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125 results for “Crowdsourcing”
Data from: Crowdsourced geometric morphometrics enable rapid large-scale collection and analysis of phenotypic data
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Data from social learning development, during COVID-19 how international students use media platforms as crowdsource technology to solve learning difficulties
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A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing
<p>We provide a large data set consisting of <strong>2,057 sentences</strong> from 90 news articles and annotations of crowdworkers with respect to <strong>bias itself</strong> and the following <strong>bias dimensions</strong>:</p> <ol> <li><strong>hidden assumptions</strong></li> <li><strong>subjectivity</strong></li> <li><strong>representation tendencies</strong></li> </ol> <p>Our data set contains <strong>44,547 labels in total</strong> (43,197 sentence labels and 1,350 article labels).</p> <p>The news articles deal with the <strong>Ukraine crisis</strong>. They were published in 33 countries in total and were selected based on the data set of Cremisini et al. (Cremisini, A., Aguilar, D., & Finlayson, M. A. <em>A Challenging Dataset for Bias Detection: The Case of the Crisis in the Ukraine</em>, Proc. of SBP-BRiMS'19, pp. 173-183, 2019).</p> <p>Each sentence was annotated by 5 crowdworkers. In total, we spent $ 3,335 for the crowdworkers annotations.</p> <p>More information can be found in our <a href="https://github.com/michaelfaerber/ukraine-news-bias">GitHub repository</a>. A description of the used file format is given in the codebook attached to the dataset.</p> <p>Please cite our data set as follows:</p> <pre><code>@unpublished{Faerber2020Bias, author = {Michael F{\"{a}}rber and Victoria Burkard and Adam Jatowt and Sora Lim}, title = {{A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing}}, year = {2020} }</code></pre>
Figure 1 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 1 - Amazon's Mechanical Turk Web site.
Figure 8 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 8 - Example artifacts in mPower voice recordings.
Figure 4b from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 4b - Analytics screenshot
Figure 3e from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3e - Eyewire neural reconstruction with scoring leaderboard
Figure 3d from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3d - EyeWire tutorial
Figure 1 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 1 - From Amunts et al. (2013)
Figure 3c from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3c - EteRNA tutorial
Figure 3b from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3b - Nanocrafter tutorial
Figure 3a from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3a - FoldIt tutorial
Figure 4a from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 4a - Screenshot
Notre Dame: Indoor+Outdoor (crowdsourced)
Model obtained from different sources (mainly youtube videos, google maps, ect) Interior & exterior Source: Objaverse 1.0 / Sketchfab
PregSource: Crowdsourcing to Understand Pregnancy
ClinicalTrials.gov study NCT02577536. IPD Sharing: YES. Countries: 1. Publications: 0.
Crowdsourcing an Open COVID-19 Imaging Repository for AI Research
ClinicalTrials.gov study NCT05384912. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing Performance on Speech Tasks Via Crowdsourced Participants
ClinicalTrials.gov study NCT05298501. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Patient Engagement Via Crowdsourcing
ClinicalTrials.gov study NCT03264521. IPD Sharing: NO. Countries: 1. Publications: 0.
Reshaping the Motor Engram - An Online Crowdsourcing Study.
ClinicalTrials.gov study NCT05511480. IPD Sharing: YES. Countries: 1. Publications: 0.
Invalid Dataset for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"
<p>This dataset is invalid. <strong>The updated version of this Dataset is here:</strong> <a href="https://zenodo.org/record/3678559#.Xl9-Ji97FhE">https://zenodo.org/record/3678559#.Xl9-Ji97FhE</a></p> <p> </p> <p>Dataset for the "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior" paper, published in ICWSM 2018. The full text of the paper can be found <a href="https://arxiv.org/pdf/1802.00393.pdf">here</a>. </p> <p>The dataset provided here includes an updated version of the original dataset, with ~100k tweets annotated using the CrowdFlower platform: </p> <ul> <li> <p>hatespeech_labels.csv: contains ~100K rows, where every row consists of a unique Tweet ID and its according to majority annotation </p> </li> </ul> <p>UPDATE: It has come to our understanding that a number of the tweets are not available anymore for download on Twitter. Therefore, under request, we can provide one more file with the full ~100K tweet text, their associated majority label, and the number of votes for the majority label. The tweets are shuffled so that there is no connection between tweet IDs and texts (in order to be in line with the T&C of Twitter). To obtain the file contact the authors through email. </p> <p> </p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@inproceedings{founta2018large, <br> title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior}, <br> author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas}, <br> booktitle={11th International Conference on Web and Social Media, ICWSM 2018}, <br> year={2018}, <br> organization={AAAI Press} <br> } </p> <p>For any further questions contact a.m.founta at gmail dot com AND markos.charalambous at eecei.cut.ac.cy </p>
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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.