Research Data Management

Information on services and activities related to Research Data Management and Research Software Engineering

Research Data & Software Publications

Teaching & Infrastructure

Research Software Engineering

Networking & Involvement

[Photos: Pages, Presentation, Floppy Disk, Globa Connection, Iconset "Lynny“ by Matthew Skiles on 1001FreeDownloads.com]

The current RDM strategy and data guidelines for the Cluster of Excellence IntCDC are continuously being further developed. We consider it as a fundamental task of RDM to advice, train, and inform researchers on issues related to research data, in particular on questions of data storage, data documentation and making data accessible. Thereby, we acknowledge legal requirements on the one hand, and open-science practices on the other (e.g. FAIR Guideline Principles). According to IntCDC's research data policy, the goal is that each publication should be accompanied by a corresponding published dataset. IntCDC researchers are supported in designing sustainable research software, what is part of research software engineering (RSE). Needs-based training at a scientific level is designed, enabling researchers to develop and deliver software in a sustainable manner. Additionally, quality criteria for the recognition of research data and research software as a full publication at the University of Stuttgart are developed.

Photo: Based on work by Gaelen Pinnock, http://scarletstudio.net, CC BY-SA

COMISSION

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

INTERNAL

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. We can either meet online via WebEx or in person

Further Information

Additional comprehensive information is provided on the internal knowledge base.

TEAM

Matthias Braun

RDM Portal of the University of Stuttgart

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
  - Link
  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

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

The Data Repository of the University of Stuttgart (DaRUS) 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 which is also based on Dataverse: EXC IntCDC Datasets on Edmond

IntCDC Publication List

IntCDC Publication List

Data Repository of the University of Stuttgart (DaRUS)

Search the EXC IntCDC Dataverse on DaRUS:

Training

The following recurring events are organized or co-organized by the research data management team of IntCDC.

IntCDC Key Skills Workshops

The following recurring workshops are organized by the research data management team of IntCDC, partially in co-operation with other RDM or RSE groups at the University of Stuttgart.

Practical Data Publication

The Practical Data Publication workshop takes place on regular bases and aims to introduce the researchers interactively to the IntCDC-specific RDM workflow. The participants will learn the processes hands-on and afterwards will be able to plan and document their data via the data or software management plan and publish their data on the FAIR data repository DaRUS. The workshop is complemented by regular office hours for individual support.

Check the ILIAS course page for upcoming dates and more information.

Software Carpentry

In cooperation with FOKUS, SimTech, and TIK we offer Software Carpentry Workshops on a half-yearly basis. Contents of the workshop are important basics for reproducibility of research with software, like Linux Bash, Version Control with Git and Programming and Plotting in Python. Professional teaching material is maintained by the Carpentries community. As a certified Carpentries Instructor, Matthias Braun is allowed to organize and hold Carpentry Workshops. Registration is open to all students and PhD students of IntCDC and the University of Stuttgart.

Check the ILIAS course page for upcoming dates and more information.

Research Software Engineering 102

In cooperation with SimTech, the RSE 102 workshop builds on the RSE basics (Unix shell, Git, Python), such as those learned in a Software Carpentry workshop, and deals with methods and tools used for good software engineering: Git workflows, containerization, testing and continuous integration, building and packaging, and software design principles.

Skills in these areas are crucial for developing or contributing to quality-assured software in collaborative environments and are very useful in today’s research landscape.

Material and more information can be found on the GitHub repository page.

Management Plans

Data Management Plan

FAIR Principles The IntCDC 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.

The generic Data Management Plan template of IntCDC is based on the Horizon 2020 template and is published on DaRUS. As stated in the project proposal of IntCDC, each Research Project (RP) has to provide its own Data Management Plan.

Software Management Plan

As with research data, IntCDC considers research software not as a by-product but as an integral part of science. Software Management Plans (SMP) support organization of project management for research software, ensure transparency and allow quality assurance, and enable researchers to reflect on their own approach to new research software projects in a structured manner.

Within IntCDC, we rely on the Software Management Wizard provided by ELIXIR. This online tool provides a state-of-the-art implementation of the FAIR Principles for research software and is continuously improved. The Wizard supports completing the questionnaire with additional hints and meta information. The SMP can be exported to various file formats at any stage, including a machine actionable format.

Collaborative Infrastructure

The following infrastructure and services are provided by the University of Stuttgart and are made available to all participants of IntCDC to support and implement collaborative tasks related to RDM and RSE.

Collaborative Infrastructure

The following publically accessible infrastructure and services are provided by the University of Stuttgart and are made available to all participants of IntCDC to support and implement collaborative tasks related to RDM and RSE.

For internal collaboration IntCDC utilizes Confluence by Atlassian which is a web-based corporate wiki software that enables collaborative knowledge documentation. The University of Stuttgart runs their own on-premise Confluence Data Centre, which is available to all participants of IntCDC and serves as central knowledge base. The network share also provided by the University of Stuttgart allows the internal storage and exchange of large data and is the storage location for currently processed research data and administrative data.

