4.5 Article

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

期刊

ECOSPHERE
卷 10, 期 5, 页码 -

出版社

WILEY
DOI: 10.1002/ecs2.2753

关键词

big data; computational ecology; computer programming; cyberinfrastructure; information technology; interdisciplinary; modeling; quantitative literacy; software; team science

类别

资金

  1. National Science Foundation (NSF) [ACI 1234983, ACI 1339737, CNS 1737424, CNS 1527415, EF 1702506, ICER 1517823]
  2. PRAGMA-GLEON workshop
  3. larger PRAGMA community

向作者/读者索取更多资源

In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high-frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long-term and high-frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long-term.

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