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Special theme: Scientific Data Sharing and Re-use

ERCIM News - January 2015
Publié le 08 janvier 2015 par Thérèse Hameau

Scientific Data Sharing and Re-use – Introduction to the Special Theme

by Costantino Thanos and Andreas Rauber

Research data are essential to all scientific endeavours. Openness in the sharing of research results is one of the norms of modern science. The assumption behind this openness is that scientific progress requires results to be shared within the scientific community as early as possible in the discovery process.

The emerging cultures of data sharing and publication, open access to, and reuse of data are the positive signs of an evolving research environment. Data sharing and (re)usability are becoming distinct characteristics of modern scientific practice, as they allow reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. However, several challenges still prevent the research community from realizing the full benefits of these practices.

Data sharing/reusability has four main dimensions: policy, legal, technological and economic. A legal and policy framework should favour the open availability of scientific data and allow legal jurisdictional boundaries to be overcome, while technology should render physical and semantic barriers irrelevant. Finally, data sharing/reuse involves economic support: who will pay for public access to research data?

To make scientific data shareable and usable it should be discoverable, i.e. scholars must be able to quickly and accurately find data that supports scientific research; understandable to those scrutinizing them; and assessable enabling potential users to evaluate them. An emerging ‘best practice’ in the scientific method is the process of publishing scientific data. Data Publication refers to a process that allows a data user to discover, understand, and make assertions about the trustworthiness and fitness for purpose of the data. In addition, it should allow data creators to receive academic credit for their work.

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