http://lod.bco-dmo.org/id/dataset/963375
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2025-05-30
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Input data and code to test hypotheses between genetic and environmental effects on phenotypes
2025-06-05
publication
2025-06-05
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2025-06-05
publication
https://doi.org/10.26008/1912/bco-dmo.963375.1
Katie Lotterhos
Northeastern University
principalInvestigator
Geoffrey C. Trussell
Northeastern University
principalInvestigator
Molly Albecker
Northeastern University
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Lotterhos, K., Trussell, G. C., Albecker, M. (2025) Input data and code to test hypotheses between genetic and environmental effects on phenotypes. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-06-05 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.963375.1 [access date]
Input data and code (CovGE) Dataset Description: Methods and Sampling: <p>There were three major phases to this study which are pertinent for data storage and collection.&nbsp;</p>
<p>1. The first phase was a quantitative review in which the literature was systematically surveyed, assessed for inclusion, and relevant data extracted. This process resulted in the dataset "Extraction_Initialize.csv", which contains metadata from all studies initially considered for data collection. In some cases, studies were later deemed inappropriate and excluded from subsequent phases, but their metadata remains in this file. Once data collection was completed for the 102 studies that met the inclusion criteria, all phenotypic data—used in the second phase—were compiled into a single .csv file, "Meta_data_checked.csv".&nbsp;</p>
<p>2. The second phase involved data analysis, where the phenotypic data and metadata were imported into R, formatted, and processed using a for-loop to estimate CovGE and ΔG×E for each phenotype independently. This loop fully implements CovGE estimation following the methods outlined in Albecker et al. (2022) and also generates confidence intervals for CovGE and p-values for ΔG×E. This analysis was executed in lines 1–220 of the "CovGE_Meta_Analysis.R" script, with essential functions housed in the companion script "CovarianceDataFunctions.R". Results of this analysis were saved as, "Meta_output_Nov27_2024.csv"&nbsp;</p>
<p>3. The third phase involved testing the questions/hypotheses outlined in the manuscript (e.g., Is CnGV or CoGV more prevalent in nature?). These analyses were conducted in the "CovGE_Meta_Analysis.R" script, following the conclusion of the primary analysis on Line 220. This phase also included all plotting and formatting code. Any modifications made to plots in PowerPoint were documented with notes. All code is fully annotated for clarity and reproducibility.</p>
<p>&nbsp;</p>
<p>Files in this dataset:</p>
<p>1. Code file - "CovGE_Meta_Analysis.R" - R code including all analyses, data formatting, and plotting. See phase 2 and 3&nbsp;<br />
2. Code file - "CovarianceDataFunctions.R" - R companion code storing all essential functions for analyses. See phase 2 and 3.&nbsp;&nbsp;Compiled dataset with the extracted phenotypic, genotypic, and environmental data for each study included in quantitative review. See Phase 1 in the description of Contents for additional details.&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1764316 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1764316
completed
Katie Lotterhos
Northeastern University
781-581-7370 X304
Northeastern University Marine Sciences Center 430 Nahant Rd.
