Research Software vs. Research Data II: Protocols for Research Data dissemination and evaluation in the Open Science context

Abstract

Background: Open Science seeks to render research outputs visible, accessible and reusable. In this context, Research Data and Research Software sharing and dissemination issues provide real challenges to the scientific community, as consequence of recent progress in political, legal and funding requirements. Methods: We take advantage from the approach we have developed in a precedent publication, in which we have highlighted the similarities between the Research Data and Research Software definitions. Results: The similarities between Research Data and Research Software definitions can be extended to propose protocols for Research Data dissemination and evaluation derived from those already proposed for Research Software dissemination and evaluation. We also analyze FAIR principles for these outputs. Conclusions: Our proposals here provide concrete instructions for Research Data and Research Software producers to make them more findable and accessible, as well as arguments to choose suitable dissemination platforms to complete the FAIR framework. Future work could analyze the potential extension of this parallelism to other kinds of research outputs that are disseminated under similar conditions to those of Research Data and Research Software, that is, without widely accepted publication procedures involving editors or other external actors and where the dissemination is usually restricted through the hands of the production team.

Research Software vs. Research Data I: Towards a Research Data definition in the Open Science context

“…To include these elements in the released RS correspond to best practices that facilitate RS reuse. In our view, the release of a RD (see Section 4 and 12 ) can follow similar practices, that is, to include a documentation, some use examples, a license, a data management plan … this is to be decided by the producer team.…”

“… 53 also provides a classification of scientific data in four types: observational, experimental, computational and reference data sets. The FAIR guiding principles 17 are studied in the article that follows this work 12 . The recent and relevant introduction of the term Big Data 24 , that refers to the exploitation of larger amounts of data.…”

“…Indeed, let us remark that, as mentioned in 11 , 12 , both RS and RD dissemination might involve the use of licenses to set their sharing conditions, such a core issue. Information about RS licenses and licensing can be found at the Free Software Foundation (FSF) 6 , the Open Source Initiative (OSI) 7 , and the Software Package Data Exchange (SPDX) 8 .…”

“…Leaving aside the common issues in RS and RD for licensing and management plans, that have been already studied in the above mentioned references, the RS and RD dissemination and evaluation analogies are more closely analyzed in the article 12 that follows the present work, including FAIR related issues 17 and 5Stars Open Data 11 . On the other hand, in the current article we focus on the conceptual analogies of RS and RD, and their consequences (see Section 5 ).…”

“…The last and final section concludes with the proposition of some specific answers to Borgman’s conundrum challenges 22 . Let us remark that these conundrum challenges involve as well RD dissemination issues that are studied in detail in the article that follows this work 12 , which also includes the analysis of RD evaluation and FAIR issues.…”