Open Collaboration Data Factories

The Open Collaboration Data Factories (OCDF) initiative focuses on actively prototyping open knowledge infrastructures as solutions to gaps in research approaches and methods in the study of knowledge creation in open online communities, such as those that create Wikipedia, open source software, and citizen science. We are building a community of scholars who address differences in research aims, data, and methods to enable a new, interdisciplinary knowledge production. The Open Knowledge Lab has leveraged  student course assignments to develop a prototype research data directory with detailed documentation of openly available data for the study of online communities. Information Management graduate students in Dr. Wiggins’ INFM 600 (Information Environments) course learn valuable professional skills by searching, evaluating, interrogating, documenting, licensing, and citing open data while creating value-added resources for scientific research on open...

Citizen Science Data Usage

    To understand the potential value and applications of freely available, carefully curated open citizen science data, our research team is working with partners from the University of Michigan and the Cornell Lab of Ornithology on a small study of eBird data users’ practices and outcomes. Survey responses are currently under analysis; respondents documented a broad range of uses for eBird data across distinct contexts, including numerous conservation applications, academic research studies, educational uses at every level, and leisure-related uses, such as record-keeping in the birding community. The inclusion of effort information—documentation of the time and place that observations were made—was considered critical for most uses of the...

Human Computer Learning Network

To improve scalability and data quality, the eBird Human-Computer Learning Network (HCLN) blends emerging techniques that integrate the speed and scalability of mechanical computation, using advances in Artificial Intelligence, with the real intelligence of human computation to solve computational problems that are beyond the scope of existing algorithms. In addition to developing emergent filters and new models of contributor expertise, this work makes extensive use of the semantic links between observations and observers to mine additional information from the existing data in order to strategically address data quality issues. Our extended research team have found evidence of learning-through-doing and improvements in participant performance with accumulated project experience. Initial analyses also confirmed expectations about localization of observers’ knowledge of species, and showed that measures of performance effectively distinguish between observers who are and are not able to detect and identify “secretive” or ambiguous species. This project is a collaboration with the Cornell Lab of Ornithology, eBird, University of Michigan, and Oregon State...