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764 results for “Reproducibility”
Dataset for "Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model"
<p>Results of random sampling the segment polarity network with the simulator COPASI. These results correspond to Fig. 2 and Table 2 of von Dassow et. al (2000) (doi:10.1038/35018085). The random sampling was carried out with file vonDassow2000_1x4_alt.cps with COPASI version 4.39 selecting the appropriate parameter set named (1-7) and setting the number of repeats in the parameter scan task to the desired number. Full results of sampling are in files prefixed with the row number of Table 2 of von Dassow et. al (2000) and extension .tsv. Results with scores below 0.2 are in corresponding files with the word "-hits" in the filename. Includes also results from a time course simulation of this model using four different simulators (COPASI, Tellurium, Amici, and VCell). Finally also contains a study on multistability carried out by random sampling of parameters and initial conditions (run with COPASI). Markdown file README.md contains more detailed explanation. See also https://github.com/pmendes/models/tree/main/vonDassow2000</p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Initial conditions to reproduce figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system"
<p>The directory includes the initial conditions of the simulations needed to reproduce the figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system". </p> <p>The simulations were done with Posidonius version v2019.07.30. The information to download and use Posidonius can be found here:<br><a href="https://www.google.com/url?q=https://www.blancocuaresma.com/s/posidonius&sa=D&source=hangouts&ust=1580820901945000&usg=AFQjCNEmxwuCiIgh9d-aTfx-bqB0_u_9BA">https://www.blancocuaresma.com/s/posidonius</a>. <br>That version was only slightly modified to use a maximum timestep size for the IAS15 algorithm. </p> <p>The directory includes:</p> <ul> <li>a README giving all the information defining the test directories</li> <li>test directories with a file called trappist1.json, which can be used to launch Posidonius:<br>posidonius start --silent trappist1.json data_dir/trappist1.bin data_dir/trappist1_history.bin</li> </ul>
R code and radiocarbon dates to reproduce figures and results of our paper "Population dynamics ..."
<p>R Codes and dataset to reproduce figures and results of the paper <strong><em>Population dynamics during the Neolithic transition and the onset of megalithism in Portugal according to summed probability distribution of radiocarbon determinations</em>. </strong></p>
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake GeoTIFF
<p>An elevation model of Crater Lake, Oregon, USA</p> <p>Landform features: caldera, cinder cone, lava flow</p> <p>Resolution: 3.33 meter, 5,200 x 5,200 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Valdez GeoTIFF
<p>Multiscale elevation models centered on Valdez, Alaska, USA</p> <p>Resolutions: 3.3, 7.5, 15, 30, 90, 250, 500, 1,000, and 2,000 meters, 1500 x 1,500 height samples each</p> <p>File format: GeoTIFF</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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes GeoTIFF
<p>An elevation model of Great Sand Dunes, Colorado, USA</p> <p>Landform features: active dune field, sand sheet, sabkha</p> <p>Resolution: 3.3 meter, 5,300 x 5,300 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain ASCII
<p>An elevation model of Massanutten Mountain, Virginia, USA</p> <p>Landform features: folded ridges, hogback, water gap, meander</p> <p>Resolution: 10 meter, 3,900 x 3,900 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 2.0.0 replaced the previous erroneous elevation model of another geographic area.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3.0T
<p>3D MR Fingerprinting T1/T2/M0 maps of twelve healthy volunteers obtained in eight different sites (1.5T and 3.0T scanners, single vendor). Each subject/site dataset includes two acquisitions (test-retest) to assess repeatability of the measurement.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog GeoTIFF
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon GeoTIFF
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon ASCII
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms
<p>This is a set of elevation models of archetypal landforms: </p> <ul> <li>volcanic caldera (Crater Lake, Oregon, USA),</li> <li>active sand dunes (Great Sand Dunes, Colorado, USA),</li> <li>a braided riverbed (Jackson Hole, Wyoming, USA),</li> <li>folded ridges (Massanutten Mountain, Virginia, USA),</li> <li>stabilized sand dunes (Sandhills, Nebraska, USA),</li> <li>crater of a shield volcano (Kilauea, Hawaii, USA),</li> <li>karst plateau (Kočevje Rog, Slovenia),</li> <li>narrow rock formations, aka hoodoos (Bryce Canyon, USA)</li> </ul> <p>All elevation models were derived from NED LiDAR sources with cell sizes ranging from 1 to 10 meters. The size of the models varies between approximately 4,000 × 4,000 and 5,500 × 5,500 height samples. The elevation models are available in georeferenced GeoTIFF and Esri ASCII file formats.</p> <p>Version 2 adds models of Kilauea, Hawaii, USA, Kočevje Rog, Slovenia, and Bryce Canyon, USA.</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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea GeoTIFF
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: GeoTIFFI</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea ASCII
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation 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>
Reproducibility in science: calculated kinetic isotope effects for cyclopropyl carbonyl radical.
<p>Calculated kinetic isotope effects, without tunnelling corrections, for the ring opening of cyclopropylcarbinyl radical using a variety of different Hamiltonians and basis sets.</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.