Integrating Large Language Models (LLMs) such as GPT into organizations’ data workflows is a complex process with various challenges. These obstacles include but are not limited to technical, operational, ethical, and legal dimensions, each presenting hurdles that organisations must navigate to harness the full potential of LLMs effectively.

This blog post is the last of a four part series based on the keynote presentation by Stefanie Haustein at the Swiss Year of Scientometrics lecture and workshop series at ETH Zurich on June 7, 2023. Responsible use of metrics As a last topic, I want to address a very important area that I think for a very long time has been ignored by the scientometric community—namely the use of the metrics that it

This blog post is the third in a four part series based on the keynote presentation by Stefanie Haustein at the Swiss Year of Scientometrics lecture and workshop series at ETH Zurich on June 7, 2023.

This blog post is the second in a four part series based on the keynote presentation by Stefanie Haustein at the Swiss Year of Scientometrics lecture and workshop series at ETH Zurich on June 7, 2023.

This blog post is the first in a four part series based on the keynote presentation by Stefanie Haustein at the Swiss Year of Scientometrics lecture and workshop series at ETH Zurich on June 7, 2023.

Data management is a core aspect of the research process, helping scholars organize their work, share it with others, and collaborate effectively.

In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible. The attributes of A[cite]10.1038/sdata.2016.18[/cite] include: (meta)data are retrievable by their identifier using a standardized communication protocol. the protocol is open, free and universally implementable.  the protocol allows for an authentication and authorization procedure.

Many efforts at building data infrastructures for the “average researcher” have been funded, designed and in some cases even built. Most of them have limited success. Part of the problem has always been building systems that solve problems that the “average researcher” doesn’t know that they have.

The Research Data Management movement is moving on apace. Tools are working and adoption is growing. Policy development is starting to back up the use of those tools and there are some big ambitious goals set out for the next few years. But has the RDM movement taken the vision of data intensive research to its heart? Does the collection, sharing, and analysis of data about research data management meet our own standards?