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764 results for “Reproducible”
Validation Videos - Robotic System for Reproducible Mobile Networking Experimentation in Anechoic Chambers (Master Thesis)
<p><strong>Note on Robot's Referential:</strong></p> <p>The robot's referential can be inferred in the recording via the "Safety Position." The safety position is the same for both the Digital Model (Gazebo) and the Real Robot (Joint Position = [0.0, -1.57, 1.57, 0.0, 0.0, 0.0]).</p> <p>In the safety position, the robot is approximately aligned with the X-axis, with its end-effector on the positive side of the axis. The end-effector faces perpendicular to the Y-axis. The positive Z-axis points upwards towards the ceiling.</p> <p> </p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten ASCII
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: Esri ASCII grid</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Dataset to reproduce the paper "A new framework to evaluate urban design using urban microclimatic modelling in future climatic conditions"
<p>This dataset has been generated with the paper " A new framework to evaluate urban design using urban microclimatic<br> modelling in future climatic conditions" (https://doi.org/10.3390/su10041134). A Python notebook is also included to conduct the analysis.</p> <ol> <li>Data analysis - Sustainability paper.ipynb : Python notebook</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2039 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2039_cim : Climate file for the year 2039 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2069 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2069_cim : Climate file for the year 2069 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2099 : Climate file for the year 2099 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2099_cim : Climate file for the year 2099 obtained from RCA4-CIM</li> <li>heating_2039 : Heating demand from CitySim for the year 2039</li> <li>heating_2039_cim : Heating demand from CitySim-CIM for the year 2039</li> <li>heating_2069 : Heating demand from CitySim for the year 2069</li> <li>heating_2069_cim : Heating demand from CitySim-CIM for the year 2069</li> <li>heating_2099 : Heating demand from CitySim for the year 2099</li> <li>heating_2099_cim : Heating demand from CitySim-CIM for the year 2099</li> <li>heating_2099_minP : Heating demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>heating_2099__minP_cim : Heating demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>cooling_2039 : Cooling demand from CitySim for the year 2039</li> <li>cooling_2039_cim : Cooling demand from CitySim-CIM for the year 2039</li> <li>cooling_2069 : Cooling demand from CitySim for the year 2069</li> <li>cooling_2069_cim : Cooling demand from CitySim-CIM for the year 2069</li> <li>cooling_2099 : Cooling demand from CitySim for the year 2099</li> <li>cooling_2099_cim : Cooling demand from CitySim-CIM for the year 2099</li> <li>cooling_2099_minP : Cooling demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>cooling_2099__minP_cim : Cooling demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>temp_cim : simulated temperature from CIM using Meteonorm</li> <li>temp_meteonorm : temperature from Meteonorm</li> <li>u_cim : simulated wind speedfrom CIM using Meteonorm</li> <li>u_meteonorm : wind speed from Meteonorm</li> </ol> <p> </p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 2]
<p>3D micro surface data processed in ConfoMap v7.4.8633 (a derivative of MountainsMap Imaging Topography developed by Digital Surf, Besançon, France).</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 4]
<p>Python script and results of the Bayesian Multi-factor ANOVA.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Syntactical Carving of PNGs and Automated Generation of Reproducible Datasets
<p>This is the dataset we used in the evaluation of our paper “Syntactical Carving of PNGs and Automated Generation of Reproducible Datasets”.</p>
Code and data to reproduce the results of the paper: "Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece"
<p>Code and data to reproduce the results of Knitter et al. (2019): Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece. ERL.</p>
Reproducibility by Default: Jupyter on Chameleon (screencast)
<p>The Jupyter notebook is an interactive environment that allows users to tell the story of an experiment by combining explanations in text, capturing the process in code, and representing results as images or graphs.</p> <p>The Notebook a particularly useful tool for expressing reproducible experiments because it allows you to record and share the experimental process – as well as the reasoning that went with it -- rather than just the results. Sharing a notebook allows others to easily repeat – and potentially also modify your experiment. This makes “standing on the shoulders of giants” a much easier proposition than ever before.</p>
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]
<p>This file contains the pretrained models and the evaluation of the pipelines described in the paper <em>Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation</em>.</p> <p>Source code can be downloaded at: <a href="https://github.com/aramis-lab/AD-DL">https://github.com/aramis-lab/AD-DL</a></p> <p>Also, single files can be obtained at: <a href="https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/">https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/</a></p> <p>The structure of the compressed file is as follows:</p> <p>clinicadl_models/<br> ├── 2D_slice<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── AD_CN_dataleakage<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ └── AD_CN<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_patch<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_ROI_based<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_subject<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── autoencoders<br> │ ├── 3D_patch<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ ├── 3D_ROI_based<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ └── 3D_subject<br> │ └── baseline<br> │ ├── extensive<br> │ └── minimal<br> └── svm<br> ├── baseline<br> │ ├── AD_CN<br> │ │ ├── all_subjects.tsv<br> │ │ └── classifier<br> │ └── sMCI_pMCI<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── longitudinal<br> ├── AD_CN<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── sMCI_pMCI<br> ├── all_subjects.tsv<br> └── classifier</p> <p>We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a <strong><em>.pth.tar</em> </strong>file (<em>Pytorch</em> format) inside the <em>best_model</em> folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The <em>performances </em>folder contains the computed metrics for the correponding model (ACC, BA, etc). <em> </em></p> <p>For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, <em>tsv</em> files with the subject list.</p>
Replication data for: "Revisiting the 'East African Paradox': CMIP6 models also struggle to reproduce strong observed Long Rain drying trends."
