Research data management tools

Practical tools support the structured documentation, organisation and publication of research data in accordance with the FAIR data principles. The Plasma-MDS.org community uses and develops specific tools tailored to the requirements in low-temperature plasma research.

Plasma-MDS

Plasma-MDS is the plasma metadata schema and core of Plasma-MDS.org community developments. Current developments focus on the extension of the schema for different application scenarios, its integration into RDM workflows, and its use for the development of ontologies and knowledge graphs for LTP research.

eLabFTW

eLabFTW is a free and open-source electronic laboratory notebook. It supports the documentation of experiments, the organisation of laboratory resources and the traceable management of research workflows. Installations of eLabFTW are already operated in research groups at RUB, CAU and INP. Access can be gained via local contacts.

Adamant

Adamant is a JSON schema-based metadata creation tool for research data management workflows. It is developed and supported by INP and can be used alongside eLabFTW for schema-based metadata collection.

Repositories

INPTDAT and RDPCIDAT are repositories for the publication of data sets from LTP research. The generic data repository Zenodo is supported for the publication of, e.g. presentations, posters and training materials (guideline). The registry re3data helps to find relevant research data repositories.

Data management plan

A data management plan (DMP) describes how research data will be created, processed, documented, stored, shared and preserved throughout a project. It should be prepared at the start of a project, reviewed regularly and updated whenever experimental setups, software, data flows or collaboration arrangements change. Some funding organisations require a structured DMP with submission of the proposal. It typically contains the following general aspects along the full research-data lifecycle:

  • Data types and volume: Describe which data will be generated or reused, such as measurement data, images, videos, diagnostic signals, simulation output, analysis scripts, laboratory records and calibration files. Estimate expected data volumes and growth rates.
  • Documentation and metadata: Define how experiments, samples, instruments, software versions, processing steps and data quality checks will be documented. Metadata should make data understandable independently of the people who created it.
  • File formats and organisation: Prefer open, well-documented and sustainable file formats where possible, for example CSV, TXT, HDF5, NetCDF, TIFF, PDF/A or open source-code formats. Establish consistent folder structures, file names and versioning conventions.
  • Storage, backup and security: Specify where active data will be stored, how often it will be backed up, who can access it and how data integrity will be checked. Institutional storage services should normally be preferred for primary project data.
  • Roles and responsibilities: Assign responsibility for data collection, metadata creation, quality assurance, storage, publication and archiving. This is particularly important for collaborative experiments and long-running facilities.
  • Legal, ethical and contractual aspects: Consider copyright, licences, personal data, export-control restrictions, confidentiality agreements and intellectual-property rights.
  • Sharing and publication: Define which data, software and metadata will be made available, when this will happen and under which licence. Select a suitable disciplinary or general-purpose repository and ensure that published datasets receive persistent identifiers, such as DOIs.
  • Long-term preservation: Clarify which data must be retained after the end of the project, for how long, and where the archived version will be stored.

A DMP is not only a funding requirement: it helps research groups make data reusable within the project, supports reproducible science and clarifies responsibilities for data stewardship, access rights and long-term preservation.

Different funding organisations often define their own specific requirements, guidelines and/or templates for data management plans. Researchers should therefore consult the requirements of the relevant funding organisation at an early stage of proposal preparation.

Special requirements for the German Research Foundation (DFG)

The DFG does not generally prescribe a separate data-management standard specifically for plasma physics. However, DFG-funded projects are expected to address research-data handling appropriately for the discipline, project type and expected reuse potential.

Low-temperature plasma research often combines complex experimental setups, diagnostics, imaging, numerical modelling and material, chemical, or biological analysis. A DMP should therefore describe the relationship between raw data, calibration information, data-processing workflows and final results in sufficient detail to allow later interpretation and use of the data. Particularly relevant for plasma research, depending on the focus of the project, are the subject-specific recommendations for

The structure of Section 2.4 (handling of research data) for proposals in the research grants programme submitted to the DFG can have the following structure and refer to developments within the Plasma-MDS.org community:

  1. Data description

    The proposal should provide a concise overview of the types, sources, formats and expected volumes of research data generated or reused in the project. In low-temperature plasma research, this may include raw and processed diagnostic data, electrical waveforms, optical spectra, images, simulation and modelling data, calibration files, sample information, laboratory records and analysis software. Wherever possible, quantities should be reported in the International System of Units (SI), or include sufficient information for conversion to SI units. Established community formats should be used where available; otherwise, open, well-documented and sustainable formats should be preferred to support interoperability and long-term reuse.

    • Identify generated, reused and derived data.
    • Estimate data volumes, growth rates and associated costs.
    • Describe data formats, units and conversion procedures.
    • Use established disciplinary standards and formats where available.

