Data citation is core to DataCite's mission and DataCite is involved in several projects that try to facilitate data citation, including THOR, Data Citation Implementation Pilot (DCIP), Research Data Alliance (RDA), and COPDESS. The biggest roadblock for wider data citation adoption might be insufficient incentives for individual researchers, but another major challenge is that implementing data citation is still too complicated.

This week some of us from DataCite are attending CSVconf in Berlin, and we are a conference sponsor and co-organizer. One important reason we are at CSVconf is that providing persistent identifiers and starndard metadata for research data, which in most cases are stored in tabular data formats such as CSV, is central to what DataCite is doing.

In a guest post two weeks ago Elizabeth Hull explained that only 6% of Dryad datasets associated with a journal article are found in the reference list of that article, data she also presented at the IDCC conference in February (Mayo, Hull, & Vision, 2015). This number has increased from 4% to 8% between 2011-2014, but is still low.

When I started as DataCite Technical Director four months ago, my first post (Fenner, 2015) on this blog was about what I called Data-Driven Development . The post included a lot of ideas on how to approach development and technical infrastructure. In this post I want to take a second look.

This Monday ORCID, CrossRef and DataCite announced (ORCID post, CrossRef post, DataCite post) the new auto-update service that automatically pushes metadata to ORCID when an ORCID identifier is found in newly registered DOI names. This is the first joint announcement by the three organizations, and shows the close collaboration between ORCID, CrossRef and DataCite.

This blog post provides more detail for a short presentation I will give today at the Software Credit Workshop in London.

The Force11 Joint Declaration of Data Citation Principles (Data Citation Synthesis Group, 2014) highlight the importance of giving scholarly credit to all contributors: The EC-funded THOR project that DataCite is involved in addresses these issues, and I have summarized the findings of one of our first reports in a previous blog post.

One of the first tasks for DataCite in the European Commission-funded THOR project, which started in June, was to contribute to a comparison of the ORCID and DataCite metadata standards.

CSV (comma-separated values) is a popular file format for data. It is popular because it is very simple: CSV is text-based and any application that can open text files can read or write CSV. This makes it a good fit for digital preservation. We don't know how many of the datasets in DataCite use CSV because the format metadata attribute is not used much (this query gives you some examples), but we know that the number is big.

In the first post of this new blog a few weeks ago I talked about Data-Driven Development, and that service monitoring is an important aspect of this. The main service DataCite is providing is registration of digital object identifiers (DOIs) for scholarly content, in particular research data.