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.
15,277
datasets available to search
ShareScore release 0.9.0
Dataset results
15,277 results for “GENETICS”
Figure 3 in Rare genetic admixture and unidirectional gene flow between Vipera aspis and Vipera berus at their contact zone in western France
Figure 3. Principal Coordinates Analysis (PCoA) based on 10 microsatellite markers showing the distribution of morphologically intermediate individuals in between the pure V. aspis and pure V. berus individuals in the Loire Atlantique department (France). Individual 4.14 was morphologically intermediate but genetically assigned to pure V. aspis.
Figure 2 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 2. Maximum-likelihood tree from combined data (Cytochrome b and ND4, totalling 1920 bp) for different subspecies of Vipera ursinii. Values of bootstrap support for maximum likelihood (first) maximum parsimony (middle) are shown for nodes found in more than 50% of 1000 trees, as well as posterior probability from Bayesian inference (right). The population number (see fig. 1 and supplementary table S1) where the haplotypes have been found are added to the haplotype label. Drawing of Vipera ursinii rakosiensis courtesy of Márton Zsoldos.
Figure 3 in Genetic homogeneity in a Pontocaspian crested newt species (Triturus karelinii) suggests recent isolation of its three allopatric range sections
Figure 3. DISTRUCT plots showing allocation of Triturus karelinii individuals to gene pools (k) based on the different Bayesian clustering programs (noted on the left with the preferred k value). See the main text for details. Population numbers (noted at the top) correspond to fig. 1 and table 1.
Figure 5 in Genetic homogeneity in a Pontocaspian crested newt species (Triturus karelinii) suggests recent isolation of its three allopatric range sections
Figure 5. Species distribution model for Triturus karelinii projected on climate layers for the present, the mid-Holocene (mid-Holo, ∼6 Ka), the Last Glacial Maximum (LGM, ∼22 Ka) and the Last Interglacial (∼130 Ka). For the mid-Holocene and the Last Glacial Maximum projections on data layers based on the CCSM4 (CCSM) and MIROC-ESM (MIROC) model are shown. Warmer colours reflect a higher predicted suitability.
Figure 2 in Genetic homogeneity in a Pontocaspian crested newt species (Triturus karelinii) suggests recent isolation of its three allopatric range sections
Figure 2. Phylogeny (left) and haplotype network for Triturus karelinii mtDNA. Haplotype codes (the prefix 'TkarA' has been excluded in the haplotype network) correspond to supplementary table S1. Colours reflect the three regions where haplotypes originate from. Values at nodes are posterior probability values (<0.5 not shown). The outgroup is not shown.
Figure 1 in Genetic homogeneity in a Pontocaspian crested newt species (Triturus karelinii) suggests recent isolation of its three allopatric range sections
Figure 1. Sampling scheme for Triturus karelinii. Numbered localities were sequenced for nuclear DNA and correspond to table 1. The black squares reflect additional localities for which mtDNA has been sequenced and the white stars reflect localities that were on top of that used to create the species distribution model. Grey background shading reflects elevation with darker colours reflecting higher elevation.
Figure 1 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 1. Location of the samples used in the study: squares represent mtDNA data, round symbols represent microsatellites data. The size of the round symbols is proportional to the number of samples used. Locality numbers correspond with supplementary table S1 (in black when microsatellite data are available; in white when mtDNA data). The colours of the marks are different between subspecies: green: V. ursinii rakosiensis, yellow: V. u. moldavica, blue: V. u. macrops, grey: V. u. macrops from Bistra Mt., red: V. renardi. White striped grids show distribution of each subspecies/species on a 100x100 UTM grid resolution (after Sillero et al., 2014). Distribution area of V. greaca (from IUCN red list, Mizsei et al., 2018) is colored in pink. On the top left, insert A shows a zoom in the V. ursinii macrops region, while insert B illustrates the location of study area within Europe.
