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Open research questions in Research Data Management Practices

215 unresolved questions extracted from the limitations and future-work sections of 2,463 Research Data Management Practices papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • the need for comprehensive biodiversity data - the lack of collaboration and data sharing among stakeholders - the importance of stakeholder engagement in national biodiversity data infrastructures

    Experiences from national biodiversity data infrastructures in Europe: advancing data integration and community engagement · 2026 · DOI

  • Deduplication is therefore an important, time-consuming step often underreported or described only briefly.

    deduplicate.it: A Simple Open-Source Tool to Remove Duplicates From Literature Searches · 2026 · DOI

  • Reproducibility failure mostly stems from unavailable, poorly documented, or incomplete code.

    Code sharing and reproducibility in survey-based social research: evidence from a large-scale audit · 2026 · DOI

  • While OS practices are gaining popularity—particularly through bottom-up initiatives—their adoption rate among early career researchers remains unclear.

    The struggle to make transparency mainstream: initial evidence for a slow uptake of open science practices in PhD theses · 2025 · DOI

  • Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe.

    Challenges of open data in aquatic sciences: issues faced by data users and data providers · 2024 · DOI

  • Establishing how EOSC, Copernicus, Destination Earth, EuroHPC and AI Factories can function as truly complementary layers needs clarification on integration mechanisms and operational coordination.

    Strengthening Europe’s sovereignty and interoperability in Earth observation data · 2026 · DOI

  • The trajectory of future work must be guided by thorough analysis of past achievements and accumulated experience. Minimising duplication is essential, but beyond that, data analysis-augmented by machine learning or domain expertise-can provide valuable foresight in identifying promising research directions.

    Evolve with your research: stepwise system evolution from document-driven to fact-centric research data management in materials science · 2026 · DOI

  • The digitalisation of research requires data management systems capable of supporting a broad spectrum of usage scenarios. Document-oriented systems generally fail to meet the key FAIR requirements.

    Evolve with your research: stepwise system evolution from document-driven to fact-centric research data management in materials science · 2026 · DOI

  • The accuracy of the models is constrained by the number and content of the selected few-shot examples. The quality of the data annotation is limited by the subclassification of specific fields. The depth of current incident analysis is constrained by the availability of public incident reports.

    Leveraging LLMs for Structured Information Extraction and Analysis from Cloud Incident Reports (Work In Progress Paper) · 2026 · DOI

  • The lack of a public dataset and long-term analysis of cloud incident reports. The difficulty of extracting and analyzing key information from unstructured cloud incident reports.

    Leveraging LLMs for Structured Information Extraction and Analysis from Cloud Incident Reports (Work In Progress Paper) · 2026 · DOI

  • The traditional scholarly publishing model is struggling to cope with the sheer volume of articles. Researchers are having difficulty keeping track of the latest developments in their field.

    Research information systems and knowledge graphs: a review · 2026 · DOI

  • Development of reporting guidelines for Minimum Information about Chemical Investigation (MIChI) will be supported in the long term as metadata templates.

    From Chemistry Data to Cross-Consortium Services: Integrating RADAR4Chem within the NFDI Infrastructure · 2026 · DOI

  • There is no machine-actionable bridge between producer-side quality metrics and user-side fitness assessments. Local uncertainty and use-case validation remain buried in narrative documentation.

    Operationalizing Data Fitness-for-Purpose Through Standardized Metrics, Local Uncertainty, and LLM-Extracted Quality Reasoning · 2026 · DOI

  • The absence of standardized frameworks and effective monitoring tools for KPIs in scientific contexts. Limited adoption of KPIs in scientific contexts due to the absence of standardized frameworks and effective monitoring tools.

    Evolving bioinformatics services – the journey of KPI metrics with Scorpion · 2026 · DOI

  • The transition from paper-based documentation to digital documentation. The need for a structured digital representation of the relevant data. The integration with external databases.

    Establishing digital reference material documentation infrastructure for chemical metrology · 2026 · DOI

  • The development of more tools to integrate different DRMD formats into a centralized database of reference materials. The increase of user awareness about digital reference material documentation.

    Establishing digital reference material documentation infrastructure for chemical metrology · 2026 · DOI

  • The European OPERAS research infrastructure and the Dutch SSH research landscape need to be connected.

    OPERAS-NL and the Dutch Research Landscape · 2026 · DOI

  • on Open Science https://www.budapestopenaccessinitiative.org/read/ - https://openaccess.mpg.de/Berlin-Declaration - http://viennaprinciples.org/v1/ - https://doi.org/10.54677/MNMH8546. 9 Diagram by Bianca Gualandi, Mario Marino, Giulia Caldoni UNESCO Open Science Recommendation The term Open Science covers a variety of efforts and practices focused on making scientific research more open to society, thus making it transparent, accessible, collaborative, and reproducible. The aims of Open Science practices: • make multilingual scientific knowledge • openly available, accessible and reusable for everyone, increase scientific collaborations and sharing of information for the benefits of science and society, • open the processes of scientific knowledge creation, evaluation and communication to societal actors beyond the traditional scientific community.

    2026-05-21_Data-Management-Plans-Psicostat-UNIPD-Vasone-Visentin · 2026 · DOI

  • The integration of heterogeneous and distributed information. The need to provide a detailed presentation of provenance information. The challenge of understanding the object as a dynamic hub of events, actors, places, and meanings.

    The Object Biography in NFDI4Objects · 2026 · DOI

  • The need for a new understanding of the creation and representation of collection data. The lack of a structure to understand the object as a dynamic hub of events, actors, places, and meanings.

    The Object Biography in NFDI4Objects · 2026 · DOI

  • The current workflows do not guarantee FAIR data citation. Reference managers are unable to generate error-free references for datasets.

    FAIRness in Reference Managers usage: utopia or reality · 2026 · DOI

  • Empirical evidence of the integration of open science into assessment reform remains limited. There is a gap between policy aspirations and organizational commitments.

    Advancing open science through research assessment reform? Content analysis of CoARA action plans · 2026 · DOI

  • Future extensions, including Fitness-for-Purpose feedback and provisional assessment status. The potential for the template to be used in conjunction with other standards and vocabularies.

    Summary Report on the RDF Template for the QUANTUM DQ&U Label · 2026 · DOI

  • The need for a standardized template for the QUANTUM Data Quality, Utility and Maturity label. The lack of a machine-readable representation of dataset-level quality assessments.

    Summary Report on the RDF Template for the QUANTUM DQ&U Label · 2026 · DOI

  • The lack of control over data affecting Indigenous lives, lands, cultures, and futures. The need for a governance model and guidelines for Indigenous data. The gap in embedding Indigenous data governance within Australia's national digital research infrastructure.

    Framework for the Governance of Indigenous Data: HASS and Indigenous Research Data Commons · 2026 · DOI

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215 open questions have been extracted from the limitations and future-work passages of 2,463 Research Data Management Practices papers in our 4.5M-paper local library. Each one below links back to the study that raised it, so you can read the original claim in context.

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