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2 results for “ConFire”
ConFire Model input/output for South America
<p>ConFIRE input and output used in the submission of Kelley et al. "Low Climatic Influence found in 2019 Amazonia Fires".</p> <p>"input" dir including "amazon_inference_data-2002-MCDBA_obs-TERRA_M__T.csv", just contains a csv file of all common grid data in other "input" subdirectories, which can be used in "optimise_run_model/bayesian_inference.ipynb" notebook. The files in the subdirectories are then used to make gridded model output using "optimise_run_model/make_model_output.ipynb".</p> <p>Output, also provided, is summerized in the files in "outputs/sampled_posterior_ConFire_solutions/constant_post_2018_full_2002_BG2020/"</p> <p>The three files contain:</p> <ul> <li>fire_summary_frequancy_of_counts.nc - Contains one spatial variable (long name "firecount frequency of occurrence") is the probability of a fire count (along the model_level_number dimension) at a particular month (time dimension) according to the models full posterior, <span class="math-tex">\(P(y_j)\)</span>(see papers supplementary). All months start on Jan 2001. </li> <li>fire_summary_precentile.nc - Contains one spatial variable (long name "firecount at percentile"), the fire count at each percentile of the full posterior in 1% increments from 1-99% (along the model_level_number dimension). Note, by definition, the 0a nd 100% percentile is 0 and <span class="math-tex">\(\infty \)</span> .</li> <li>fire_summary_observed_liklihood.nc - the position ( "variable_0") and p-value ("variable") of MCD64A1 in the model posterior.</li> <li>model_summary.nc - Summary of each variable of ConFire: <ul> <li>"burnt_area": burnt area or fire count summary, described as percentiles as per "fire_summary_precentile", but this time just assessing parameter uncertainty, i.e <span class="math-tex">\(P(\beta | Y_s)\)</span> in supplementary of paper.</li> <li>All other variables describe different model controls in the same "burnt_area". Full information on how controls are constructed can be found in Kelley et al. (2019) with updates listed in supplementary of this paper. <ul> <li>"fuel_continuity", "moisture_content", "ignitions", "suppression" are the actual values of the control</li> <li>"standard_<<control>>" is the standard limitation imposed by the control.</li> <li>"potential_<<control>> is the potential limitation</li> <li>"sensitivity_<<control>> is the senstivity of fire to a particular control.<br> <br> See Kelley et al. 2019 for the definition of limitation types and sensitivity</li> </ul> </li> </ul> </li> </ul> <p>Kelley, D.I., Bistinas, I., Whitley, R. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9, </strong>690–696 (2019) doi:10.1038/s41558-019-0540-7</p> <p> </p>
State of Wildfires 2023-24 - ConFire data
<div> <p>This contains driving and output data used by ConFire in the State of Wildfire’s 2023/24 report. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over the three regions used and defined in the report.</p> <p> </p> <p><strong>Driving Data</strong></p> <p>The “Driving_data” directory contains data used to run the ConFire model and produce analyses. This directory is divided into three focal regions, with NW_Amazon corresponding to the report's “Western Amazonia”. Each region contains the following files:</p> </div> <div> <ul> <li><strong>raw_burnt_area.nc</strong>: The original 0.25-degree burnt area dataset before being regridded for use in ConFire.</li> <li><strong>nrt</strong>: Near Real Time (NRT) driving data used for driver identification.</li> <li><strong>isimip3a</strong>: ISIMIP3a data used for attribution.</li> <li><strong>isimip3b</strong>: ISIMIP3b GCM bias-corrected data used for future projections.</li> </ul> </div> <div> <p><strong>NRT</strong></p> <p>Within the <strong>nrt</strong> directory, data is organized by periods, with the numbers corresponding to the year range. The report utilizes the <strong>period_2013_2023</strong> directory, which contains the NetCDF files in the table below.</p> <p>Filename ending with the following show:</p> <p>12Annual – 12 month running mean</p> <ul> <li>12monthMax – 12 month running maximum</li> <li>Deficity – current month over 12 month running mean</li> <li>Quarter – 3 month running mean</li> </ul> <p><br>Not all were used in the final analysis. For full data info, see Table 3 of the report <a href="https://doi.org/10.5194/essd-2024-218" target="_blank" rel="noopener">https://doi.org/10.5194/essd-2024-218</a>:</p> </div> <div> <div> <div> <div> <table> <tbody> <tr> <th>NetCDF File</th> <th>Variable</th> <th>Used/Not Used</th> <th>Source</th> <th>Notes</th> </tr> </tbody> <tbody> <tr> <td>burnt_area.nc</td> <td>Burnt Area</td> <td>As training data</td> <td> </td> <td> </td> </tr> <tr> <td>cropland.nc</td> <td>Cropland</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>d2m.nc</td> <td>2m Dewpoint Temperature</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>DeadFuelFoilage-cvh_C.nc</td> <td>Dead Foliage Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelFoilage-cvl_C.nc</td> <td>Dead Foliage Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelFoilage.nc</td> <td>Dead Foliage Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelWood-cvh_C.nc</td> <td>Dead Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelWood-cvl_C.nc</td> <td>Dead Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelWood.nc</td> <td>Dead Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-12Annual.