The (Re)usable Data Project
Creators: name: Carbon, Seth
Publisher: name: F1000 Research Limited
Year: 2019

description: The goal of the (Re)usable Data Project (RDP, http://reusabledata.org) is to draw attention to the licensing issues that make the reuse of valuable biomedical data challenging and complicated. The RDP is meant to provide an explorable resource that looks at some of the issues around the reuse of scientific data and open a conversation about how to deal with them. The RDP exists as an element in the environment of the FAIR Data Principles and the FAIR-TLC evaluation framework; it is focused on licensing issues—narrowly scoped to reusability—and seeks to draw attention to the pervasiveness of current practice failures and their effects. As the current centerpiece of this project, the RDP has put together a rubric that attempts the objective evaluation of a data resource’s license and basic data accessibility from the perspective of reuse on a linear scale.<br><br>While the criteria can be quite detailed (http://reusabledata.org/criteria), we have attempted to balance many needs (credit, mutability, commercialization, redistribution, etc.) and focus on trying to objectively evaluate how licenses can interact across resources. The RDP assesses licenses for the following criteria:<br><br>Clearly stated:<br>A clearly stated, unambiguous, and hopefully standard, license for data use is critical for any (re)use of data: if there is no license to be found, then rights are unclear and one needs to assume the default: all<br>rights reserved.<br><br>Comprehensive and non-negotiated:<br>Data that is mixed under different licenses, only partially available, or must be in some way negotiated creates barriers to the (re)use of data.<br><br>Accessible:<br>Data must be accessible in a reasonable and manner to be useful to the broader community.<br><br>Avoid restrictions on kinds of (re)use:<br>Data should be able to be copied, built upon, edited, and modified as freely as possible.<br><br>Avoid restrictions on who may (re)use:<br>Data should be available to as many people as possible for their (re)use.<br><br>Over the past couple of years, the RDP has processed and evaluated about sixty resources and their licenses using the developed rubric. We will present the rubric that we created, highlight some of the results of this examination, and explore license choices in the current licensing landscape.<br><br>Overall, the results of the RDP’s examination indicate that there are ongoing issues with how resources license and present their data. While a great number and variety of publicly-funded biomedical datasets are ostensibly "open", complex licensing issues may be hindering them from being put to their best use.<br><br>A lack of licensing rigor and standardization can force downstream consumers to laboriously seek, essential reuse and redistribution permissions. Issues we observed include missing licenses, nonstandard licenses, and license provisions that are restrictive or incompatible. The legal interpretation of, and compliance with, database license and reuse agreements has become a significant burden and expense for many fields in the scientific community, where a complex and lengthy set of legal negotiations may be required for a data integration project to legally and freely redistribute all of its relevant data. This landscape does not benefit data providers, users, or scientific progress, especially from the perspective of reuse.<br><br>We hope that the efforts of the RDP will encourage the community to work together and generate discussions that help improve licensing practices, facilitating broad and long-term access to reusable scientific resources. Reusing data, especially when aggregating and synthesizing, comes with numerous challenges that we believe can be overcome with community engagement in these critical issues. The rapidly evolving landscape of funder and journal data sharing policies offers an opportunity to provide explicit direction to upstream data contributors about licensing and other practices that are consistent with the FAIR data principles and positively impact reuse.