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Module 6: Data Sharing and Publication

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The training curriculum is currently undergoing final revisions and quality checks. All materials will be released shortly. Until the official release, please refrain from using, distributing, or implementing any part of these resources.

Learning Objectives

  • Learning Objective 1 (LO1): Recognise the benefits, requirements, and limitations of sharing/publishing data.
  • Learning Objective 2 (LO2): Identify different forms of data publication and infrastructure to make outputs discoverable.

Total Module Duration

2 hours 45 minutes

Learning Objective 1

LO1: Recognise the benefits, requirements, and limitations of sharing/publishing data.

Learning Activities

  • Lecture (45 mins): Prepare a lecture on the benefits of data sharing/publication for researchers, the research community, funders, and the public. The lecture can also cover limitations (confidentiality, industrial exploitation, data protection) and what to do if the data cannot be made publicly available (for example, archive data and publish metadata with access rules). Requirements from institutional policies, funders and publishers (like data availability statements) should also be addressed.
  • Group Discussion (30 mins): Learners are confronted with a case in which a researcher resists sharing data and they need to provide arguments to convince the researcher. The activity can be done in small breakout groups, with results from each group discussed in plenum.

Materials to Prepare

  • Presentation slides on benefits, limitations, and requirements of data sharing and publication.
  • Prepare a small input for the group task. The input should describe a detailed scenario of a researcher who is reluctant to sharing his data. Include some specific arguments from the researcher that the learners need to refute.

Instructor Notes

Lecture:

  • These are some of the key takeaways the instructor needs to cover in their presentation (see Resources 1–10 for information and inspiration):
    • Sharing data promotes collaboration, accelerates discoveries, and supports evidence-based decision-making across disciplines.
    • Open data allows for verification of results, reducing errors, bias, and misconduct in research and data-driven initiatives.
    • While open data drives progress, responsible sharing requires balancing openness with privacy, security, and proprietary concerns.
    • Sometimes there are recommendations on where to publish your data.
    • Making sure that guidelines from different stakeholders are known and followed is important to not violate any laws or good scientific practice (see Resources 11–14 for examples).
  • If the audience is known, search for relevant requirements from institution/funders/publishers for the audience (see generic example requirements under Resources).
  • For further details about repositories and data infrastructure, please also refer to the module Data Preservation and Archiving of this curriculum.

Resources

Benefits/Limitations of sharing your data – for instructors:

  1. NFDI4Chem. Data Publishing | NFDI4Chem Knowledge Base. https://knowledgebase.nfdi4chem.de/knowledge_base/docs/data_publishing/. Accessed 11 Nov. 2024.
  2. DataONE Community Engagement & Outreach Working Group. Data Sharing. 2017. https://dataoneorg.github.io/Education/lessons/02_datasharing/index.html.
  3. Colavizza, Giovanni, et al. "The Citation Advantage of Linking Publications to Research Data." PLOS ONE, edited by Jelte M. Wicherts, vol. 15, no. 4, Apr. 2020, p. e0230416. DOI.org (Crossref), https://doi.org/10.1371/journal.pone.0230416.
  4. Lortie, Christopher J. "The Early Bird Gets the Return: The Benefits of Publishing Your Data Sooner." Ecology and Evolution, vol. 11, no. 16, Aug. 2021, pp. 10736--40. DOI.org (Crossref), https://doi.org/10.1002/ece3.7853.
  5. Piwowar, Heather A., and Todd J. Vision. "Data Reuse and the Open Data Citation Advantage." PeerJ, vol. 1, Oct. 2013, p. e175. DOI.org (Crossref), https://doi.org/10.7717/peerj.175.
  6. Smith, Jade. LibGuides: Research Data Management: Why Share Research Data. 28 Mar. 2025. https://libguides.ucd.ie/data/share.
  7. GRIC UPRM. LibGuides UPRM: Research Data Management (RDM): Publishing Your Data. 25 Aug. 2023. https://libguides.uprm.edu/datamanagement-en/share.https://libguides.uprm.edu/datamanagement-en/share.
  8. Eynden, Veerle van den. Managing and Sharing Data: Best Practice for Researchers. 3rd ed., Fully rev, UK Data Archive, 2011. Open WorldCat. https://dam.ukdataservice.ac.uk/media/622417/managingsharing.pdf.
  9. Schönbrodt, Felix, et al. Open Science - Open Data and Open Material I. 17 Oct. 2019. https://oer.vhb.org/edu-sharing/components/render/14521893-fda4-413b-ad4b-7434cb4ea983.
  10. RDA Session 8 - Persistant Identifiers, Data Citation and Open Data. Directed by CODATA, 2021. Vimeo. https://vimeo.com/620062523.

