%0 Generic %1 garkov2022research %A Garkov, Dimitar %A Müller, Christoph %A Braun, Matthias %A Weiskopf, Daniel %A Schreiber, Falk %D 2022 %K %R 10.18419/darus-3144 %T "Research Data Curation in Visualization : Position Paper" (Data) %X Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided.Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.

@misc{garkov2022research, abstract = {Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided.Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards. }, added-at = {2023-09-18T09:55:51.000+0200}, affiliation = {Garkov, Dimitar/Department of Computer and Information Science, University of Konstanz, Müller, Christoph/Visualization Research Center (VISUS), University of Stuttgart, Braun, Matthias/Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC), University of Stuttgart, Weiskopf, Daniel/Visualization Research Center (VISUS), University of Stuttgart, Schreiber, Falk/Department of Computer and Information Science, University of Konstanz}, author = {Garkov, Dimitar and Müller, Christoph and Braun, Matthias and Weiskopf, Daniel and Schreiber, Falk}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2fc7720388a5a136217ddafab0e88e2a3/unibiblio}, doi = {10.18419/darus-3144}, howpublished = {Dataset}, interhash = {583c5c282c01743097ab20e390500cd9}, intrahash = {fc7720388a5a136217ddafab0e88e2a3}, keywords = {}, note = {Related to: Garkov, D., Müller, C., Braun, M., Weiskopf, D., Schreiber, F., “Research Data Curation in Visualization : Position Paper”, in 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), IEEE, 2022. doi: 10.1109/BELIV57783.2022.00011}, orcid-numbers = {Garkov, Dimitar/0000-0002-2134-1686, Müller, Christoph/0000-0002-9979-3682, Braun, Matthias/0000-0001-8591-3690, Weiskopf, Daniel/0000-0003-1174-1026, Schreiber, Falk/0000-0002-9307-3254}, timestamp = {2023-09-18T07:55:51.000+0200}, title = {"Research Data Curation in Visualization : Position Paper" (Data)}, year = 2022 }