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.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “COSMO-CLM”
Regional climate simulations of surface precipitation and temperature for West Africa using COSMO-CLM based on MPI-LR (ECHAM6) and RCP4.5
<p>Regional climate model COSMO-CLM (CCLM) simulations with a horizontal resolution of 0.11° (approx. 12 km) for sub-Saharan West Africa under current and future climate conditions. The CCLM is driven by initial and lateral boundary conditions from the MPI-LR (ECHAM6), based on the emission scenario RCP4.5. The downscaled MPI-LR (ECHAM6) data for surface precipitation (P) and surface temperature (Tmin, Tmax) are provided for the baseline period (1981-2010) and two future time slices, i.e. the 2021–2050 and the 2071–2100 period. </p> <p> </p>
Cyclone tracks from 1901 to 2010 in dynamically downscaled ERA-20C reanalysis (COSMO-CLM+NEMO)
<p>The database contains two files: one with all cyclone trajectories from 1901 to 2010, and another one only with the so-called Vb-cyclones that propagate from the Mediterranean Sea north-eastward to Central Europe.</p> <p>We detected the cyclone trajectories with the method of Wernli and Schwierz (2006) and Sprenger et al. (2017) and classified all cyclone trajectories that crossed the 47°N latitude between 12°E and 22°E as Vb-cyclones following Hofstätter and Blöschl (2019). The cyclone tracking was based on mean sea level pressure data of dynamically downscaled ERA-20C reanalysis. The downscaling was performed over Europe [including MED-CORDEX (Somot et al. 2018) and EURO-CORDEX (Giorgi et al. 2009)] from 1901 to 2010 with an interactively coupled high-resolution atmosphere-ocean model (COSMO-CLM+NEMO) by Cristina Primo. More details on the data basis can be found in Primo et al. (2019) and Krug et al. (2020).</p> <p> </p> <p>Giorgi, F., Jones, C. & Asrar, G. Addressing climate information needs at the regional level: the CORDEX framework.<em> WMO Bulletin</em> <strong>58</strong>, 175–183 (2009).</p> <p>Hofstätter, M. & Blöschl, G. Vb Cyclones Synchronized With the Arctic-/North Atlantic Oscillation. <em>J. Geophys. Res. Atmos.</em> <strong>124</strong>, 3259–3278 (2019).</p> <p>Krug, A., Primo, C., Fischer, S., Schumann, A. & Ahrens, B. On the temporal variability of widespread rain-on-snow floods. <em>Meteorol. Zeitschrift</em> <strong>29</strong>, 147–163 (2020).</p> <p>Primo, C., Kelemen, F. D., Feldmann, H., Akhtar, N. & Ahrens, B. A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance. <em>Geosci. Model Dev.</em> <strong>12</strong>, 5077–5095 (2019).</p> <p>Somot, S. <em>et al.</em> Editorial for the Med-CORDEX special issue. <em>Clim. Dyn.</em> <strong>51</strong>, 771–777 (2018). doi: 10.1007/s00382-018-4325-x</p> <p>Sprenger, M. <em>et al.</em> Global climatologies of Eulerian and Lagrangian flow features based on ERA-Interim. <em>Bull. Am. Meteorol. Soc.</em> (2017). doi:10.1175/BAMS-D-15-00299.1</p> <p>Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. <em>J. Atmos. Sci.</em> <strong>63</strong>, 2486–2507 (2006).</p>
2000-2002 Dataset [1/7] 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 1/7 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 2000-2002. 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 part 1/7 of the dataset also includes files related 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>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2009-2011 Dataset [4/7] 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 4/7 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 2009-2011. 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>
2012-2014 Dataset [5/7] 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 5/7 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 2012-2014. 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>
2015-2017 Dataset [6/7] 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 6/7 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 2015-2017. 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>
2006-2008 Dataset [3/7] 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 3/7 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 2006-2008. 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>
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>
COSMO-CLM outputs Central Asia Russo et al. 2019
<p>Monthly values of 2-meter temperature (T2M), total precipitation (TOT_PREC) and diurnal temperature range (DTR) derived from the simulations performed with the Regional Climate Model COSMO-CLM (Roeckel et al. 2008) for the CORDEX Central Asia Domain, presented in Russo et al. 2019 ( https://doi.org/10.5194/gmd-2019-22, 2019).</p> <p>The data have a spatial resolution of ~25 km. The data cover the period 1996-2005. For some realization, a file covering the period 2005-2015 is also present. All simulations are initialized on the 01.01.1991. The only exception are 4 simulations used to investigate the model internal variability, whose initial time has been shifted of +1,-1,+3 and -3 with respect to the default initial date.</p>
Additional data simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>The data presented here are the additional data used for the performance of the COSMO-CLM Mid-Holocene and Pre-Industrial simulations, used for the analysis of the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The data available here are:</p> <p>-ext_data_044.nc: The external parameters used for the simulations, including information on soil type, albedo, land mask, etc. <br> -W_SO_ref_half_rel_soil_moisture_18650401.nc: Soil Moisture at 50% saturation level on the first of April of the 15th year of the simulations. The data are used as reference for the simulation with different soil moisture.</p> <p> </p>
Data PI simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>Postprocessed data of the PRE-Industrial (PI) simulations performed for the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The structure of the directories of the data is as follow:</p> <p>--PPE_exp: data of the Physically Perturbed Ensembles<br> Inside this directory there are 3 subfolders:<br> -day_anm: daily mean anomalies<br> -mon_bias: decadal monthly means<br> -mon_fld: spatial averages of monthly means<br> In each of these subfolders, there are data for each of the considered variables:<br> -T_2M; CLCT; TOT_PREC<br> <br> For more specific Information on the presented data, please refer to the corresponding paper:</p>
Data MH simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>Postprocessed data of the Mid-Holocene (MH) simulations performed for the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The structure of the directories of the data is as follow:</p> <p>--PPE_exp: data of the Physically Perturbed Ensembles<br> Inside this directory there are 3 subfolders:<br> -day_anm: daily mean anomalies<br> -mon_bias: decadal monthly means<br> -mon_fld: spatial averages of monthly means<br> In each of these subfolders, there are data for each of the considered variables:<br> -T_2M; CLCT; TOT_PREC<br> --soil_pert: sensitivity tests with different initial soil moisture on 1st of April at MH<br> soil_pert only has data for MH for T_2M<br> <br> For more specific Information on the presented data, [please refer to the corresponding paper.</p> <p> </p> <p> </p> <p> </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>
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.