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50 results for “data issues”
Raw data for the submitted manuscript entitled "Prospective Scenarios for Addressing the Agricultural Plastic Waste Issue: Results of a Territorial Analysis"
<p><span>Agricultural activities have been positively affected by the use of plastic products, but this has resulted in the production of plastic waste and led to an increase in environmental pollution. </span><span>This file concerns plastic waste indices to different crop types and plastic products allowed quantifying and georeferencing actual plastic waste production. Two improved scenarios were considered, the first consisted of extending the lifespan of some plastics, and the second entailed the introduction of some biodegradable alternatives. </span></p>
MOSAiC Cloudnet issue data set
<p>This data set contains information on possible data issues caused by external drivers (e.g. tethered balloon artefacts in the observations) related to the MOSAiC Cloudnet data set. Flagged data must be handled with care and should be excluded from statistical analyses. Issues tracking flags are identified by tethered balloon operation periods and experienced-eye observations of MOSAiC staff.</p>
Documentary sources of case studies on the issues a data protection officer faces on a daily basis
<p>The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].</p> <p>The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.</p> <p>The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.</p> <p>The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).</p> <p>The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified.<br> The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.</p> <p>The available files are as follows:</p> <ul> <li><strong>documents-texts.zip</strong> --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.</li> <li><strong>documents-metadata.csv</strong> --> Contains a CSV file with the metadata for each document used as a source for the case studies.</li> </ul> <p>This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].</p> <p>[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.</p> <p>[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.</p> <p>[3] R. K. Yin, Case study research and applications. Sage, 2018.</p>
The datasets for "Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data" paper
<p>Paper title: Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data</p> <p>Conference: ICSME 2021</p>
GIRT-Data: Sampling GitHub Issue Report Templates
<p><strong>GIRT-Data</strong> is the first and largest dataset of <strong>issue report templates (IRTs)</strong> in both YAML and Markdown format. This dataset and its corresponding open-source crawler tool are intended to support research in this area and to encourage more developers to use IRTs in their repositories. The stable version of the dataset, containing <code>1_084_300</code> repositories, that <code>50_032</code> of them support IRTs.</p> <p>For more details see the GitHub page of the dataset: <a href="https://github.com/kargaranamir/girt-data">https://github.com/kargaranamir/girt-data</a></p> <p><br> The dataset is accepted for <a href="https://conf.researchr.org/track/msr-2023/msr-2023-data-showcase">MSR 2023</a> conference, under the title of "GIRT-Data: Sampling GitHub Issue Report Templates" <a href="https://scholar.google.com/scholar?q=GIRT-Data:+Sampling+GitHub+Issue+Report+Templates">Search in Google Scholar</a>.</p>
Raw Data related to Research Article: Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects, Optics Express, volume 29, issue 3, 2021
<p>These are the plotted and raw data used to obtain figures shown in:</p> <p>P. Tiwari, P. Wen, D. Caimi, S. Mauthe, N. Vico Triviño, M. Sousa, and K. E. Moselund, Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects., Optics Express, volume 29, issue 3, 2021</p> <p>Please comply with copyright rules of the Optical Society of America under the terms of the OSA Open Access Publishing Agreement.:</p> <p>https://www.osapublishing.org/library/license_v1.cfm#VOR-OA</p> <p> </p>
Code and Data for: Donor activity is associated with US legislators' attention to political issues
<p>Contains data, code, and annotations for:</p> <blockquote> <p>Goel P, Malkin N, Gaynor SW, Jojic N, Miler K, Resnik P (2023) Donor activity is associated with US legislators’ attention to political issues. PLoS ONE 18(9): e0291169. https://doi.org/10.1371/journal.pone.0291169</p> </blockquote>
Data on authorship issues in Cochrane manuscripts
<p><span>Anonymized data on authorship issues in Cochrane manuscripts after inplementation of an authorship declaration form in Cochrane Colorectal Group.</span></p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Data used in the paper A Taxonomy of Testable HTML5 Canvas Issues
