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800 results for “issues”
Investigating the "Too Bright" Issue Pertaining to Non-PBL Clouds over the South Pacific Trade-Wind Region in CMIP6 Global Climate Models
<p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.SON_ANN.tar.g</a>z</p> <p>CESM2-CAM6 with falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p> </p> <p>The data includes with netcdf self description.</p> <p>f09.C6.B-hist.h01_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_tauy_ANN_climo-CDO.nc</p> <p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.NOS_ANN.tar.g</a>z</p> <p>CESM2-CAM6 without falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p><br>f09.C6.B-hist.nos81_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CDNUMC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_taux_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_tauy_ANN_climo-CDO.nc</p>
Replication package for Journal paper title "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"
<p>Replication package for the study "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"</p> <p> </p>
The dataset of DOM events interactions flakiness issues in web tests
Open the record for dataset details and reuse information.
Dataset para "Formas tipo cantanon/cantano: más datos sobre la convergencia geolectal galaico-asturleonesa", Dialectologia. Special Issue 13.
<p>Contén os datos da cuestión 404 "Abandonaron o seu tío/Abandonaron a su tío" do ALPI = García Mouton, Pilar (coord.), Inés Fernández-Ordóñez, David Heap, Maria Pilar Perea, João Saramago, Xulio Sousa, 2016, <em>ALPI-CSIC [www.alpi.csic.es], edición dixital de Navarro Tomás, Tomás (dir.), Atlas Lingüístico de la Península Ibérica</em>, Madrid, CSIC.</p> <p>Só se inclúen os datos de Galicia, Portugal, Asturias, León, Zamora, Salamanca, Cáceres, Badajoz e Santander</p>
Accessibility Issues in Ad-Driven Web Appliactions
Open the record for dataset details and reuse information.
Ethical issues in patient-reported outcomes (PROs) among various populations - a special perspective from dental practice
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r14)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r14)</p>
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r2)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r2)</p>
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r6)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r6)</p>
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r3)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r3)</p>
IoT bugs based on CVEs and Issues
<p>we created two datasets of IoT-related vulnerable clones.</p>
MaintainoMATE: Issue Assignment Dataset
<p>Contains issues from the used for training classification models of MaintainoMATE</p>
Submission to Special Issue of Quantitative Science Studies "Scientific Knowledge Graphs and Research Impact Assessment"
<p>To be added</p>
Graphical integrity issues in open access publications: detection and patterns of proportional ink violations
<p>Here are all the bar charts we annotated manually and used to train the graphical integrity detector. For more detail about our method, please visit our GitHub repository. (https://github.com/sciosci/Graphical_Integrity_Issues)</p>
Dataset used for paper "Issues-Driven Features for Software Fault Prediction"
<p> </p> <div>Dataset used for paper "Issues-Driven Features for Software Fault Prediction".</div> <div> </div> <div> </div> <div> </div> <div>The dataset contains 86 projects from the open source organizations Apache and Spring were written in Java that managed their source code using the Git version control system and an issue tracking system (JIRA or BUGZILLA). </div> <div> </div> <div>For each project, we extracted data for software fault prediction (SFL) task as follows:</div> <div> </div> <div>First, we filtered out projects without reported resolved bugs or less than 5 released versions.<br> Then we iterated the resolved bugs and mapped them to the commits that fixed them.<br> Next, for each version, we labeled the faulty files in the version. A faulty file is a file that was modified in a commit in the version that resolved a bug. <br> The methodology for labeling the files is a variant of the very known approach implemented in the {\it SZZ} algorithm, which accounts for its vulnerabilities.</div> <div>Finally, for each project, we filtered out versions with faulty files' ratios lower than 5\% and higher than 30\%. The remaining set includes a good representation of bugs and reduces the class imbalance produced by the low number of defects. In addition, filtering versions with a low number of bugs helps to prune outliers, such as a version created to fix specific issues.</div> <div> </div> <div>Extracted using Beirut repository mining tool:</div> <p>https://github.com/beirut-repository-mining/repository_mining</p> <p> </p>
GreenVerse Issues Log_ITACA
<p>User stories gathered during the "GreenVerse" app pilot. GreenVerse is one of the apps developed as part of the GreenSCENT project.</p>
Understanding Security Issues in the NFT Ecosystem
<p>The dataset used in the ACM CCS 2022 paper titled "<a href="https://arxiv.org/abs/2111.08893">Understanding Security Issues in the NFT Ecosystem</a>". It contains the following items.</p> <ul> <li> <p>The asset (e.g., collection name, asset URI, metadata URI, etc.) and event (e.g., mint, buy, sell, auction creation, placing of a bid, acceptance of a bid, transfer, etc.) data collected from the top 8 NFT marketplaces (OpenSea, Axie, CryptoPunks, Rarible, Superrare, Sorare, Foundation, and Nifty) are stored in their respective databases. Since the OpenSea dataset contains events for the same set of assets, crawled three times, with a three-month interval between two subsequent crawls: in June 2021, September 2021, and finally in December 2021, therefore respective asset tables are appropriately timestamped.</p> </li> <li> <p>The `coins` database contains the Historical prices of the cryptocoins as provided by <a href="https://www.coingecko.com">CoinGecko</a>.</p> </li> <li>The `stats` database contains the availability of the source code of the token contracts in <a href="https://etherscan.io">Etherscan</a>.</li> </ul> <p><strong>Usage</strong></p> <ol> <li>Download all the parts of the dataset zip.</li> <li>Combine them into one single zip file. The MD5 hash of the resulting `nft-marketplaces.zip` would be `2e27cd3a319d0fb187a1a51589156e96`. <pre><code class="language-bash">cat nft-marketplaces.zip.part* > nft-marketplaces.zip</code></pre> </li> <li>Extract the zip file containing the database dumps. <pre><code class="language-bash">unzip nft-marketplaces.zip</code></pre> </li> <li>Restore databases to a PostgreSQL instance: <pre><code class="language-bash">pg_restore -U $DB_USER -h $DB_HOST -v -Fc -Fd -C -d postgres -j $CONCURRENCY $DB_DIR > $LOG_DIR/pg_$DB.log 2>&1</code></pre> Substitute variables with appropriate values. $DB_DIR is the directory containing the database backup files. $LOG_DIR is the directory containing the restoration logs.</li> </ol> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite our ACM CCS '22 paper.</p> <pre><code>@inproceedings{nft-study, author = {Dipanjan Das and Priyanka Bose and Nicola Ruaro and Christopher Kruegel and Giovanni Vigna}, booktitle = {Proceedings of the ACM Conference on Computer and Communications Security (CCS)}, title = {Understanding Security Issues in the NFT Ecosystem}, year = {2022} }</code></pre>
A Model Instability Issue in the NCEP Global Forecast System Version 16 and Potential Solutions
<p>This dataset includes the GFSv16 model source code and the scripts and data to plot the figures for the manuscript</p> <p>" A Model Instability Issue in the NCEP Global Forecast System Version 16 and Potential Solutions" submitted to GMD</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Replication package for: Beyond Dividing the Pie: Multi-Issue Bargaining in the Laboratory
<p>Replication package for the article Bochet, Olivier, Manshu Khanna, Simon Siegenthaler (2023), Beyond Dividing the Pie: Multi-Issue Bargaining in the Laboratory. The Review of Economic Studies.</p> <p>The replication package contains the data and software code to replicate the statistical analysis, tables, and figures published in the article and the online appendix. It only contains the experimental instructions.</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.