DOI content negotiation is one of the oldest DataCite services, launched in 2012. Content negotiation makes it easy to fetch DataCite metadata in other metadata formats, for example BibTeX or schema.org , or as formatted citation in one of more than 5,000 citation styles. For example: curl -LH "Accept: application/x-bibtex" https://doi.org/10.5438/0000-0C2G In 2017 we updated the service, both adding new content types (e.g.

The connections between scholarly resources generated by persistent identifiers (PIDs) and associated metadata form a graph: the PID Graph [Fenner & Aryani (2019)]. We developed this PID Graph concept in the EC-funded FREYA project, and have identified important use cases and technical requirements. In May, DataCite introduced a GraphQL API to standardize and simplify how users can contribute to and consume the PID Graph [Fenner (2019b)].

Two weeks ago DataCite announced the pre-release version of a GraphQL API [Fenner (2019)]. GraphQL simplifies complex queries that for example want to retrieve information about the authors, funding and data citations for a dataset with a DataCite DOI. These connections together form the PID Graph [Fenner &

DataCite DOIs describe resources such as datasets, samples, software and publications with rich metadata. An important part of this metadata is the description of connections between resources that use persistent identifiers (PIDs) provided by DataCite and others (Crossref, ORCID, ROR, ISNI, IGSN, etc.). Together these resources and their connections form a graph, the PID Graph (Fenner &

DOI metadata provenance is describing the history of a particular DOI metadata record, i.e. what changes were made when and by whom. This information is now stored and provided via an API for all DOI registrations since March 10, 2019.

Persistent identifiers (PIDs) are not only important to uniquely identify a publication, dataset, or person, but the metadata for these persistent identifiers can provide unambiguous linking between persistent identifiers of the same type, e.g. journal articles citing other journal articles, or of different types, e.g. linking a researcher and the datasets they produced.

Today we are announcing our first new functionality of 2019, a much improved search for DataCite DOIs and metadata. While the DataCite Search user interface has not changed, changes under the hood bring many important improvements and are our biggest changes to search since 2012.

All DataCite DOIs have associated metadata, described in the DataCite Metadata Schema Documentation (DataCite Metadata Working Group (2017)), validated and stored as XML in the DataCite Metadata Store (MDS). These metadata are then made available via DataCite APIs and services. For these services XML is not always the best format, and we are thus providing the metadata in other formats, most notably JSON.

There is a need for the consistent and credible reporting of research data usage. Such usage metrics are required as an important component in understanding how publicly available research data are being reused.

We know that software is important in research, and some of us in the scholarly communications community, for example, in FORCE11, have been pushing the concept of software citation as a method to allow software developers and maintainers to get academic credit for their work: software releases are published and assigned DOIs, and software users then cite these releases when they publish research that uses the software.