Tom Wiesing
Hi, my name is Tom Wiesing and I am a PhD candidate in the KWARC Group at FAU Erlangen-Nürnberg and work at the FAU Competence Center for Research Data and Information
In my free time I like to code and make random websites.
I have visited the National Institute of Standards and Technology four times between 2017 and 2021 in order to help work on LaTeXML, a software that transforms LaTeX documents into annotated XHTML.
I was formerly associated to Jacobs University Bremen, where I achieved aMaster Of Science in Data Engineering (Class of 2017) and a Bachelor Of Science in Applied & Computational Mathematics (Class of 2015). I was also the President of the Graduate Student Association of Jacobs University for the majority of my final two semesters.
You may best contact me by sending an email to tom@tkw01536.de. I prefer plain text emails in english, but I also speak german.
Modern science relies on an ever-increasing amount of data, a fact that is broadly acknowledged by politics, funding agencies and universities alike. Scientists author, curate, and eventually use or search through varying amounts of data and datasets, which frequently revolve around objects. Some examples of such objects include artifacts of cultural heritage or mathematical structures.
It is well established that datasets should be Findable, Accessible, Interoperable and Reuseable - FAIR for short - in order to become useable in a broader and possibly public context. Typical research questions involve more than a single dataset, instead relying on a large group of datasets that effectively act as a single (possibly federated) dataset. Such Research Data Commons accelerate science and prevent duplicate datasets.
I asked the following research questions:
- How can researchers be enabled to answer their questions using a Research Data Commons?
- What types of software are required for a Research Data Commons? In particular, is it necessary to provide an explicit infrastructure?
- Which costs and benefits are associated with designing, developing, deploying and using a Research Data Commons?
To answer these questions, I explored the design space of Research Data Commons software by designing three systems - MathDataHub, the WissKI Distillery and the WissKI Data Viewer - which demonstrate that there is a disconnect between the costs and benefits of Research Data Commons. Costs need to be payed upfront at the beginning of a research project, whereas the benefits only come into effect much later. The latter are also conditional: They only arise if other researchers invest likewise. To minimize its effect, and enable the formation of Research Data Commons, it becomes necessary to decrease costs or to increase benefits.