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51 results for “jupyter”
Datasets and Jupyter notebook for the structural analysis of protein-RNA interface evolution
<p>The present repository contains data and code related to our manuscript "Structural comparison of protein-RNA homologous interfaces reveals widespread overall conservation contrasted with versatility in polar contacts". In the manuscript, we analyze the evolution of protein-RNA interfaces by building a dataset of protein-RNA interologs (homologous interfaces) and exploring how interface contacts are conserved between homologous interfaces, as well as possible explanations for non-conserved contacts.</p> <p>This repository contains the following files:</p> <ul> <li>DataAnalysisNotebook.ipynb is a Jupyter notebook to reproduce contact conservation analysis and all figures from our manuscript, and to explore data</li> <li>env.yaml is an environment file in order to build a Conda/Mamba environment to run the Jupyter notebook </li> <li>2022-02-21-PDB.csv contains data from the PDB about 3D structures of complexes containing interacting protein and RNA chains (PDB structure identifier, chain identifiers, experimental technique and resolution)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.tsv contains more detailed information about interacting protein and RNA chains from these complexes (PDB and chain identifiers, protein and RNA size, interface size and number of contacts)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.txt.selectXE_2.50_p30_r10_pi5_ri5_rep_bc-100.out_RNAcl_0.99.tsv contains the same detailed information, restricted to the filtered dataset used as a starting point in our interolog search pipeline</li> <li>PDBinterfaceAlign.csv contains information about the structural alignment of pairs of protein-RNA interactions (structural alignment TM-scores, sequence identity and coverage)</li> <li>DataInterologsParam.tsv contains information about a pre-filtered set of 2587 potential interologs (including interface RMSD, sequence identity and coverage and interface size)</li> <li>DataInterologsContactsFixedSASA.tsv contains detailed information about conserved and non-conserved contacts in the final set of 2022 interologs (atomic contacts, apolar contacts, hydrogen bonds, salt bridges and stacking information for aminoacid-nucleotide pairs, as well as information about whether each belongs to the interface, secondary structures, and the aminoacid surface accessibility and evolutionary conservation metrics) - compared to version 1, the calculation of solvent accessibility was fixed for a number of interolog pairs</li> <li>DataCons.csv contains precomputed contact conservation metrics for each of the 2022 interolog pairs, for fast reproduction of manuscript figures</li> <li>DataInterologsContactsResampledMaintainStructSeqId.tsv, DataInterologsContactsShuffled.tsv and DataInterologsShuffled.tsv relate to baselines computed for contact conservation assessment</li> <li>clan.txt, clan_membership.txt, ecod.latest.domains.uniq.txt, rfam_interfaces_977.txt, DataGroupsECOD.tsv, DataGroupesRFAM.tsv, DataGroupsRFAMClan.tsv, DataInterfaceGroupsECOD.tsv and DataInterfaceGroupsRFAM.tsv relate to the ECOD (respectively Rfam) classification of protein domains (respectively RNA) in protein-RNA interfaces from our dataset</li> <li>ListeIntraHbonds.pkl and ListeIntraSaltBridges.pkl are pickle-format data files containing intra-molecular hydrogen bonds and salt bridges (respectively) that are used to analyse scenarii of compensation for non-conserved polar contacts.</li> </ul>
Outputs of the Jupyter Notebook - Detecting floating objects using Deep Learning and Sentinel-2 imagery
<p>The dataset contains the outputs of the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery" published in the ocean modelling section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Marc Rußwurm (author), EPFL-ECEO, <a href="https://github.com/MarcCoru">@marccoru</a></p> </li> </ul>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
Outputs of the Jupyter Notebook - Tree crown detection using DeepForest
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" 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>Matt Allen (reviewer), Department of Geography - University of Cambridge, <a href="https://github.com/mja2106">@mja2106</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Ben Weinstein (maintainer & developer), University of Florida, <a href="https://github.com/bw4sz">@bw4sz</a></p> </li> <li> <p>Henry Senyondo (support maintainer), University of Florida, <a href="https://github.com/henrykironde">@henrykironde</a></p> </li> <li> <p>Ethan White (PI and author), University of Florida, <a href="https://github.com/ethanwhite">@weecology</a></p> </li> <li> <p>Other contributors are listed in the <a href="https://github.com/weecology/DeepForest/graphs/contributors">GitHub repo</a></p> </li> </ul> <p><em>Modelling publications</em></p> <ul> <li> <p>Ben G Weinstein, Sergio Marconi, Mélaine Aubry-Kientz, Gregoire Vincent, Henry Senyondo, and Ethan P White. Deepforest: a python package for rgb deep learning tree crown delineation. <em>Methods in Ecology and Evolution</em>, 11:1743–1751, 2020. URL: <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472</a>, <a href="https://doi.org/https://doi.org/10.1111/2041-210X.13472">doi:https://doi.org/10.1111/2041-210X.13472</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, and Ethan White. Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. <em>Remote Sensing</em>, 2019. URL: <a href="https://www.mdpi.com/2072-4292/11/11/1309">https://www.mdpi.com/2072-4292/11/11/1309</a>, <a href="https://doi.org/10.3390/rs11111309">doi:10.3390/rs11111309</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie A Bohlman, Alina Zare, and Ethan P White. Cross-site learning in deep learning rgb tree crown detection. <em>Ecological Informatics</em>, 56:101061, 2020. URL: <a href="https://www.sciencedirect.com/science/article/pii/S157495412030011X">https://www.sciencedirect.com/science/article/pii/S157495412030011X</a>, <a href="https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101061">doi:https://doi.org/10.1016/j.ecoinf.2020.101061</a>.</p> </li> </ul>
Outputs of the Jupyter Notebook - SEVIRI Level 1.5
<p>The dataset contains the outputs of the notebook "SEVIRI Level 1.5" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samuel Jackson (author), Science & Technology Facilities Council, <a href="https://github.com/samueljackson92">@samueljackson92</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a>, 18/01/22 (latest revision)</p> </li> </ul> <p><em>Dataset originator/creator</em></p> <p>SEVIRI Level 1.5 Image Data - MSG - 0 degree</p> <ul> <li> <p>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</p> </li> </ul> <p>FRPPIXEL</p> <ul> <li> <p>Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)</p> </li> </ul> <p><em>Dataset authors</em></p> <p>SEVIRI Level 1.5 Image Data - MSG - 0 degree</p> <ul> <li> <p>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</p> </li> </ul> <p>FRPPIXEL</p> <ul> <li> <p>Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Martin Wooster, Jiangping He, Weidong Xu, and Alessio Lattanzio. Frp - product user manual. URL: <a href="https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq">https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq</a> (visited on 2021-11-18).</p> </li> <li> <p>MJ Wooster, G Roberts, PH Freeborn, W Xu, Y Govaerts, R Beeby, J He, A Lattanzio, D Fisher, and R Mullen. Lsa saf meteosat frp products–part 1: algorithms, product contents, and analysis. <em>Atmospheric Chemistry and Physics</em>, 15(22):13217–13239, 2015.</p> </li> </ul>
Outputs of the Jupyter Notebook - Tree crown delineation using detectreeRGB
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge, <a href="https://github.com/shmh40">@shmh40</a></li> <li>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></li> </ul> <p><em>Modelling codebase</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge <a href="https://github.com/shmh40">@shmh40</a></li> <li>James G. C. Ball (contributor), University of Cambridge <a href="https://github.com/PatBall1">@PatBall1</a></li> <li>David A. Coomes (contributor), University of Cambridge</li> <li>Toby Jackson (contributor), University of Cambridge</li> </ul>
Outputs of the Jupyter Notebook - Sea ice forecasting using IceNet
<p>The dataset contains the outputs of the notebook "Sea ice forecasting using IceNet" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></li> <li>Tom R. Andersson (reviewer), British Antarctic Survey, <a href="https://github.com/tom-andersson">@tom-andersson</a></li> <li>Nick Barlow (reviewer), The Alan Turing Institute, <a href="https://github.com/nbarlowATI">@nbarlowATI</a></li> </ul> <p><em>Modelling codebase</em></p> <ul> <li>Tom R. Andersson (author), British Antarctic Survey, <a href="https://github.com/tom-andersson">@tom-andersson</a></li> <li>James Byrne (contributor), British Antarctic Survey, <a href="https://github.com/JimCircadian">@JimCircadian</a></li> <li>Tony Phillips (contributor), British Antarctic Survey</li> </ul>
Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort.</p> <p>The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p><strong>Note this data should be used only for non-commercial purposes.</strong></p>
Outputs of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)
<p>The dataset contains the outputs of the notebook "Exploring Land Cover Data (Impact Observatory)" published in The Environmental Data Science Book.</p>
Jupyter Usage in Institutions with Coordinates
<p>A dataset with the coordinates of several Institutions which are using Jupyter along with some metadata</p>
Supporting Jupyter Python notebook for "A new class of efficient randomized benchmarking protocols"
<p>Python notebook containing the code used to generate the data for figure 2 in the appendix of "A new class of efficient randomized benchmarking protocols" (arXiv:1806.02048).</p>
Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)
<p>The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.</p>
Modelica Models and Jupyter Notebooks for System Analysis of Glucose Insulin Regulation
<p>This dataset contains source code of Modelica models of Glucose-Insulin regulation using different techniques.</p> <p>Accompanying Jupyter notebook is demo for system analysis (parameter estimation) of artificial data and to match model simulation able to be used in Teaching class.</p> <ul> <li><strong>ModelicaIdentification.ipynb</strong> - default notebook - code contains ellipsis which needs to be replaced as per instruction in text</li> <li><strong>ModelicaIdentificationResolution.ipynb - </strong>notebook - code with exemplar solution to default notebook</li> <li><strong>glucoseinsulin.mo - </strong>Modelica source code</li> <li><strong>PatientInsulinConcentration.csv</strong> - sample data to be fitted against model</li> <li><strong>seminar11hw.GIExperiment.fmu</strong> - FMU exported from Modelica in order to run simulation in Python and PyFMI library</li> </ul> <p>Thanks to the MYBINDER service, the Jupyter notebook can be viewed and executed as</p> <ul> <li><a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/">https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/</a> note that you need to launch terminal first in Jupyter -> New -> Terminal and install pyfmi and matplotlib by:</li> </ul> <pre><code class="language-bash">conda install -c conda-forge pyfmi matplotlib</code></pre> <ul> <li>Most recent version with other models and notebooks <a href="https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/">https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/</a></li> </ul>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </p>
2016 Jupyter Education Survey
<p>This dataset is the responses for the Jupyter education survey conducted in May 2016. This includes the following files:</p> <ul> <li>questions.pdf A PDF containing the questions that were asked.</li> <li>responses.csv A CSV file containing the survey responses.</li> </ul> <p>This survey was designed by Jessica Hamrick (@jhamrick) and was sourced primarily from the Jupyter, Jupyter Education, and Software Carpentry email lists between 04/22/2016 and 05/07/2016.</p> <p>Available at: https://github.com/jupyter/datasets/tree/master/surveys/2016-05-education-survey</p>
DistilKaggle: a distilled dataset of Kaggle Jupyter notebooks
<h2><strong>Overview</strong></h2> <p>DistilKaggle is a curated dataset extracted from Kaggle Jupyter notebooks spanning from September 2015 to October 2023. This dataset is a distilled version derived from the download of over 300GB of Kaggle kernels, focusing on essential data for research purposes. The dataset exclusively comprises publicly available Python Jupyter notebooks from Kaggle. The essential information for retrieving the data needed to download the dataset is obtained from the MetaKaggle dataset provided by Kaggle.</p> <h2><strong>Contents</strong></h2> <p>The DistilKaggle dataset consists of three main CSV files:</p> <p><strong>code.csv:</strong> Contains over 12 million rows of code cells extracted from the Kaggle kernels. Each row is identified by the kernel's ID and cell index for reproducibility.</p> <p><strong>markdown.csv:</strong> Includes over 5 million rows of markdown cells extracted from Kaggle kernels. Similar to <strong>code.csv</strong>, each row is identified by the kernel's ID and cell index.</p> <p><strong>notebook_metrics.csv:</strong> This file provides notebook features described in the accompanying paper released with this dataset. It includes metrics for over 517,000 Python notebooks.</p> <h2><strong>Directory Structure</strong></h2> <p>The <strong>kernels</strong> directory is organized based on Kaggle's Performance Tiers (PTs), a ranking system in Kaggle that classifies users. The structure includes PT-specific directories, each containing user ids that belong to this PT, download logs, and the essential data needed for downloading the notebooks.</p> <p>The <strong>utility</strong> directory contains two important files:</p> <p><strong>aggregate_data.py:</strong> A Python script for aggregating data from different PTs into the mentioned CSV files.</p> <p><strong>application.ipynb:</strong> A Jupyter notebook serving as a simple example application using the metrics dataframe. It demonstrates predicting the PT of the author based on notebook metrics.</p> <p><strong>DistilKaggle.tar.gz: </strong>It is just the compressed version of the whole dataset. If you downloaded all of the other files independently already, there is no need to download this file.</p> <h2><strong>Usage</strong></h2> <p>Researchers can leverage this distilled dataset for various analyses without dealing with the bulk of the original 300GB dataset. For access to the raw, unprocessed Kaggle kernels, researchers can request the dataset directly.</p> <h2><strong>Note</strong></h2> <p>The original dataset of Kaggle kernels is substantial, exceeding 300GB, making it impractical for direct upload to Zenodo. Researchers interested in the full dataset can contact the dataset maintainers for access.</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset in your research, please cite the accompanying paper or provide appropriate acknowledgment as outlined in the documentation.</p> <p>If you have any questions regarding the dataset, don't hesitate to contact me at <a href="mailto:mohammad.abolnejadian@gmail.com">mohammad.abolnejadian@gmail.com</a></p> <p>Thank you for using DistilKaggle!</p>
