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450 results for “Spatio-Temporal”
Fig. 3 in Patterns of spatio-temporal distribution as criteria for the separation of planktic foraminiferal species across the Danian-Selandian transition in Spain
Fig. 3. Quantitative stratigraphic distribution of planktic foraminiferal species across the Danian–Selandian transition at Caravaca. The shown stratigraphic interval does not include the lower part of the A. uncinata Zone, where Globoconusa species were found (see Arenillas and Molina 1997).
Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter".
<p>Inputs or the experiments included in the manuscript <a href="https://doi.org/10.5194/egusphere-2024-1404">Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter</a>. </p> <p>Three experiment's inputs (forcing, observations and config file) for the Multiple Snow data Assimilation system (<a href="https://doi.org/10.5281/zenodo.11147258">MuSA</a>, v2.1) for the experimental catchment of Izas in the Spanish Pyrenees. All the experiments use ERA5 data downscaled to 20 m spatial resolution with the statistical downscaling tool <a href="https://doi.org/10.21105/joss.05059">TopoPySCALE</a>. The experiments assimilate different variables. </p> <p> C) assimilation of fSCA retrieved from Sentinel-2;</p> <p> D) assimilation of snow depth profiles retrieved with ICESat-2;</p> <p> J) joint assimilation of variables in C) and D).</p> <p> </p> <p>All the experiments assimilate the observations with the deterministic ensemble smoother with multiple data assimilation (DES-MDA) scheme.</p>
A relative-motion method for parsing spatio-temporal behaviour of dyads using GPS relocation data
<p>In this paper, we introduce a novel method for classifying and computing the frequencies of movement modes of intra- and interspecific dyads, focusing in particular on distance-mediated approach, retreat, following and side by side movement modes. Besides distance, other factors such as time of day, season, sex, or age can be included in the analysis to assess if they cause frequencies of movement modes to deviate from random. By subdividing the data according to selected factors, our method allows us to identify those responsible for (or correlated with) significant differences in the behaviour of dyadic pairs. We demonstrate and validate our method using both simulated and empirical data. Our simulated data were obtained from a relative-motion, biased random-walk (RM-BRW) model with attraction and repulsion components. Our empirical data were GPS relocation data collected from African elephants in Etosha National Park, Namibia. The simulated data were primarily used to validate our method while the empirical data were used to illustrate the types of behavioural assessment that our methodology reveals. Our method facilitates automated, observer-bias-free analysis of the locomotive interactions of dyads using GPS relocation data, which are becoming increasingly ubiquitous as telemetry and related technologies improve. It should open up a whole new vista of behavioural-interaction type analyses to movement and behavioural ecologists.</p>
Fig. 3 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 3. Kernel density estimates of daily predator activity patterns. A, tiger, leopard, and dhole; B, tiger and leopard; C, tiger and dhole; D, leopard and dhole. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 5 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 5. Kernel density estimates of daily activity patterns of leopards and prey species. A, leopard and gaur; B, leopard and sambar; C, leopard and barking deer; D, leopard and tapir; E, leopard and wild boar. The dashed lines are kernel density estimates for leopards and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 4 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 4. Kernel density estimates of daily activity patterns of tigers and prey species. A, tiger and gaur; B, tiger and sambar; C, tiger and tapir; D, tiger and barking deer; E, tiger and wild boar. The dashed lines are kernel density estimates for tigers and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 6 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 6. Kernel density estimates of daily activity patterns of dholes and prey species. A, dhole and gaur; B, dhole and sambar; C, dhole and tapir; D, dhole and barking deer; E, dhole and wild boar. The dashed lines are kernel density estimates for dholes and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 6 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil
Fig. 6. Least-square means and 95% confidence intervals from ANCOVA of the first three environmental factors from PCA. Different markers represent significantly different means as detected by planned contrasts with 5% significance level, first comparing lakes with any non-native species with those without them, and then comparing the two categories of lakes with non-natives (non-piscivores vs. piscivores).
