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227 results for “Crowding out”
Data and results for the paper: "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC"
<div> <div>We provide the following files used in the study "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC".</div> <div>The paper has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.</div> <div>The preprint is available on <a href="http://arxiv.org/abs/2501.15181" target="_blank" rel="noopener">arXiv</a>.</div> <br> <div><strong>User stories e-commerce.xlsx</strong></div> <div>307 real-world user stories from 3 different eCommerce projects.</div> <br> <div><em>Project A</em> defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.</div> <div><em>Project B</em> contains a partial set of requirements for a B2C focused onlineshop of a bookseller.</div> <div><em>Project C</em> includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.</div> <div>Most of the user stories come with additional acceptance criteria, written in unstructured natural language.</div> <br> <div>The user stories have been anonymized and the merchant's real names were replaced with neutral terms.</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned across all projects</div> <div>- Project: user story belongs to project A, B or C</div> <div>- Connextra: user story in connextra pattern</div> <div>- Acceptance Criteria: acceptance criteria that came with the user story</div> <br> <div><strong>User stories CMS.xlsx</strong></div> <div>34 CMS related user stories from a dataset that was originally created by</div> <div>*Lucassen, G., Dalpiaz, F., van der Werf, J.M.E., Brinkkemper, S.: Visualizing user</div> <div>story requirements at multiple granularity levels via semantic relatedness. In: Con-</div> <div>ceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November</div> <div>14-17, 2016, Proceedings 35. pp. 463–478. Springer (2016)*</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned</div> <div>- Connextra: user story in connextra pattern</div> <br> <div><strong>Issues e-commerce.xlsx</strong></div> <div>54,396 issues, we harvested from seven different issue trackers between June 2011 and July 2024</div> <div>- magento2 (https://github.com/magento/magento2/issues)</div> <div>- nopCommerce (https://github.com/nopSolutions/nopCommerce/issues)</div> <div>- OpenCart (https://github.com/opencart/opencart/issues)</div> <div>- PrestaShop (https://github.com/PrestaShop/PrestaShop/issues)</div> <div>- Shopware5 (https://issues.shopware.com/?products=SW-5)</div> <div>- Shopware6 (https://issues.shopware.com/?products=SW-6)</div> <div>- WooCommerce (https://github.com/woocommerce/woocommerce/issues)</div> <br> <div>Columns</div> <div>- id: unique ID we have assigned</div> <div>- Issue Tracker: issue tracker this issue originates from</div> <div>- Title: title of the original issue</div> <div>- Body: body / description of the original issue</div> <div>- Preprocessed: result of preprocessing the issue as described in the paper</div> <div>- Sample: issue was part of our 3,500 sample issues we used to evaluate CrUISE-AC</div> <br> <div><strong>Issues CMS.xlsx</strong></div> <div>64,500 issues, we harvested from two different issue trackers between April 2002 and August 2024</div> <div>- Moodle (https://github.com/magento/magento2/issues)</div> <div>- Umbraco (https://github.com/nopSolutions/nopCommerce/issues)</div> <br> <div>Columns are the the same as for "Issues e-commerce.xlsx"</div> <br> <div><strong>trivia-trainingdata.csv</strong></div> Manually labelled dataset to train the trivia classifier. <div>The dataset contains 1916 phrases with an even distribution of 958 trivia and 958 non-trivia phrases.</div> <br> <div>- Label = 1: this sentence is trivia</div> <div>- Label = 0: this sentence is not considered trivia</div> <br> <div>Any source code was replaced by [CODE] to simplify the classification process. Source code in markdown could be identified easily as it is enclosed by a special character https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks</div> <br><strong>Prompts</strong><br> <div><em>prompt_match.txt</em>: prompt we used across all LLMs to assess, if an issue potentially might affect a given user story</div> <em>prompt_generate.txt</em>: GPT4-turbo prompt to convert an issue text into gherkin-style acceptance criteria for a given user story<br> <div><em>prompt_evaluate.txt</em>: GPT4-turbo prompt to assess the usefulness of a newly generated acceptance criteria for a given user story</div> <br> <div><strong>Evaluation e-commerce.xlsx</strong></div> issue / user story pairs, generated acceptance criteria and result of manual evaluation.