General Information

Keeping research data accessible, reusible and verifiably in the long term through Research Data Management

IntCDC is committed to the publication of the relevant research data in conjunction with the traditional publication of research results and will adhere to the ‘FAIR Data Principles’ in order to maximise the potential of data assets, increase the visibility and citation of research, improve the reproducibility and reliability of research, and align with international standards and approaches.

Published Research Data & Software

Emphasizing that research data and research software are actively recognized as an essential part of scientific output, published datasets and software are listed next to the paper publications on the IntCDC website:

The Data Repository of the University of Stuttgart (DaRUS) is based on the open source research data repository software Dataverse and is the preferred FAIR repository for data and software that are published as part of IntCDC's research. Additional data is published on EDMOND, the Open Research Data Repository of the Max Planck Society.

IntCDC Publication List

Data Repository of the University of Stuttgart (DaRUS)

Search the EXC IntCDC Dataverse on DaRUS:

Guidelines

University of Stuttgart
- Research Data Policy of the University of Stuttgart [PDF] - Competence Centre for Research Data Management at the University of Stuttgart

Max Planck Institute for Intelligent Systems
- Rules of Good Scientific Practice [PDF]

German Research Foundation
- Code of Conduct - Guidelines for Safeguarding Good Research Practice [PDF]
- Guidelines on the Handling of Research Data [PDF]
- Specification of Requirements Relating to the Handling of Research Data in Funding Proposals. See supplemental catalogue of questions [PDF]

Internal Information

Comprehensive information, best practices and how-tos are provided on the internal knowledge base on Confluence.

Office Hours

Every Thursday from 14:00 to 15:00 the office hours are held for all questions about research data management and research software engineering. Meetings can be online via WebEx or in person.

Data Management Commission

The main goals of the commission are quality assurance and transparency for digitally oriented research, publication of research data for re-use following the FAIR principles, and support of co-design within IntCDC.