<p>This repository contains data used in the paper <code>A Taxonomy of Testable HTML5 Canvas Issues</code>, submitted to IEEE Transactions on Software Engineering.</p> <p><strong>Data description:</strong></p> <table> <thead> <tr> <th>file name</th> <th>description</th> <th>fields</th> </tr> </thead> <tbody> <tr> <td>extracted_projects.csv</td> <td>180 open-source <canvas> projects (extracted from GitHub)</td> <td>nameWithOwner</td> </tr> <tr> <td>extracted_bug_reports.csv</td> <td>2,403 <canvas> issue reports (extracted from the 180 GitHub projects)</td> <td>url</td> </tr> <tr> <td>classified_bug_reports.csv</td> <td>332 classified issue reports (sampled from extracted_bug_reports.csv)</td> <td>url, type</td> </tr> </tbody> </table>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
Environmental issues of software & its labelling: questionnaire and survey data
<p>Questionnaire (original one in German, translated into English) and the survey data of my survey on environmental issues of software and its labelling, conducted in August to October 2016 (doctoral studies).</p>
Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"
<p>Contains research data underlying the following research paper:</p> <blockquote> <p>Davide Di Staso, Lærke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ ’24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798</p> </blockquote> <p>The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).</p>
Aggregated data issued from the JOBIM 2021 'Gender equality observational study'
<p>JOBIM 2021 Gender analysis</p> <p>This repository contains data and script related to our observation study of gender impact on asking behavior during JOBIM 2021. In agreement with our <a href="https://research.pasteur.fr/en/project/jobim-2021-pilot-project-gender-speaking-differences-in-academia/">data policy and RGPD regulations</a>, only aggregated, anonymous and/or publicly available information are posted in this repository. Zoom exports, registration survey and observation files containing names of askers and their accompanying scripts remains private.</p> <p>Citation</p> <p>If you wish to use our data please cite our manuscript: .https://www.biorxiv.org/content/10.1101/2022.03.07.483337v3</p>
Newspaper review and analysis data on media coverage of budget issues in Nigeria
<p>The data was collected from analysis of six newspaper publications to establish citizen engagement and media coverage of the budget discourse from 2009 to 2013 as part of the investigation of the use of the online national budget of Nigeria. The work was part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</p>
Data Package for "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns"
<p>This Zenodo repository contains the data supporting the findings of the journal paper, titled "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns", published on IEEE Transactions on Software Engineering, including:</p> <ol> <li><strong>Heuristic Linguistic Pattern Set</strong>: <span>we listed the 80 HLP we derived from </span><span>Apache's JIRA issue tracking system</span><span>. Column "</span><span>Category" </span><span>lists the type of each pattern. Namely, LEX represents lexical pattern, STR represents structural pattern, SEM represents semantic pattern, and PRF represents profiling pattern. Column "Name" is a descriptive name we give to each pattern. Column "Definition" defines the detailed content in each pattern.</span></li> <li><strong>Manual Tagging Results</strong>: manual_tagging.xlsx spreadsheet <span>comprises both sentence-level and issue-level manually tagging results for three datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', '</span><span>Dataset-</span><span>2: Apache Jira's Heterologous Evaluation', and '</span><span>Dataset-</span><span>3: Other Platform's Evaluation'. The tagging results are segmented into sentence-level tabs ("Dataset-1 Sen", "Dataset-2 Sen", "Dataset-3 Sen") and issue-level tabs ("Dataset-1 Issue", "Dataset-2 Issue", "Dataset-3 Issue").</span></li> <li><span><strong>RQ Findings</strong>: </span> <p><span>This section contains detailed data findings from six research questions (RQ1 to RQ6).