Data Literacy with Focus on Research Data Management: Upskilling your data competencies for your thesis (SUB/GGG) in person

Dr. Barbara Löhde studied Political Science and Geography at the University of Mainz and earned her PhD in Anthropology at the University of Göttingen, where she also taught courses in anthropology. Through various research projects and extended fieldwork stays, particularly in West African countries, she has gained extensive experience in international and interdisciplinary collaborative research.

Dr. Merle Schatz studied Sinology, Mongolian Studies, and Japanese Studies, and holds a PhD in Mongolian Studies. She has taught at the universities of Leipzig, Cologne, and Beijing, and is a lecturer at Göttingen University, and has authored and edited numerous academic publications. She currently works as a subject specialist at the SUB Göttingen, where she is responsible for Sociology, Political Science, and Educational and Higher Education Studies, and works on projects related to Open Science and research data management.

As team members of the State Initiative Research Data Management Lower Saxony, Dr. Barbara Löhde and Dr. Merle Schatz are jointly committed to building the data literacy skills of students and researchers, and are happy to share their combined expertise in teaching in higher education and in the management of qualitative, ethnographic, and text-based data.

Some sessions will be supported by specialized experts (cf. Content Details).

This course introduces you to the topic of Data literacy (DL) and Research Data Management (RDM). It teaches essential skills in handling data in accordance with the FAIR principles and focuses on developing data literacy across five key competence areas: data collection, data management, data evaluation, data application, and the conceptual framework. Special attention is given to data management which is practiced in all phases of the research data life cycle using students' own PhD projects as example.

• Research Data Management, Data Management Plan (Repositories, GWDG Services)
• Open Science: FAIR and CARE principles
• Data storage and documentation: storage devices, file structure (data organization in spread sheets, documentation and storage)
• Data Quality: KODAQS Toolbox and Academy
• Repositories: metadata, archiving, licences, GRO.Data, TextGrid
• Data protection: legal and ethical aspects, anonymisation / pseudonymisation
• RDM tools: data cleaning, GRO.Plan

Session 1: Introduction to Research Data Management
02.06.2026, 10:00-13:15
Human errors or hardware failure can easily result in the loss of valuable research data if it has never been properly backed up. Datasets may become uninterpretable due to poorly named files or disorganized folders. A journal may reject a manuscript if the underlying data were collected from research participants without obtaining their informed consent.
This workshop offers PhD researchers an introduction to the concepts and practical aspects of Research Data Management (RDM). Participants will learn why funders and institutions increasingly require good data management practices – and how adopting these practices can strengthen the quality, transparency, and reproducibility of their own research projects. The session introduces the role of Data Management Plans (DMPs) in the research process and familiarizes participants with key frameworks for responsible data practices, including the FAIR and CARE principles. Through practical examples and interactive discussion, the workshop addresses strategies for secure data storage, effective file organization, and sustainable documentation. It also introduces key considerations for data sharing and reuse.
Designed as an accessible entry point, the workshop equips doctoral researchers with the knowledge and practical skills needed to manage research data responsibly and effectively throughout the research data lifecycle helping them to avoid a few preventable data disasters along the way.

Session 2: Data Management Plans, Data Repositories and GWDG Services for Research Data Management
03.06.2026, 10:00-13:15
After participating in “Session 1: Introduction to Research Data Management”, PhD students can join this session to explore Data Management Plans (DMPs) and data repositories in greater depth. In the first half of the session, participants will learn about the key components of a DMP – from data collection, documentation, and storage to preservation and sharing – ensuring well managed, secured, and transparent research. Additionally, data repositories for archiving and sharing datasets are introduced. Participants will explore different types of repositories, learn how to choose an appropriate platform, and consider licensing options and access levels.
Göttingen University offers dedicated data services for both DMP creation (GRO.Plan) and data archiving and publishing (GRO.Data), which are available to researchers at all higher education institutions across Lower Saxony. In the second half, participants will take part in a hands-on walkthrough of these services. Using GRO.Plan, they will learn how to record and organize key information needed for planning the management of their research data. Finally, they will be introduced to GRO.Data, a repository where they can secure data, edit with version control, share datasets, and publish data with metadata and persistent identifiers.

Session 3: Introduction to Data Quality for Social Sciences
05.06.2026, 10:00-13:15
Specialized expert contributing to this session: Dr. Jessica Daikeler, KODAQS coordinator and team leader , GESIS Leibniz Institute for the Social sciences
High-quality data are essential for drawing valid conclusions in social science research. This workshop developed from the BMFTR funded the Competence Center for Data Quality in the Social sciences (KODAQS) introduces central concepts of data quality with a particular focus on intrinsic data quality, addressing two core dimensions, first representation: about whom do we want to make statements, and about whom do we actually make inferences? In addition, the workshop examines the measurement dimension of data quality: what do we intend to measure, and what is actually captured by our instruments and data sources?
Using examples from substantial Social science research, participants will be introduced to selected tools and learning resources that support data quality assessment in empirical research. This includes a demonstration of two established survey-related tools from the KODAQS toolbox that illustrate how data quality can be assessed in practice. Selected learning opportunities from the KODAQS Academy will also be presented.
In the hands-on part, we will analyze in groups a publication , assess its data quality and make suggestions for improvements .

