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3,673 results for “Temporal”
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>
Sexual recombination and temporal gene flow maintain host resistance and genetic diversity
<p>Infectious disease can threaten host populations. Hosts can rapidly evolve resistance during epidemics, with this evolution often modulated by fitness trade-offs (e.g., between resistance and fecundity). However, many organisms switch between asexual and sexual reproduction, and this shift in reproductive strategy can also alter how resistance in host populations persists through time. Recombination can shuffle alleles selected for during an asexual phase, uncoupling the combinations of alleles that facilitated resistance to parasites and altering the distribution of resistance phenotypes in populations. Furthermore, in host species that produce diapausing propagules (e.g., seeds, spores, or resting eggs) after sex, accumulation of propagules into and gene flow out of a germ bank introduce allele combinations from past populations. Thus, recombination and gene flow might shift populations away from the trait distribution reached after selection by parasites. To understand how recombination and gene flow alter host population resistance, we tracked the genotypic diversity and resistance distributions of two wild populations of cyclical parthenogens. In one population, resistance and genetic diversity increased after recombination whereas, in the other, recombination did not shift already high resistance and genetic diversity. In both lakes, resistance remained high after temporal gene flow. This observation surprised us: due to costs to resistance imposed by a fecundity-resistance trade-off, we expected that high population resistance would be a transient state that would be eroded through time by recombination and gene flow. Instead, low resistance was the transient state, while recombination and gene flow re-established or maintained high resistance to this virulent parasite. We propose this outcome may have been driven by the joint influence of fitness trade-offs, genetic slippage after recombination, and temporal gene flow via the egg bank.</p>
Temporal dynamics of mother-offspring relationships in Bigg's killer whales: opportunities for kin-directed help by post-reproductive females
<p>Age-related changes in the patterns of local relatedness (kinship dynamics) can be a significant selective force for shaping the evolution of life history and social behaviour. In humans and some species of toothed whales, average female relatedness increases with age which can select for a prolonged post-reproductive lifespan in older females due to both the costs of reproductive conflict and the benefits of late-life helping of kin. Killer whales (<em>Orcinus</em> <em>orca</em>) provide a valuable system for exploring social dynamics related to such costs and benefits in a mammal with an extended post-reproductive female lifespan. We use >40 years of demographic data to investigate the opportunities for helping and harming in the mammal-eating Bigg's killer whale by quantifying how mother-offspring social relationships change with offspring age. Our results suggest a high degree of male philopatry and female-biased budding dispersal in Bigg's killer whales, with some variability in the dispersal rate for both sexes. These patterns of dispersal provide opportunities for late-life helping of particularly adult sons, while partly mitigating the costs of mother-daughter reproductive conflict. This is an important step towards understanding the evolution of menopause in the few species it occurs.</p>
Data: Effects of anterior temporal lobe resection on cortical morphology
<p>Data used for analysis for the paper <a href="http://doi.org/10.48550/arXiv.2212.06529">Effects of anterior temporal lobe resection on cortical morphology</a>.</p> <p>Code used for the analysis can be found on github: <a href="https://github.com/cnnp-lab/2023Leiberg_ATLRmorphology">https://github.com/cnnp-lab/2023Leiberg_ATLRmorphology</a>.</p> <p>The folder "not_corrected" contains morphological data for each subject (pre and post surgery for individuals with TLE) and vertex before application of the gam correction, and corresponding meta data. File names indicate metrics (T=average cortical thickness, At=pial surface area, Ae=exposed surface area), hemispheres (lh=left hemisphere, rh=right hemisphere), and onset sides (RTLE=subjects with right onset TLE, LTLE=subjects with left onset TLE). Controls are included in each file, processed without the temporal lobe for rh_RTLE and lh_LTLE.</p> <p>The folder "age_sex_corrected" contains the data for subjects with TLE with age, sex, and scanning protocol effects removed. Both onset sides have been combined (RTLE hemispheres are switched), and the files contain data for both hemispheres pre- and postoperatively.</p>
Spatio-temporal Dynamics of Coworking Spaces Development: The Case of Selected Central European Capitals
<p>The dataset provides geolocation coordinates and years of activity of coworking spaces in the selected Central European capitals - Bratislava, Budapest, Prague, and Warsaw.</p>
Figure 4 in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses
Figure 4. Phylogenetic tree from Bayesian inference for Nilotanypus Kieffer and outgroups (Table 1) based on concatenated gene fragments. Posterior probabilities (PP) and Bootstrap support (BS from Maximum Likelihood analysis) are indicated above branches only for nodes with PP> 0.95 or BS> 70. Maximal supported nodes are indicated with an asterisk. A dash (–) for either PP or BS indicates a value below the threshold for support; unlabelled nodes lack support under both criteria.
