FAIR Social Science Data Enhancement

Štebe, Janez

doi:10.5281/zenodo.17279388

FAIR Social Science Data Enhancement

Štebe, Janez

Publisher: Zenodo
DOI: 10.5281/zenodo.17279388
Repository: zenodo.org
Language: en

Abstract

Research data infrastructures aiming to improve FAIR principles often focus on enhancing metadata. However, most FAIR principles apply to both data and metadata. Interoperability aspects, in particular, can be emphasised, such as standardised knowledge representation, the use of FAIR-compliant vocabularies, and explicit references to other data (Bahim et al. 2020). This presentation discusses areas of data planning and processing that can make data more FAIR. Enhancing FAIRness increases the overall reusability and analytic potential of data, including the possibilities of merging and combining different data sources. It is important to train researchers in established best practices that increase reusability (e.g. http://www.dlib.si/?URN=URN:NBN:SI). For example, comparability of data and conceptual clarity can be improved by using standard social science variables. Standard social science classifications can be derived using shared computer code (see https://www.gesis.org/en/missy/materials/EU-SILC/tools/datahandling). Data infrastructure services are often asked for advice regarding Data Management Planning, which requires maximising FAIR aspects. Historical research projects that use printed archival resources from the past (Mezzoli, 2022, https://doi.org/10.17898/ADP_HDS47_V1) have unified the coding framework from different periods and locations. The variable operationalisations from key social science studies have known reliability and validity characteristics, which the CESSDA European Question Bank, with full survey question text in DDI format, will provide. The use of FAIR-compliant vocabularies, such as the European Language Social Science Thesaurus, to document the conceptual content of data at a granular level can add an additional layer of FAIRness (see https://www.cessda.eu/Tools). In conclusion, a higher return on investment can be expected by publishing FAIR-compliant data (Inau et al. 2023, https://www.jmir.org/2023/1/e45013). Repositories can establish evaluation criteria for data that respect different aspects of FAIR data (e.g. https://pubmet2024.unizd.hr/janez-stebe-abstract/).

