cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30615452
title: Code + Data for COG Identification

description: To identify clusters of orthologous genes (COGs) that correlate with nutrient limitation in the modern ocean, we examined the Ocean Microbial Reference Catalog v2 (OM-RGC.v2) from the <i>Tara</i> Oceans Project. The OM-RGC.v2 includes relative gene abundances of all COGs (<i>n</i> = 4,787) in 139 <i>Tara</i> Oceans metagenomic samples, along with metadata information including phosphate, oxygen, and nitrate/nitrite concentrations. (Nitrate/nitrite values were reported together for OM-RGC v2.) Iron concentrations for <i>Tara</i><i> </i>Oceans samples were not available and were thus estimated using the PISCES2 model based on iron concentration model predictions for <i>Tara</i><i> </i>Oceans sampling locations as described in Table S1 of Caputi et al., 2019. Iron concentrations were predicted for surface and the deep chlorophyll maximum (DCM) only; iron concentrations for samples from the mesopelagic zone were not available under the PISCES2 model. All other metadata for <i>Tara</i><i> </i>Oceans samples were directly obtained from Salazar et al., 2019.Estimation of correlations between COGs and metadata information was performed using regression models. Compound poisson linear models were fitted in bulk using the MaAsLin2 software package (v. 1.18.0). Separate models were fit for each COG to analyze the effect of metadata variables on individual COG abundances. While the main focus was to investigate correlation with nutrient abundance, environmental metadata was included in the model to control for as many potential confounding effects as the data allowed. The following predictors were included in the final model (based on variables available from the Tara Oceans dataset): the size fraction at which the sample was taken, mean temperature, depth, salinity, mean oxygen concentration, PO<sub>4</sub> concentration, NO<sub>2</sub> + NO<sub>3</sub> concentration, iron concentration, and absolute latitude. Of these, the following predictors were log-transformed to allow greater model fit: depth, PO<sub>4</sub> concentration, NO<sub>2</sub> + NO<sub>3</sub> concentration. To the same end, the iron concentration was transformed by taking the square root, and the absolute value of the latitude was taken. Otherwise, no transformations or normalization was performed. No abundance cutoff was applied, but COGs present in less than one-third of the Tara Oceans samples were discarded in order to ensure that the COGs identified by the statistical model were meaningful.
DOI: 10.6084/m9.figshare.30615452.v1
title: Code + Data for COG Identification

