Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
281
datasets available to search
ShareScore release 0.9.0
Dataset results
281 results for “source code”
Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"
<p>Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"</p>
Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"
<p>This repository contains the replication package for the paper <em>"</em>An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023).</p> <p>The paper is deposited on arXiv, will be available later at the publisher's site (<a href="https://ieeexplore.ieee.org/Xplore/home.jsp">IEEE</a>), and a copy is included in this repository.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.8058667">10.5281/zenodo.8058667</a>. The data is distributed under the CC BY 4.0 license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <pre><code>@inproceedings{hort2023:sharing, title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code}, author = {Max Hort and Anastasiia Grishina and Leon Moonen}, booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)}, year = {2023}, publisher = {IEEE} note = {To appear. Pre-print on arXiv.} }</code></pre>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
E3SMv2 source codes
<p>This archive contains the E3SMv2 source codes and use license for the manuscript submitted to GMD journal.</p>
Stellar Sources data for the Weltgeist code
<p>Dataset for stellar feedback in the Weltgeist code for simulations of stellar feedback in 1D spherical coordinates. Further information about the software, including source code and installation instructions can be found here: https://github.com/samgeen/Weltgeist</p>
Data and code for: "An open-source GIS approach to understanding dunefield morphologic variability at Kati Thanda (Lake Eyre), central Australia"
<p>Data and reproducabel code</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part II: Main runs)
<p>This is Part II of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for every simulations used in the paper.</p> <p>Each zip file corresponds to a model run. </p> <p>TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100.<br>TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200.<br>TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY.</p> <p>Main scenarios:<br>- 01: Spartina (Figures 1-5, S3-S10)<br>- 02: Salicornia (Figures 1-5, S3-S10)<br>- 83: No vegetation (Figures 1-5, S3, S8-S10)</p> <p>Additional scenarios:<br>- 146: Spartina, low bulk drag coefficient (Figure S3)<br>- 147: Spartina, very low bulk drag coefficient (Figure S3)<br>- 148: Salicornia, low bulk drag coefficient (Figure S3)<br>- 149: Salicornia, very low bulk drag coefficient (Figure S3)<br>- 122: Spartina, low settling velocity (Figure S8)<br>- 123: Spartina, high settling velocity (Figure S8)<br>- 124: Salicornia, low settling velocity (Figure S8)<br>- 125: Salicornia, high settling velocity (Figure S8)<br>- 126: No vegetation, low settling velocity (Figure S8)<br>- 127: No vegetation, high settling velocity (Figure S8)<br>- 128: Spartina, low critical bed erosion shear stress (Figure S8)<br>- 129: Spartina, high critical bed erosion shear stress (Figure S8)<br>- 130: Salicornia, low critical bed erosion shear stress (Figure S8)<br>- 131: Salicornia, high critical bed erosion shear stress (Figure S8)<br>- 132: No vegetation, low critical bed erosion shear stress (Figure S8)<br>- 133: No vegetation, high critical bed erosion shear stress (Figure S8)<br>- 134: Spartina, low Partheniades constant (Figure S8)<br>- 143: Spartina, high Partheniades constant (Figure S8)<br>- 136: Salicornia, low Partheniades constant (Figure S8)<br>- 144: Salicornia, high Partheniades constant (Figure S8)<br>- 138: No vegetation, low Partheniades constant (Figure S8)<br>- 145: No vegetation, high Partheniades constant (Figure S8)<br>- 150: Spartina, low sediment dry bulk density (Figure S8)<br>- 151: Spartina, high sediment dry bulk density (Figure S8)<br>- 152: Salicornia, low sediment dry bulk density (Figure S8)<br>- 153: Salicornia, high sediment dry bulk density (Figure S8)<br>- 154: No vegetation, low sediment dry bulk density (Figure S8)<br>- 155: No vegetation, high sediment dry bulk density (Figure S8)<br>- 76: Spartina, replicate #1 (Figures S9-S10)<br>- 77: Spartina, replicate #2 (Figures S9-S10)<br>- 78: Spartina, replicate #3 (Figures S9-S10)<br>- 88: Spartina, replicate #4 (Figures S9-S10)<br>- 80: Salicornia, replicate #1 (Figures S9-S10)<br>- 81: Salicornia, replicate #2 (Figures S9-S10)<br>- 82: Salicornia, replicate #3 (Figures S9-S10)<br>- 89: Salicornia, replicate #4 (Figures S9-S10)<br>- 85: No vegetation, replicate #1 (Figures S9-S10)<br>- 86: No vegetation, replicate #2 (Figures S9-S10)<br>- 87: No vegetation, replicate #3 (Figures S9-S10)<br>- 90: No vegetation, replicate #4 (Figures S9-S10)</p>
Data and Code for: Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America
