Following the launch of Google Dataset Search we received many questions from DataCite members about schema.org, indexing by Google Dataset Search, and data citation in general. We wanted to make sure you had all the answers, so we organized a webinar and invited Natasha Noy from Google.

This blog post was cross-posted from the Crossref blog We’ve mentioned why data citation is important to the research community. Now it’s time to roll up our sleeves and get into the ‘how’. This part is important, as citing data in a standard way helps those citations be recognized, tracked, and used in a host of different services. This week A Data Citation Roadmap for Scientific Publishers was published in Scientific Data.

A couple of weeks ago we shared with you that data citation is here, and that you can start doing data citation today. But why would you want to? There are always so many priorities, why should this be at the top of the list? I’m sure you heard this before, but data sharing and data citation are important for scientific progress.

Your new Member Support Manager here, excited about my recent initiation into the world of persistent identifiers, and keen to learn more about the DataCite community. I entered into the world of technical support working at a large global information company, a different set up to DataCite, to say the least, but the principles are the same. I love working with people, information, and now I’m a strong advocate of Open Knowledge.

We frequently receive questions from our members and clients about our current approach to testing. Some of you have accounts in our test system, some of you have the “demo” accounts introduced earlier this year, and all of you have access to the test prefix 10.5072 as part of your regular production account. What’s the difference? Which one are you supposed to use for what?

This blog post was cross-posted from the Make Data Count blog. With Make Data Count now in its second year, the focus is shifting from building infrastructure to driving adoption of our open data-level metrics infrastructure.

If you’ve gone through the effort of sharing your data, you’d like other people to find, view, download, and reuse your data. Sometimes the repository that holds your dataset will make that information available, but up to now there hasn’t really been a single system that provides views, downloads and citations for datasets across repositories.

Data citation is seen as one of the most important ways to establish data as a first-class scientific output. At Crossref and DataCite, we are seeing growth in journal articles and other content types citing data, and datasets making the link the other way. Our organizations are committed to working together to help realize the data citation community’s ambition, so we’re embarking on a dedicated effort to get things moving.

In last month’s overview of our current API landscape, we introduced the idea of our consolidated REST API, the one place to go to use all DataCite services. As part of this grand consolidation, we’re pleased to say that DOI creation is now available as part of the REST API. We’re calling on all interested early adopters who want to try it out to drop us a line. Wait. Didn’t you already have a REST API? Yes, we did.

More and more funders and publishers require research data to be made available in appropriate repositories, but determining which repository to choose or what counts as an “appropriate repository” can take up a lot of time. What is a researcher to do? Last year the American Geophysical Union (AGU) convened representatives of the international Earth, space and environmental science community to work on the Enabling FAIR Data project.

Crossposted from the COUNTER announcement from September 13, 2018. 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.