{"title":"DRAT:大学与产业合作的数据风险评估工具","authors":"J. Sikorska, S. Bradley, M. Hodkiewicz, R. Fraser","doi":"10.1017/dce.2020.13","DOIUrl":null,"url":null,"abstract":"Abstract For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an individual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/dce.2020.13","citationCount":"2","resultStr":"{\"title\":\"DRAT: Data risk assessment tool for university–industry collaborations\",\"authors\":\"J. Sikorska, S. Bradley, M. Hodkiewicz, R. Fraser\",\"doi\":\"10.1017/dce.2020.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an individual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.\",\"PeriodicalId\":34169,\"journal\":{\"name\":\"DataCentric Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2020-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1017/dce.2020.13\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DataCentric Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/dce.2020.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DataCentric Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dce.2020.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
DRAT: Data risk assessment tool for university–industry collaborations
Abstract For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an individual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.