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zenodo52/100

ICARIA: climate projections from statistical downscaling outputs

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed:          -----                                      The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). <div> <div> <table> <tbody> <tr> <td> CMIP6 MODELS </td> <td> Resolution </td> <td> Responsible Centre </td> <td> References </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> 1,875º x 1,250º </td> <td> Australian Community Climate and Earth System Simulator (ACCESS), Australia </td> <td> Bi, D. et al (2020) </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> 1,125º x 1,121º </td> <td> Beijing Climate Center (BCC), China Meteorological Administration, China. </td> <td> Wu T. et al. (2019) </td> </tr> <tr> <td> CanESM5 </td> <td> 2,812º x 2,790º </td> <td> Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </td> <td> Swart, N.C. et al. (2019) </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> 1,000º x 1,000º </td> <td> Centro Mediterraneo sui Cambiamenti Climatici (CMCC). </td> <td> Cherchi et al, 2018 </td> </tr> <tr> <td> CNRM-ESM2-1 </td> <td> 1,406º x 1,401º </td> <td> CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </td> <td> Seferian, R. (2019) </td> </tr> <tr> <td> EC-EARTH3 </td> <td> 0,703º x 0,702º </td> <td> EC-EARTH Consortium </td> <td> EC-Earth Consortium. (2019) </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> 0,938º x 0,935º </td> <td> Max-Planck Institute for Meteorology (MPI-M), Germany. </td> <td> Müller et al., (2018) </td> </tr> <tr> <td> MRI-ESM2-0 </td> <td> 1,125º x 1,121º </td> <td> Meteorological Research Institute (MRI), Japan. </td> <td> Yukimoto, S. et al. (2019) </td> </tr> <tr> <td> NorESM2-MM </td> <td> 1,250º x 0,942º </td> <td> Norwegian Climate Centre (NCC), Norway. </td> <td> Bentsen, M. et al. (2019) </td> </tr> <tr> <td> UKESM1-0-LL </td> <td> 1,875º x 1,250º </td> <td> UK Met Office, Hadley Centre, United Kingdom </td> <td> Good, P. et al. (2019) </td> </tr> </tbody> </table>   The results shared here are developed over each of the observational locations that were retrieved to run the statistical downscaling. Both the observational datasets and the future climate change projections can be found here in a TXT format for each of the locations where they were developed. Observations include the main variables retrieved after a quality and homogeneity control, and climate projections together with extreme indicators include each of the 10 models, the 4 Tier 1 SSPs and data until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Thermal indicators </td> </tr> <tr> <td> TX90 / TX10 </td> <td> Warm/cold days </td> <td> Zhang et al. (2011) </td> <td> TX </td> <td> nd </td> <td> 90 / 10% </td> </tr> <tr> <td> HD </td> <td> Heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> EHD </td> <td> Extreme heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 35 °C </td> </tr> <tr> <td> TR </td> <td> Tropical nights </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> > 20 °C </td> </tr> <tr> <td> EQ </td> <td> Equatorial nights </td> <td> AEMet 2020, ICARIA </td> <td> TN </td> <td> nd </td> <td> > 25 °C </td> </tr> <tr> <td> IN </td> <td> Infernal nights </td> <td> ICARIA </td> <td> TN </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> FD </td> <td> Frost days </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> < 0 °C </td> </tr> <tr> <td> Max consec </td> <td> Max spell