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-12monthMax.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-Deficity.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-Quater.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood-12Annual.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood-12monthMax.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood-Deficity.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-12Annual.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-12monthMax.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-Deficity.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live.nc</td> <td>Live Fuel Moisture Content</td> <td>Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-Quater.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>grazing_land.nc</td> <td>Grazing Land</td> <td>Not Used</td> <td> </td> <td> </td> </tr> <tr> <td>lightn.nc</td> <td>Lightning</td> <td>Used</td> <td>LIS/OTD</td> <td>Cecil et al., 2014</td> </tr> <tr> <td>LiveFuelFoilage-cvh_C.nc</td> <td>Live Leaf Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelFoilage-cvl_C.nc</td> <td>Live Leaf Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelFoilage.nc</td> <td>Live Leaf Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelWood-cvh_C.nc</td> <td>Live Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelWood-cvl_C.nc</td> <td>Live Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelWood.nc</td> <td>Live Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>pasture.nc</td> <td>Pasture</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>population_density.nc</td> <td>Population Density</td> <td>Used</td> <td> </td> <td> </td> </tr> <tr> <td>rangeland.nc</td> <td>Rangeland</td> <td>Not Used</td> <td> </td> <td> </td> </tr> <tr> <td>rural_population.nc</td> <td>Rural Population</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>snowCover.nc</td> <td>Snow Cover</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>t2m.nc</td> <td>2m Temperature</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>total_irrigated.nc</td> <td>Irrigated Area</td> <td>Not Used</td> <td> </td> <td> </td> </tr> <tr> <td>tp-12Annual.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp-12monthMax.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp-Deficity.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp.nc</td> <td>Precipitation</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp-Quater.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>urban_population.nc</td> <td>Urban Population</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>VOD-12Annual.nc</td> <td>Mean & Max VOD</td> <td>Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD-12monthMax.nc</td> <td>Mean & Max VOD</td> <td>Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD-Deficity.nc</td> <td>Vegetation Optical Depth (VOD)</td> <td>Not Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD.nc</td> <td>Vegetation Optical Depth (VOD)</td> <td>Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD-Quater.nc</td> <td>Vegetation Optical Depth (VOD)</td> <td>Not Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> </tbody> </table> </div> </div> </div> </div> <div> <div> </div> <div> </div> <div> <div> <p><strong>ISIMIP3a</strong></p> <p>The <strong>isimip3a</strong> directory follows the structure: <strong><<experiment>>/<<reanalysis_source>>/period_yyyy_yyyy/</strong>.</p> </div> <div> <ul> <li><strong><<experiment>></strong>: Can be either:</li> </ul> </div> <div> <ul> <ul> <li><strong>obsclim</strong>: Reanalysis targeting observed climate.</li> <li><strong>counterclim</strong>: Detrended obsclim approximating climate without climate change.</li> </ul> </ul> </div> <div> <ul> <li><strong><<reanalysis_source>></strong>: Currently contains only GSWP3-W5E5, with more sources to follow in subsequent years.</li> <li><strong>yyyy_yyyy</strong>: Corresponds to the year range.