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18,621 results for “development;”
Perceptions on the utility of community question and answer websites like Stack Overflow to software developers (Replication package)
<p>Interview Questions on the perception of the utility of CQAs like Stack Overflow to software developers. In this study, we focused on the questions highlighted in yellow.</p>
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
The PANORAMA Challenge: Public Training and Development Dataset (3)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>3</strong> <strong>out of 4</strong></em></p>
The PANORAMA Challenge: Public Training and Development Dataset (4)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>4</strong> <strong>out of 4</strong></em></p>
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
Survey with game development companies on personal data protection
<p><strong>Dataset linked to the article: </strong>Investigating the Implementation of Data Protection Laws in Brazilian Game Companies: An Initial Study</p>
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
Self-reported data for Sustainable Development from people living in rural and remote areas
<p>Anonymous self-reported data from people living in rural/remote areas as part of a research project on Sustainable Development.</p> <p>The data collection has been approved by the University of Technology Sydney (UTS HREC REF NO. ETH24-9191).</p> <p>The version 1.0 of the dataset includes 212 valid answers to 40 core questions (+ additional info) collected in 2024 in Saudi Arabia.</p>
(No) Influence of Continuous Integration on the Development Activity in GitHub Projects — Dataset
<p>This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 and 2017-07-17.</p> <p>We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:</p> <ol> <li>used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),</li> <li>were active for at least one year (365 days) before the first build with Travis CI (before_ci),</li> <li>used Travis CI at least for one year (during_ci),</li> <li>had commit or merge activity on the default branch in both of these phases, and</li> <li>used the default branch to trigger builds.</li> </ol> <p>To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 113 projects. Of these projects, 89 are Ruby projects and 24 are Java projects. For our analysis, we only consider the activity one year before and after the first build.</p> <p>We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).</p> <p>We also retrieved a random sample of GitHub project to validate the effects we observed in the CI project sample. We only considered projects that:</p> <ol> <li>have Java or Ruby as their project language</li> <li>used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)</li> <li>have commit activity for at least two years (730 days)</li> <li>are engineered software projects (at least 10 watchers)</li> <li>were not in the TravisTorrent dataset</li> </ol> <p>In total, 8,046 projects satisfied those constraints. We drew a random sample of 800 projects from this sampling frame and retrieved the commit and merge data in the same way as for the CI sample. We then split the development activity at the median development date, removed projects without commits or merges in either of the two resulting time spans, and then manually checked the remaining projects to remove the ones with CI configuration files. The final comparision sample contained 60 non-CI projects.</p> <p>This dataset contains the following files:</p> <p><strong>tr_projects_sample_filtered_2.csv</strong><br> A CSV file with information about the 113 selected projects.</p> <p><strong>tr_sample_commits_default_branch_before_ci.csv<br> tr_sample_commits_default_branch_during_ci.csv</strong><br> One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:</p> <p>project: GitHub project name ("/" replaced by "_").<br> branch: The branch to which the commit was made.<br> hash_value: The SHA1 hash value of the commit.<br> author_name: The author name.<br> author_email: The author email address.<br> author_date: The authoring timestamp.<br> commit_name: The committer name.<br> commit_email: The committer email address.<br> commit_date: The commit timestamp.<br> log_message_length: The length of the git commit messages (in characters).<br> file_count: Files changed with this commit.<br> lines_added: Lines added to all files changed with this commit.<br> lines_deleted: Lines deleted in all files changed with this commit.<br> file_extensions: Distinct file extensions of files changed with this commit.</p> <p><strong>tr_sample_merges_default_branch_before_ci.csv<br> tr_sample_merges_default_branch_during_ci.csv</strong><br> One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:</p> <p>project: GitHub project name ("/" replaced by "_").<br> branch: The destination branch of the merge.<br> hash_value: The SHA1 hash value of the merge commit.<br> merged_commits: Unique hash value prefixes of the commits merged with this commit.<br> author_name: The author name.