Academic Productivity

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).
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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)

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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:

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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.