Collaborative Research: Explaining Differential Success in Biodiversity Knowledge Commons

Project: Research project

Project Details


Collaborative Research: Explaining Differential Success in Biodiversity Knowledge Commons Collaborative Research: Explaining Differential Success in Biodiversity Knowledge Commons Project Title: Explaining Differential Success in Biodiversity Knowledge Commons ASU: PI Beckett Sterner, Co-PI Steve Elliott; Purdue: Co-PI Zoe Nyssa OVERVIEW Scientists increasingly create and contribute to digital knowledge commons, including community-governed data portals. While these innovations have improved access to data and other scientific products, they are expensive to implement and frequently fail due to lack of sustained adoption and investment. Their impacts on research, industry, and policy-making are also challenging to evaluate as their usage often fails to map onto traditional scientometrics such as publication and citation counts. Mirroring trends in the economy at large, scientists are increasingly adopting platforms as an attractive model for lowering infrastructure costs while generating network benefits. However, no study of scientific data commons has investigated how these platforms operate or leveraged them to conduct a systematic analysis of why portals using the same platform still typically show a large variation in outcomes. The project will be the first to utilize rich tracking data available from the Symbiota platform for biodiversity data portals and analyze these data to identify key determinants of portal outcomes for 37 active and 4 inactive data portals. The Symbiota platform itself is one of the largest and earliest scientific data platforms still under continual development, with hundreds of participating collections and several dozen individually managed portals. To represent and analyze variation of data portal outcomes, the project will collect a range of quantitative and qualitative data from each portal and use fuzzy set Qualitative Comparative Analysis (fsQCA) to identify causally relevant configurations for portal outcomes. Adopting a platform-based study design enables the project to integrate prior research results on digital knowledge commons in other domains, such as open source software and online peer production communities such as Wikipedia, and test their explanatory value in the new domain of open scientific data. INTELLECTUAL MERIT The project is the first systematic comparative study of causal factors for sustained growth in scientific data portals. It develops a set of causal generalizations that can inform future studies of scientific data portals beyond the biodiversity domain. By further evaluating the scope to which the set of causal generalizations apply, researchers will be able to explain and predict the growth and success of knowledge commons across the range of social and structural scales. The project develops an innovative procedure that integrates qualitative and quantitative data to describe portals and their outcomes, empirically identifies and tests causal hypotheses for those outcomes, and is the first application of fsQCA methods to knowledge commons. The project also be the first application of the knowledge commons framework to the empirical study of biodiversity data portals, and it expands insights into the sustainability of knowledge commons to a context with a very different culture, history, and societal relevance than prior studies on genomics and biomedicine. BROADER IMPACTS The project addresses the needs for the long-term sustainability of biodiversity collections as discussed in the 2020 National Academies of Science Consensus Report commissioned by the NSF. The project also characterizes issues of diversity, equity, and inclusion (DEI) in biodiversity communities through data collection about demographics in a survey of over 5,000 data managers and many times more users, and questions about DEI in interviews with portal leaders. At the broadest level, societal benefit from internet technologies depends on open, equal access, and on effective governance to ensure collective benefit and avoid injustice through exploitation of less powerful groups. The project advances these outcomes through improved knowledge about the outcomes and effective governance of scientific knowledge commons. In addition to disseminating results to relevant stakeholder communities, PI Sterner will develop and lead a special issue on research metrics and analytics for data portals to share results and techniques enabling cross-sector comparisons with other digital knowledge commons, e.g. open source software and peer-production communities such as Wikipedia.
Effective start/end date1/1/2212/31/23


  • National Science Foundation (NSF): $292,095.00


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