<br> <div> </div> <div>Columns</div> <div>- StoryID: unique ID of the user story (refer to User stories e-commerce.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues e-commerce.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- Existing AC: acceptance criteria that originally came with the user story</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E2: evaluation result by expert 2 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E3: evaluation result by expert 3 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 3/4 majority: did at least 3 experts assess this AC as relevant (1 = yes; 0 = no)</div> <br> <div><strong>Evaluation CMS.xlsx</strong></div> <div>- StoryID: unique ID of the user story (refer to User stories CMS.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues CMS.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E5: evaluation result by expert 5 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 2/3 majority: did at least 2 experts assess this AC as relevant (1 = yes; 0 = no)</div> </div>
Dataset for the manuscript "Crowding results from optimal integration of visual targets with contextual information"
<p>There are seven experimental datasets, two program with which data are collected, two supplemetary programs needed to run the main code and one program to analyse data. Two .txt files are included, where we describe how to use the stimulation and analysis programs.</p>
After the Crowds Disperse
<p>Over the past decade, cultural heritage organisations have increasingly turned to volunteers through citizen research (or crowdsourcing) platforms to help make their holdings digitally accessible for discovery and research. These platforms have proved to be highly successful, both in attracting and retaining the interest of volunteers, and in the rate and quality of data collected. They have provided a new and deeper level of engagement and attracted more diverse audiences to national collections. However, there are still significant hurdles to overcome to achieve seamless sharing and movement of data between institutional Collection Management Systems (CMS) and crowdsourcing platforms, and back again. If crowdsourcing is to fulfil the potential of its generous volunteers this break in the data cycle must be closed.</p>
Text-fig. 11. Acer Post Hammer species 1, UF 279-34456. a, b: Diffuse-porous wood with distinct growth rings, marked by marginal parenchyma, vessels solitary and in short radial multiples, TS. c: Crowded alternate intervessel pits, TLS. d: Simple perforation plates, helical thickenings in vessel elements, TLS. e: Rays 2–3-seriate, gum deposit in vessel element, TLS. Acer Post Hammer species 2. UF 279-34466. f: Diffuse-porous wood with distinct growth rings, marked by marginal parenchyma, vessels solitary and in short radial multiples, TS. g: Alternate intervessel pits, helical thickenings in vessel elements, TLS. h: Rays 1–4(–5)-seriate, TLS. i: Crystalliferous strand, multiseriate ray, TLS. Trochodendron beckii, UF 279-24558. j, k: Distinct growth rings, abrupt transition from earlywood to latewood, vesselless, wide rays noded at growth ring boundaries, TS. l: Rays of two distinct sizes, uniseriate and multiseriates>10-seriate, TLS. m. Scalariform intertracheary pits. RLS. Scale bars: 500 µm in j; 200 µm in a, f, k, l; 100 µm in b, h; 50 µm in c, d, e, g, i, m. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 11. Acer Post Hammer species 1, UF 279-34456. a, b: Diffuse-porous wood with distinct growth rings, marked by marginal parenchyma, vessels solitary and in short radial multiples, TS. c: Crowded alternate intervessel pits, TLS. d: Simple perforation plates, helical thickenings in vessel elements, TLS. e: Rays 2–3-seriate, gum deposit in vessel element, TLS. Acer Post Hammer species 2. UF 279-34466. f: Diffuse-porous wood with distinct growth rings, marked by marginal parenchyma, vessels solitary and in short radial multiples, TS. g: Alternate intervessel pits, helical thickenings in vessel elements, TLS. h: Rays 1–4(–5)-seriate, TLS. i: Crystalliferous strand, multiseriate ray, TLS. Trochodendron beckii, UF 279-24558. j, k: Distinct growth rings, abrupt transition from earlywood to latewood, vesselless, wide rays noded at growth ring boundaries, TS. l: Rays of two distinct sizes, uniseriate and multiseriates>10-seriate, TLS. m. Scalariform intertracheary pits. RLS. Scale bars: 500 µm in j; 200 µm in a, f, k, l; 100 µm in b, h; 50 µm in c, d, e, g, i, m.
