Human-Data Interaction: Thinking Beyond Individual Datasets

interactions, vol. 32, pp. 34–38

Abstract

sharing and coding within the data science community, such as Kaggle for machine learning competitions, or Hugging Face, which focuses more on natural language processing technologies.Using data created by others, however, is still challenging.Despite the increase in the number of data-sharing websites and platforms, there is still a lack of understanding about social interactions and collaborations when working with data.We argue that attention should be paid to these and other aspects of human-data interaction, focusing on the issues and affordances of howWe live in data-centric times.Many of the world's greatest challenges, from advancing science to improving government services and tackling climate change, require access to large amounts of data.In recent years, avenues that allow individuals to share and use data online have proliferated.Scientists use open platforms, such as GitHub or Zenodo, to collaborate on data projects and archive them.Governments establish data hubs for publishing and facilitating data use, and policymakers and industry develop "data spaces" for safe sharing across multiple parties.There are also more-informal platforms for data W

Authors 3

1. Laura M. Koesten

   University of Vienna

   Affiliation as printed

   University of Vienna

2. Jude Yew

   Google (United States)

   Affiliation as printed

   Google

3. Kathleen Gregory Aachen

   Leiden University

   Affiliation as printed

   Leiden University