Collaborative Research: Explaining Differential Success in Biodiversity Knowledge Commons

合作研究：解释生物多样性知识共享的不同成功

• 批准号: 2122818
• 负责人: Beckett Sterner
• 金额: $ 29.21万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122818&HistoricalAwards=false
• 关键词: Collaborative; Research; Explaining; Differential; Success

Scientists increasingly rely on community-governed digital portals to store and access open data. While these digital portals have improved access to data and other scientificproducts, portals are expensive to implement, challenging to maintain, and often fail to have uptake among key stakeholders. Like those working in other areas of the digital economy, scientists have increasingly adopted platforms to implement and tailor portals to particular communities and needs, a model that lowers infrastructure costs and enables benefits of scale across networked portals. Yet portals built from the same platform nonetheless show a large variation in their outcomes. Participation in any particular portal is often short-lived and the impacts of data portals on the scientific process are challenging to evaluate; portal usage typically fails to map onto traditional measures of research productivity such as publications and citation counts. This project is the first to systematically investigate scientific data portals built from a common platform in order to understand portal communities and outcomes. The research design, which compares biodiversity data portals, will inform studies of other platforms and digital knowledge commons, such as open source software and peer-production communities. Specific findings and recommendations will be shared with biodiversity portal stakeholders regarding the effective design and use of these portals in order to improve uptake and access to these important species data and facilitate science and decision making around environmental change.This project uses fuzzy set Qualitative Comparative Analysis (fsQCA) to analyze open data portals as a kind of knowledge commons that impose minimal restrictions on access or reuse. The study sample comprises the 37 active and 4 inactive biodiversity data portals built from the Symbiota platform, one of the largest and earliest scientific data platforms still under continual development, with hundreds of participating biodiversity collections and several dozen individually managed portals. In 2020, Symbiota portals collectively providedaccess to over 60 million biodiversity data records and accounted for 90% of Web traffic accessing specimens digitized through the NSF’s Advanced Digitization of Biodiversity Collections (ADBC) program, which has invested over $50 million in this area to date. The project synthesizes across multiple types and sources of quantitative and qualitative data to identify why some of these portals achieve sustained growth and others do not. To do so, the project collects and analyzes up to ten years of analytics and other information from the portals, including tracked usage data, community building activities, features of portal governance, and resource inputs, as well as observation data collected during portal site visits and interviews with a stratified sample of portal managers. The fsQCA approach enables comparative inferences about those portal features most likely to foster productive and sustained outcomes, including collective benefit and building inclusive scientific communities.This 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.

科学家们越来越依赖社区管理的数字门户来存储和访问开放数据。虽然这些数字门户改善了对数据和其他科学产品的获取，但门户的实施成本高昂，维护起来具有挑战性，而且往往无法在关键利益攸关方中得到应用。与那些在数字经济的其他领域工作的人一样，科学家们越来越多地采用平台来实施和定制门户，以满足特定的社区和需求，这种模式降低了基础设施成本，并实现了跨联网门户的规模效益。然而，建立在同一平台上的门户网站的结果却有很大不同。对任何特定门户的参与往往是短暂的，数据门户对科学进程的影响难以评估；门户的使用通常无法与传统的研究生产力衡量标准相对应，如出版物和引文计数。该项目是第一个系统调查建立在公共平台上的科学数据门户的项目，以便了解门户社区和成果。这项研究设计比较了生物多样性数据门户，将为其他平台和数字知识共享的研究提供信息，如开放源码软件和同行生产社区。将与生物多样性门户网站的利益相关者分享关于有效设计和使用这些门户网站的具体结果和建议，以改善对这些重要物种数据的吸收和获取，并促进围绕环境变化的科学和决策。该项目使用模糊集比较分析(FsQCA)来分析开放数据门户网站，将其作为一种对访问或重复使用施加最小限制的知识共享空间。研究样本包括从共生区平台建立的37个活跃和4个非活跃的生物多样性数据门户，该平台是仍在持续开发的最大和最早的科学数据平台之一，有数百个参与的生物多样性收集和数十个单独管理的门户。2020年，共生区门户网站总共提供了超过6000万条生物多样性数据记录的访问，占访问通过美国国家科学基金会生物多样性收集高级数字化(ADBC)计划数字化的标本的网络流量的90%，该计划迄今已在这一领域投资超过5000万美元。该项目综合了多种类型和来源的定量和定性数据，以确定为什么其中一些门户网站实现了持续增长，而另一些门户网站则没有。为此，该项目从门户网站收集和分析长达十年的分析和其他信息，包括跟踪的使用数据、社区建设活动、门户网站治理的特点和资源投入，以及在门户网站访问和与门户网站经理分层抽样面谈期间收集的观察数据。FsQCA方法能够对最有可能促进生产性和可持续成果的门户功能进行比较推断，包括集体利益和建立包容性科学社区。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。