Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
867
datasets available to search
ShareScore release 0.9.0
Dataset results
867 results for “spatiotemporal”
Lake browning generates a spatiotemporal mismatch between DOC and limiting nutrients, 2018 spatial survey, modeled light limitation and whole-lake productivity changes in long-term Adirondack lake survey 1994-2012
This data set contains information on a spatial survey of dissolved organic matter (DOM) across lakes and wetlands in the Northeast and Midwest, USA and modeled long-term changes in light limitation and whole-lake productivity in a suite of lakes in the Adirondack State Park, New York, USA. Widespread long-term increases in DOM have been observed in many lakes in a process known as browning. This data set enables the assessment of potential changes in dissolved absorbance and dissolved organic nutrients associated with browning. This data set accompanies a manuscript in review at Limnology and Oceanography: Letters.
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>original daily data for 'Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants'</p>
Figure 6 in Spatiotemporal distribution of planktonic copepod communities in Tokyo Bay where Oithona davisae Ferrari and Orsi dominated in mid-1980s
Figure 6. Horizontal distributions of the identified community groups in Tokyo Bay (the letters in parentheses in legends show the indicator species).
Data from: Do introduced apex predators suppress introduced mesopredators? A multiscale spatiotemporal study of dingoes and feral cats in Australia suggests not
<p>1. The role of apex predators in structuring ecosystems through the suppression of mesopredator activity and abundance is receiving increasing attention, largely due to the potential benefits for biodiversity conservation. In Australia, invasive mesopredators such as feral cats (Felis catus) have been identified as major contributors to Australia's mass mammal extinctions since European arrival. The introduced dingo (Canis familiaris) has been proposed as a novel way to suppress the impacts of feral cats, however scientific evidence of the dingo's suppressive role is equivocal. 2. We used camera traps to investigate whether a large introduced predator (dingo) suppresses the activity of an established introduced mesopredator (feral cat) across a national park site conserving endangered species, and an agricultural site supporting cattle grazing enterprises. 3. Feral cats and dingoes exhibited marked overlap in both temporal and spatial activity, indicating coexistence. Some temporal separation was evident at the agricultural site, however this reflected higher diurnal activity by dingoes, not a responsive shift in cat activity. Cat activity times were unrelated to dingo presence and did not differ between areas occupied by dingoes and dingo-free areas. There was no evidence of dingoes excluding cats from patches at either site, nor was there evidence of within-night fine-scale spatiotemporal avoidance of dingoes by cats. 4. Species co-occurrence models revealed dingoes had no negative effect on the probability of cat presence. The probability of detecting a cat on the national park was significantly higher in areas with dingoes than in dingo-free areas, while on agricultural land, cat detectability did not differ between areas with and without dingoes. Cats remained active, abundant and widespread across both sites, with evidence of cats hunting and breeding successfully in areas occupied by dingoes. 5. Synthesis and applications. Our findings indicate that feral cats can coexist with dingoes, without apparent suppression of cat activity, abundance, or fitness. Proposals to reintroduce or restore dingoes and other large predators to suppress invasive mesopredators and conserve biodiversity should be carefully evaluated on a site-by-site basis, as their ability to suppress cats and protect species of conservation significance will likely be context dependent.</p>
XIS: A daily spatiotemporal machine-learning model for environmental exposures in the contiguous United States
<p>These Parquet files contain the outputs used for many analyses and plots in the linked papers. For temperature and humidity, the full sets of observations for cross-validation aren't included because we used restricted-use MADIS data.</p>
Processed ERA5, IMERG and TRMM PR/GPM DPR precipitation data for Nicolas & Boos - "Understanding the spatiotemporal variability of tropical orographic rainfall using convective plume buoyancy."