<br> author_email: The author email address.<br> author_date: The authoring timestamp.<br> commit_name: The committer name.<br> commit_email: The committer email address.<br> commit_date: The commit timestamp.<br> log_message_length: The length of the git commit messages (in characters).<br> file_count: Files changed with this commit.<br> lines_added: Lines added to all files changed with this commit.<br> lines_deleted: Lines deleted in all files changed with this commit.<br> file_extensions: Distinct file extensions of files changed with this commit.<br> pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message).<br> source_user: GitHub login name of the user who initiated the pull request (extracted from log message).<br> source_branch : Source branch of the pull request (extracted from log message).</p> <p><strong>comparison_project_sample_800.csv</strong><br> A CSV file with information about the 800 projects in the comparison sample.</p> <p><strong>commits_default_branch_before_mid.csv<br> commits_default_branch_after_mid.csv</strong><br> One CSV file with information about all commits to the default branch before and after the medium date of the commit history. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the commits tables described above.</p> <p><strong>merges_default_branch_before_mid.csv<br> merges_default_branch_after_mid.csv</strong><br> One CSV file with information about all merges into the default branch before and after the medium date of the commit history. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the merge tables described above.</p>
Data and material for: "Content classification of development emails"
<p>This data and material support the paper "Content classification of development emails" published in the proceedings of the 34th International Conference on Software Engineering (ICSE 2012).</p> <p>Every software system has a history. <strong>We find traces of a system's history in software repositories</strong>, which are used by developers when building and maintaining their systems. Each repository tells us a part of the history, from its perspective: Issue repositories murmur dark events involving defective and flawed entities; versioning system repositories narrate about restless artifacts and classes that nobody would ever touch; mailing list archives report of unexpected stories on developers’ interactions and opinions.</p> <p>But... <strong>can we seriously trust these repositories?</strong> Can we just listen to what they tell us and behave accordingly? Many wise researchers warmly warned us about the risks of showing such a naive faith in data repositories: Versioning system repositories might seduce us with enchanting stories of always changing entities, but in reality many of these entities may just modify their make up and maintain their old behaviour; or issue repositories might tell us a partial truth about certain very special entities, or developers. We do agree with these researchers: Especially <strong>natural language documents contain information in different languages, surrounded by much noise</strong>. We must pay a special attention when using them.</p> <p>We created <strong>MUCCA</strong>, a classification method to use when dealing with natural language documents. It recognizes source code fragments, patches, stack traces, noise, and natural language with significantly high accuracy. In this way, it allows one to subsequently apply ad hoc analysis techniques to exploit the peculiarities of each category, and extract reliable information.</p> <p>This Zenodo upload supports the paper that describe our work on this topic.</p> <p> </p> <p><strong>1. Source code & Virtual Image</strong></p> <p>MUCCA is written in <a href="http://www.cincomsmalltalk.com/main/products/visualworks/overview/">Cincom VisualWorks Smalltalk</a> and is composed of several components.<br> You can download the source code of the following MUCCA components from this upload (<code>mucca-source_code</code> folder):</p> <ul> <li>Miler2, the core of MUCCA, including metamodels, importers, classification engine, etc.;</li> <li>MailPeek, our web application for the manual classification of email content;</li> <li>PetitIsland, our grammar to generate island parsers;</li> <li>PetitJava, the grammar of Java, which we implemented for PetitParser;</li> <li>PetitSTrace, our island parser for java stack traces.</li> </ul> <p>Note that, in order to make Miler2 run, you will also need the following external Smalltalk components: <a href="http://www.moosetechnology.org/">Moose</a>, <a href="http://www.glorp.org/">Glorp</a>, <a href="http://www.seaside.st/">Seaside</a>, TwoFlower, MetaDB, <a href="http://www.lukas-renggli.ch/blog/petitparser-1">PetitParser</a>.</p> <p>In addition, we make use of the <a href="http://www.cs.waikato.ac.nz/ml/weka/">Weka</a> workbench, for the machine learning tasks. You can download the two trained classifiers that compose MUCCA (<code>mucca-classifiers</code> folder): Naive Bayes based classifier (<code>classifier1-nb.model</code>), Decision Tree based classifier (<code>classifier2-dt.model</code>).