<p>This submission contains all the code and data necessary for reproducing 1) the dataset, 2) the main results, and 3) all supplemental analyses appearing in the manuscript titled: <em>Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America</em> (Ramirez-Parada, Park, Record, Davis, Ellison, and Mazer, 2023). A preprint of this manuscript can be accessed at: https://doi.org/10.21203/rs.3.rs-3131821/v1.</p> <p> </p> <p>Extracting the compressed file will generate a folder titled "Project folder", containing sub-folders named "Data" and "R code". In order for the code to work, users need to preserve the folder structure of the code and data, as the R Markdown files in the "R code" folder have relative file paths that read and write data within the "Data" folder. Moving either would require re-writing the filepaths across Rmds for the code to run.</p> <p><br> To replicate the results, the following R Markdowns must be run in sequence (once they have been run, the Rmds for supplemental analyses can be used in any order):</p> <p><br> <em>"1. Subsetting Dataset.Rmd"</em></p> <p>This file processes a specimen dataset of ca. 2.3 million specimens that we assembled for this project (publicly available on Dryad: <a href="https://doi.org/10.25349/D9WP6S">https://doi.org/10.25349/D9WP6S</a>), filtering out duplicates, specimens out of the spatial scope of the PRISM data used for all analyses, and subsetting to only those species represented by a minimum of 300 specimens. This filtering yields a dataset of 1,038,047 specimens in flower across 1,605 species.</p> <p>For an in-depth description of the starting dataset, please refer to the "READ ME.txt" file within the "Project folder", and visit its corresponding Dryad repository (linked above).</p> <p><br> <em>"2. Main Analysis - Estimating S_space, S_time, and S_diff.Rmd"</em></p> <p>This file uses the subset dataset produced by the previous Rmd to fit the varying-intercepts, varying-slopes model that produced the estimates of apparent plasticity and apparent adaptation underlying all main analyses. This Rmd exports a dataset of species-specific estimates of S<sub>space</sub>, S<sub>time</sub>, and S<sub>space</sub> - S<sub>time</sub> that is used to recreate Figures 2, 3, and 4 of the main text in the next step. This is the most time consuming R Markdown file to run, as each MCMC chain used to fit the model in Stan must be run on a dedicated processor (limiting the usefulness of parallel computation). Fitting the model using 3 MCMC chains, 1000 iterations for warmup, and 4000 iterations for sampling, took approximately 24 hours using an Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz processor. </p> <p> </p> <p><em>"3. Main Analysis - Figures 2, 3, and 4.Rmd"</em></p> <p>Finally, this Rmd uses the dataset of species-specific estimates to conduct all analyses underlying Figures 2, 3, and 4, recreating each of these figures.</p> <p><strong><em>For detailed descriptions of all materials (code and data) and instructions for using them, please refer to the "READ ME.txt" file within "Project folder". </em></strong></p> <p> </p>
Raw dataset of "Supersonic: Learning to Generate Source Code Optimisations in C/C++"
<p>The raw unfiltered dataset of "<a href="https://arxiv.org/abs/2309.14846">Supersonic: Learning to Generate Source Code Optimisations in C/C++</a>".</p>
Data and source code of "Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices"
<p>Data and source code related to the article "Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices".</p> <ul> <li>The script "generate_artificial_landscapes.R" was used to produce the artificial landscapes located in the folder "ARTIFICAL_LANDSCAPES". The package rflsgen was used to produce these landscapes.</li> <li>The script "evaluation_artificial_landscapes.R" was used to evaluate these artificial landscapes, in accordance with what is presented in the article. It relies on the intra R package also introduced in the article.</li> <li>The script "evaluation_real_landscape.R" was used to evaluate the Koniambo massif landscape (New Caledonia), according to what is presented in the article. It also relies on the intra R package.</li> </ul>
Source Code: Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution
Open the record for dataset details and reuse information.
Data and Python codes used in "A New Approach to Estimate Total Nitrogen Concentration in a Seasonal Lake Based on Multi-Source Data Methodology"
<p>These are the dataset and python codes used in a manuscript titled "A New Approach to Estimate Total Nitrogen Concentration in a Seasonal Lake Based on Multi-Source Data Methodology"</p>
Source data and code for: Climate change transforms the functional identity of Mediterranean coralligenous assemblages
Open the record for dataset details and reuse information.
R code from: Diagnosing common sources of lack of fit to composition data in fisheries stock assessment models using One-Step-Ahead (OSA) residuals
Open the record for dataset details and reuse information.
Improved representation of clouds in the atmospheric component LMDZ6A of the IPSL Earth system model IPSL-CM6A : Source codes and supporting files
<p>Source codes and supporting files of the paper by J-B Madeleine et al., 2020, entitled "Improved representation of clouds in the atmospheric component LMDZ6A of the IPSL Earth system model IPSL-CM6A" published in the Journal of Advances in Modeling Earth Systems. See the README file for more information.</p>
Recovering Architectural Variability from Source Code
<p>Video presentation of the SBES 2020 paper: Recovering Architectural Variability from Source Code.</p> <p>Presented by Crescencio Lima.</p>
Unvalidated R source code
<p>Univalidated R source code implmenting a performance comparison of adaptive sample size recalculation rules including in particular the new performance score by Herrmann et al. (2019)</p>
Learning from Source Code History to Identify Performance Failures
<p>This dataset accompanies the article ICPE 2016 "Learning from Source Code History to Identify Performance Regressions". This dataset provides the source code and runtime metrics associated with 17 Pharo applications. Further information about this dataset can be found in the README.txt file.</p>
A Large Corpus of C Source Code based on Gentoo packages
<p>Corpus of C packages extracted from the Gentoo packages, created for the JSEP publication.</p>
A Curated Corpus of Java Source Code based on Sourcerer (2015)
<p>Java corpus constructed for the JSEP paper based on the Sourcerer corpus.</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.