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70 results for “supervised training”
Sentence/Table Pair Data from Wikipedia for Pre-training with Distant-Supervision
<p>This is the dataset used for pre-training in "<em>ReasonBERT: Pre-trained to Reason with Distant Supervision</em>", EMNLP'21.</p> <p>There are two files:</p> <p>sentence_pairs_for_pretrain_no_tokenization.tar.gz -> Contain only sentences as evidence, Text-only</p> <p>table_pairs_for_pretrain_no_tokenization.tar.gz -> At least one piece of evidence is a table, Hybrid</p> <p>The data is chunked into multiple tar files for easy loading. We use <a href="https://github.com/webdataset/webdataset">WebDataset</a>, a PyTorch Dataset (IterableDataset) implementation providing efficient sequential/streaming data access.</p> <p>For pre-training code, or if you have any questions, please check our GitHub repo https://github.com/sunlab-osu/ReasonBERT</p> <p>Below is a sample code snippet to load the data</p> <pre><code class="language-python">import webdataset as wds # path to the uncompressed files, should be a directory with a set of tar files url = './sentence_multi_pairs_for_pretrain_no_tokenization/{000000...000763}.tar' dataset = ( wds.Dataset(url) .shuffle(1000) # cache 1000 samples and shuffle .decode() .to_tuple("json") .batched(20) # group every 20 examples into a batch ) # Please see the documentation for WebDataset for more details about how to use it as dataloader for Pytorch # You can also iterate through all examples and dump them with your preferred data format</code></pre> <p>Below we show how the data is organized with two examples.</p> <p>Text-only</p> <pre><code>{'s1_text': 'Sils is a municipality in the comarca of Selva, in Catalonia, Spain.', # query sentence 's1_all_links': { 'Sils,_Girona': [[0, 4]], 'municipality': [[10, 22]], 'Comarques_of_Catalonia': [[30, 37]], 'Selva': [[41, 46]], 'Catalonia': [[51, 60]] }, # list of entities and their mentions in the sentence (start, end location) 'pairs': [ # other sentences that share common entity pair with the query, group by shared entity pairs { 'pair': ['Comarques_of_Catalonia', 'Selva'], # the common entity pair 's1_pair_locs': [[[30, 37]], [[41, 46]]], # mention of the entity pair in the query 's2s': [ # list of other sentences that contain the common entity pair, or evidence { 'md5': '2777e32bddd6ec414f0bc7a0b7fea331', 'text': 'Selva is a coastal comarque (county) in Catalonia, Spain, located between the mountain range known as the Serralada Transversal or Puigsacalm and the Costa Brava (part of the Mediterranean coast). Unusually, it is divided between the provinces of Girona and Barcelona, with Fogars de la Selva being part of Barcelona province and all other municipalities falling inside Girona province. Also unusually, its capital, Santa Coloma de Farners, is no longer among its larger municipalities, with the coastal towns of Blanes and Lloret de Mar having far surpassed it in size.', 's_loc': [0, 27], # in addition to the sentence containing the common entity pair, we also keep its surrounding context. 's_loc' is the start/end location of the actual evidence sentence 'pair_locs': [ # mentions of the entity pair in the evidence [[19, 27]], # mentions of entity 1 [[0, 5], [288, 293]] # mentions of entity 2 ], 'all_links': { 'Selva': [[0, 5], [288, 293]], 'Comarques_of_Catalonia': [[19, 27]], 'Catalonia': [[40, 49]] } } ,...] # there are multiple evidence sentences }, ,...] # there are multiple entity pairs in the query }</code></pre> <p>Hybrid</p> <pre><code>{'s1_text': 'The 2006 Major League Baseball All-Star Game was the 77th playing of the midseason exhibition baseball game between the all-stars of the American League (AL) and National League (NL), the two leagues comprising Major League Baseball.', 's1_all_links': {...}, # same as text-only 'sentence_pairs': [{'pair': ..., 's1_pair_locs': ..., 's2s': [...]}], # same as text-only 'table_pairs': [ 'tid': 'Major_League_Baseball-1', 'text':[ ['World Series Records', 'World Series Records', ...], ['Team', 'Number of Series won', ...], ['St. Louis Cardinals (NL)', '11', ...], ...] # table content, list of rows 'index':[ [[0, 0], [0, 1], ...], [[1, 0], [1, 1], ...], ...] # index of each cell [row_id, col_id]. we keep only a table snippet, but the index here is from the original table. 'value_ranks':[ [0, 0, ...], [0, 0, ...], [0, 10, ...], ...] # if the cell contain numeric value/date, this is its rank ordered from small to large, follow TAPAS 'value_inv_ranks': [], # inverse rank 'all_links':{ 'St._Louis_Cardinals': { '2': [ [[2, 0], [0, 19]], # [[row_id, col_id], [start, end]] ] # list of mentions in the second row, the key is row_id }, 'CARDINAL:11': {'2': [[[2, 1], [0, 2]]], '8': [[[8, 3], [0, 2]]]}, } 'name': '', # table name, if exists 'pairs': { 'pair': ['American_League', 'National_League'], 's1_pair_locs': [[[137, 152]], [[162, 177]]], # mention in the query 'table_pair_locs': { '17': [ # mention of entity pair in row 17 [ [[17, 0], [3, 18]], [[17, 1], [3, 18]], [[17, 2], [3, 18]], [[17, 3], [3, 18]] ], # mention of the first entity [ [[17, 0], [21, 36]], [[17, 1], [21, 36]], ] # mention of the second entity ] } } ] }</code></pre> <p> </p> <p> </p> <p> </p>