length for above thermal indicators </td> <td> ICARIA </td> <td> - </td> <td> nd </td> <td> - </td> </tr> <tr> <td> Nº events </td> <td> Number of above thermal indicators events </td> <td> ICARIA </td> <td> - </td> <td> ne </td> <td> > 3 days </td> </tr> <tr> <td> TXm </td> <td> Mean maximum temperatures </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TNm </td> <td> Mean minimum temperatures </td> <td> ICARIA </td> <td> TN </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TM </td> <td> Mean temperatures </td> <td> ICARIA </td> <td> TA </td> <td> °C </td> <td> - </td> </tr> <tr> <td> HWle </td> <td> Heatwave length </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWim/HWix </td> <td> Mean and maximum heatwave intensity </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWf </td> <td> Heatwave frequency </td> <td> ICARIA </td> <td> TX </td> <td> ne </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWd </td> <td> Heatwave days </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HI - P90 </td> <td> Heat Index (percentile 90) </td> <td> NWS (1994) </td> <td> TX, RH </td> <td> °C </td> <td> TX>27 °C, HR> 40% </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index </td> <td> Bröde et al. (2012) </td> <td> TA RH, W </td> <td> - </td> <td> - </td> </tr> <tr> <td> UHI </td> <td> Isla de calor (BCN) anual y estacional </td> <td> AMB, Metrobs 2015 </td> <td> T </td> <td> °C </td> <td> TM1-TM2 > 0 °C </td> </tr> <tr> <td> Precipitation indicators </td> </tr> <tr> <td> R20 </td> <td> Number of heavy precipitation days </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> >20 mm </td> </tr> <tr> <td> R50, R100 </td> <td> Days with extreme heavy rain </td> <td> AMB et al. (2017) </td> <td> P </td> <td> nd </td> <td> >50mm >100mm </td> </tr> <tr> <td> Ra </td> <td> Yearly and seasonal rainfall relative change </td> <td> ICARIA </td> <td> P </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> IDF - CCF </td> <td> IDF Curves - Climate Change Factor </td> <td> Arnbjerg-Nielsen (2012) </td> <td> P </td> <td> - </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Forest fire indicators </td> </tr> <tr> <td> Mean FWI </td> <td> Mean Canadian FWI in fire season </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> . </td> <td> June- September </td> </tr> <tr> <td> Very High FWI </td> <td> Very High Canadian FWI </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> nd </td> <td> FWI > 38 </td> </tr> </tbody> </table> </div>   Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Drought indicators </td> </tr> <tr> <td> CDDx </td> <td> Maximum dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> CDDm </td> <td> Mean dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> SPI </td> <td> SPI  of 1, 3, 6, 12, 24 & 36 months </td> <td> McKee et al. (1993)  </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> SPEI </td> <td> SPEI  of 1, 3, 6, 12, 24 & 36 months </td> <td> Vicente-Serrano et al.  (2010) </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Oceanic indicators </td> </tr> <tr> <td> SS </td> <td> Storm surge </td> <td> Bryant et al. (2016) </td> <td> MT </td> <td> cm </td> <td> - </td> </tr> <tr> <td> OW </td> <td> Significant/maximum wave height </td> <td> ICARIA </td> <td> WH </td> <td> m </td> <td> - </td> </tr> <tr> <td> Wind indicators </td> </tr> <tr> <td> EWG </td> <td> Extreme wind gusts </td> <td> ICARIA </td> <td> W </td> <td> km/h </td> <td> - </td> </tr> </tbody> </table> </div>   </div> </div>

