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Overview

Stephen Burgess is affiliated with the University of Cambridge in the United Kingdom. Their research focuses primarily on Environmental Science, with contributions spanning various related subfields.

The main fields of study in their work include:

  • Environmental Science

Within Environmental Science, their subfields of study cover:

  • Global and Planetary Change
  • Ecology
  • Nature and Landscape Conservation
  • Atmospheric Science

The main topics addressed in their research are:

  • Plant Water Relations and Carbon Dynamics
  • Ecology and Vegetation Dynamics Studies
  • Remote Sensing in Agriculture
  • Tree-ring Climate Responses
  • Hydrology and Sediment Transport Processes

Stephen Burgess has contributed to several recent papers, including:

  • How Climate Shapes the Functioning of Tropical Montane Cloud Forests, 2020, published in Current Forestry Reports
  • Maximum Heat Ratio: Bi-directional Method for Fast and Slow Sap Flow Measurements, 2021, published in Plant and Soil
  • Correction to: Maximum Heat Ratio: Bi-directional Method for Fast and Slow Sap Flow Measurements, 2023, published in Plant and Soil

Their frequent co-authors include:

  • Jose Gutiérrez López
  • Thomas G. Pypker
  • Julián Licata
  • Heidi Asbjornsen
  • Cleiton B. Eller

The primary venues where their work has been published are:

  • Plant and Soil
  • Current Forestry Reports

Best Publications

  • Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

    Jack Bowden;George Davey Smith;Stephen Burgess

  • Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator.

    Jack Bowden;George Davey Smith;Philip C. Haycock;Stephen Burgess

  • The MR-Base platform supports systematic causal inference across the human phenome

    Gibran Hemani;Jie Zheng;Benjamin Elsworth;Kaitlin H Wade

  • Mendelian randomization analysis with multiple genetic variants using summarized data.

    Stephen Burgess;Adam Butterworth;Simon G. Thompson

  • Interpreting findings from Mendelian randomization using the MR-Egger method

    Stephen Burgess;Simon G. Thompson

  • Avoiding bias from weak instruments in Mendelian randomization studies

    Stephen Burgess;Simon G Thompson

  • A review of instrumental variable estimators for Mendelian randomization

    Stephen Burgess;Dylan S Small;Simon Gregory Thompson

  • PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations

    Mihir A Kamat;James A Blackshaw;Robin Young;Praveen Surendran

  • MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data.

    Olena O Yavorska;Stephen Burgess

  • Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants

    Stephen Burgess;Jack Bowden;Tove Fall;Erik Ingelsson

  • Guidelines for performing Mendelian randomization investigations.

    Stephen Burgess;George Davey Smith;Neil M Davies;Frank Dudbridge

  • PhenoScanner: a database of human genotype-phenotype associations.

    James R. Staley;James A. Blackshaw;Mihir A. Kamat;Steve Ellis

  • Bias due to participant overlap in two-sample Mendelian randomization

    Stephen Burgess;Neil M. Davies;Simon G. Thompson

  • Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

    Stephen Burgess;Robert A. Scott;Nicholas J. Timpson;George Davey Smith

  • A macroscope in the redwoods

    Gilman Tolle;Joseph Polastre;Robert Szewczyk;David Culler

  • Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators

    Brandon L. Pierce;Stephen Burgess

  • Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods

    Stephen Burgess;Frank Dudbridge;Simon Gregory Thompson

  • Re: “Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects”

    Stephen Burgess;Frank Dudbridge;Simon G. Thompson

  • Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

    Angela M. Wood;Stephen Kaptoge;Adam S. Butterworth;Peter Willeit

  • An improved heat pulse method to measure low and reverse rates of sap flow in woody plants.

    Stephen S. O. Burgess;Mark A. Adams;Neil C. Turner;Craig R. Beverly

Frequent Co-Authors

Susanna C. Larsson
Susanna C. Larsson Karolinska Institute
John Danesh
John Danesh University of Cambridge
Adam S. Butterworth
Adam S. Butterworth University of Cambridge
Simon G. Thompson
Simon G. Thompson University of Cambridge
George Davey Smith
George Davey Smith University of Bristol
Emanuele Di Angelantonio
Emanuele Di Angelantonio University of Cambridge
Nicholas J. Wareham
Nicholas J. Wareham University of Cambridge
Claudia Langenberg
Claudia Langenberg Queen Mary University of London
Børge G. Nordestgaard
Børge G. Nordestgaard University of Copenhagen
Peter Willeit
Peter Willeit Innsbruck Medical University

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