Text-fig. 8. Pterocaryoxylon sp., a–c, e: UF 279-85024; d, f: UF 279-24551. a, b: Wood semi-ring-porous, vessels solitary and in short radial multiples, axial parenchyma scanty vasicentric, marginal, and in narrow lines, TS. c: Crowded alternate intervessel pitting, simple perforation plate (PP), TLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e: Rays mostly 1–3 cells wide, occasionally 4 cells, uniseriate rays probably mostly square to upright cells, TLS. f: Rays heterocellular, body cells procumbent. Scale bars: 200 µm in a, b; 100 µm in e, f; 50 µm in c; 20 µm in d. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 8. Pterocaryoxylon sp., a–c, e: UF 279-85024; d, f: UF 279-24551. a, b: Wood semi-ring-porous, vessels solitary and in short radial multiples, axial parenchyma scanty vasicentric, marginal, and in narrow lines, TS. c: Crowded alternate intervessel pitting, simple perforation plate (PP), TLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e: Rays mostly 1–3 cells wide, occasionally 4 cells, uniseriate rays probably mostly square to upright cells, TLS. f: Rays heterocellular, body cells procumbent. Scale bars: 200 µm in a, b; 100 µm in e, f; 50 µm in c; 20 µm in d.
Data for: Field data and simulation results of the Seoul Halloween crowd-crush
<div> <div>This data contains field data and simulation results related to the Seoul Halloween crowd-crush that occurred in Itaewon, Seoul, in 2022. The dataset includes original mobile LTE data from the event, conversations between pedestrians and police, and simulation codes and results. The simulations were conducted using MATLAB and Fortran to analyze crowd dynamics and potential safety measures.</div> </div>
Datasets for Crowd-based Requirements Engineering and aspect-based detection of learning-centered emotion from the text in Serbian language
<h1>Datasets for the paper "Enhancing Software and Learning with Serbian Student Feedback Corpora"</h1> <p>These datasets include student feedback on an Intelligent Tutoring System written in Serbian, annotated with categories for Crowd-based Requirements Engineering (CrowdRE) and aspect-based detection of learning-centered emotions. Four annotators manually annotated each sentence. </p> <p>The CrowdRE dataset includes two JSON files:</p> <ul> <li><strong>crowdre_english.json</strong> - annotated text with columns and classes written in English. Columns are: <ul> <li><em>Comment</em> - the entire student feedback</li> <li><em>Sentence</em> - sentence extracted from the feedback that was annotated</li> <li><em>Intention </em>- class representing the intention of the sentence</li> <li><em>Topic </em>- class representing the topic of the sentence</li> </ul> </li> <li><strong>crowdre_srpski.json</strong> - annotated text with columns and classes written in Serbian. Columns are:<br> <ul> <li><em>Komentar</em> - the entire student feedback</li> <li><em>Recenica </em>- sentence extracted from the entire feedback that was annotated</li> <li><em>Namera </em>- class representing the intention of the sentence</li> <li><em>Tema </em>- class representing the topic of the sentence.</li> </ul> </li> </ul> <p>The dataset for the aspect-based detection of learning-centered emotions includes two JSON files:</p> <ul> <li><strong>emotions_english.json </strong>- annotated text with columns and classes written in English. Columns are: <ul> <li><em>Comment </em>- the entire student feedback</li> <li><em>Sentence </em>- sentence extracted from the feedback that was annotated</li> <li><em>Aspect </em>- class representing the aspect of the sentence</li> <li><em>Emotion </em>- class representing the learning-centered emotion of the sentence</li> </ul> </li> <li><strong>emocije_srpski.json </strong>- annotated text with columns and classes written in Serbian. Columns are: <ul> <li><em>Komentar </em>- the entire student feedback</li> <li><em>Recenica</em> - sentence extracted from the entire feedback that was annotated</li> <li><em>Aspekt</em> - class representing the aspect of the sentence</li> <li><em>Emotion </em>- class representing the learning-centered emotion of the sentence.</li> </ul> </li> </ul> <p>Annotators annotated the dataset based on the annotation procedure and guidelines available <a href="https://github.com/Clean-CaDET/student-feedback-mining">here</a>. </p> <h2>Citation</h2> <p>If you use this in your research, please cite:</p> <blockquote> <p>Vidaković, D., Luburić, N., Kovačević, A., & Slivka, J. Enhancing software and learning with Serbian student feedback corpora. Language Resources & Evaluation (2025). https://doi.org/10.1007/s10579-025-09855-y</p> </blockquote> <p> </p>
Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11)
<p>The Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11) contains 7,859 candidate paraphrases obtained from Mechanical Turk crowdsourcing. The corpus is made up of 4,067 accepted paraphrases, 3,792 rejected non-paraphrases, and the original texts. These samples have formed part of <a href="https://doi.org/10.5281/zenodo.3250123">PAN 2010</a> international plagiarism detection competition, but were not previously available separate to rest of the competition data.</p> <p>We provide the dataset as a single folder in a Zip archive. Each paraphrase is represented by three files, containing the original text (e.g.: "1-original.txt"), the paraphrase text (e.g.: "1-paraphrase.txt"), and a file containing metadata (e.g.: "1-metadata.txt"), with information about the task identifier, task author identifier, time taken, and whether the paraphrase was accepted or rejected.</p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon
<p>We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories.</p> <p>The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German <a href="We%20present%20one%20of%20the%20first%20comprehensive%20field%20datasets%20capturing%20dense%20pedestrian%20dynamics%20across%20multiple%20scales,%20ranging%20from%20macroscopic%20crowd%20flows%20over%20distances%20of%20several%20hundred%20meters%20to%20microscopic%20individual%20trajectories,%20including%20approximately%207,000%20recorded%20trajectories.%20%20The%20dataset%20also%20includes%20a%20sample%20of%20GPS%20traces,%20statistics%20on%20contact%20and%20push%20interactions,%20as%20well%20as%20a%20catalog%20of%20non-standard%20crowd%20phenomena%20observed%20in%20video%20recordings.%20Data%20were%20collected%20during%20the%202022%20Festival%20of%20Lights%20in%20Lyon,%20France,%20within%20the%20framework%20of%20the%20French-German%20MADRAS%20project,%20covering%20pedestrian%20densities%20up%20to%204%20individuals%20per%20square%20meter." target="_blank" rel="noopener">MADRAS project</a>, covering pedestrian densities up to 4 individuals per square meter.</p>
Linked collectors and determiners for: On the sand and among the crowds: a new species of Woodworthia gecko (Reptilia Diplodactylidae) from Auckland, Aotearoa / New Zealand.
Natural history specimen data linked to collectors and determiners held within, "On the sand and among the crowds: a new species of Woodworthia gecko (Reptilia Diplodactylidae) from Auckland, Aotearoa / New Zealand". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/291f6104-0ef0-41a6-977c-858c8e126552">https://bionomia.net/dataset/291f6104-0ef0-41a6-977c-858c8e126552</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/291f6104-0ef0-41a6-977c-858c8e126552">https://gbif.org/dataset/291f6104-0ef0-41a6-977c-858c8e126552</a>. Formatted as a Frictionless Data package.
Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials - Data and results
<p>This repository contains the input data and the generated results utilized as part of the research article titled <em>"Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials"</em> by Saarimäki & Fratello et al.</p> <h1>Data</h1> <p>This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety.</p> <h2>Additional Data Sources</h2> <p>This entry further includes additional data from the following sources:</p> <ul> <li><strong>Saarimäki et al. (2021)</strong>: Preprocessed data and primary physicochemical characteristics are available on <a href="https://doi.org/10.5281/zenodo.6425445">Zenodo</a>.</li> <li><strong>Gallud et al. (2020)</strong>: Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148705">GSE148705</a>.</li> <li><strong>del Giudice et al. (2023)</strong>: The advanced descriptors are accessible through the associated <a href="https://doi.org/10.5281/zenodo.7674574">Zenodo repository</a>.</li> <li><strong>Labouta et al. (2019)</strong>: The harmonized dataset of ENMs cell viability assays is available as <a href="https://pubs.acs.org/doi/suppl/10.1021/acsnano.8b07562/suppl_file/nn8b07562_si_001.xlsx">supporting information of the reasearch article</a>.</li> </ul> <h1>Outputs</h1> <ul> <li><strong>Statistical modeling:</strong> The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.