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28,952 results for “Distributed”
FIG. 2 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 2. — Mangroves on the east coast of the municipality of Salvaterra, Marajó Island, Pará: A, B, mangrove in inland zone; C, D, fringe zone.
FIG. 1 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 1. — Location map of collection points in Marajó Island, Pará, Brazilian Amazon:A, localization of Marajó Island in Pará, Brazil, South America (red rectangle); B, localization of Salvaterra in Marajó Island; C, localization of sampling points on the east coast of the Salvaterra, with 1 km between the fringe zone and the inland zone in each area (map prepared by P.W.P. Gomes).
Data associated to the paper "Phase diagram detection via Gaussian fitting of number probability distribution"
<p>We investigate the number probability density function that characterizes subportions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.<br><br>The dataset contains the entanglement spectra of several configurations of the extended Bose-Hubbard model ground state for different systems' sizes. </p>
Fig. 6 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 6. Hermatobates djiboutensis. Specimens from Meerufenfushi Island, Kaafu (North Malé) Atoll, 14 November 2006. Scale bar = 0.5 mm. A, male, dorsal view; B, male ventral view (length ca. 3.8 mm); C, male front leg showing teeth and tubercles on tibia (length of femur ca. 0.8 mm); D, female, dorsal view (length ca. 3.6 mm); E, female, ventral view.
Fig. 5 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 5. Halobates germanus. Specimens collected inside Haa Alifu Atoll at night, January 2007. Scale bar = 0.5 mm. A, adult female, dorsal view (body length = 3.7 mm); B, male genitalia, dorsal view (genital segment length = 1.9 mm); C, male genitalia, ventral view (genital length = 1.5 mm).
Fig. 4 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 4. Halobates formidabilis. Specimens from Maalhendhoo Island, Noonu Atoll, 5 May 2013. Scale bars = 0.5 mm. A, 5th instar nymph, dorsal view, showing colour pattern (length = 4.2 mm); B, adult male, dorsal view (length = 5.6 mm); C, male genitalia, dorsal view (genital segment length = 2.0 mm); D, male genitalia, ventral view (genital length = 1.4 mm).
Fig. 3. Halobates formidabilis. 5 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 3. Halobates formidabilis. 5th instar nymph, live individual, Dhidhdhoofinolhu Island, Alifu Dhaalu Atoll, 13 May 2006; not measured, approximate length 5 mm. A, dorsal view; B, ventral view, note uniform pale colouration.
Figure 5. a in Systematics, morphometry, and distribution of Eptesicus fuscus miradorensis (H. Allen, 1866) (Chiroptera: Vespertilionidae), with notes on baculum morphology and natural history
Figure 5. a. Suitability map of Eptesicus miradorensis in America using the Maxent algorithm. The higher suitability values are present in México,Guatemala,north Colombia, and Venezuela. b. Binary distribution map of Eptesicus miradorensis using the 10-percentile threshold value. Red points represent the species records.
Figure 1. a in Systematics, morphometry, and distribution of Eptesicus fuscus miradorensis (H. Allen, 1866) (Chiroptera: Vespertilionidae), with notes on baculum morphology and natural history
Figure 1. a. Map of the distribution of the currently recognized subspecies of Eptesicus fuscus. b. Bayesian gene trees of Cyt-b and COI of the E. fuscus complex. Upper values of branches show the posterior probability of the Bayesian inference. Values below the branches indicate the maximum likelihood inference's nonparametric (SH-aLRT) and ultrafast (UFBoot) bootstrap values.Country abbreviations. COL:Colombia,CAN:Canada,DOM: Dominican Republic, GUA:Guatemala, PAN: Panama, USA: United States,VEN:Venezuela.
Figure 2 in Systematics, morphometry, and distribution of Eptesicus fuscus miradorensis (H. Allen, 1866) (Chiroptera: Vespertilionidae), with notes on baculum morphology and natural history
Figure 2. Details of the skull (ICN 17189; female from Department of Santander, Colombia) of E. miradorensis. a. Ventral view. b. Dorsal view. c. Lateral view. d. Alive specimen from Serranía del Perijá, Colombia (ICN uncatalogued) shows long brownish hair and a dark, naked face.
