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.
423
datasets available to search
ShareScore release 0.9.0
Dataset results
423 results for “spatial use”
Figure 4b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 4b "Network Edition" example of use (II). - Network resulting from clicking on "Rebuild linear network" in the situation of a
Figure 7 from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 7 Example of a linear road network following usual notation for the edges (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} e_{i} \end{equation*} \end{varwidth} \end{document} ) and vertex (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} v_{i} \end{equation*} \end{varwidth} \end{document} ). Arrows represent the direction of traffic flow.
Figure 6a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 6a "Network Direction" features. - A zone of a road network introduced as an input in the "Network Direction" section of the SpNetPrep application
Figure 4a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 4a "Network Edition" example of use (II). - Another use of the "Join vertex" (in green) option of the "Network Edition" section
Figure 5b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 5b Example of use of the SimplifyLinearNetwork function. - Simplified version of the network in a after the application of the SimplifyLinearNetwork function with parameters Angle = 25 and Length = 65
Figure 1 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 1 Phlebotomine sand fly capture sites, land use, and land cover in the municipality of Paracambi, Rio de Janeiro State, Brazil. Detail below: example of the extraction of the annual percentages of land use and land cover in a 200-meter buffer in one of the capture sites (1992-1994 and 2001-2003). Spatial resolution of the land cover data is 30 meters (Source: mapbiomas.org).
Figure 3 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 3 Kernel density map of the total number of sand flies captured in Paracambi, RJ, Brazil, 1992-1994 and 2001-2003.
A spatial autocorrelation analysis of environmental factors related to Dengue using Moran's I spatial statistics: A study from Nepal 2020-2023
Open the record for dataset details and reuse information.
A spatial autocorrelation analysis of Road Traffic Accidents by severity using Moran's I spatial statistics: A study from Nepal 2019-2022
Open the record for dataset details and reuse information.
Understanding tumor heterogeneity in melanoma brain metastasis using spatial transcriptomics and multi-regional bulk sequencing
<p>Melanoma brain metastasis (MBM) exhibits extensive inter- and intra-tumor heterogeneity, driven by a complex tumor microenvironment (TME). The aim with this study was to profile the MBMs by using a multi-omics approach, integrating spatial transcriptomics with bulk exome, proteome, and transcriptome profiling. We identified significant patient-specific variations in immune cell infiltration, particularly in B/plasma cells, myeloid cells, and cancer-associated fibroblasts (CAFs). Notably, immunotherapy-treated patients showed enrichedpathways related to EMT, IFN-γ signaling, oxidative phosphorylation, T-cell signaling, inflammation and DNA damage, which aligned with distinct cellular compositions observed in the spatial analysis. We also uncovered considerable intra-tumor heterogeneity, especially at the protein level, revealing differential expression patterns of key tumor and immune-related markers. The correlation between mRNA and protein data highlighted consistent enrichment of critical pathways across multi-omics layers. These findings provide a comprehensive view of MBM's molecular and cellular landscape, emphasizing the importance of addressing tumor heterogeneity in developing effective therapeutic strategies.</p>
Assessment of fish communities using eDNA: effect of spatial sampling design in lentic systems of different sizes
<p>Freshwater fish biodiversity is quickly decreasing and requires effective monitoring and conservation. Environmental DNA (eDNA)-based methods have been shown to be highly sensitive and cost-efficient for aquatic biodiversity surveys, but few studies have systematically investigated how spatial sampling design affects eDNA-detected fish communities across lentic systems of different sizes. We compared the spatial patterns of fish diversity determined using eDNA in three lakes of small (SL; 3 ha), medium (ML; 122 ha) and large (LL; 4,343 ha) size using a spatially explicit grid sampling method. A total of 100 water samples (including 9, 17, and 18 shoreline samples and 6, 14, and 36 interior samples from SL, ML, and LL, respectively) were collected, and fish communities were analyzed using eDNA metabarcoding of the mitochondrial 12S region. Together, 30, 35, and 41 fish taxa were detected in samples from SL, ML, and LL, respectively. We observed that eDNA from shoreline samples effectively captured the majority of the fish diversity of entire waterbodies, and pooled samples recovered fewer species than individually processed samples. Significant spatial autocorrelations between fish communities within 250 m and 2 km of each other were detected in ML and LL, respectively. Additionally, the relative sequence abundances of many fish species exhibited spatial distribution patterns that correlated with their typical habitat occupation. Overall, our results support the validity of a shoreline sampling strategy for eDNA-based fish community surveys in lentic systems but also suggest that a spatially comprehensive sampling design can reveal finer distribution patterns of individual species.</p>
Fig. 4 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 4. Frequency of Sus scrofa group sizes observed in 167 unique camera trap observations from May 2016 to August 2016.
Figure 3 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Figure 3 - Detailed figure of Gruta Helictites showing different land uses within the 50, 100 and 250 m buffers.
Figure 2 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Figure 2 - Study area location, sampling design used in sampled caves at "Parque Estadual do Sumidouro", and the Buffers of 50m, 100m, and 250m for analyzing the effect of spatial scale on the explanatory power of environmental variables in the cave invertebrate communities.
Figure 1 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Figure 1 - Spatial characterization of landscape at "Parque Estadual do Sumidouro". Different colors represent distinct vegetation cover or land-use types. The numbers indicate the sampled caves, indicated by name. Legend: 1 Gruta Ninho de Pérolas 2 Gruta Macaco das Cavernas 3 Lapa da Várzea 4 Gruta do Grilão 5 Gruta Helictites 6 Lapa das Pacas 7 Gruta do Sumidouro 8 Gruta Lagoa Seca 9 Gruta do Feneme 10 Gruta do Lixo.
The m-Dimensional Spatial Nyquist Limit Using the Wave Telescope for Larger Numbers of Spacecraft Dataset
<p>Dataset for: The m-Dimensional Spatial Nyquist Limit Using the Wave Telescope for Larger Numbers of Spacecraft. Includes model results of 2D and 3D modelling. </p>
Supplementary material 5 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
MaxEnt Supplementary Info
Supplementary material 4 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Moran's I Correlograms
Supplementary material 3 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sampled PUD occurrence
Supplementary material 2 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
InVEST model configuration
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.