Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD)
AbstractBackground:High-quality data are critical to the entire scientific enterprise, yet the complexity and effort involved in data curation are vastly under-appreciated. This is especially true for large observational, clinical studies because of the amount of multimodal data that is captured and...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2020-08-01
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| Series: | Journal of Clinical and Translational Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866120000242/type/journal_article |
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| author | Ari Ercole Vibeke Brinck Pradeep George Ramona Hicks Jilske Huijben Michael Jarrett Mary Vassar Lindsay Wilson |
| author_facet | Ari Ercole Vibeke Brinck Pradeep George Ramona Hicks Jilske Huijben Michael Jarrett Mary Vassar Lindsay Wilson |
| author_sort | Ari Ercole |
| collection | DOAJ |
| description | AbstractBackground:High-quality data are critical to the entire scientific enterprise, yet the complexity and effort involved in data curation are vastly under-appreciated. This is especially true for large observational, clinical studies because of the amount of multimodal data that is captured and the opportunity for addressing numerous research questions through analysis, either alone or in combination with other data sets. However, a lack of details concerning data curation methods can result in unresolved questions about the robustness of the data, its utility for addressing specific research questions or hypotheses and how to interpret the results. We aimed to develop a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data.Methods:Forty-six experts participated in a modified Delphi process to reach consensus on indicators of data curation that could be used in the design and reporting of studies.Results:We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies.Conclusion:The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines are the first comprehensive set of data quality indicators for large observational studies. They were developed around the needs of neuroscience projects, but we believe they are relevant and generalisable, in whole or in part, to other fields of health research, and also to smaller observational studies and preclinical research. The DAQCORD Guidelines provide a framework for achieving high-quality data; a cornerstone of health research. |
| first_indexed | 2024-04-10T04:55:20Z |
| format | Article |
| id | doaj.art-ecb3999166264257b2a734ee4af7ada5 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-04-10T04:55:20Z |
| publishDate | 2020-08-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-ecb3999166264257b2a734ee4af7ada52023-03-09T12:30:17ZengCambridge University PressJournal of Clinical and Translational Science2059-86612020-08-01435435910.1017/cts.2020.24Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD)Ari Ercole0https://orcid.org/0000-0001-8350-8093Vibeke Brinck1Pradeep George2https://orcid.org/0000-0001-6771-2828Ramona Hicks3https://orcid.org/0000-0001-7418-6221Jilske Huijben4https://orcid.org/0000-0002-2892-5406Michael Jarrett5https://orcid.org/0000-0003-0292-4728Mary Vassar6https://orcid.org/0000-0001-6748-6939Lindsay Wilson7https://orcid.org/0000-0003-4113-2328Department of Medicine, Division of Anaesthesia, University of Cambridge, Cambridge, UKQuesGen Systems, Inc, Burlingame, CA, USAInternational Neuroinformatics Coordinating Facility, Karolinska Institutet, Stockholm, SwedenOne Mind, Rutherford, CA, USADepartment of Public Health, Center for Medical Decision Sciences, Erasmus MC, Rotterdam, The NetherlandsQuesGen Systems, Inc, Burlingame, CA, USADepartment of Neurological Surgery, University of California, San Francisco, CA, USADivision of Psychology, University of Stirling, Stirling, UKAbstractBackground:High-quality data are critical to the entire scientific enterprise, yet the complexity and effort involved in data curation are vastly under-appreciated. This is especially true for large observational, clinical studies because of the amount of multimodal data that is captured and the opportunity for addressing numerous research questions through analysis, either alone or in combination with other data sets. However, a lack of details concerning data curation methods can result in unresolved questions about the robustness of the data, its utility for addressing specific research questions or hypotheses and how to interpret the results. We aimed to develop a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data.Methods:Forty-six experts participated in a modified Delphi process to reach consensus on indicators of data curation that could be used in the design and reporting of studies.Results:We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies.Conclusion:The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines are the first comprehensive set of data quality indicators for large observational studies. They were developed around the needs of neuroscience projects, but we believe they are relevant and generalisable, in whole or in part, to other fields of health research, and also to smaller observational studies and preclinical research. The DAQCORD Guidelines provide a framework for achieving high-quality data; a cornerstone of health research.https://www.cambridge.org/core/product/identifier/S2059866120000242/type/journal_articleData qualitycurationobservational studiesDelphi processdesignreporting |
| spellingShingle | Ari Ercole Vibeke Brinck Pradeep George Ramona Hicks Jilske Huijben Michael Jarrett Mary Vassar Lindsay Wilson Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Journal of Clinical and Translational Science Data quality curation observational studies Delphi process design reporting |
| title | Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) |
| title_full | Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) |
| title_fullStr | Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) |
| title_full_unstemmed | Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) |
| title_short | Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) |
| title_sort | guidelines for data acquisition quality and curation for observational research designs daqcord |
| topic | Data quality curation observational studies Delphi process design reporting |
| url | https://www.cambridge.org/core/product/identifier/S2059866120000242/type/journal_article |
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