CAREER: What’s next? Developing novel quantitative tools to address conflicting evidence in temporal ecology

大数据革命正在改变科学家解释和预测生态系统行为的方式。传感器网络、生态观测站和从经典实验中共享的开放数据可以汇集在一起，提供更全面的图景。然而，由于环境的可变性和（重要的是）由于人类的选择导致的观察方式的细微差异，在组合来自多个来源的数据时经常会出现问题。当研究过程如何随着时间的推移而展开，导致相互矛盾的解释时，这些问题可能会加剧。例如，最近几项研究调查了全球范围内昆虫数量下降的证据，根据所选择的数据和研究计算的方式，得出了一系列可能的结论。这个职业奖的目标是通过建立和评估结合生态数据的工具来理解和解释这些差异，并为生态学家提供新的方法来评估他们在生态系统中观察到的长期模式的不确定性。综合教育和推广计划将使在职科学家能够灵活、批判性和透明地看待数据和分析，并将为学生如何概念化数据和促进公众数据素养提供见解。有效综合大尺度生态数据是解决各种环境问题的关键，并有可能成为生态预测的基础。由于生态系统通常具有非线性行为，了解动态系统中的过程轨迹和变化点是描述其未来行为的关键方面，但对数据质量和综合问题的不充分诊断可能掩盖或混淆这些模式。该职业奖通过三个具体目标解决了技术和文化差距，这些差距阻碍了生态学中时间过程的有意义的综合。目标1)开发数据集成的新方法和评估生态时间序列数据趋势可靠性的工具。这将通过比较实地实验来评估生物多样性监测策略、数据挖掘方法和开发用于处理时间数据的新型软件产品来实现。目标2)开发一个新的研究生定量方法课程，整合了关键的计算能力和历史背景。目标3)与社会学家和教育专家合作开发一个跨学科播客，以促进社会对数据和信息的理解，并突出早期职业和少数族裔科学家的工作，为“知道”和数据问题提供不同的背景。该项目的结果，包括出版物、课程和播客的链接，可以在https://bahlailab.org/This上找到。奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The big data revolution is changing how scientists approach explaining and predicting the behavior of ecological systems. Sensor networks, ecological observatories, and open data shared from classical experiments can be brought together to provide more holistic picture. However, problems often arise when combining data from multiple sources because of both environmental variability and, importantly, subtle differences in howobservations are made as a result of human choices. These issues can be exacerbated when studying how processes that unfold over time, leading to contradictory interpretations. For example, several recent studies which examined evidence of insect populations declining at a global level have found a full range of possible conclusions, depending on the data selected and the way in which the studies’ calculations were performed. The objective of this CAREER award is to understand and explain these discrepancies by building and evaluating tools for combining ecological data and to provide new approaches for ecologists to evaluate the uncertainty in the patterns they observe over time in ecological systems. An integrated education and outreach program will enable scientists-in-training to take a flexible, critical and transparent view of data and analytics, and will also offer insights into how students conceptualize data and promote public data literacy. Effective synthesis of broad scale ecological data is key to tackling a wide variety of environmental problems and has the potential to form the basis of ecological forecasting. Because ecological systems often have non-linear behavior, understanding process trajectory and changepoints in dynamical systems is a key aspect in describing their future behavior, but inadequate diagnostics for data quality and synthesis issues can mask or confound these patterns. This CAREER award addresses both technical and cultural gaps preventing meaningful synthesis of temporal processes in ecology through three specific aims. Aim 1) develop novel approaches to data integration and tools for evaluating the reliability of trends in ecological timeseries data. This will be achieved through comparative field experiments to assess biodiversity monitoring strategies, data mining approaches, and development of novel software products for processing temporal data. Aim 2) develop a new graduate quantitative methods course which integrates critical numeracy and historical context. Aim 3) develop an interdisciplinary podcast in collaboration with sociologists and education specialists to advance societal understanding of data and information, as well as highlight the work of early-career and minoritized scientists to give diverse context to issues of "knowing" and data. Results of this project, including links to publications, curriculum, and podcast episodes can be found at https://bahlailab.org/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.