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11 to 18 of 18 Results

Dataset belonging to publication "Evaluation of three soil moisture profile sensors using laboratory and field experiments" Jul 21, 2023 Nieberding, Felix; Huisman, Johan Alexander; Huebner, Christof; Schilling, Bernd; Weuthen, Ansgar; Bogena, Heye Reemt, 2023, "Dataset belonging to publication "Evaluation of three soil moisture profile sensors using laboratory and field experiments"", https://doi.org/10.26165/JUELICH-DATA/NAWMCS, Jülich DATA, V1 The dataset contains the raw and processed data, plots and statistics, as well as the R scripts used to generate these. It is meant as supplementary material accompanying the upcoming publication in MDPI Sensors topic "Metrology-Assisted Production in Agriculture and Forestry": h...
Replication Data for: https://arxiv.org/abs/2210.07833v2 May 16, 2023 Singh, Juhi; Zeier, Robert; Calarco, Tommaso; Motzoi, Felix, 2023, "Replication Data for: https://arxiv.org/abs/2210.07833v2", https://doi.org/10.26165/JUELICH-DATA/FJO68W, Jülich DATA, V1 The files contains all the data used for generating all figures in the manuscript. All the folders the named according to the figure numbers.
Replication Data for: On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots Feb 23, 2023 Hader, Fabian; Vogelbruch, Jan; Humpohl, Simon; Hangleiter, Tobias; Eguzo, Chimezie; Heinen, Stefan; Meyer, Stefanie; van Waasen, Stefan, 2023, "Replication Data for: On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots", https://doi.org/10.26165/JUELICH-DATA/QIIBZV, Jülich DATA, V1 Sensor dot measurement data (Matlab format) and format description used in the evaluation of IEEE TQE paper: On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots, 2023
Data used in: Spanning Fermi arcs in a two-dimensional magnet Sep 7, 2022 Chen, Ying-Jiun; Jan-Philipp Hanke; Markus Hoffmann; Gustav Bihlmayer; Yuriy Mokrousov; Stefan Blügel; Claus M. Schneider; Christian Tusche, 2022, "Data used in: Spanning Fermi arcs in a two-dimensional magnet", https://doi.org/10.26165/JUELICH-DATA/CXWKMJ, Jülich DATA, V1 We provide here the raw data used to produce the figures in the publication Y.J. Chen et al. XXXXXXX, XX, XXXX, DOI: XXXXXXXX. All metadata necessary to analyze the data are contained in the files.
Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Aug 29, 2022 Tesch, Tobias; Kollet, Stefan; Garcke, Jochen; Katragkou, Eleni; Kartsios, Stergios, 2022, "Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'", https://doi.org/10.26165/JUELICH-DATA/YO3JCM, Jülich DATA, V1 This is a data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe', submitted to Geophysical Research Letters. It contains data from a convection-permitting (CP) simulation across central Europe. The simulation was perf...
Advancing AI-based pan-European groundwater monitoring Mar 28, 2022 Ma, Yueling; Montzka, Carsten; Naz, Bibi; Kollet, Stefan, 2021, "Advancing AI-based pan-European groundwater monitoring", https://doi.org/10.26165/JUELICH-DATA/ZBLDIR, Jülich DATA, V2 This study proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is nam...
An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Dec 13, 2021 Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 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", https://doi.org/10.26165/JUELICH-DATA/AMQ6NI, Jülich DATA, V1 This study introduced a number of hydrometeorological variables in addition to precipitation anomaly (pr_a) in the construction of Long Short-Term Memory (LSTM) networks to arrive at improved water table depth anomaly (wtd_a) at individual pixels over Europe in various experiment...
Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe May 31, 2021 Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 2021, "Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe", https://doi.org/10.26165/JUELICH-DATA/WPRA1F, Jülich DATA, V1 This study utilized spatiotemporally continuous precipitation anomaly (pr_a) and water table depth anomaly (wtd_a) from integrated hydrologic simulation results (i.e., the TSMP-G2A data set) over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the tim...

