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Data and code files for co-occurrence modeling project
Files included are original data inputs on stream fishes (fish_data_OEPA_2012.csv), water chemistry (OEPA_WATER_2012.csv), geographic data (NHD_Plus_StreamCat); modeling files for generating predictions from the original data, including the R code (MVP_R_Final.txt) and Stan code (MV_Probit_Stan_Final.txt); and the model output file containing predictions for all NHDPlus catchments in the East Fork Little Miami River watershed (MVP_EFLMR_cooc_Final).
This dataset is associated with the following publication:
Martin, R., E. Waits, and C. Nietch. Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 613(614): 1228-1239, (2018).
Resources
6 resources available
-
MVP_EFLMR_cooc_Final.txt
TEXT/PLAIN -
MVP_R_Final.txt
TEXT/PLAIN -
MV_Probit_Stan_Final.txt
TEXT/PLAIN -
NHD_Plus_StreamCat.csv
APPLICATION/VND.MS-EXCEL -
OEPA_WATER_2012.csv
APPLICATION/VND.MS-EXCEL -
fish_data_OEPA_2012.csv
APPLICATION/VND.MS-EXCEL
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "020:00" ] |
| contactPoint |
{ "fn": "Roy Martin", "hasEmail": "mailto:martin.roy@epa.gov" } |
| describedBy | https://pasteur.epa.gov/uploads/10.23719/1407576/documents/EFLMR_cooc_data_dictionary_Final.xlsx |
| describedByType | application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
| description | Files included are original data inputs on stream fishes (fish_data_OEPA_2012.csv), water chemistry (OEPA_WATER_2012.csv), geographic data (NHD_Plus_StreamCat); modeling files for generating predictions from the original data, including the R code (MVP_R_Final.txt) and Stan code (MV_Probit_Stan_Final.txt); and the model output file containing predictions for all NHDPlus catchments in the East Fork Little Miami River watershed (MVP_EFLMR_cooc_Final). This dataset is associated with the following publication: Martin, R., E. Waits, and C. Nietch. Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 613(614): 1228-1239, (2018). |
| distribution |
[ { "title": "MVP_EFLMR_cooc_Final.txt", "mediaType": "text/plain", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1407576/MVP_EFLMR_cooc_Final.txt" }, { "title": "MVP_R_Final.txt", "mediaType": "text/plain", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1407576/MVP_R_Final.txt" }, { "title": "MV_Probit_Stan_Final.txt", "mediaType": "text/plain", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1407576/MV_Probit_Stan_Final.txt" }, { "title": "NHD_Plus_StreamCat.csv", "mediaType": "application/vnd.ms-excel", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1407576/NHD_Plus_StreamCat.csv" }, { "title": "OEPA_WATER_2012.csv", "mediaType": "application/vnd.ms-excel", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1407576/OEPA_WATER_2012.csv" }, { "title": "fish_data_OEPA_2012.csv", "mediaType": "application/vnd.ms-excel", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1407576/fish_data_OEPA_2012.csv" } ] |
| identifier | https://doi.org/10.23719/1407576 |
| keyword |
[ "Bayesian statistics", "Co-occurrence model", "Co-occurrence model predictions", "East Fork Little Miami River watershed data", "Fish community bioassessment data", "R code", "Stan code", "Water chemistry bioassessment data", "co-occurrence models", "ecological exposure assessment", "ecological risk mapping" ] |
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2017-07-13 |
| programCode |
[ "020:095" ] |
| publisher |
{ "name": "U.S. EPA Office of Research and Development (ORD)", "subOrganizationOf": { "name": "U.S. Environmental Protection Agency", "subOrganizationOf": { "name": "U.S. Government" } } } |
| references |
[ "https://doi.org/10.1016/j.scitotenv.2017.08.301" ] |
| rights |
null
|
| title | Data and code files for co-occurrence modeling project |
