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800 results for “Issues”
Identifying required APIs for solving Open SourceSoftware issues
<p>Data set to classify skills from a project in GitHub. </p>
Supplementary materials for Weiss et al, 2020, Neuron Volume 105 issue 1
<p>Supplementary materials for the paper "<a href="https://doi.org/10.1016/j.neuron.2019.10.006">Human Olfaction without Apparent Olfactory Bulbs</a>". by Weiss et al, published in Neuron Volume 105 Issue 1, pages 35-45.</p>
Aligning repository networks to support international sharing of COVID-19 resources and other current issues
<p>Recording of a panel "Aligning repository networks to support international sharing of COVID-19 resources", organized by COAR. This panel presented several national approaches to managing and sharing COVID-19 resources from around the world (Africa, Canada, Europe, Latin America, Asia), followed by a discussion about how we can work more closely across countries and regions to ensure that content can be integrated internationally. Participants: Kathleen Shearer - COAR, Paolo Manghi - OpenAIRE, Europe, Kazuhiro Hayashi, National Institution of Science and Technology Policy, Japan, Ansie Van de Wasthuizen, UNISA, South Africa, Cesar Olivares, CONCYTEC, Peru and LA Referencia, Latin America, Geoff Harder, Canadian Association of Research Libraries<br> <br> Organization Identifiers and Open Repositories: ROR-ing Together – Maria Gould, California Digital Library, University of California Office of the President.<br> <br> Research Data Management: a Stellenbosch University Journey – Samuel Simango, Stellenbosch University Library.<br> <br> Towards Open Data Metrics: enabling data repositories to demonstrate reuse – Kristian Garza, DataCite.</p> <p>DSpace-CRIS 7: What is Coming? – Susanna Mornati, 4Science</p>
Re-evaluating deep neural networks for phylogeny estimation: the issue of taxon sampling
Deep neural networks (DNNs) are powerful machine learning models that are widely used for classification problems, and have been recently proposed for quartet tree phylogeny estimation (Survorov et al. Systematic Biology 2020 and Zou et al. Molecular Biology and Evolution 2020). Here we present a study evaluating recently trained DNNs (from Zou et al., MBE 2020) in comparison to a collection of standard phylogeny estimation methods, including UPGMA, neighbor joining, maximum parsimony, and maximum likelihood, on a heterogeneous collection of 20-sequence datasets simulated under the same models that were used to train the DNNs, and also under similar conditions but with higher rates of evolution. Our study shows that using DNNs with quartet amalgamation (to combine quartet trees into a tree on the full dataset) is only more accurate than UPGMA, and otherwise is less accurate than all standard phylogeny estimation methods we explore (maximum likelihood, neighbor joining, and maximum parsimony). We further find that while DNNs can provide good quartet tree accuracy, some standard phylogeny estimation methods match or improve on DNNs for quartet accuracy, especially, but not exclusively, when used in a global manner (i.e., the tree on the full dataset is computed and then the induced quartet trees are extracted from the full tree). Thus, our study provides evidence that a major challenge impacting the utility of current DNNs for phylogeny estimation is their restriction to estimating quartet trees which must subsequently be combined into a tree on the full dataset: in contrast, global methods -- i.e., those that estimate trees from the full set of sequences -- are able to benefit from taxon sampling, and hence have higher accuracy on large datasets.
