Research Data Management at the University of Applied Sciences Potsdam
Research data management is an elementary component of any research project dealing with research data. We have compiled background information, examples of data management plans and tools for creating them, as well as a collection of links to external training and qualification opportunities.
What is Research Data?
In short, virtually all data generated in the research process is research data.
The DFG provides a comprehensive definition: "Research data include measurement data, laboratory values, audiovisual information, texts, survey data, objects from collections or samples that are created, developed or evaluated in the course of scientific work. Methodological test procedures such as questionnaires, software and simulations can represent central results of scientific research and should therefore also be included under the term research data." Source: DFG (2015): Guidelines on the Handling of Research Data.
There are numerous other definitions, including from the Alliance of Science Organisations.
Research Data Management in Practice
The aim is to provide data for these data in accordance with FAIR-principles, to find an appropriate processing for this data according to FAIR principles. FAIR stands for Findable – Accessible – Interoperable - Reuseable. It is about long-term and person-independent findability, accessibility, re-usability and verifiability of the data, usability and verifiability of the data as well as the consideration of legal aspects (e. g. data protection). aspects (e. g. data protection). The details and background to the FAIR principles can be found at the Technical Information Library.
Research Data Guideline
The University of Applied Sciences Potsdam supports the cultural change towards more openness and transparency in science, research and teaching, among other things, through its guideline on handling research data. This was published in the official announcements of the university on 4 November 2011. Work is currently underway on an accompanying handout with recommendations for the specific subject culture of the University of Applied Sciences Potsdam.
Statutes for ensuring good scientific practice and dealing with scientific misconduct at the University of Applied Sciences Potsdam
The Statutes was published on 28.07.2022 and defines the principles of the University of Applied Sciences Potsdam (UAS) to ensure good scientific practice and to deal with accusations of scientific misconduct.
The UAS is aware of its task to convey the principles of good scientific practice to students and young academics in particular and to familiarise them with the techniques and methods of scientific work.
Information on the documentation and archiving of research data and results can be found in the statutes under point 9.
The foundations of these statutes are the "Code: Guidelines for Ensuring Good Scientific Practice"of the German Research Foundation (2019), the "Recommendations on Scientific Integrity" of the German Council of Science and Humanities (2015) and the recommendations of the Hochschulrektorenkonferenz on "Good Scientific Practice at German Universities"(2013) as well as Section 4 (5) of the Brandenburg University Act (BbgHG, 2014).
Research Data Strategy for the State of Brandenburg
On 18 July 2022, a joint research data strategy for the state of Brandenburg was adopted by the Brandenburg Ministry of Science, Research and Culture and the Brandenburg State Conference of University Presidents. The strategy supports the goal of establishing institutionalised and sustainable research data management at Brandenburg's universities.
Background to Research Data Management
Expectations of the funding agencies
Increasingly, funding organisations are calling for research data to be addressed, e.g. the DFG according to its guidelines for proposal submission (see DFG form 54.01). In addition, the DFG offers further information on the topic on its website and publishes a checklist.
For EU project applications, data management plans usually have to be submitted as deliverables within the first six months of the project. However, you should already deal with your research data in detail in the application (cf. Horizon Europe Programme Guide, p. 41ff).
Consideration in financial and work planning
Funds can often be calculated for the additional costs incurred for data management as well as for subsequent uses of the data when applying for research projects. This is usually indicated in the announcements and funding guidelines. Costs may be incurred for infrastructure such as servers or personnel, for example.
At the same time, the handling of research data should also be sufficiently taken into account in work planning, e. g. by scheduling (sub-)work packages for project-specific data management.
For further information, please consult the general pages www.forschungsdaten.org, www.forschungsdaten.info as well as the subject-specific portal www.forschungsdaten-bildung.de for educational sciences.
Further assistance in handling research data is also provided by the"PARTHENOS Guide to the "FAIRification" of Data Management and Enabling the Re-use of Data" .
Data Management Plan
A data management plan (DMP) is a suitable means of determining at the beginning of a research process how data are to be documented, metadata and standards described, and criteria established according to which data are later to be secured and made available. There are no one-size-fits-allstandard guidelines on how a data management data management plan structured should be. When submitting applications, it is advisable to adhere to the regulations of the respective funding organisation.
A data management plan can be created free of charge using web-based tools and geared to the requirements of the funding organisations. The University of Applied Sciences Potsdam offers the Research Data Management Organiser (RDMO) for this purpose, which was developed as part of a DFG project (access with the Campus.Account). On the RDMO website you will find an introductory video and further assistance.
A data management plan should include, in addition to the project key data (name, funding organisation, funding programme, project management with contact, brief project description) approximately the following topics address:
- Characteristics of the existing data and new data to be generated,
- Information on versioning, structuring and quality assurance,
- information on possible subsequent use by third parties.
Documentation and metadata
- Explanations of the procedure so that others understand how data management is done
- Consideration of relevant metadata
Findability, access and use
- Which data types are likely to be published when in which data archives/repositories?
- How are they to be accessed, what licence will be issued? Will DOIs be assigned?
- How will access rights for personal/sensitive data be regulated?
Storage and preservation
- During and after the project duration: Which data types are selected for storage and archiving according to which criteria?