Dataset belonging to publication "Evaluation of three soil moisture profile sensors using laboratory and field experiments"

Jul 21, 2023

Nieberding, Felix; Huisman, Johan Alexander; Huebner, Christof; Schilling, Bernd; Weuthen, Ansgar; Bogena, Heye Reemt, 2023, "Dataset belonging to publication "Evaluation of three soil moisture profile sensors using laboratory and field experiments"", https://doi.org/10.26165/JUELICH-DATA/NAWMCS, Jülich DATA, V1

The dataset contains the raw and processed data, plots and statistics, as well as the R scripts used to generate these. It is meant as supplementary material accompanying the upcoming publication in MDPI Sensors topic "Metrology-Assisted Production in Agriculture and Forestry": h...

Replication Data for: https://arxiv.org/abs/2210.07833v2

May 16, 2023

Singh, Juhi; Zeier, Robert; Calarco, Tommaso; Motzoi, Felix, 2023, "Replication Data for: https://arxiv.org/abs/2210.07833v2", https://doi.org/10.26165/JUELICH-DATA/FJO68W, Jülich DATA, V1

The files contains all the data used for generating all figures in the manuscript. All the folders the named according to the figure numbers.

Replication Data for: On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots

Feb 23, 2023

Hader, Fabian; Vogelbruch, Jan; Humpohl, Simon; Hangleiter, Tobias; Eguzo, Chimezie; Heinen, Stefan; Meyer, Stefanie; van Waasen, Stefan, 2023, "Replication Data for: On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots", https://doi.org/10.26165/JUELICH-DATA/QIIBZV, Jülich DATA, V1

Sensor dot measurement data (Matlab format) and format description used in the evaluation of IEEE TQE paper: On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots, 2023

Data used in: Spanning Fermi arcs in a two-dimensional magnet

Sep 7, 2022

Chen, Ying-Jiun; Jan-Philipp Hanke; Markus Hoffmann; Gustav Bihlmayer; Yuriy Mokrousov; Stefan Blügel; Claus M. Schneider; Christian Tusche, 2022, "Data used in: Spanning Fermi arcs in a two-dimensional magnet", https://doi.org/10.26165/JUELICH-DATA/CXWKMJ, Jülich DATA, V1

We provide here the raw data used to produce the figures in the publication Y.J. Chen et al. XXXXXXX, XX, XXXX, DOI: XXXXXXXX. All metadata necessary to analyze the data are contained in the files.

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

Aug 29, 2022

Tesch, Tobias; Kollet, Stefan; Garcke, Jochen; Katragkou, Eleni; Kartsios, Stergios, 2022, "Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'", https://doi.org/10.26165/JUELICH-DATA/YO3JCM, Jülich DATA, V1

This is a data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe', submitted to Geophysical Research Letters. It contains data from a convection-permitting (CP) simulation across central Europe. The simulation was perf...

Advancing AI-based pan-European groundwater monitoring

Mar 28, 2022

Ma, Yueling; Montzka, Carsten; Naz, Bibi; Kollet, Stefan, 2021, "Advancing AI-based pan-European groundwater monitoring", https://doi.org/10.26165/JUELICH-DATA/ZBLDIR, Jülich DATA, V2

This study proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is nam...

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

Dec 13, 2021

Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 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", https://doi.org/10.26165/JUELICH-DATA/AMQ6NI, Jülich DATA, V1

This study introduced a number of hydrometeorological variables in addition to precipitation anomaly (pr_a) in the construction of Long Short-Term Memory (LSTM) networks to arrive at improved water table depth anomaly (wtd_a) at individual pixels over Europe in various experiment...

Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

May 31, 2021

Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 2021, "Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe", https://doi.org/10.26165/JUELICH-DATA/WPRA1F, Jülich DATA, V1

This study utilized spatiotemporally continuous precipitation anomaly (pr_a) and water table depth anomaly (wtd_a) from integrated hydrologic simulation results (i.e., the TSMP-G2A data set) over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the tim...

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