The Impacts of Sentiments and Tones in Community-Generated Issue Discussions
<p>The dataset, data analysis code, and complete results accompanying the paper published in CHASE2021, titled <em>The Impacts of Sentiments and Tones in Community-Generated Issue Discussions</em>.</p>
The Walking Dead Comic Issue 1 (Animated)
Issue 1 is the first and the debut issue of Image Comics' The Walking Dead and the first part of Volume 1: Days Gone Bye. It was originally published on October 8, 2003. It was re-released in full color on October 9, 2013 as a special edition to celebrate the tenth anniversary of The Walking Dead. An artist proof edition was released on August 26, 2015. Info Link: https://walkingdead.fandom.com/wiki/Issue_1 I have a Patreon Join now! :https://www.patreon.com/user?u=14434838 Source: Objaverse 1.0 / Sketchfab
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>
Issue close time: datasets + prediction classifiers
<p>This project contains experiments on predicting the amount of time required to close issue reports in software repositories. Namely, it contains (a) issue lifetime datasets from 10 large software projects and (b) experiment scripts to generate decision tree classifiers that predict issue close time.</p> <p><strong>To run the cross-validation experiment:</strong><br> 1. Compile the Java classes by running "make" or "make compile-java" on the command line<br> 2. Configure the experimental setup by changing the variables at the top of run.sh<br> 3. Run "bash run.sh" on the command line<br> 4. Results can be found in out/</p> <p><strong>To run the round robin experiment:</strong><br> 1. Compile the Java classes by running "make" or "make compile-java" on the command line<br> 2. Run "bash roundRobin.sh" on the command line<br> 3. Results can be found in out/roundRobin</p> <p> </p> <p>The latest version of this project can be found on GitHub: https://github.com/reesjones/issueCloseTime</p>
SIF datasets (50 m) and demos of network structure designs on the study of the STP-SIF issue
<p>The SIF datasets (i.e., SIF2019 and SIF2020) accompany the paper "Regional-Scale Cotton Yield Forecast via Data-Driven Spatio-Temporal Prediction (STP) of Solar-Induced Chlorophyll Fluorescence (SIF)" that was published in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425723004121">Remote Sensing of Environment</a> on October 20, 2023. They have a spatial resolution of about 50 m and a monthly temporal resolution. Each of them has seven bands, corresponding to April to October. Please refer to our previous work, "Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network", which was published in <a href="https://www.sciencedirect.com/science/article/pii/S0168169922005737">Computers and Electronics in Agriculture</a> on August 14, 2022, for the development and detailed description.</p><p>The geographic reference (ESPG: 4326 (WGS_1984)) is the same for the two dataset, conforming to that in the geotiff file.</p><p><strong>Citation</strong>:</p><p>[1] Kang, X., Huang, C., Zhang, L., Wang, H., Zhang, Z., Lv, X., 2023. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment 299, 113861. doi:10.1016/j.rse.2023.113861</p><p>[2] Kang, X., Huang, C., Zhang, L., Zhang, Z., Lv, X., 2022. Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network. Computers and Electronics in Agriculture 201, 107260. doi:10.1016/j.compag.2022.107260</p><p>[3] Kang, X., Huang, C., Chen, J.M., Lv, X., Wang, J., Zhong, T., Wang, H., Fan, X., Ma, Y., Yi, X., Zhang, Z., Zhang, L., Tong, Q., 2023. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 10, 688. doi:10.1038/s41597-023-02584-3</p><p>[4] Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., Tong, Q., 2023. Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science 13, 1048479. doi:10.3389/fpls.2022.1048479</p>
Dataset for the Paper: Issues and Their Causes in WebAssembly Applications: An Empirical Study
<p>This dataset accompanies the paper titled 'Issues and Their Causes in WebAssembly Applications: An Empirical Study.' The dataset is stored in a Microsoft Excel file, which comprises multiple worksheets. A brief description of each worksheet is provided below.</p><p>(1) The '<strong>Selected Systems</strong>' worksheet contains information on the 12 chosen open-source WebAssembly applications, along with the URL for each application.</p><p>(2) The '<strong>GitHub-Raw Data</strong>' worksheet contains information on the initially retrieved 6,667 issues, including the titles, links, and statuses of each individual issue discussion.</p><p>(3) The '<strong>SOF-Raw Data</strong>' worksheet contains information on the initially retrieved 6,667 questions and answers, including the details of each question and answer, respective links, and associated tags.</p><p>(4) The '<strong>GitHubData Random Selected</strong>' worksheet contains a list of issues randomly selected from the initial pool of 6,667 issues, as well as extracted data from the discussions associated with these randomly selected issues.</p><p>(5) The '<strong>GitHub-(Issues, Causes)</strong>' worksheet contains the initial codes categorizing the types of issues and causes.</p><p>(6) The '<strong>SOF (Issues, Causes)</strong>' worksheet contains information gleaned from a randomly selected subset of 354 Stack Overflow posts. This information includes the title and body of each question, the associated link, tags, as well as key points for types of issues and causes.</p><p>(7) The '<strong>Combine (Git and SOF) Data</strong>' worksheet contains the compiled issues and causes extracted from both GitHub and Stack Overflow.</p><p>(8) The <strong>'Issue Taxonomy</strong>' worksheet contains a comprehensive issue taxonomy, which is organized into 9 categories, 20 subcategories, and 132 specific types of issues.</p><p>(9) The '<strong>Cause Taxonomy</strong>' worksheet contains a comprehensive cause taxonomy, which is organized into 10 categories, 35 subcategories, and 283 specific types of causes.</p>
Map of Extant Ceremonial Court Ordinances issued between 1300-1556 and the Marital Relationships of the Burgudian Dukes
<p>Map of Extant Ceremonial Court Ordinances issued between 1300-1556 and the Marital Relationships of the Burgudian Dukes. ArcGIS (Esri). Hatched polygon indicate the boundaries of the Burgundian territories during the reign of Charles the Bold. Dots indicate extant ceremonial court ordinances issued between 1300-1556. Blue dots indicate Burgundian ceremonial court ordinances issued between 1300-1556. Red lines indicate the marital relationships between the Burgundian dukes and their spouses from their place of birth.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Map of Extant Court Ordinances issued between 1300-1556 in Western- and Central Europe.