Phononic crystals dataset for supervised training of surrogate deep learning model
<p>The dataset contains shapes of unit cells of phononic crystals (inputs) in the form of images and corresponding dispersion diagrams (outputs). The dataset is used for deep learning (DL) model training.<br> Outputs are in the form of .mat files which contain vectors of reduced wavevector and corresponding frequencies, and also displacements u, v, w which can be used for polarization calculation.</p> <p>The dataset contains 11000 cases.</p> <p>Note: Ignore names "labels" as these are actually inputs to the DL model, not labels.</p>
Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images
<p>Codes, trained model, and datasets for the paper "Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images".</p>
Small PASTIS training dataset config: Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>Files to run the small dataset experiments used in the preprint "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. This .csv files enables to generate balanced small dataset from the <a href="https://zenodo.org/record/5012942#.ZFDfUJHP1H4">PASTIS dataset</a>. These files are required to run the experiment with a small training data-set, from the open source code <a href="https://src.koda.cnrs.fr/iris.dumeur/ssl_ubarn.git">ssl_ubarn</a>. In the .csv file name selected_patches_fold_{FOLD}_nb_{NSITS}_seed_{SEED}.csv :</p> <ul> <li>FOLD: id which corresponds to one of the 5 experiments run due to PASTIS K-fold.</li> <li>NSITS: Number of SITS selected to construct this training data-set</li> <li>SEED: the randomness used to create this small dataset</li> </ul> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TUVU): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30UVU</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T30TYQ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYQ</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TDJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TDJ</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TFN): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TFN</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TDL): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TDL</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TGJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TGJ</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31UEP): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31UEP</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
l-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of large size.</p>
s-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of small size.</p>
Weekly supervised Multilingual Data Set to train Named Entity Recognition for Symptom Extraction
<p>Data Sets were generated using the Weakly Supervised NER pipeline (https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) to train the symptom extraction NER models. </p> <p><strong>Supported Languages and dataset locations for the specific language:</strong></p> <p> English (base language): https://huggingface.co/HUMADEX/english_medical_ner<br> German: https://huggingface.co/HUMADEX/german_medical_ner<br> Italian: https://huggingface.co/HUMADEX/italian_medical_ner<br> Spanish: https://huggingface.co/HUMADEX/spanish_medical_ner<br> Greek: https://huggingface.co/HUMADEX/german_medical_ner<br> Slovenian: https://huggingface.co/HUMADEX/slovenian_medical_ner<br> Polish: https://huggingface.co/HUMADEX/polish_medical_ner<br> Portuguese: https://huggingface.co/HUMADEX/portugese_medical_ner</p> <p> </p> <p><strong>Dataset Building </strong></p> <ul> <li>Data Integration and Preprocessing</li> <li>Data Cleaning</li> <li>Annotation with Stanza's i2b2 Clinical Model </li> <li>Translation into the targeted language</li> <li>Word Alignment </li> <li>Data Augmentation </li> </ul> <p><strong>Acknowledgement</strong><br>This dataset had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.</p> <p><strong>Authors:</strong><br>dr. Izidor Mlakar, Rigona Sallauka, dr. Umut Arioz, dr. Matej Rojc</p> <p><strong>Please cite as:</strong></p> <p><span>Article title: Weakly-Supervised Multilingual Medical NER For Symptom Extraction For Low-Resource Languages</span><br><span>Doi: 10.20944/preprints202504.1356.v1</span><br><span>Website: </span><a title="https://www.preprints.org/manuscript/202504.1356/v1" href="https://www.preprints.org/manuscript/202504.1356/v1">https://www.preprints.org/manuscript/202504.1356/v1</a></p>
Progressive Supervised Home-based Strength Training in Children With Spastic Cerebral Palsy
ClinicalTrials.gov study NCT03863197. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Point-of-Care Ultrasound (PoCUS) Low-Contact Curriculum Using Near-Peer Training and Remote Supervision
ClinicalTrials.gov study NCT06777641. IPD Sharing: YES. Countries: 1. Publications: 7.
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Supervision Models in Training Environments
ClinicalTrials.gov study NCT05075278. IPD Sharing: NO. Countries: 1. Publications: 4.
Effectiveness of an HIV-adapted IMCI Training and Supervision Programme for Community Health Workers
ClinicalTrials.gov study NCT01774136. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.