description: To identify clusters of orthologous genes (COGs) that correlate with nutrient limitation in the modern ocean, we examined the Ocean Microbial Reference Catalog v2 (OM-RGC.v2) from the <i>Tara</i> Oceans Project. The OM-RGC.v2 includes relative gene abundances of all COGs (<i>n</i> = 4,787) in 139 <i>Tara</i> Oceans metagenomic samples, along with metadata information including phosphate, oxygen, and nitrate/nitrite concentrations. (Nitrate/nitrite values were reported together for OM-RGC v2.) Iron concentrations for <i>Tara</i><i> </i>Oceans samples were not available and were thus estimated using the PISCES2 model based on iron concentration model predictions for <i>Tara</i><i> </i>Oceans sampling locations as described in Table S1 of Caputi et al., 2019. Iron concentrations were predicted for surface and the deep chlorophyll maximum (DCM) only; iron concentrations for samples from the mesopelagic zone were not available under the PISCES2 model. All other metadata for <i>Tara</i><i> </i>Oceans samples were directly obtained from Salazar et al., 2019.Estimation of correlations between COGs and metadata information was performed using regression models. Compound poisson linear models were fitted in bulk using the MaAsLin2 software package (v. 1.18.0). Separate models were fit for each COG to analyze the effect of metadata variables on individual COG abundances. While the main focus was to investigate correlation with nutrient abundance, environmental metadata was included in the model to control for as many potential confounding effects as the data allowed. The following predictors were included in the final model (based on variables available from the Tara Oceans dataset): the size fraction at which the sample was taken, mean temperature, depth, salinity, mean oxygen concentration, PO<sub>4</sub> concentration, NO<sub>2</sub> + NO<sub>3</sub> concentration, iron concentration, and absolute latitude. Of these, the following predictors were log-transformed to allow greater model fit: depth, PO<sub>4</sub> concentration, NO<sub>2</sub> + NO<sub>3</sub> concentration. To the same end, the iron concentration was transformed by taking the square root, and the absolute value of the latitude was taken. Otherwise, no transformations or normalization was performed. No abundance cutoff was applied, but COGs present in less than one-third of the Tara Oceans samples were discarded in order to ensure that the COGs identified by the statistical model were meaningful.
DOI: 10.25346/s6lqjm3j/o7v9cu
title: msXL_344v_MMSI+RTI+W400G58_033_F.tif
DOI: 10.25346/s6lqjm3j/t2t9up
title: msXL_344v_MMSI+RTI+W400B47_037_F.tif
DOI: 10.25346/s6lqjm3j/vgwbrl
title: msXL_344v_MMSI+RTI+W385R25_028_F.tif
DOI: 10.25346/s6lqjm3j/mui6wb
title: msXL_344v_MMSI+RTI+W385G58_032_F.tif
DOI: 10.25346/s6lqjm3j/n4q51l
title: msXL_344v_MMSI+RTI+W385B47_036_F.tif
DOI: 10.25346/s6lqjm3j/fbigqx
title: msXL_344v_MMSI+RTI+W365R25_027_F.tif
DOI: 10.25346/s6lqjm3j/iogefq
title: msXL_344v_MMSI+RTI+W365G58_031_F.tif
DOI: 10.25346/s6lqjm3j/icvccu
title: msXL_344v_MMSI+RTI+W365B47_035_F.tif
DOI: 10.25346/s6lqjm3j/vojq1p
title: msXL_344v_MMSI+RTI+TX940IR_025_F.tif
DOI: 10.25346/s6lqjm3j/u74mad
title: msXL_344v_MMSI+RTI+TX735IR_024_F.tif
DOI: 10.25346/s6lqjm3j/vpkmnj
title: msXL_344v_MMSI+RTI+TX615RO_023_F.tif
DOI: 10.25346/s6lqjm3j/fxiyio
title: msXL_344v_MMSI+RTI+TX615IR_023_F.tif
DOI: 10.25346/s6lqjm3j/xpftma
title: msXL_344v_MMSI+RTI+TX1050R_026_F.tif
DOI: 10.25346/s6lqjm3j/fwg1ab
title: msXL_344v_MMSI+RTI+TX1050R_022_F.tif
DOI: 10.25346/s6lqjm3j/dviwrz
title: msXL_344v_MMSI+RTI+M1050IR_021_F.tif
DOI: 10.25346/s6lqjm3j/a5zahf
title: msXL_344v_MMSI+RTI+M1050IR_020_F.tif
DOI: 10.25346/s6lqjm3j/vlsjhy
title: msXL_344v_MMSI+RTI+M1050IR_019_F.tif
DOI: 10.25346/s6lqjm3j/hw4gym
title: msXL_344v_MMSI+RTI+M0940IR_018_F.tif
DOI: 10.25346/s6lqjm3j/gi6ku2
title: msXL_344v_MMSI+RTI+M0850IR_017_F.tif
DOI: 10.25346/s6lqjm3j/burn6q
title: msXL_344v_MMSI+RTI+M0780IR_016_F.tif
DOI: 10.25346/s6lqjm3j/wukwcw
title: msXL_344v_MMSI+RTI+M0735IR_015_F.tif
DOI: 10.25346/s6lqjm3j/yqgx34
title: msXL_344v_MMSI+RTI+M0700IR_014_F.tif
DOI: 10.25346/s6lqjm3j/oetgpn
title: msXL_344v_MMSI+RTI+M0655DR_013_F.tif
DOI: 10.25346/s6lqjm3j/0vegl9
title: msXL_344v_MMSI+RTI+M0630RD_012_F.tif
DOI: 10.25346/s6lqjm3j/b3zclo
title: msXL_344v_MMSI+RTI+M0615RO_011_F.tif
DOI: 10.25346/s6lqjm3j/tnlv7r
title: msXL_344v_MMSI+RTI+M0590AM_010_F.tif
DOI: 10.25346/s6lqjm3j/l2xppw
title: msXL_344v_MMSI+RTI+M0560LI_009_F.tif
DOI: 10.25346/s6lqjm3j/jswfuu
title: msXL_344v_MMSI+RTI+M0530GN_008_F.tif
DOI: 10.25346/s6lqjm3j/oakqle
title: msXL_344v_MMSI+RTI+M0505CN_007_F.tif
DOI: 10.25346/s6lqjm3j/uwz1vc
title: msXL_344v_MMSI+RTI+M0470LB_006_F.tif
DOI: 10.25346/s6lqjm3j/vur6wz
title: msXL_344v_MMSI+RTI+M0450RB_005_F.tif
DOI: 10.25346/s6lqjm3j/edvzxe
title: msXL_344v_MMSI+RTI+M0420VI_004_F.tif
DOI: 10.5281/zenodo.17604922
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114a Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114a Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.25346/s6lqjm3j/a6ejcm
title: msXL_344v_MMSI+RTI+M0400UV_003_F.tif
DOI: 10.5281/zenodo.17604921
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114a Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114a Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.25346/s6lqjm3j/ykccww
title: msXL_344v_MMSI+RTI+M0385UV_002_F.tif
DOI: 10.25346/s6lqjm3j/atrih0
title: msXL_344v_MMSI+RTI+M0365UV_001_F.tif
DOI: 10.25346/s6lqjm3j/fdf0ce
title: msXL_344v_MMSI+RTI+Arc_G15_096_F.tif
DOI: 10.25346/s6lqjm3j/r25vys
title: msXL_344v_MMSI+RTI+Arc_G13_095_F.tif
DOI: 10.25346/s6lqjm3j/hg8pgv
title: msXL_344v_MMSI+RTI+Arc_G11_094_F.tif
DOI: 10.25346/s6lqjm3j/wzgyot
title: msXL_344v_MMSI+RTI+Arc_G09_093_F.tif
DOI: 10.25346/s6lqjm3j/45mhar
title: msXL_344v_MMSI+RTI+Arc_G07_092_F.tif
DOI: 10.25346/s6lqjm3j/k2xc7w
title: msXL_344v_MMSI+RTI+Arc_G05_091_F.tif
DOI: 10.25346/s6lqjm3j/dp8bz1
title: msXL_344v_MMSI+RTI+Arc_G03_090_F.tif
DOI: 10.25346/s6lqjm3j/lfloj6
title: msXL_344v_MMSI+RTI+Arc_G01_089_F.tif
DOI: 10.25346/s6lqjm3j/razsaz
title: msXL_344v_MMSI+RTI+Arc_F16_088_F.tif
DOI: 10.25346/s6lqjm3j/seebun
title: msXL_344v_MMSI+RTI+Arc_F14_087_F.tif
DOI: 10.25346/s6lqjm3j/hc7rjd
title: msXL_344v_MMSI+RTI+Arc_F12_086_F.tif
DOI: 10.25346/s6lqjm3j/c3pjxf
title: msXL_344v_MMSI+RTI+Arc_F10_085_F.tif
DOI: 10.25346/s6lqjm3j/umvevo
title: msXL_344v_MMSI+RTI+Arc_F08_084_F.tif
DOI: 10.25346/s6lqjm3j/yddjuf
title: msXL_344v_MMSI+RTI+Arc_F06_083_F.tif
DOI: 10.15468/dl.eyr95g
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-105.7188 20.84913, -105.5688 20.84913, -105.5688 20.99913, -105.7188 20.99913, -105.7188 20.84913))"
 ]
}

