Research data is generated during the research process and varies depending on the discipline. The German Research Foundation (DFG) offers the following definition:

“Research data form an essential basis for scientific work. The diversity of such data reflects the diversity of different scientific disciplines, research interests and research methods. Research data includes, amongst other things, measurement data, laboratory results, audiovisual information, texts, survey data, objects from collections or samples that are generated, developed or analysed in the course of scientific work. Methodological testing procedures, such as questionnaires, software and simulations, can also constitute key findings of scientific research and should therefore also be included under the term ‘research data’.”

Source: DFG (2015)Guidelines on the Handling of Research Data

Research data management involves a structured and standardised approach to handling the data generated during the course of a research project.The research data lifecycle representsthe entire process that the data undergoes, from planning and generation through to publication and re-use. Each phase places different demands on the handling of research data.

The collection of research data is time-consuming and costly. This makes it valuable not only for verifying research findings, but also for re-use in addressing further research questions. The aim is to prevent the loss of knowledge and to produce traceable and sustainable research data in accordancewith the FAIR principles. FAIR stands for Findable, Accessible, Interoperable and Reusable. Data processed in accordance with these principles can be reused for other research questions, increasing the visibility and transparency of data generation and collection, and thereby also enhancing the quality of the research and the institution’s scientific reputation.

• Funding: Some funding bodies now require a data management plan at the application stage. This is intended to ensure that the academic community and society as a whole derive the maximum benefit from the public funds allocated and have access to the data and results obtained.
• Integrity: Data management measures contribute to compliancewith the requirements of good scientific practiceand thereby strengthen the credibility of science.
• Visibility: Publishing data can increase the visibility of one’s own research. A meaningful description using metadata and reusable file formats makes it easier to locate and reuse research data.
• Security: The risk of data loss is minimised through careful documentation and systematic data backup and archiving. This ensures the integrity and long-term availability of the data.
• Re-use: Published data can foster the generation of new insights, thereby forming the basis for follow-up research and also sparking collaborations.

Research funding bodies, such as the DFG, the BMBF and the EU, are paying increasing attention to research data and have drawn up corresponding guidelines. The requirements vary depending on the funding body, programme line or academic discipline. In some cases, detailed information on how the data will be handled is required at the application stage, or complete data management plans are expected.

German Research Foundation (DFG)

• For DFG projects,the handling of research data andthe objects on which it is based must be carefully planned, documented and described. Furthermore, they should be published as soon as possible, insofar as data protection, copyright and other considerations permit. It is advisable to submit a data management plan. For certain programmes, this is even a requirement.
• A checklist helpsto describe the handling of research data in the DFG project as comprehensively as possible and to explain the framework conditions.
• Costs incurred in preparing and making research data accessible may be applied for and approved as project-specific costs. Source: DFG (as at 24 January 2022):Information on available funding
• In addition, the DFG providessubject-specific guidance onthe handling of research data.

Federal Ministry of Education and Research (BMBF)

• The BMBF hasdrawn up an action planaimed at strengthening data culture in education, science and research and promoting compliance with the FAIR principles in data collection, compilation, use and preservation.
• Specific requirements relating to research data management will be set out in the relevant calls for proposals. These will also specify the extent to which the costs of research data management are eligible for funding.

European Commission / Horizon Europe programme

• In its Horizon Europe programme (2021–2027), the European Commission has stipulated that the preparation of a data management plan is mandatory for all EU projects that generate or reuse data. A brief description of the methodological approach to data handling must be submitted at the time of application.
• A first version of the DMP must be submitted no later than six months after the project has been approved and must be updated regularly thereafter.
• The programme requires the open-access publication of research data and the information necessary for its reuse or validation in a trusted repository (‘as open as possible, as closed as necessary’).
• TheHE Programme Guideexplains the programme’s objectives regarding open and sustainable scientific practices in Chapter 16, ‘Open Science’, and provides recommendations for action.A template for a DMPis provided, and its use is recommended.
• Costs incurred in connection with the open provision of research data and arising during the project period may be claimed as eligible costs.