Nahant
MA
01908
USA
k.lotterhos@northeastern.edu
pointOfContact
Geoffrey C. Trussell
Northeastern University
781-581-7370 (ext. 300)
Marine Science Center 430 Nahant Road
Nahant
MA
01908
USA
g.trussell@northeastern.edu
pointOfContact
Molly Albecker
Northeastern University
Department of Marine and Environmental Sciences
Boston
MA
01908
USA
maalbecker@uh.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
Study_ID_phenotype
First_Author
gen_factor
nat_env_factor
exp_env_factor
phen_n
phen_data
phen_SD
phen_mean_SE
phen_mean_lowCI_095
phen_mean_highCI_095
Source
theme
None, User defined
sample identification
author name (publication)
sample description
standard deviation
standard error
document_name
featureType
BCO-DMO Standard Parameters
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
RCN: Evolution in Changing Seas
https://rcn-ecs.github.io/
RCN: Evolution in Changing Seas
<p><em>NSF abstract:</em><br />
How marine species will react to changing environment and climate is not well understood. While the interaction between oceanographic and ecological processes has yielded considerable insight into the ecology of marine species, the evolutionary responses of marine species are not well integrated into this framework. This project research coordinated network on "Evolution in Changing Seas" (ECSRCN), will bring marine scientists together with evolutionary biologists having expertise in population genetics, eco-evolutionary dynamics, and phylogenetics to better understand and predict the evolutionary responses of marine species to climate stressors. ECS-RCN will increase the impact of evolutionary studies in marine systems through increased collaboration among scientists from diverse fields. Furthermore, the empirical robustness of these studies will also be improved through the development of standards for experimental design and statistical analysis, especially for genomics data analysis. ECS-RCN will build a diverse network through a dedicated workshop for early-career participants, by advertising with diversity groups, and by dedicating funds to increase diversity. This project will support one postdoctoral researcher who will play a key role in coordinating scientific activities of the network as well as receive interdisciplinary training through network activities, strongly positioning them to become a leader in the field. ECS-RCN will also build the foundation for a lasting network through establishment of a listserv, open access to publications, development of a website, and development of teaching modules for undergraduate and graduate curriculum.</p>
<p>Specifically, ECS-RCN will consider how coupling between oceanographic and evolutionary processes shape adaptive and plastic responses to climate change, from the fundamental level of genomes scaled up to entire populations. Under this theme, the objectives of ECS-RCN are to synthesize the current state of knowledge, to prioritize lines of inquiry that will advance knowledge in marine and evolutionary biology, to determine the appropriate experimental designs and statistical approaches for robustly testing these lines of inquiry (including genomics approaches), and to build a foundation for a diverse and lasting network. These goals will be realized over the course of 3 years, starting with a Synthesis Workshop in Year 1 where working groups will be established, followed by working group meetings and formation of a Genomics Subcommittee in Year 2, and ending with an Integration and Training Workshop aimed at early career scientists in Year 3. To promote synthesis and self-organization at workshops, the workshops will employ the Open Space format. ECS-RCN will promote evolutionary thinking in biological oceanography and integrate unique aspects of marine life-histories into evolutionary principles. ECS-RCN will also advance knowledge in both marine and evolutionary biology through synthesis and the development of frameworks for merging genomics and ecology. The activities will provide novel insights into pressing questions in both marine and evolutionary ecology, such as: what drives geographic patterns of local (mal)adaptation and plasticity?; what are the mechanisms that generate adaptive vs. nonadaptive plasticity?; what is the role of genotype dependent dispersal in adaptation?; what are the genetic constraints on adaptation of function-valued traits to climate change?; and how do epigenetic modifications act as a mediator between adaptation and plasticity? Ultimately, the RCN aims to develop a quantitative understanding of the relative importance of ecological versus evolutionary responses to climate change.</p>
<p>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.</p>
RCN ECS
largerWorkCitation
project
eng; USA
oceans
2025-06-05
United States
0
BCO-DMO catalogue of parameters from Input data and code to test hypotheses between genetic and environmental effects on phenotypes