Session 4: Data organization in Spread Sheets
08.06.2026, 10:00-13:15
Organizing, cleaning and merging research data in tabular form is a very common activity, in particular as preparation for data analysis and visualization. Using spreadsheet software like MS Excel or OpenCalc can however become a stressful and error-prone task when data from different sources, in different formats or with ambiguous or incomplete values are involved. Handling date formats, mixed text and numbers, and preparing data for efficient analysis with other tools can also pose challenges.
This workshop provides you with solid knowledge on dealing with these problems by guiding you through exercises using a real scientific survey dataset. No prior knowledge is required, but you should have available a running instance of MS Excel or other spreadsheet software like LibreOffice Calc.

Session 5: OpenRefine – Cleaning and Transforming Messy Data
09.06.2026, 10:00-13:15
This hands-on session introduces OpenRefine, a powerful, free, and open-source tool for exploring, cleaning, and transforming messy data. Using a real dataset and working step by step, participants will learn how to examine their data, identify and correct inconsistencies, standardize values, and efficiently transform it into a format ready for analysis. The session emphasizes transparency and reproducibility, demonstrating how every step in the cleaning and transformation process can be reviewed, shared, and reused. By the end of the session, participants will feel confident using OpenRefine to turn raw data into a structured, reliable dataset suitable for further analysis across a wide range of research contexts.

Session 6: Managing Personal Data in Research: ethics, law and security considerations
11.06.2026, 13:00-17:15
Research projects frequently involve the collection, processing, and storage of personal data. Ensuring that such data are handled responsibly is essential not only for legal compliance but also for maintaining ethical research standards and protecting the rights of research participants. This workshop introduces researchers to key principles for managing personal data in research contexts.
Participants will begin by exploring what constitutes personal data and how such data appear in research settings, with particular attention to special categories of personal data that require enhanced protection. The session will cover the legal frameworks governing personal data in research and their implications for research practice. Key elements of the legal basis for collecting and processing personal data include safeguards such as informed consent, anonymization, and pseudonymization, as well as comprehensive data protection measures.
Building on this foundation, the session provides practical guidance on implementing these safeguards. This includes obtaining informed consent in fieldwork settings, using suitable templates, and documenting and storing consent forms appropriately. Participants will also learn about storage solutions, including suitable media and encryption options, as well as GDPR-compliant tools and open-source software that support the safe processing, transcription, and anonymization of sensitive research data. By the end of the workshop, participants will gain a clearer understanding of how to identify personal data in their projects, apply relevant legal and ethical requirements, and implement practical measures to ensure responsible and secure data management in research.

Session 7: Finding and Publishing Digital Edition and Corpora in TextGrid Repository
12.06.26, 10:00-13:15
The humanities are diverse, yet one common feature of many humanists and their projects is the use of texts as primary sources. In many cases, texts are digitized or projects aim to create digital versions of them. Texts exist in many known formats (Word, PDF, TXT, or XML), but one well-established standard is the Text Encoding Initiative (TEI), to which workshop participants will be introduced. The Göttingen Library hosts a repository dedicated to such materials: the TextGrid Repository. It contains large collections of texts from different projects, genres, languages, periods, number of documents, and subject matter.
In this workshop, we will demonstrate how the repository can be used both as a data source and as a platform for publishing research data. The online portal provides several ways to search for texts, for example, by project, genre, language, or author. Users also have direct access to tools that can be applied to individual texts or entire collections. In addition, the repository provides access to data via APIs and a Python library. Participants will have the opportunity to practice using some of these tools. The workshop will introduce the workflow for publishing digital editions or corpora in TEI and guide participants in publishing their own materials. The portal is open to projects of any language, genre, or period, and of any size – from a single document to several thousand. Publishing research data in this way is a valuable addition to research, increasing visibility and opening up further possibilities for the reuse of materials.

The workshop allows for flexible participation to accommodate students’ interests and discipline-specific needs in RDM applications and topics. Students may choose sessions based on their individual goals. The workshop includes one Basics of RDM session and six specialization sessions. Completion of the Basics of RDM session is mandatory before enrolling in any specialization session.

Please upload a document (word or pdf file) in the registration form including the following information:

• Title of your thesis:
• Institute / Department:
• What kind of data have you collected?
• How are you processing/preparing the data as part of your thesis / project?
• Are you using any specific tools or software to work with the data?
• Which topic interests you most in this course, and why?
• What are your expectations for this course?

Partial participation is possible. We enable a stacking of credits.

You will receive a certificate of participation for the sessions you attend. Participation in session 1 (Introduction to Research Data Management) is a mandatory prerequisite for participation in other sessions (specializations).

Recognition of credits is structured as follows:

• completed component: RDM basics (session 1) > 0 ECTS
• completed components: RDM basics + 1 specialization > 1 ECTS
• completed components: RDM basics + 2 specializations + handing in a Data Management Plan (DMP) > 1 ECTS
• completed components: RDM basics + 3 specializations + DMP > 2 ECTS
• completed components: RDM basics + 4 specializations + DMP > 2 ECTS
• completed components: RDM basics + 5 specializations + DMP > 3 ECTS
• completed components: RDM basics + 6 specializations + DMP > 3 ECTS
• completed components: RDM basics + 6 specializations + DMP + application > 4 ECTS

Consequently, a maximum of 4 ECTS is awarded for...
• completing all sessions (contribute to the discussions and exercises),
• handing in a Data Management Plan (DMP), and
• practical application of a Research Data Management (RDM) tool.


Recognition: PhD candidates in Social Sciences who are doing their doctorate according to the doctoral degree regulations of 2024 can have the workshop credited for the module “P.SOWI.160: Data Literacy with Focus on Research Data”. If you have any further questions about recognition, please contact your respective degree program coordinator (Studiengangskoordination).