Figs 11–19 in A New Species Of The Planthopper Genus Polychornum Gnezdilov, 2021 (Hemiptera: Caliscelidae: Ommatidiotinae) Extends The Distribution Of The Genus And Tribe Augilini Baker To Africa
Figs 11–19. Polychornum centroafricanum sp. n., holotype, male genitalia: 11 = genital block, lateral view; 12 = lower margin of pygofer, ventral view; 13 = anal tube, lateral view; 14 = anal tube, dorsal view; 15 = penis, dorsal view; 16 = penis, ventral view; 17 = penis, lateral
Figs 6–10 in A New Species Of The Planthopper Genus Polychornum Gnezdilov, 2021 (Hemiptera: Caliscelidae: Ommatidiotinae) Extends The Distribution Of The Genus And Tribe Augilini Baker To Africa
Figs 6–10. Polychornum centroafricanum sp. n., holotype: 6 = head, frontal view; 7 = head, lateral view; 8 = head, dorsal view; 9 = right forewing; 10 = apex of left forewing. Not to scale
Initial values for distribution function
<p>Contains initial conditions for turbulent channel flow simulations using Discrete Unified Gas Kinetic Scheme (DUGKS). Binary representation of distribution function values is stored in a column major format. Data resolution is 19x384x256x256 eight byte floating point (FP64) numbers.</p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Distribution grid data generated by ding0
<p>Distribution grid data generated with ding0 in the <a href="https://ego-n.org/" target="_blank" rel="noopener">eGo^n project</a>.<br>Data from pre-release v0.3.0-alpha using branch <em>ding0_run/2023_04_06</em>, head: <a href="https://github.com/openego/ding0/tree/9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c" target="_blank" rel="noopener">9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c</a>.</p> <p>Input data from eGon-data, branch <a href="https://github.com/openego/eGon-data/tree/continuous-integration/run-everything-2022-11-10" target="_blank" rel="noopener">run-everything-2022-11-10</a>.</p> <p>See <code>README.md</code> for details.</p> <p> </p> <p> </p>
Livestock activity shifts large herbivore temporal distributions to their crepuscular edges
<p>Wildlife species are transitioning to greater crepuscular and nocturnal activity in response to high human densities. This plasticity in temporal niches may partially mitigate the impacts of human activity but may also result in underestimating human effects on species foraging, predator-prey relationships, and community level interactions. We deployed remote cameras to characterize shifts in herbivore diel activity in protected habitat vs pastoralist landscapes. We then compared species traits including body mass, dietary preferences, and behavioral characteristics as potential predictors of species sensitivity to livestock. Our data capture a significant temporal shift away from core cattle activity for nearly every herbivore species in our study, leading to more crepuscular activity patterns. As livestock were primarily diurnal and predators primarily nocturnal in pastoralist habitat, species that decreased their overlap with livestock were more likely to increase their overlap with potential predators. Other than species' typical daytime activity levels, we found no evidence that any particular trait significantly predicted temporal shifts in response to livestock. Instead, species generally trended toward greater activity levels at dawn, suggesting that cattle have a homogenizing effect on community-wide activity patterns. Our findings highlight how cohabitation with livestock can profoundly alter the temporal niches of wild herbivores. Shifts in diel activity patterns may reduce herbivore foraging time or efficiency and potentially have cascading shifts on predator-prey dynamics. Given that species traits could not predict responses to livestock, our analysis suggests that conservation strategies should consider each species separately when designing interventions for wildlife management.</p>
Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"
<p>Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618</p> <p> </p> <p>There are 2 files in this upload:</p> <p> </p> <p>1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.</p> <p> </p> <p>2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file.</p> <p>· The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1).</p> <p>· The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’.</p> <p>· The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first.</p> <p>· The subfolder “Data” contains the Matlab data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”).</p> <p>· The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file.</p> <p>· The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes.</p> <p>· The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures.</p> <p>· The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes.</p> <p>When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.</p>
Heat pumps for all? Distributions of the costs and benefits of residential air-source heat pumps in the United States
<p>Dataset for the "<span>Heat pumps for all? Distributions of the costs and benefits of residential air-source </span><span>heat pumps in the United States" paper</span></p>
Maps of the diversity and distribution of Raunkiær's life forms in European vegetation
<p>This repository contains raster files (TIF format) with a 50 km × 50 km resolution (over UTM grid EPSG:32633), showcasing the diversity and distribution of Raunkiær’s life forms in European vegetation. The maps are based on two key metrics: (i) the proportion (%) of species within each life form and (ii) the diversity of life forms, including richness and evenness.</p> <p>To generate these maps, we averaged plot-level metric values across a comprehensive dataset comprising 546,501 vegetation plots sourced from the European Vegetation Archive (EVA; Project 163; <a href="https://euroveg.org" target="_new">https://euroveg.org</a>). These plots cover diverse habitats, including 173,190 forests, 260,884 grasslands, 52,517 scrubs, and 59,910 wetlands.</p> <p>The maps encompass the entire dataset, offering a visualization of the geographical distribution patterns of life forms across Europe. Additionally, we created habitat-specific maps by subsetting the dataset to explore unique patterns within each habitat type (forest, grassland, scrub, and wetland).</p> <p>Furthermore, we generated additional maps based on standardised effect sizes (SES) of diversity metrics. Through 500 species identity shuffles without replacement, specific to each habitat type, we examined the deviations from random expectations. SES values outside the range of -1.96 to 1.96 indicate significantly lower or higher metric values than expected at random, respectively. </p> <p> </p> <table> <tbody> <tr> <td><strong>Folder name</strong></td> <td><strong>Description of TIF raster values</strong></td> </tr> <tr> <td>full.div</td> <td>Mean richness and evenness of life forms across all habitat types</td> </tr> <tr> <td>full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types</td> </tr> <tr> <td>habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>SES.full.div</td> <td>Mean richness and evenness of life forms across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> </tbody> </table> <p><br>Additional information is available in our publication:<br><br>Midolo, G., Axmanová, I., Divíšek, J., Dřevojan, P., Lososová, Z., Večeřa, M., Karger, D. N., Thuiller, W., Bruelheide, H., Aćić, S., Attorre, F., Biurrun, I., Boch, S., Bonari, G., Čarni, A., Chiarucci, A., Ćušterevska, R., Dengler, J., Dziuba, T., Garbolino, E., Jandt, U., Lenoir, J., Marcenò, C., Rūsiņa, S., Šibík, J., Škvorc, Ž., Stančić, Z., Stanišić-Vujačić, M., Svenning, J. C., Swacha, G., Vassilev, K., & Chytrý, M. (2024) Diversity and distribution of Raunkiær’s life forms in European vegetation.<em> Journal of Vegetation Science. </em>Accepted on the 10th of December 2023</p>
Dataset: Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management
<p>This data set is used to employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed. A case of implications for flood management. Observational raw data from Thailand were provided by the Climate Information Services (CIS) at https://www.tmd.go.th/cis/main.php.</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.