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Discovery (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for multilingual research. </p> <p>Filmed and edited by Fabian Fess.</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>
Open Chat Series: How-to-Advocacy "How to Advocate for Innovation in Scholarly Communication?"
<p>On 25. November 2022 Magdalena Wnuk moderated the Open Chat with Maciej Maryl from IBL PAN and talked about how to advocate for innovation in scholarly communication. Together with our guest speaker and the audience we discussed the advocacy goals and challenges for the SSH community while advocating for innovation in open science. </p>
Text-fig. 2. Archaeotetraodon winterbottomi. Specimen NMNHU-P PI 1995. Sheshory locality, Ukraine (Rybnytsia Member, lowermost part of the Menilite Series, Oligocene). a: photo of the specimen; b: interpretative drawing of the (a). in New Material Of The Puffer Fish Archaeotetraodon Winterbottomi Tyler Et Bannikov, 1994 (Tetraodontidae) From The Oligocene Of The Eastern Paratethys
Text-fig. 2. Archaeotetraodon winterbottomi. Specimen NMNHU-P PI 1995. Sheshory locality, Ukraine (Rybnytsia Member, lowermost part of the Menilite Series, Oligocene). a: photo of the specimen; b: interpretative drawing of the (a).
Code and data used for the study: 'BioDeepTime: a database of biodiversity time series for modern and fossil assemblages'
<p>The repository includes code and data to reproduce the results in the manuscript ‘BioDeepTime: a database of biodiversity time series for modern and fossil assemblages' by Smith et al. (<code>analysis_biodeeptime.zip</code>).</p>
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
PB preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
AL preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed AL data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=137 representing 137 solar plants ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 34.48MB<br> *** Key: 'data_TR_AL'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL_AL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE_AL'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'position_TR_AL'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_AL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_AL'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</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 (validation): 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 validation data</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,T30UVU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <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 : Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This repository list all the available repositories, to load the unlabeled Sentinel 2 (S2) L2A dataset used in the article<a href="https://ieeexplore.ieee.org/document/10414422/"> "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series"</a>. This dataset is composed of patch time series acquired over France. For further details, see section IV.A of the pre-print article, 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> <ul> <li>The validation dataset is available here : <a href="https://doi.org/10.5281/zenodo.7890452">10.5281/zenodo.7890452</a></li> <li>The training dataset is composed of 9 zenodo repositories, one for each S2 tiles. Here are the available repositories: <ul> <li>T31UEP<a href="http://https://doi.org/10.5281/zenodo.7899943"> 10.5281/zenodo.7899943</a></li> <li>T31TGJ <a href="https://doi.org/10.5281/zenodo.7899237">10.5281/zenodo.7899237</a></li> <li>T30TYS <a href="https://doi.org/10.5281/zenodo.7924193">10.5281/zenodo.7924193</a></li> <li>T31TFN <a href="https://doi.org/10.5281/zenodo.7896621">10.5281/zenodo.7896621</a></li> <li>T31TDL <a href="http://10.5281/zenodo.7896082">10.5281/zenodo.7896082</a></li> <li>T31TDJ <a href="https://doi.org/10.5281/zenodo.7895498">10.5281/zenodo.7895498</a></li> <li>T30UVU <a href="https://doi.org/10.5281/zenodo.7892410">10.5281/zenodo.7892410</a></li> <li>T30TYQ<a href="https://doi.org/10.5281/zenodo.7890542"> 10.5281/zenodo.7890542</a></li> <li>T30TXT <a href="https://doi.org/10.5281/zenodo.7875977">10.5281/zenodo.7875977</a></li> </ul> </li> </ul> <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, 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>
Time Series Measurement Data of Office Building in Jakarta Indonesia - Incoming Transformer of 20 kV | 0.4 kV
<p>Time Series Measurement Data of Office Buildings in Jakarta Indonesia - Incoming Three Transformers of 2 MVA rating (20 kV | 0.4 kV)<br> The data was taken by Standardized Power Quality Analyzer within minutes span data during eight days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included from with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</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.