Citizen science projects advance scientific inquiry by enlisting large crowds of volunteers to clean, code, and categorize large datasets in areas where humans still outperform machines. Though the usefulness of citizen science is no longer seriously in doubt, obstacles remain that prevent it from reaching its full potential. A lack of common standards for citizen science data projects makes it difficult to share or repurpose data; regulatory barriers inhibit federal agencies from using citizen science effectively; and the lack of a common repository of information on citizen science projects prevents researchers from taking advantage of what has already been learned. This grant supports efforts by the Commons Lab at the Woodrow Wilson International Center for Scholars to ease some of these barriers. Over the next two years, a team led by Anne Bowser will join with members of the citizen science community to spearhead a grassroots effort to develop common metadata standards; create a database of citizen science projects, develop a platform and API to facilitate citizen science data-sharing, examine the ethical and regulatory barriers to using unpaid volunteers in research projects, and conduct outreach to federal agencies and policymakers about the way in which citizen science can and is being used to further the aims of federal initiatives.

The basic process of moving large tabular data from one environment to another is fraught with issues. Ambiguous column headings and messy metadata can make it difficult and time consuming to understand exactly what a data file contains. As researchers move data from repository to research tool (and often through a series of research tools), the opportunities for error proliferate. Rufus Pollock of the Open Knowledge Foundation has developed a lightweight approach to structuring metadata about tabular datasets. With the Pollock approach, tabular datasets are packaged and moved with files that describe the data—datatypes, formatting, source, etc.—allowing research tools like Matlab, Excel, and Stata to appropriately parse the data inside. He describes this “data package” model as the equivalent to a shipping container for data, making it easier to standardize the entire logistics process. Funds from this grant continue development of the Pollock’s “data package” standard. Funded activities include the development of validators and extensions that would make it easy to export and import data packages from standard research tools (essentially adding new “Save As” and “Open” options); outreach to specific user communities to model use of the specification for individual disciplinary communities; the launch of several pilot projects integrating the data package model into existing user workflows; and building a broader development community around the need for better tools for efficient and trusted storage, transport, and analysis of large tabular data.

This grant provides partial funding for eight postdoctoral fellowships in Data Curation for the Sciences and Social Sciences. Though the fellowship program is administered by the Council on Library and Information Resources (CLIR), fellows are appointed at host institutions, where they work on digital initiatives that marshal a university’s technical, archival, and library resources in service to the data curation and management needs of the institution’s researchers.  Of the eight fellowships supported, four will focus on software curation, the growing archival field that seeks to preserve the software programs and platforms developed for and as a result of scientific research.  In addition to providing fellowship support, grant funds will expand the fellowship program to include improved education and training on software curation, both among the fellows and at participating host institutions.

There are currently no academic social science repositories that can routinely handle terabytes of data. This despite the fact that the rise of the Internet and new sensing technologies are creating large new datasets of potential interest to social scientists, like phone usage data or geospatial social media data. This grant supports efforts by Gary King at Harvard University to expand the popular Dataverse platform so that it becomes the first data archiving and management application capable of handling social science data at the terabyte scale. Fully open source, Dataverse is a decentralized web application that allows individual institutions to download and run their own instances. Universities and research labs can manage their data easily while at the same time configuring the system to meet their own needs and comply with their own institutional policies. Funds from this grant will fund the technical development of the Dataverse platform to accommodate the immense logistical and resource challenges posed by “big data” datasets, expanding the power of an increasingly important resource for social scientists everywhere.