I read a good quote the other day:

“Bollocks to waiting 10 years for progress. I want people to know about it now, and then do something about it” – Dr Paul Fisher

So why do we wait? Why isn’t there immediate publication, analysis and dissemination of data? Publication of Scientific data as it stands is a broken business model…for the most part. The advent of journals like PLoS and their subsequent success shows that the scientific community is taking note of what steps need to be taken. In my short life as a scientist, there has always been one thing that really annoys me. The inefficiency of scientific publishing and subsequent global sharing of knowledge. In terms of making significant advances available to wide audiences as peer reviewed publications, PLoS has it covered. But what about the rest of your research?

What percentage of the figures that went into your undergrad, masters or doctorate thesis were ever published? The ones that you didnt publish were probably good basic science, or figures that didnt tell a complete story. As a PhD student, I became very aware of the fact that a large amount of my data, although good, would never be published as it did not show significant differences. I then began wondering how many times experiments had been repeated globally unnecessarily. And so FigShare started life as an idea for researchers to publish all of their data that would otherwise never leave their lab books. By categorising and tagging the research, it becomes very searchable and other scientists should not reproduce experiments and waste money when they have been conducted several times by other labs. Following the alpha release, FigShare received a lot of attention and a lot of feedback. This caused the site to develop and it now allows the upload of Figures, Datasets and most recently media (eg. videos).

This is not a new idea, and big data and data sharing projects have won several big JISC grants, but your average researcher needs this to be simple in order to adopt. The CKAN repository is a fantastic project which allows you to upload data from any field, such as government finances, weather forecasts and traffic reports. Where I feel this becomes inaccessible for scientists is the ease of uploads. A choice from 50 licenses for your data is intimidating enough to make most postdocs turn and run. For a project like FigShare, the more research that is uploaded, the more useful the site becomes. In order for this to happen, uploading research needs to be simple. This is what FigShare gives you. Give your bit of research, be it a figure, dataset or some other media format a name, hit upload, add details like your name, some tags and you have a nicely presented, citable, published figure.

FigShare now also serves as a repository for preprints figures. ie. Figures that will one day be published but feedback is requested on the prelimonary data. FigShare can be used as a platform to collaborate where users can contact one another and request to use figures in their publications etc. This means that previous unused figures, maybe from unfinished postdocs or PhD projects can be published, gaining the author more publications.

There is also the ability to easily share your figures, datasets and videos via a host of social media platforms through ‘share buttons’ on every page. This is a new way of bringing scientific research online and to a new audience. An example of how this can benefit science is already producing examples such as this one – A lot of scientists hear how social media can benefit research and yet there has been little evidence of how these tools can be exploited to make science more efficient. Imagine real time discussions about science you did yesterday, not last year when you first submitted your paper.

FigShare is a permanent database of your research. To further ensure this, FigShare is supported by Systems Institute. Systems Institute is a not for profit which is providing ongoing support for the hosting of FigShare as it expands. This also allows FigShare to make backups of all of your data each and every day.

So please, upload your data now and do your bit to help science progress in an efficient manner. It’ll probably do wonders for your academic career too!

Koblenz (Germany), 14-15 June 2011
An ACM Web Science Conference 2011 Workshop

Call for papers

The increasing quantity and velocity of scientific output is presenting scholars with a deluge of data. There is growing concern that scholarly output may be swamping traditional mechanisms for both pre-publication filtering (e.g peer review) and post-publication impact filtering (e.g. the Journal Impact Factor).

Increasing scholarly use of Web2.0 tools like CiteULike, Mendeley, Twitter, and blog-style article commenting presents an opportunity to create new filters. Metrics based on a diverse set of social sources could yield broader, richer, and more timely assessments of current and potential scholarly impact. Realizing this, many authors have begun to call for investigation of these “altmetrics.” (see altmetrics.org)

Despite the growing speculation and early exploratory investigation into the value of altmetrics, however, there remains little concrete, objective research into the properties of these metrics: their validity, their potential value and flaws, and their relationship to established measures. Nor has there been any large umbrella to bring these multiple perspectives together. The altmetrics11 workshop aims to  encourage both these. Submissions are invited from a variety of areas:

• New metrics based on social media
• Tracking science communication on the Web
• Relation between traditional metrics and altmetrics
• Peer-review and altmetrics
• Tools for gathering, analyzing, disseminating altmetrics

Important Dates

Submissions

Prospective authors should submit 2-page extended abstracts (max. 1000 words, not including references). If necessary, the workshop organizers will select the most relevant, original, and significant abstracts for presentation. Experimental results will be given preference, followed by technical reports on working altmetrics tools and position papers. All selected submissions will be published online for open peer review and discussion. Authors are encouraged to participate in the discussions of their work. Based on the presentations and online discussion, selected authors may be asked to submit full papers for peer-reviewed proceedings.