Figure 2. Nilotanypus Kieffer. Pupa. A, B in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses
Figure 2. Nilotanypus Kieffer. Pupa. A, B. Thoracic horn; C. Wing sheath; D–F. Abdomen, male); D. dorsal, E. ventral. Larva. F. Head capsule, left side ventral, right side dorsal; G. Dorsal head capsule; H. Antenna; I. Antennal apex, detail; J. Mandible; K. Ligula, paraligula; l. Maxilla; M. Submentum; N. Anterior parapod small comb claw; O. Posterior body; P. Posterior parapod comb claw. A, D–F, H–O. N. haplochelus sp. n.; B, C, G, P. N. ctenochelus sp. n. Abbreviations: S5 – S10 – cephalic setae, DP – dorsal pit, VP – ventral pit.
Figure 1. Nilotanypus Kieffer. Adult. A–D in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses
Figure 1. Nilotanypus Kieffer. Adult. A–D. Head, anterior view, right side, ♂, A, C. ♀, B, D.; E–F. Thorax, E. dor- sal, F. lateral; G. Mid-dorsal sensory pit; H. Wing (male); I, J. Tibial apices, I. P1, J. P3; K. Male hypopygium; L. Gonostylus; M. Female genitalia left side only; N. Anterior vaginal cavity, detail. A–B, E–K, M–N. N. haplochelus sp. n.; C–D, L. N. ctenochelus sp.n. Abbreviations: fr–frontal setae, iv–inner vertical setae, ped–pedestal setae, ov– outer vertical setae, sc-scape setae. Fig. 1G after Roback, 1986.
Figure 5. BEAST chronogram from a data set corresponding with Table 1 in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses
Figure 5. BEAST chronogram from a data set corresponding with Table 1. Values at nodes are time to most recent common ancestor (tmrca) with HPD (95% Highest Posterior Density) intervals in parentheses. The time scale is in millions of years before present.
Figure 3 in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses
Figure 3. Nilotanypus ctenochelus sp. n. A. Wing sheath, tubercle row; B. Larval posterior parapod, comb claw.
The MALaria Spatio-temporal Wind-Outbreak Trajectories Simulation (MALSWOTS)
<p>A mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level.</p>
Spatial and temporal variations in nutrient and Chl a concentration in a Faroese fjord
<p>The datasett is a two-year time series of seawater nitrate, silicate, phosphorous and Chl a content in in a Faroese fjord. Weekly or biweekly sampling from April to September at four depths at two stations.</p>
Spatial and temporal zooplankton abundance in a Faroese Fjord
<p>The dataset is a two-year time series of the zooplankton community in a Faroese fjord. Weekly or biweekly sampling from April to September at five stations.</p>
Data from: Insect communities under skyglow: diffuse night-time illuminance induces spatio-temporal shifts in movement and predation
<p>We conducted our experiment at the iDiv Ecotron experimental facility, which is an indoor mesocosm facility consisting of independent, experimental chambers called “EcoUnits”. The Ecotron is located in Bad Lauchstädt, Saxony-Anhalt, Germany, at the Experimental Research Station of the Helmholtz Centre for Environmental Research (UFZ, 51.3917° N, 11.8762° E). Multiple environmental conditions in the EcoUnits can be fully controlled (e.g., nutrient supply and irrigation). Each EcoUnit has internal dimensions of 1.46 m × 1.46 m × 1.50 m (L × W × H, aboveground) and 1.24 m × 1.24 m × 0.80 m (L × W × H, belowground) with the soil surface area measuring 1.54 m².</p> <p>To assess the interactive effects of diffuse nighttime illuminance and landscape structure on animal movement patterns, we established a patch-grassland system which consisted of four meadow patches within each of the corners of an EcoUnit, separated by an area of bare ground. The EcoUnits were filled with 1.23 m<sup>3</sup> of unsterilised and homogenised soil from the vicinity of the iDiv Ecotron, and plant communities of 16 plant species were sown on February 4th 2020. We allowed for a settlement phase of roughly 5 months before starting our measurements.</p> <p> </p> <p>Each heading below describes columns in the two datasets that describe the movement activity (RFID sensor detections) and predation (bite marks left on artificial caterpillar prey dummies) by the experimental insect communities.</p> <p> </p> <p><strong>timestamp:</strong> Continuous value indicating the time and date (format: DD/MM/YYYY hh:mm:ss, timezone: UTC+2) at which a tagged individual was detected by an RFID sensor. We defined detections as distinct and only counted them when they (1) occurred on different sensors or when (2) at least 10 seconds had elapsed (without detection on the same sensor) between two consecutive detections on the same sensor. This prevented the repeated detection of resting or dead animals.</p> <p><strong>block_ID: </strong>Categorical value indicating whether an observation was made within the first or second temporal experimental block (b1, b2). Each block corresponded to a period of approximately one lunar cycle (i.e., 28 days: experimental block b1: 21.07.2020 - 18.08.2020, experimental block b2: 15.09.2020 - 13.10.2020).</p> <p><strong>rep_ID: </strong>Categorical value indicating the replicate prey dummy exposures (r2, r3, r4, r5) that took place within each 28-day temporal experimental block: r2 and r3 took place successively within block b1; r4 and r5 took place successively within block b2. NAs identify periods during each experimental block where prey dummies had not yet been deployed or were in the process of being collected/re-deployed. </p> <p><strong>unit_ID: </strong>Categorical value indicating the identity of each of the 12 EcoUnits at the iDiv Ecotron experimental facility.</p> <p><strong>patch_ID: </strong>Categorical value indicating the identify of each of the four meadow patches within each EcoUnit. Patch identities correspond to their coordinates within the EcoUnit.