Preparing, curating and describing qualitative research data – the RDC Qualiservice approach

gefördert von: Preparing, curating and describing qualitative research data – the RDC Qualiservice approach Kati Mozygemba, Susanna Prepeliczay, Noemi Betancort Cabrera International Secure Data Facility Professionals Network (ISDFPN) 10th September 2025, online DOI: 10.5281/zenodo.17533208 Overview The RDC Qualiservice: social science disciplines and types of research data Focus workflow of data preparation, curation and archiving: Part 1 – Cooperative preparation of research data (legal base IC, contextualisation, anonymisation) Part 2 – Data submission process (agreement) Part 3 – Curation process in the Safe Center Part 4 – Metadata (model, cataloguing, output) Part 5 – Data archiving The Research Data Center (RDC) •RDC for all kinds of qualitative research materials (i.e., research data & context materials) in the social & behavioral sciences •This includes e.g. interview transcripts, observation protocols, field notes, images/photos, audiovisual data etc. •Located at the University of Bremen; Germany, head: Prof. Dr. Betina Hollstein •Accredited by the German Data Forum (RatSWD); member of KonsortSWD-NFDI4Society within the National Research Data Infrastructure (NFDI) https://www.qualiservice.org/en/ Focus on “sensitive“ (e.g., personal) data and the related ethical, legal, professional, and technical requirements Workflow for data preparation and data curation at Central characteristics – Research Friendliness: •Close cooperation between researchers and RDC during the whole process •RDC staff is experienced in qualitative research •Offer flexibility for different research data types or project specific requirements •Further development of workflow(s) in cooperation with researchers from different disciplines (use studies) Part 1 – „Cooperative Data Preparation“ Workflow for data preparation and data curation at Workflow Part 1 – „Cooperative Data Preparation“ •Takes place during the research process, outside the RDC •Qualiservice supports researchers throughout the entire research project in the steps that are important for data sharing, from the application stage onwards, •The aim is to integrate data preparation into the research project •Create synergies (e.g. with regard to documentation) •Prevent a backlog of tasks at the end of the project •Find the best solution for the project: taking into account content-related aspects, research ethics and data protection aspects, while maximizing reuse value •Includes consultation and support of researchers with a focus on project-specific data sharing requirements •Collaboration in designing the data set(s) •providing handouts and tools •Consult the completing of the data transfer agreement (prerequisite for transfer to the RDC) Workflow Part 1 – „Cooperative Data Preparation“ Alternatives to written Informed Consent http://dx.doi.org/10.26092/elib/1070 IC-Template for primary research IC-Template for data sharing •Qualiservice supports researchers in preparing informed consent, •provides legally verified templates for primary research, archiving and data sharing •as well as information on practices of informed consent and alternatives to it. Workflow Part 1 – „Cooperative Data Preparation“ Supporting Informed Consent Workflow Part 1 – „Cooperative Data Preparation“ Part 2 – Data submission based on agreement with data providers Workflow for data preparation and data curation at Comprises conditions for data usage rights and scientific re-use Individual reuse conditions for each dataset defined by primary researchers (data providers): e.g. different purposes (research/secondary analyses vs. academic teaching) Scientific Use File (SUF) and/or Campus Use File (CUF) with different levels of anonymization •Define scope of authorized citations (e.g. length; paraphrasing) •E.g., embargo periods (until project publications finalised; time span protecting interviewees) •Re-use with/out approval by data providers or information to data providers •Onsite-Use for sensible documents (transcripts, videos etc.) at Bremen University •Time-limited periods of scientific re-use related to specific purpose (research or teaching) •Usage agreement annexed to data submission agreement   Secure transfer of (anonymized/pseudonymized) research data via protected upload space Part 3 – Curation process in the Safe Center Workflow for data preparation and data curation at Part 3 – Review and Curation process in Safe Center Workflow for data preparation and data curation at •Transfer of research data and materials to internal drive in the Safe Center •Completeness checks & sorting of files into curation directories; technical functionality checks •Review of context materials: study report, interview guidelines, vignettes, other materials •Check of information about files and sample properties provided in project overview table Part 3 – Curation process in the Safe Center Workflow for data preparation and data curation at Part 3 – Curation process in the Safe Center Workflow for data preparation and data curation at Control of anonymization by domain experts •Anonymization concept delivered by data providers methodic basis, rules •Use of QualiAnon anonymization tool reading of transcripts word by word •Completion of replacements where necessary •Documentation of all changes in table overview (place of origin/file/row, identified problem or question(s), applied solution, name(s) of curator(s) •AI Chatbot tool with instructions for curators : currently in implementation Feedback process with data providers •Different degrees of anonymization selection of files for SUF and CUF •Materials that cannot be anonymized sufficiently to warrant protection of participants without too much loss of scientific relevant information Onsite use Part 3 – Curation process in Safe center Workflow for data preparation and data curation at Teil 3 – Curation process in Safe center: compilation of micro-metadata Workflow for data preparation and data curation at Part 4 – Metadata (model, cataloguing, output) Workflow for data preparation and data curation at The Qualiservice Metadata Model Requirements by designing the Qualiservice metadata schema: •(Inter)national standards und initiatives: DDI Specifications, DataCite, DCAT, REFI-QDA and QuDEx •Recording of metadata: data creators know their data better (term suggestions and free text entries) + data curators record it homogeneously in the system •Product: a machine-readable and FAIR version of the study and data description •Project networks and collaborative research centres can be recorded as well as individual qualitative projects and various data types, waves of data collection and methods Part 4 – Metadata Based on DDI Lifecycle www.ddialliance.org The Qualiservice Metadata Model Part 4 – Metadata Metadata in Qualiservice (cataloguing system) PANGAEA Editorial System: „DDI-Profile“ Part 4 – Metadata Metadata in Qualiservice (cataloguing system) Controlled Vocabularies Part 4 – Metadata Dataset metadata Part 4 – Metadata Dataset metadata Part 4 – Metadata FAIR Metadata: •PIDs for the dataset and for referencing other resources: oStudy report: Contextualises the dataset and it is referenced in the metadata. The study report can be searched in full text on the search portal oQuantitative data: Qualiservice cooperates with GESIS to jointly present mixed-methods studies referencing the respective project part via DOI in the metadata so that the study context remains visible. Dataset metadata Part 4 – Metadata Dataset metadata Part 4 – Metadata FAIR Metadata: •Use of controlled vocabularies for interoperability •Provenance, license information, and •Micrometadata providing more granular information at the data object level for reusability Dataset metadata Part 4 – Metadata •Non-sensitive information about the case itself (e.g. socio-demographic data), •Technical information on the data object and the event (duration of the interview, file format, location of the interview etc.), •Allow an overview of the entire data set at this level of detail, •Important for assessing the data fit or selecting suitable interviews (as a data set can contain a large number of interviews (over 100 or more) Micrometadata at Qualiservice Part 4 – Metadata Micrometadata at Qualiservice Part 4 – Metadata Micrometadata at Qualiservice Part 4 – Metadata