Quality Assurance for Spatial Research Data
In Earth System Sciences (ESS), spatial data are increasingly used for impact research and decision-making. To support the stakeholders’ decision, the quality of the spatial data and its assurance play a major role. We present concepts and a workflow to assure the quality of ESS data. Our concepts a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/11/6/334 |
| _version_ | 1797486701520617472 |
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| author | Michael Wagner Christin Henzen |
| author_facet | Michael Wagner Christin Henzen |
| author_sort | Michael Wagner |
| collection | DOAJ |
| description | In Earth System Sciences (ESS), spatial data are increasingly used for impact research and decision-making. To support the stakeholders’ decision, the quality of the spatial data and its assurance play a major role. We present concepts and a workflow to assure the quality of ESS data. Our concepts and workflow are designed along the research data life cycle and include criteria for openness, FAIRness of data (findable, accessible, interoperable, reusable), data maturity, and data quality. Existing data maturity concepts describe (community-specific) maturity matrices, e.g., for meteorological data. These concepts assign a variety of maturity metrics to discrete levels to facilitate evaluation of the data. Moreover, the use of easy-to-understand level numbers enables quick recognition of highly mature data, and hence fosters easier reusability. Here, we propose a revised maturity matrix for ESS data including a comprehensive list of FAIR criteria. To foster the compatibility with the developed maturity matrix approach, we developed a spatial data quality matrix that relates the data maturity levels to quality metrics. The maturity and quality levels are then assigned to the phases of the data life cycle. With implementing openness criteria and matrices for data maturity and quality, we build a quality assurance (QA) workflow that comprises various activities and roles. To support researchers in applying this workflow, we implement an interactive questionnaire in the tool RDMO (research data management organizer) to collaboratively manage and monitor all QA activities. This can serve as a blueprint for use-case-specific QA for other datasets. As a proof of concept, we successfully applied our criteria for openness, data maturity, and data quality to the publicly available SPAM2010 (crop distribution) dataset series. |
| first_indexed | 2024-03-09T23:37:00Z |
| format | Article |
| id | doaj.art-71a8326cd7474bc78a58040a26bdb982 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T23:37:00Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-71a8326cd7474bc78a58040a26bdb9822023-11-23T16:58:58ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-06-0111633410.3390/ijgi11060334Quality Assurance for Spatial Research DataMichael Wagner0Christin Henzen1Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Helmholtzstr. 10, 01062 Dresden, GermanyWorking Group Geo UX, German UPA, Technische Universität Dresden, Helmholtzstr. 10, 01062 Dresden, GermanyIn Earth System Sciences (ESS), spatial data are increasingly used for impact research and decision-making. To support the stakeholders’ decision, the quality of the spatial data and its assurance play a major role. We present concepts and a workflow to assure the quality of ESS data. Our concepts and workflow are designed along the research data life cycle and include criteria for openness, FAIRness of data (findable, accessible, interoperable, reusable), data maturity, and data quality. Existing data maturity concepts describe (community-specific) maturity matrices, e.g., for meteorological data. These concepts assign a variety of maturity metrics to discrete levels to facilitate evaluation of the data. Moreover, the use of easy-to-understand level numbers enables quick recognition of highly mature data, and hence fosters easier reusability. Here, we propose a revised maturity matrix for ESS data including a comprehensive list of FAIR criteria. To foster the compatibility with the developed maturity matrix approach, we developed a spatial data quality matrix that relates the data maturity levels to quality metrics. The maturity and quality levels are then assigned to the phases of the data life cycle. With implementing openness criteria and matrices for data maturity and quality, we build a quality assurance (QA) workflow that comprises various activities and roles. To support researchers in applying this workflow, we implement an interactive questionnaire in the tool RDMO (research data management organizer) to collaboratively manage and monitor all QA activities. This can serve as a blueprint for use-case-specific QA for other datasets. As a proof of concept, we successfully applied our criteria for openness, data maturity, and data quality to the publicly available SPAM2010 (crop distribution) dataset series.https://www.mdpi.com/2220-9964/11/6/334quality assurancedata maturitymaturity matrixspatial data qualityFAIR |
| spellingShingle | Michael Wagner Christin Henzen Quality Assurance for Spatial Research Data ISPRS International Journal of Geo-Information quality assurance data maturity maturity matrix spatial data quality FAIR |
| title | Quality Assurance for Spatial Research Data |
| title_full | Quality Assurance for Spatial Research Data |
| title_fullStr | Quality Assurance for Spatial Research Data |
| title_full_unstemmed | Quality Assurance for Spatial Research Data |
| title_short | Quality Assurance for Spatial Research Data |
| title_sort | quality assurance for spatial research data |
| topic | quality assurance data maturity maturity matrix spatial data quality FAIR |
| url | https://www.mdpi.com/2220-9964/11/6/334 |
| work_keys_str_mv | AT michaelwagner qualityassuranceforspatialresearchdata AT christinhenzen qualityassuranceforspatialresearchdata |