Exemplary requirements or guidelines for funding or publishing:

Funding from EU and related institutions:

  1. European Commission. Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020. 21 Mar. 2017. https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf.
  2. Open Research Europe. Open Data, Software and Code Guidelines. https://open-research-europe.ec.europa.eu/for-authors/data-guidelines. Accessed 2 Apr. 2025.

National funders:

  1. Deutsche Forschungsgesellschaft (DFG). "Providing Public Access to Research Results." Wissenschaftliche Integrität. https://wissenschaftliche-integritaet.de/en/code-of-conduct/providing-public-access-to-research-results/. Accessed 2 Apr. 2025.
    Guideline from the German Research Association (DFG)

Publishers:

  1. MIT Libraries. Journal Requirements | Data Management. https://libraries.mit.edu/data-management/share/journal-requirements/. Accessed 2 Apr. 2025.
    List of some guidelines from publishers

Learning Objective 2

LO2: Identify different forms of data publication and infrastructure to make outputs discoverable.

Learning Activities

  • Lecture (45 mins): Lecture on different forms of data publication, infrastructures, persistent identifiers. The lecture should include a live demonstration of publishing data to some demo infrastructure.
  • Discussion activity (45 mins): Learners should take a dataset from their research and:
    • Choose the best form and license for publishing their dataset (take into consideration possible relevant laws or guidelines from institutions).
    • Find a data journal suitable for a given dataset.
    • Create a network of different related publications (datasets, research papers, software); the publications may not yet exist, but can also be planned for the future.

Materials to Prepare

  • Presentation slides on data publication, infrastructures, persistent identifiers.
  • Prepare notes and practice live demonstration of publishing a dataset to a demo repository (for example, Zenodo Sandbox, see Resource 18).
  • If available, learners should bring their own datasets for the discussion activity. If they do not have any datasets, the instructor should prepare some sample datasets that can be used by the learners.

Instructor Notes

Lecture:

  • Resources 1–10 already provide content (some under an open licenses) that can be used for creating the slides.
  • Content to include in lecture or presentation:
    • Different forms of data publication (see Resources 1–7)
      • Standalone Dataset
      • Attachment to research paper
      • Data paper/publication
    • Data Citaitons (see Resource 8)
    • Licenses for dats (see Resource 9)
    • Promote your data publication (see Resource 10)
    • Infrastructure
      • Data repositories/Data registries/Research data centres (see Resources 11–20)
        • discipline-specific
        • institutional repositories
        • general repositories
        • national & international repositories
      • How to determine trustworthy repositories (see Resources 21–26)
        • Criteria:
          • moderation of deposit
          • guarantee about sustainability
          • offering of persistent identifiers
          • listed in registry such as re3data or fair sharing
          • TRUST principles
        • Certification
          • CoreTrustSeal
      • Data journals (see Resources 27–30)
    • Persistent identifiers (PID; see Resources 31–33)
      • DOI
      • ROR
      • ORCID
      • Accession numbers (for library collections)
  • If applicable, adopt license part to country specific legislation.
  • If available, include research data policy from the home institution or discipline of the learners.