Full text

FAIR social science data enhancement Janez Štebe, UL/ADP 11TH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE METHODOLOGY, Naples, Thursday, September 25th, 11:00-13:00 Session 12C: 8-Embracing Open Science: Methodological and Epistemological Challenges in Research Infrastructures for the Social Sciences II Introduction Increasing volume of data accessible Intent: Facilitate data Re-use via secondary analysis FAIR principles is all about increasing data reusability One of the mission of data repositories: to support data producers in FAIR data enhancement By providing resources and guidance in planning and processing data for increasing the reuse potential There is room for improvement in making data specifically more FAIR Examples of resources and their utilization for data FAIRification Research Data Alliance FAIR Data Maturity Model Working Group. ‘FAIR Data Maturity Model: Specification and Guidelines’, 2020. https://doi.org/10.15497/RDA00050. FAIR principles references for Data (without ‚meta‘-) FAIR ID Indicator F1 RDA-F101D Data is identified by a persistent identifier F1 RDA-F102D Data is identified by a globally unique identifier A1 RDA-A102D Data can be accessed manually (i.e. with human intervention) A1 RDA-A103D Data identifier resolves to a digital object A1 RDA-A104D Data is accessible through standardised protocol A1 RDA-A105D Data can be accessed automatically (i.e. by a computer program) A1.1 RDA-A1.101D Data is accessible through a free access protocol A1.2 RDA-A1.201D Data is accessible through an access protocol that supports authentication and authorisation I1 RDA-I1-01D Data uses knowledge representation expressed in standardised format I1 RDA-I1-02D Data uses machine-understandable knowledge representation I2 RDA-I2-01D Data uses FAIR-compliant vocabularies I3 RDA-I3-01D Data includes references to other data I3 RDA-I3-02D Data includes qualified references to other data R1.3 RDA-R1.301D Data complies with a community standard R1.3 RDA-R1.302D Data is expressed in compliance with a machine-understandable community standard Example: Standard occupation coding (ISCO by ILO) Based on it, the international ESeC and Occupational prestige score can be derived Shared social science concepts and operationalisations Classifications and historical harmonisation tables, computer code syntax, reference data and metadata sets etc. Utilisation of standard demographics variables in new data production planing and management Some resources: •Harry Ganzeboom’s Tools for deriving occupational status measures from ISCO-08 with interpretative notes to ISCO-08 http://www.harryganzeboom.nl/isco08/index.htm •Hughes BT, Srivastava S, Leszko M, Condon DM (2024): Occupational Prestige: The Status Component of Socioeconomic Status. Collabra: Psychology 16 January 2024; 10 (1): 92882. https://doi.org/10.1525/collabra.92882 •GML, German Microdata Lab (2025): MISSY - Metadata for Official Statistics: Data Handling & Analysis: EU-SILC. GESIS. Accessed on May 2025. https://www.gesis.org/en/missy/materials/EUSILC/tools/datahandling •BIBB-FDZ (2025): Metadata Catalogue. The Federal Institute for Vocational Education and Training (BIBB). Accessed May 2025. https://metadaten.bibb.de/en/ •Van Leeuwen, MHD, 2016, "Files from HISCO database", https://hdl.handle.net/10622/JA9B8O, IISH Data Collection, V1. Examples: GeoNames identifiers resolving the language and political changes over time (e.g. Mezzoli, 2022) Measured characteristics of the named entities from parallel sources as contextual data ‚Macro‘ data keept as a resource ready for integration with ‚micro‘ data (e.g. ESS 2019 and CSES 2023) Cross disciplinary data integration chalenging (e.g. Harrison, 2024; Gregory et. al. 2023) Consistent use of standard identifiers for named entities (locations, political parties, peoples...) References: •Mezzoli, E (2022): Historical data of the labour force, 1945-1947: Zone A-AMG-Venezia Giulia (Allied Military Government) [Data file]. Ljubljana: UL/Slovenian Social Science Data Archives. https://doi.org/10.17898/ADP_HDS47_V1. •CSES Comparative Study of Electoral Systems (2023): Data Bridging. Accessed on May 2025. https://cses.org/datadownload/data-bridging/ •ESS European Social Survey (2019): Contextual information for Round 8 now available. Accessed on May 2025. https://www.europeansocialsurvey.org/news/article/contextualinformation-round-8-now-available •Harrison, E (2024). Climate and Air Quality Data in Attitudinal Research: A Feasibility Use-Case. Zenodo. https://doi.org/10.5281/zenodo.10581886 •Gregory A, Wackerow J, Orten H (2023) Reuse and Reproducibility: Describing Cross-Domain Research Data in the Science Project Climate Neutral and Smart Cities. ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e115047 Concerns about the proliferation of constructs and measures ("jingle/jungle fallacies") (NASEM, 2022). Difficulties in variables standardisation and data harmonisation from multiple single studies Challenges in reusing existing (psychological) measures in different research design settings (Ryser, 2023). Ontology resources (e.g. ELSST Thesaurus) Repositories of existing measures like European Question Bank (EQB) and REPOPSI Existing concepts and operationalisations of key social science variables for Findability and Reuse References and resources: •NASEM, National Academies of Sciences, Engineering, and Medicine (2022): Ontologies in the Behavioral Sciences: Accelerating Research and the Spread of Knowledge. Washington, DC: The National Academies Press. https://doi.org/10.17226/26464 . •Ryser, V.-A. (2023). Measuring Psychological constructs. FORS Guide No. 22, Version 1.0. Lausanne: Swiss Centre of Expertise in the Social Sciences FORS. https://doi.org/10.24449/FG-2023-00022 •EQB and ELSST on CESSDA Digital Tools (2025). https://www.cessda.eu/Tools •FAIRsharing.org (2024b): REPOPSI; Repository of Psychological Instruments in Serbian, https://doi.org/10.25504/FAIRsharing.d8fec9 •FAIRification improves data quality and utility •Data transparency fosters cumulative knowledge production, and other •Motivating and training data producers for engaging in deeper anotation and use of resources •Enabling research community and infrastructure providers efforts for sustaining such resources Conclusions University of Ljubljana Faculty of Social Sciences Social Science Data Archive Kardeljeva ploščad 5 1000 Ljubljana Slovenia www.adp.fdv.uni-lj.si [email protected]i-lj.si Arhiv.Druzboslovnih.Podatkov @ArhivPodatkov Thank you!