Further information and resources on research data:

• fdm-bayern.org– Networking and exchange platform
• National Research Data Infrastructure (NFDI) e.V.
• Forschungsdaten.info –German-language information portal on research data management (RDM)
• QualidataNet– the network for research data management, data sharing and the secondary use of qualitative research data: support for the preparation, archiving and sharing of qualitative data in social research
• Law & Ethics(Research Data in Education Consortium, VerbundFDB)
• Subject-specific recommendations on the handling of research data(DFG)

The storage and backup of data collected during the project, and where applicable the software applications used, must be adequately planned in order to protect the data from loss due to human or technical error, as well as from tampering and unauthorised access. There are various storage locations and media, such as hard drives, portable storage devices, institutional and external cloud services, which may be available for this purpose and should be assessed for their suitability. Furthermore, regular backups are important, and the backup files should be stored on a different medium and kept separate from the original data. An important preparatory step is to develop a file-naming scheme and create a clear folder structure.

Recommendations and guidance:

• In the case of sensitive and/or personal data, the issue of pseudonymisation or anonymisation must be clarified.
• Furthermore, it is important to secure the files and folders with password protection and, where appropriate, to use access control to assign specific access rights.
• Simply moving data to the Recycle Bin is not sufficient for deletion . To ensure the data is actually destroyed, it must be overwritten or the storage medium destroyed. The Federal Office for Information Security provides guidance on how data can be permanently deleted from hard drives and smartphones.
• The 3-2-1 rule provides helpful guidance for backups. It states that all files should be backed up in triplicate on at least two different media, with one of these copies stored off-site. The backup strategy should also specify which data is to be backed up, how often backups should be carried out, and how long the copies should be retained.
• The DFG’s Guidelines on Good Scientific Practice stipulate that research data must be available in the long term for a period of ten years. To comply with this requirement, it is important to consider the durability of data storage media, choose suitable data formats and provide adequate documentation.
• Cloud services from commercial providers must be critically assessed in terms of data security, data protection, confidentiality and integrity. Institutional cloud services are generally to be preferred.

A detailed description of research data facilitates its discoverability, enhances its comprehensibility and interpretability, and thereby promotes the reproducibility of research. It is also important for those generating the data to ensure traceability at a later stage. The extent of the documentation may vary depending on the research project. Where possible, standardised and machine-readable metadata schemas should be used, and care must be taken to ensure that the metadata is unambiguously assigned to the object.

Documentation may take the form of structured metadata or an electronic laboratory notebook, but may also be provided as a ReadMe file or a project-internal wiki. Based on the research data lifecycle, the following aspects should be taken into account:

• Information on the research project (project title, project objectives and hypotheses)
• Information on data collection (e.g. purpose of collection, collection method, time period and location of collection, technology/instruments used, data sources)
• Measures for data preparation and analysis (e.g. cleaning, anonymisation/pseudonymisation)
• Structure of the dataset (naming conventions, data versions and changes contained therein)
• Conditions of access

Metadata is structured information about data, often referred to as ‘data about data’. It serves to describe resources and fulfils several functions: facilitating the management of objects, ensuring they can be found, providing information about the objects described and ensuring the re-use of the data.

Metadata can be divided into the following categories:

• bibliographic metadata: This includes details such as the title of the data publication, authors, institution, project, year of creation and keywords. It enables data to be cited and makes it easier to locate.
• administrative metadata: This provides information on file types, locations, access rights and licences, and assists with the management and long-term preservation of the data.
• Content-descriptive metadata: This provides information on the content and context of the data and may be structured differently depending on the discipline. For research data, this type of metadata is particularly important for discoverability and traceability. Consequently, various subject-specific metadata standards exist. The Metadata Standards Catalogue (Research Data Alliance) and the ‘Disciplinary Metadata’ overview (Digital Curation Centre) list metadata schemas for data from different academic disciplines.

In the interests of consistency and interoperability, it is recommended to use standardised metadata schemas in which a specific set of descriptive attributes is defined and the individual attributes are clearly specified.

