Today's science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)'s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMed's goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of datasets, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as a Schema.org annotated serialization to be used beyond DataMed, for example, in projects like DataCite.

Two weeks ago we started assigning DOIs to every post on this blog (Fenner, 2016c). The process we implemented uses a new command line utility and integrates well with our the publishing workflow, with (almost) no extra effort compared to how we published...

1 Introduction 1.1 The DataCite Consortium 1.2 DataCite Community Participation 1.3 The Metadata Schema 1.4 Version 4.0 Update 2 DataCite Metadata Properties 2.1 Overview 2.2 Citation 2.3 DataCite Properties 3 XML Example 4 XML Schema 5 Other DataCite Services Appendices Appendix 1: Controlled List Definitions Appendix 2: Earlier Version Update Notes

Five years ago, a global infrastructure to uniquely attribute to researchers their scientific artefacts (articles, data, software…) appeared technically and socially infeasible. Since then, DataCite has minted over 3.5m unique identifiers for data. ORCID has deployed an open solution for identification of contributors with over 850,000 registrants in less than 2 years. THOR will leverage these emerging global infrastructures to support the H2020 goal to make every researcher digital and increase creativity and efficiency of research, while bridging the R&D divide between developed and less-developed regions. We will establish interoperability between existing resources, linking digital identifiers across platforms and propagating attribution information. We will integrate PID services across the research lifecycle and data publishing workflows in four advanced research communities, and then roll-out core services and service building blocks for the wider community. These open resources will foster an open and sustainable e-infrastructure across stakeholders to avoid duplications, give economies of scale, richness of services and the ability to respond rapidly to opportunities for innovation. THOR is not just relevant to the EINFRA-7-1024 Call, but will become a pervasive element of the EINFRA family of e-Infrastructure resources over the next 3 years. It will allow data-management and curation services to exploit knowledge of data location and attribution; provide robust and persistent mechanism for linking literature and data; enable search and resolving services and generate incentives for Open Science; deliver provenance and attribution mechanisms to underpin data exchange; and provide minting and resolving services for data citation workflows. Its impact will enable third-party services, no-profit and commercial, to leverage the scholarly record.

Ruby gem and command-line utility for conversion of DOI metadata from and to different metadata formats, including schema.org.

An API for DOI content negotiation. Also available as Docker container.