<h3><strong>Intro</strong></h3> <p>This repository contains replication data for Schwarzwald, Kevin and Richard Seager (2024), "'Revisiting the “East African Paradox': CMIP6 models also struggle to reproduce strong observed MAM long rain drying trends." (under revision at Journal of Climate). This includes the data necessary to replicate all main text figures and most figures in Supplementary Materials. Additional figures in Supplementary Materials require raw precipitation time series, detailed below. This repository also includes a copy of the code necessary to replicate the entire study; the latest version of the code, in addition to instructions on how to use it, is kept at <a href="https://github.com/ks905383/gha_trends">this GitHub archive</a>. </p> <h3><strong>Structure</strong></h3> <p>The repository is structured as follows: </p> <ul> <li><code>code</code>: Static / stable version of reproduction code for Schwarzwald and Seager (2024) that uses and creates these data (see <a href="https://github.com/ks905383/gha_trends">here</a> for more detailed instructions)</li> <li><code>figures</code>: Static / stable versions of main and supplemental figures for Schwarzwald and Seager (2024).</li> <li><code>climate_raw</code>: "raw" (often pre-processed) climate data files; only some files are included, see below for more details</li> <li><code>climate_proc</code>: Processed climate data files upon which the analysis is based, used by main and supplemental figure code</li> <li><code>aux_data</code>: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code. </li> </ul> <h3><strong>Notes on raw climate data</strong></h3> <p>Due to space limitations (and a desire to not create yet another cloud copy of CMIP6 data), this repository only contains processed CMIP6 data: the calculated linear trends of rainfall, sea surface temperatures, and 500 hPa geopotential height used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://aims2.llnl.gov/search/cmip6/">ESGF</a>, and consists of every available CMIP6 monthly rainfall, sea surface temperature (SST), and geopotential height file on the archive at the time of processing for the experiments detailed in the manuscript. For precipitation, only a bounding box (-3 S to 12.5 N, 32 E to 55 E) around East Africa was downloaded, and saved with the file suffix "_HoAfrica" (see replication code README for more details). One example precipitation file (one ensemble member of ACCESS-CM2 historical precipitation) is included in this repository for reference. </p> <p>Similarly, this repository only contains processed NMME data: calculated linear trends of rainfall used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/">IRI Data Library</a> and consists of every available monthly rainfall hindcast / forecast file from the NMME archive for the models used (see manuscript Table S1). As above, only a bounding box around East Africa was downloaded, and files were standardized to a partial CMIP* file format (one file per variable, with CMIP file and variable name conventions, but with forecast lead time as an additional dimension). Preprocessing is a bit more extensive than for CMIP6 models: first, hindcasts and forecasts were concatenated into a single file (since hindcasts are saved in the archive only up to the time when the model was operationalized), and then reindexed such that the "time" variable refers to the time <em>for which the forecast is made</em>, and not the time <em>at</em> which the forecast is made. One example precipitation file (CanCM4i rainfall hindcasts/forecasts) is included in this repository for reference. </p> <p>This repository does, however, contain preprocessed copies of the "raw" gridded observational rainfall data products used (in addition to the processed trends), since harmonizing and standardizing the data into a format easily compatible with the CMIP6 data was a nontrivial amount of work that would be tedious to replicate. For the ten gridded observational data products used, monthly rainfall was brought into the CMIP* file format (one file per variable, with CMIP file and variable name conventions), with one notable exception: all observational rainfall is saved in units of mm/day. </p> <p>Reanalysis and gridded ocean observations can be downloaded from each product's respective