<p>The dataset contains processed data from large datasets that are freely available online. <br>All data cover the period 01/2001 - 12/2020. The file names describe the months & region that each file contains. Variable codes for ERA5 data (all files starting in e5.) are:</p><p> - 228_246_100u : 100m u-wind<br> - 228_247_100v : 100m v-wind<br> - qL : 900-600hPa averaged specific humidity<br> - thetaeb : surface - 900hPa averaged equivalent potential temperature<br> - thetaeL : 900-600hPa averaged equivalent potential temperature<br> - thetaeLstar : 900-600hPa averaged saturation equivalent potential temperature<br> - tL : 900-600hPa averaged temperature<br> - uBL : surface - 900hPa averaged u wind<br> - vBL : surface - 900hPa averaged v wind<br> - 128_034_sstk : sea surface temperature<br> - 162_071_viwve : eastward component of vertically integrated water vapor transport<br> - 162_072_viwvn : northward component of vertically integrated water vapor transport</p><p> </p>
Data from: Spatiotemporal-social association predicts immunological similarity in rewilded mice
<p>Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal cooccurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. And we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.<span><br></span></p>
Data used for "Assessing spatiotemporal change in coral reef social-ecological systems"
<p>Coral reef data used for Eason, T. and Garmestani, A. S., Assessing spatiotemporal change in coral reef ecosystems. Under Review (Ecology and Society)</p> <p>The raw data was gathered from a coral bleaching study performed by Sully et al (2019). In our current study, we used data spanning from 2003-2016. We amalgamated station names and associated data when the station locations (latitude and longitude) remained essentially the same, but were named slightly different from year to year. </p> <p>Reference: Sully S, Burkepile DE, Donovan MK, Hodgson G, van Woesik R. A global analysis of coral bleaching over the past two decades. Nat Commun. 2019;10(1):1264</p>
Land cover preferences and spatiotemporal associations of ungulates within a Scottish mammal community
<p>In the degraded and modified environment of the Scottish Highlands, novel ungulate communities have arisen following local extinctions, reintroductions, and the introduction of non-native species. An understanding of the dynamics and interactions within these unique mammal communities is important as many of these mammals represent keystone species with disproportionate impacts on the environment. Using a camera trap survey, we investigated land cover preferences and spatiotemporal interactions within a Scottish ungulate community: the sika deer (<em>Cervus nippon</em>), the roe deer (<em>Capreolus capreolus</em>), the red deer (<em>Cervus elaphus</em>), and the wild boar (<em>Sus scrofa</em>). We used generalised linear models to assess land cover preferences and the effect of human disturbance; spatiotemporal interactions were characterised using time interval modelling. We found sika deer and roe deer preferred coniferous plantations and grasslands, with sika deer additionally preferring woodland. For red deer, we found a slight preference for wetland over woodland; however, the explained variance was low. Finally, wild boar preferred grassland and woodland and avoided coniferous plantations, heathland, and shrubland. Contrary to our expectations, we found no evidence that human disturbance negatively impacted ungulates distributions, potentially because ungulates temporally avoid humans or because dense vegetation cover mitigates the impacts of humans on their distributions. Furthermore, we detected a spatiotemporal association between sika deer and roe deer. Although the underlying cause of this is unknown, we hypothesise that interactions such as grazing facilitation or an anti-predator response to culling could be driving this pattern. Our work provides a preliminary analysis of the dynamics occurring within a novel ungulate community, but also highlights current knowledge gaps in our understanding of the underlying mechanisms dictating the observed spatiotemporal associations.</p>
Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
<p>Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.</p>
Spatiotemporally distinct responses to mechanical forces shape the developing seed of Arabidopsis
<p><span>Source Data File related to the manuscript:</span></p> <p><a name="_Hlk141697088"></a><span> </span></p> <p><span>Spatiotemporally distinct responses to mechanical forces</span></p> <p><span>shape the developing seed of Arabidopsis</span></p> <p> </p> <p><span>See Description Dataset file for full description</span></p> <p><span> </span></p>
Plant traits shape global spatiotemporal variations in photosynthetic efficiency
<p>Dataset to reproduce key results in the following work: Plant traits shape global spatiotemporal variations in photosynthetic efficiency</p>
Spatiotemporal distribution of global peatland area during the Holocene
<p>The global peatland area dataset comprises netCDF files, which consist of 13 sets of maps showing the global extent of peatlands at a spatial resolution of 0.5° × 0.5°. All maps are provided at 1,000-year time intervals between 12 and 0 ka BP. The peatland area files named “Global_peatland_area_BA_*” were reconstructed using the BA method, and the files named “Global_peatland_area_IDW_*” were reconstructed using the IDW method. The global peatland records included data on location, latitude, longitude, peat type, basal ages, and end ages. The global pollen of <em>Sphagnum</em> records included latitude, longitude,<em> Sphagnum</em> content, and peat basal and end ages.</p>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
Spatiotemporal checkins with social connections