</span></p> <ul> <li> <p><span>The RQ1 tab provides an evaluation of our HLP-based approach, showing the precision, recall, and F1-Score of eight classifiers. These results are juxtaposed with the corresponding values from baseline methods, at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ2 tab illustrates the precision, recall, and F1-Score of eight classifiers under two training conditions: a balanced training dataset (BT+HLP) and an imbalanced training dataset (UBT+HLP). These outcomes are contrasted with the equivalent values from baseline methods, also trained under balanced (BT+BLM) and imbalanced (UBT+BLM) conditions. The results are shown at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ3 tab evaluates the dataset transferability of our HLP-based approach in comparison to baseline methods. It achieves this by analyzing the precision, recall, and F1-Score metrics for eight classifiers under two different "training/testing" dataset conditions, i.e., 'D1/D1' and 'D1/D3'. These conditions allow for a direct comparison of performance when applied to the same dataset ('D1/D1') versus when transferred to a different dataset ('D1/D3'). Additionally, the tab includes an 'Avg Change' and 'p-value' section, summarizing the statistical change in performance metrics between the two dataset conditions. </span></p> </li> <li> <p><span>The RQ4 tab presents a direct comparison between strict and fuzzy HLP matching approaches, assessed through precision, recall, and F1-Score metrics across eight issue classifiers.</span></p> </li> <li> <p><span>The RQ5 tab examines the influence of sentence order on the accuracy of eight classifiers within our approach. It shows the change in precision, recall, and F1-Score when the sentence order feature is taken into consideration versus when it is not.</span></p> </li> <li> <p><span>The RQ6 tab explores the impact of feature selection algorithms on both issue and sentence-level tagging accuracy. This tab presents the average precision, recall, and F1-Score for three experiments: Boruta, Recursive Feature Elimination (RFE), and the usage of all 80 features. </span></p> </li> </ul> </li> <li><strong>Qualitative Analysis</strong>: <p><span>This spreadsheet offers a comprehensive examination of the data supporting Section 6.1, which focuses on Qualitative Analysis. It is organized into several tabs, each dedicated to specific research questions (RQs) as outlined below:</span></p> <ul> <li> <p><span>Tab "RQ-1" showcases performance issue reports accurately detected by our High-Level Performance (HLP) approach's top model, XGBoost, which were not identified by the benchmark method's leading model, BERT. This highlights the comparative advantage of our approach in identifying nuanced performance issues.</span></p> </li> <li> <p><span>Tab "RQ-2" continues the exploration of performance issue reports, presenting cases with specific details (to be added).</span></p> </li> <li> <p><span>Tab "RQ-3" delves into the unique capabilities of XGBoost, the leading model in our HLP approach, showcasing its ability to detect performance issues missed by the baseline's top model, BERT. This comparison is drawn under distinct conditions: with pre-training (Dataset 1) and without pre-training (Dataset 3), illustrating the robustness and adaptability of our model.</span></p> </li> <li> <p><span>Tab "RQ-4" focuses on performance issue reports uniquely identified through the implementation of Fuzzy HLP Matching within our HLP approach. This method underscores the innovative matching techniques that enhance issue detection.</span></p> </li> <li> <p><span>Tab "RQ-5" presents performance issue reports pinpointed exclusively by applying the Issue HLP Matrix within our approach. This tab demonstrates the effectiveness of our matrix-based analysis in isolating and identifying specific performance concerns.</span></p> </li> <li> <p><span>Tab "RQ-6" is dedicated to performance issue reports uniquely detected by incorporating feature selection techniques into our HLP approach. This illustrates the value of advanced feature selection in improving the precision of performance issue identification.</span></p> </li> </ul> </li> <li><strong>LLM Experiment Data</strong>: presents the tagging outcomes of Large Language Models (LLMs), specifically ChatGPT-3.5 and ChatGPT-4, across three distinct datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', 'Dataset-2: Apache Jira's Heterologous Evaluation', and 'Dataset-3: Evaluation on Other Platforms'. The results are organized into three separate tabs: 'Dataset-1 Issue', 'Dataset-2 Issue', and 'Dataset-3 Issue'.</li> <li><strong>ChatGPT Operation Python Script</strong>: crafted for automating the evaluation and tagging of issue reports in Excel using Large Language Models (LLMs) like ChatGPT-3.5 and ChatGPT-4. It underscores the importance of administrative rights for file modifications and outlines procedures for reading from and writing responses to Excel files. Key functions include querying LLMs with issue descriptions, processing their responses, and updating the spreadsheet with 'Yes' or 'No' labels and explanatory reasons, thereby facilitating an organized review of LLM performance across different datasets.</li> </ol>