 The dataset includes 25 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032097-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6lqjm3j/dtgzrv
title: msXL_344v_MMSI+RTI+Arc_F04_082_F.tif
DOI: 10.25346/s6lqjm3j/b6httx
title: msXL_344v_MMSI+RTI+Arc_F02_081_F.tif
DOI: 10.25346/s6lqjm3j/pzmb5k
title: msXL_344v_MMSI+RTI+Arc_E15_080_F.tif
DOI: 10.25346/s6lqjm3j/gybwg2
title: msXL_344v_MMSI+RTI+Arc_E13_079_F.tif
DOI: 10.25346/s6lqjm3j/g0qwzz
title: msXL_344v_MMSI+RTI+Arc_E11_078_F.tif
DOI: 10.25346/s6lqjm3j/ztngro
title: msXL_344v_MMSI+RTI+Arc_E09_077_F.tif
DOI: 10.25346/s6lqjm3j/1zeraz
title: msXL_344v_MMSI+RTI+Arc_E07_076_F.tif
DOI: 10.25346/s6lqjm3j/mbl5pz
title: msXL_344v_MMSI+RTI+Arc_E05_075_F.tif
DOI: 10.25346/s6lqjm3j/aag4yu
title: msXL_344v_MMSI+RTI+Arc_E03_074_F.tif
DOI: 10.25346/s6lqjm3j/xur6aj
title: msXL_344v_MMSI+RTI+Arc_E01_076_F.tif
DOI: 10.71789/db79-2m04
title: Climate Vulnerability Assessment Radiata Pine Data