Members

Research Data & Software Manager

Matthias Braun

PUBLICATIONS

2023
1. Goth, F., Alves, R., Braun, M., Castro, L. J., Chourdakis, G., Christ, S., Cohen, J., Erxleben, F., Grad, J.-N., Hagdorn, M., Hodges, T., Juckeland, G., Kempf, D., Lamprecht, A.-L., Linxweiler, J., Schwarzmeier, M., Seibold, H., Thiele, J. P., von Waldow, H., & Wittke, S. (2023). Foundational Competencies and Responsibilities of a Research Software Engineer (p. 28). https://doi.org/10.48550/arXiv.2311.11457
  - BibTeX
  - Link
  BibTeX
  @misc{goth2023foundational, abstract = {The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of fundamental competencies as well as values that define the general profile of an RSE. On this basis, we elaborate on the progression of these skills along different dimensions, looking at specific types of RSE roles, proposing recommendations for organisations, and giving examples of future specialisations. An appendix details how existing curricula fit into this framework.}, archiveprefix = {arXiv}, author = {Goth, Florian and Alves, Renato and Braun, Matthias and Castro, Leyla Jael and Chourdakis, Gerasimos and Christ, Simon and Cohen, Jeremy and Erxleben, Fredo and Grad, Jean-Noël and Hagdorn, Magnus and Hodges, Toby and Juckeland, Guido and Kempf, Dominic and Lamprecht, Anna-Lena and Linxweiler, Jan and Schwarzmeier, Moritz and Seibold, Heidi and Thiele, Jan Philipp and von Waldow, Harald and Wittke, Samantha}, doi = {10.48550/arXiv.2311.11457}, eprint = {2311.11457}, month = {11}, pages = 28, primaryclass = {cs.SE}, title = {Foundational Competencies and Responsibilities of a Research Software Engineer}, url = {https://doi.org/10.48550/arXiv.2311.11457}, year = 2023 }
  Link
  https://doi.org/10.48550/arXiv.2311.11457
2022
1. Garkov, D., Müller, C., Braun, M., Weiskopf, D., & Schreiber, F. (2022). Research Data Curation in Visualization : Position Paper. 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), 56–65. https://doi.org/10.1109/BELIV57783.2022.00011
  - BibTeX
  BibTeX
  @inproceedings{Garkov2022Resea, abstract = {Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.}, author = {Garkov, Dimitar and Müller, Christoph and Braun, Matthias and Weiskopf, Daniel and Schreiber, Falk}, booktitle = {2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)}, doi = {10.1109/BELIV57783.2022.00011}, eventdate = {17 October 2022}, note = {Related to: Garkov, D., Müller, C., Braun, M., Weiskopf, D., & Schreiber, F. (2022). Research Data Curation in Visualization : Position Paper (Data), DOI: 10.18419/darus-3144}, pages = {56-65}, publisher = {IEEE}, title = {Research Data Curation in Visualization : Position Paper}, year = 2022 }
2. Garkov, D., Müller, C., Braun, M., Weiskopf, D., & Schreiber, F. (2022). Research Data Curation in Visualization : Position Paper (Data). https://doi.org/10.18419/darus-3144
  - BibTeX
  BibTeX
  @dataset{garkov2022research, abstract = {Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided.Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards. }, affiliation = {Garkov, Dimitar/Department of Computer and Information Science, University of Konstanz, Müller, Christoph/Visualization Research Center (VISUS), University of Stuttgart, Braun, Matthias/Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC), University of Stuttgart, Weiskopf, Daniel/Visualization Research Center (VISUS), University of Stuttgart, Schreiber, Falk/Department of Computer and Information Science, University of Konstanz}, author = {Garkov, Dimitar and Müller, Christoph and Braun, Matthias and Weiskopf, Daniel and Schreiber, Falk}, doi = {10.18419/darus-3144}, howpublished = {Dataset}, note = {Related to: Garkov, D., Müller, C., Braun, M., Weiskopf, D., Schreiber, F., “Research Data Curation in Visualization : Position Paper”, in 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), IEEE, 2022, DOI: 10.1109/BELIV57783.2022.00011}, orcid-numbers = {Garkov, Dimitar/0000-0002-2134-1686, Müller, Christoph/0000-0002-9979-3682, Braun, Matthias/0000-0001-8591-3690, Weiskopf, Daniel/0000-0003-1174-1026, Schreiber, Falk/0000-0002-9307-3254}, title = {Research Data Curation in Visualization : Position Paper (Data)}, year = 2022 }