Discussion:

  • Discuss the advantages of different publication forms. No single form of data publication fits all needs. Researchers should select the most appropriate (or combine multiple) method(s) based on their goals, the nature of their data, and disciplinary norms:
    • Use dataset repositories when enabling direct access and reuse is a priority.
    • Publish a data paper when contextualisation and academic recognition are important.
    • Attach supplementary materials when data primarily supports a research article without independent reuse.
  • Assessing FAIRness (for instance with F-UJI or FAIReva, see Resources 34, 35) ensures compliance with best practices, making data more useful for future research and innovation.
  • A clear data license defines how others can access, use, and build upon research. Selecting the right license balances openness with necessary restrictions, fostering trust and ethical reuse while protecting intellectual contributions.
  • Data citations, just like research article citations, give proper credit to data creators and encourage responsible reuse.
  • Connecting datasets to code, research software, and publications provides the provenance of the research process and help the advancement of science. Breaking a single big journal publication containing everything into these smaller pieces values each step of the research process; the researcher does not only focus on publishing interpretations, but also puts effort into data collection and publishing. This supports reproducibility, verification, and reuse.
  • If the audience is known, modify the examples for the discussion to match the discipline or institution of the learners.

Resources

Learning materials that can be reused:

  1. NFDI4Ing. Datenlebenszyklus Phase 4: Daten Teilen Und Publizieren. https://nfdi4ing.pages.rwth-aachen.de/education/education-pages/dlc-datalifecycle/html_slides/dlc4new.html#/. Accessed 2 Apr. 2025.
    Self-paced learning materials about Data Sharing & Publication (CC BY 4.0, in German language)
  2. Bezjak, Sonja, et al. The Open Science Training Handbook: Open Research Data and Materials. https://open-science-training-handbook.github.io/Open-Science-Training-Handbook_EN/02OpenScienceBasics/02OpenResearchDataAndMaterials.html. Accessed 3 Feb. 2025.
    Learning Unit about Open Data and Materials (CC0 1.0 Universal)

General information for creating the training materials:

  1. CESSDA Training Team. Data Management Expert Guide: Data Publishing Routes. https://dmeg.cessda.eu/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes. Accessed 2 Apr. 2025.
    Guide on RDM in Social Sciences: Data Publishing Routes (CC BY-SA 4.0)
  2. Leibniz Information Centre for Economics (ZBW). Selecting the Suitable Repository for Research Data | Open Economics Guide of the ZBW. https://openeconomics.zbw.eu/en/knowledgebase/selecting-the-suitable-repository-for-research-data/. Accessed 2 Apr. 2025.
    Things to keep in mind when selecting repositories (CC BY 4.0)
  3. Leibniz Information Centre for Economics (ZBW). Data Repositories and Data Portals | Open Economics Guide of the ZBW. https://openeconomics.zbw.eu/en/knowledgebase/data-repositories-and-data-portals/. Accessed 31 Jan. 2025.
    Overview of where to search for data repositories and data journals (CC BY 4.0)
  4. Universität Würzburg. Research Data Management: Data Publication and Archiving. https://www.uni-wuerzburg.de/en/rdm/information/data-publication/. Accessed 3 Feb. 2025.
    Short overview of different data publication forms
  5. Charels University Open Science Support Centre. "How to Share Research Data." Open Science Support Centre. https://openscience.cuni.cz/OSCIEN-53.html. Accessed 3 Feb. 2025.
    Short overview of different data publication forms
  6. Data Citation Synthesis Group. Joint Declaration of Data Citation Principles. Force11, 2014. DOI.org (Datacite), https://doi.org/10.25490/A97F-EGYK. Video including information about data citation.
  7. Licenses for Research Data | RADAR. https://radar.products.fiz-karlsruhe.de/en/radarfeatures/lizenzen-fuer-forschungsdaten. Accessed 11 Nov. 2024.
    Information about licenses for research data (for Germany)
  8. CESSDA Training Team. Data Management Expert Guide: Promoting Your Data. https://dmeg.cessda.eu/Data-Management-Expert-Guide/6.-Archive-Publish/Promoting-your-data. Accessed 2 Apr. 2025.
    Guide on RDM in Social Sciences: Promoting Your Data (CC BY-SA 4.0).

