评估为生物医学研究设计的软件影响力：我们是否在衡量有意义的东西？
Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?
AI总结：
本文针对生物医学软件影响力评估中指标失真、小众工具价值被低估等问题，结合ITCR社区调查与软件基础设施分析，提出通用及分类评估策略，并发现文档、健康指标等基础设施与使用率提升相关。
摘要：
软件对于生物学和医学的进步至关重要。对使用情况和影响力指标进行分析，可以帮助开发者确定用户和社区的参与度，证明追加资金的合理性，鼓励进一步使用，识别意料之外的用例，并帮助确定需要改进的领域。然而，这些分析也面临一些挑战，包括扭曲或具有误导性的指标，以及伦理和安全方面的担忧。需要更加关注在各类生物学软件中捕捉影响力所涉及的细微差别。此外，某些工具可能对一小部分受众特别有益，但可能并不具备引人注目的典型使用指标。我们提出了更通用的指导原则，以及针对更具体软件类型的策略。我们重点指出了各社区在如何衡量或评估软件影响力方面尚未解决的问题。为了更深入地了解当前软件评估的实践，我们对由美国国家癌症研究所（NCI）资助的癌症研究信息技术（ITCR）项目的参与者进行了一项调查。我们还调查了该社区及其他社区中的软件，以评估支持此类评估的基础设施的实施频率，以及这如何影响描述该软件使用情况的论文比例。我们发现，开发者认识到分析软件使用情况的效用，但难以为此类分析找到时间或资金。我们还发现，社交媒体存在、更深入的文档、软件健康指标的存在以及关于如何联系开发者的清晰信息等基础设施，似乎与使用率的提高相关。我们的研究结果可以帮助科学软件开发者充分利用对其软件的评估。
英文摘要：
Software is vital for the advancement of biology and medicine. Analysis of usage and impact metrics can help developers determine user and community engagement, justify additional funding, encourage additional use, identify unanticipated use cases, and help define improvement areas. However, there are challenges associated with these analyses including distorted or misleading metrics, as well as ethical and security concerns. More attention to the nuances involved in capturing impact across the spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have compelling typical usage metrics. We propose more general guidelines, as well as strategies for more specific types of software. We highlight outstanding issues regarding how communities measure or evaluate software impact. To get a deeper understanding of current practices for software evaluations, we performed a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that developers recognize the utility of analyzing software usage, but struggle to find the time or funding for such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of evaluations of their software.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