</p> <p><strong>x: </strong>Continuous value indicating the X-coordinate position of an RFID sensor or prey dummy within the Ecotron unit with respect to the position of each EcoUnit’s control panel.</p> <p><strong>y: </strong>Continuous value indicating the Y-coordinate position of an RFID sensor or prey dummy within the Ecotron unit with respect to the position of each EcoUnit’s control panel. </p> <p><strong>day_night: </strong>Categorical value indicating whether an individual was detected during the day (treatment lights off) or during the night (treatment lights on). Night includes the periods of dawn and dusk where daylight was gradually (i.e. linearly) brightened or dimmed over the course of two hours before sunrise and sunset, respectively.</p> <p><strong>habitat: </strong>Categorical value indicating whether a tagged individual was detected within a meadow patch (Patch) or within the area of bare ground (Matrix) which separates individual patches.</p> <p><strong>tag: </strong>Categorical value indicating the unique serial number associated with an RFID-tagged individual. This column is used to estimate local densities (sum of unique tag IDs detected).</p> <p><strong>species: </strong>Categorical value indicating the scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2020). Note that <em>Harpalus rufipes </em>(De Geer) is referred to in the database by its synonym <em>Pseudoophonus rufipes</em> (De Geer). Poecilus includes the species <em>Poecilus cupreus</em> and<em> Poecilus versicolor</em>. , <em>Harpalus affinis</em>, <em>Harpalus latus</em>) are pooled together at the genus level. </p> <p><strong>bodymass_mg: </strong>Continuous value indicating the live body mass of each tagged individual, excluding the added mass of the RFID tag.</p> <p><strong>detection: </strong>Integer with a fixed value of 1 representing the detection of a unique RFID tag. This column is used to estimate movement activity (sum of detections).</p> <p><strong>bite_count: </strong>Integer value indicating the number of bite marks recorded on an individual prey dummy during a 14-day exposure. Two independent observers scored the prey dummies by identifying and counting the bite marks left by carabid predators.</p> <p><strong>treatment_lux: </strong>Continuous value indicating the treatment of diffuse nighttime illuminance in Lux.</p>
Spatial and temporal variations in temperature and salinity in a Faroese fjord
<p>The dataset contains a two-year time series of CTD profiles, including temperature, salinity, fluorescent, par and oxygen in a Faroese fjord. Weekly or biweekly sampling from April to September at seven stations.</p>
Spatio-temporal, optogenetic control of gene expression in organoids
<p>Organoids derived from stem cells become increasingly important to study human development and to model disease. However, methods are needed to control and study spatio-temporal patterns of gene expression in organoids. To this aim, we combined optogenetics and gene perturbation technologies to activate or knock-down RNA of target genes, at single-cell resolution and in programmable spatio-temporal patterns. To illustrate the usefulness of our approach, we locally activated Sonic Hedgehog (<em>SHH</em>) signaling in an organoid model for human neurodevelopment. High-resolution spatial transcriptomic and single-cell analyses showed that this local induction was sufficient to generate stereotypically patterned organoids in three dimensions and revealed new insights into <em>SHH</em>’s contribution to gene regulation in neurodevelopment.</p> <p>With this study, we propose optogenetic perturbations in combination with spatial transcriptomics as a powerful technology to reprogram and study cell fates and tissue patterning in organoids.</p>
Data for detailed temporal mapping of global human modification from 1990 to 2017
<p>Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015). <strong>Note that because of repo file size limits, the datasets for the for the HM overall for 1990 and 1995, as well as major stressors for all years, are located <a href="https://drive.google.com/drive/folders/1D1-S_IuPuPrduBSwCiRfY8b4kPjtho2f?usp=drive_link">this</a> Google Drive. </strong></p> <p>This version 1.5 provides the following updates:</p> <ol> <li> <p>Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.</p> </li> <li> <p>It provides the addition datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.</p> </li> <li> <p>The ESA 150 m water-mask dataset (<a href="https://www.mdpi.com/2072-4292/9/1/36">Lamarche et al. 2017</a>) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.</p> </li> <li> <p>The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).</p> </li> <li> <p>Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.</p> </li> </ol> <p>For more details on the approach and methods, please see: Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020.</p> <p>Version 1.5 was completed in collaboration with the Center for Biodiversity and Global Change at Yale University and supported by the E.O. Wilson Biodiversity Foundation. </p>
Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation: extended material
<p>This dataset contains extended material for the paper:</p> <p>Frantz, D., Rufin, P., Janz, A., Ernst, S., Pflugmacher, D., Schug, F., Hostert, P.<strong>: Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation. </strong><em>In revision.</em></p>
ScienceDex guides
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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.