Search engines to find repositories for data publication:

  1. re3data.org - Registry of Research Data Repositories. https://doi.org/10.17616/R3D.
  2. Datacite Commons. https://commons.datacite.org/repositories.
  3. FAIRsharing.org. https://fairsharing.org/search?page=1&recordType=repository.
  4. OpenDOAR (quality-assured directory of open access repositories). https://v2.sherpa.ac.uk/opendoar/about.html.
  5. ROAR --- Registry of Open Access Repositories. https://roar.eprints.org/.
  6. RIsources (RI = Research Infrastructure). https://risources.dfg.de/home_en.html.

Exemplary general, interdisciplinary repositories:

  1. Zenodo. https://zenodo.org/.
  2. Zenodo Sandbox. https://sandbox.zenodo.org/.
    Can be used for testing publishing data to Zenodo without actually publishing the data.
  3. Figshare. https://figshare.com/.

Exemplary research data centres:

  1. KonsortSWD -- Datenzentren. https://www.konsortswd.de/en/services/research/all-datacentres/.

Help for selecting trustworthy repositories; see also module Data Preservation/Archiving:

  1. Lazzeri, Emma. Update of the Study on the Readiness of Research Data and Literature Repositories to Facilitate Compliance with the Open Science Horizon Europe MGA Requirements. 1.0, Zenodo, 14 Oct. 2024. DOI.org (Datacite). https://doi.org/10.5281/ZENODO.13919643.
    Recommendations on selecting suitable repositories for research output (CC BY 7.0)
  2. Science Europe. Criteria for the Selection of Trustworthy Repositories. https://www.scienceeurope.org/media/ffkb51ei/se-rdm-template-2-criteria-for-the-selection-of-trustworthy-repositories.docx. Accessed 31 Jan. 2025.
    Short list with criteria of how to select trustworthy repositories (CC BY 7.0)
  3. De Lamotte, Frédéric, et al. Selecting a Trustworthy Subject-Specific Repository for Self-Depositing Data: Methodology and Analysis of Existing Services. Ministère de l'enseignement supérieur et de la recherche, 2024. DOI.org (Crossref), https://doi.org/10.52949/81.
    Report on how trustworthy repositories for different disciplines have been selected (CC BY-ND 7.0)
  4. Lin, Dawei, et al. "The TRUST Principles for Digital Repositories." Scientific Data, vol. 7, no. 1, May 2020, p. 144. DOI.org (Crossref), https://doi.org/10.1038/s41597-020-0486-7.
    TRUST: Guiding principles to demonstrate digital repository trustworthiness
  5. CoreTrustSeal Standards And Certification Board. CoreTrustSeal Requirements 2023-2025. V01.00, Zenodo, 5 Sept. 2022. DOI.org (Datacite). https://doi.org/10.5281/ZENODO.7051012.
  6. Witt, Michael, et al. RDA Common Descriptive Attributes of Research Data Repositories. 1 Dec. 2023. https://www.rd-alliance.org/wp-content/uploads/2024/01/RDA20Common20Descriptive20Attributes20of20Research20Data20Repositories_0.pdf.

Exemplary data journals:

  1. Nature Scientific Data. https://www.nature.com/sdata.
  2. Earth Science System Data. https://www.earth-system-science-data.net.
  3. List of Data Journals from forschungsdaten.info. https://www.forschungsdaten.org/index.php/Data_Journals.

Service to find an open access(data)journal to publish a (data) paper:

  1. B!SON -- the Open-Access journal recommender. https://service.tib.eu/bison/.

Important persistent Identifiers:

  1. DOI. https://www.doi.org/.
    Identifier for various (digital) objects, widely used for scientific publications.
  2. ROR. https://ror.org/.
    Identifier for research organisations.
  3. ORCID. https://orcid.org/.
    Identifier for researchers.

Tools for FAIR assessment:

  1. FAIRsFAIR. "F-UJI Automated FAIR Data Assessment Tool". FAIRsFAIR. https://www.fairsfair.eu/f-uji-automated-fair-data-assessment-tool. Accessed 2 Apr. 2025.
  2. Aguilar Gómez, Fernando, and Isabel Bernal. "FAIR EVA: Bringing Institutional Multidisciplinary Repositories into the FAIR Picture." Scientific Data, vol. 10, no. 1, Nov. 2023, p. 764. DOI.org (Crossref), https://doi.org/10.1038/s41597-023-02652-8.