<ul> <li><strong>Introduction</strong></li> </ul> <p>These three datasets are used in the analysis of human mobility research paper [1]. For each dataset, there are checkins info and friendshio info, </p> <ol> <li>Brightkite: "brightkite_checkins.csv" and "brightkite_friends.csv".</li> <li>Gowalla: "gowalla_checkins.csv" and "gowalla_friends.csv".</li> <li>Weeplaces: "weeplace_checkins.csv" and "weeplace_friends.csv"</li> </ol> <p> </p> <ul> <li><strong>Basic Description </strong></li> </ul> <p><em>BrightKite</em> [2] is a LBSN service provider that allowed registered users to connect with their existing social ties and also meet new people based on the places that they go. Once a user "checked in" at a place, they could post notes and photos to a location and other users could comment on those posts. The social relationship network was collected using their public API. The raw dataset is from SNAP <a href="https://snap.stanford.edu/data/loc-brightkite.html">https://snap.stanford.edu/data/loc-brightkite.html</a>.<br> </p> <p><em>Gowalla</em> [2] is a LBSN website where users share their locations by checking-in. In early versions of the service, users would occasionally receive a virtual "Item" as a bonus upon checking in, and these items could be swapped or dropped at other spots. Users became "Founders" of a spot by dropping an item there. This incentivises users to create new check-ins, not necessarily to check-in consistently at frequently visited locations. The social relationship network is undirected and was collected using their public API. The raw dataset is from SNAP <a href="https://snap.stanford.edu/data/loc-gowalla.html">https://snap.stanford.edu/data/loc-gowalla.html</a>.<br> <br> <em>Weeplaces</em> --This is collected from Weeplaces and integrated with the APIs of other LBSN services, e.g., Facebook Places, Foursquare, and Gowalla. Users can login Weeplaces using their LBSN accounts and connect with their social ties in the same LBSN who have also used this application. Weeplaces visualizes your check-ins on a map. Unlike Gowalla, there is no direct incentive in Weeplaces to alter one's visitation habits or check-ins, so there should be a more accurate representation of a regular person's mobility patterns.<br> The raw dataset is from the website <a href="https://www.yongliu.org/datasets/">https://www.yongliu.org/datasets/</a>.</p> <p> </p> <p>More details can be found in the data description of paper [1].</p> <p> </p> <ul> <li><strong>Reference</strong></li> </ul> <p>[1] Chen, Z., Kelty, S., Welles, B.F., Bagrow, J.P., Menezes, R. and Ghoshal, G., 2021. Contrasting social and non-social sources of predictability in human mobility. <em>arXiv preprint arXiv:2104.13282</em>.</p> <p>[2] Cho, Eunjoon, Seth A. Myers, and Jure Leskovec. "Friendship and mobility: user movement in location-based social networks." In <em>Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining</em>, pp. 1082-1090. 2011.</p> <p> </p> <ol> </ol> <p> </p> <p> </p>
Spatiotemporal attosecond control of electron pulses via subluminal terahertz waveforms
<p>The dataset contains the electron deflectograms and evanescent wave profiles. </p>
Spatiotemporal influences of climate and humans on muskox range dynamics over multiple millennia
<p>Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early-Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution, have included an over reliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Using process-explicit macroecological models that integrate modern and fossil occurrence records, spatiotemporal reconstructions of past climatic change, speciesspecific population ecology and the growth and spread of anatomically modern humans, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia, and in its expansion in North America. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years requires high dispersal abilities, large maximum densities, and a small Allee effect. Climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses reveal that the range collapse and extinction of the muskox in Europe (~ 13 thousand years ago) was caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene-Holocene transition signals a vulnerability of this species to future increased warming. By disentangling the ecological processes that shaped the distribution of the muskox through space and time, process-explicit models have important applications for the future conservation and management of this iconic species in a warming Arctic. </p>
Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate
<p>These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of spatiotemporal covariates that could potentially explain pronghorn (Antilocapra americana) mortality risk in the Northern Sagebrush Steppe (NSS) ecosystem (50.0757<sup>o</sup> N, −108.7526<sup>o</sup> W). Known-fate data were collected from 170 adult female pronghorn monitored with GPS collars from 2003-2011, which were used to construct a time-to-event (TTE) dataset with a daily timescale and an annual recurrent origin of 11 November. Seasonal risk periods (winter, spring, summer, autumn) were defined by median migration dates of collared pronghorn. We linked this TTE dataset with spatiotemporal covariates that were extracted and collated from pronghorn seasonal activity areas (estimated using 95% minimum convex polygons) to form a final dataset. Specifically, average fence and road densities (km/km2), average snow water equivalent (SWE; kg/m2), and maximum decadal normalized difference vegetation index (NDVI) were considered as predictors. We tested for these main effects of spatiotemporal risk covariates as well as the hypotheses that pronghorn mortality risk from roads or fences could be intensified during severe winter weather (i.e., interactions: SWE*road density and SWE*fence density). We also compare an analogous frequentist implementation to estimate model-averaged risk coefficients. Ultimately, the study aimed to develop the first broad-scale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts.</p> <p> </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.