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831
datasets available to search
ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Dataset for PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron
<p dir="auto">If you find our work useful in your research, please consider citing our paper:</p> <div> <pre><code>Cai Lin, Fan Taiyu, Qu Xuzhong, Zhang Ying, Gou Xianyu, Ding Quanwei, Feng Weihua, Cao Tingting, Lv Xiaohua, Liu Xiuli, Huang Qing, Quan Tingwei, Zeng Shaoqun (2024) PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron eLife 13:RP102840 https://doi.org/10.7554/eLife.102840.2</code></pre> </div>
Test data of published work titled 'Automatic Detection of Colorectal Polyps with Mixed Convolutions and its Occlusion Testing'
<p>Test data of published work titled 'Automatic Detection of Colorectal Polyps with Mixed Convolutions and its Occlusion Testing' includes:</p><ol><li>Gastrointestinal atlas-Colon Polyp dataset (test dataset)</li><li>Supplementary file containing information of Gastrointestinal atlas-Colon Polyp</li></ol><p>For more details: <a href="https://link.springer.com/article/10.1007/s00521-023-08762-z">Automatic Detection of Colorectal Polyps with Mixed Convolutions and its Occlusion Testing | Neural Computing and Applications (springer.com) </a></p>
A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Agent classification table
<p>This table classifies the agents used in the work to those which use models in their decision-making, which are model-based (MB), and those which do not, which are model-free (MF).</p>
Automatic Jorek Calibration via Batch Bayesian Optimization Data
<p>The dataset contains the essential information for replicating the Plots in the paper 'Automatic Jorek Calibration via Batch Bayesian Optimization'. The reader can also find the complete list of JOREK simulations performed.</p>
Supplementary material to 'Automatic Identification of Hate Speech – A Case-Study of Alt-Right YouTube Videos'
<p>The associated files have been created for and is analysed in a fortcoming article entitled <em>Automatic Identification of Hate Speech – A Case-Study of Alt-Right YouTube Videos'. </em>The material is divided into six tables as follows:</p> <table> <tbody> <tr> <td>Sentence top 5%</td> <td>The 19th 20-quantile predicted most hateful sentences</td> </tr> <tr> <td>Sentence bottom 5%</td> <td>The bottom 20-quantile predicted moste hatefull sentences (the least likely to contain hatespeech)</td> </tr> <tr> <td>Paragraphs</td> <td>Prediction and annotation of paragraphs</td> </tr> <tr> <td>Video top 10%</td> <td>Titles of the top decile predicted hateful videos</td> </tr> <tr> <td>Video bottom 10%</td> <td>Titles of the bottom decile predicted hateful videos</td> </tr> <tr> <td>Video bottom 10% - Alt right</td> <td>Titles of the bottom decile predicted hateful videos without History</td> </tr> </tbody> </table> <p>The data is uploaded in two formats:</p> <p><strong>Excel file: </strong>Automatic_Detection_of_Hate_Speech_a_Case-Study_of_Alt-Right_Videos.xlsx contains all six tables in one file, with a supplementary <em>codebook. </em></p> <p><strong>Tab Separated Values (TSV):</strong> Each file correspond to a single sheet from the excel file, and are named accordingly. UTF-8 Encoded.<strong><br></strong></p>
Data from Automatic Weather Station (AWS) measurements at Berlin–Mobile deployment (BEMOBI) from 2022-03-22 to 2022-08-13 [RAW]
<p>Original data files from AWS measurements.</p>
Automatic Volumetric Temperature Regulation During In Vivo MRI-guided Laser-Induced Thermotherapy (MRg-LITT) with multiple laser probes
<p>This dataset is associated with the article.</p> <p>An example of automatic regulation with three laser probes used corresponding to <strong>Experiment A</strong> of the article is provided in this dataset with :</p> <ul> <li>dataset generated from the<strong> initial low power shot</strong> containing:<br> <ul> <li>'Data' : reconstructed data of the MRI thermometry sequence (magnitude, phase, temperature) ;</li> <li>'ROIs': the three masks of ROIs that are used for regulation and are generated from this low power shot ;</li> <li>'Sources' : matrices of heating distributions of the three laser probes used for regulation and computed from this low power shot.</li> </ul> </li> <li>dataset generated from the<strong> regulation</strong> : <ul> <li>'Data': reconstructed data of the MRI thermometry sequence (magnitude, phase, temperature, thermal dose) ;</li> <li>'Curves': regulation curves (target temperature, regulated temperature and baseline);</li> <li>'Coord_regulated_points': position [slice, X,Y] in thermometry images of points used for regulation (showing maximal temperature at each repetition) for each ROI and each repetition.</li> </ul> </li> </ul>
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition
<p>The Makerere AI Lab has built an end-to-end CTC Luganda ASR model using radio data. Having encountered data challenges in working with low resource languages, we take the initiative together with our partners to release the first radio corpus for Luganda.</p> <p>The corpus of 155 hours is publicly available online under the Creative Commons BY-NC-ND 4.0 license. The dataset release is comprised of the following:</p> <ol> <li>20 hours of human transcribed radio speech. The audio is 16kHZ, mono channel and with 16 bit rate. </li> <li>Two CSV files for the 20-hour human transcribed dataset - cleaned.csv contains cleaned transcripts and uncleaned.csv contains uncleaned transcripts. The uncleaned transcripts contain extra speech details included in tags like [laughter] for laughter, and [um] for filler pauses, which speaker is talking, where each speaker is assigned an identifier A or B.</li> <li>The transcription guide used to transcribe the radio dataset.</li> <li> A multi-speaker untranscribed dataset of 6 hours of radio data. 1.4 hours of women voices and 4.6 hours of men voices. Each audio is a ten-seconds clip with a single speaker.</li> <li> 135 hours of multi-speaker untranscribed radio data.</li> </ol> <p><strong>NOTE: You can read and cite our paper published in the </strong><a href="http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.208.pdf"><strong>Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)</strong></a> The Dataset is published under Creative Commons BY-NC-ND 4.0 license and in order for us to monitor who is using it for the right license we request that you reach out to us officially. </p>
Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care
<p>Minimal dataset to replicate findings and figures for the publication Groh et al., Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.</p>
Figure 6 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 6 - The user interface field for uploading EML files into ARPHA.
Figure 4 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 4 - Download of an EML from the GBIF Integarted Publishuing Toolkit (IPT)
Figure 1 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 1 - Poll results about composition of audience during live participation.
THE HARDWARE PART OF THE AUTOMATIC SYSTEM WITH FIBER OPTIC SENSOR UNDER DEVELOPMENT
Open the record for dataset details and reuse information.
DAVI: A Dataset for Automatic Variant Interpretation
<p><span>The analysis of an individual’s genetic material may uncover genetic variants, which can be classified as disease-causing (pathogenic) or benign. Identifying pathogenic variants among millions of variants relies on the research of evidence in support of or against variant pathogenicity, a process regulated by the American College of Molecular Genetics (ACMG) guidelines, which leverages data from the scientific literature. Despite recent improvements towards automation, searching shreds of evidence for pathogenicity in the literature still requires manual curation, a time-consuming process, due to the ever-growing number of published papers. </span></p> <p><span>In this work, we built DAVI (Dataset for Automatic Variant Interpretation), a reliable, manually curated dataset comprising 1239 sentences extracted from 311 (variant, article) associations </span><span>for a pool of 41 variants. 597 sentences </span>contain (positive) evidence activating two opposing ACGM criteria, namely PS3 and BS3, while the remaining 642 do not contain (negative) evidence activating either of the two considered ACGM criteria. (variant, article) associations containing<span> at least one positive sentence are classified as positive, while (variant, article) associations containing any positive sentence are negative. Therefore DAVI also contains 154 positive and 157 negative (variant, article) associations. </span></p> <p> </p>
A proposal of metrics to automatically populate a user model for intelligent user interfaces in MDD: Experimental material
<p>Material of paper: A proposal of metrics to automatically populate a user model for intelligent user interfaces in MDD</p>
Figure 4 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 4. – Automatic otolith image orientation method.
Figure 6 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 6. – Example of otolith image standardization for the three species.
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Chamant (PACHAM) from 2022-11-25 to 2024-02-22 [RAW]
<p>Original data files from ALC measurements at Chamant (Département 60) in the rural area to the NNE of Greater Paris.</p> <p>Part 3 of 3.</p>
Data from Automatic Lidar and Ceilometer (ALC) intercomparison measurements at Paris – SIRTA Atmospheric Observatory (PASIRT) [RAW]
<p>Original data files from ALC measurements from intercomparison periods at Paris–SIRTA Atmospheric Observatory. Intercomparison periods are when two or more ALC are operated within close proximity within a given measurement station.</p>
Data from Automatic Lidar and Ceilometer (ALC) calibration measurements in the greater Paris area [RAW]
<p>Original data files from ALC measurements from calibration periods. A calibration period is when a calibration termination hood was placed on the ALC, typically interrupting a longer-term standard deployment</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.