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.
927
datasets available to search
ShareScore release 0.9.0
Dataset results
927 results for “genetic study”
Genetically predicted lipid traits, diabetes mellitus liability and carotid intima-media thickness in African ancestry individuals: a Mendelian randomization study
<p>Genetic variants used for the analysis of 'Genetically predicted lipid traits, diabetes mellitus liability and carotid intima-media thickness in African ancestry individuals: a Mendelian randomization study'.</p>
A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions - Full study data
<p><strong>Objective</strong>:</p> <p>Our objective is to evaluate the efficacy of ChatGPT 4 in accurately and effectively delivering genetic information, building on previous findings with ChatGPT 3.5. We focus on assessing the utility, limitations, and ethical implications of using ChatGPT in medical settings.</p> <p><strong>Materials and Methods</strong>:</p> <p>A structured questionnaire, including the Brief User Survey (BUS-15) and custom questions, was developed to assess ChatGPT 4's clinical value. An expert panel of genetic counselors and clinical geneticists independently evaluated ChatGPT 4's responses to these questions. We also involved comparative analysis with ChatGPT 3.5, utilizing descriptive statistics and using R for data analysis.</p> <p><strong>Results</strong>:</p> <p>ChatGPT 4 demonstrated improvements over 3.5 in context recognition, relevance, and informativeness. However, performance variability and concerns about the naturalness of the output were noted. No significant difference in accuracy was found between ChatGPT 3.5 and 4.0. Notably, the efficacy of ChatGPT 4 varied significantly across different genetic conditions, with specific differences identified between responses related to BRCA1 and HFE.</p> <p><strong>Discussion and Conclusion</strong>:</p> <p>This study highlights ChatGPT 4's potential in genomics, noting significant advancements over its predecessor. Despite these improvements, challenges remain, including the risk of outdated information and the necessity of ongoing refinement. The variability in performance across different genetic conditions underscores the need for expert oversight and continuous AI training. ChatGPT 4, while showing promise, emphasizes the importance of balancing technological innovation with ethical responsibility in healthcare information delivery.</p>
Supplementary material 1 from: Jażdżewska AM, Corbari L, Driskell A, Frutos I, Havermans C, Hendrycks E, Hughes L, Lörz A-N, Stransky B, Tandberg AHS, Vader W, Brix S (2018) A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 55–73. https://doi.org/10.3897/zookeys.731.19931
Table S1 : Explanation note: Amphipod and outgroup accession numbers in BOLD, GenBank and station data.
FIGURE 3. Phylogenetic tree for the four species under study. A in No more machismo in Callyntra (Coleoptera: Tenebrionidae): Callyntra femina, a new species discovered based on female genitalia and genetic evidence
FIGURE 3. Phylogenetic tree for the four species under study. A) Tree obtained using Bayesian Inference with mitochondrial COI and 16S genes combined. Number above the nodes correspond to the posterior probabilities. B) Clade G from the COI + 16S combined tree obtained by Zúñiga-Reinoso & Méndez (2018), highlighting the species under study with colorful branches. The colors are matching the species in both trees.
Poor implementation of non-invasive sampling in wildlife genetics studies - supplementary material
<p>Supplementary Table S1: Peer-reviewed studies on wildlife genetics in amphibians, birds, carnivores, molluscs and rodents, published in 2017-2018, which were included in the review.</p>
FIGURE9 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE9. Maximum Parsimony tree of Juglans regia species based on ITS sequences (Populations 1–7 are according to Table 1). Values above branches are bootstrap value.
FIGURE 8 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE 8. TCS Network of Juglans regia cultivars based on nrDNA ITS sequences (Populations 1–7 are according to Table 1). Culivars of Iran (Red colored) are differentiated from Italian cultivars (blue colored).
FIGURE7 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE7. UPGMA tree of Juglans regia cultivars based on nrDNA ITS sequences (Populations 1–7 are according to Table 1).
FIGURE6 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE6. PCoA of SRAP data after 1000 times permutation in the studied Persian walnut populations (Populations 1–7 are according to Table 1).
FIGURE5 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE5. STRUCTURE plot of the studied Persian walnut populations based on k = 2 (Populations 1–7 are according to Table 1).
FIGURE4 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE4. UPGMA plot of ISSR data in Persian walnut populations (Populations 1–7 are according to Table 1).
FIGURE3 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE3. PCA biplot of morphological characters in Persian walnut populations studied (Populations 1–7 are according to Table 1)
FIGURE2 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE2. UPGMA dendrogram of morphological characters in Persian walnut populations studied (Populations 1–7 are according to Table 1).
FIGURE1 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE1. Distribution map of the studied J. regia wild and cultivated populations (Populations code are according table1).
