Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707

Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707 File descriptions ArmTA.txt - Temperature and surface areas for arms of C. perspicillata after flight experiment BodyTA.txt - Temperature and surface areas for body of C. perspicillata after flight experiment HeadTA.txt - Temperature and surface areas for head of C. perspicillata after flight experiment WingTA.txt - Temperature and surface areas for wings (patagium) of C. perspicillata after flight experiment WingMorph.txt - Morphological variables measured in the body and wings of C. perspicillata HeatLoss.R - Function to estimate heat loss (Qt) PowFlight.R - Function to estimate minimum power required to fly Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed SupportingInformationFile.docx - R notebook with set of analyses performed, word format SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown) For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook ( SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.

opencc-by-4.0Aug 2019View details →

Survey of digitized newspaper interfaces (dataset and notebooks)

This record contains the datasets and jupyter notebooks which support the analysis presented in the paper "Historical Newspaper User Interfaces: A Review". Please refer to the paper or the github repository for more information (see links below), or do not hesitate to contact us!

opencc-by-4.0May 2024View details →

Datasets and Jupyter notebook for the structural analysis of protein-RNA interface evolution

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. This repository contains the following files: <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>

opencc-by-4.0Jul 2021View details →

BrainIAK IEM notebook demonstration

These datasets are used to demonstrate how to use the BrainIAK Inverted Encoding Model (IEM) class. The original data are associated with pre-existing publications & Open Science Framework repositories, but have been sorted and cleaned for easier use.   Dataset 1 Rademaker, R., Chunharas, C., Serences, J.T. 2019. Coexisting representations of sensory and mnemonic information in human visual cortex. Nature Neuroscience 22:8. Publicly available data at <a href="https://osf.io/dkx6y/">https://osf.io/dkx6y/</a>   Dataset 2  Itthipuripat, S., Sprague, T.,C., Serences, J.T. 2019. Functional MRI and EEG Index Complementary Attentional Modulations. J. Neurosci. 31:6162-6179. Publicly available data at <a href="https://osf.io/savfp/">https://osf.io/savfp/</a>

opencc-by-4.0Aug 2021View details →

Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks

This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".

opencc-by-4.0Sep 2021View details →

Research data supporting "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment"

Research data supporting the publication: Higgins SG, Nogiwa-Valdez AA, Stevens MM, Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment, Nature Protocols, 2021. This repository contains the raw survey data of 172 current and historic electronic laboratory notebook (ELN) software packages. Main files: <ul> <li>"ELN_Review_Higgins_2021_Survey.csv"  = raw survey data in 'tidy' data format</li> <li>"ELN_Review_Higgins_2021.Rmd" = an R Markdown File (R Notebook) that takes the survey data as input and produces summary statistics and plots. This file was written using R Studio as the IDE.</li> </ul> Derived files, generated from those above: <ul> <li>"ELN_Review_Higgins_2021.nb.html" = a self-contained HTML file that is automatically generated by R Studio, based on the markdown file. This can be opened in any web browser to allow manual inspection of the code and comments without the need for specialist software. Embedded within this file is also the original markdown script (i.e. a copy of the code in "ELN_Review_Higgins_2021.Rmd")</li> <li>"ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html" = an HTML file generated by the script above via the plotly package. It contains an interactive version of the ELN survey data, allowing the user to hover over the timeline and explore the data.</li> <li>"ELN_Review_Higgins_2021_Timeline.pdf" = static version of ELN timeline, used to generate figure in main manuscript.</li> <li>"ELN_Review_Higgins_2021_Releases-Per-Year.pdf" = static version of number of new ELNs per year, used to generate figure in main manuscript.</li> </ul> This survey was generated from a mixture of primary and secondary sources (see references for secondary sources).

opencc-by-4.0Jul 2023View details →

Notably Inaccessible – Data Driven Understanding of Data Science Notebook (In)Accessibility