opencc-by-4.0Apr 2024View details →

zenodo52/100

ICARIA: spatially distributed climate projections from statistical downscaling

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a 100x100m resolution grid, which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed:          -----                                      The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). <div> <div> <table> <tbody> <tr> <td> CMIP6 MODELS </td> <td> Resolution </td> <td> Responsible Centre </td> <td> References </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> 1,875º x 1,250º </td> <td> Australian Community Climate and Earth System Simulator (ACCESS), Australia </td> <td> Bi, D. et al (2020) </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> 1,125º x 1,121º </td> <td> Beijing Climate Center (BCC), China Meteorological Administration, China. </td> <td> Wu T. et al. (2019) </td> </tr> <tr> <td> CanESM5 </td> <td> 2,812º x 2,790º </td> <td> Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </td> <td> Swart, N.C. et al. (2019) </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> 1,000º x 1,000º </td> <td> Centro Mediterraneo sui Cambiamenti Climatici (CMCC). </td> <td> Cherchi et al, 2018 </td> </tr> <tr> <td> CNRM-ESM2-1 </td> <td> 1,406º x 1,401º </td> <td> CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </td> <td> Seferian, R. (2019) </td> </tr> <tr> <td> EC-EARTH3 </td> <td> 0,703º x 0,702º </td> <td> EC-EARTH Consortium </td> <td> EC-Earth Consortium. (2019) </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> 0,938º x 0,935º </td> <td> Max-Planck Institute for Meteorology (MPI-M), Germany. </td> <td> Müller et al., (2018) </td> </tr> <tr> <td> MRI-ESM2-0 </td> <td> 1,125º x 1,121º </td> <td> Meteorological Research Institute (MRI), Japan. </td> <td> Yukimoto, S. et al. (2019) </td> </tr> <tr> <td> NorESM2-MM </td> <td> 1,250º x 0,942º </td> <td> Norwegian Climate Centre (NCC), Norway. </td> <td> Bentsen, M. et al. (2019) </td> </tr> <tr> <td> UKESM1-0-LL </td> <td> 1,875º x 1,250º </td> <td> UK Met Office, Hadley Centre, United Kingdom </td> <td> Good, P. et al. (2019) </td> </tr> </tbody> </table> The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model considering diverse adjustments and topographic corrections. The results presented here are the median of the 10 models used, obtained for each of the 4 SSPs and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Thermal indicators </td> </tr> <tr> <td> TX90 / TX10 </td> <td> Warm/cold days </td> <td> Zhang et al. (2011) </td> <td> TX </td> <td> nd </td> <td> 90 / 10% </td> </tr> <tr> <td> HD </td> <td> Heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> EHD </td> <td> Extreme heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 35 °C </td> </tr> <tr> <td> TR </td> <td> Tropical nights </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> > 20 °C </td> </tr> <tr> <td> EQ </td> <td> Equatorial nights </td> <td> AEMet 2020, ICARIA </td> <td> TN </td> <td> nd </td> <td> > 25 °C </td> </tr> <tr> <td> IN </td> <td> Infernal nights </td> <td> ICARIA </td> <td> TN </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> FD </td> <td> Frost days </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> < 0 °C </td> </tr> <tr> <td> Max consec </td> <td> Max spell length for above thermal indicators </td> <td> ICARIA </td> <td> - </td> <td> nd </td> <td> - </td> </tr> <tr> <td> Nº events </td> <td> Number of above thermal indicators events </td> <td> ICARIA </td> <td> - </td> <td> ne </td> <td> > 3 days </td> </tr> <tr> <td> TXm </td> <td> Mean maximum temperatures </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TNm </td> <td> Mean minimum temperatures </td> <td> ICARIA </td> <td> TN </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TM </td> <td> Mean temperatures </td> <td> ICARIA </td> <td> TA </td> <td> °C </td> <td> - </td> </tr> <tr> <td> HWle </td> <td> Heatwave length </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWim/HWix </td> <td> Mean and maximum heatwave intensity </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWf </td> <td> Heatwave frequency </td> <td> ICARIA </td> <td> TX </td> <td> ne </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWd </td> <td> Heatwave days </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HI - P90 </td> <td> Heat Index (percentile 90) </td> <td> NWS (1994) </td> <td> TX, RH </td> <td> °C </td> <td> TX>27 °C, HR> 40% </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index </td> <td> Bröde et al. (2012) </td> <td> TA RH, W </td> <td> - </td> <td> - </td> </tr> <tr> <td> UHI </td> <td> Isla de calor (BCN) anual y estacional </td> <td> AMB, Metrobs 2015 </td> <td> T </td> <td> °C </td> <td> TM1-TM2 > 0 °C </td> </tr> <tr> <td> Precipitation indicators </td> </tr> <tr> <td> R20 </td> <td> Number of heavy precipitation days </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> >20 mm </td> </tr> <tr> <td> R50, R100 </td> <td> Days with extreme heavy rain </td> <td> AMB et al. (2017) </td> <td> P </td> <td> nd </td> <td> >50mm >100mm </td> </tr> <tr> <td> Ra </td> <td> Yearly and seasonal rainfall relative change </td> <td> ICARIA </td> <td> P </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> IDF - CCF </td> <td> IDF Curves - Climate Change Factor </td> <td> Arnbjerg-Nielsen (2012) </td> <td> P </td> <td> - </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Forest fire indicators </td> </tr> <tr> <td> Mean FWI </td> <td> Mean Canadian FWI in fire season </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> . </td> <td> June- September </td> </tr> <tr> <td> Very High FWI </td> <td> Very High Canadian FWI </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> nd </td> <td> FWI > 38 </td> </tr> </tbody> </table> </div>   Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Drought indicators </td> </tr> <tr> <td> CDDx </td> <td> Maximum dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> CDDm </td> <td> Mean dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> SPI </td> <td> SPI  of 1, 3, 6, 12, 24 & 36 months </td> <td> McKee et al. (1993)  </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> SPEI </td> <td> SPEI  of 1, 3, 6, 12, 24 & 36 months </td> <td> Vicente-Serrano et al.  (2010) </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Oceanic indicators </td> </tr> <tr> <td> SS </td> <td> Storm surge </td> <td> Bryant et al. (2016) </td> <td> MT </td> <td> cm </td> <td> - </td> </tr> <tr> <td> OW </td> <td> Significant/maximum wave height </td> <td> ICARIA </td> <td> WH </td> <td> m </td> <td> - </td> </tr> <tr> <td> Wind indicators </td> </tr> <tr> <td> EWG </td> <td> Extreme wind gusts </td> <td> ICARIA </td> <td> W </td> <td> km/h </td> <td> - </td> </tr> </tbody> </table> </div>   </div> </div>