Supplementary files and data Files for "Discordance between mitochondrial, nuclear, and symbiont genomes in aphid phylogenetics: who is telling the truth?" Zoological journal of the Linnean society, 2024, vol 1, issue 4. https://doi.org/10.1093/zoolinnean/zlae098
<p>This repository comprises</p> <ul> <li>a file with all supplementary Tables (<strong>Supplementary_Tables</strong>). Table S1: Collection details and voucher ID for aphid samples from the CBGP-Inrae collection, data origin is given for other specimens. Table S2: Amplification success of long range DNA fragments from mitochondrial genomes . Table S3: Primers used for fluidigm aplification of nuclear genes and sequencing success. Table S4. Genomic features of newly sequenced Buchnera aphidicola with aphid taxonomic affiliation. Table S5: Summary of models used for each ML analysis and corresponding log-likelihood score of the best tree.Table S6: Output of RERConverge analyses.</li> <li>two Supplementary figures: Figure S1: Workflow of phylogenetic analyses as implemented on each dataset. Figure S2: Plot depicting the genome-wide pattern of molecular evolution (dN/dS) between disymbiotic (n = 15) and monosymbiotic aphid (n = 45) branches across the ML phylogeny (horizontal bars indicate 95% CI of the means). <em>P</em> value was calculated by using Wilcoxon Rank test.</li> <li>a word file (Text S1) with : Details of protocol for obtaining mitochondrial genomes through long-range DNA amplifications and Illumina sequencing, and two-step PCR protocole.</li> <li>an archive (archive1) with the mitochondrial AA and DNA matrices and alternative phylogenetic trees under ML and Bayesian analyses ; </li> </ul> <ul> <li>an archive (archive2) with the nuclear AA and DNA matrices and alternative phylogenetic trees under ML and Bayesian analyses</li> <li>an archive (archive 3) with the twelve new <em>Buchnera</em> genome drafts.</li> <li> an archive (archive4) with the <em>Buchnera</em> AA matrices and alternative topologies </li> </ul> <p> </p>
Survey data on the demographics, motivations, mental-health issues and regrets of r/RoastMe posters
<p>Dataset including the data analysed in the "r/RoastMe: Characterising Self-Requested Online Mocking" paper describing the demographics, motivations, mental-health issues and consequences of posting on the r/RoastMe subreddit.</p>
Data related to Mapping user-related comfort conditions to identify urban planning issues for improved quality of places in cities: The case of Ljubljana, Slovenia
<p>The data consists of parameters including air temperature, noise levels, humidity, and air quality (PM 2.5), measured using a portable ICT device while cycling through the city of Ljubljana between August 1 and August 30, 2022. The data is available as raw CSV files, with each measurement session stored in a separate file. An additional QGIS Project file (qgz) is included, along with maps (in jpg format in ppt) that visually represent the results presented in the article.</p>
Data Release for "The curious case of GW200129: interplay between spin-precession inference and data-quality issues"
<p>Data Release associated with <a href="https://arxiv.org/abs/2206.11932">The curious case of GW200129: interplay between spin-precession inference and data-quality issues Data Release</a>. We release frame files and result file for selected parameter estimation runs in the paper.</p> <p> </p> <p>Each directory contains .json bilby result files for the PE run described by that directory. The specific channel names and frame files that we used in the PE runs are listed below. The frames for the PE runs with BayesWave glitch subtraction are included in this release and are in BW_frames/frame_{glitch label}.</p> <p> </p> <p>L1 data with glitch subtraction:</p> <p>Channel: L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_P1800169_v4</p> <p>Link: https://zenodo.org/record/5546680/files/L-L1_HOFT_CLEAN_SUB60HZ_C01_P1800169_v4-1264314068-4096.gwf</p> <p> </p> <p>L1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (L1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080/L-L1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p> </p> <p>H1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (H1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080HL-H1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p> </p> <p>Channels for runs with BayesWave glitch-subtracted frames--</p> <p>(Note you need to read in the correct gwf file to access each channel; for example the channel for BayesWave glitch A should be accessed after reading in the gwf file in `BW_frames/frame_A/`)</p> <p>BayesWave glitch A (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_30000</p> <p>BayesWave glitch B (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_28395</p> <p>BayesWave glitch C (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_32752</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.