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221 results for “multi-scales”
Data from: Finer-scale habitat predicts nest survival in grassland birds more than management and landscape: a multi-scale perspective
1. Birds may respond to habitat at multiple scales, ranging from microhabitat structure to landscape composition. North American grassland bird distributions predominantly reside on private lands, and populations have been consistently declining. Many of these lands are enrolled in U.S. federal conservation programmes, and properly guided management policies could alleviate declines. However, more evaluative research is needed on the effects of management policies juxtaposed with other multi-scale habitat features. Furthermore, research focused on nest survival is arguably more valuable because habitat associations with avian densities can sometimes be deceptive. 2. We investigated nest survival of a grassland facultative (red-winged blackbird, Agelaius phoeniceus) an obligate species (dickcissel, Spiza americana), and two nesting communities (ground and above-ground nesters) relative to management and multi-scale habitat (nest-site characteristics, in-field microhabitat, patch metrics, and landscape context). Our study was conducted on private lands in Illinois (2011-2014) and directly linked to policy-based management (disking, herbicidal spraying, spray/interseeding) and landowner decisions. 3. Multi-scale models explained more variation in nest survival compared to single scales or management in three of four analyses (blackbirds, dickcissels, and above-ground nesters). Finer-scale habitat variables, such as nest-site characteristics, were more often in top and among the competitive models relative to landscape factors. 4. Compared with other management types, disking (i.e. tractor-pulled disc harrows removed approximately 50% of vegetation) displayed distinct effects and positively influenced nest survival in above-ground nesters. Also, greater proportions of a field managed cumulatively and yearly, regardless of type, generally improved nest survival for dickcissels and above-ground nesters. All groups except above-ground nesters had generally higher nest survival in native grass fields. 5. Synthesis and applications. Habitat practitioners can improve nest survival for certain grassland birds by directly affecting infield-microhabitat vegetation and structure. However, characteristics associated with specific nest locations often drive nest survival. We suggest habitat managers and agency staff promote native grass practices and management, such as disking, to enhance nest survival of grassland bird populations. Management will likely be most effective in favourable unfragmented grassland landscapes with less surrounding forested areas, which also promote other important responses (e.g. colonization and persistence) of target species.
Arctic Tectonics and Volcanism: a multi-scale, multidisciplinary educational approach (Petrel and GPlates data package)
<p>The dataset includes pre-loaded and integrated data sets used in teaching the AGx51 course on Arctic Tectonics and Volcanism at the University Centre in Svalbard. </p><p>The dataset includes:</p><ul><li>a Petrel 2020 project with pre-loaded cultural, map, subsurface and borehole data from Svalbard</li><li>a GPlates project with relevant circum-Arctic datasets, exercises and plate tectonic models</li></ul><p>The data package is related to a manuscript currently in review for Geoscience Communication. </p>
Data for "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry"
<p>Neural and FEM E-field simulation data generated for Aberra AS, Wang R, Grill WM, Peterchev AV. (2023). "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry". <i>Brain Stimulation</i>. Dataset includes:</p><ul><li><i>cell_data/ </i>- Coordinates and morphology information for all model neurons</li><li><i>nrn_sim_data/</i> - Polarization data from NEURON simulations for all 25 model neurons included in the study, either in response to uniform E-field or tDCS.</li><li><i>layer_data/ - </i>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li><li><i>simnibs/</i> - E-field simulation and head mesh data generated within SimNIBS simulation environment</li></ul><p>To generate figures using these data, use the matlab code stored in the tDCSsim_Aberra2023 repository (https://github.com/Aman-A/tDCSsim_Aberra2023) </p>
Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior: resources and main figure data
<p><strong>Summary:</strong></p> <p>Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits. </p> <p><strong>Files included: </strong></p> <ul> <li>Vu, M-A. et al. (2023) - Key Resources Table.xlsx -- key resources used in this study</li> <li>README_figure_data_and_analysis.txt --a description of the organization of the files within this repository</li> <li>README_preprocessing_code.txt -- details about the preprocessing pipeline (including registration and localization pipelines) and annotation of the preprocessed data structure</li> <li>preprocessing_code.zip -- contains all the preprocessing code</li> <li>Fig01.zip -- preprocessed data and analysis code for main Figure 1</li> <li>Fig03.zip -- preprocessed data and analysis code for main Figure 3</li> <li>Fig04.zip -- preprocessed data and analysis code for main Figure 4</li> <li>Fig05.zip -- preprocessed data and analysis code for main Figure 5</li> <li>Fig06.zip -- preprocessed data and analysis code for main Figure 6</li> <li>Fig07.zip -- preprocessed data and analysis code for main Figure 7</li> <li>SuppFig01.zip -- preprocessed data and analysis code for supplemental Figure 1</li> <li>SuppFig02.zip -- preprocessed data and analysis code for supplemental Figure 2</li> <li>SuppFig03.zip -- preprocessed data and analysis code for supplemental Figure 3</li> <li>SuppFig04.zip -- preprocessed data and analysis code for supplemental Figure 4</li> <li>SuppFig05.zip -- preprocessed data and analysis code for supplemental Figure 5</li> <li>SuppFig06.zip -- preprocessed data and analysis code for supplemental Figure 6</li> <li>SuppFig07.zip -- preprocessed data and analysis code for supplemental Figure 7</li> </ul> <p>Each .zip file contains a folder corresponding to a figure. Within each figure folder will be a folder corresponding to the letter (e.g., A, B, C) of the panel within the figure. In addition to the panel folders, when applicable, there may also be a folder containing general-use functions or scripts, or interim results .mat files, that are called by the scripts within the subfolders. In each subfolder there will be 3 things:</p> <ol> <li>a folder of preprocessed data (see README_preprocessing_code.txt)</li> <li>a folder of the analysis scripts that analyzed the preprocessed data and generated the figure</li> <li>a README text file detailing the contents of both folders and the analysis workflow.</li> </ol> <p> </p>
