{"title":"研究项目中研究数据管理的质量评估","authors":"Max Leo Wawer, Johanna Wurst, Roland Lachmayer","doi":"10.52825/cordi.v1i.420","DOIUrl":null,"url":null,"abstract":"In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality Assessment for Research Data Management in Research Projects\",\"authors\":\"Max Leo Wawer, Johanna Wurst, Roland Lachmayer\",\"doi\":\"10.52825/cordi.v1i.420\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].\",\"PeriodicalId\":359879,\"journal\":{\"name\":\"Proceedings of the Conference on Research Data Infrastructure\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Conference on Research Data Infrastructure\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52825/cordi.v1i.420\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Conference on Research Data Infrastructure","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52825/cordi.v1i.420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quality Assessment for Research Data Management in Research Projects
In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].