</li> </ul> </div> <div> <p>For attribution experiments in the report, the following directories are used:</p> </div> <div> <ul> <li><strong>Factual</strong>: <strong>obsclim/GSWP3-W5E5/period_2002_2019/</strong></li> <li><strong>Counterfactual</strong>: <strong>counterclim/GSWP3-W5E5/period_2002_2019/</strong></li> <li><strong>Early Industrial</strong>: <strong>counterclim/GSWP3-W5E5/period_1901_1920/</strong></li> </ul> </div> <div> <p>Additional details on setting the temporal range for the report can be found <a href="https://github.com/douglask3/Bayesian_fire_models/tree/SoW?tab=readme-ov-file#configuration-settings" target="_new">here</a>.</p> <p><strong>ISIMIP3b</strong></p> <p>The <strong>isimip3b</strong> directory structure is similar to ISIMIP3a: <strong><<experiment>>/<<GCM>>/period_yyyy_yyyy/</strong>.</p> </div> <div> <ul> <li><strong><<experiment>></strong> includes:</li> </ul> </div> <div> <ul> <ul> <li><strong>historical</strong>: Historical GCM output.</li> <li><strong>ssp126</strong></li> <li><strong>ssp370</strong></li> <li><strong>ssp585</strong></li> </ul> </ul> </div> <div> <ul> <li><strong><<GCM>></strong>: Refers to the General Circulation Model used.</li> <li><strong>yyyy_yyyy</strong>: Corresponds to the year range.</li> </ul> </div> <div> <p>Both ISIMIP3a and ISIMIP3b contain the same NetCDF files, as follows:</p> <table> <tbody> <tr> <th><strong>netcdf file</strong></th> <th><strong>variable</strong></th> <th><strong>used/not used</strong></th> <th><strong>source</strong></th> <th><strong>Notes</strong></th> </tr> </tbody> <tbody> <tr> <td>consec_dry_mean.nc</td> <td>Max. consecutive dry days</td> <td>used</td> <td> <p>ISIMIP3a/3b</p> </td> <td>Based on precipitation</td> </tr> <tr> <td>crop_jules-es.nc</td> <td>Cropland</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Interpolated from annual to monthly</td> </tr> <tr> <td>debiased_nonetree_cover_jules-es.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Non-tree vegetated cover simulated by JULES and bias-corrected</td> </tr> <tr> <td>debiased_tree_cover_jules-es.nc</td> <td>Tree Cover</td> <td>not used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Annual mean tree cover bias-corrected to VCF</td> </tr> <tr> <td>dry_days.nc</td> <td>No. dry days</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Fractional number of days with rainfall < 0.1mm/m</td> </tr> <tr> <td>filled_debiased_nonetree_cover_jules-es.nc</td> <td>Total vegetation cover</td> <td>used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Filled and bias-corrected non-tree vegetated cover</td> </tr> <tr> <td>filled_debiased_tree_cover_jules-es.nc</td> <td>Tree Cover</td> <td>used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Filled and bias-corrected tree cover</td> </tr> <tr> <td>filled_debiased_vegCover_jules-es.nc</td> <td>Total vegetation cover</td> <td>used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Filled and bias-corrected vegetation cover</td> </tr> <tr> <td>lightning.nc</td> <td>Lightning</td> <td>used</td> <td>ISIMIP3a</td> <td>Climatology</td> </tr> <tr> <td>nonetree_cover_jules-es.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td>JULES-ES-ISIMIP </td> <td>Non-tree vegetated cover simulated by JULES</td> </tr> <tr> <td>pasture_jules-es.nc</td> <td>Pasture</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Interpolated from annual to monthly</td> </tr> <tr> <td>pr_mean.nc</td> <td>Precipitation</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Monthly mean precipitation</td> </tr> <tr> <td>tas_max.nc</td> <td>Maximum monthly temperature</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Maximum of maximum daily temperature within the month</td> </tr> <tr> <td>tas_mean.nc</td> <td>Mean monthly temperature</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Daily mean temperature</td> </tr> <tr> <td>tree_cover_jules-es.nc</td> <td>Tree Cover</td> <td>not used</td> <td>JULES-ES-ISIMIP </td> <td>Annual mean tree cover bias-corrected to VCF</td> </tr> <tr> <td>urban_jules-es.nc</td> <td>Urban fraction</td> <td>used</td> <td>JULES-ES</td> <td>Urban area fraction</td> </tr> <tr> <td>vpd_max.nc</td> <td>Maximum monthly VPD</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Maximum of daily VPD values</td> </tr> <tr> <td>vpd_mean.nc</td> <td>Mean monthly VPD</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Mean of daily VPD values</td> </tr> <tr> <td>nontree_cover_VCF-obs.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td>VCF</td> <td>Non-tree vegetated cover observed</td> </tr> <tr> <td>nontree_raw_VCF-obs.