Data Champion Award

Just as the role of a Data Steward is defined differently in contexts such as industry [1] compared to academia [2,3], a Data Champion is also not a precisely defined role. Data Champions are, on one hand, discipline-specific scientists [4] who are supported by (permanent) Data Stewards [5], and on the other hand, in industry, Data Champions are seen as managers and promoters of data as an asset [6].

I interpret the “Champion” as an advocate for OpenData, promoting the accessibility of all research data to everyone on this planet. OpenData is a part of OpenScience, which is about conducting science without access restrictions, similar to the OpenSource movement that has been paving the way for some time. In this sense, I am very pleased to be seen as a promoter of these related values.

[1]   Plotkin, David: Data Stewardship. An Actionable Guide to Effective Data Management and Data Governance. Academic Press 2021
[2]   Mons, Barend: Data Stewardship for Open Science. Implementing FAIR Principles. CRC 2018
[3]   Aliaksandra Shutsko, Birte Lindstädt: Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten – NFDI4Health: Pilotprojekt zu Bibliotheken und Forschungsdatenkompetenzzentren als Multiplikatoren („Data Steward”). GMS Medizin 2020
[4]   Clare, Connie / Cruz, Maria / Papadopoulou, Elli / Savage, James / Teperek, Marta / Wang, Yan / Witkowska, Iza / Yeomans, Joanne: Engaging Researchers with Data Management. The Cookbook. OpenBook 2019
[5]   Marta Teperek, Maria Cruz, Ellen Verbakel, Jasmin Böhmer, Alastair Dunning: Data Stewardship – addressing disciplinary data management needs. International Digital Curation Conference 2018
[6]   Virginia Collins and Joel Lanz: Managing Data as an Asset. CPA J. 2019

OpenData requires a cultural shift in which data is not merely seen as a functional means to confirm one's own scientific hypotheses. It’s about making this individually generated and used data available to others, thereby significantly increasing its value through potential reuse by other scientists, especially through the application of machine learning methods.

OpenData necessitates the introduction of both software tools and methods to establish data literacy, as well as specialists like Data Stewards who are knowledgeable in both a specific field and in computer and information sciences. Therefore, a holistic approach to research data management (RDM) is essential for this cultural shift.

For this reason, it is important to me to address the various levels of data literacy at my institute. This includes easy-to-implement guidelines for uniformly structured storage of research data, the implementation and introduction of various software tools such as Electronic Laboratory Notebooks (ELN) or Git repositories, and training for all staff.

In addition to these internal improvements, I see the exchange of experiences with others as another crucial element. The NFDI provides nationwide connections, and we can learn a great deal from each other and build structures. Even within a single university, contributions from various disciplines provide new impulses and broaden our horizons. This is one of the reasons I am very happy to lead the Data Steward Network within the UBRA.

Networking and visibility are crucial to guiding many small scientific workgroups that have yet to establish comprehensive research data management (RDM) systems. This requires, on one hand, well-promoted, low-threshold cooperative points of contact that are attractive to these workgroups. On the other hand, it requires institutional resources, which are available with the Data Science Center and the DataNord Competence Center.

The exchange among Data Stewards remains necessary and beneficial. So far, experts from other federal states have already contributed interesting insights within the UBRA Data Stewardship exchange group. Why not also integrate Data Stewards from Oldenburg, Hamburg, Hannover, and beyond, just as the ELB-NRW (Electronic Laboratory Notebooks) has now become active nationwide, focusing on a specific ELN (eLabFTW)? This network demonstrates that there is a need for specializations alongside the more general Data Stewardship, such as sample management.

From this, I derive the need for multiple networks operating at different data literacy levels:

1. The Data Steward Network already exists and could be expanded to include other universities, but it is too general for discipline-specific Data Champions.
2. Therefore, a low-threshold Data Champions Network should be established within the framework of UBRA/DataNord. This network would address specific topics such as sample management and continually train new members, most of whom will move on after their PhD.
3. Additionally, technical staff should be included in a Data Technician Network, as they have received little attention in RDM, particularly in the natural and technical sciences. This group is a crucial component for OpenScience because a textual description of a complex experimental setup without accompanying technical drawings contributes little to the reproducibility of research. To fulfill Good Scientific Practice (GSP), the entire workflow must be documented, which requires specialized tools.
4. The various activities in these networks could be orchestrated within an overarching new network (working title: Data Community Club).