Overview This dataset artifact contains the intermediate datasets from pipeline executions necessary to reproduce the results of the paper. We share this artifact in hopes of providing a starting point for other researchers to extend the analysis on notebooks, discover more about their accessibility, and offer solutions to make data science more accessible. The scripts needed to generate these datasets and analyse them are shared in the <a href="https://github.com/make4all/notebooka11y">GitHub repository</a> for this work. <blockquote> The dataset contains large files of approximately 60 GB so please exercise caution when extracting the data from compressed files. </blockquote> <blockquote> The dataset contains files which could take a significant amount of run time of the scripts to generate/reproduce. </blockquote> Dataset Contents We briefly summarize the included files in our dataset. Please refer to the <a href="https://github.com/make4all/notebooka11y/blob/main/pipeline/README.md">documentation</a> for specific information about the structure of the data in these files, the scripts to generate them, and runtimes for various parts of our data processing pipeline. <ol> <li><code>epoch_9_loss_0.04706_testAcc_0.96867_X_resnext101_docSeg.pth</code>: We share this model file, originally provided by <a href="https://github.com/jobinkv/DocFigure">Jobin et al.</a>, to enable the classification of figures found in our dataset. Please place this into the `model/` <a href="https://github.com/make4all/notebooka11y/tree/main/model">directory</a>.</li> <li><code>model-results.csv</code>: This file contains results from the classification performed on the figures found in the notebooks in our dataset. <blockquote> Performing this classification may take upto a day. </blockquote> </li> <li> a11y-scan-dataset.zip: This archive contains two files and results in datasets of approximately 60GB when extracted. Please ensure that you have sufficient disk space to uncompress this zip archive. The archive contains: <ul> <li> <code>a11y/a11y-detailed-result.csv</code>: This dataset contains the accessibility scan results from the scans run on the 100k notebooks across themes. <blockquote>The detailed result file can be really large (> 60 GB) and can be time-consuming to construct.</blockquote> </li> <li> <code>a11y/a11y-aggregate-scan.csv</code>: This file is an aggregate of the detailed result that contains the number of each type of error found in each notebook. <blockquote>This file is also shared outside the compressed directory.</blockquote> </li> </ul> </li> <li> <code>errors-different-counts-a11y-analyze-errors-summary.csv</code>: This file contains the counts of errors that occur in notebooks across different themes. </li> <li> <code>nb_processed_cell_html.csv</code>: This file contains metadata corresponding to each cell extracted from the html exports of our notebooks. </li> <li> <code>nb_first_interactive_cell.csv</code>: This file contains the necessary metadata to compute the first interactive element, as defined in our paper, in each notebook. </li> <li> <code>nb_processed.csv</code>: This file contains the necessary data after processing the notebooks extracting the number of images, imports, languages, and cell level information. </li> <li> <code>processed_function_calls.csv</code>: This file contains the information about the notebooks, the various imports and function calls used within the notebooks. </li> </ol>

opencc-by-4.0Jan 2021View details →

Notebook Critiques d'Art 1 : les pseudonymes

This is a tutorial to make Notebook Jupyter which queries the API of the Art Critics site to filter the pseudonyms. C'est un tuto pour faire Notebook Jupyter qui interroge l'API du site critiques d'Art pour filtrer les pseudonymes. Es ist ein Tutorial, um ein Jupyter-Notebook zu erstellen, das die API der Art Critics Site abfragt, um Pseudonyme zu filtern.

opencc-by-4.0Jan 2022View details →

Outputs of the Jupyter Notebook - Detecting floating objects using Deep Learning and Sentinel-2 imagery

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. Contributions Notebook <ul> <li> Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a> </li> <li> Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a> </li> <li> Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> </ul> Modelling codebase <ul> <li> Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a> </li> <li> Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a> </li> <li> Marc Rußwurm (author), EPFL-ECEO, <a href="https://github.com/MarcCoru">@marccoru</a> </li> </ul>

Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data

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. Contributions Notebook <ul> <li> Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a> </li> <li> Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> </ul> Dataset originator/creator <ul> <li> Met Office Informatics Lab (creator) </li> <li> Microsoft (support) </li> <li> European Regional Development Fund (support) </li> </ul> Dataset authors <ul> <li> Met Office </li> </ul> Dataset documentation <ul> <li> 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>. </li> </ul>

Outputs of the Jupyter Notebook - Tree crown detection using DeepForest

The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book. Contributions Notebook <ul> <li> Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> <li> Matt Allen (reviewer), Department of Geography - University of Cambridge, <a href="https://github.com/mja2106">@mja2106</a> </li> </ul> Modelling codebase <ul> <li> Ben Weinstein (maintainer & developer), University of Florida, <a href="https://github.com/bw4sz">@bw4sz</a> </li> <li> Henry Senyondo (support maintainer), University of Florida, <a href="https://github.com/henrykironde">@henrykironde</a> </li> <li> Ethan White (PI and author), University of Florida, <a href="https://github.com/ethanwhite">@weecology</a> </li> <li> Other contributors are listed in the <a href="https://github.com/weecology/DeepForest/graphs/contributors">GitHub repo</a> </li> </ul> Modelling publications <ul> <li> 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. Methods in Ecology and Evolution, 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>. </li> <li> 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. Remote Sensing, 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>. </li> <li> Ben G Weinstein, Sergio Marconi, Stephanie A Bohlman, Alina Zare, and Ethan P White. Cross-site learning in deep learning rgb tree crown detection. Ecological Informatics, 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>. </li> </ul>

