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

点击翻译按钮获取中文摘要

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.