Dataset of structural and energetic descriptors for optimized geometries of the pyrene dimer in the S1 state and Jupyter Notebbok used for the unsupervised clustering, analysis and visualization
<p>Geometrical and energy data extracted from a set of 188 optimized geometries for the pyrene dimer in the first excited singlet state at the TD-CAMB3LYP + D3BJ / 6-31G* / C-CPCM(Cyclohexane) level. </p> <p>Coordinates (in xyz format) of the 188 optimized geometries considered.</p> <p>Jupyter notebook used to perform unsupervised clustering and analysis of the available structures.</p>
Dataset of Jupyter Notebooks from the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"
<pre>This archive contains the dataset of properly-licensed Jupyter notebooks from the MSR'22 paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts". The dataset contains 847,881 notebooks stored in the PostgreSQL dump file. You can find the details about the database in the README file. To transform the notebooks into this convenient format and to calcuate the structural metrics, we used our library called Matroskin, which can be found here: <a href="https://github.com/JetBrains-Research/Matroskin">https://github.com/JetBrains-Research/Matroskin</a>. </pre>
Inputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the inputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of the public 2013-2019 COSMOS-UK dataset, daily and subhourly observations and metadata for four stations: WYTH1, WADDN, SHEEP and CHIMN. These stations represent the first sites to prototype COSMOS sensors in the UK, see further details in Evans et al. (2016) and they are situated in human-intervened areas (grassland and cropland), except for one in a woodland land cover site.</p> <p>Data from COSMOS-UK up to the end of 2019 are available for download from the UKCEH Environmental Information Data Centre (EIDC). The data are accompanied by documentation that describes the site-specific instrumentation, data and processing including quality control. The full dataset is available for <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">download</a> under the terms of the Open Government License.</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>
Outputs of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series
<p>The dataset contains the outputs of the notebook "Concatenating a gridded rainfall reanalysis dataset into a time series" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Timothy Lam (author), University of Exeter, <a href="https://github.com/timo0thy">@timo0thy</a></p> </li> <li> <p>Marlene Kretschmer (author), University of Reading, <a href="https://github.com/MarleneKretschmer">@MarleneKretschmer</a></p> </li> <li> <p>Samantha Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Rachel Prudden (author), Met Office Informatics Lab, <a href="https://github.com/RPrudden">@RPrudden</a></p> </li> <li> <p>Elena Saggioro (author), University of Reading, <a href="https://github.com/ESaggioro">@ESaggioro</a></p> </li> <li> <p>Nick Homer (reviewer), University of Edinburgh, <a href="https://github.com/NHomer">@NHomer</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>NOAA National Center for Environmental Prediction (creator)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Eugenia Kalnay, Director, NCEP Environmental Modeling Center</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, Roy Jenne, and Dennis Joseph. The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3):437 – 472, 1996. URL: <a href="https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml">https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml</a>, <a href="https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2">doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2</a>.</p> </li> </ul> <p><em>Pipeline documentation</em></p> <ul> <li> <p>Marlene Kretschmer, Samantha V. Adams, Alberto Arribas, Rachel Prudden, Niall Robinson, Elena Saggioro, and Theodore G. Shepherd. Quantifying causal pathways of teleconnections. Bulletin of the American Meteorological Society, 102(12):E2247 – E2263, 2021. URL: <a href="https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml">https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml</a>, <a href="https://doi.org/10.1175/BAMS-D-20-0117.1">doi:10.1175/BAMS-D-20-0117.1</a>.</p> </li> </ul>
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