<p>Map depicting the collected extant court ordinances issued between 1300 and 1556 at the Noble Courts of Western- and Central Europe. ArcGIS (Esri). Blue dots refer to court ordinances that describe ceremonial life at court. Red dots refer to court ordinances that do not describe ceremonial life at court.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Dataset of Extant Court Ordinances issued between 1300-1556 in Western- and Central Europe
<p>List of collected extant court ordinances published between 1300 and 1556 from the princely territories of Western and Central Europe, with a focus on the court ordinances published by the dukes of Burgundy in this period.</p><p>The dataset makes a subjective distinction between court ordinances that do or do not provide information on ceremonial and spatial regulations of the court.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Dataset: Very-small-aperture 3-D infrasonic array for volcanic jet observation at Stromboli Volcano, Geophysical Journal International, Volume 229, Issue 1, Pages 459–471, https://doi.org/10.1093/gji/ggab487
<p>This is the dataset of the infrasound observation of Yamakawa et al. (GJI, 2022).</p>
A Survey on Usability Evaluation in Digital Health and Potential Efficiency Issues
<p><strong>A Survey on Usability Evaluation in Digital Health and Potential Efficiency Issues</strong> is a research study published at HEALTHINF24 that aimed to collect and analyse data from 144 usability experts on their experiences with usability evaluation of digital health applications.</p> <ol> <li>This file, "<strong>Usability Survey Paper Appendix A (Questionnaire)</strong>", contains the comprehensive set of questions and materials used in the usability survey discussed in the main paper. It serves as an essential resource for readers and researchers aiming to gain in-depth insights into the survey's questionnaire design, structure, and content. This appendix offers a detailed overview of the questionnaire, including the questions' wording, order, and categorizations, enabling an in-depth understanding of the survey's methodology and findings. <ul> <li>The survey had three different parts with a total of 19 questions including <ul> <li>1) the introductory and screening part,</li> <li>2) the demographic part,</li> <li>and 3) the main part asking usability-related questions (see survey questionnaires).</li> </ul> </li> <li>The questionnaire used in the study included both fixed-alternative questions (such as true/false, multiple choice, checkbox, and rating scale) and open-ended free-text responses. It incorporates an initial set of response options for the fixed-alternative questions, which were primarily derived from the relevant literature and grey literature. Almost every question included open-text fields to accommodate a broader range of responses, allowing participants to share answers not listed in the fixed alternative questions.</li> <li>The introductory and screening part includes survey information and screening them based on a non-leading mandatory screening question to determine whether volunteers meet the eligibility criteria. </li> <li>The demographic part consists of eight questions designed to know participants better regarding their demographics and previous experiences in assessing and ensuring the usability of digital health applications (see Part II of survey questionnaire). The questions asked participants about their gender, age, job position or title, and their experiences regarding evaluating the usability of applications in the digital health sector. They were also asked about the types of digital health systems/services/technologies they had evaluated for usability.</li> <li>The main part contains ten usability evaluation related questions, with one attention check question placed in the middle of the survey. This section focuses on usability evaluation tools, methods, and approaches to understand how usability experts assess and ensure the usability of digital healthcare software. Participants were also asked about the usability characteristics that are covered during the usability evaluation in digital healthcare. Furthermore, another sub-part of this section explores the benefits of using tools during usability evaluation, as well as the overall perceived benefits and challenges encountered during conducting usability evaluation of digital health apps.</li> </ul> </li> <li><strong>Appendix B</strong>, titled "<strong>Usability Survey Paper Appendix B (Detailed Results in Tabular Form)</strong>," provides an exhaustive compilation of the results obtained from the usability survey detailed in the main paper. The file contains organized, tabulated data offering insights into participants' responses, practices, and experiences. Each table is carefully structured to ensure clarity, making the data accessible and interpretable for subsequent analysis, review, and comparison.</li> <li>The <strong>quantitative dataset </strong>(filename: <strong>Usability Survey Paper Dataset.xlsx</strong>) includes the responses of usability experts to questionnaires about their demographics, experience with usability testing, and the frequency of use of different usability evaluation methods. This dataset also includes quantitative data on the ranking of the importance of different aspects of usability evaluation, such as ease of use, efficiency, and satisfaction. The qualitative dataset also includes the responses of usability experts to open-ended field of survey questions.</li> </ol>
