Jülich DATA

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Jülich DATA is the central institutional repository for research data of Forschungszentrum Jülich.

It serves as a platform for all research data generated at Forschungszentrum Jülich or created in this context. Please feel welcome to index your research data here.

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18,691 to 18,700 of 20,109 Results

LSTM_E2_1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: b7ec5b5accae616765f768c0becd8e2ad6fab4eb1daaaaee9d0af7ee64cdc357 Monthly water table depth anomalies generated from the proposed LSTM networks of E2.1 (precipitation anomaly, soil moisture anomaly and scaled yearly averaged snow water equivalent)
LSTM_E2_2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: 005b78e53e7a65064e34348b66ff975a8880336d85f8bb383aaa9d8728c7e4d7 Monthly water table depth anomalies generated from the proposed LSTM networks of E2.2 (precipitation anomaly and soil moisture anomaly at the selected pixels and adjacent pixels)
LSTM_E2_3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: d090fd16b6a96db6e91c6f418d3ac8fa4e793d0c1145269fce502124c7b80b25 Monthly water table depth anomalies generated from the proposed LSTM networks of E2.3 (precipitation anomaly and soil moisture anomaly at the selected pixels close to rivers and river stage anomaly at the adjacent pixels)
TSMP_G2A_monthly_evapotranspiration_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: a1260d6aed328110afbf60df25cd63a48ede47973954ce3eaea09c136a81a5b8 Monthly evapotranspiration anomalies calculated from the TSMP-G2A data set
TSMP_G2A_monthly_soil_moisture_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: 6290fd173e798af8c3aa343c1f44445997042ffbe93dd27c78c3e05dc06eb436 Monthly soil moisture anomalies calculated from the TSMP-G2A data set
TSMP_G2A_yearly_averaged_snow_water_equivalent_FZJ-IBG3_TSMP_0.1_degree_v1.2003_2010.txt Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Plain Text - 2.5 MB - SHA-256: e85a674dacaaafd8431b78dac8ad82237a1c3eebe89cc736cce963a777389cac Yearly averaged snow water equivalent calculated from the TSMP-G2A data set
Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images Dec 13, 2021 - Peter Grünberg Institut – Quantum Nanoscience (PGI-3) Leis, Arthur; Cherepanov, Vasily; Voigtländer, Bert; Tautz, F. Stefan, 2021, "Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images", https://doi.org/10.26165/JUELICH-DATA/YRLPL0, Jülich DATA, V1 We provide here the raw data used to produce the figures in the publication Leis et al. Rev. Sci. Instrum., 2021, ??, DOI: ??. fig1a.png: Optical microscopy data used to produce figure 1a. fig2a.png, fig2b.png: Optical microscopy data used to produce figures 2a, 2b. fig2c.stp, fi...
000_kalibrierung_tip1_-9500mV_145458x81820nm.bmp Dec 13, 2021 - Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images Bitmap Image - 2.6 MB - SHA-256: d8aa315d790e5d97019b0198a833d5b62489a6f7bec3982c4e8d130e2f0ee3bb Optical microscope image used to produce figures 3a, 4.
001_kalibrierung_tip1_-9000mV_145458x81820nm.bmp Dec 13, 2021 - Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images Bitmap Image - 2.6 MB - SHA-256: 53f6073d9d80772b12ba93c9152b11d2239327091db19c9ec5b0516e185c5d6c Optical microscope image used to produce figure 4.
002_kalibrierung_tip1_-8500mV_145458x81820nm.bmp Dec 13, 2021 - Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images Bitmap Image - 2.6 MB - SHA-256: 2e9bfd29d888deaf08eba1c7f6e1e8cd829dec93b219a97be7420da9c838474c Optical microscope image used to produce figure 4.

LSTM_E2_1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: b7ec5b5accae616765f768c0becd8e2ad6fab4eb1daaaaee9d0af7ee64cdc357

Monthly water table depth anomalies generated from the proposed LSTM networks of E2.1 (precipitation anomaly, soil moisture anomaly and scaled yearly averaged snow water equivalent)

LSTM_E2_2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: 005b78e53e7a65064e34348b66ff975a8880336d85f8bb383aaa9d8728c7e4d7

Monthly water table depth anomalies generated from the proposed LSTM networks of E2.2 (precipitation anomaly and soil moisture anomaly at the selected pixels and adjacent pixels)

LSTM_E2_3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: d090fd16b6a96db6e91c6f418d3ac8fa4e793d0c1145269fce502124c7b80b25

Monthly water table depth anomalies generated from the proposed LSTM networks of E2.3 (precipitation anomaly and soil moisture anomaly at the selected pixels close to rivers and river stage anomaly at the adjacent pixels)

TSMP_G2A_monthly_evapotranspiration_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: a1260d6aed328110afbf60df25cd63a48ede47973954ce3eaea09c136a81a5b8

Monthly evapotranspiration anomalies calculated from the TSMP-G2A data set

TSMP_G2A_monthly_soil_moisture_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: 6290fd173e798af8c3aa343c1f44445997042ffbe93dd27c78c3e05dc06eb436

Monthly soil moisture anomalies calculated from the TSMP-G2A data set

TSMP_G2A_yearly_averaged_snow_water_equivalent_FZJ-IBG3_TSMP_0.1_degree_v1.2003_2010.txt

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Plain Text - 2.5 MB - SHA-256: e85a674dacaaafd8431b78dac8ad82237a1c3eebe89cc736cce963a777389cac

Yearly averaged snow water equivalent calculated from the TSMP-G2A data set

Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images

Dec 13, 2021 - Peter Grünberg Institut – Quantum Nanoscience (PGI-3)

Leis, Arthur; Cherepanov, Vasily; Voigtländer, Bert; Tautz, F. Stefan, 2021, "Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images", https://doi.org/10.26165/JUELICH-DATA/YRLPL0, Jülich DATA, V1

We provide here the raw data used to produce the figures in the publication Leis et al. Rev. Sci. Instrum., 2021, ??, DOI: ??. fig1a.png: Optical microscopy data used to produce figure 1a. fig2a.png, fig2b.png: Optical microscopy data used to produce figures 2a, 2b. fig2c.stp, fi...

000_kalibrierung_tip1_-9500mV_145458x81820nm.bmp

Dec 13, 2021 - Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images

Bitmap Image - 2.6 MB - SHA-256: d8aa315d790e5d97019b0198a833d5b62489a6f7bec3982c4e8d130e2f0ee3bb

Optical microscope image used to produce figures 3a, 4.

001_kalibrierung_tip1_-9000mV_145458x81820nm.bmp

Dec 13, 2021 - Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images

Bitmap Image - 2.6 MB - SHA-256: 53f6073d9d80772b12ba93c9152b11d2239327091db19c9ec5b0516e185c5d6c

Optical microscope image used to produce figure 4.

002_kalibrierung_tip1_-8500mV_145458x81820nm.bmp

Dec 13, 2021 - Data used in: Nanoscale tip positioning with a multi-tip scanning tunneling microscope using topography images

Bitmap Image - 2.6 MB - SHA-256: 2e9bfd29d888deaf08eba1c7f6e1e8cd829dec93b219a97be7420da9c838474c

Optical microscope image used to produce figure 4.

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