</li> <li><strong>Machine Learning classifiers:</strong> The performances of several machine learning classifiers trained on the consensus responses to learn models that can predict safety concerns of new nanomaterials based on transcriptomics and physicochemical descriptors data.</li> <li><strong>Feature Importance:</strong> The relevant features extracted from the models analyzed to understand which aspects of the molecular responses to ENMs and which physicochemical properties are most important in driving the predictions.</li> </ul> <h1>Contents</h1> <p><code>data/Combined_cleaned_responses_anon.xlsx</code>: Contains the anonymized responses from the expert panel.</p> <p><code>data/enms_grouping.txt</code>: Contains a categorization of the ENMs based on their core material.</p> <p><code>data/expert_bibliographies_anon.pickle</code>: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled.</p> <p><code>data/gex.csc.gz</code>: Contains the gene expression after exposure to the ENMs.</p> <p><code>data/phenodata.txt</code>: Contains the meta-data of the experiments</p> <p><code>data/physicochemical_descriptors.txt</code>: Contains the physicochemical descriptors of the ENMs.</p> <p><code>data/external/nn8b07562_si_001.xlsx</code>: Contains a panel of harmonized ENMs cytotoxicity assays.</p> <p><code>outputs/concern_scores.csv</code>: Contains the consensus scores inferred by the statistical model.</p> <p><code>outputs/cross_validation/</code>: Contains the logs and performances of all the machine learning classification runs .</p> <p><code>outputs/important_{genes,physchem}_weighted.xlsx</code>: Feature relevance scores of both views.</p> <p><code>outputs/model_inference_anon.nc</code>: Inferred parameters of the statistical model.</p>
Crowd simulation (CrowdSim2) for tracking and object detection
<p>CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). </p> <table align="center"> <caption>Information summarizing number of folders, seconds and frames of data for different weather conditions</caption> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Folders</th> <th scope="col">Seconds</th> <th scope="col">Frames</th> </tr> </thead> <tbody> <tr> <td>Sun</td> <td>2899</td> <td>86 970</td> <td>2 174 250</td> </tr> <tr> <td>Rain</td> <td>1633</td> <td>48 990</td> <td>1 224 750</td> </tr> <tr> <td>Fog</td> <td>1653</td> <td>49 590</td> <td>1 239 750</td> </tr> <tr> <td>Snow</td> <td>1646</td> <td>49 380</td> <td>1 234 500</td> </tr> </tbody> </table> <p>Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: <a href="https://crowdsim.aei.polsl.pl/">CrowdSim</a></p> <p><strong>Acknowledgments</strong> </p> <p>This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. </p> <p> </p> <p><strong>Citing the Crowdsim 2</strong></p> <p>The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people</p> <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> <p>and this Zenodo Dataset</p> <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre> <p> </p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
List of crowd accidents from 1900 to 2019
<p>This list contains information about crowd accidents that occurred worldwide between 1900 and 2019. The files included in this dataset are described as follows:</p> <ul> <li><strong>accident_data_raw.csv </strong>- CSV file containing information for all accidents as given in the header. Date, country or location are provided using commonly used standard. Information such as fatalities or the number of people injured are reported using expressions found in the employed references (for example "a dozens of people" is given as "dozens"). References are given in the form of text files as reported below.</li> <li><strong>references.zip -</strong> ZIP file containing the references used to collect information on each accident. The date of the accident is used as filename (and reported in the last column of the list above). Sources for each accident are given on each line of the relative text file.</li> <li><strong>accident_data_numeric.csv</strong> - CSV file containing only numeric values corresponding to word expressions. The conversion scheme used here is provided below. Alternative approaches are possible.</li> <li><strong>number_conversion.csv</strong> - CSV file providing the conversion scheme used to convert word expressions into number.</li> </ul>