description: This asset is a dataset containing geospatial outputs from the Radiata pine model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for Radiata pine in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/exuymi
title: msXL_344v_MMSI+RTI+Arc_E01_073_F.tif
DOI: 10.25346/s6lqjm3j/xmfomb
title: msXL_344v_MMSI+RTI+Arc_D16_072_F.tif
DOI: 10.25346/s6lqjm3j/wg8sie
title: msXL_344v_MMSI+RTI+Arc_D14_071_F.tif
DOI: 10.25346/s6lqjm3j/vpjzih
title: msXL_344v_MMSI+RTI+Arc_D12_070_F.tif
DOI: 10.71789/3ed9-mn35
title: Climate Vulnerability Assessment Bonito Data

description: This asset is a dataset containing geospatial outputs from the bonito model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for bonito, both historically (1993-2012 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/6wwuql
title: msXL_344v_MMSI+RTI+Arc_D10_069_F.tif
DOI: 10.25346/s6lqjm3j/bud3jl
title: msXL_344v_MMSI+RTI+Arc_D08_068_F.tif
DOI: 10.25346/s6lqjm3j/jdaatp
title: msXL_344v_MMSI+RTI+Arc_D06_072_F.tif
DOI: 10.71789/e0p4-m548
title: Climate Vulnerability Assessment Dolphinfish Data

description: This asset is a dataset containing geospatial outputs from the dolphinfish model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for dolphinfish, both historically (1993-2012 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/hedn3x
title: msXL_344v_MMSI+RTI+Arc_D06_067_F.tif
DOI: 10.25346/s6lqjm3j/oamypq
title: msXL_344v_MMSI+RTI+Arc_D04_066_F.tif
DOI: 10.25346/s6lqjm3j/j191xx
title: msXL_344v_MMSI+RTI+Arc_D02_065_F.tif
DOI: 10.25346/s6lqjm3j/0pwgej
title: msXL_344v_MMSI+RTI+Arc_C15_064_F.tif
DOI: 10.71789/hr7p-f283
title: Climate Vulnerability Assessment Kingfish Data

description: This asset is a dataset containing geospatial outputs from the kingfish model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for kingfish, both historically (1993-2012 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/bhe2kc
title: msXL_344v_MMSI+RTI+Arc_C13_063_F.tif
DOI: 10.25346/s6lqjm3j/jblb3b
title: msXL_344v_MMSI+RTI+Arc_C11_062_F.tif
DOI: 10.25346/s6lqjm3j/tto6pz
title: msXL_344v_MMSI+RTI+Arc_C09_061_F.tif
DOI: 10.25346/s6lqjm3j/lpqkoj
title: msXL_344v_MMSI+RTI+Arc_C07_060_F.tif
DOI: 10.25346/s6lqjm3j/pdy3ak
title: msXL_344v_MMSI+RTI+Arc_C05_059_F.tif
DOI: 10.71789/wmz6-kk85
title: Climate Vulnerability Assessment Spanish Mackerel Data

description: This asset is a dataset containing geospatial outputs from the Spanish mackerel model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for Spanish mackerl, both historically (1993-2012 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/lncfm5
title: msXL_344v_MMSI+RTI+Arc_C03_058_F.tif
DOI: 10.25346/s6lqjm3j/dvnt8c
title: msXL_344v_MMSI+RTI+Arc_C01_057_F.tif
DOI: 10.25346/s6lqjm3j/1mgzxf
title: msXL_344v_MMSI+RTI+Arc_B16_056_F.tif
DOI: 10.71789/msk3-5k62
title: Climate Vulnerability Assessment Spotted Mackerel Data

description: This asset is a dataset containing geospatial outputs from the spotted mackerel model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for spotted mackerel, both historically (1993-2012 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/k6w6a0
title: msXL_344v_MMSI+RTI+Arc_B14_055_F.tif
DOI: 10.25346/s6lqjm3j/yum1cv
title: msXL_344v_MMSI+RTI+Arc_B12_054_F.tif
DOI: 10.15468/dl.tme7dy
title: Occurrence Download

description: A dataset containing 4734 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-163.97239 23.37258,-51.02761 23.37258,-51.02761 68.62742,-163.97239 68.62742,-163.97239 23.37258))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Microtus montanus",
 "Year 1970-2020"
 ]
}