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
<p>Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.</p> <p> </p> <p>ET_fluxes.csv<br>- Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations</p> <p>GW_levels.csv<br>- Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment</p> <p>Q_runoff.csv<br>- Observed specific discharge [mm/d] at the LJO catchment outlet</p> <p>THETA_kenttarova.csv<br>- Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations</p> <p>THETA_spatial.csv<br>- Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment</p>
Long-term monitoring in endangered woodlands shows effects of multi-scale drivers on bird occupancy
<p>Occupancy predictor data, detection predictor data, and species detections from sites in remnant Box Gum Grassy Woodland patches in south-eastern Australia. Only sites, species, and predictors used in our statistical analysises included. For privacy, predictors have been standardised (mean = 0, standard deviation = 1) and latitude and longitude have been offset by random vectors.</p>
A Multi-Scale Spatial Model of Hepatitis-B Viral Dynamics
<p>Dataset related to an accepted manuscript:</p> <p>A Multi-Scale Spatial Model of Hepatitis-B Viral Dynamic. Cangelosi Q., Means S., Ho H. PLOS One</p> <p>Dataset documented in a ReadMe file.</p>
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
Multi-scale Gaussian process dataset
<p>A multi-scale hierarchical dataset comprised of Gaussian processes. For more information regarding the dataset and usage see https://github.com/dexgen/backdrop/.</p>
Data used in "Persistent multi-scale fluctuations shift European hydroclimate to its millennial boundaries"
<p>Data used for evaluation of "Persistent multi-scale fluctuations shift European hydroclimate to its millennial boundaries"</p>
FIG. 8 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 8. Daily rainfall (mm) during the 2018 incubation period and average 15-minute soil saturation (%) at the bottom (solid line) and top (dashed line) of turtle nests (red, n ¼ 6) and haphazard sites (gray, n ¼ 6).
FIG. 6 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 6. Mean (6 range) daily soil temperature (8C) at the depth of the nest chamber center during the 2018 incubation season for turtle nests (n ¼ 6, red) and paired haphazard sites (n ¼ 6, light gray).
FIG. 7 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 7. Diel soil temperature (8C) pattern for turtle nests (n ¼ 6, red line) and paired haphazard sites (n ¼ 6, gray line) measured hourly (points) at depths equivalent to the bottom (A) and top (B) of the nest chambers during the 2018 summer incubation period.
FIG. 2 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 2. Mean (695% confidence interval) hourly soil temperature (8C) at the depth of the nest chamber top (A) and bottom (B) for turtle nests during the 2018 (n ¼ 6) and 2019 (n ¼ 6) incubation period. Nest were laid in sites with a crevice (red line, n ¼ 3), ledge (gray line, n ¼ 5), or flat (black line, n ¼ 4) bedrock morphology.
FIG. 5 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 5. Mean (6 SE) soil saturation (%) recession curves after rainfall events for sections of the turtle nest cavities with 100% hatch success (red line, n ¼ 8) and 0% hatch success (gray line, n ¼ 9) during the 2018 and 2019 incubation periods (A). Mean (6 SE) soil saturation (%) recession curves after rainfall events for turtle nests (red line, n ¼ 6) and paired haphazard sites (gray line, n ¼ 6) during the 2018 incubation period (B).
FIG. 3 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 3. Predicted probability (695% confidence intervals) of turtle egg hatch success (n ¼ 105) in relation to mean daily soil temperature (8C) when variance of percent soil saturation during incubation was high (standard deviation of 20% saturation, gray) compared to low (standard deviation of 10% saturation, red). Mean daily incubation temperature is shown for each turtle egg and black circles represent sample size (1–3 eggs [small circle], 4–6 eggs [medium circle], or 7þ eggs [large circle]).
FIG. 1 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success
FIG. 1. In a rock barrens landscape in the eastern Georgian Bay region (A), turtles nest in shallow soil deposits underlain by bedrock which can be classified as having either a crevice (B), ledge (C), or flat (D) morphology.
Data from: Genetic diversity, clonality and connectivity in the scleractinian coral Pocillopora damicornis: a multi-scale analysis in an insular, fragmented reef system
Clonality and genetic structure of the coral Pocillopora damicornis sensu lato were assessed using five microsatellites in 12 populations from four islands of the Society Archipelago (French Polynesia) sampled in June 2008. The 427 analysed specimens fell into 132 multilocus genotypes (MLGs), suggesting that asexual reproduction plays an important role in the maintenance of these populations. A haploweb analysis of ITS2 sequences of each MLG was consistent with all of them being conspecific. Genetic differentiation was detected both between and within islands, but when a single sample per MLG was included in the analyses, the populations turned out to be nearly panmictic. These observations provide further evidence of the marked variability in reproductive strategies and genetic structure of P. damicornis throughout its geographic range; comparison with results previously obtained for the congeneric species Pocillopora meandrina underlines the importance of life history traits in shaping the genetic structure of coral populations.
Figure 2 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 2. Non-metric multi-dimensional scaling (MDS) ordination showing hemipteran composition for all sampling periods with selected plant species superimposed.
Figure 5 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 5. Annual cyclic pattern of the proportion of the effectively specialized fauna (squares) and singleton species (circles) for the total number of hemipteran species from each sampling period.
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.