Towards tailored data curation workflows in a trusted repository: Strategies in a collaborative research centre in neuroscience
Creators: name: Pacharra, Marlene
name: Frenzel, Johannes
name: Winter, Nina O. C.
name: Otto, Tobias
Publisher: name: Zenodo
Year: 2024
Subjects: research data management
review workflow
quality control
Hyrax
data curation
metadata
repository
neuroscience

description: In the interdisciplinary Collaborative Research Centre (CRC) 1280 "Extinction Learning", involving more than 90 researchers at four different institutions, research data management (RDM) is a particular challenge: Researchers from biology, psychology, medicine, and computational neuroscience collect heterogeneous data with multiple data channels and modalities (e.g., electrophysiology, electroencephalography) as well as large-scale data (e.g., live cell microscopy, functional magnetic resonance imaging) in proprietary formats that may fall under data protection regulation (e.g., patient data). Moreover, there are few discipline-specific standards for data and metadata, and not all scientists have sufficient RDM skills (Klingner et al. 2023), highlighting the need for tailored data curation and a trusted repository solution. Our proposed poster will highlight how CRC 1280 addresses these challenges while promoting collaboration and Open Science.

To make all RDM coordination processes in the CRC participatory and transparent, a recognized Research Management Board which consists of members of different career levels was established. Through iterative feedback loops with the whole CRC, a RDM policy has been designed and implemented (Pacharra et al., 2022), encompassing documentation and organization of data in accordance with FAIR principles, and emphasizing the CRC’s commitment to discipline-specific quality control of data both at the level of doctoral and principal researchers. Researchers are supported in the implementation of the policy by a dynamic internal knowledge base that can be adapted to meet evolving RDM needs, and by a data steward who provides technical advice and hands-on data curation assistance. 

The CRC’s research data are stored with metadata in the newly developed institutional data repository of the Ruhr University Bochum (Germany) ReSeeD based on Hyrax 3 from the Samvera community (2021). This open-source repository supports the entire research data lifecycle: It enables researchers to store, share, publish, and archive data, while facilitating precise rights and roles management. In alignment with CRC 1280's policy and organizational structure, a three-tier roles and rights system has been established, encompassing researcher, group manager, CRC’s data steward, and central publication manager roles. Each role carries specific responsibilities, which may evolve as a workflow for either data publication and archiving progresses, such as discipline-specific metadata checks by the CRC’s data steward, or DataCite metadata verification by the central library’s publication manager. Importantly, these workflows are transparent and accessible to all parties involved fostering constant exchange regarding data curation practices in the CRC. With extended beta testing phases this system is continuously adapted and improved to fit researchers' needs creating a trusted and accepted solution for researchers, to aid their daily RDM work while meeting policy requirements.

References

Klingner, C. M., Denker, M., Grün, S., Hanke, M., Oeltze-Jafra, S., Ohl, F. W., Radny, J., Rotter, S., Scherberger, H., Stein, A., Wachtler, T., Witte, O. W., & Ritter, P. (2023). Research Data Management and Data Sharing for Reproducible Research-Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience. eNeuro, 10(2), ENEURO.0215-22.2023. https://doi.org/10.1523/ENEURO.0215-22.2023

Pacharra, M., Winter, N. O. C., Kumsta, R., Uengoer, M., Caviola, J. K., Ernst, T. M., Reichert, R., Yavari, F., Merz, C. J., Cheng, S., Linn, S., Wolf, O. T., Güntürkün, O., & Otto, T. (2022). Research Data Management Policy of the collaborative research centre SFB 1280 "Extinction Learning" (v1.0). Zenodo. https://doi.org/10.5281/zenodo.8004432

Samvera community (2021). Hyrax (v3.0) [Source code]. Accessed 2023-07-13. https://github.com/samvera/hyrax