Optimized OPEP Force Field for Simulation of Crowded Protein Solutions
<p>Initial structures (in the PDB format) and LBMD trajectories (in the GROMACS XTC format) of crowded protein solutions simulated using the OPEPv7 force field and presented in the article entitled <em>Optimized OPEP Force Field for Simulation of Crowded Protein Solutions </em>(<a href="https://doi.org/10.1021/acs.jpcb.3c00253">https://doi.org/10.1021/acs.jpcb.3c00253</a>).</p>
Crowds & Machines Next level: Meditteranean wheat classification labels from gamified crowd-sourcing
<p>Machine learning (and especially deep learning) algorithms need lots of training and validation datasets, which are often unavailable. Creating on-ground datasets is costly and time consuming. Within the European Space Agency funded project ‘Crowds & Machine – Next Level’ (by <a href="https://www.blackshore.eu">Blackshore B.V.</a>, <a href="https://www.52impact.nl">52impact B.V.</a> and <a href="https://hcss.nl">The Hague Centre for Strategic Studies</a>), we aimed to solve this issue by generating labelled data effectively using an innovative gamified crowdsourced-based method.</p> <p>The objective of the project ‘Crowds & Machines Next Level’ was to generate labelled data for the training and validation of machine learning algorithms to classify the crop wheat. We make those labelled datasets freely available as open data to organisations that use machine learning for their activities, mainly companies and knowledge institutes. As part of the project we developed example scripts (Jupyter notebooks) that enable organisations to use the crowdsourced generated data smoothly for their own machine learning systems. </p> <p>BlackShore has developed the online platform Cerberus to enable large scale generation of labelled datasets, which is deployed on twenty locations around the Mediterranean Sea to generate labelled datasets of wheat and other land cover classes (see table). Those different locations encompass a diversity of climate regions, harvest cultures and crop calendars, posing a challenge to the training of machine learning algorithms. Gamers click on hexagons plotted on top of very high resolution satellite imagery (captured during the harvest period in 2021), and by combining 3 different hexagon grids those clicks are converted into triangles. Each triangle has a number of clicks (by different users) per land cover category, which provides a measure of accuracy to the label.</p> <p>52impact developed example tutorials to use the data to train pixel-based (Random Forest) and segmentation-based (U-Net) machine learning models, using Sentinel-2 imagery (provided in the data folder), which can be forked here: <a href="https://bitbucket.org/52impact/crowds-machines">https://bitbucket.org/52impact/crowds-machines</a>.<br> </p> <table> <caption><strong>Overview of locations</strong></caption> <thead> <tr> <th scope="col">ID</th> <th scope="col">location_id</th> <th scope="col">Country</th> <th scope="col">Region</th> <th scope="col">Shape</th> <th scope="col">Harvest period</th> <th scope="col">VHR image date</th> <th scope="col">S-2 pre-harvest</th> <th scope="col">S-2 harvest</th> <th scope="col">S-2 post-harvest</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>portugalAlentejo</td> <td>Portugal</td> <td>Alentejo</td> <td>01_Portugal_Alentejo_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>07/07/2021</td> <td>14/05/2021</td> <td>13/07/2021</td> <td>22/08/2022</td> </tr> <tr> <td>02</td> <td>spainAndalusia</td> <td>Spain</td> <td>Andalusia</td> <td>02_Spain_Andalusia_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>02/07/2021</td> <td>16/05/2021</td> <td>15/07/2021</td> <td>03/09/2021</td> </tr> <tr> <td>03</td> <td>spainAragon</td> <td>Spain</td> <td>Aragon</td> <td>03_Spain_Aragon_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>26/10/2021</td> <td>20/05/2021</td> <td>19/07/2021</td> <td>05/09/2021</td> </tr> <tr> <td>04</td> <td>franceAude</td> <td>France</td> <td>Aude</td> <td>04_France_Aude_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>22/09/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>05</td> <td>franceCamargue</td> <td>France</td> <td>Camargue</td> <td>05_France_Camargue_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>07/10/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>06</td> <td>franceProvence</td> <td>France</td> <td>Provence</td> <td>06_France_Provence_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>26/10/2021</td> <td>19/05/2021</td> <td>17/08/2021</td> <td>20/11/2021</td> </tr> <tr> <td>07_08</td> <td>italyMarche</td> <td>Italy</td> <td>Marche (East and West)</td> <td>07_08_Italy_Marche_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>09/08/2021</td> <td>26/05/2021</td> <td>25/07/2021</td> <td>20/11/2021</td> </tr> <tr> <td>09</td> <td>italySardinia</td> <td>Italy</td> <td>Sardinia</td> <td>09_Italy_Sardinia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>31/08/2021</td> <td>26/05/2021</td> <td>22/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>10</td> <td>italySicily</td> <td>Italy</td> <td>Sicily</td> <td>10_Italy_Sicily_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/09/2021</td> <td>22/05/2021</td> <td>26/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>11</td> <td>italyPugliaNorth</td> <td>Italy</td> <td>Puglia (North)</td> <td>11_Italy_PugliaNorth_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>06/10/2021</td> <td>11/06/2021</td> <td>31/07/2021</td> <td>04/10/2021</td> </tr> <tr> <td>12</td> <td>italyPuglia</td> <td>Italy</td> <td>Puglia</td> <td>12_Italy_Puglia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/08/2021</td> <td>03/06/2021</td> <td>02/08/2021</td> <td>21/10/2021</td> </tr> <tr> <td>13</td> <td>greeceWest</td> <td>Greece</td> <td>West</td> <td>13_Greece_West_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>02/09/2021</td> <td>27/07/2021</td> <td>05/10/2021</td> <td>14/12/2021</td> </tr> <tr> <td>14</td> <td>greeceThessaly</td> <td>Greece</td> <td>Thessaly</td> <td>14_Greece_Thessaly_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>14/07/2021</td> <td>27/07/2021</td> <td>25/09/2021</td> <td>19/12/2021</td> </tr> <tr> <td>15</td> <td>greeceMacedoniaCentral</td> <td>Greece</td> <td>Macedonia (Central)</td> <td>15_Greece_MacedoniaCentral_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>22/07/2021</td> <td>13/05/2021</td> <td>22/07/2021</td> <td>15/09/2021</td> </tr> <tr> <td>16</td> <td>greeceMacedoniaEast</td> <td>Greece</td> <td>Macedonia (East)</td> <td>16_Greece_MacedoniaEast_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>05/08/2021</td> <td>25/05/2021</td> <td>29/07/2021</td> <td>27/10/2021</td> </tr> <tr> <td>17</td> <td>greeceRhodes</td> <td>Greece</td> <td>Rhodes</td> <td>17_Greece_Rhodes_SELECTION</td> <td>15 May - 1 Jul</td> <td>09/05/2021</td> <td>25/03/2021</td> <td>24/05/2021</td> <td>22/08/2021</td> </tr> <tr> <td>18</td> <td>cyprusLarnaca</td> <td>Cyprus</td> <td>Larnaca</td> <td>18_Cyprus_Larnaca_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>19/03/2021</td> <td>07/06/2021</td> <td>21/08/2021</td> </tr> <tr> <td>19</td> <td>turkeyCyprus</td> <td>Cyprus (T)</td> <td>Farmagusta</td> <td>19_Turkey_Cyprus_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>29/03/2021</td> <td>17/06/2021</td> <td>26/08/2021</td> </tr> <tr> <td>20</td> <td>egyptBehera</td> <td>Egypt</td> <td>Behera</td> <td>20_Egypt_Behera_SELECTION</td> <td>1 Apr - 1 Jul</td> <td>06/03/2021</td> <td>26/01/2021</td> <td>07/03/2021</td> <td>19/08/2021</td> </tr> </tbody> </table> <p>The following data is provided:</p> <ul> <li>Triangulated_data.zip: contains per region and per category a geopackage (gpkg) file containing triangular polygons with the number of clicks per polygon. The filename of the polygon files depends on the location and category. For example, a file that contains the triangles corresponding to Cattle in Alentejo, Portugal, is called: 01_Portugal_Alentejo_Cattle.gpkg</li> <li>Data.zip: all data necessary to run the Jupyter notebooks, i.e., location data, cropped Sentinel-2 satellite imagery (for training location IDs 01, 02, 12 and 15, and validation locations near IDs 02 and 15) and also the triangulated polygons.</li> <li>Models.zip: pre-trained random forest and U-Net models based on the data, which can be generated by the Jupyter notebooks.<br> </li> </ul>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
Data from: Modulating peptide co-assembly via macromolecular crowding: Recipes for co-assembled structures
Open the record for dataset details and reuse information.
Data from: Crowding reduces per-capita parasite infection risk in a butterfly host
Open the record for dataset details and reuse information.
ScienceDex guides
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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.