repositories. As before, the code assumes the data have been preprocessed into something akin to the CMIP* file format. </p> <h3><strong>Notes on processed climate data</strong></h3> <p>Note that the repository includes a file (<code>aux_data/pr_doyavg_CHIRPS_historical_seasstats_dunning_19810101-20141231_HoAfrica.nc</code>) containing CHIRPS seasonal characteristics in East Africa, created as part of Schwarzwald et al., 2023, <em>Climate Dynamics</em>. This file is primarily used to set the boundaries of the study region, see the manuscript for details of how it was calculated. </p> <h3><strong>Licensing, citing, questions</strong></h3> <p>These data are offered under a CC 4.0 license, which allows redistribution and reuse as long as they are correctly cited; note that for much of these data (especially for "raw" data), this requires citations to the original creators. </p> <p>For questions, please feel free to reach out to corresponding author Kevin Schwarzwald. </p>
Data files and computer code scripts for reproducing the results of a manuscript on the measurement and ranking of cotton drought tolerance capacity
<p>This upload contains the data files and computer code scripts for reproducing the main results, esp. figures, of the manuscript entitled<br>"Rapid measurement and statistical ranking of leaf drought tolerance capacity in cotton," by X. Dong, D. A. Mott, J. Garg, Q. Zhou, J. Sunoj V. S., and B. M. McKnight. The manuscriptt is currently under peer review.</p>
Dataset for PanoSpace-reproducibility
<p>These datasets are used to implement the reproducibility of PanoSpace within Visium Breast Cancer (https://github.com/hehuifeng/PanoSpace/blob/main/Visium_Breast_Reproducibility.ipynb). </p>
Data bundle for powerd-data: A transparent and reproducible data processing pipeline for energy system modeling based on egon-data
<div> <p><strong>powerd-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. Is is a fork from the open-source tool <strong>egon-data</strong>. </p> <p>powerd-data and egon-data retrieve and process data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources, we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li>district_heating_shares: <ul> <li>Assumed district heating share for all European countries in 2050</li> <li>Source: Own representation</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>egon_demandregio_cts_ind:<br> <ul> <li>Industrial and CTS demands per branch and NUTS3 region in Germany for the year 2050</li> <li>Source: egon-data, based on data from DemandRegio disaggregator tool</li> <li>License: Data license Germany – © FfE 2019, © Statistisches Bundesamt (Destatis), 2008-2017 – version 2.0</li> </ul> </li> <li>industrial_gas_demand: <ul> <li>This folder contains 5 files. The files CH4_for_industry_eGon100RE.json, CH4_for_industry_eGon2035.json, H2_for_industry_eGon100RE.json and H2_for_industry_eGon2035.json contain the industrial hourly demands for hydrogen and methane in NUTS3 resolution for the scenarios eGon100RE and eGon2035. The file region_corr.json provides information that make it possible to correlate each load to a geographical position.</li> <li>License: Attribution 4.0 International (CC BY 4.0) © FfE, eXtremOS Project</li> </ul> </li> </ol> <p> </p> </div>
Sample WebAssembly Data Files for Reproducible Analysis and Visualization of iEEG (RAVE)
<p>The data was derived from the following work and packaged into WebAssemply via Emscripten. The modification includes removing large data files and only keep up with the minimal requirements.</p> <blockquote> <p>Magnotti, J. F., Wang, Z., & Beauchamp, M. S. (2020). RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. <em>NeuroImage</em>, <em>223</em>, 117341.</p> </blockquote> <p> </p>
Data for the Reproducibility of the Report: Collaboration between IRCC Candiolo and OSR TIGET, 2024
<p>Collection of processed <code>.Robj</code> files (primarily Seurat spatial transcriptomics datasets) and the original publicly available data for reproducibility of the report generated in collaboration between OSR-TIGET and IRCC Candiolo (academic year 2024). For complete reproduction, visit: https://github.com/carloelle/Report_OSR_Candiolo_2024 .</p> <p>All data provided here is publicly available, and no data leakage has occurred.</p>