</p> <p>Alternatively, we created a <a href="http://www.virtualbox.org/">VirtualBox</a> image with a pre-configured VisualWorks environment, which includes all the MUCCA components, and pre-requisites (both Smalltalk and Java): <code>MUCCA.ova</code> (Both user and password are <code>muccauser</code>).</p> <p> </p> <p><strong>2. Benchmark</strong></p> <p>To train machine-learning classifiers and evaluate the effectiveness of the different approaches, we <strong>manually</strong> create a benchmark, in which emails are classified at character granularity.</p> <p>Given the time and effort needed to create such a benchmark, we humbly think it is a valuable contribution to the community. With the help of this benchmark, other researchers can reproduce our experiments and devise new classification methods, which can be immediately compared to ours.</p> <p>You can download the dataset from the <a href="https://github.com/ilredeitopi/mucca-dataset">GitHub repository</a> (a dump of the GitHub repository is uploaded here (<code>benchmark/githubDump.zip</code>), or download the full database dump in PostgreSQL format (<code>benchmark/benchmarkDump.tar.bz2</code>).</p>
Developer Expertise Dataset on JavaScript Libraries
<p>This dataset contains an anonymized list of surveyed developers who provided their expertise level on three popular JavaScript libraries:</p> <ol> <li><a href="https://github.com/facebook/react">ReactJS</a>, a library for building enriched web interfaces </li> <li><a href="https://github.com/mongodb/node-mongodb-native">MongoDB</a>, a driver for accessing MongoDB databased </li> <li><a href="https://github.com/socketio/socket.io">Socket.IO</a>, a library for realtime communication </li> </ol>
The Polycomb Group protein Rnf2/Ring1b is essential for zebrafish development and cardiogenesis
<p>This dataset contains zebrafish (<em>Danio rerio</em>) raw RNA and ChIP (paired-end) sequencing data:</p> <ul> <li>NDC-TLF-3dpf-repl*: 5 biological replicates of RNA-seq data from 3dpf wild-type (TL background) whole embryo lysates</li> <li>TLF-3dpf-replicate*: 3 biological replicates of RNA-seq data from 3dpf wild-type (TL background) whole embryo lysates</li> <li>WT[12]-3dpf-ChIP-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> <li>WT[12]-3dpf-H3K27me3-spikein*: 2 biological replicates of H3K27me3 ChIP-seq data (with Drosophila H2Ay spike in) from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> <li>WT[12]-3dpf-Rnf2-spikein*: 2 biological replicates of Rnf2 ChIP-seq data (with Drosophila H2Ay spike in) from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> <li>WT2-3dpf-input-spikein*: input ChIP-seq data (with Drosophila H2Ay spike in) from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> </ul>
A Panel Data Set of Cryptocurrency Development Activity on GitHub
<p>Contents:</p> <ul> <li><strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all data, sorted by date. This file contains some imputed values for missing data, and all fields across all repositories and normalized to "null". This is the most convenient form to use.</li> <li><strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all, sorted by date. It is the raw data after processing the raw format.</li> <li><strong>raw-data-2018-01-21-to-2019-02-04.tar.gz</strong>: The raw format of data collected (S-expressions). Contains additional contributor data and CoinMarketCap data not currently in the CSV datasets.</li> <li><strong>recovered.patch</strong>: The modification on <strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong> after recovering (imputing) data<strong>, </strong>showing what was recovered.</li> <li><strong>recovered-normalized.patch</strong>: The modification of <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>after normalizing the recovered data set. Thus, patching <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>with<strong> recovered.patch</strong>, then <strong>recovered-normalized.patch </strong>gives <strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong></li> <li><strong>missing-dates.txt</strong>: Days for which we missed GitHub data collection (partial or completely).</li> </ul> <p>Related publications:</p> <pre><code>@inproceedings{van-tonder-crypto-oss-2019, title = {{A Panel Data Set of Cryptocurrency Development Activity on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "{van~Tonder}, Rijnard and Trockman, Asher and {Le~Goues}, Claire", series = {MSR '19}, year = 2019 } @inproceedings{trockman-striking-gold-2019, title = {{Striking Gold in Software Repositories? An Econometric Study of Cryptocurrencies on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "Trockman, Asher and {van~Tonder}, Rijnard and Vasilescu, Bogdan", series = {MSR '19}, year = 2019 }</code></pre> <p>Related code: <a href="https://github.com/rvantonder/CryptOSS">https://github.com/rvantonder/CryptOSS</a></p>
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
The LAVA mutants defective in auxin-regulated primary or lateral root development.
