This proposal is based on the results gained within the OpenUP project (http://openup-h2020.eu/). In particular, one of pilot carried out during the OpenUP project in the field of Social sciences aimed at identifying strengths and weaknesses in the process of dataset review and validation, outlining best practices towards transparency, data dissemination, reliability and reuse. To reach this aim we selected the scientific community that originated the Human Mortality Database (HMD), a well well-known data source providing open access data for demography and population studies. We interviewed the HMD managers to investigate motivations and organizational features that prompted the community to make their data available as well as the quality assurance process. Moreover, to analyze attitudes and re-use behaviors a questionnaire was designed in collaboration with HMD management and sent to HMD end users. Our research questions were: To what extent these very practical and real requirements are fulfilled by the principles expressed by the HMD managers? Taking for granted that they provide important indications for the improvement of the data repository, do they suggest the implementation of further principles? Considering the FAIR principles as a reference point, does the resulting matching of the two perspectives suggest the implementation of new features that make HMD FAIR complaint? The poster will display a matching exercise between the practical users' requirements and the principles expressed by the HMD managers to finally investigate how they fit in the FAIR principles. Preliminary results outlined community-specific principles that partially overlap the FAIR principles, but meet user expectations, that are generally related to data quality assurance. Moreover, while some FAIR criteria in the description of data are easy to adopt (metadata schema, PID, etc.), others, such as interoperability, are more difficult to implement requiring a broad consensus on the use of common standards and protocols.

Users' needs and research practices in data reuse: translating the FAIR principles in to reality

Ruggieri R;Pisacane L;Luzi D
2019

Abstract

This proposal is based on the results gained within the OpenUP project (http://openup-h2020.eu/). In particular, one of pilot carried out during the OpenUP project in the field of Social sciences aimed at identifying strengths and weaknesses in the process of dataset review and validation, outlining best practices towards transparency, data dissemination, reliability and reuse. To reach this aim we selected the scientific community that originated the Human Mortality Database (HMD), a well well-known data source providing open access data for demography and population studies. We interviewed the HMD managers to investigate motivations and organizational features that prompted the community to make their data available as well as the quality assurance process. Moreover, to analyze attitudes and re-use behaviors a questionnaire was designed in collaboration with HMD management and sent to HMD end users. Our research questions were: To what extent these very practical and real requirements are fulfilled by the principles expressed by the HMD managers? Taking for granted that they provide important indications for the improvement of the data repository, do they suggest the implementation of further principles? Considering the FAIR principles as a reference point, does the resulting matching of the two perspectives suggest the implementation of new features that make HMD FAIR complaint? The poster will display a matching exercise between the practical users' requirements and the principles expressed by the HMD managers to finally investigate how they fit in the FAIR principles. Preliminary results outlined community-specific principles that partially overlap the FAIR principles, but meet user expectations, that are generally related to data quality assurance. Moreover, while some FAIR criteria in the description of data are easy to adopt (metadata schema, PID, etc.), others, such as interoperability, are more difficult to implement requiring a broad consensus on the use of common standards and protocols.
2019
Istituto di Ricerche sulla Popolazione e le Politiche Sociali - IRPPS
Open data
FAIR principles
research life cycle
data re-use behaviors
data reuse in Social science
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/389506
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