Location

The workshop is hosted by the ACM Web Science Conference 2011 (Koblenz, Germany). This interdisciplinary conference focuses on advances in studying the full range of social-technical relationships on the Web. Please visit the Web Science site for more information.

Organizers

• Paul Groth – VU University Amsterdam, NL
• Jason Priem –University of North Carolina at Chapel Hill, USA
• Dario Taraborelli – Wikimedia Foundation, USA

The organizers have an interdisciplinary background covering Sociology, Information and Library Science and Computer Science.

An excellent article published last month in the Chronicle celebrates Wikipedia’s 10th anniversary by observing that today the project doesn’t represent “the bottom layer of authority, nor the top, but in fact the highest layer without formal vetting” and, as such, it can serve as “an ideal bridge between the validated and unvalidated Web”. An increasing number of university students use Wikipedia for “pre-research”, as part of their course assignments or research projects. Yet many among academics, scientists and experts turn their noses up at the thought of contributing to Wikipedia, despite a growing number of calls from the scientific community to join the project (see for instance this recent initiative of the Association for Psychological Science or this call for biomedical experts to help contribute rigorous public health information in Wikipedia).

A survey has been launched by the Wikimedia Research Committee to understand why scientists, academics and other experts do (or do not) contribute to Wikipedia, and whether individual motivation aligns with shared perceptions of Wikipedia within different communities of experts. The survey is anonymous and takes about 20 min to complete. Whether you are an active Wikipedia contributor or not, you can take the survey and help Wikipedia think of ways around barriers to expert participation.

No one can read everything. We rely on filters to make sense of the scholarly literature, but the narrow, traditional filters are being swamped. However, the growth of new, online scholarly tools allows us to make new filters; these alt-metrics reflect the broad, rapid impact of scholarship in this burgeoning ecosystem. We call for more tools and research based on alt-metrics.

As the volume of academic literature explodes, scholars rely on filters to select the most relevant and significant sources from the rest.

Unfortunately, scholarship’s three main filters for importance are failing:

• Peer-review has served scholarship well, but is beginning to show its age. It is slow, encourages conventionality, and fails to hold reviewers accountable. Moreover, given that most papers are eventually published somewhere, peer-review fails to limit the volume of research.
• Citation counting measures are useful, but not sufficient. Metrics like the h-index are even slower than peer-review: a work’s first citation can take years. Citation measures are narrow; influential work may remain uncited. These metrics are narrow; they neglect impact outside the academy, and also ignore the context and reasons for citation.
• The JIF, which measures journals’ average citations per article, is often incorrectly used to assess the impact of individual articles. It’s troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy.

Tomorrow’s filters: alt-metrics

In growing numbers, scholars are moving their everyday work to the web. Online reference managers Zotero and Mendeley each claim to store over 40 million articles (making them substantially larger than PubMed); as many as a third of scholars are on Twitter, and a growing number tend scholarly blogs.

These new forms reflect and transmit scholarly impact: that dog-eared (but uncited) article that used to live on a shelf now lives in Mendeley, CiteULike, or Zotero–where we can see and count it. That hallway conversation about a recent finding has moved to blogs and social networks–now, we can listen in. The local genomics dataset has moved to an online repository–now, we can track it. This diverse group of activities forms a composite trace of impact far richer than any available before. We call the elements of this trace alt-metrics.

Alt-metrics expand our view of what impact looks like, but also of what’s making the impact. This matters because expressions of scholarship are becoming more diverse. Articles are increasingly joined by:

• The sharing of “raw science” like datasets, code, and experimental designs
• Semantic publishing or “nanopublication,” where the citeable unit is an argument or passage rather than entire article.
• Widespread self-publishing via blogging, microblogging, and comments or annotations on existing work.