Replication package - Potential Effectiveness and Efficiency Issues in Usability Evaluation within Digital Health: A Systematic Literature Review
<p>1. File: <strong>Maqbool_SLR_2023_JSS_Inclusion_610.xlsm.</strong></p> <p>There are two sheets in file. A. <strong>Final_Selected_papers</strong>, (sheet) aims to provide a comprehensive list of articles (n=610) selected for our SLR, whose process and data items specified and detailed in the article. </p> <p>B. <strong>Rejected_After_Full_Review</strong>, (sheet) aims to provide a comprehensive list of articles (n=153) rejected for our SLR based on inclusion or exclusion criteria after full article review process, whose process and data items specified and detailed in the article. </p> <p>2. File: <strong>Maqbool_SLR_2023_JSS_Data_Extraction_Form.pdf</strong></p> <p>This file aims to provide a comprehensive data extraction form, whose process and data items specified and detailed in the article. The form was used to elicit data relevant to answer the postulated research questions. This form served as the foundation for the additional information presented in the final paper.</p> <p>3. File: <strong>Bilal_SLR_JSS_Primary_Studies_References.pdf</strong></p> <p>This file contains the primary selected studies (n=610) for the systematic literature review. The systematic review aims to explore and analyse research literature related to usability evaluation methods and their effectiveness and efficiency in the context of digital health applications. This file will help to identity reference of the primary selected study that is cited in the paper using a prefix (S, e.g. S137). This file can be used for peer review, ensuring the reliability and correctness of findings.</p> <p>4. File: <strong>SLR_Analysis_updated_2023.nvp</strong></p> <p>The data extracted from each article was recorded in a worksheet (Excel) and then coded in NVivo 12/14 to categorise (classify) and compare extracted facets. Each data item's category and related paper id are coded in the given Excel file. Papers were not included in the NVivo project due to copyright concerns. Relevant papers can be tracked using the provided spreadsheet file (see Paper ID cell).<br>The file(s) are cleaned as much as reasonable and other raw data is removed. This file does not include the matrix tables or codes, which were produced and analysed run-time during the analysis phase. Although the given package allows for re-generation.</p> <p>-------- UPDATE: --------</p> <p>5. File: <strong>SLR_Analysis_updated_2023_for_MAC.nvpx</strong></p> <p>This is an extra copy of NVivo project, created for the MAC user.</p> <p> </p> <p>This replication package is produced and published here. Research conducted by Karlstad University researchers. We publish data sets to improve coverage and accessibility. For more info or concerns, contact us.</p> <p> </p> <p>Linked paper published at: Maqbool, Bilal, and Sebastian Herold. "Potential effectiveness and efficiency issues in usability evaluation within digital health: A systematic literature review." <em>Journal of Systems and Software</em> (2023): 111881.</p> <p>DOI: <a href="https://doi.org/10.1016/j.jss.2023.111881">https://doi.org/10.1016/j.jss.2023.111881</a><br> </p> <p>This work was funded, in parts, by Region Värmland through the DHINO project, Sweden (Grant: RUN/220266) and Vinnova through the DigitalWell Arena (DWA) project, Sweden (Grant: 2018-03025).</p>
Figure 5 in Practical issues related to cladistics and the classification of spiders
Figure 5. Hypothetical phylogeny of the Salticidae, showing only the species depicted in Figure 4 (after Maddison 2015).
Figure 4 in Practical issues related to cladistics and the classification of spiders
Figure 4. Representative members of the salticid group (Salticidae). Relatedness of these species is shown in Figure 5. Photo credit and ©: 2, Wynand Uys; 4, Reynante Martinez; 9, Jonghyun Park; 10, sunnyjosef.
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>
Artifacts for Demystifying Device-specific Compatibility Issues in Android apps
<p>This artifact is for the paper "Demystifying Device-specific Compatibility Issues in Android Apps."</p> <p>We include our repositories link, commit hash, our GitHub crawler, its output, and some representative examples for Device-specific compatibility issues in this artifact.</p> <p>Please checkout the README.pdf first. The main artifact pack is the `device-specific-artifacts.zip`.</p> <p>Other zips starting with "repo-" are repositories archives.</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.