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.

A Data Citation Roadmap for Scholarly Data Repositories

“…The roadmap presented here aims to provide practical guidance for repositories on implementing these data citation principles with a focus on life sciences, based on earlier work in this area, in particular Starr et al (2015) and Altman and Crosas (2013), and are consistent with recent recommendations regarding data, code and workflows (Smith, Katz, & Niemeyer, 2016;Stodden et al, 2016).…”

“…The persistent identifier expressed as HTTP URL must not resolve to the data itself (Starr et al, 2015), or to other representations of the metadata, unless special protocols such as content negotiation are used (see recommendation #7 below).…”

“…http://dx.doi.org/10.1101/097196 doi: bioRxiv preprint first posted online Dec. 28, 2016; Landing pages provide definitive information (metadata) on how the dataset should be cited, other descriptive information about the dataset, as well as data accessibility and licensing information. Repositories should provide a landing page for every dataset or collection of datasets intended to be cited, which could be single entries, sets of entries, the entire repository or a curated database (Starr et al, 2015).…”

“…Data persistence policies will vary by repository but should be clearly described (Starr et al, 2015). The copyright holder for this preprint (which was not .…”

The Information Sharing Statement Grows Some Teeth

“…Access barriers to data reuse are sometimes necessary in order to assure human subjects ethics compliance, but the onerousness of these barriers must be kept to the minimum necessary. Requirements for citation of the data source (for appropriate credit to data acquirers and funders) is expected 9 , and the process of data archival should adhere to the FAIR standards (findable, accessible, interoperable and reusable) 10 and include provisions for unique and permanent citation.…”