Fig. 4 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil
Fig. 4. Scatterplot of species body size (mean standard length) vs. a relative index of native affinity to lakes containing piscivorous invaders (the proportion of biomass of a given native species in lakes with piscivorous invaders). The estimated regression line is also presented (Y = 0.044*X - 0.279; R2 = 0.443; p = 0.007). Species codes: ast = Astyanax sp.; aus = Australoheros facetus; cyp = Cyphocharax gilbert; cre = Crenicichla lacustris; geo = Geophagus brasiliensis; gym = Gymnotus gr. carapo; hop = Hoplias malabaricus; lep = Leporinus steindachneri; lor = Loricariidae (unidentified species); lyc = Lycengraulis sp.; moe = Moenkhausia doceana; oli = Oligosarcus solitarius; pac = Pachyurus adspersus; pro = Prochilodus vimboides; tra = Trachelyopterus striatulus.
Fig. 5 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil
Fig. 5. Least-square means and 95% confidence intervals from ANCOVA of mean individual size and temporal turnover as related to the three lake categories. Different markers represent significantly different means as detected by planned contrasts with 5% significance level, first comparing lakes with any non-native species with those without them, and then comparing the two categories of lakes with non-natives (non-piscivores vs. piscivores).
Fig. 2 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil
Fig. 2. Alpha (mean) and beta richness. a) Comparison among the temporal and spatial components of richness. b) Species richness for each lake. The alpha (mean) and beta richness were taken along the temporal component. Lake codes: No = Nova; Ca = Capim; Fe = Ferrugem; Cr = Crentes; Po = Poço Redondo; Ro = Romoalda; Ti = Timburé; Ag = Águas Claras; Pa = Palmeirinha; Ar = Ariranha. "Natives" represents lakes without non-native species; "Non-piscivores" represents lakes with non-piscivorous non-native species; "Piscivores" represents lakes with invasive piscivorous species.
Fig. 1 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 1. Locations of the two studied lagoons in the right bank of the lower Orinoco river, between the cities of Puerto Ordaz and Ciudad Bolívar, Bolívar State, Venezuela. The arrows in black indicate the lagoons.
Fig. 3 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 3. Percentage abundance of total species (S) and number of species for orders in each habitats of the lagoons. The abbreviations of the habitats are explained in the Fig. 2.
Fig. 4 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 4. Mean values (+ confidence interval) of abundance, biomass and richness by habitats and hydrological phases between lagoons. The abbreviations of the hydrological phases and habitats are explained in the Fig. 2.
Fig. 6. nMDS analysis during high waters and low waters. a and b in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 6. nMDS analysis during high waters and low waters. a and b = gill nets sampling; c and d = seine net sampling. Each symbol represents one sample, filled symbols belongs to Las Arhuacas (arh) and those of open symbols to Los Cardonales (car). The dissimilarity between the sampling is approximately proportional to the distance, that is to say to greater distance greater dissimilarity. The abbreviations of the habitats are explained in the Fig. 2.
Fig. 2 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 2. Percentage distribution abundance and biomass of fish by habitats in each hydrological phase in both lagoons. DW = Descent water, LW = Low water, RW = Raise water and HW = High water. RO = Rocky outcrops, FGF = Flooded grass fields, FF = Floodplain forests, B = Beach, AV = Aquatic vegetation, LZFT = Littoral zone with fallen trunks and LZOW = Littoral zone and open waters.
Earthquake catalog in QuakeML format from: "Spatio-Temporal Evolution of Intermediate-Depth Seismicity Beneath the Himalayas: Implications for Metamorphism and Tectonics"
<p>Earthquake catalog of the intermediate-depth seismicity beneath the central Himalayas in QuakeML format. The information included for each event contains location, phase pick, local magnitude information. For more details refer to the Frontiers publication:<a href="https://doi.org/10.3389/feart.2021.742700"> Michailos et al., 2021</a></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>
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>
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>
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.