opencc-by-4.0Jul 2024View details →

zenodo44/100

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula

The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”. The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncMay 2022View details →

zenodo44/100

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast

The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncJun 2022View details →

zenodo44/100

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay

The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling  was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncMay 2022View details →

zenodo44/100

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea

The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncMay 2022View details →

zenodo44/100

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea

The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.   Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncMay 2022View details →

zenodo44/100

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea

The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncMay 2022View details →

zenodo44/100

Occupations on the map: Using a super learner algorithm to downscale labor statistics, data

This repository contains all the input and output data (including maps) related to <a href="https://doi.org/10.1371/journal.pone.0278120">Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics</a>. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These  files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of  this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm. In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

opencc-by-4.0Dec 2021View details →

zenodo44/100

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset

Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows:    The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R2) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.

opencc-by-4.0Apr 2023View details →

edi44/100

Statistically downscaled future precipitation for the Luquillo Mountains, Puerto Rico

This dataset contains climate predictions that serve as the basis for the analysis in Ramseyer et al. (2019), which projected a trend toward drier conditions in eastern Puerto Rico during the mid- and late-21st century. The analysis was informed by computing nine atmospheric variables, which had been shown by previous research to related to precipitation in Puerto Rico (Ramseyer and Mote 2016) from four GCMs. These nine variables were used to train an artificial neural network (ANN) to predict the binary occurrence of a wet (>= 5 mm of precipitation) versus dry (<5 mm) day using in-situ daily precipitation observations from El Verde Field Station in northeast Puerto Rico. The nine atmospheric variables used to train the ANN were: 1000- 850-, 700-, and 500-hPa daily specific humidity, 1000–700-hPa bulk wind shear (BWS), the Gálvez-Davison Index (GDI), and the GDI's three component terms (the column buoyancy index, mid-level warming index, and a trade-wind inversion index). These same nine variables were then extracted on a daily basis from four GCMs for the eastern Caribbean early rainfall season (April-July) between 2041-2060 and 2081-2100, and fed through the ANN. These data are the daily predicted values of wet (1) or dry (0) conditions for each of the four GCMs in the ensemble. Because ERS total precipitation at El Verde is strongly correlated with the percentage of ERS dry days (R2=0.95 for years with <10% missing data), the GCM predictions were used to estimate future ERS precipitation using the following formula: ERS precipitation (mm) = 3373-37.6*(ERS dry-day percentage) Applying this formula to each of the GCM dry-day projections yielded an ensemble mean ERS precipitation total of 771 mm by 2041-2060 and 974 mm by 2081-2100. See Ramseyer et al. (2019) for a complete description of the neural network and its predictions. Ramseyer, C., P. Miller, and T. Mote, 2019: Future precipitation variability during the early rainfall season in the El Yunque National Fore

openCC (other)Mar 2024View details →

zenodo40/100

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.

opencc-by-4.0Dec 2021View details →

zenodo36/100

Supporting material for PyESDv1.0.1 An open-source Python framework for empirical-statistical downscaling of climate information

The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (pyESD; github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of code. The package is highly modular and flexible and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even glacial retreat. The dataset presented here serves as supporting material for the package description and evaluation manuscript

opencc-by-4.0Mar 2023View details →

zenodo32/100

Statistical downscaling of significant wave height: data

The cfsr file contains the Climate Forecast System Reanalysis wind data over the North Atlantic and homere contains the Homere sea state data at a location in the Bay of Biscay (latitude=25.4N, longitude=1.6W). data_prep contains the processed data, which are used to run the statistical downscaling model. All files are in RDS format ( R data Serialization).

opencc-by-4.0Jan 2022View details →

zenodo32/100

Data and code: High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method

Data and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method -  Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file.

opencc-by-4.0May 2023View details →

zenodo28/100

Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2024View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record