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3,673 results for “Temporal”
Spacial and temporal genetic pattern of Semaprochilodus insignis (Prochilodontidae), the most popular fish from the Amazon basin
<p>Dataset of 8 genotyped microsatellite loci for 180 individuals of Semaprochilodus insignis from 11 locations in the Amazon basin.</p>
Fig. 2 in Temporal variations in the diversity of true crabs (Crustacea: Brachyura) in the St Lucia Estuary, South Africa
Fig. 2. Distribution of Hymenosoma projectum (red), Paratylodiplax blephariskios (grey) and Neosarmatium africanum (blue) in 1948 and 2012.
Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model
<p>Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance (LC) food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had a minor effect, increasing metabolic rate associated with 1–2˚C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch. However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to e.g. assess potential future community-level responses to ongoing environmental changes.</p>
Temporal and spatial heat exposure in Colombo - dataset
<p>The dataset consists of two shapefiles that provide information on outdoor heat stress and anthropogenic heat flux in Colombo, Sri Lanka in 500 m resolution.</p> <p>1. "heat_stress_indicator.shp" contains the outdoor heat stress indicators, including heat index (HI), humidex (HD), and discomfort index (DI). These indicators were calculated using the modelled variables of urban land surface model SUEWS, and the values represent the averages at 7:00, 14:00, 19:00, and 23:00 during a heatwave (23-28, 2020) in Colombo, Sri Lanka. The results are for each 500 m grid across Colombo.</p> <p>2. "QF.shp" contains the calculated anthropogenic heat flux for each 500 m grid in Colombo, Sri Lanka. Published as: Blunn, L., Xie, X., Grimmond, S., Luo, Z., Sun, T., Perera, N., Ratnayake, R. and Emmanuel, R., 2024. Spatial and temporal variation of anthropogenic heat emissions in Colombo, Sri Lanka. Urban Climate, 54, p.101828. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.101828" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.uclim.2024.101828</span></span></a></p> <p> </p>
The practice and promise of temporal genomics for measuring evolutionary responses to global change
<p>Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographicic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomics studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.</p>
Flood water depth prediction with Convolutional Temporal Attention Networks
<p>SwissFlood dataset from Flood water depth prediction with Convolutional Temporal Attention Networks paper.</p>
Climate-driven spatial and temporal patterns in peatland pool biogeochemistry
<p>This archive entry contains the original datasets used in the article "Climate-driven spatial and temporal patterns in peatland pool biogeochemistry" as CSV files. The Global_dataset.csv file is a synthesis of the morphological, biogeochemical and climate properties of peatland pools from eastern Canada, southern Patagonia, and the United Kingdom and comprises a total of 240 observations. The GPB_full_dataset.csv file includes the morphological and biogeochemical properties of nine pools of a peatland of eastern Canada that have been sampled regularly over the 2016 to 2021 summers. The GPB_aggregated_dataset.csv file shows the average pool biogeochemical and climate properties of the same peatland of eastern Canada, for 50-day windows between day of year 150 to 300. Statistical analyses shown in the "Climate-driven spatial and temporal patterns in peatland pool biogeochemistry" article are based on the GPB_aggregated_dataset.csv dataset.</p>
Spatio-temporal dynamics of the proton motive force on single bacteria - dataset
<p>Data set used in our manuscript "Spatio-temporal dynamics of the proton motive force on single bacteria" [<a href="https://www.biorxiv.org/content/10.1101/2023.04.03.535353v1">Biorxiv</a>].</p> <p> </p> <p>To produce fig1 and fig2, unzip file in bash:</p> <pre><code class="language-bash">$ 7z e data_fig1_fig2.7z</code></pre> <p>Open fig1 data in python:</p> <pre><code class="language-python">> b = pickle.load(open('fig1.p', 'rb')) > b {'speed_Hz': array([-34.01139986, 13.07744575, 79.66060694, ..., -4.79440373, -4.88539275, -0.1366687 ]), 'speed_Hz_f': array([-11.11722186, 15.74953016, 32.24685265, ..., 20.11726694, -3.54263861, -36.89432049]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 5000.0}</code></pre> <p>where</p> <p>b['speed_Hz'] : speed trace in Hz</p> <p>b['speed_Hz_f'] : speed trace in Hz, savgol filtered (5th order, 41 points)</p> <p>b['laser'] : laser trace in arbitrary units</p> <p>b['FramesPerSecond'] : camera frame acquisition rate</p> <p> </p> <p>Open fig2 