DaRUS

DaRUS, the data repository of the University of Stuttgart, offers a secure location for research data and codes, be it for the administration of own data, for exchange within a research group, for sharing with selected partners or for publishing. Data published on DaRUS complies with the FAIR Principles and is suitable for so called "cold" data that is in a final state and e.g. part of a publication. A persistent identifier (DOI) is assigned to each published dataset. All published datasets are easily citable and reusable for the research community.

EXC IntCDC Dataverses

Data Repository of the University of Stuttgart (DaRUS)

Search the EXC IntCDC Dataverse on DaRUS:

RESEARCH SOFTWARE ENGINEERING

Software Projects

Research software projects at IntCDC are individually supported and also new long-term software projects that support the research within IntCDC are initiated.

Visual Analytics Framework for Grasshopper

VisuAlFroG is a framework that provides various visualization types to support visual analytics for the algorithmic modelling software Grasshopper. Grasshopper for its part is a plug-in of the commercial 3D computer graphics and computer-aided design application software Rhinoceros. The basic architecture of VisuAlFroG consists of a core framework providing general functionality and a modular extension mechanism that easily allows to integrate new visualization types or visualization libraries.

- Project Start: March 2023
- Current Status: Prototype
- Contact: RDM team (see below)

Github Repository

Data Repository of the University of Stuttgart (DaRUS)

Search the EXC IntCDC Dataverse on DaRUS:

NETWORKING & INVOLVEMENT

Networking

University of Stuttgart

Competence Center for Research Data Management (FoKUS): Supports all researchers at the University of Stuttgart and their partners in the management, documentation and publication of their research data and research software.
FDM-AG: Research data management working group consisting of interested researchers and representatives of infrastructure institutions developing processes, guidelines and best practices for research data management at the University of Stuttgart

RDM Portal of the University of Stuttgart

Germany

Carpentries Community Discussions: Regional DACH Call
Member of Research Data Alliance (RDA) Deutschland e.V.
Member of deRSE e.V.
German National Research Data Infrastructure (NFDI)
- Seed Fund Project of NFDI4Ing: "RDM-Workflows for construction engineering and architecture" (Proposal, 2022)
- As a participant in the second funding period of NFDI4Ing, IntCDC will interact with the consortium and contribute to its work programme on a sustained basis, mainly in the Task Area BETTY.

Worldwide

Research Data Alliance (RDA) - Working/Interest Groups:
- Sharing Rewards and Credit IG (SHARC)
- FAIR for Research Software WG (FAIR4RS)
- Research Data Management in Engineering IG (RDME)

Policies & Commitment

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]

Data Repository of the University of Stuttgart (DaRUS)

Search the EXC IntCDC Dataverse on DaRUS:

Networking & Involvement

Additional Services

Technical Services for Information and Communication

The Technical Services for Information and Communication of the University of Stuttgart (TIK) offer an extensive IT-service portfolio. See all services listed on the overview.

Highlights

Cloud service for sharing and collaboration on Office documents

Infrastructure as a Service (IaaS) for operating own servers on virtual machines

ZENDAS

See ZENDAS (Central Data Protection Office of the Universities in Baden-Württemberg ) for information on data protection issues (Note: Some pages are only accessible from within the university network).

Here you can find official English samples for research projects that require a declaration of consent:
- Sample Declaration of Consent for Research Projects [DOC]
- Sample Information According to Art. 13 GDPR [DOC]

Recommended Websites

NFDI (Association German National Research Data Infrastructure) promotes science and research through a national research data infrastructure that establishes and develops comprehensive research data management in Germany and increases the efficiency of the entire German science system.

NFDI4Ing (National Research Data Infrastructure for Engineering Sciences) brings together the engineering communities to work towards proper research data management (RDM) that implements the FAIR data principles.

NFDI4Culture (Consortium for Research Data on Material and Immaterial Cultural Heritage) establishes a needs-based infrastructure for research data ranging from architecture, art history and musicology to theatre, dance, film and media studies.

Software Sustainability Institute facilitated the advancement of software in research.

Forschungsdaten.info is the German-language information portal on research data management.

Contact

Write e-mail

Research Data Management

Research Data & Software Publications

Teaching & Infrastructure

Research Software Engineering

Networking & Involvement

COMISSION

Members

INTERNAL

Office Hours

Further Information

TEAM

PUBLICATIONS

2023

BibTeX

Link

2022

BibTeX

BibTeX

BibTeX

Link

2021

BibTeX

Link

Published Research Data & Software

Data Repository of the University of Stuttgart (DaRUS)

Training

IntCDC Key Skills Workshops

Management Plans

Data Management Plan

Software Management Plan

Collaborative Infrastructure

Collaborative Infrastructure

Data Repository of the University of Stuttgart (DaRUS)

RESEARCH SOFTWARE ENGINEERING

Software Projects

Data Repository of the University of Stuttgart (DaRUS)

NETWORKING & INVOLVEMENT

Networking

Policies & Commitment

Data Repository of the University of Stuttgart (DaRUS)

Networking & Involvement

Additional Services

Highlights

Contact

IntCDC Research Data Management

Cluster of Excellence IntCDC

Audience

Formalities

Services

Organization