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51 results for “jupyter”
Outputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the outputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Doran Khamis (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/dorankhamis">@dorankhamis</a></p> </li> <li> <p>Matt Fry (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/mattfry-ceh">@mattfry-ceh</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>UK Centre for Ecology & Hydrology (creator)</p> </li> <li> <p>Natural Environment Research Council (support)</p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>S. Stanley, V. Antoniou, A. Askquith-Ellis, L.A. Ball, E.S. Bennett, J.R. Blake, D.B. Boorman, M. Brooks, M. Clarke, H.M. Cooper, N. Cowan, A. Cumming, J.G. Evans, P. Farrand, M. Fry, O.E. Hitt, W.D. Lord, R. Morrison, G.V. Nash, D. Rylett, P.M. Scarlett, O.D. Swain, M. Szczykulska, J.L. Thornton, E.J. Trill, A.C. Warwick, and B. Winterbourn. Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]. 2021. URL: <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>, <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">doi:10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>.</p> </li> </ul> <p><strong>Further references</strong></p> <ul> <li> <p>Jonathan G. Evans, H. C. Ward, J. R. Blake, E. J. Hewitt, R. Morrison, M. Fry, L. A. Ball, L. C. Doughty, J. W. Libre, O. E. Hitt, D. Rylett, R. J. Ellis, A. C. Warwick, M. Brooks, M. A. Parkes, G. M.H. Wright, A. C. Singer, D. B. Boorman, and A. Jenkins. Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk. <em>Hydrological Processes</em>, 30:4987–4999, 12 2016. <a href="https://doi.org/10.1002/hyp.10929">doi:10.1002/hyp.10929</a>.</p> </li> <li> <p>M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem. Cosmos: the cosmic-ray soil moisture observing system. <em>Hydrology and Earth System Sciences</em>, 16(11):4079–4099, 2012. URL: <a href="https://hess.copernicus.org/articles/16/4079/2012/">https://hess.copernicus.org/articles/16/4079/2012/</a>, <a href="https://doi.org/10.5194/hess-16-4079-2012">doi:10.5194/hess-16-4079-2012</a>.</p> </li> </ul>
Dataset for FOSS4G 2022 workshop: Unleash the power of GRASS GIS with Jupyter
<p>Dataset for FOSS4G 2022 workshop <em>Unleash the power of GRASS GIS with Jupyter.</em></p>
Dataset and Jupyter notebook for "pyDARN: A Python Software for Visualizing SuperDARN Radar Data"
<p>SuperDARN radar dataset and Jupyter notebook used to generate figures for "pyDARN: A Python Software for Visualizing SuperDARN Radar Data".</p>
Trajectories_Jupyter_Tutorial
<p>These datasets and Jupyter notebooks are used in the single cell trajectories tutorial on the Galaxy Training Network site.</p>
Density field data, VTK files and Jupyter notebook for dislocation embedding analysis (with periodic boundary condition)
<p><strong>pbc_dataset.zip</strong></p> <p>The density field data is only for 0 degrees of misorientation with loading directions along [100], [110], [111], [234]. </p> <p>Folder paths for low density and low resolution simulation are:</p> <ol> <li>0deg/dir100/2.5e+13/config3/10x10x10</li> <li>0deg/dir110/2.5e+13/config3/10x10x10</li> <li>0deg/dir111/2.5e+13/config3/10x10x10</li> <li>0deg/dir234/2.5e+13/config3/10x10x10</li> </ol> <p>Folder paths for high density and high resolutions are:</p> <ol> <li>0deg/dir100/1e+14/config1/20x20x20</li> <li>0deg/dir110/1e+14/config1/20x20x20</li> <li>0deg/dir111/1e+14/config1/20x20x20</li> <li>0deg/dir234/1e+14/config1/20x20x20</li> </ol> <p>Each set of simulation has 2000 density field data files. Total files : 16000</p> <p>Please ensure that above paths are entered in the Jupyter notebook script file.</p> <p> </p> <p><strong>vtk.zip</strong></p> <p>VTK files for each simulation. Each simulation has 2000 files. </p> <p><strong>Dislocation_embeddings_pbc.ipynb</strong></p> <p>Jupyter notebook to generate dislocation embeddings for uploaded dataset.</p> <p> </p>
The code and data for paper: "Observing Fine-Grained Changes in Jupyter Notebooks During Development Time"
<div> <p>This package represents supplementary materials for the paper "Observing Fine-Grained Changes in Jupyter Notebooks During Development Time". Please refer to README in the archive for details.</p> </div>
Datasets, scripts and Jupyter Notebook for "Two distinct magma storage regions at Ambrym volcano detected by satellite geodesy", Geophysical Research Letters
<p>This repository includes scripts and files necessary to create Figure 1 (<strong>S1.zip </strong>and <strong>plot_TS_Ambrym_2019_2022.py</strong>) in "Two distinct magma storage regions at Ambrym volcano detected by satellite geodesy", <em>Geophysical Research Letters</em>. We also include the Jupyter Notebook used to run the EnKF data assimilation (<strong>enkf_notebook.zip) </strong>and produce Figures 2 and 3. The Jupyter Notebook and files used to produce Figure 4b,c can be found on <a href="http://github.com/tshreve/jupyterNBs/">GitHub</a>.</p> <p>This version corrects a bug in the code used to plot the cross-sections in Figure 3 with <strong>enkf_notebook.zip</strong>.</p>
tashley/particle_tracking_data: Added Jupyter notebook
<p>Data and code related to the manuscript "Probability distributions of particle hop distance and travel time over equilibrium mobile bedforms" (Ashley et al, in revision)</p>
Error Identification Strategies for Python Jupyter Notebooks
<p>Replication package for the paper "<strong>Error Identification Strategies for Python Jupyter Notebooks</strong>".</p>
Mars Meets Jupyter: Navigating the Interdisciplinary Divide Between Software Engineers and Domain Experts (Appendix)
Open the record for dataset details and reuse information.
Trained vectors and jupyter notebook for exploration
<p>Supplemental material to the submitted paper, containing a collection of trained vector as induced by different embedding techniques and a Jupyter Notebook to reproduce some of the results of the qualitative analysis section, as well as the experimental results.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.