specific social networking application. I think the big names are more likely to reach a wider audience of like-minded folk.
Remember you’re broadcasting to the world. Only put stuff in that you think others will be interested in. This is a public face for you and your career.
I am a strong believer in keeping the personal parts of my life private (the entire world doesn’t need – or want – to know about my cat or see the pictures of my nephew) while at the same time making sure that I am really easy to reach for work-related discussions and collaborations. Through my blog, and my social networking, I am gaining a fuller appreciation of the work going on in the research community around me and contributing to the resulting large experiment in informal data integration.
It is fun: I meet new people and have interesting conversations. It is useful to my career: my blogging has resulted in an invitation to co-author two conference reports, and shows me new things happening in my field earlier than before. I’m all about sharing biological knowledge. I’m researching the formal side of data integration and sharing, and I’m using informal knowledge sharing to help me do my work.
While I am on Facebook, I do not use it for work purposes, and therefore cannot comment on its applicability for scientists.
Lister A, Charoensawan V, De S, James K, Janga SC, & Huppert J (2009). Interfacing systems biology and synthetic biology. Genome biology, 10 (6) PMID: 19591648 Saunders, N., Beltrão, P., Jensen, L., Jurczak, D., Krause, R., Kuhn, M., & Wu, S. (2009). Microblogging the ISMB: A New Approach to Conference Reporting PLoS Computational Biology, 5 (1) DOI: 10.1371/journal.pcbi.1000263
Over on Friendfeed this week, I started a discussion (both in The Life Scientists room and in the Science 2.0 room) about ontologies and licensing them. I am creating a couple, and was trying to determine whether I should use some flavor of CC license or perhaps an LGPL license or similar. CC people say that their licenses shouldn’t be used for software. But is an ontology software, a document, data, or something else entirely? I feel that it is a model or representation of knowledge, and a way to conceptualize what you need to describe. That doesn’t really provide an answer, however. As Egon pointed out in the FF discussion, it has no real inputs or outputs, and as such isn’t software. However, reasoners can present logical inferences as outputs when the ontology is given as an input… The situation is tricky, and I suggest that you head over to FF to get an idea of what people are saying about it.
I also asked some Science Commons people (thanks to Frank for the idea) what they had decided to do for this situation. Here is their reply, and based on their thoughts, I think a CC license is definitely OK for ontologies, and I will choose among them according to the policies of my boss and my university! Thanks to SC for the help, and for their permission to reproduce their thoughts:
Whether an ontology qualifies for copyright protection under U.S. Copyright law depends on whether it contains a sufficient degree of creative expression. For example, an ontology that draws entirely on facts or ideas in the public domain would not qualify for copyright protection. While there does not appear to be any legal cases that directly address the issue of copyright protection for ontologies, there have been some cases in medical ontologies (particularly in medical procedure coding) that have upheld copyright claims in classification schemes that might resemble ontologies.
Thus, the determination of whether an ontology qualifies for copyright protection may require case-by-case analysis. For sharing ontologies in a community or publicly, it would be prudent to think about copyright and licensing. For example, the ontology creator could say that “to the extent I may have copyright in my ontology, I license it in the following way.” In that way, she can reassure the community that even in the event copyright is later found to exist, they may rely upon her offer of a license. This provides an important “safety net” for the community of users, given the uncertainty about whether a given ontology may be copyrightable.
There are several reasonable ways to license ontologies. But it must be kept in mind that an important goal of publicly shared ontologies is to foster community involvement (which necessitates granting rights to modify and extend the ontology) and interoperability (we want to avoid license conflicts in the future if ontologies have to be combined or made to interoperate). The best way to avoid license conflicts is to place an ontology in the public domain—that is, to release it without restrictions. This can be done using CC0 (http://creativecommons.org/license/zero). This gives users maximum freedom and ensures maximum compatibility with all other licenses.
However, some creators may want to retain rights of attribution. In that case, they may make use of licenses that require attribution only, such as the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). The drawback of this license is that since attribution is mandated, it may over time become more of a burden than a benefit (because as the list of contributors grows very large, the attribution requirements results in “attribution stacking”, where the number of people who need attribution become so large that it becomes not only meaningless but also a significant administrative and legal burden on future users).
For creators of ontologies who are concerned about protecting the quality of their distribution, trademark may offer an alternative form of protection. Unlike copyright, trademark does not protect the work itself, but it protects the “branding” of the work. An analogy would be while everyone can offer a different distribution of Linux, only Red Hat, Inc. can claim to offer “Red Hat” Linux. Thus, branding is used to protect the quality and integrity of the product, rather than copyright control.
Update: There is additional talk on this post at FF, including a post by Pierre about the UMLS Metathesaurus. Basically what you need to do is follow the 3 FF links I’ve provided to keep up-to-date, as comments on this post will post likely happen there rather than here! 🙂
‘ll start off by saying that I’m new to the whole Friendfeed thing, and I’ve also only recently started using Citeulike in a more comprehensive way. I started out on the former through the recommendation of Frank over at peanutbutter, and it’s one of the best things I’ve done recently with respect to my working life (subscribe to my friendfeed). Citeulike also began via a recommendation from Frank, but it has really been useful to me as I start to slowly gather references that a) interest me in general, and/or b) will be useful when I start writing up my PhD thesis (my citeulike library).
Just today it really twigged in me how useful these two tools, in combination, can be. I credit Frank with two nice things he said about this grouping of two apps in a chat we had today: 1) “don’t need to do pubmed searches anymore”, and 2) “organise, share and discover” (update: Frank would like to say he wasn’t the originator of the quote, which is very good of him. Of course, it still holds true that you said it in our chat 😉 ). Certainly the joining of these two apps facilitates the latter, and my pubmed searching, while still extant, is now nicely supplemented by what my friends are reading.
I shall illustrate my point with some examples. (Please note that all
the people mentioned in the following images have their friendfeeds set
to public, and therefore I will not be compromising anyone’s privacy by
using these examples.)
It all started this morning, when Simon added this paper into his citeulike library:
Then, I liked the look of it – having seen it in my friendfeed – and added it to my library with just two clicks:
Next, via friendfeed’s comment mechanism,
I received plaudits for adding to my very new citeulike library:
Then, others noticed Simon’s or my additions, and added it themselves. First, it was Dan:
And then it was Frank. However, before I show Frank’s feed, I should mention that earlier in the day, Duncan had posted a review from Nature for a book we had been discussing:
And I decided I also liked this review:
So, when Frank had a look at Friendfeed, he found two things he liked, and it was reported by Friendfeed as so:
I’m sure others have experienced this already, but it’s new to me, and just shows me how using social apps like Friendfeed in a work context can really increase my knowledge in an efficient and fun way. It’s fantastic, even it if is a little circular and self-referencing. After all, this post about Friendfeed will shortly appear on my Friendfeed. But then, Friendfeed is a great forum for discussing things, and getting ideas to blog about. Neil and others have already done this. Thanks to everyone whose feeds I read 😉