Implementing Effective Data Practices

开放科学运动在整个学术界获得了动力。自美国国家科学、工程和医学院（NAS）于2018年发布《设计的开放科学：实现21世纪世纪研究愿景》报告以来，许多机构、组织和教师已经开始评估其当前的实践和基础设施，以支持更开放的研究生态系统。为了充分实现开放科学和学术的愿景，利益相关者需要采用必要的关键基础设施、标准和实践，以促进负责任的数据实践。从2019年5月NSF Dear Colleague Letter（DCL）等来源中汲取灵感，组织者提议召开专家会议，讨论数据集的持久性标识符（PID）并创建机器可读数据管理计划（DMP）。 该会议由加州数字图书馆（CDL）和研究图书馆协会（ARL）与美国大学协会（AAU）和公立和赠地大学协会（APLU）合作组织。该会议将于2019年冬季在华盛顿举行为期多日的研讨会，邀请约40名专家参加，目的是确定和确定：1.在NSF DCL 2中实施广泛认可的良好实践仍然存在哪些障碍。什么样的模型工作流程可以解决这些障碍，同时最大限度地减少教师负担3。NSF DCL的实施对机构数据治理意味着什么（例如，在校园单位之间，机构之间和公共之间共享DMP）将研究结果反馈给决策者、供资机构和其他类似机构5。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The open science movement is gaining momentum across the academic landscape. Since the National Academies of Science, Engineering, and Medicine (NAS) published the Open Science by Design: Realizing a Vision for 21st Century Research report in 2018, many institutions, organizations, and faculty have begun assessing their current practices and infrastructure to support a more open research ecosystem. To fully realize the vision for open science and scholarship, stakeholders need to adopt key infrastructure, standards, and practices necessary to facilitate responsible data practices. Drawing inspiration from sources such as the May 2019 NSF Dear Colleague Letter (DCL) the organizers propose an expert convening to discuss persistent identifiers (PIDs) for datasets and creating machine readable data management plans (DMPs). The conference is organized by California Digital Library (CDL) and Association of Research Libraries (ARL), in partnership with the Association of American Universities (AAU) and the Association of Public and Land-grant Universities (APLU). The conference will engage approximately 40 experts in a multi-day workshop in Washington DC winter of 2019 with the goal to identify and determine:1. What barriers remain to implementing the widely recognized good practices in the NSF DCL2. What kinds of model workflows might address those barriers, while minimizing faculty burden3. What implementation of the NSF DCL means for institutional data governance (e.g. sharing DMPs across campus units, between institutions, and publicly)4. Findings to bring back to policymakers, funding agencies, and other similar institutions5. Recommendations of effective practices for grants offices, including guidance to their researchersThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.