- Information on total size, data transfer, backups?
- What happens after the retention period of 10 years?
Legal and ethical aspects
- Reference to further legal and ethical aspects
- Information on the collection and subsequent use of sensitive data (e.g. research on/with children or older people)
Responsibilities and resources
- Statements on responsibilities,
- Information on necessary resources and (expected) costs
Overview of Training & Qualification Offers
Students created overviews of training materials and qualification opportunities as part of the project course "WBD01b Research Data Management" with Prof. Dr. Heike Neuroth in the summer semester 2020.
- "Crash Course in Research Data Management" by the University of Leipzig with a good all-round view of research data management, 2020.
- "Forschungsdaten leben länger" by RWTH Aachen University describes a typical project progression along the life cycle of research data in an instructional video, 2018.
- "Forschungsdaten managen - Anforderungen, Methoden, Hilfsmittel" by TIB Hannover provides an overview of various aspects of research data management including practical tips and exercises via workshop slides and working materials, 2020.
- "Warum Forschungsdatenmanagement?" by KIM University of Konstanz provides a fundamental basis for research data management, 2020. DOI: 10.5281/zenodo.3762983.
- "Was sind Forschungsdaten?" by the Research Data Management Initiative of HU Berlin provides the most important key data on research data management in the form of an online tutorial, 2016. DOI: 10.18450/dataman/90.
- "Forschungsdatenmanagement auf einen Blick - eine Online-Einführung" by the FOKUS project is a web-based training course for getting started with research data management, including exercises and various materials, 2019.
- "Forschungsdatenmanagement" by Swissuniversities offers basic information on the life cycle, subsequent use and publication of research data in four modules on an e-learning platform, 2017.
- "Lernmaterialien - Datenmanagementpläne & RDMO" by Friedrich-Alexander-Universität Erlangen-Nürnberg introduces data management plans and presents the data management programme RDMO, 2019.
- "Lernmaterialien - Forschungssoftware & Datenformate in den Geistes- und Sozialwissenschaften" by Friedrich-Alexander-Universität Erlangen-Nürnberg gives a selection of software and digital solutions that help with acquisition, analysis, storage or publication incl. practical exercises, 2019.
- "Lernmaterialien - Forschungsdaten suchen & nachnutzen" by Friedrich-Alexander-Universität Erlangen-Nürnberg teaches core skills required for searching for and reusing research data incl. exercises for self-assessment, 2019.
- "Glossar Forschungsdatenmanagement" by Friedrich-Alexander-Universität Erlangen-Nürnberg provides definitions of key terms used in research data management.
- "Forschungsdatenmanagement in der empirischen Bildungsforschung" by the Association for Research Data Education addresses selected topics of research data management for educational researchers (e. g. metadata, copyright, data protection) in annual workshops, 2019.
- "Webinar: Daten teilen - Wo fange ich an?" of the Association for Research Data in Education deals with open access and the handling of research data in educational research, 2017.
"Erhaltung von Forschungsdaten: Komplementäre Kompetenzen von Forschungsgruppen und Bibliotheken" Conference presentation by ETH Zurich at the 103rd German Librarians' Day on the topic of "Preservation of Research Data", 2014.
"Forschungsdaten dokumentieren und publizieren" by the University of Hannover explains the process steps in research data management and presents tools and standards in each case, 2019.
"Train-the-Trainer Konzept zum Thema Forschungsdatenmanagement" by the BMBF project FDMentor offers teaching content on research data management as well as numerous working materials, lecture slides and worksheets, 2019.
"Webinar: Datenaufbereitung und Anonymisierung" by the Association for Research Data in Education teaches the basics of correct preparation and anonymisation of quantitative research data, 2019.
"Rechtliche Rahmenbedingungen des FDM - Grundlagen und Praxisbeispiele" by TU Dresden provides an overview of legal areas and covers legal frameworks for the collection, processing, archiving and publication of research data, 2020.
"Der Weg zum PID: Praktische Einführung in DOI und ePIC" with information on Persistent Identifiers (PID), their basics, development and comparison of ePIC PID and DOI, 2017.
"Persistente Identifikatoren (PID) - Services für Forschungsdaten im Bereich Lebenswissenschaften" of the ZB MED Publication Portal Life Sciences on the data life cycle, the FAIR Data Principles as well as the goals and benefits of Persistent Identifiers (PID), 2019.
The internal working group on research data management (AG FDM@FHP) promotes the structural anchoring of research data management at University of Applied Sciences Potsdam and at the same time expands the advisory and support services offered by the research service with reference to the topic. Thus, for example, special topics from the above-mentioned enquiries are also consulted collegially from time to time. The group therefore includes not only experts who are familiar with the application process, but also those from IT, data protection, the library and academics with specialist knowledge.
Currently, we can help you in the following areas:
- Support in the formulation of statements on the handling of research data in research proposals, for which we have text modules and formulation suggestions available
- Advice on research data management in the context of data protection or ethical issues
- Provision of a tool for the creation of data management plans: Research Data Management Organiser (RDMO)
- Advice on the preparation of data management plans
- Regular provision of information on innovations in research data management, e.g. new requirements from funding bodies, new publications etc.
Enquiries can be made via the e-mail address firstname.lastname@example.org.