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Related Concept Videos

Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...

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Related Experiment Video

Updated: May 22, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

Synthetic control methods enable stronger causal inference using participatory science data in cities.

Asia Kaiser1, Julian Resasco2, Laura E Dee2

  • 1Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA. asiakaiser@gmail.com.

Nature Ecology & Evolution
|May 20, 2026
PubMed
Summary

Urban biodiversity monitoring is difficult. Using iNaturalist data with synthetic controls revealed a 15.5-20.9% decline in bee observations after Hurricane Ida, a finding missed by traditional methods.

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Last Updated: May 22, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
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Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

Area of Science:

  • Urban ecology
  • Biodiversity science
  • Environmental science

Background:

  • Urban environments present unique challenges for biodiversity monitoring due to fragmented land ownership.
  • Participatory science platforms like iNaturalist provide extensive urban biodiversity data but inferring causality is difficult.

Purpose of the Study:

  • To develop a framework combining iNaturalist data with synthetic-control methods for quasi-experimental analysis in urban ecology.
  • To assess the impact of Hurricane Ida on bee abundance in Philadelphia using iNaturalist data.

Main Methods:

  • Utilized iNaturalist records as a proxy for bee abundance.
  • Applied synthetic-control methods, a quasi-experimental approach, to estimate the impact of Hurricane Ida.
  • Compared results with conventional ecological analyses including interrupted time-series regression, before-after comparison, and before-after control impact design.

Main Results:

  • The synthetic-control method estimated a 15.5-20.9% decline in bee observations in the two years following Hurricane Ida.
  • Conventional ecological analyses failed to detect this significant decline in bee observations.

Conclusions:

  • Synthetic-control methods offer a powerful tool for estimating urban biodiversity responses to climate events and policy interventions.
  • This approach enhances the utility of participatory science data for urban ecology research.