Cyberinfrastructure studies overview

In their introduction to the special issue on sociotechnical studies of cyberinfrastructure (CI) and e-research Ribes and Lee identify current themes and methodologies of CI studies (Computer Supported Cooperative Work (CSCW), 2010, Volume 19, Issue 3, pp 231-244, doi: 10.1007/s10606-010-9120-0)

Cyberinfrastructure (CI) is one of the current terms for the technologies that support scientific activities such as collaboration, data sharing and dissemination of findings. CI features that distinguish it from other CSCW work include: community wide and cross-disciplinary scope, computational orientation, and end-to-end (data-to-knowledge-to-user) integration.

Themes in CI studies:

1. Relationality. What is supporting the work of another and who is sustaining those relationships?
2. Integration of heterogeneity. CI involves computer specialists, data and information managers, domain scientists, and so on, but also non-human actors such as sensors and databases.
3. Sustainability. What makes CI a long-term resource?
4. Standardization. Ways to achieve integration on the technical and human levels.
5. Scale. How to plan for change and growth in the number of collaborators, the quantity of data, and the geographical reach.
6. The distribution between human work and technological delegation. 

Methods include historical, ethnographic, documentary, and interview-based approaches that focus on the following:

• Investigations of ongoing planning, development and deployment efforts 
• Activities of maintenance, upgrade and breakdown
• Adoption of certain expressions of scientific activity and changes in their use
• Adoption of new technological artifacts

Units of analysis can be a project or CI as a whole (focus on national policies and funding incentives). The introduction concludes by calling for more studies:

The stories of cyberinfrastructure are revealed by looking across multiple levels of granularity, various facets of social life, and diverse technological actors. Much remains to be studied in the areas of supporting domain specific practice, data sharing and curating, and infrastructural organizings. This is an exciting time for CI studies. Research is occurring in new and unexpected places, drawing on and bringing together the traditions of CSCW, information science, organizational studies, and science and technology studies. This cross-pollination, as exemplified by the papers in this issue, seems to be not only fruitful, but also very necessary.

Philosophy and science: A need for Ph in PhD

An interesting piece published recently in the Science magazine: Shaking Up Science by Jennifer Couzin-Frankel. It's behind the paywall and pretty long, so here is a quick summary.

The essay is a story about two biology scientists, Ferric Fang from University of Washington and Arturo Casadevall from Albert Einstein College of Medicine in the Bronx, New York. They were brought together by "disenchantment", i.e., they both had worries about what is going on in academia and science:

Discovery for its own sake was being sidelined by a push to publish in high-impact journals. Funding was scarcer than ever. Scientists focused on narrow fields and often couldn't communicate their professional passions at a cocktail party.

They were both editors of an immunology journal, so they started writing opinion pieces about grants, peer review process and so on. At some point they got interested in research misconduct, more specifically, how many papers are being retracted, where and why. First, they wanted to see if there is a connection between a journal's impact factor and its retraction rate. They searched the PubMed database and found a robust correlation - the higher the impact factor, the more retractions the journal had.

Then they looked closely to retractions between 1977 and 2000 and found that about 67% of all the retractions were attributed to scientific misconduct, including fraud and plagiarism.

The next step was (and it's usually the most difficult one) to figure out why it happens. I like the possible explanation, but it's not clear from the essay whether it was supported with evidence or not. It makes a lot of sense though.

The scientists believe that the race for grants and funding encourages misconduct.

"It's all about money," Fang says. "How can you be sure that you get money?" The answer comes back to publications—and sometimes skirting the rules to get them.

The story up to this point is more or less obvious. A lot of people talk about problems with depending on grants in funding science and scientists (soft money) and peer review. What is interesting is what kind of solutions are proposed. The scientists argue for more generalized science education instead of the extreme specialization. And for more philosophical training, particularly in epistemology and metaphysics that encourages asking questions like "What is it that you know?" and "How do you know what you know?"

Even though we may never go back to making philosophy a required subject (which I had as part of my graduate studies in Russia), I think it'd be great. Asking broad questions about the nature of knowledge and, more importantly, its justifications and limits, encourages people to step back, look at the larger picture and think critically about what they're doing. By doing that the sciences can be what they're supposed to be - a self-correcting institution based on the Mertonian norms of communalism, universalism, disinterestedness, originality and skepticism.