Picture, if you will, the labyrinthine world of academic information management, where metadata schema mapping serves as a vital underpinning for the exchange and intermingling of data across diverse platforms and systems. This arena has long been dominated by the venerable metadata schema crosswalk, which, though serviceable, has begun to show its age.

Large language models have been making waves in artificial intelligence for a good reason. These models have the ability to understand and generate human language, which has far-reaching implications for the future of technology. One area where these models have a significant impact is in the realm of software development.

Scientists rarely cite research software they use as part of a research project. As a consequence, the software and the time spent developing and maintaining it becomes an invisible scholarly contribution. Furthermore, this lack of visibility means that incentives to produce high quality, sustainable software are missing. Among many reasons why software is not cited, one is the lack of a clear citation information from package developers.

At rOpenSci, our R package peer review process relies on the the hard work of many volunteer reviewers. These community members donate their time and expertise to improving the quality of rOpenSci packages and helping drive best practices into scientific software. Our open review process, where reviews and reviewers are public, means that one benefit for reviewers is that they can get credit for their reviews.

When going for Text Mining a collection of resources pretty much the first step is to check for the language. If it is a multi-language collection may be there is at least some metadata. At SUB we often use the METS/MODS standard based on XML.

For the last few years, we have been working on the development of new Drosophila flight simulators. Now, finally, we are reaching a stage where we are starting to think about how to store the data we’ll be capturing both with Open Science in mind, but particularly keeping in mind that this will likely be the final major overhaul of this kind of data until I retire in 20 years.

In the first post in this series I identified a series of challenges in scholarly publishing while stepping through some of the processes that publishers undertake in the management of articles. A particular theme was the challenge of managing a heterogenous stream of articles and their associated heterogeneous formats and problems, in particular at a large scale.

[Europe PMC has a new provider of External Links: links from articles on Europe PMC to related content on 3 ^rd party websites. Our most recent addition provides links from over 300,000 articles to entries in Wikipedia: the free encyclopaedia.

[[Recently Europe PMC released new export format options, to help users get Europe PMC’s wealth of metadata (and our open access papers) into the file formats they like to use.]{style=“font-size: 11.0pt; line-height: 115%; mso-ansi-language: EN-GB; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US;

Scholarly metadata - the meta-information surrounding articles - can be super useful. Although metadata does not contain the full content of articles, it contains a lot of useful information, including title, authors, abstract, URL to the article, etc. One of the largest sources of metadata is provided via the Open Archives Initiative Protocol for Metadata Harvesting or OAI-PMH.