OPUS: CRS--Experiments, Meta-analysis and Big Data in Advancing Ecological Understanding

OPUS：CRS——促进生态理解的实验、荟萃分析和大数据

• 批准号: 1909233
• 负责人: Jessica Gurevitch
• 金额: $ 22.97万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909233&HistoricalAwards=false
• 关键词: OPUS; CRS; Experiments; Meta; analysis

Science relies on both theory and data. Data provide the information that guides and tests theory. Together, theory and data advance our understanding of the natural world. Ecological data once emphasized the observations and controlled environment experiments of individual scientists, but progressed to field experiments in natural systems, then to multi-investigator studies and research synthesis, and more recently, to the analysis of "big data," studies based on large electronic databases that include climate, geography, land use, and ecological data. Experiments are the most rigorous way to test hypotheses and disentangle potential confounding factors, but face challenges and limitations. For example, experiments in natural populations have the great advantage of realism, but are limited because they are almost always conducted at local spatial scales. This project will examine how individual experiments, the synthesis of data from many experiments using meta-analysis, and analyses of "big data" at large spatial scales, have brought different perspectives and understanding to ecology over the past 50 years, and changed our understanding of the natural world. Broader impacts of this research will include seminars and training for freshman, graduate students and professionals, presentations, and a YouTube video on how to record, save, organize, and make scientific data in ecology available and transparent.We have witnessed and will continue to experience a major shift in emphasis in how we gather information used to understand the natural world. Can we still answer important fundamental and applied questions through experiments carried out by individual researchers, or is ecology best situated within multi-investigator research programs? Are questions different in individual experiments, large group studies, meta-analyses and analysis of large databases? This study will examine five hypotheses, focusing on plant population, community and macro-ecology. H1: The role of individual investigator field experiments in advancing ecological understanding ecology, once central, is now more secondary; H2: Field experiments have declined from a peak from the mid-1970s to 1990s, to the present; H3: Observational macroecological observational studies using "big data" have concordantly increased in frequency, funding, visibility and prestige; H4: The role of meta-analysis and research synthesis has increased exponentially from the late 1990s to the present; and H5: The focus of work addressed by individual experiments, large group experiments, meta-analysis and the analysis of observational big data differ systematically in spatial scale, topics, questions asked, and insights and understanding gained.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.

科学既依赖于理论，也依赖于数据。数据提供了指导和检验理论的信息。理论和数据共同推动了我们对自然世界的理解。生态数据曾经强调科学家个人的观察和受控环境实验，但发展到自然系统的实地实验，然后是多研究者研究和研究综合，最近又发展到“大数据”分析，即基于包括气候、地理、土地利用和生态数据的大型电子数据库的研究。实验是检验假设和解开潜在混杂因素的最严格方法，但面临挑战和局限性。例如，自然种群中的实验具有现实性的巨大优势，但也受到限制，因为它们几乎总是在局部空间尺度上进行。该项目将研究单个实验，使用元分析的许多实验数据的合成，以及在大空间尺度上对“大数据”的分析，如何在过去50年中为生态学带来不同的视角和理解，并改变我们对自然世界的理解。这项研究的更广泛的影响将包括研讨会和培训新生，研究生和专业人士，演示文稿，以及YouTube视频如何记录，保存，组织，并使生态学科学数据可用和透明。我们已经见证了，并将继续经历一个重大转变，重点是我们如何收集用于了解自然世界的信息。我们仍然可以通过个别研究人员进行的实验来回答重要的基础和应用问题，或者生态学最好位于多研究者研究计划中？在个体实验、大群体研究、荟萃分析和大型数据库分析中，问题是否不同？本研究将检验五个假设，重点是植物种群，群落和宏观生态。H1：个人研究者实地实验在促进生态学理解方面的作用曾经是核心，现在更是次要的; H2：实地实验从20世纪70年代中期到20世纪90年代的高峰下降到现在; H3：使用“大数据”的观察性宏观生态观察研究在频率、资金、知名度和声望方面一致增加; H4：从20世纪90年代末到现在，荟萃分析和研究综合的作用呈指数级增长; H5：个体实验、大群体实验、荟萃分析和观察性大数据分析所解决的工作重点在空间尺度、主题、问题、该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。