Achieving human and machine accessibility of cited data in scholarly publications

Abstract

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.

Interoperability and FAIRness through a novel combination of Web technologies

“…Data must therefore be considered, and treated, as first-order scientific output, upon which there may be many downstream derivative works, among these, the familiar research article (Starr et al, 2015). But as the volume and complexity of data continue to grow, a data publication and distribution infrastructure is beginning to emerge that is not ad hoc, but rather explicitly designed to support discovery, accessibility, (re)coding to standards, integration, machine-guided interpretation, and re-use.…”

14 Years of PID Services at the German National Library of Science and Technology (TIB): Connected Frameworks, Research Data and Lessons Learned from a National Research Library Perspective

“…While the technical challenge facing the complexity of data is addressed by multiple working groups across the world (e.g. European Union 2010; Crosas 2011; Lecarpentier et al 2013;Starr et al 2015) and seems to make good progress, the social (human) side is still staggering and changing slowly (Data Citation Synthesis Group 2014; Roche et al 2015). A successful examples is the the COPDESS Statement of Commitment (http://www.copdess.org/statement-of-commitment/) which includes a recommendation to archive data in public data repositories, and the acceptance of dataset DOIs in reference lists of Journal articles.…”

Strategies and guidelines for scholarly publishing of biodiversity data

“…It also encompasses the latest international efforts to standardise the data and software citation mechanisms carried out within the CODATA, FORCE11 and RDA networks (CODATA/ITSCI 2013, Starr et al 2015, Rauber et al 2016). …”