Francisca Onaolapo Oladipo, Sakinat Folorunso, E. Ogundepo, Obinna Osigwe, A. Akindele
{"title":"Curriculum Development for FAIR Data Stewardship","authors":"Francisca Onaolapo Oladipo, Sakinat Folorunso, E. Ogundepo, Obinna Osigwe, A. Akindele","doi":"10.1162/dint_a_00183","DOIUrl":null,"url":null,"abstract":"Abstract The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable (FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub (DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"991-1012"},"PeriodicalIF":1.3000,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1162/dint_a_00183","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 4
Abstract
Abstract The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable (FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub (DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.
摘要FAIR指南试图使数字数据可查找、可访问、可互操作和可重用(FAIR)。为了准备FAIR数据,一个被称为数据管理的新数据科学学科正在兴起,随着FAIR指南越来越被接受,预计对数据管理人员的需求会增加。因此,有必要制定课程,通过有效的知识交流培养数据管理方面的专业技能。已经采取了一些举措,旨在通过正式和非正式方案弥合FAIR数据管理培训方面的差距。本文描述了在数字创新和技能中心(DISH)项目下为FAIR数据管理培训开发数字计划的经验。FAIR数据管理课程在12周内提供6个简短的按需证书模块。模块分为两组:FAIR数据和数据科学。核心主题包括数据科学的基本主题、监管框架、FAIR数据管理、FAIR data Point安装的中级到高级主题,以及医疗保健和语义数据管理中的FAIR数据。根据所提供的资源,参与者每周需要花7-8小时自学模块。一旦满足了所有要求,学生就被认证为FAIR数据科学家,并有资格担任FAIR数据管理员和分析师。预计由不同参与者进行的深入而有针对性的课程开发将为数据能力中心建立FAIR数据科学家的核心,并鼓励快速采用FAIR研发指南。