 The dataset includes 4734 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032098-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6lqjm3j/j6wegx
title: msXL_344v_MMSI+RTI+Arc_B10_053_F.tif
DOI: 10.25346/s6lqjm3j/oejjhs
title: msXL_344v_MMSI+RTI+Arc_B08_052_F.tif
DOI: 10.71789/x6sc-hk15
title: Climate Vulnerability Assessment Blueberry Data

description: This asset is a dataset containing geospatial outputs from the blueberry model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for blueberry in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/akavif
title: msXL_344v_MMSI+RTI+Arc_B06_051_F.tif
DOI: 10.25346/s6lqjm3j/hgjwpd
title: msXL_344v_MMSI+RTI+Arc_B04_050_F.tif
DOI: 10.25346/s6lqjm3j/zhm94u
title: msXL_344v_MMSI+RTI+Arc_B02_049_F.tif
DOI: 10.25346/s6lqjm3j/huigxy
title: msXL_344v_MMSI+RTI+Arc_A15_048_F.tif
DOI: 10.71789/91cb-rb58
title: Climate Vulnerability Assessment Cherry Data

description: This asset is a dataset containing geospatial outputs from the cherry model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for cherry in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/h8znal
title: msXL_344v_MMSI+RTI+Arc_A13_047_F.tif
DOI: 10.25346/s6lqjm3j/jetdiu
title: msXL_344v_MMSI+RTI+Arc_A11_046_F.tif
DOI: 10.25346/s6lqjm3j/h66ulm
title: msXL_344v_MMSI+RTI+Arc_A09_045_F.tif
DOI: 10.25346/s6lqjm3j/rckb0g
title: msXL_344v_MMSI+RTI+Arc_A07_044_F.tif
DOI: 10.25346/s6lqjm3j/0rv96g
title: msXL_344v_MMSI+RTI+Arc_A05_043_F.tif
DOI: 10.15468/dl.sxnwfe
title: Occurrence Download

description: A dataset containing 506723 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "GadmGid is one of (COL.19_2)",
 "StateProvince is one of (La Guajira)",
 "TaxonKey is one of (Aves [GBIF Backbone Taxonomy])"
 ]
 }
 ]
}

 The dataset includes 506723 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032074-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6lqjm3j/oxpilq
title: msXL_344v_MMSI+RTI+Arc_A03_042_F.tif
DOI: 10.25346/s6lqjm3j/4qycm3
title: msXL_344v_MMSI+RTI+Arc_A01_041_F.tif
DOI: 10.25346/s6lqjm3j/t8eq7j
title: msXL_344r_MMSI+RTI+W530R72_039_F.tif
DOI: 10.25346/s6lqjm3j/krjae9
title: msXL_344r_MMSI+RTI+W505665_040_F.tif
DOI: 10.71789/xgs7-k219
title: Climate Vulnerability Assessment Citrus Data

description: This asset is a dataset containing geospatial outputs from the citrus model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for citrus in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/cmonn6
title: msXL_344r_MMSI+RTI+W450R25_030_F.tif
DOI: 10.25346/s6lqjm3j/eeliox
title: msXL_344r_MMSI+RTI+W450G58_034_F.tif
DOI: 10.25346/s6lqjm3j/k6c8z2
title: msXL_344r_MMSI+RTI+W450B47_038_F.tif
DOI: 10.25346/s6lqjm3j/sum21w
title: msXL_344r_MMSI+RTI+W400R25_029_F.tif
DOI: 10.71789/tmar-ry69
title: Climate Vulnerability Assessment Chickpea Data

