Workshop on Supporting Scientific Discovery through Norms and Practices for Software and Data Citation and Attribution

科学研究人员，尤其是学术研究人员，植根于声誉经济。通过有影响力的研究获得终身职位、晋升和赞誉。科学家分享数据和软件的动机很少，而且任期和晋升决策缺乏对这些活动的考虑；使问题更加复杂的是，还有一些不利因素，比如冒着失去归属的风险。一些科学家不信任软件和数据的公共访问模式，他们更倾向于只在个人要求的情况下共享数据和软件，这种模式通过个人接触和隐性社会契约来确保归因。还缺乏完善的指标来评估科学软件和数据的影响和质量。研究界需要在软件和数据的引用和归属方面采取新的做法和激励措施，以便数据生产者、软件和工具开发人员以及数据管理员的贡献得到认可。本次研讨会将促进对软件和数据引用和归属的新规范和实践的全国性跨学科探索，目标是为科学与创新政策（SciSIP）和持续创新的软件基础设施（SI2） NSF项目提供信息。将确定当前软件开发和数据生成工作面临的社会和技术挑战，参与者将探索可行的方法和指标，以支持科学研究界的软件和数据归因。本次讲习班将讨论软件和数据的登记、软件和数据的存储库、跟踪软件和数据使用的方法、软件和数据注释、收集和管理软件和数据的元数据、确保软件和数据用户的适当归属、传统的归属出版模式的替代方案、软件和数据归属的商业模式的改编、分配归因度量以匹配软件开发和数据生成中的努力程度和角色，并为开放科学建立奖励度量。研讨会成果将包括可操作的计划，使更广泛的研究界能够实施由研讨会参与者确定和推进的软件和数据归因实践。

Scientific researchers, and particularly academic researchers, are embedded in a reputation economy. Tenure, promotion, and acclaim are achieved through influential research. There are few incentives for scientists to share data and software, and tenure and promotion decisions lack consideration of such activities; and to compound the problem, there are disincentives such as risking the loss of attribution. Some scientists distrust the public access model for software and data, and prefer to share data and software only by personal request, which assures attribution through personal contact and implicit social contract. There is also a lack of well-developed metrics with which to assess the impact and quality of scientific software and data. New practices and incentives are needed in the research community for software and data citation and attribution, so that data producers, software and tool developers, and data curators are credited for their contributions. This workshop will facilitate a national, interdisciplinary exploration of new norms and practices for software and data citation and attribution, with the goal of informing the Science of Science and Innovation Policy (SciSIP) and Software Infrastructure for Sustained Innovation (SI2) NSF programs. Social and technical challenges facing current software development and data generation efforts will be identified and participants will explore viable methods and metrics to support software and data attribution in the scientific research community. This workshop will address registration of software and data, repositories for software and data, methods for tracking software and data usage, software and data annotation, collecting and curating metadata on software and data, ensuring appropriate attribution by software and data users, alternatives to traditional publication models for attribution, adaptation of commercial models for software and data attribution, proportioning attribution metrics to match degree of effort and role in software development and data generation, and establishing reward metrics for open science. Workshop outcomes will include actionable plans to enable the broader research community to implement the software and data attribution practices that are identified and advanced by the participants of the workshop.