A few weeks ago Kafkas et al. (2013) published a paper looking at current patterns of how datasets o biological databases are cited in research articles, based on an analysis of the full text Open Access articles available from Europe PMC. They identified data citations by: 1. Accession numbers available in articles as publisher-supplied, structured content; 2. Accession numbers identified in articles by text mining;

Scholarly documents often need metadata that describe them: typically author(s), title and location (DOI or URL), but possibly many other things. For some metadata it makes sense to store them in the document text, e.g. as is typically done for citations. The problem is that this can make it hard to make the metadata machine-readable.

Abstract Sharing data is increasingly considered to be an important part of the scientific process. Making your data publicly available allows original results to be reproduced and new analyses to be conducted. While sharing your data is the first step in allowing reuse, it is also important that the data be easy to understand and use.

After the post last week and the crazy discussion that followed I would understand that you feel you have heard enough about citations in markdown. But I had the feeling last week that something was still missing, and I have done some more thinking.

One important outcome of the recent Markdown for Science workshop was an overall agreement that all the different implementations (or flavors) of markdown that currently exist are a big problem for the adoption of Scholarly Markdown and that we need: A reference implementation with documentation and tests As described by Karthik Ram (31 flavors is great for ice cream but not markdown), me and others, there is really a large number of markdown

In the comments on Monday’s blog post about the Markdown for Science workshop, Carl Boettiger had some good arguments against the proposal for how to do citations that we came up with during the workshop. As this is a complex topic, I decided to write this blog post. Citations of the scholarly literature are an essential part of scholarly texts and therefore have to be supported by scholarly markdown.

One of the important discussions taking place at the Markdown for Science workshop last weekend was about the definition of Scholarly Markdown.

This is the last Gobbledygook post on PLOS Blogs, and at the same time the first post at the new Github blog location. I have been blogging at PLOS Blogs since the PLOS Blogs Network was launched in September 2010, so this step wasn’t easy. But I have two good reasons. In May 2012 I started to work as technical lead for the PLOS Article-Level Metrics project.

Earlier this week re3data.org – the Registry of Research Data Repositories – officially launched. The registry is nicely described in a preprint also published this week. re3data.org offers researchers, funding organizations, libraries and publishers and overview of the heterogeneous research data repository landscape. Information icons help researchers to identify an adequate repository for the storage and reuse of their data.

This Thursday I take part in a panel discussion at the Joint ORCID – Dryad Symposium on Research Attribution. Together with Trish Groves (BMJ) and Christine Borgman (UCLA) I will discuss several aspects of attribution. Trish will speak about ethics, Christine will highlight problems, and I will add my perspective on metrics. This blog post summarizes the main points I want to make.

A new service allows researchers to add research datasets – and other content with DataCite DOIs, including all figshare content – to their ORCID profile by integrating with the DataCite Metadata Store. The tool is an adaption (or fork) of the CrossRef Metadata Search developed by Karl Ward, and was developed by Gudmundur Thorisson and myself as part of work in the EU-funded ODIN project. More details can be found here.