Litcius/Paper detail

Information needs on research data creation

Lisa Börjesson, Isto Huvila, Olle Sköld

2022Information Research an international electronic journal17 citationsDOIOpen Access PDF

Abstract

Researchers’ data related information needs are growing. This paper reports the findings of a study with archaeologists and cultural heritage professionals focussing on data reuse related meta-information needs.Methods. Interviews with (N=)10 archaeologists and cultural heritage professionals. Qualitative coding and content analysis. Four types of paradata needs (data on processes, e.g. data creation) are identified, including 1) scope, 2) provenance, 3) methods and 4) knowledge organisation and representation paradata. Knowledge organisation and representation paradata has been least explored both in research and practises so far. The findings point to a need to develop the understanding of the needs and means of documentation of knowledge organisation and representation. The findings contribute to the data literacy of researchers producing and using data descriptions, and to the study of how paradata can be created and used. Further, the findings indicate that distance-to-data is a significant parameter in determining whether information needs are continuous or discrete. Further, the most likely type of reuse should guide the level and type of paradata. Finally, the findings underline that in spite of the comprehensiveness of available meta-information, it will be incomplete. Complementary means — including collaboration with data creators and meta-information extraction approaches — are needed to increase information reusability.

Topics & Concepts

Computer scienceReuseDocumentationExternal Data RepresentationCoding (social sciences)Representation (politics)Scope (computer science)Point (geometry)Knowledge managementInformation needsData scienceWorld Wide WebSociologyOperating systemProgramming languagePolitical scienceSocial scienceGeometryBiologyPoliticsEcologyMathematicsLawSemantic Web and OntologiesResearch Data Management PracticesData Quality and Management