<p><span>Regulation of PIN activity, polarity as well as auxin gradient generation and its canalisation remain crucial topics in plant developmental biology especially in the context of organogenesis like the formation new lateral roots. Here, we are presenting the LAVA (LR Alterations Visualised after Auxin) collection of 278 mutants, defective in auxin-induced lateral root (LR) morphogenesis. Those mutants were obtained from a forward genetic screen in which synthetic auxin 1-Naphtyl Acetic Acid (1-NAA) was used to induce LR formation in the mutagenized PIN3::PIN3-GFP population. Our database contains mutant root phenotyping and for a subset of the collection, we recorded PIN polarity and subcellular trafficking, cotyledon vasculature development, primary and LR gravitropism as well as aerial phenotypes with some reminiscent to auxin-regulated organogenesis aberrations. We are convinced that our dataset can serve as a unique tool to identify novel components of auxin signalling, transport, and cell polarity but also be of interest to the broader plant research community interested in the roots system architecture that is vital for plant survival, growth and adaptation to environmental conditions.</span></p> <p><span>The phenotype analysis of the mutant collection is summarized and organised in an Excel spreadsheet (2024-08-07_mutant_database_Table S1). The photographic material is organised in folder form (see Supporting Data S1 in this repository), where each folder number corresponds to a particular mutant and entry in the excel table.</span></p> <p><span> Mutant seeds will be available in the European Arabidopsis Stock Centre (NASC). The seed sending to the repository is in progress. </span></p> <p><span>The manuscript describing this work is currently being submitted to the research journal. Its version will be available on the open access Masaryk University repository.</span></p>
Insect Development
<p>Data on the <a href="http://eol.org/schema/terms/DevelopmentalMode">developmental modes</a> of insects derived from the following sources:</p> <p>Belles, Xavier. 2011. Origin and Evolution of Insect Metamorphosis. In: Encyclopedia of Life Sciences (ELS). John Wiley & Sons, Ltd: Chichester. <a href="https://doi.org/10.1002/9780470015902.a0022854">https://doi.org/10.1002/9780470015902.a0022854</a></p> <p>Štys, P. and Šobotník, J., 1999. Comments on classifications of insect ontogenies, and occurrence of proneometabolous wing development in termite genus Prorhinotermes (Hexapoda: Isoptera). Acta Soc. Zool. Bohem, 63, pp.483-492.</p> <p>Truman, J.W., 2019. The evolution of insect metamorphosis. Current Biology, 29(23), pp.R1252-R1268. <a href="https://doi.org/10.1016/j.cub.2019.10.009">https://doi.org/10.1016/j.cub.2019.10.009 </a></p> <p>Vea, I. M. and Minakuchi, C. 2021. Atypical insects: molecular mechanisms of unusual life history strategies. Current Opinion in Insect Science 43:46-53. <a href="https://doi.org/10.1016/j.cois.2020.09.016">https://doi.org/10.1016/j.cois.2020.09.016</a></p>
Data associated with "Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors"
<p>Data used for the publication Microbiota-derived metabolites inihibit Salmonella virulent subpopulation development by acting on single-cell behaviors. </p> <p> </p> <p>all_hi_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the presence of SCFAs.</p> <p>all_no_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the absence of SCFAs.</p> <p>odmeasurements.csv OD measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p> <p>gfpmeasurements.csv GFP measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p>
Kuangetal-PPIG24-Designing-a-multimodal-IDE-with-developers
<p>This replication package includes:</p> <ol> <li>The conceptual design and personas.</li> <li>The co-design workshop output and the slides used in the workshop.</li> <li>The IDE prototype exported in PDF for a quick overview and in PNG for reuse on Figma.</li> <li>A demo of the IDE prototype on Figma, which is used for the user test.</li> </ol>
Fuτure - dataset for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons
<h1> Data description</h1> <h2>MC Simulation</h2> <p><br>The <strong>Fuτure</strong> dataset is intended for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons. The dataset is generated with Pythia 8, with the full detector simulation being performed by Geant4 with the CLIC-like detector setup CLICdet (CLIC_o3_v14) setup. Events are reconstructed using the Marlin reconstruction framework and interfaced with Key4HEP. Particle candidates in the reconstructed events are reconstructed using the PandoraPF algorithm.</p> <p>In this version of the dataset no γγ -> hadrons background is included.</p> <h2>Samples</h2> <p><br>This dataset contains e+e- samples with Z->ττ, ZH,H->ττ and Z->qq events, with approximately 2 million events simulated in each category.