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Jülich DATA (Forschungszentrum Jülich GmbH)
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Unknown - 5.9 MB - SHA-256: 19f47bbbde9639866f9111326d79c57725f2e4a09c41f7392ff0fc5c6be7c2b7
Unknown - 24.7 KB - SHA-256: c5824069ca9f1cbead7dff3728d5737df4d838d1bb7cce8496e795b2131e2206
Unknown - 5.9 MB - SHA-256: 55ed4eefdca50d523179d96f7a21487a82e4ec8edd3d3c20fab30a816e09a383
Unknown - 24.7 KB - SHA-256: cbeeb5dfde812eed3ab93745c6cc56258c4b5ab73ae9ae1e1a582c7e26bc2deb
Unknown - 5.9 MB - SHA-256: df5aa47da1ac18b6f76baba14718a10dadf9d144d59a5c5280afdeb80e5fa50d
Unknown - 24.7 KB - SHA-256: d5ba495071a357b91053e8815df5ea86b67ac7bed03bb197208b020e0821cf68
Jul 27, 2021
The dataverse of the Institute of Neuroscience and Medicine – Nuclear Chemistry (INM-5)
Network Common Data Form - 335.9 MB - SHA-256: a0ac27eb52a9fbffc8620acdb5ba8eebe44ad11daa0001aeaf59a78b4aa8744f
Monthly groundwater table depth anomalies generated from the proposed LSTM networks
Network Common Data Form - 167.9 MB - SHA-256: cb307f74a193b7dc06443d50c61357fd50625c8bd8610094ba485309e4c4445f
Monthly groundwater table depth anomalies calculated from the TSMP-G2A data set
Network Common Data Form - 167.9 MB - SHA-256: 25188351e4f16eac5a147ff03c402b4699183a02ff2cd28940fb2b0effd4da4f
Monthly precipitation anomalies calculated from the TSMP-G2A data set
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