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.
324
datasets available to search
ShareScore release 0.9.0
Dataset results
324 results for “spatial genetics”
Spatial genetic structure in a crustacean herbivore highlights the need for local considerations in Baltic Sea biodiversity management
Open the record for dataset details and reuse information.
Data from: Patterns of fine-scale spatial genetic structure and pollen dispersal in giant sequoia (Sequoiadendron giganteum)
Open the record for dataset details and reuse information.
Genetically Engineered Brain Organoids Recapitulate Spatial and Developmental States of Glioblastoma Progression [Xenium Spatial Transcriptomics]
GEO Series GSE283497. Homo sapiens. 6 samples. Type: Other.
Genetically Engineered Brain Organoids Recapitulate Spatial and Developmental States of Glioblastoma Progression [scRNA-seq]
GEO Series GSE283496. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Spatial-temporal tracking of Brca1-driven breast tumorigenesis from sporadic mutant cells with a genetic mosaic mouse model
GEO Series GSE214433. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Genetic profiles of small intestinal and colonic metaplastic Paneth cells by spatial transcriptomics
GEO Series GSE306374. Homo sapiens. 2 samples. Type: Other.
Deep Spatial Profiling of Venezuelan Equine Encephalitis Virus Reveals Increased Genetic Diversity Amidst Neuroinflammation and Cell Death During Brain Infection
GEO Series GSE213725. Mus musculus. 223 samples. Type: Expression profiling by high throughput sequencing.
Genetically- and spatially-defined basolateral amygdala neurons control food consumption and social interaction
GEO Series GSE244860. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Temporal, spatial, and genetic constraints contribute to the patterning and penetrance of murine Neurofibromatosis-1 optic glioma
GEO Series GSE149946. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
A Lactate-induced SREBF2-dependent genetic program drives an immunotolerant dendritic cell population during cancer progression [Spatial transcriptomics]
GEO Series GSE253588. Homo sapiens. 3 samples. Type: Other.
Spatial genetic structure to identify populations at risk
<p>Microsatellite genotypes, locality information, and raster data for Miller et al</p>
Data from: Evolutionary processes driving spatial patterns of intra-specific genetic diversity in river ecosystems
Describing, understanding and predicting the spatial distribution of genetic diversity is a central issue in biological sciences. In river landscapes, it is generally predicted that neutral genetic diversity should increase downstream, but there have been few attempts to test and validate this assumption across taxonomic groups. Moreover, it is still unclear what are the evolutionary processes that may generate this apparent spatial pattern of diversity. Here, we quantitatively synthesized published results from diverse taxa living in river ecosystems, and we performed a meta-analysis to show that a downstream increase in intraspecific genetic diversity (DIGD) actually constitutes a general spatial pattern of biodiversity that is repeatable across taxa. We further demonstrated that DIGD was stronger for strictly waterborne dispersing than for overland dispersing species. However, for a restricted data set focusing on fishes, there was no evidence that DIGD was related to particular species traits. We then searched for general processes underlying DIGD by simulating genetic data in dendritic-like river systems. Simulations revealed that the three processes we considered (downstream-biased dispersal, increase in habitat availability downstream and upstream-directed colonization) might generate DIGD. Using random forest models, we identified from simulations a set of highly informative summary statistics allowing discriminating among the processes causing DIGD. Finally, combining these discriminant statistics and approximate Bayesian computations on a set of twelve empirical case studies, we hypothesized that DIGD were most likely due to the interaction of two of these three processes and that contrary to expectation, they were not solely caused by downstream-biased dispersal.
Data from: The genetics of phenotypic plasticity. XII. Temporal and spatial heterogeneity
In order to understand empirical patterns of phenotypic plasticity, we need to explore the complexities of environmental heterogeneity and how it interacts with cue reliability. I consider both temporal and spatial variation separately and in combination, the timing of temporal variation relative to development , the timing of movement relative to selection, and two different patterns of movement: stepping-stone and island. Among-generation temporal heterogeneity favors plasticity, while within-generation heterogeneity can result in cue unreliability. In general, spatial variation more strongly favors plasticity than temporal variation, and island migration more strongly favors plasticity than stepping-stone migration. Negative correlations among environments between the time of development and selection can result in seemingly maladaptive reaction norms. The effects of higher dispersal rates depends on the life history stage when dispersal occurs and the pattern of environmental heterogeneity. Thus, patterns of environmental heterogeneity can be complex and can interact in unforeseen ways to affect cue reliability. Proper interpretation of patterns of trait plasticity require consideration of the ecology and biology of the organism. More information on actual cue reliability and the ecological and developmental context of trait plasticity is needed.
Data from: Spatial and temporal genetic structure of a river-resident Atlantic salmon (Salmo salar) after millennia of isolation
The river-resident Salmo salar ("småblank") has been isolated from other Atlantic salmon populations for 9,500 years in upper River Namsen, Norway. This is the only European Atlantic salmon population accomplishing its entire life cycle in a river. Hydropower development during the last six decades has introduced movement barriers and changed more than 50% of the river habitat to lentic conditions. Based on microsatellites and SNPs, genetic variation within småblank was only about 50% of that in the anadromous Atlantic salmon within the same river. The genetic differentiation (FST) between småblank and the anadromous population was 0.24. This is similar to the differentiation between anadromous Atlantic salmon in Europe and North America. Microsatellite analyses identified three genetic subpopulations within småblank, each with an effective population size Ne of a few hundred individuals. There was no evidence of reduced heterozygosity and allelic richness in contemporary samples (2005–2008) compared with historical samples (1955–56 and 1978–79). However, there was a reduction in genetic differentiation between sampling localities over time. SNP data supported the differentiation of småblank into subpopulations and revealed downstream asymmetric gene flow between subpopulations. In spite of this, genetic variation was not higher in the lower than in the upper areas. The meta-population structure of småblank probably maintains genetic variation better than one panmictic population would do, as long as gene flow among subpopulations is maintained. Småblank is a unique endemic island population of Atlantic salmon. It is in a precarious situation due to a variety of anthropogenic impacts on its restricted habitat area. Thus, maintaining population size and avoiding further habitat fragmentation are important.
Data from: Evolutionary processes driving spatial patterns of intra-specific genetic diversity in river ecosystems
Open the record for dataset details and reuse information.
Data from: The genetics of phenotypic plasticity. XII. Temporal and spatial heterogeneity
Open the record for dataset details and reuse information.
Data from: Spatial and temporal genetic structure of a river-resident Atlantic salmon (Salmo salar) after millennia of isolation
Open the record for dataset details and reuse information.
Data from: Dispersal of Amur tiger from spatial distribution and genetics within the eastern Changbai mountains of China
Open the record for dataset details and reuse information.
Data from: Dioecy, more than monoecy, affects plant spatial genetic structure: the case study of Ficus
Open the record for dataset details and reuse information.
Genetic vulnerability to healing reveals a spatially resolved epithelial restitution program
GEO Series GSE235743. Mus musculus. 54 samples. Type: Expression profiling by high throughput sequencing; Other.
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.