Reproducibility Case Study and Survey: Machine Learning-based Additive Manufacturing Process Monitoring and Quality Prediction
<p><span>Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). However, the reproducibility of the proposed ML-based AM monitoring systems in published works has not been investigated due to a lack of evaluation methods. In the paper 'Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing,' we propose a reproducibility investigation pipeline and conduct two case studies to validate the pipeline. This dataset records the data generated by one of the case studies. This dataset also contains the reproducibility survey results.</span></p>
Reproducibility packs for the article: Generation and decay of Higgs mode in a strongly interacting Fermi gas
<p>The supplementary material contains a reproducibility pack for results presented in the paper:</p> <p>A. Barresi, A. Boulet, G. Wlazłowski, P. Magierski,<br><em>Generation and decay of Higgs mode in a strongly interacting Fermi gas</em>,<br><a href="https://www.nature.com/articles/s41598-023-38176-9">Sci. Rep. 13, 11285 (2023)</a></p> <p>For more info see: README.txt</p>
Reproducibility pack for article: Quantum turbulence, superfluidity, non-Markovian dynamics, and wave function thermalization
<p>The supplementary material contains a reproducibility pack for results presented in the paper:</p> <p><em>Quantum turbulence, superfluidity, non-Markovian dynamics, and wave function thermalization</em><br>Aurel Bulgac, Matthew Kafker, Ibrahim Abdurrahman, and Gabriel Wlazłowski<br><a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.L042003">Phys. Rev. Research 6, L042003 (2024)</a></p> <p>The packs contain full information needed to restore the numerical simulation of dynamics of 12 quantum vortices.<br>To be able to restore the results of calculations, you need to use the <a href="https://wslda.fizyka.pw.edu.pl/">W-SLDA Toolkit</a>.<br>See the documentation of the <a href="https://wslda.fizyka.pw.edu.pl/">W-SLDA Toolkit</a> to learn how to use the code and the reproducibility packs.</p>
Reproducing the FeS2 Example from the Demeter Tutorials
<p>These files are provided to complement the learning materials for managing RO-Crates in Galaxy.</p> <p>The files included are:</p> <ul> <li>A crystal structure file for performing a feff fit (1564889.cif).</li> <li>A XAS spectra file to process and analyse (fes2_rt01_mar02.xmu).</li> <li>A Galaxy workflow file to perform the process and analysis (Galaxy-Workflow-FeS2_Analysis.ga)</li> <li>A Galaxy RO-Crate file which can be restored to see the processing and analysis directly.</li> </ul> <p>The workflow and RO-Crate reproduce the EXAFS fitting example for athena and artemis as described by <a href="https://github.com/bruceravel/demeter/tree/master/examples/recipes/FeS2">Bruce Ravel</a>. Instead of using the FeS2.inp file in the original example, the workflow uses a crystal structure file (<a href="https://www.crystallography.net/cod/cif/1/56/48/1564889.cif">1564889.cif</a>) from the Crystallography Open Database (COD).</p> <p>This RO is published as part of the research data submitted for the paper <strong>Facilitating Reproducibility in Catalysis Research with Managed Workflows and RO-Crates: A Galaxy Case Study</strong>, ChemCatChem, DOI: 10.1002/cctc.202401676.</p>
How to Drawjectory? - Trajectory Planning using Programming by Demonstration (Reproducibility Package)
<p>The experiments were conducted following the scenario description from <em>scenario_description.pdf. </em>The scripts containing the DSL commands used for programming the trajectories can be found under <em>/dsl_scripts.</em></p> <p>The evaluation was conducted using the R (v3.6.3 and tested on 4.4.1 too) programming language. The main script is <em>experiments/evaluation.R</em> which includes and sources all other necessary scripts. To re-run this script, make sure to open the R project (<em>experiments/xperiment.Rproj</em>) and have the following packages installed (note: other versions may also work): </p> <ul> <li>jsonlite (1.8.8)</li> <li>magrittr (2.0.3)</li> <li>dplyr (1.1.4)</li> <li>ggplot2 (3.5.1)</li> <li>wesanderson (0.3.7)</li> <li>pracma (2.4.4)</li> <li>SimilarityMeasures (1.4)</li> <li>progress (1.2.3)</li> </ul>
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.