Data from: A study on genetic variants of Fibroblast Growth Factor Receptor 2 (FGFR2) and the risk of breast cancer from North India
Genome-Wide Association Studies (GWAS) have identified Fibroblast growth factor receptor 2 (FGFR2) as a candidate gene for breast cancer with single nucleotide polymorphisms (SNPs) located in intron 2 region as the susceptibility loci strongly associated with the risk. However, replicate studies have often failed to extrapolate the association to diverse ethnic regions. This hints towards the existing heterogeneity among different populations, arising due to differential linkage disequilibrium (LD) structures and frequencies of SNPs within the associated regions of the genome. It is therefore important to revisit the previously linked candidates in varied population groups to unravel the extent of heterogeneity. In an attempt to investigate the role of FGFR2 polymorphisms in susceptibility to the risk of breast cancer among North Indian women, we genotyped rs2981582, rs1219648, rs2981578 and rs7895676 polymorphisms in 368 breast cancer patients and 484 healthy controls by Polymerase chain reaction-Restriction fragment length polymorphism (PCR-RFLP) assay. We observed a statistically significant association with breast cancer risk for all the four genetic variants (P<0.05). In per-allele model for rs2981582, rs1219648, rs7895676 and in dominant model for rs2981578, association remained significant after bonferroni correction (P<0.0125). On performing stratified analysis, significant correlations with various clinicopathological as well as environmental and lifestyle characteristics were observed. It was evident that rs1219648 and rs2981578 interacted with exogenous hormone use and advanced clinical stage III (after Bonferroni correction, P<0.000694), respectively. Furthermore, combined analysis on these four loci revealed that compared to women with 0–1 risk loci, those with 2–4 risk loci had increased risk (OR = 1.645, 95%CI = 1.152–2.347, P = 0.006). In haplotype analysis, for rs2981578, rs2981582 and rs1219648, risk haplotype (GTG) was associated with a significantly increased risk compared to the common (ACA) haplotype (OR = 1.365, 95% CI = 1.086–1.717, P = 0.008). Our results suggest that intron 2 SNPs of FGFR2 may contribute to genetic susceptibility of breast cancer in North India population.
Data from: A comparative study on genetic effects of artificial and natural habitat fragmentation on Loropetalum chinense (Hamamelidaceae) in Southeast China
Elucidating the demographic and landscape features that determine the genetic effects of habitat fragmentation has become fundamental to research in conservation and evolutionary biology. Land-bridge islands provide ideal study areas for investigating the genetic effects of habitat fragmentation at different temporal and spatial scales. In this context, we compared patterns of nuclear microsatellite variation between insular populations of a shrub of evergreen broad-leaved forest, Loropetalum chinense, from the artificially created Thousand-Island Lake (TIL) and the Holocene-dated Zhoushan Archipelago of Southeast China. Populations from the TIL region harboured higher levels of genetic diversity than those from the Zhoushan Archipelago, but these differences were not significant. There was no correlation between genetic diversity and most island features, excepting a negative effect of mainland–island distance on allelic richness and expected heterozygosity in the Zhoushan Archipelago. In general, levels of gene flow among island populations were moderate to high, and tests of alternative models of population history strongly favoured a gene flow-drift model over a pure drift model in each region. In sum, our results showed no obvious genetic effects of habitat fragmentation due to recent (artificial) or past (natural) island formation. Rather, they highlight the importance of gene flow (most likely via seed) in maintaining genetic variation and preventing inter-population differentiation in the face of habitat 'insularization' at different temporal and spatial scales.
Data from: Genetic structure of a naturally regenerating post-fire seedling population: Pinus halepensis as a case study
To study the effects of wildfire on population genetics of a wind pollinated and wind dispersed tree, we have analyzed the genetic structure of a post-fire, naturally regenerating seedling population of Pinus halepensis Miller, on Mt. Carmel, Israel. We tested the existence of spatial genetic structure, which is expected due to the special spatial demographic structure of the post-fire seedling and sapling populations of this species. Explicitly, we asked whether or not seedlings that germinated under large, burned, dead pine trees are also their offspring. The results revealed that the post-fire seedling population is polymorphic, diverse, and reflects the pre-fire random mating system. In contrast to our prediction, we found no division of the post-fire seedling population to distinct sub-populations. Furthermore, as a result of post-fire seed dispersal to longer range than the average pre-fire inter-tree distance, seedlings found under individual burned trees were not necessarily their sole offspring. Although the population as a whole showed a Hardy-Weinberg equilibrium, significant excess of heterozygotes was found within each tallest seedlings group growing under single, large, burned pine trees. Our finding indicates the possible existence of intense natural selection for the most vigorous heterozygous genotypes that are best adapted to the special post-fire regeneration niche, which is the thick ash bed under large, dead, pine trees.
Figure 9 in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 9. The Bayesian tree for COI gene sequences assigned to Trochulus. Two sequences of Trochulus lubomirskii (= Trichia lubomirskii = Plicuteria lubomirskii) and one Petasina bielzi were also included as the out-group. Labels for Trochulus hispidus, Trochulus plebeius, and Trochulus phorochaetius were coloured in red, blue, and green, respectively. Additional acronyms beside the species name or an ampersand symbol '&' indicate 100% sequence identity for the joined names. Numbers at nodes, in the order shown, correspond to posterior probabilities estimated in MrBayes (MB), as well as bootstrap support values calculated in TreeFinder (TF), and PAUP using the weighted least squares (LS) method. Values of the posterior probabilities and bootstrap percentages lower than 0.50 and 50%, respectively, were omitted or indicated by a dash '-'.
Figure 5 in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 5. Variation of the ratio of the umbilicus minor to its major diameter (u/U) in Trochulus phorochaetius and Trochulus plebeius. Numbers over the box plots indicate mean values. Letters 'a' and 'b' indicate significant differences determined by the Kruskal–Wallis test (P <0.001). Abbreviations are as defined in Table 1.
Figure 2. A in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 2. A, Trochulus phorochaetius specimen from Sassenage; B, Trochulus plebeius specimen from Échallon; C, T. plebeius specimen from Coiserette; D, Trochulus hispidus specimen from Schramberg. Scale bar: 5 mm.
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.