Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data

Abstract

Edited by Yvonne M. Socha PREFACEThe growth in the capacity of the research community to collect and distribute data presents huge opportunities. It is already transforming old methods of scientific research and permitting the creation of new ones. However, the exploitation of these opportunities depends upon more than computing power, storage, and network connectivity. Among the promises of our growing universe of online digital data are the ability to integrate data into new forms of scholarly publishing to allow peer-examination and review of conclusions or analysis of experimental and observational data and the ability for subsequent researchers to make new analyses of the same data, including their combination with other data sets and uses that may have been unanticipated by the original producer or collector.The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, and interpret them. Data citations provide necessary support for these functions, as well as other functions such as attribution of credit and establishment of provenance. References to data, however, present challenges not encountered in references to literature. For example, how can one specify a particular subset of data in the absence of familiar conventions such as page numbers or chapters? The traditions and good practices for maintaining the scholarly record by proper references to a work are well established and understood in regard to journal articles and other literature, but attributing credit by bibliographic references to data are not yet so broadly implemented.Recognizing the needs for better data referencing and citation practices and investing effort to address those needs has come at different rates in different fields and disciplines. As competing conventions and practices emerge in separate communities, inconsistencies and incompatibilities can interfere with promoting the sharing and use of research data. In order to reconcile this problem, sharing experiences across communities may be necessary, or at least helpful, to achieving the full potential of published data.Practical and consistent data citation standards and practices are thus important for providing the incentives, recognition, and rewards that foster scientific progress. New requirements from funding agencies to develop data management plans emphasize the need to develop standards and data citation practices.

Data Citation Practices in the CRAWDAD Wireless Network Data Archive

“…More broadly, various working groups have been examining data citation and proposing ways to improve citation practices [1,9]. The aforementioned data citation principles (Section 4) are another example of this trend.…”

Towards new tools for bioresource use and sharing

“…In any such case though, compliance to the guideline is not guaranteed unless it is strictly verified by either reviewers or editorial staff (or made mandatory). Associations of editors such as the EASE 4 are of great help in reaching and empowering journal editors and authors of scientific publications. EASE Guidelines for authors and translators of articles to be published in English already include the necessity to mention the origin and identity of experimental materials used in the methods section, and refer to the CoBRA guideline.…”

“…As described in 2015 OECD Making open science a reality report, an important alternative way to foster sharing could be through data citation: the possibility for researchers to be acknowledged for their work of data/samples collection and curation through mechanisms similar to the one already in place for citations of academic articles [4,14,18]. This would lead to reward mechanisms that are currently under discussion and that include widespread use of dataset citation and/or proper acknowledgment of open science and data, sharing efforts as criteria to be actually taken into account in career advancement mechanisms or in grant attribution to research teams.…”

Open Science strategies for NIH data management, sharing, and citation

“…FORCE11 and its stakeholder communities have already undertaken very significant policy development work enabling common definition of data citation practices [1,2,4,5,[7][8][9] [13]; (f) a set of Software Citation Principles [14].…”