data:</p> <pre><code class="language-python">> a = pickle.load(open('fig2.p','rb')) > a {11: {'speed_Hz': array([ 16.2828179 , 38.42508915, 94.68772452, ..., -12.24519659, 160.68335892, 114.39589274]), 'speed_Hz_f': array([ 25.8833788 , 31.87792607, 37.33062434, ..., 66.97264585, 91.38908923, 122.73732123]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 12: {'speed_Hz': array([ 57.41541418, 24.4936895 , 248.68571544, ..., 86.08667522, -46.11733599, 18.16993041]), 'speed_Hz_f': array([102.04557049, 91.82376088, 84.51288552, ..., 33.63965888, 16.31075159, -5.36834171]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 21: {'speed_Hz': array([ -57.50587524, 74.55546084, 65.87878605, ..., -197.26554361, 140.95754077, 52.72452236]), 'speed_Hz_f': array([-24.36465769, 22.9146126 , 49.48957346, ..., 47.67536653, 43.44702708, 36.80127664]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 22: {'speed_Hz': array([ 56.78413066, -147.32742392, -14.28783413, ..., -29.51455248, 15.66125098, 41.38001828]), 'speed_Hz_f': array([-23.57073686, -18.61154574, -15.85582681, ..., 7.05157621, 18.45809539, 37.52083946]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}} </code></pre> <p>where</p> <p>a[11] : dictionary for motor 1 trace, laser on motor 1, composed as above.</p> <p>a[12] : dictionary for motor 1 trace, laser on motor 2.</p> <p>a[21] : dictionary for motor 2 trace, laser on motor 1.</p> <p>a[22] : dictionary for motor 2 trace, laser on motor 2.</p>
Temporal Synchrony in Satellite-Derived Ocean Parameters in the Inner Sea of Chiloé, Northern Patagonia, Chile
<p>GENERAL INFORMATION</p> <p>Title of Dataset: Replication data for: Long-term synchrony in satellite-derived ocean parameters in northern Patagonia, Chile.</p> <p>- General Bathymetry of the Ocean (GEBCO),northern Patagonia.</p> <p>- Daily Chlorophyll-a (Chla, mg m-3), Fluorescence Line Height (nFLH; W m-2 μm-1 sr-1) and surface reflectance at 645nm (Rrs 645). MODIS Level2.</p> <p>- Daily river discharge (m3/s) from Puelo and Futaleufú stations. Dirección General de Aguas (DGA, Chile).</p> <p>- Climatic indices: ENSO Multivariate (MEI v2) and Southern Annular Mode (SAM).</p> <p> </p>
Spatio-temporal change of selected soil physico-chemical properties in grevillea-banana agroforestry systems
<p>This is a data base containin raw data (soil and litter data) as well as the R scripts used for their analyses</p>
Temporal trends of surface water area in India's rivers and basins
<p>This dataset quantifies the extent and annual rate of change in surface water area (SWA) in India's rivers and basins over a period of 30 years from 1991 to 2020. Visit <a title="Surface Water Trends - India" href="https://sites.google.com/view/surface-water-trends-india/" target="_blank" rel="noopener">Surface Water Trends - India</a> for an interactive web interface to explore these results, and for additional data and information.</p> <p>It is derived from the <a href="https://global-surface-water.appspot.com/" target="_blank" rel="noopener">Global Surface Water Explorer</a> which maps terrestrial surface water globally using historical Landsat satellite imagery. (Pekel, J. et al., Nature 540, 418-422 (2016). (doi:10.1038/nature20584)). The data files contain zipped archives of shapefiles and CSV (comma separated values) files.</p> <p>Shapefiles are one for each season (dry, wet and permanent) and scale (river basin and reach) of our analysis, and contain annual trends in surface water area. To open and explore them in a GIS software (eg. QGIS), un-ZIP them and include them as vector datasets.</p> <p>CSV files are one for each scale (river basin and reach (transect)) of our analysis, and contain time series of surface water areas from 1991 to 2020. To open and explore them, for analysis or to explore in a table editing software, un-ZIP them and read them in.</p> <p>Refer to 00_README.txt for details on feature and table attributes in the files. </p>
Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years
<p>Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we developed the Temporal Population Structure (TPS), the first DNA-based dating method for ancient genomes ranging from the Late Mesolithic to modern-days, and applied it to 3,591 ancient and 1,307 modern Eurasians. We show that TPS predictions align with their known dates and correctly account for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We demonstrated how TPS improved the ability to study phenotypic traits over time.</p>
Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments
<p>To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking <em>Drosophila</em>. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.</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>
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.