description: This asset is a dataset containing geospatial outputs from the chickpea model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for chickpea in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/eptyau
title: msXL_344r_MMSI+RTI+W400G58_033_F.tif
DOI: 10.25346/s6lqjm3j/5wfgfg
title: msXL_344r_MMSI+RTI+W400B47_037_F.tif
DOI: 10.25346/s6lqjm3j/iwghng
title: msXL_344r_MMSI+RTI+W385R25_028_F.tif
DOI: 10.25346/s6lqjm3j/pnq5j6
title: msXL_344r_MMSI+RTI+W385G58_032_F.tif
DOI: 10.71789/e1kk-v661
title: Climate Vulnerability Assessment Dryland Wheat Data

description: This asset is a dataset containing geospatial outputs from the dryland wheat model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for dryland wheat in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/so6wmf
title: msXL_344r_MMSI+RTI+W385B47_036_F.tif
DOI: 10.25346/s6lqjm3j/xk6nqr
title: msXL_344r_MMSI+RTI+W365R25_027_F.tif
DOI: 10.25346/s6lqjm3j/8rb1yz
title: msXL_344r_MMSI+RTI+W365G58_031_F.tif
DOI: 10.25346/s6lqjm3j/yu1lf6
title: msXL_344r_MMSI+RTI+W365B47_035_F.tif
DOI: 10.15468/dl.bge5ag
title: Occurrence Download

description: A dataset containing 35494 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-105.5688 20.84913, -105.4188 20.84913, -105.4188 20.99913, -105.5688 20.99913, -105.5688 20.84913))"
 ]
}

 The dataset includes 35494 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032099-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.71789/jh15-vg93
title: Climate Vulnerability Assessment Irrigated Wheat Data

description: This asset is a dataset containing geospatial outputs from the irrigated wheat model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for irrigated wheat in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/xu5fum
title: msXL_344r_MMSI+RTI+TX940IR_025_F.tif
DOI: 10.25346/s6lqjm3j/15vysx
title: msXL_344r_MMSI+RTI+TX735IR_024_F.tif
DOI: 10.25346/s6lqjm3j/z4ubal
title: msXL_344r_MMSI+RTI+TX615RO_023_F.tif
DOI: 10.25346/s6lqjm3j/20mt92
title: msXL_344r_MMSI+RTI+TX615IR_023_F.tif
DOI: 10.71789/c75y-be79
title: Climate Vulnerability Assessment Dryland Barley Data

description: This asset is a dataset containing geospatial outputs from the dryland barley model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for dryland barley in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/gnrxt6
title: msXL_344r_MMSI+RTI+TX1050R_026_F.tif
DOI: 10.25346/s6lqjm3j/b9nipg
title: msXL_344r_MMSI+RTI+TX1050R_022_F.tif
DOI: 10.25346/s6lqjm3j/olaukr
title: msXL_344r_MMSI+RTI+M1050IR_021_F.tif
DOI: 10.25346/s6lqjm3j/hg7w4z
title: msXL_344r_MMSI+RTI+M1050IR_020_F.tif
DOI: 10.71789/rzvg-5398
title: Climate Vulnerability Assessment Dryland Canola Data

description: This asset is a dataset containing geospatial outputs from the dryland canola model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for dryland canola in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/bkhesx
title: msXL_344r_MMSI+RTI+M1050IR_019_F.tif
DOI: 10.25346/s6lqjm3j/857ytn
title: msXL_344r_MMSI+RTI+M0940IR_018_F.tif
DOI: 10.25346/s6lqjm3j/etmx4y
title: msXL_344r_MMSI+RTI+M0850IR_017_F.tif
DOI: 10.25346/s6lqjm3j/qzcc5s
title: msXL_344r_MMSI+RTI+M0780IR_016_F.tif
DOI: 10.71789/4g86-4074
title: Climate Vulnerability Assessment Irrigated Cotton Data

description: This asset is a dataset containing geospatial outputs from the irrigated cotton model produced by the Climate Vulnerability Assessment Project. It contains the climate suitability for irrigated cotton in NSW, both historically (1981-2010 baseline) and using future climate projections (2036-2065) for two emissions scenarios (RCP4.5 and RCP8.5), for each element of the model.
DOI: 10.25346/s6lqjm3j/ywwdgu
title: msXL_344r_MMSI+RTI+M0735IR_015_F.tif