Images as data – modelling data interactions in social science and humanities research

Purpose The expanded reuse of images as research data in the social sciences and humanities necessitates the understanding of scholars’ real-life interactions with the type of data. The aim of this study is to analyse activities constituting image data interactions in social science and humanities research and to provide a model describing the data interaction process. Design/methodology/approach The study is based on interviews with 21 scholars from various academic backgrounds utilising digital and print images collected from external sources as empirical research data. Qualitative content analyses were executed to analyse image data interactions throughout the research process in three task types: contemporary, historical and computational research. Findings The findings further develop the task-based information interaction model (Järvelin et al ., 2015) originally created to explain the information interaction process. The enhanced model presents five main image data interaction activities: Data gathering, Forming dataset, Working with data, Synthesizing and reporting and Concluding, with various sub-activities. The findings show the variety of image data interactions in different task types. Originality/value The developed model contributes to understanding critical points in image data interactions and provides a model for future research analysing research data interactions. The model may also be used, for example, in designing better research services and infrastructures by identifying support needs throughout the research process.

Authors

Institutions

Publication Details

Journal
Journal of Documentation
Published
2024-10-31
DOI
https://doi.org/10.1108/jd-08-2024-0195
Citations
3
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
2.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Images as data – modelling data interactions in social science and humanities research

Inés Matres, Anna Sendra, Elina Late, Sanna Kumpulainen
3 citations
Journal of Documentation
Research Data Management Practices
2.34
article

Images as data – modelling data interactions in social science and humanities research

Inés Matres, Anna Sendra, Elina Late, Sanna Kumpulainen
article en
3 citations

Abstract

Purpose The expanded reuse of images as research data in the social sciences and humanities necessitates the understanding of scholars’ real-life interactions with the type of data. The aim of this study is to analyse activities constituting image data interactions in social science and humanities research and to provide a model describing the data interaction process. Design/methodology/approach The study is based on interviews with 21 scholars from various academic backgrounds utilising digital and print images collected from external sources as empirical research data. Qualitative content analyses were executed to analyse image data interactions throughout the research process in three task types: contemporary, historical and computational research. Findings The findings further develop the task-based information interaction model (Järvelin et al ., 2015) originally created to explain the information interaction process. The enhanced model presents five main image data interaction activities: Data gathering, Forming dataset, Working with data, Synthesizing and reporting and Concluding, with various sub-activities. The findings show the variety of image data interactions in different task types. Originality/value The developed model contributes to understanding critical points in image data interactions and provides a model for future research analysing research data interactions. The model may also be used, for example, in designing better research services and infrastructures by identifying support needs throughout the research process.

Journal of DocumentationVol. 80(7)
University of Helsinki (FI), Tampere University (FI)
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
Research Data Management Practices
2.34
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.