The DataCite metadata schema is a widely used generic standard for describing datasets. It contains mandatory fields (e.g. author and title), recommended fields (e.g. subject area and description) and optional fields (e.g. funding and access rights). A DataCite Best Practice Guide (PDF, GitHub accompanying document) is available as a guide, offering general advice and recommendations on the individual fields. In addition, the DataCite Metadata Generator (GitHub) has been developed as an online tool that provides an input form and links to the explanations in the Best Practice Guide. The result is a DataCite XML file containing the entered data, which can be used to deposit the metadata in a repository. The tool is available on the LMU website, but can also be downloaded from GitHub and opened in a web browser.

The research datasets selected for publication can be published as a data supplement to a text-based publication or independently in a data repository .

Data repositories are storage locations for the long-term archiving and, as a rule, publication of digital research data. They can be divided into three categories based on their subject specialisation and their operator. Generic repositories ( e.g. Zenodo), subject-specific repositories and institutional repositories are available , in which datasets, including metadata, can be stored for a certain retention period (usually 10 years). A certified and trustworthy repository is the ideal storage location.

Various search systems assist in finding suitable repositories for the storage and publication of data, and also serve as sources for re-usable datasets:

• Registry of Research Data Repositories (re3data)
  re3data is a project funded by the German Research Foundation (DFG) that lists subject-specific repositories for many disciplines worldwide. It uses its own metadata schema to describe the repositories, which allows for a wide range of filtering options, e.g. by subject, content type or country. The use of re3data is recommended by various funding bodies, such as the EU.
• Directory of Open Access Repositories (OpenDOAR)
  OpenDOAR is a quality-assured global directory listing academic open-access repositories, including those for research data.
• RIsource (DFG platform)
  With RIsource, the German Research Foundation (DFG) provides an information portal for German research infrastructures to support the planning and implementation of research projects. This includes just under 100 research data repositories.

Before the data is published, all necessary consents regarding the use of third-party materials and information requiring protection must be obtained and documented. Confidential and sensitive data that cannot be made freely available, or that may only be made accessible after a embargo period, must be subject to appropriate access restrictions. In such cases, for example, only the metadata may be made visible to provide information about the data.

Reliable and long-term discoverability and citability of research data are ensured by a persistent identifier (such as the DOI – Digital Object Identifier). Persistent identifiers refer to a digital object itself, meaning they remain valid regardless of changes to the URL. Many repositories offer automatic assignment as a service. The Open Researcher and Contributor ID (ORCID) service provides a unique identifier for academic authors, whilst the Research Organisation Registry (ROR) does the same for institutions. OTH Regensburg is already registered with ROR: https://ror.org/04b9vrm74

Copyright protection for research data often arises only through processes of analysis, classification or organisation (e.g. analysis scripts, descriptive text, models, etc.). Primary data or raw data, such as pure measurement data, do not generally meet the required threshold of originality. By granting licences, the permitted re-use by third parties beyond the scope of copyright can be clearly defined. CC licences constitute usage agreements and are now widely used in the open access sector. The six different licence types cover the most common usage scenarios, enabling rights holders to specify which forms of re-use they permit.

By the end of the research project at the latest, a decision should be made as to which of the data collected should only be retained in the short term (for example, only for the duration of the research project) and for which it makes sense to ensure long-term archiving, so that the data can still be retrieved after the research project has been completed. The DFG Code of Practice for Good Scientific Practice recommends that research data be retained ‘for an appropriate period’, generally ten years. In this context, it is also necessary to determine which data archives or repositories are suitable for the specified type of archiving.

Data relevant to the reproducibility of the research results should be archived on a storage medium that ensures the required retention period, audit trail integrity, identifiability and retrievability. It is also particularly important that file names provide information about the content and are standardised and consistent, and that suitable file formats are used.

Unless open and standardised file formats have already been used, the data should be converted into appropriate formats.

Formats particularly suitable for archiving:

• ‘Office’ documents: PDF/A A (Portable Document Format)
• Text (unstructured): TXT
• Tables (structured text data): csv (Comma Separated Values), tsv (Tab Separated Values)
• Relational databases: SIARD (Software Independent Archival of Relational Databases)
• Graphics: TIFF (Tagged Image File Format)
• Audio files: WAV
• Video files: MPEG-4
• Structured data: XML, JSON