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Castro E, Garnett A.

Building a Bridge Between Journal Articles and Research Data: The PKP-Dataverse Integration Project

. International Journal of Digital Curation [Internet]. 2014;9(1):176-184. Publisher's VersionAbstract
A growing number of funding agencies and international scholarly organizations are requesting that research data be made more openly available to help validate and advance scientific research. Thus, this is an opportune moment for research data repositories to partner with journal editors and publishers in order to simplify and improve data curation and publishing practices. One practical example of this type of cooperation is currently being facilitated by a two year (2012-2014) one million dollar Sloan Foundation grant, integrating two well-established open source systems: the Public Knowledge Project’s (PKP) Open Journal Systems (OJS), developed by Stanford University and Simon Fraser University; and Harvard University’s Dataverse Network web application, developed by the Institute for Quantitative Social Science (IQSS). To help make this interoperability possible, an OJS Dataverse plugin and Data Deposit API are being developed, which together will allow authors to submit their articles and datasets through an existing journal management interface, while the underlying data are seamlessly deposited into a research data repository, such as the Harvard Dataverse. This practice paper will provide an overview of the project, and a brief exploration of some of the specific challenges to and advantages of this integration.
Honaker J, D'Orazio V.

Statistical Modeling by Gesture: A graphical, browser-based statistical interface for data repositories

. Extended Proceedings of ACM Hypertext 2014 [Internet]. 2014. Publisher's VersionAbstract
We detail our construction of TwoRavens, a graphical user interface for quantitative analysis that allows users at all levels of statistical expertise to explore their data, describe their substantive understanding of the data, and appropriately construct and interpret statistical models. The interface is a browser-based, thin client, with the data remaining in an online repository, and the statistical modeling occurring on a remote server.  In our implementation, we integrate with tens of thousands of datasets from the Dataverse repository, and the large library of statistical models available in the Zelig package for the R statistical language.  Our interface is entirely gesture-driven, and so easily used on tablets and phones.  This, in combination with being browser-based, makes data exploration and quantitative reasoning easily portable to the classroom with minimal infrastructure or technology overhead.
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