The International Geo Sample Number (IGSN) grew out of an initial need to foster better citation of geological samples. As of mid-2018, there are over 6.5 million individual physical specimens represented within the IGSN, and a network of 25 IGSN “allocating agents” across five continents. After a number of years of growth within geoscience, the IGSN is confronted with increased interest from other disciplines; for example, the IGSN has already been used to register IDs for biological specimens and archaeological artifacts. Rather than encourage the development of a number of different discipline-specific registries, Lehnert and an international team of collaborators plan to redesign IGSN to support physical samples and specimens from across the sciences. Funds from this grant support technical development of the IGSN platform and a series of working meetings to bring together current IGSN registrars, other stakeholders, and persistent identifier (PID) experts to strategically plan the organizational and technical future of the initiative.

This grant provides three years of support for gatherings of WikiCite, a project within the Wikipedia ecosystem designed to both improve citation within Wikipedia and to expand the Wikidata project in ways useful to the scientific community. Grant funds will support a dedicated annual WikiCite meeting, as well as a series of smaller satellite meetings at other Wikimedia events. The organizers will also maintain a strong presence at other scholarly communication meetings, bringing the energy and technical sophistication of the Wikimedia community to bear on innovation in scholarly communication more broadly.

This grant funds a project led by Jonathan Crabtree, Director of Cyberinfrastructure at the University of North Carolina’s Odum Institute, to improve and expand the capabilities of the Dataverse open source data repository platform. Odum is responsible for executing and implementing the Replication and Verification Policy for the American Journal of Political Science (AJPS) and uses Dataverse as the underlying platform where authors who publish in AJPS can upload their data and software code to ensure results may be replicated. Because Dataverse was originally designed for data and not software, however, the process can be unwieldy and time consuming. Crabtree and his team plan to use the Jupyter computing platform and the open source software “containerization” toolkit Docker to create a “Confirmable Reproducible Research (CoRe2) environment” for Dataverse that would combine the ability to containerize computational research with communication and workflow tools. This would greatly speed and partially automate the process of verifying that submitted research results can be verified using the code and data uploaded. Grant funds will provide support for this project for three years.  years.

This grant funds a series of four projects by Mercи Crosas, Chief Data Science and Technology Officer at Harvard’s Institute for Quantitative Social Science to expand and improve software handling capabilities of the Dataverse open source data repository platform. First Crosas will integrate Dataverse with Encapsulator, an open source tool that allows the creation of a computational “time capsule” that preserves the exact computational environment used to conduct a piece of data analysis. Second, Crosas will create links between Dataverse and Code Ocean, a computational reproducibility platform that was spun out of Cornell Technion’s incubator program. Third, Crosas will develop a set of metadata versioning and exploration tools that will increase incentives for data curation by returning richer usage statistics to data providers and publishers. Finally, Crosas will model and pilot a fee-based curation service that would allow the sustainable scaling of data and code curation in Dataverse. This work, like all other development on and organizational innovation within the Dataverse community, will be freely available and useful to the dozens of other institutions running the software to power their own data archives.

The Center for Open Science’s (COS) preprint platform was designed to serve a variety of scholarly communities, especially lowering barriers to entry for those disciplines new to preprint publication whose needs were not being served by the larger, more highly powered preprint servers like arXiv. This grant funds a project by COS founder Brian Nosek to use the COS preprint platform as the setting for a series of experiments that will test how user trust is affected by different preprint platform features. Nosek proposes to use the launch of already-planned features like annotation and visual icons to run a set of experiments on the assignment of trust by readers of scientific research. While the budget includes some technical development, the bulk of the requested funding will support the COS “metascience” team to take a mixed-methods research approach, combining surveys with analysis of usage data from the preprint servers to understand the impact of annotation and “reproducibility badges” on readers’ perceptions of trustworthiness of individual preprints and of the preprint server overall.

