There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

as information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold.

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After a spectacular case of data fraud in the field of social psychology surfaced in The Netherlands in September 2011, the Dutch research community was confronted with a number of questions. Is this an isolated case or is scientific fraud with data more common? Is the scientific method robust enough to uncover the results of misconduct and to withstand the breach of trust that fraud causes? How responsible and reliable are researchers when they collect, process, analyse and report on data? How can we prevent data fraud? Do we need to adapt the codes of conduct for researchers or do we need stricter rules for data management and data sharing?This paper discusses the conclusions and recommendations of two reports that were published recently in consequence of this data fraud. The reports are relevant for scientific integrity and trustworthy treatment of research data. Next, this paper reports on the outcomes of enquiries in data cultures in a number of scientific disciplines. The concluding section of this paper contains a number of examples that show that the approach towards data sharing is improving gradually. The data fraud case can be regarded as a wake-up call.