Because alt-metrics are themselves diverse, they’re great for measuring impact in this diverse scholarly ecosystem. In fact, alt-metrics will be essential to sift these new forms, since they’re outside the scope of traditional filters. This diversity can also help in measuring the aggregate impact of the research enterprise itself.

Alt-metrics are fast, using public APIs to gather data in days or weeks. They’re open–not just the data, but the scripts and algorithms that collect and interpret it. Alt-metrics look beyond counting and emphasize semantic content like usernames, timestamps, and tags. Alt-metrics aren’t citations, nor are they webometrics; although these latter approaches are related to alt-metrics, they are relatively slow, unstructured, and closed.

How can alt-metrics improve existing filters?

With alt-metrics, we can crowdsource peer-review. Instead of waiting months for two opinions, an article’s impact might be assessed by thousands of conversations and bookmarks in a week. In the short term, this is likely to supplement traditional peer-review, perhaps augmenting rapid review in journals like PLoS ONE, BMC Research Notes, or BMJ Open. In the future, greater participation and better systems for identifying expert contributors may allow peer review to be performed entirely from alt-metrics. Unlike the JIF, alt-metrics reflect the impact of the article itself, not its venue. Unlike citation metrics, alt-metrics will track impact outside the academy, impact of influential but uncited work, and impact from sources that aren’t peer-reviewed. Some have suggested alt-metrics would be too easy to game; we argue the opposite. The JIF is appallingly open to manipulation; mature alt-metrics systems could be more robust, leveraging the diversity of of alt-metrics and statistical power of big data to algorithmically detect and correct for fraudulent activity. This approach already works for online advertisers, social news sites, Wikipedia, and search engines.

The speed of alt-metrics presents the opportunity to create real-time recommendation and collaborative filtering systems: instead of subscribing to dozens of tables-of-contents, a researcher could get a feed of this week’s most significant work in her field. This becomes especially powerful when combined with quick “alt-publications” like blogs or preprint servers, shrinking the communication cycle from years to weeks or days. Faster, broader impact metrics could also play a role in funding and promotion decisions.

Road map for alt-metrics

Speculation regarding alt-metrics (Taraborelli, 2008; Neylon and Wu, 2009; Priem and Hemminger, 2010) is beginning to yield to empirical investigation and working tools. Priem and Costello (2010) and Groth and Gurney (2010) find citation on Twitter and blogs respectively. ReaderMeter computes impact indicators from readership in reference management systems. Datacite promotes metrics for datasets. Future work must continue along these lines.

Researchers must ask if alt-metrics really reflect impact, or just empty buzz. Work should correlate between alt-metrics and existing measures, predict citations from alt-metrics, and compare alt-metrics with expert evaluation. Application designers should continue to build systems to display alt-metrics, develop methods to detect and repair gaming, and create metrics for use and reuse of data. Ultimately, our tools should use the rich semantic data from alt-metrics to ask “how and why?” as well as “how many?”

Alt-metrics are in their early stages; many questions are unanswered. But given the crisis facing existing filters and the rapid evolution of scholarly communication, the speed, richness, and breadth of alt-metrics make them worth investing in.

Jason Priem (University of North Carolina-Chapel Hill)
Dario Taraborelli (University of Surrey)
Paul Groth (VU University Amsterdam)
Cameron Neylon (Science and Technology Facilities Council)

Source: http://altmetrics.org/manifesto 

I just found this presentation, and thought it’s worth bringing it to the attention of ap.com readers:

Anita de Waard is the director of Disruptive Technologies at Elsevier. A company that has a position with such a name has my sympathy. Looks like publishers are slowly realizing that they can have a huge impact on how science is done, and how fast it moves, if they simply paid more attention to modern trends.

Only habit prevents us researchers from realizing that the media we use the most, a paper article with a review cycle of years, is woefully wrong in this day and age.

