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.
365
datasets available to search
ShareScore release 0.7.1
Dataset results
365 results for “Spatial modeling”
Data set for "Spatially explicit ecological modeling improves empirical characterization of dispersal"
<p>Data set used and created in the simulations, analysis and figures of the associated paper.</p>
Figure 2 in A two-species distribution model for parapatric newts, with inferences on their history of spatial replacement
Figure 2. Two-species distribution model derived from Triturus cristatus and Triturus marmoratus records over France along with a suite of environmental variable (for details, see main text), extrapolated over neighbouring areas. The colours show the probability for any eligible locality to be occupied by T. cristatus (P c), from deep red for T. cristatus to deep blue for T. marmoratus. Intermediate colours, such as orange and green, represent intermediate probabilities (see the colour scale, which ranges from P c at zero to P c at unity). Areas in black have an elevation of> 1500 m a.s.l. A, model with forestation as documented. B, C, the mutual species distribution under the assumption that western Europe would be completely forested (full forest; B) and devoid of forestation (zero forest; C). The white line approximates the mutual species border as modelled in A. Note that large areas in the south-east of France are devoid of Triturus newts (cf. Fig. 1) and that Italy has another crested newt species (Triturus carnifex), but that a parapatric contact zone is being modelled nevertheless.
Figure 1. A in A two-species distribution model for parapatric newts, with inferences on their history of spatial replacement
Figure 1. A, the outer range borders of the crested newt, Triturus cristatus (c; southern border shown by continuous line) and the marbled newt, Triturus marmoratus (m; northern and eastern border shown by dashed line) in continental France, after Castanet & Guyetant (1989) and Lescure & De Massary (2012). Departments mentioned in the text are as follows: DS, Deux-Sevres; M, Mayenne; V, Vienne. The Lower Rhône T. cristatus population is indicated by LR. The base map was downloaded from MapsLand (https://www.mapsland.com), under a Creative Commons Attribution-ShareAlike 3.0 Licence. B, the area of T. cristatus–T. marmoratus range overlap in Mercator projection, with the generalized species border as inferred from a two-species distribution model (see Fig. 2). The open circles represent documented species occurrences that strongly contradict the model, for T. cristatus (probability of occurrence, Pc ≤ 0.2, in red) and T. marmoratus (Pc ≥ 0.8, in blue). Large symbols represent multiple observations at close range. The drawings of animals, with T. cristatus at the top and T. marmoratus at the bottom, are by Bas Blankevoort, Naturalis Biodiversity Center.
Figure 4 in A two-species distribution model for parapatric newts, with inferences on their history of spatial replacement
Figure 4. Model of the two-species distribution of Triturus cristatus and Triturus marmoratus for climatic conditions as foreseen for 50 years from now under the CMCC-ESM2_ SSP126 scenario. Results were simplified to a binary representation, with red for T. cristatus (probability of occurrence, Pc> 0.5) and blue for T. marmoratus (Pc <0.5). Grey areas predict the presence of one species or the other, depending on zero or full forestation (for details, see main text). The white line approximates the mutual species border as modelled for the present day (Fig. 2A). Note that the contact zone would have to move at a pace of> 10 km/ year to keep up with the projected change. Three other scenarios yielded even larger contact zone displacements (Supporting Information, Fig. S2).
Figure 3 in A two-species distribution model for parapatric newts, with inferences on their history of spatial replacement
Figure 3. Models of the two-species distribution of Triturus cristatus and Triturus marmoratus for climatic conditions as reconstructed for the Holocene, under the assumption that western Europe would be entirely forested (left panel) or entirely deforested (right panel). Colour key as in Figure 2. Results for nine different scenarios (for details, see main text) were averaged; for scenarios shown individually, see the Supporting Information (Fig. S1). The white line approximates the mutual species border as modelled for the present day in Figure 2.
Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
<p>Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. </p>
IMPROVING THE AVAILABILITY AND USABILITY OF PLANETARY SPATIAL DATA RESEARCH BY SPATIAL DATA INFRASTRUCTURE OF CELESTIAL BODIES MODELING
<p>The rapid development of space technology and the increased interest in space exploration have resulted in the intensifying of observation of celestial bodies, mostly in the solar system, over the past decade with the prospect of an upward trend in the future. Data collected by space missions are stored and provided to users for use through the archives of individual space agencies and specialized portals of space missions. Users often encounter many problems when searching and retrieving data of interest, despite the fact that access to data is open for all groups of users. Current ways of storing and shearing this valuable data set are focused on their long-term archiving and are largely adapted for space scientists with inadequate access and search functionalities that do not meet the needs of a wider group of users. To search for data, users must have some prior knowledge and invest a lot of time and effort, and the available functionalities gives too many of the same or similar data filtering results that, in most cases, cannot be visualized before downloading. For this reason, the data remains unused, and in order to solve this problem, large amounts of collected data on space bodies, of which most are spatially defined, impose the need to develop the spatial data infrastructure of celestial bodies (SDICB) at the general level in order to enable standardized organization and storage of these data, and their efficient use and exchange. In order to approach to the development of such an infrastructure, it is necessary to investigate what data, as well as how and to what extent, are collected through the space observation, either from Earth, Earth orbit or from space probes. It is also necessary to investigate how this data can be obtained and to explore concepts of spatial data infrastructure, the possibilities of its establishment and operationalization. This doctoral dissertation provides a detailed overview of current ways of storing and distributing space research data and their shortcomings and explores the possibility of modeling the establishment of SDICB. In order to adequately approach the development of the model, user needs assessment and analysis of the current data archiving situation was conducted. These results served as input parameters for modeling SDICB and the adoption of guidelines (recommendations) for the establishment. The proposed model is focused on user needs and improving the functionality of data access by applying international standards of spatial data and open-source technologies to make space data available to the general public and enable their easy search, download and interpretation. For the proposed model, an implementation project with a five-year implementation period was created, for which a feasibility study was conducted, and the benefits of SDICB implementation for all involved stakeholders were investigated.</p>
A new method for runoff forecasting in ungauged areas based on the Xinanjiang model incorporating multiple spatial geographical distribution data
<p>This dataset includes two parts:</p> <p>(1) The SHP format layer data of 256 watersheds in North America and 2 watersheds in ChinaThe SHP format layer data of 256 watersheds in North America and 2 watersheds in China.</p> <p>(2) A reference meta-watershed database of spatial data was established, covering a spatial database of 256 watersheds. This provides a data foundation for runoff forecasting in other ungauged watersheds.</p>
A Model to Identify Specific Predictors of Spatial Neglect Recovery
ClinicalTrials.gov study NCT00990353. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: samc: An R package for connectivity modeling with spatial absorbing Markov chains
Open the record for dataset details and reuse information.
Data from: Exploring the interaction of avian frugivory and plant spatial heterogeneity and its effect on seed dispersal kernels using a simulation model
Open the record for dataset details and reuse information.
Data from: How large spatially-explicit optimal reserve design models can we solve now? an exploration of current models’ computational efficiency
Open the record for dataset details and reuse information.
Data from: Conservation versus livelihoods: spatial management of non-timber forest product harvests in a two-dimensional model
Open the record for dataset details and reuse information.
Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches
Open the record for dataset details and reuse information.
Data from: Ignoring spatial effects results in inadequate models for variation in littoral macroinvertebrate diversity
Open the record for dataset details and reuse information.
Data from: Spatial modeling improves understanding patterns of invasive species defoliation by a biocontrol herbivore
Open the record for dataset details and reuse information.
Data from: A multistate dynamic site occupancy model for spatially aggregated sessile communities
Open the record for dataset details and reuse information.
Data from: Using data from related species to overcome spatial sampling bias and associated limitations in ecological niche modeling
Open the record for dataset details and reuse information.
Data from: Integrating passive acoustic and visual data to model spatial patterns of occurrence in coastal dolphins
Open the record for dataset details and reuse information.
Data from: Spatial heterogeneity in soil nutrient supply modulates nutrient and biomass responses to multiple global change drivers in model grassland communities
Open the record for dataset details and reuse information.
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.