Somewhere between 50% and 60% of internet users use Google Chrome to browse the web. Chrome, unfortunately, doesn’t natively display mathematics using the standard XML markup language MathML. This forces sites like Wikipedia to generate static images of mathematical notation from the underlying MathML when Chrome can’t render the markup on its own. Not only does this have implications for accessibility, it also inhibits the development of innovative new interfaces and applications that would rely on dynamic interaction with mathematical notation via browser-based programming languages like JavaScript. Funds from this grant will support a project led by the National Information Standards Organization to implement full MathML rendering in Chromium (the open source codebase underlying the Chrome web browser). Technical development will be undertaken by developers at Igalia, an open source software consultancy that has played a key role in MathML integration in other major web engines.

This grant funds a project led by Brian Granger to expand and enhance Jupyter Notebooks—a powerful, popular, and open-source scientific computing platform—to enhance its handling of private, proprietary, or otherwise sensitive data. Planned features to be developed and implemented include a nuanced permissions structure that can be used to ensure that only properly credentialed individuals can see or manipulate data, stronger event logging and internal telemetry, and encryption of data both at rest and in transit. To test the software, Granger will work closely with a wide variety of data holders including a U.S. company dealing with health data; a German warehouse for financial data; and NYU’s Center for Urban Science and Progress (CUSP), which collect all kinds of municipal data. Grant funds support software development along with a host of dissemination activities that will promote and test Jupyter’s expanded capabilities among researchers dealing with sensitive data.

This grant provides funds for the creation and operation of the Administrative Data Research Institute (ADRI), a national membership organization designed to provide services and set standards for the global network of Administrative Data Research Facilities (ADRFs). An ADRF is a data intermediary, i.e., an institution that facilitates researcher access to private or sensitive data owned or held by corporations and government entities. To become a member of the new ADRI, member ADRF’s would have to provably maintain high data standards and practices, particularly regarding data privacy and security. In return, the ADRI would provide member institutions with expert advice and leadership concerning the many technical, legal, political, or privacy challenges that data intermediaries face. Above all, the ADRI would help engender trust in at least three domains where it is much needed: among the member intermediaries when it comes to arrangements for linking their data; among the data producers when it comes to negotiating data use agreements with member intermediaries; and among the public and their policymakers when it comes to allowing administrative data containing sensitive information to be reused through member intermediaries for research purposes. Grant funds support initial start-up and operational costs of the new network for a period of two years.

Funds from this grant support three different large-scale and randomized field experiments, devised by economists Matthew Gentzkow at Stanford and Hunter Allcott at New York University, that aim to help us better understand the various ways social media and smartphones impact our lives. In the first, Gentzkow and Allcott study the welfare implications of Facebook usage by measuring the impact of cutting off access to that platform. In the second experiment, the team studies how limiting smartphone access to social media and other apps affects the welfare of college students. The third experiment concerns the demand for “fake news” and investigates what happens to consumer behavior when incentives to find accurate information increase. Grant funds support the fielding and analysis of all three experiments as well as documentation of methods to enable easy replication by other scholars.

Nudging consists of changing how alternatives are presented in ways designed to help choosers select what they say they would ideally want. Substantial empirical evidence shows that nudges can significantly—and sometimes substantially—modify behaviors. Yet much remains mysterious about nudging, especially with regard to heterogeneous effects and behavioral welfare economics. How is it possible to create effective choice architectures when not everyone should be nudged the same way? This grant funds a series of experiments by Eric Johnson of Columbia University. He has devised a series of experiments about what he calls “smart nudges.” In contrast to the one-size-fits-all approach, these take into account how individuals differ. One idea is to set different defaults for different people depending on their background characteristics. Another technique, called “preference checklists,” presents a prospective decision-maker with a list of criteria that other people sometimes take into account when considering similar choices. Checking off the criteria that the decision-maker thinks should apply in this case is a way of bringing to mind factors that might otherwise be forgotten or ignored. Johnson’s hypothesis is that choice architectures that incorporate smart defaults and preference checklists will be welfare-enhancing compared to traditional one-size-fits-all nudges. Grant funds support the fielding and analysis of these experiments, and the publication of two papers on the results.