3. Brecher, C., Buchmeiser, M. R., Burkert, A., Busemeyer, M. R., Conermann, S., Ertl, T., Friedrich, M., Helmig, R., Hohmann, V., Johnston, A. J., Kollmeier, B., Larkum, M., Louis, J., Menges, A., Morgner, U., Müller, J., Niessen, C., Ohlberger, M., Schäffner, W., … Flemisch, B. (2022). Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden : Commitment to active data and software management in large research alliances : Bausteine Forschungsdatenmanagement : 2022, 1. https://doi.org/10.17192/bfdm.2022.1.8412
  - BibTeX
  - Link
  BibTeX
  @misc{brecher2022commitment, abstract = {Wir erkennen die Wichtigkeit von Forschungsdaten und -software für unsere Forschungsprozesse an und ordnen die Veröffentlichung von Forschungsdaten und -software als wesentlichen Bestandteil der wissenschaftlichen Publikationstätigkeit ein. Dafür unterstützen wir als Verbund unsere Forschenden im Umgang mit Daten und Software nach den FAIR-Prinzipien in Einvernehmen mit dem DFG-Kodex “Leitlinien zur Sicherung guter wissenschaftlicher Praxis”. In Zusammenarbeit mit unseren Institutionen und Fachcommunities stellen wir adäquate Forschungsdatenmanagement-Werkzeuge und -Dienste bereit und befähigen unsere Forschenden zum Umgang damit. Dabei bauen wir vorzugsweise auf existierenden Angeboten auf und bemühen uns im Gegenzug um deren Anpassung an unsere Bedürfnisse. Wir streben Maßnahmen für die Definition und Sicherstellung der Qualität unserer Forschungsdaten und -software an. Wir verwenden vorzugsweise existierende Daten-/Metadatenstandards und vernetzen uns nach Möglichkeit für die Erstellung und Implementierung neuer Standards mit entsprechenden nationalen und internationalen Initiativen. Wir verfolgen die Entwicklungen im Bereich des Forschungsdaten- und -softwaremanagements und prüfen neu entstehende Empfehlungen und Richtlinien zeitnah auf ihre Umsetzbarkeit.}, author = {Brecher, Christian and Buchmeiser, Michael R. and Burkert, Andreas and Busemeyer, Marius R. and Conermann, Stephan and Ertl, Thomas and Friedrich, Michael and Helmig, Rainer and Hohmann, Volker and Johnston, Andrew James and Kollmeier, Birger and Larkum, Matthew and Louis, Jan and Menges, Achim and Morgner, Uwe and Müller, Jürgen and Niessen, Carien and Ohlberger, Mario and Schäffner, Wolfgang and Schmidt, Piet and Schmitz, Dietmar and Seeger, Werner and Stammer, Detlef and Thomas, Arne and Traninger, Anita and Wegener, Martin and Colomb, Julien and Hermann, Sibylle and Kopsch-Xhema, Jenny and Range, Jan and Flemisch, Bernd}, doi = {10.17192/bfdm.2022.1.8412}, source-work-id = {BASE:b04b75f28f30ecd0ef47c3ac5f880556ae9011b5d8fa06208f2692086907eaf8}, title = {Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden : Commitment to active data and software management in large research alliances : Bausteine Forschungsdatenmanagement : 2022, 1}, url = {https://dx.doi.org/10.17192/bfdm.2022.1.8412}, year = 2022 }
  Link
  https://dx.doi.org/10.17192/bfdm.2022.1.8412
2021
1. Braun, M. (2021). EXC IntCDC - Data Management Plan Template for Research Projects. DaRUS. https://doi.org/10.18419/darus-2116
  - BibTeX
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  BibTeX
  @dataset{braun2021intcdc, abstract = {This Data Management Plan describes the data management life cycle for the data, a Research Project of EXC IntCDC will collect, process and/or generate. Moreover, it describes whether and how this data is being used and/or made publicly available for verification and re-use and how the data will be curated and preserved after the end of the project.}, author = {Braun, Matthias}, doi = {10.18419/darus-2116}, publisher = {DaRUS}, title = {EXC IntCDC - Data Management Plan Template for Research Projects}, unf = {UNF:6:Al3ljb79iWiURj4OdonArw==}, url = {https://doi.org/10.18419/darus-2116}, version = {V1}, year = 2021 }
  Link
  https://doi.org/10.18419/darus-2116

General Information

Published Research Data & Software

Data Repository of the University of Stuttgart (DaRUS)

Guidelines

Internal Information

Office Hours

Data Management Commission

Members

Research Data & Software Manager

PUBLICATIONS

2023

BibTeX

Link

2022

BibTeX

BibTeX

BibTeX

Link

2021

BibTeX

Link

Matthias Braun

Audience

Formalities

Services

Organization

General Information

Published Research Data & Software

Data Repository of the University of Stuttgart (DaRUS)

Guidelines

Internal Information

Office Hours

Data Management Commission

Members

Research Data & Software Manager

PUBLICATIONS

2023

BibTeX

Link

2022

BibTeX

BibTeX

BibTeX

Link

2021

BibTeX

Link

Matthias Braun

Cluster of Excellence IntCDC

Audience

Formalities

Services

Organization