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71 to 80 of 98 Results
subjective.csv
Comma Separated Values - 857 B - SHA-256: 4cbceea79d600519bebd9dc067d0cd51d7fb6e05bbc2b5f2a9bf628feff96abc
survey.zip.archive
Unknown - 2.1 MB - SHA-256: 3db4264cf0970091bcaa651256e71ec7e20e2174821e0768010f5912d4acba9c
tracings.zip.archive
Unknown - 3.5 MB - SHA-256: b9f49559119a1c8967627e4e5ab2d0ffc2d5661f62ca0ec6d4f4d5cf34844617
Remove the .archive ending before unzipping
training_111x111x190.raw
Bitmap Image - 2.2 MB - SHA-256: e0a60bf9a79c0ead7419162504f96528f5d00767180f9f2a510ff433a9c4707b
Spacing 0.5 0.5 1
ueq.csv
Comma Separated Values - 2.0 KB - SHA-256: aba3039b25deb6966ab8363cb45d5f2820b439a6c0f2c56bd2b0d97c39ea6fa0
Spectroscopy Data and Model Parameters for: Observation of Josephson Harmonics in Tunnel Junctions
Nov 29, 2023 - Quantum Information Processing
Willsch, Dennis; Rieger, Dennis, 2023, "Spectroscopy Data and Model Parameters for: Observation of Josephson Harmonics in Tunnel Junctions", https://doi.org/10.26165/JUELICH-DATA/LGRHUH, Jülich DATA, V2
This repository contains the spectroscopy data and the model parameters along with a Jupyter notebook that demonstrates how to diagonalize the transmon-resonator Hamiltonians using tensorflow, verify the model parameters, and reproduce some of the figures from the corresponding p...
README.txt
Plain Text - 97 B - SHA-256: 862af1955f50b373cbc7005cc97e6708dd6b575fba57489637be514034f5d4a0
AIRS/Aqua Observations of non-LTE Emissions
Mar 16, 2023 - Earth System Science
Hoffmann, Lars, 2023, "AIRS/Aqua Observations of non-LTE Emissions", https://doi.org/10.26165/JUELICH-DATA/PHL4EE, Jülich DATA, V1
This data repository provides access to observations of carbon dioxide non-LTE emissions provided by the Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite. Information on carbon dioxide non-LTE emissions is derived by a brightness temperature differencing technique...
2m Temperature Forecast by Deep Learning
Aug 31, 2022 - Earth System Science
Gong, Bing; Langguth, Michael; Ji, Yan; Mozaffari, Amirpasha; Stadtler, Scarlet; Mache, Karim; Schultz, Martin G., 2022, "2m Temperature Forecast by Deep Learning", https://doi.org/10.26165/JUELICH-DATA/X5HPXP, Jülich DATA, V2
This repository provides the preprocessed datasets, which are used in the study Temperature forecasting by deep learning methods by Gong et al. (2022). This allows the user to reproduce the presented results without running the preprocessing chain from the raw ERA5 data. Data des...
md5sum.txt
Plain Text - 471 B - SHA-256: 63f2e461ba77a72bb4c47e4dc600c02015209f175e6849f18b0fad3f7cb6a859
md5 hash values for data stored on datapub
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