  2. Documentation and data quality

    The proposal should explain how data acquisition, processing, quality assurance and documentation will be organised. For low-temperature plasma experiments, this includes recording the experimental configuration, gas composition and flow, pressure, power and voltage settings, diagnostic parameters, calibration data, sample identifiers and relevant environmental conditions. Clear and persistent identifiers for experiments, measurements and samples are essential for traceability between laboratory records, raw data, processed datasets, figures and publications. Electronic laboratory notebooks, structured metadata and version-controlled analysis scripts can support reproducibility; all project members involved in data generation should receive appropriate training in these workflows.

    • Document experimental setups, operating conditions and diagnostics / modelling.
    • Record calibration procedures, uncertainty information and quality controls.
    • Assign unambiguous identifiers to samples, measurements and datasets.
    • Maintain links between raw data, processing steps, scripts and publications.
    • Define responsibilities for quality assurance and staff training.
    • Relevant tools supported by Plasma-MDS.org: Plasma-MDS, eLabFTW, Adamant, Plasma-O, Plasma-KG.
    • FAIRsharing helps to find further relevant standards.

  3. Storage and technical archiving during the project

    The proposal should describe how research data will be stored, backed up, protected from unauthorised modification and made accessible to authorised project members. A structured storage system should distinguish clearly between raw data, processed data, analysis scripts, documentation and publication-ready datasets. In plasma research, high-speed imaging, oscilloscope traces, spectroscopy and simulation output can create substantial data volumes. Storage capacity, redundant backup procedures, access and usage rights, write-protection, data-transfer workflows and the institutional storage infrastructure should therefore be planned at an early stage. In general, DFG-funded research data must be retained for ten years in accordance with the DFG Guidelines for Safeguarding Good Research Practice, unless valid reasons prevent retention.

    • Use structured, secure and preferably institutionally managed storage.
    • Implement redundant backups and define backup frequencies.
    • Define access rights, write protection and protection against unauthorised access.
    • Separate raw, processed, and publication-ready data.
    • Plan for high-volume data acquisition, transfer and retention requirements.

  4. Legal obligations and conditions

    Legal, contractual and institutional requirements should be assessed before the project begins and briefly addressed in the proposal. These requirements may affect the collection, documentation, storage, access, publication, licensing, archiving and reuse of research data. Low-temperature plasma projects may involve proprietary reactor designs, industrial collaborations, patented processes, restricted technical information or third-party data. Where data cannot be openly published, the reasons, applicable access conditions, ownership arrangements and any planned embargo period should be stated clearly.

    • Clarify copyright, usage rights, ownership and licensing conditions.
    • Check agreements with project partners, industrial collaborators and data providers.
    • Consider patent rights, confidentiality and export-control restrictions where relevant.
    • Obtain permission for the reuse of externally provided data where required.
    • Specify publication restrictions, controlled access or embargo periods.

  5. Data exchange and long-term data accessibility

    The proposal should identify which data are suitable for external archiving and long-term access and explain the criteria used for their selection. Relevant and reproducible data underlying publications should be archived for the long term and made accessible wherever legally, ethically and technically possible. For low-temperature plasma research, reusable datasets should ideally include relevant raw or reduced measurement data, calibration information, experimental parameters, processing procedures, metadata and analysis software. Data should be deposited in appropriate, trustworthy repositories using sustainable formats, rich metadata, persistent identifiers such as DOIs and clearly defined access conditions. Final long-term storage should be implemented in a storage environment separate from the active project storage system.

    • Define criteria for selecting data for publication, reuse and deletion.
    • Archive data, metadata, software and workflows supporting published results.
    • Use suitable certified or trustworthy repositories and data centres.
    • Provide persistent identifiers, preferably DOIs, and clear licence information.
    • Use sustainable formats and sufficient metadata for independent reuse.
    • Plan repository selection and contact repository operators early in the project.
    • Relevant tools supported by Plasma-MDS.org: Repositories.
    • re3data and FAIRsharing help to find further relevant research data repositories.

  6. Responsibilities and resources

    The proposal should clearly assign responsibilities for data collection, documentation, quality control, storage, publication and long-term archiving. While individual tasks may be delegated to project members, data stewards or shared infrastructures, overall responsibility for sound research data management and final quality control normally remains with the project leadership. Required resources should be planned from the outset, including staff time, training, storage capacity, backup services, metadata curation, software maintenance and repository fees where applicable. In collaborative plasma research, responsibilities, access arrangements and interfaces between participating institutions should be explicitly documented.

    • Assign named roles for data generation, documentation, curation and archiving.
    • Define the responsibilities of project leadership, staff and infrastructure providers.
    • Provide training for researchers who collect or process data.
    • Budget staff time, storage, curation, software and repository costs.
    • Document responsibilities and data-exchange procedures in collaborations.
    • Specify who will curate and maintain data after the project ends.