Campus Collection

A "catch all" like collection of datasets for research data not fitting elsewhere.

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

1,771 to 1,780 of 1,803 Results

zmla_2014.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: a49c80f9eceaf984da0e1ee01b8fd8b3cc27999380831c8609e504256c6b5794
Example_script_for_the_implementation_of_LSTM_TL.ipynb Mar 28, 2022 - Advancing AI-based pan-European groundwater monitoring Jupyter Notebook - 82.0 KB - SHA-256: 9fae22a6005caf9cb3361e20ba0bd5954550e833e3444ef24a917abe41a77b5b A Jupyter Notebook showing an example about the implementation of LSTM-TL
COSMO_REA6_monthly_precipitation_anomaly_FZJ-IBG3_COSMO_0.1_degree_v1.1995_082019.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 197.3 MB - SHA-256: 93abe0a605585b816742fe50b77030254c284fd236ea4319b5d7216c1bbce371 Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S. and Steinke, S.: Towards a high‐resolution regio...
ERA5_bias_corrected_monthly_precipitation_anomaly_FZJ-IBG3_WFDE5_0.1_degree_v1..nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 327.9 MB - SHA-256: e5a3559843458d2ed2e2db5a4572539081d9cbf5c8d4b7a4d420bd019a1270ce Monthly precipitation anomalies (pr_a) derived from the ERA5 bias corrected dataset (referred to: Muñoz Sabater, J.: Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds....
ERA5_Land_monthly_precipitation_anomaly_FZJ-IBG3_ERA5_Land_0.1_degree_v1.1981_052021.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 323.2 MB - SHA-256: ad0ab24cc07a4a66b7e36408f4bd24c979c30dc305399d394cc4bf6039dddb12 Monthly precipitation anomalies (pr_a) derived from the ERA5 Land dataset (referred to: Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds.e2161bac, 2021.)
ERA5_Land_monthly_soil_moisture_anomaly_FZJ-IBG3_ERA5_Land_0.1_degree_v1.1981_052021.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 323.2 MB - SHA-256: 651e2e853d4421bed46ef7f5fab012dc1570a07f449fd0cc5d40606bb0fa4555 Monthly soil moisture anomalies (θ_a) derived from the ERA5 Land dataset (referred to: Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds.e2161bac, 2021.)
GLEAM_monthly_soil_moisture_anomaly_FZJ-IBG3_GLEAM_0.1_degree_v1.1980_2020.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 327.9 MB - SHA-256: af89272f98158109edaa1d23e07452328d923457a4b59f3a0a0acc099dafb5f9 Monthly soil moisture anomalies (θ_a) derived from the GLEAM dataset (referred to: Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A. and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporati...
LSTM_m_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 335.9 MB - SHA-256: a79f2a29e8e09028af96db6fdc7039b8602ce65ce79d412f946761a5e15e1492 Monthly water table depth anomalies (wtd_a) estimates at pixels with water table depth (wtd) observations over Europe obtained by LSTM networks with modeling results as input
LSTM_obs_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 335.9 MB - SHA-256: 88fdab991dbe77b5d112190612fe9d012607efa22908d5152ae10558669c2670 Monthly water table depth anomalies (wtd_a) estimates at pixels with water table depth (wtd) observations over Europe obtained by LSTM networks trained on observations
LSTM_TL_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1996_2016.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 335.9 MB - SHA-256: 93be9a8cda7a0ecdbeeb65bbdaf7a4ada323a3e2cf28b021531ec98c54c5261d Monthly water table depth anomalies (wtd_a) estimates at pixels with water table depth (wtd) observations over Europe obtained by LSTM-TL

zmla_2014.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: a49c80f9eceaf984da0e1ee01b8fd8b3cc27999380831c8609e504256c6b5794

Example_script_for_the_implementation_of_LSTM_TL.ipynb

Mar 28, 2022 - Advancing AI-based pan-European groundwater monitoring

Jupyter Notebook - 82.0 KB - SHA-256: 9fae22a6005caf9cb3361e20ba0bd5954550e833e3444ef24a917abe41a77b5b

A Jupyter Notebook showing an example about the implementation of LSTM-TL

COSMO_REA6_monthly_precipitation_anomaly_FZJ-IBG3_COSMO_0.1_degree_v1.1995_082019.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 197.3 MB - SHA-256: 93abe0a605585b816742fe50b77030254c284fd236ea4319b5d7216c1bbce371

Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S. and Steinke, S.: Towards a high‐resolution regio...

ERA5_bias_corrected_monthly_precipitation_anomaly_FZJ-IBG3_WFDE5_0.1_degree_v1..nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 327.9 MB - SHA-256: e5a3559843458d2ed2e2db5a4572539081d9cbf5c8d4b7a4d420bd019a1270ce

Monthly precipitation anomalies (pr_a) derived from the ERA5 bias corrected dataset (referred to: Muñoz Sabater, J.: Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds....

ERA5_Land_monthly_precipitation_anomaly_FZJ-IBG3_ERA5_Land_0.1_degree_v1.1981_052021.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 323.2 MB - SHA-256: ad0ab24cc07a4a66b7e36408f4bd24c979c30dc305399d394cc4bf6039dddb12

Monthly precipitation anomalies (pr_a) derived from the ERA5 Land dataset (referred to: Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds.e2161bac, 2021.)

ERA5_Land_monthly_soil_moisture_anomaly_FZJ-IBG3_ERA5_Land_0.1_degree_v1.1981_052021.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 323.2 MB - SHA-256: 651e2e853d4421bed46ef7f5fab012dc1570a07f449fd0cc5d40606bb0fa4555

Monthly soil moisture anomalies (θ_a) derived from the ERA5 Land dataset (referred to: Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds.e2161bac, 2021.)

GLEAM_monthly_soil_moisture_anomaly_FZJ-IBG3_GLEAM_0.1_degree_v1.1980_2020.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 327.9 MB - SHA-256: af89272f98158109edaa1d23e07452328d923457a4b59f3a0a0acc099dafb5f9

Monthly soil moisture anomalies (θ_a) derived from the GLEAM dataset (referred to: Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A. and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporati...

LSTM_m_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 335.9 MB - SHA-256: a79f2a29e8e09028af96db6fdc7039b8602ce65ce79d412f946761a5e15e1492

Monthly water table depth anomalies (wtd_a) estimates at pixels with water table depth (wtd) observations over Europe obtained by LSTM networks with modeling results as input

LSTM_obs_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 335.9 MB - SHA-256: 88fdab991dbe77b5d112190612fe9d012607efa22908d5152ae10558669c2670

Monthly water table depth anomalies (wtd_a) estimates at pixels with water table depth (wtd) observations over Europe obtained by LSTM networks trained on observations

LSTM_TL_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1996_2016.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 335.9 MB - SHA-256: 93be9a8cda7a0ecdbeeb65bbdaf7a4ada323a3e2cf28b021531ec98c54c5261d

Monthly water table depth anomalies (wtd_a) estimates at pixels with water table depth (wtd) observations over Europe obtained by LSTM-TL

Add Data

Share Dataverse

Link Dataverse

Reset Modifications