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Helping the Consumers and Producers of standards,Repositories and Policies to Enable FAIR data

data Intelligence 2020年第1期2卷 151-157,312页

作者： Peter McQuilton Dominique Batista Oya Beyan Ramon Granell Simon Coles Massimiliano Izzo Allyson L.Lister Robert Pergl Philippe Rocca-Serra Ben Schaap Hugh Shanahan Milo Thurston Susanna-Assunta Sansone Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK Fraunhofer Institute for Applied Information Technology(FIT) 53754 Sankt AugustinGermany School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic Wageningen University Wagengen 6700 HBThe Netherlands Royal Holloway University of LondonLondon Surrey TW200EXUK

Thousands of community-developed(meta)data guidelines,models,ontologies,schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases,across all disciplines.These resources are necessary to meet government,funder and publisher expectations of greater transparency and access to and preservation of data related to research publications.This obligates researchers to ensure their data is FAIR,share their data using the appropriate standards,store their data in sustainable and community-adopted repositories,and to conform to funder and publisher data policies.FAIR data sharing also plays a key role in enabling researchers to evaluate,re-analyse and reproduce each other’s work.We can map the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.In this paper,we show how the work of the GO-FAIR FAIR standards,Repositories and Policies(StRePo)Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards,repositories and data policies in general.Pivotal to this effort,the FAIRsharing,an endorsed flagship resource of the Research data Alliance that maps the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.Lastly,we highlight a number of activities around FAIR tools,services and educational efforts to raise awareness and encourage participation.

关键词： Convergence data repositories data policies data standards FAIR data FAIR enabling community standards

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Open HD map service model:an interoperable high-Definition map data model for autonomous driving

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International Journal of Digital Earth 2023年第1期16卷 2089-2110页

作者： Fengyuan Zhang Wenzhong Shi Min Chen Wei Huang Xintao Liu Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityKowloonHong Kong Smart Cities Research Institute The Hong Kong Polytechnic UniversityKowloonHong Kong Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjingPeople’s Republic of China College of Surveying and Geo-Informatics Tongji UniversityShanghaiPeople’s Republic of China Guangdong–Hong Kong-Macao Joint Laboratory for Smart Cities ShenzhenPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China State Key Laboratory Cultivation Base of Geographical Environment Evolution(Jiangsu Province) NanjingPeople’s Republic of China

With the growth in the vehicle industry,autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors.Maps,as pivotal geospatial information carriers,play a vital role in route planning and navigation service.Compared with conventional maps,high-definition(HD)maps possesses higher precision,richer information,and various services and are regarded as critical infrastructure for autonomous driving.However,heterogeneous HD map data standards and models have different characteristics and advantages,and thus they rarely meet all autonomous driving requirements for different driving objectives.This research presents an interoperable map data model,the Open HD Map Service Model(OHDMSM),to provide a reference for HD map development.The designed OHDMSM,which contains three data layers and a set of corresponding interfaces,demonstrates high interoperability for HD map data fusion and application.As a proof of concept,an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM.The design and development of OHDMSM data structures,interfaces and systems will benefit data requesting,updating,and interoperation for HD map data worldwide,which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.

关键词： HD map data interoperation autonomous driving data standards smart cities

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Implementation of the FAIR data Principles for Exploratory Biomarker data from Clinical Trials

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data Intelligence 2021年第4期3卷 630-661页

作者： Alexander Arefolov Laura Adam Shoshana Brown Yelena Budovskaya Cong Chen Diya Das Chen Farhy Rebecca Ferguson Hongmei Huang Kimberly Kanigel Christina Lu Oksana Polesskaya Tracy Staton Rajeev Tajhya Maryann Whitley Jee-Yeon Wong Xiangpei Zeng Mark McCreary Rancho BioSciences LLC. San DiegoCA 92127USA Development Sciences Informatics Genentech Inc.South San FranciscoCA 94080-4990USA Development Sciences OMNI-Biomarker Development Genentech Inc.South San FranciscoCA 94080-4990USA

The FAIR data guiding principles have been recently developed and widely adopted to improve the Findability,Accessibility,Interoperability,and Reuse of digital assets in the face of an exponential increase of data volume and complexity.The FAIR data principles have been formulated on a general level and the technological implementation of these principles remains up to the industries and organizations working on maximizing the value of their data.Here,we describe the data management and curation methodologies and best practices developed for FAIRification of clinical exploratory biomarker data collected from over 250 clinical studies.We discuss the data curation effort involved,the resulting output,and the business and scientific impact of our work.Finally,we propose prospective planning for FAIR data to optimize data management efforts and maximize data value.

关键词： data management data curation Clinical studies FAIR data Biomarkers data model Metadata data standards data governance

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英国政府数据共享与开放的元数据标准建设及启示

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图书馆学情报学 2021年第8期

作者：翟军翟玮裴心童李剑锋

[目的/意义]为促进政府数据的开放共享和高效使用，英国率先在元数据领域采纳开放标准，对其系统介绍可为我国政府数据资源的元数据标准建设提供一定的参考和借鉴。[方法/过程]采用文献分析和网络调查方法，分析和介绍2020年4月成立的... 详细信息

[目的/意义]为促进政府数据的开放共享和高效使用，英国率先在元数据领域采纳开放标准，对其系统介绍可为我国政府数据资源的元数据标准建设提供一定的参考和借鉴。[方法/过程]采用文献分析和网络调查方法，分析和介绍2020年4月成立的英国政府数据标准局的第一批成果——共享和开放政府数据的元数据标准、实施指南和应用情况。[结果/结论]采纳开放标准代表着国际上元数据标准建设的最新发展方向。对我国而言，应在将元数据标准看作重要的数据基础设施、重视引进和鼓励使用开放标准、持续提升元数据管理的成熟度水平上借鉴先进经验。

关键词：政府数据元数据开放标准数据标准数据共享文献分析法英国　　government data metadata open standards data standards data sharing literature analysis U.K.

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