Figure 4. Comparative phylogenetic relationship between the 11 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 4. Comparative phylogenetic relationship between the 11 regions with both mtDNA (left) and nDNA (right). left: Mitochondrial DNA tree based on the genetic distances of the different haplotypes (combining cytochrome b and ND4; 1920 bp) within each region. right: Nuclear tree based on Cavalli-Sforza and Edwards Dc distances (Cavalli-Sforza and Edwards, 1967) calculated with the software POPULATIONS 1.2.28 (Langella, 1999) based on 5 microsatellites markers. Dashed branches correspond to discrepancies between both phylogenetic reconstructions. Both trees were not rooted. The colours are different between subspecies: green: V. ursinii rakosiensis, yellow: V. u. moldavica, blue: V. u. macrops, grey: V. u. macrops from Bistra Mt., red: V. renardi.
Figure 1 in Rare genetic admixture and unidirectional gene flow between Vipera aspis and Vipera berus at their contact zone in western France
Figure 1. Structure analysis (K = 2) of V. aspis and V. berus samples collected in the Loire Atlantique department (France). Morphologically intermediate individuals include individual 4.14 which was genetically assigned to V. aspis. The hybridization level of individuals 14 and 71 could not be clearly assigned.
Figure 4 in Genetic homogeneity in a Pontocaspian crested newt species (Triturus karelinii) suggests recent isolation of its three allopatric range sections
Figure 4. Results of a principal component analysis of nuclear genetic data for Triturus karelinii. Population numbers correspond to fig. 1 and table 1.
Challenges of ELA-based Function Evolution using Genetic Programming - Reproducability files
<p>This repository contains the data and code for the paper "Challenges of ELA-based Function Evolution using Genetic<br> Programming"</p> <p> </p> <p>This repository consists of separated folders, which contain the following data:</p> <p>## Code:</p> <p>This is the main code used to run the GP functions. The main executable is 'main_gp.py', which executes a single run of the GP system (based on the passed-in argument, which is an index from 0-71 in our experiments).</p> <p>The data for the BBOB functions are generated using the 'preliminary' folder and the 'get_ela_preliminary.py' file.</p> <p> </p> <p>## Data_GP:</p> <p>This contains the full logs from each GP run, separated by target function and dimension.</p> <p> </p> <p>## data_random_func:</p> <p>This contains the same kind of data but for the Random Function Generator.</p> <p> </p> <p>## Reproducibility:</p> <p>This contains all code used to analyse and visualize the resulting data. The notebook is structured in the same way as the paper, separated by figure.</p>
The genotype data accompanying the paper titled "Reanalyzing the genetic history of Kra-Dai speakers from Thailand and new insights into their genetic interactions beyond Mainland Southeast Asia"
<p>The newly generated genome-wide genotype data for 10 Lao individuals used in the paper "Reanalyzing the genetic history of Kra-Dai speakers from Thailand and new insights into their genetic interactions beyond Mainland Southeast Asia" ( https://www.nature.com/articles/s41598-023-35507-8 )</p>
Data from: Publication and use of genetics tools in conservation management applications: a systematic review
<p><span>Genetic tools are used in applied conservation management for taxonomic identification, delineation of management units, management of wild populations, captive breeding and reintroduction, and control of invasive species, disease, and hybridisation. </span></p> <p><span>To assess the extent to which genetics tools are being used for applied conservation management, we conducted a systematic literature review of over 53,767 papers focussing on wildlife research that reported results on species delineation, translocations, and population augmentation. We synthesised information on papers that used genetics tools in an expressly applied manner across all wildlife species. </span></p> <p><span>We found that the application of genetics tools in conservation management was biased towards fishes, mammals, and birds and northern hemisphere locations, especially the USA and Europe. </span></p> <p><span>Despite genetics tools being a highly published topic, it was difficult to find published applications of these tools in both the primary and the grey literature. Of the 115 papers on 152 species that could be considered an applied use of a genetics tool expressly for conservation management, only 49 had definable applied outcomes. The remaining 66 made recommendations, but it was often unclear if the recommendations were ever used to make conservation management decisions because of the time-lag between publication of the initial recommendation and publication of the results of the use of the tool in a conservation management situation, as well as the lack of dissemination in the primary literature. </span></p> <p><span>Our study highlights the relatively low publication rate of applications of genetics tools compared to the general conservation genetics field. These tools appear to have either a low percentage of translations into publication ('conservation genetics publishing gap') or a poor uptake among wildlife conservation managers ('conservation genetics gap')—the two are indistinguishable in this review. </span></p> <p><span><em>Policy implications</em>. Conservation genetics tools must be brought to the forefront of conservation policy and management. Users should support the use of systems and accessible databases to increase the uptake of genetic tools for conservation in applied management decisions for wildlife, reducing barriers to disseminating the results to other end users and interested parties.</span></p>
p1_unict_zenodo_data_genetic_purity_intraspecific
<p>Bresov brassica data</p>
p1_unict_zenodo_data_genetic_purity_interspecific
<p>p1_unict_zenodo_data_genetic_purity_interspecific</p>
p1_unict_zenodo_data_genetic_purity_mads_box
<p>MADS box genes</p>
Genetics and Genomics of Cardiac Channelopathies in India
<p>Appendix dataset for thesis.</p> <p>Appendix 1. List of 30 predicted loss-of-function variations with their ACMG/AMP classification</p> <p>Appendix 2. List of 440 nonsynonymous variations with their ACMG/AMP classification</p> <p>Appendix 3. List of 114 unique variations in IndiGenomes with their ACMG/AMP classification</p> <p>Appendix 4: List of variations (as per ClinVar) covered in Cardiac Channelopathy amplicon based multiplex assay </p> <p>Appendix 5: Primer sequences for validation of respective variations</p>
Data for Selfing species has greater genetic diversity and less structure than related outcrossing species due to seed dispersal and population history in Roscoea (Zingiberaceae)
<p>Data matrix of two species with nexus format.</p>
Genetic factors predict hybrid formation in the British flora
<p>Natural hybridization can have a profound evolutionary impact, with consequences ranging from the extinction of rare taxa to the origin of new species. Natural hybridization is particularly common in plants; however, our understanding of the general factors that promote or prevent hybridization is hampered by the highly variable outcomes in different lineages. Here, we quantify the influence of different predictors on hybrid formation across species from an entire flora. We combine estimates of hybridization with ecological attributes and a new species-level phylogeny for over 1,100 UK flowering plant species. Our results show that genetic factors, particularly parental genetic distance, as well as phylogenetic position and ploidy, are key determinants of hybrid formation, whereas many other factors such as range overlap and genus size explain much less variation in hybrid formation. Overall, intrinsic genetic factors shape the evolutionary and ecological consequences of natural hybridization across species in a flora.</p>
Origin and genetic variability of populations of the invasive plant Rumex alpinus L. in the Giant (Krkonoše) Mountains
<p><span>Monk's rhubarb, <em>Rumex</em> <em>alpinus</em> L. (<em>R. alpinus</em>), is a perennial plant native to the mountains of Central and Southern Europe. Currently, the distribution of <em>R. alpinus</em> has been partly affected by its utilization as a vegetable and a medicinal herb. In the mountains of the Czech Republic, it is considered an invasive plant, probably introduced into the Krkonoše Mountains by colonists from the Alps. </span></p> <p><span>This study's main aim was to verify whether <em>R. alpinus</em> was introduced into the Krkonoše Mountains by alpine colonists or whether it was anthropogenically introduced from the Carpathians. Furthermore, the genetic structure of native and introduced populations of <em>R. alpinus</em> was determined.</span></p> <p><span>For the evaluation of genetic structure, 417 samples of <em>R. alpinus</em> were collected from the Alps, Carpathians, Balkan, Pyrenees, and Czech Mountains. In total, 12 simple sequence repeat (SSR) markers were applied.</span></p> <p><span>The results of AMOVA showed a high 60% variation within populations, 27% variation among groups, and 13% among the population within groups. The overall unbiased gene diversity was high (ĥ = 0.55). The higher level of genetic differentiation among populations (<em>F</em><sub>ST</sub> = 0.35; <em>p</em> < 0.01) indicated restricted gene flow between populations. Compared to native populations, limited genetic variability was observed in the nonnative populations. It was concluded that local adaptation, low gene exchange, and genetic drift affected the genetic diversity of nonnative <em>R. alpinus</em>.</span></p> <p><span>The results support a genetic link between Alpine and Czech genotypes of <em>R. alpinus</em>, while the Carpathians genotypes corresponded to the Balkan genotype.</span></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.