The Scientific Software Network Map

越来越复杂的科学依赖于越来越复杂的软件。从分析数据、模拟现象到控制仪器，软件在所有科学领域的功能都越来越重要。软件不仅仅是一种辅助服务，它还可以是创新的源泉，并可以通过增加透明度、再现性、可转移性和规模来加强科学。然而，目前对软件工作对科学进步的贡献的科学理解很少；通常甚至不清楚什么软件被用来推动科学，以及谁应该为该软件的贡献获得荣誉。由于缺乏这一引文机制，年轻的科学家显然不愿从事开发广泛有用的软件所需的工作。改进现有软件的合作也无意中受到了阻碍。发布一个包可能会造成一个不幸的结果，一个包的成功带来了支持和维护负担，使其创建者不堪重负，阻碍了新兴项目的发展，并阻碍了未来的分享。无论是理解软件工作在科学中的作用，还是克服阻碍其进展的障碍，都关键取决于了解实际使用的软件、如何在科学项目中编排各种包的使用，以及在哪里可以最有效地部署资源以促进软件开发、发展和支持。为了不断地获取这些知识，本项目正在设计和构建科学软件网络地图。这张地图将通过收集以下数据来衡量科学-软件生态系统：科学中使用了哪些软件工具导致出版、从该软件到其他软件包的依赖性和互补性网络，以及软件使用如何随着时间的推移而演变。该项目正在与以软件为主的领域的科学家合作，研究和设计数据收集技术，收集数据，并设计适当的分析。该项目还将为科学地理解软件在科学和科学创新中的作用提供初步的基础。更广泛的影响：该项目的成果将改善软件支持的科学的实际行为，并为与软件在科学、研究和创新项目中的应用有关的科学政策决策提供信息。公开提供的地图结果将在三个相辅相成的方面更广泛地有用。首先，它将是一个资源，供贡献软件的科学家展示他们的工作的用处和影响，为他们积极、持续的贡献提供基础，并提高科学软件项目的可持续性。其次，它将作为一个信息源，让其他科学家了解他们所在领域正在使用的软件，产生共同的知识，减少成本高昂的重新实施，并帮助社区团结起来，走向共享平台。第三，它将帮助科学资助者识别软件生态系统中不断发展的问题和机会--例如，快速采用带来了沉重但意想不到的支持负担的挑战，或者关于社区围绕新生的核心凝聚的早期警报，这些核心在及时、集中的资金支持下，可以转变为将推动未来科学创新的软件平台。

Increasingly sophisticated science depends on ever more complex software. Software is increasingly vital across all scientific fields for functions as diverse as analyzing data, simulating phenomena, and controlling instruments. Software, more than simply a supporting service, can be a source of innovation and can enhance science by increasing its transparency, reproducibility, transferability and scale. Currently, however, there is little scientific understanding of the contribution of software work towards progress in science; it is often not even clear what software was used in advancing science and who ought to receive credit for that software's contribution. Lacking this citation mechanism, young scientists are implicitly discouraged from the work needed to build broadly useful software. Collaboration to improve existing software is also unintentionally discouraged. Releasing a package can create a unfortunate outcome, where a package's success brings support and maintenance burdens that overwhelm its creators, stalling emerging projects and discouraging future sharing. Both understanding the role of software work in science and overcoming the obstacles slowing its progress depend critically on knowing what software is actually used, how the use of various packages is orchestrated in scientific projects, and where resources can be most effectively deployed to facilitate software development, evolution, and support. In order to continuously acquire such knowledge, this project is designing and building the Scientific Software Network Map. This map will measure the scientific-software ecosystem by collecting data on which software tools have been used in the science leading to publications, the network of dependencies and complementarities from that software to other software packages, and how software use evolves over time. The project is working with scientists in software-heavy fields to research and design data-collection techniques, gather data, and design appropriate analyses. This project will also provide an initial foundation for a scientific understanding of the role of software in science and scientific innovation. Broader Impacts: The results of the project will improve the practical conduct of software-supported science as well as informing science policy decision-making related to software's application in science, research, and innovation projects. The publicly available Map that results will be more broadly useful in three mutually reinforcing ways. First, it will be a resource for software-contributing scientists to demonstrate the usefulness and impact of their work, providing a foundation for their motivated, sustained contribution and improving the sustainability of scientific software projects. Second, it will function as an information source for other scientists to know what software is being used in their field, generating common knowledge that reduces costly reimplementation and helping communities coalesce towards shared platforms. Third, it will help science funders identify evolving issues and opportunities in the software ecosystem -- such as the challenge of rapid adoption driving heavy but unanticipated support burdens, or early alerts about communities coalescing around nascent cores which, with timely, focused funding support, can be turned into the software platforms that will drive future scientific innovation.