</p> <p>The following processes e+e- were simulated with Pythia 8 at sqrt(s) = 380 GeV:</p> <ul> <li>p8_ee_qq_ecm380 [Z -> qq events]</li> <li>p8_ee_ZH_Htautau [ZH -> Ztautau]</li> <li>p8_ee_Z_Ztautau_ecm380 [ZH -> Ztautau]</li> </ul> <p>The .root files from the MC simulation chain are eventually processed by the software found in <a href="https://github.com/HEP-KBFI/ml-tau-en-reg">Github</a> in order to create flat ntuples as the final product.</p> <h2><br>Features</h2> <p><br>The basis of the ntuples are the particle flow (PF) candidates from PandoraPF. Each PF candidate has four momenta, charge and particle label (electron / muon / photon / charged hadron / neutral hadron). The PF candidates in a given event are clustered into jets using generalized kt algorithm for ee collisions, with parameters p=-1 and R=0.4. The minimum pT is set to be 0 GeV for both generator level jets and reconstructed jets. The dataset contains the four momenta of the jets, with the PF candidates in the jets with the above listed properties.</p> <p>Additionally, a set of variables describing the tau lifetime are calculated using the software in <a href="https://github.com/HEP-KBFI/ml-tau-en-reg">Github</a>. As tau lifetime is very short, these variables are sensitive to true tau decays. In the calculation of these lifetime variables, we use a linear approximation.</p> <p>In summary, the features found in the flat ntuples are:</p> <p> </p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>reco_cand_p4s</td> <td>4-momenta per particle in the reco jet.</td> </tr> <tr> <td>reco_cand_charge</td> <td>Charge per particle in the jet.</td> </tr> <tr> <td>reco_cand_pdg</td> <td>PDGid per particle in the jet.</td> </tr> <tr> <td>reco_jet_p4s</td> <td>RecoJet 4-momenta.</td> </tr> <tr> <td>reco_cand_dz</td> <td>Longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dz_err</td> <td>Uncertainty of the longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dxy</td> <td>Transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dxy_err</td> <td>Uncertainty of the transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>gen_jet_p4s</td> <td>GenJet 4-momenta. Matched with RecoJet within a cone of radius dR < 0.3.</td> </tr> <tr> <td>gen_jet_tau_decaymode</td> <td>Decay mode of the associated genTau. Jets that have associated leptonically decaying taus are removed, so there are no DM=16 jets. If no GenTau can be matched to GenJet within dR < 0.4, a fill value is used.</td> </tr> <tr> <td>gen_jet_tau_p4s</td> <td>Visible 4-momenta of the genTau. If no GenTau can be matched to GenJet within dR<0.4, a fill value is used.</td> </tr> </tbody> </table> <p>The ground truth is based on stable particles at the generator level, before detector simulation. These particles are clustered into generator-level jets and are matched to generator-level τ leptons as well as reconstructed jets. In order for a generator-level jet to be matched to generator-level τ lepton, the τ lepton needs to be inside a cone of dR = 0.4. The same applies for the reconstructed jet, with the requirement on dR being set to dR = 0.3. For each reconstructed jet, we define three target values related to τ lepton reconstruction:</p> <ul> <li> a binary flag <strong>isTau</strong> if it was matched to a generator-level hadronically decaying τ lepton. <strong>gen_jet_tau_decaymode</strong> of value -1 indicates no match to generator-level hadronically decaying τ.</li> <li> the categorical decay mode of the τ <strong>gen_jet_tau_decaymode</strong> in terms of the number of generator level charged and neutral hadrons. Possible <strong>gen_jet_tau_decaymode</strong> are {0, 1, . . . , 15}.</li> <li> if matched, the visible (neglecting neutrinos), reconstructable pT of the τ lepton. This is inferred from the <strong>gen_jet_tau_p4s</strong></li> </ul> <h2>Contents:</h2> <ul> <li>qq_test.parquet</li> <li>qq_train.parquet</li> <li>zh_test.parquet</li> <li>zh_train.parquet</li> <li>z_test.parquet</li> <li> z_train.parquet</li> <li>data_intro.ipynb</li> </ul> <h2>Dataset characteristics</h2> <p> </p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong># Jets</strong></td> <td><strong>Size</strong></td> </tr> <tr> <td>z_test.parquet</td> <td> <pre>870 843</pre> </td> <td>171 MB</td> </tr> <tr> <td>z_train.parquet</td> <td> <pre>3 483 369</pre> </td> <td>681 MB</td> </tr> <tr> <td>zh_test.parquet</td> <td> <pre>1 068 606</pre> </td> <td>213 MB</td> </tr> <tr> <td>zh_train.parquet</td> <td> <pre>4 274 423</pre> </td> <td>851 MB</td> </tr> <tr> <td>qq_test.parquet</td> <td> <pre>6 366 715</pre> </td> <td>1.4 GB</td> </tr> <tr> <td>qq_train.parquet</td> <td> <pre>25 466 858</pre> </td> <td>5.6 GB</td> </tr> </tbody> </table> <p>The dataset consists of 6 files of 8.9 GB in total.</p> <h2>How can you use these data?</h2> <p>The .parquet files can be directly loaded with the Awkward Array Python library.<br>An example how one might use the dataset and the features is given in <strong>data_intro.ipynb</strong></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.