A somewhat related idea are the 5 stars of open linked data:

★ make your stuff available on the web (whatever format)

★★ make it available as structured data (e.g. excel instead of image scan of a table)

★★★ non-proprietary format (e.g. csv instead of excel)

★★★★ use URLs to identify things, so that people can point at your stuff

★★★★★ link your data to other people’s data to provide context

If scientists and publishers have opendata in mind (and the trend is there!) doing research becomes more fun immediately (no more mails to the authors asking for data that get no response). Seeing that the academic publishing industry has at least one person (Anita) that gets it makes me feel good. Looks like Elsevier has a head-start.

I’m not sure how big of a problem selecting classes is for students, and how much it can be automated, but now there’s a tool specifically solving this problem. CourseRank tracks scheduling conflicts, together with some other Interesting features. For example, it gathers course/professor reviews, workload estimations and aggregates questions and answers.

Right now the selection of universities is not that great. It makes sense since the service is specifically tailored to each university, so I can imagine the implementation can take a while.

Judith Simon and Diego Ponte from the LiquidPub project are seeking participants for a a survey about scientific publishing and the Web 2.0.

The aim of the survey is to gauge the potential acceptance of a Web 2.0 inspired production and dissemination of scientific publications by different scientific communities and by practitioners. The survey is hence tailored for researchers in all domains as well as for people working in the publishing industry.

Judith promised to report back on the results of the survey

Call for participation for a workshop at CSCW 2010
[submission deadline: November 20, 2009]

The confluence of two major trends in scientific research is leading to an upheaval in standard scientific practice and collaborative technologies. A new generation of scientists, working in large-scale collaborations, is repurposing social software for use in collaborative science. Existing social tools such as chat, IM, and FriendFind are being adopted and modified for use as group problem-solving facilities. At the same time, exponentially greater and more complex datasets are being generated at a rate that is challenging the limits of current hardware, software, and human cognitive capability. A concerted effort to create software that will support new scientific practices and handle this data tsunami is redefining the collaboratory and represents a new frontier for computer supported cooperative work.

This follow-on event to a similarly themed workshop at CHI 2009 is intended to foster community among researchers and practitioners from multiple disciplines interested in the changing dynamics of scientific collaborations.

We encourage papers on the following topics, especially those with a focus on changing practices in these areas:

• Collaborative scientific applications concerning data gathering,
  analysis, sharing, and visualization
• Case studies concerning data gathering, analysis, sharing and visualization
• Socio-technical research on scientific collaborations
• Social networks of scientists
• Repurposing social software for science
• Participatory design and/or rapid prototyping for scientific software
• Distributed data gathering and analysis
• Time-critical scientific applications
• Studies of generational differences in how science is done
• Cross-functional applications and comparisons of a scientific to
  a non-scientific field

For more information, please see the workshop web site:
http://www.sci.utah.edu/cscw2010/

Organizers:

• Cecilia Aragon, Lawrence Berkeley National Lab, CRAragon@lbl.gov
• Jeffrey Heer, Stanford University, jheer@cs.stanford.edu
• Charlotte Lee, University of Washington, cplee@u.washington.edu
• Claudio Silva, University of Utah, csilva@sci.utah.edu

My colleague Alastair is conducting a survey about online academic collaboration, use of tools and attitudes to technology in the Academia as part of the Qlectives project. All participants who supply an email address (and complete the questionnaire by the 14 November) will be entered into a prize draw.

The Qlectives project based at the University of Surrey is conducting a survey to investigate how scientists collaborate and use technology. The questionnaire should take around 20 minutes to complete and is being conducted anonymously and confidentially.

As a way of saying thank you to those who take part, we will hold a prize draw for 5 x £20 Amazon vouchers when we have finished
collecting data (we anticipate that this will be around the 14 November 2009). To be considered in the prize draw, simply leave your
email address on the final page of the questionnaire, and we will contact the lucky winners.

A more detailed description of the Qlectives project and this study can be found on the first page of the questionnaire. Please also feel
free to share the study with other colleagues who you think would be interested in taking part.

CC-licensed image courtesy of Enrique Burgos Garcia

Google Scholar is probably the most useful tool on the web today for academics. However, there’s no API for it, and seems to add little to no features with time. I don’t think Google is going to give it the Axe any time soon, but … I can’t imagine ads getting clicked on scholar pages. And Google is a for-profit, so one never knows. In any case, it would not hurt to show Google that we care, and there’s one simple thing to do. If you want to support the creation of the API, you could drop by the google API forums and express your interest.