Outputs of the Jupyter Notebook - SEVIRI Level 1.5

The dataset contains the outputs of the notebook "SEVIRI Level 1.5" published in The Environmental Data Science Book. Contributions Notebook <ul> <li> Samuel Jackson (author), Science & Technology Facilities Council, <a href="https://github.com/samueljackson92">@samueljackson92</a> </li> <li> Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a>, 18/01/22 (latest revision) </li> </ul> Dataset originator/creator SEVIRI Level 1.5 Image Data - MSG - 0 degree <ul> <li> European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) </li> </ul> FRPPIXEL <ul> <li> Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF) </li> </ul> Dataset authors SEVIRI Level 1.5 Image Data - MSG - 0 degree <ul> <li> European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) </li> </ul> FRPPIXEL <ul> <li> Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF) </li> </ul> Dataset documentation <ul> <li> 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). </li> <li> 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. Atmospheric Chemistry and Physics, 15(22):13217–13239, 2015. </li> </ul>

opencc-by-4.0Mar 2022View details →

Outputs of the Jupyter Notebook - Tree crown delineation using detectreeRGB

opencc-by-4.0Apr 2022View details →

Outputs of the Jupyter Notebook - Sea ice forecasting using IceNet

The dataset contains the outputs of the notebook "Sea ice forecasting using IceNet" published in The Environmental Data Science Book. Contributions Notebook <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> Modelling codebase <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>

opencc-by-4.0Jun 2021View details →

MERRIN: MEtabolic Regulation Rule INference from time series data (Docker image and notebooks)

This record contains notebooks and Docker image for reproducing the results of the paper "MERRIN: MEtabolic Regulation Rule INference from time series data" published as part of the ECCB 2022 conference. Notebooks can be executed interactively within the Docker image <code>bioasp/merrin:v1</code> which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code> Also see <a href="https://github.com/bioasp/merrin-covert">https://github.com/bioasp/merrin-covert</a> The Docker image can be executed as follows: <pre><code class="language-bash">docker pull bioasp/merrin:v1 docker run -it --rm -p 8888:8888 bioasp/merrin:v1 </code></pre> then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>. The image can be imported using the command <code>docker load</code> with the image file provided in this record: <pre><code>docker load -i image.tar.gz</code></pre> or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>: <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.6670165</code></pre>

opencc-by-4.0Aug 2022View details →

StarDist Adipocyte Segmentation Training data, Training Notebook and Model

Data from H&E human bone marrow whole slide scanner images used in the paper: "MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology" (https://doi.org/10.21203/rs.3.rs-1860140/v1)   292 image patches Ground truth were manually annotated using QuPath and split into 263 images for training and 29 for validation. Training in StarDist was done on a Windows 10 PC with an RTX 2080 GPU. The requirements file for installing a Python 3.7 environment to run the attached notebooks is provided (stardist-val.txt). The StarDist model configuration can be found in the Jupyter Notebook : <pre><code>Adipocyte Training.ipynb</code></pre> Model validation and metrics can be performed by running the notebook after finishing the Adipocyte Training notebook. <pre><code>Quality Control.ipynb</code></pre>

Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data

The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book. 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. 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. Contributions Notebook <ul> <li> Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a> </li> <li> Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> </ul> Dataset originator/creator <ul> <li> Met Office Informatics Lab (creator) </li> <li> Microsoft (support) </li> <li> European Regional Development Fund (support) </li> </ul> Dataset authors <ul> <li> Met Office </li> </ul> Dataset documentation <ul> <li> 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>. </li> </ul> Note this data should be used only for non-commercial purposes.

opencc-by-4.0Sep 2022View details →

Outputs of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)

The dataset contains the outputs of the notebook "Exploring Land Cover Data (Impact Observatory)" published in The Environmental Data Science Book.

opencc-by-4.0Jan 2019View details →

Supporting Jupyter Python notebook for "A new class of efficient randomized benchmarking protocols"

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).