Publishing Fine-Grained Standardized Metadata: Lessons Learned from Three Research Data Centers

FAIRness of research data, meaning that data are managed according to the principles of being Findable, Accessible, Interoperable, and Reusable, has become a ubiquitous requirement in research data policies as well as in general guidelines for research data management. Meeting this requirement largely depends on the availability of rich and standardized DDI-metadata—based on the Data Documentation Initiative family of metadata standards—which is of particular importance for tabular data resulting from surveys and other structured observations, is often lacking (Wenzig and Han, 2024). The lack of such metadata can largely be attributed to the absence of lightweight approaches that integrate its creation into existing data preparation workflows. Against this background, a project funded by KonsortSWD-NFDI4Society brought together three research data centers (SOEP, LIfBi, and FDZ-DZHW) to investigate the requirements for converting existing metadata into a standardized DDI format and publishing it using a common protocol (OAI-PMH). The results demonstrate that generating fine-grained DDI-metadata is easy to implement, even with limited resources. Contrary to expectations, OAI-PMH did not prove to be a straightforward approach for publishing metadata. Based on these findings, the authors evaluate FAIR signposting as an alternative approach, which shows considerable potential. The results of this study may therefore serve as a best-practice example for institutions, especially from survey-based research domains seeking to implement fine-grained standardized DDI metadata, as well as a starting point for further research on approaches to publishing fine-grained standardized metadata.

Institutions

Publication Details

Journal
Data Science Journal
Published
2026-01-01
DOI
https://doi.org/10.5334/dsj-2026-013
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Publishing Fine-Grained Standardized Metadata: Lessons Learned from Three Research Data Centers

Data Science Journal
Research Data Management Practices
article

Publishing Fine-Grained Standardized Metadata: Lessons Learned from Three Research Data Centers

article en

Abstract

FAIRness of research data, meaning that data are managed according to the principles of being Findable, Accessible, Interoperable, and Reusable, has become a ubiquitous requirement in research data policies as well as in general guidelines for research data management. Meeting this requirement largely depends on the availability of rich and standardized DDI-metadata—based on the Data Documentation Initiative family of metadata standards—which is of particular importance for tabular data resulting from surveys and other structured observations, is often lacking (Wenzig and Han, 2024). The lack of such metadata can largely be attributed to the absence of lightweight approaches that integrate its creation into existing data preparation workflows. Against this background, a project funded by KonsortSWD-NFDI4Society brought together three research data centers (SOEP, LIfBi, and FDZ-DZHW) to investigate the requirements for converting existing metadata into a standardized DDI format and publishing it using a common protocol (OAI-PMH). The results demonstrate that generating fine-grained DDI-metadata is easy to implement, even with limited resources. Contrary to expectations, OAI-PMH did not prove to be a straightforward approach for publishing metadata. Based on these findings, the authors evaluate FAIR signposting as an alternative approach, which shows considerable potential. The results of this study may therefore serve as a best-practice example for institutions, especially from survey-based research domains seeking to implement fine-grained standardized DDI metadata, as well as a starting point for further research on approaches to publishing fine-grained standardized metadata.

Data Science JournalVol. 25
German Institute for Economic Research (DE), Leibniz Institute for Educational Trajectories (DE)
Partnerships for the goals
Openalex Percentile: Top 10%
Research Data Management Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Publishing Fine-Grained Standardized Metadata: Lessons Learned from Three Research Data Centers · Data Science Journal (2026) | TGRS Research Map | TGRS