"Research Data Curation in Visualization : Position Paper" (Data)

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Garkov, Dimitar; Müller, Christoph; Braun, Matthias; Weiskopf, Daniel; Schreiber, Falk, 2022, ""Research Data Curation in Visualization : Position Paper" (Data)", https://doi.org/10.18419/darus-3144, DaRUS, V1, UNF:6:yUhRXAoSoLD387EnHtthFg== [fileUNF]

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Description	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.
Subject	Computer and Information Science
Keyword	Data Curation, Visualization, Research--Data Processing, Data Processing Service Centers--Management, Paper--Preservation, Digital Preservation, Reproducible Research, Replication (Experimental Design), Head-Mounted Displays, Virtual Reality Headsets
Related Publication	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
Notes	Data collection methodology and sources are also described inside the Excel (.xlsx) data file.
License/Data Use Agreement	CC BY 4.0

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	1 to 2 of 2 Files	Original Format Archival Format (.tab)
	VisDataPolicies_2.1.tab Tabular Data - 15.3 KB Published Sep 29, 2022 70 Downloads 9 Variables, 23 Observations UNF:6:yUhRXAoSoLD387EnHtthFg== File coverted from "VisDataPolicies_2.1.xlsx".	Preview "VisDataPolicies_2.1.tab" Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata Variable Metadata Data File Citation EndNote XML RIS BibTeX
	VisDataPolicies_2.1.xlsx MS Excel Spreadsheet - 21.2 KB Published Sep 29, 2022 64 Downloads MD5: f5f7ea5348a99f2ba6ddfbcf61fabb66	Access File File Access Public Download Options MS Excel Spreadsheet Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.18419/darus-3144
Publication Date	2022-09-29
Title	"Research Data Curation in Visualization : Position Paper" (Data)
Author	Garkov, Dimitar (Department of Computer and Information Science, University of Konstanz) - ORCID: 0000-0002-2134-1686 Müller, Christoph (Visualization Research Center (VISUS), University of Stuttgart) - ORCID: 0000-0002-9979-3682 Braun, Matthias (Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC), University of Stuttgart) - ORCID: 0000-0001-8591-3690 Weiskopf, Daniel (Visualization Research Center (VISUS), University of Stuttgart) - ORCID: 0000-0003-1174-1026 Schreiber, Falk (Department of Computer and Information Science, University of Konstanz) - ORCID: 0000-0002-9307-3254
Point of Contact	Use email button above to contact. Schreiber, Falk (University of Konstanz)
Description	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.
Subject	Computer and Information Science
Keyword	Data Curation https://id.loc.gov/authorities/subjects/sh2015001855.html (LCSH) Visualization http://id.loc.gov/authorities/subjects/sh85143939 (LCSH) Research--Data Processing http://id.loc.gov/authorities/subjects/sh85113022 (LCSH) Data Processing Service Centers--Management http://id.loc.gov/authorities/subjects/sh89004335 (LCSH) Paper--Preservation http://id.loc.gov/authorities/subjects/sh85097586 (LCSH) Digital Preservation http://id.loc.gov/authorities/subjects/sh95004496 (LCSH) Reproducible Research https://id.loc.gov/authorities/subjects/sh2013002574 (LCSH) Replication (Experimental Design) http://id.loc.gov/authorities/subjects/sh94003448 (LCSH) Head-Mounted Displays http://id.loc.gov/authorities/subjects/sh2017002969 (LCSH) Virtual Reality Headsets http://id.loc.gov/authorities/subjects/sh2017002969 (LCSH)
Related Publication	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 https://doi.org/10.1109/BELIV57783.2022.00011
Notes	Data collection methodology and sources are also described inside the Excel (.xlsx) data file.
Language	English
Producer	Garkov, Dimitar (Department of Computer and Information Science, University of Konstanz)
Production Date	2022-06
Funding Information	DFG: 251654672 DFG: EXC 2120/1 - 390831618
Project	SFB/Transregio 161 (Level 0) EXC IntCDC (Level 0)
Depositor	Garkov, Dimitar
Deposit Date	2022-09-18
Data Type	Aggregate Data
Data Source	In the data files themselves.

Process Metadata

Processing Methods

Data collection (To collect the list we did a search in notable online collections, such as the InfoVis Wiki (https://infovis-wiki.net/wiki/Category:Conferences), the VRVis Calendar (https://confcal.vrvis.at/conferences/), Scimago (https://www.scimagojr.com/journalrank.php?area=1700&category=1704) and CORE (https://www.core.edu.au/conference-portal), feedback by non-author researchers who publish in the field also informed the identification procedure. We narrowed down the list by looking at the (1) how recent the conference is (was it in 21/22?), and (2) is it well-ranked. Well-ranked for journals means the journal has an IF over two, i.e. avgerage citation rate of at least two for the last two years. As a result we collected 8 journals. For conferences, we primarily used CORE (970 ranked conferences for 2021), where we chose all suitable conference with at least A ranking, where A is "excellent" and A* - "flagship". An exception to conferences is CHI, where the two-year average citation rate is tracked in Scimago as the premier HCI venue. We focused on predominantly visualization-related conferences and journals, and less interdisciplinary ones. With the above procedure we arrived at 12 conferences (all with A/A* rank), and omited DIS (A and HCI-focus) instead including EuroVis (B, Visualization), PacificVis (B, Visualization), and VISSOFT (B, Visualization/Software) to have a predominant focus on Visualization for 15 conferences in total. We have selected 6 VIS, 4 HCI, 3 VR and 2 other closely related conference venues. We found VRVis Calendar thanks to Liu et al.'s 2021 paper "Visualization Resources: A Starting Point". We were pointed to the InfoVis Wiki collection by an expert in the field.)
Data sharing and long-term preservation index (The index determines the score with regards to the policy on data sharing and long-term preservation. The result can be found in column 4 of the Excel/CSV file. This is as follows: Scala: 0. None, 1. Encouraged submitting, 2. Encouraged sharing of parameters, data sets (important parts thereof), 3. Encouraged sharing, 4. Encouraged sharing and preservation (repository), 5. Required DAS and encouraged sharing and preservation, 6. Required sharing/Encouraged preservation, 7. Required sharing and encouraged preservation, 8. Required sharing and preservation)

Privacy Metadata

Personal Data	no

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Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

Creative Commons Attribution 4.0 International License. CC BY 4.0

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