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td> VCF</td> <td>Raw non-tree vegetated cover observed</td> </tr> <tr> <td>nonveg_cover_VCF-obs.nc</td> <td>Non-vegetated cover</td> <td>not used</td> <td> VCF</td> <td>Observed non-vegetated cover</td> </tr> <tr> <td>nonveg_raw_VCF-obs.nc</td> <td>Non-vegetated cover</td> <td>not used</td> <td> VCF</td> <td>Raw observed non-vegetated cover</td> </tr> <tr> <td>tree_cover_VCF-obs.nc</td> <td>Tree Cover</td> <td>not used</td> <td> VCF</td> <td>Observed tree cover</td> </tr> <tr> <td>tree_raw_VCF-obs.nc</td> <td>Tree Cover</td> <td>not used</td> <td> VCF</td> <td>Raw observed tree cover</td> </tr> </tbody> </table> </div> <p>ISIMIP3a/3b is detailed in Frieler et al. (2024) and raw data can be obtained from <strong><a href="https://data.isimip.org/">https://data.ISIMIP.org</a></strong></p> <p>While not used as driving data, VCF is used to proceed bias corrected driving data. VCF is taken from MODIS Vegetation Continuous Fields collection 6.1 remote sensed data for <60॰N DiMiceli et al. (2022) and collection 6 for <60॰N DiMiceli et al. (2015). JULES-ES (Mathison et al. 2023) was driven using the corresponding ISIMIP datasets.</p> <div> <p><strong>Outputs</strong></p> <p>Outputs contain the ConFire outputs when driven with the provided datasets. The directories are named according to the regions, and for each region, there are four sets of outputs:</p> </div> <div> <ul> <li>isimip-evaluation1</li> <li>isimip-final.tar</li> <li>nrt-evaluation1</li> <li>nrt-final</li> </ul> </div> <div> <p>Each of these directories contains the following files necessary for rerunning the model without redoing the optimization. While you are unlikely to need to look at these files, they are useful for setting up your own model experiments (see the <a href="https://github.com/douglask3/Bayesian_fire_models/tree/SoW?tab=readme-ov-file#configuration-settings" target="_new">ConFire configuration settings</a>):</p> </div> <div> <ul> <li>scalers-_*.csv</li> <li>trace-_*.nc</li> <li>variables_info-_*.txt</li> </ul> </div> <div> <p>Additionally, there are two other directories:</p> </div> <div> <ul> <li><strong>figs</strong>: Contains automatically generated figures and some of their outputs.</li> <li><strong>sample</strong>: Contains model outputs.</li> </ul> </div> <div> <p>Within the <strong>sample</strong> directory, there is a subdirectory indicating the model run name, which contains a series of experiments. These experiments differ for each run (see below), and each experiment contains some or all of the following directories:</p> </div> <div> <ul> <li><strong>Evaluate</strong>: Contains the burnt area from the full model including stochastic parameters. Often used for evaluation (see report supplement for more information).</li> <li><strong>Control</strong>: Contains burnt area driven purely by driving datasets with stochasticity. Used as the control in most of the analysis.</li> <li><strong>Standard_X</strong>: A series of directories with burnt areas from individual controls. This describes the burnt area in the presence of that control in otherwise ideal burning conditions. The numbers are:</li> </ul> </div> <div> <ul> <ul> <li>0: Fuel load for all runs</li> <li>1: Fuel moisture for all runs</li> <li>2: Fire weather for NRT and ignitions for ISIMIP3a</li> <li>3: Ignitions for ISIMIP3a and suppression for ISIMIP3a</li> <li>4: Suppression for NRT</li> <li>5: Snow for NRT</li> </ul> </ul> </div> <div> <p>Within each of these directories is a series of ensemble members <strong>sample-predX.nc</strong>. Within the same optimization (i.e., the same model run, so across all experiments), samples are paired, meaning the <strong>sample-predX.nc</strong> corresponds to the sample in another experiment.</p> <p><strong>Experiments</strong></p> <p><strong>isimip-evaluation1 & nrt-evaluation1</strong></p> <p>The only experiment for evaluation is called <strong>baseline</strong>, which has an 'Evaluate' and 'Control' run and is used to evaluate the model. Automatically generated evaluation figures can be found in the <strong>figs/</strong> directory.</p> <p><strong>nrt-final</strong></p> <p>This also contains only one run, <strong>baseline</strong>, but includes runs for each of the controls.</p> <p><strong>isimip-final</strong></p> <p>This has more runs:</p> </div> <div> <ul> <li><strong>factual</strong>: Uses the ISIMIP3a obsclim driving dataset (see driving dataset above).</li> <li><strong>counterfactual</strong>: Uses the ISIMIP3a counterclim dataset.</li> <li><strong>early_industrial</strong>: Uses the early period ISIMIP3a counterclim dataset.</li> <li><strong>historical/<<GCM>>/</strong>, <strong>ssp126/<<GCM>>/</strong>, <strong>ssp370/<<GCM>>/</strong>, <strong>ssp585/<<GCM>>/</strong>: Uses the ISIMIP3b datasets outlined above, where <<GCM>> is one of each of the five GCMs used in ISIMIP3b.</li> </ul> </div> <div> <p><strong>Additional Analysis</strong></p> <p>The analysis in the report also utilizes 95th and 90th percentile burnt area totals. These aren't as neatly organized as the NetCDF files yet, but we’re getting there. They can be found in:</p> <p>figs/ _13-frac_points_0.5-<<experiment>>-control_TS/<<GCM>>-control_TS/pc-%%/ points-<<run>>.csv</p> </div> </div> </div>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.