Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
28
datasets available to search
ShareScore release 0.9.0
Dataset results
28 results for “dynamical downscaling”
2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'
<p>This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'</p> <p>The data set contains output from LADDIE simulations, including basal melt rates.</p> <p>The main simulations used in the man text are:</p> <p>- Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc">CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc </a><br> - Filchner-Ronne: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/FRIS_1.0_linear_S134.8_T1-2.3_720.nc">FRIS_1.0_linear_S134.8_T1-2.3_720.nc </a><br> </p> <p>The additional simulations included in the Appendix are:</p> <p>- 3D forcing of Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_mitgcm_2003_2008_100.nc">CrossDots_0.5_mitgcm_2003_2008_100.nc </a><br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/PIG.nc">PIG.nc </a><br> </p>
Data for "Dynamical Downscaling of Climate Simulations in the Tropics"
<p>Precipitation, radiation and vertical mass flux data. 'MPI' indicates conventional downscaling results. 'biascor' indicates bias-corrected downscaling results. 'sstcor' indicates SST-corrected downscaling results.</p>
Local sea-level rise caused by climate change in the northwest Pacific marginal seas using dynamical downscaling
Open the record for dataset details and reuse information.
Analysis of future heatwaves in the Pearl River Delta through CMIP6-WRF dynamical downscaling
<p>Heatwave datasets</p>
2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 7/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains a sample of the input data for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
High Mountain Asia 4-km Dynamically Downscaled Meteorological Data, 2000-2015 V001
This High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions – 36 km, 12 km, and 4 km – and 9 pressure levels spanning 1000 hPa – 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.
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.