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/963581.rdf
Name: Study_ID_phenotype
Units: unitless
Description: <p>Identifier combining study id and phenotype</p>
http://lod.bco-dmo.org/id/dataset-parameter/963582.rdf
Name: First_Author
Units: unitless
Description: <p>First name of author of study</p>
http://lod.bco-dmo.org/id/dataset-parameter/963583.rdf
Name: gen_factor
Units: unitless
Description: <p>Assigned name for genotype with "G_" and numeric</p>
http://lod.bco-dmo.org/id/dataset-parameter/963584.rdf
Name: nat_env_factor
Units: unitless
Description: <p>The native environment of each genotype (started with N_ and switched to E_ later - in code, all are switched to E_).</p>
http://lod.bco-dmo.org/id/dataset-parameter/963585.rdf
Name: exp_env_factor
Units: unitless
Description: <p>Assigned categorical label for each environmental treatment - one must correspond with a native environment. These all begin with "E_" and end with numeric.</p>
http://lod.bco-dmo.org/id/dataset-parameter/963586.rdf
Name: phen_n
Units: unitless
Description: <p>How many samples were collected in each genotype/environment</p>
http://lod.bco-dmo.org/id/dataset-parameter/963587.rdf
Name: phen_data
Units: unitless
Description: <p>The data for the phenotype from the genotype in that environment</p>
http://lod.bco-dmo.org/id/dataset-parameter/963588.rdf
Name: phen_SD
Units: unitless
Description: <p>If means and standard deviation were collected, enter SD in this column</p>
http://lod.bco-dmo.org/id/dataset-parameter/963589.rdf
Name: phen_mean_SE
Units: unitless
Description: <p>If means and standard error were collected, enter SE in this column</p>
http://lod.bco-dmo.org/id/dataset-parameter/963590.rdf
Name: phen_mean_lowCI_095
Units: unitless
Description: <p>If means and confidence intervals were collected, enter lower CI value in this column</p>
http://lod.bco-dmo.org/id/dataset-parameter/963591.rdf
Name: phen_mean_highCI_095
Units: unitless
Description: <p>If means and confidence intervals were collected, enter upper CI value in this column</p>
http://lod.bco-dmo.org/id/dataset-parameter/963592.rdf
Name: Source
Units: unitless
Description: <p>Lists original, unique xlsx file</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
6583598
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/b663ca8e-4ce7-41c6-b9ed-cc6b9af0357b/content
download
https://doi.org/10.26008/1912/bco-dmo.963375.1
download
onLine
dataset
<p>There were three major phases to this study which are pertinent for data storage and collection.&nbsp;</p>
<p>1. The first phase was a quantitative review in which the literature was systematically surveyed, assessed for inclusion, and relevant data extracted. This process resulted in the dataset "Extraction_Initialize.csv", which contains metadata from all studies initially considered for data collection. In some cases, studies were later deemed inappropriate and excluded from subsequent phases, but their metadata remains in this file. Once data collection was completed for the 102 studies that met the inclusion criteria, all phenotypic data—used in the second phase—were compiled into a single .csv file, "Meta_data_checked.csv".&nbsp;</p>
<p>2. The second phase involved data analysis, where the phenotypic data and metadata were imported into R, formatted, and processed using a for-loop to estimate CovGE and ΔG×E for each phenotype independently. This loop fully implements CovGE estimation following the methods outlined in Albecker et al. (2022) and also generates confidence intervals for CovGE and p-values for ΔG×E. This analysis was executed in lines 1–220 of the "CovGE_Meta_Analysis.R" script, with essential functions housed in the companion script "CovarianceDataFunctions.R". Results of this analysis were saved as, "Meta_output_Nov27_2024.csv"&nbsp;</p>
<p>3. The third phase involved testing the questions/hypotheses outlined in the manuscript (e.g., Is CnGV or CoGV more prevalent in nature?). These analyses were conducted in the "CovGE_Meta_Analysis.R" script, following the conclusion of the primary analysis on Line 220. This phase also included all plotting and formatting code. Any modifications made to plots in PowerPoint were documented with notes. All code is fully annotated for clarity and reproducibility.</p>
<p>&nbsp;</p>
<p>Files in this dataset:</p>
<p>1. Code file - "CovGE_Meta_Analysis.R" - R code including all analyses, data formatting, and plotting. See phase 2 and 3&nbsp;<br />
2. Code file - "CovarianceDataFunctions.R" - R companion code storing all essential functions for analyses. See phase 2 and 3.&nbsp;&nbsp;Compiled dataset with the extracted phenotypic, genotypic, and environmental data for each study included in quantitative review. See Phase 1 in the description of Contents for additional details.&nbsp;</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact