Opening Your Research Data in FAIR Way

ESA RN12 Environment & Society, Summer School for Early Career Researchers “Methodological Perspectives for Social Research on Energy and Environmental Issues”, 22-23 September, 2025 Venue: Faculty of Social Sciences, Arts and Humanities, Kaunas University of Technology Opening Your Research Data in FAIR Way Prof. Vaidas Morkevičius LiDA & DAtA Centre, Kaunas University of Technology Centre for Data Analysis and Archiving 1. Why open data? 2. FAIR data principles for opening data 3. Data repositories for FAIR data Topics Centre for Data Analysis and Archiving ●Empirical science/research generates data ●Open data (science) – strategic priority ●Not only publications should be open, but also data (software and other research outputs) ●Goods created using public money (money from public funding agencies) should be public ●Data management plans (almost every public funding institution requires them) ●How the data generated implementing projects are managed, where they are put and curated? Would they be opened (if possible)? Why open data? Centre for Data Analysis and Archiving ●Basic principle of opening data ●As Open as Possible, as Closed as Necessary ●There are legitimate reasons for restricting openness of data ●Short term – until a research group (researcher) finalizes publications and other outputs ●Long term – due to confidentiality of the research subjects Why open data? Centre for Data Analysis and Archiving ●Opening data is simple (put somewhere on the web)? ●Quality of opening data? (Open Access vs. Open/FAIR data) ●In 2014 workshop in Leiden (the Netherlands) Jointly Designing a Data FAIRPORT ●The meeting concluded with a draft formulation of a set of foundational principles that were subsequently elaborated in greater detail – namely, that all research objects should be Findable, Accessible, Interoperable and Reusable (FAIR) both for machines and for people. These are now referred to as the FAIR Guiding Principles (Wilkinson et al. 2016). Why open data? Centre for Data Analysis and Archiving ●One more standard among many other, or something more “serious”? ●Seems to be a long-term regulation ●What does it mean? ●How to implement it (for researcher, science administrators etc.) ●How to measure FAIRness? ●Research data will not become nor stay FAIR by magic. We need skilled people, transparent processes, interoperable technologies and collaboration to build, operate and maintain research data infrastructures. ●Mari Kleemola, Finnish Social Science Data Archive/CoreTrustSeal Board, Secretary https://tietoarkistoblogi.blogspot.com/2018/11/being-trustworthy-and-fair.html FAIR data principles for opening data Centre for Data Analysis and Archiving Images by kenwoodpress.com, Good Ware by flaticon.com, freebeesupply.com, openlibrary.org ●Data lifecycle ●Usually, researcher begins from the planing/design FAIR data principles for opening data Centre for Data Analysis and Archiving UK Data Archive: https://ukdataservice.ac.uk/learning-hub/research-data-managemen t ●Data lifecycle ●Usually, researcher begins from the planing/design ●However, the process of opening data starts at the last stage ●Re-using data ●You have to know your data, in order to use them properly FAIR data principles for opening data Centre for Data Analysis and Archiving UK Data Archive: https://ukdataservice.ac.uk/learning-hub/research-data-managemen t ●Trustworthiness (trust in the used data) is most important ●A person re-using the data needs to know ●Where these data come from? ●How they were generated? ●How they were processed? ●How they can be re-used? ●A person who produced the data wants to be sure that ●The generated data will be used and interpreted properly FAIR data principles for opening data Centre for Data Analysis and Archiving FAIR data principles for opening data Centre for Data Analysis and Archiving Mons et al., 2017 ●To make opened data compliant with FAIR principles is essentially impossible for an individual researcher ●For this purpose multitude of data repositories were developed – virtual infrastructures, where data (and other digital objects) managed, stored, processed and published (curated) ●Institutional (institution or department) ●Domain specific (social science) ●Generalist (zenodo.org) ●All of them set certain requirements ●Data re-use ●File formats and data structure ●Metadata standards Data repositories for FAIR data Centre for Data Analysis and Archiving ●Why users (researchers) should worry about FAIR data, data repositories, and data repositories compliant with FAIR principles? ●Advantages of data repositories ●Researchers may worry less about proper opening of their data ●Openness strengthens scientific integrity and trust in scientific products ●Deposited data become ●Accessible ●Understandable ●Re-usable ●Data repositories make your data FAIR and curate them according to FAIR principles Data repositories for FAIR data Centre for Data Analysis and Archiving ●How data become FAIR in repositories? ●A few examples ●PID is issued on publication ●Long-term findability and proper citation ●Findability via public search catalogues ●Effective data findability is an essential premise of sharing data ●Proper licensing of data and metadata ●Clear terms and conditions of data (re)use ●(Meta)data standards implementation and development ●(Meta)data interoperability Data repositories for FAIR data Centre for Data Analysis and Archiving ●How data is stored in repositories according to FAIR standards? ●Data repositories ensure long-term curation of FAIR digital objects, ensuring that data remain accessible and FAIR-compliant over time ●Advise and support data depositors (e.g. (meta)data standards, restricted access) ●Advise and support data users (e.g. citation, how to use data) Data repositories for FAIR data Centre for Data Analysis and Archiving ●How to find a suitable data repository? ●Find a certified data repository ●Use domain specific repository ●Geriausiai išmano jums tinkamus (meta)duomenų standartus ●Use institutional repository (if long-term preservation is available) ●Sometimes more efective and convenient ●Generalist repository – only is options above are not available ●(Meta)data sandards are very general (suitable for all data types) ●Searching for your repository ●www.re3data.org (most comprehensive) ●https://repositoryfinder.datacite.org Data repositories for FAIR data Centre for Data Analysis and Archiving ●Trustworthy repositories usually are certified ●Provides services that ensure compatibility of data sets with the FAIR principles ●Assessed regularly according to established standards ●There are several certification methods that can be used to assess the trustworthiness of a data repository (ISO, DIN) ●The most commen certification system: CoreTrustSeal www.coretrustseal.org ●Infrastructure is also important ●www.dataverse.org platform is among the most-compliant with FAIR principles Data repositories for FAIR data Centre for Data Analysis and Archiving ●Trustworthiness is the most important aspect when sharing and re-using data ●Measurement of FAIRness can be done at multiple levels and should include infrastructure (repository) ●FAIR-compliant data repositories ensure long-term data availability, understandability, and reusability ●FAIR data “live” in trusted data repositories ●Certified repositories curate FAIR data according to FAIR principles ●CoreTrustSeal requirements are compatible with FAIR data principles Data repositories for FAIR data Centre for Data Analysis and Archiving Data repositories for FAIR data Centre for Data Analysis and Archiving Mokrane, 2018 Data repositories for FAIR data Centre for Data Analysis and Archiving Doorn, Dijk & Grootveld, 2017