Skip to main content
AI Crawler DoS Attacks | Dataverse Creation Restriction – Please note:
  • We are experiencing a coordinated attack by AI training crawler bots, which slow down the service. We are working on mitigating the problem and apologize for any inconveniences.
  • Institutional or project level dataverses have to be created with confirming permissions. See details in guide.
Jülich DATA (Forschungszentrum Jülich GmbH)
Metrics
156,976 Downloads
Contact Share
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.

Find Advanced Search

41 to 50 of 340 Results
Thieme 1 2019-2023.csv
Comma Separated Values - 71 B - SHA-256: 53e4da879637f92f3a783c1fba8a9bd7eb5b67c78cccc6ba354f60095e720c99
Thieme 2 2021-2025.csv
Comma Separated Values - 158 B - SHA-256: ffaa58894d97a395d1e8c16a1bed9d06c292016f16d0f34be646d85bea4c8370
Wiley Gold (DEAL) 2019-2022.csv
Comma Separated Values - 11.3 KB - SHA-256: 0cbc829822008393dab5c4690c68306f876751126f38e36db72cc18a11a096d5
Wiley Hybrid (DEAL) 2019-2022.csv
Comma Separated Values - 69.6 KB - SHA-256: 012f5dcf469abf0e72c8fb089b5ba56f02d63d0f3d91ee51e82ce808571bef03
OAM_OA-Zeitschriften_DFG-Anträge_2022.csv
Comma Separated Values - 2.8 MB - SHA-256: 51356edb147190e53e7cf406baeaad241589dc1573d2dc0e60cc8bf5c400d9b5
Updated list with input from ISSN-GOLD-OA 5.0
Example_script_for_the_implementation_of_LSTM_TL.ipynb
Jupyter Notebook - 82.0 KB - SHA-256: 9fae22a6005caf9cb3361e20ba0bd5954550e833e3444ef24a917abe41a77b5b
A Jupyter Notebook showing an example about the implementation of LSTM-TL
SI.txt
Plain Text - 529.8 KB - SHA-256: 8ff9b3781efbff553f254dd6cbb13c84b154064189286a39f7a204bbebd941a4
Quantum chemical data for the OH-initiated oxidation of DMS
COSMO_REA6_monthly_precipitation_anomaly_FZJ-IBG3_COSMO_0.1_degree_v1.1995_082019.nc
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
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
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.)
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.

Contact Jülich DATA Support

Jülich DATA Support

Please fill this out to prove you are not a robot.

+ =