Data Citation and Reuse

How, where, and by whom is data used?


Although data is essential to research and discovery, and has—through efforts by the open science community—gained some importance in the scholarly communication process, the scientific reward system has not yet caught up by valuing the role of data in the digital era. Even if many funding agencies now require data sharing and detailed research data management plans, data collection, cleaning, and curation are not yet considered valuable contributions to scientific advancement. The scientific reward system lags behind in recognizing open data practices, particularly with regards to tenure and promotion.

The Meaningful Data Counts research project aims to provide empirical evidence on data usage and data citation behaviour and to improve the understanding of the role that datasets play in scholarly communication. We seek to discover data sharing and citation patterns across academic disciplines and researchers’ career stages as well as underlying motivations to (not) share or cite datasets.

The project will pave the way for future bibliometric research and inform the development of data metrics in order to incentivize researchers to share, reuse, and cite datasets. It will conduct empirical research using a mixed-methods approach combining bibliometric analysis to discover data sharing and citation patterns with survey research and semi-structured interviews to explain underlying motivations and behaviors. The empirical research will be complemented by the development of a theoretical framework that helps to explain the role of data in scholarly communication. The project will generate empirical evidence on open data practices essential to the development of meaningful data metrics. In close collaboration with the Make Data Count team (see below) we will develop incentive structures to elevate the status of research datasets to first-class scholarly outputs. The findings are expected to improve data sharing policies.

The Meaningful Data Counts project is part of the larger Make Data Count (MDC) initiative, which drives adoption of the building blocks for open data metrics: standardized data usage and data citation practices at repositories and publishers. The initiative is focused on addressing the significant social as well as technical barriers to widespread development of open data metrics and is working to develop and deploy infrastructure for data-level metrics. The initiative’s goals are to produce evidence based studies on researcher behavior in citing and using data, as well as drive a community of practice around open, normalized data usage and data citation.

All research output of the Meaningful Data Counts project will be made available at https://zenodo.org/communities/meaningfuldatacounts.

Collaborators

Stefanie Haustein (PI), Isabella Peters (Co-PI), Daniella Lowenberg, Felicity Tayler, Rodrigo Costas, Peter Kraker, Philippe Mongeon, Nicolas Robinson-Garcia