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.
81
datasets available to search
ShareScore release 0.7.1
Dataset results
81 results for “Genealogy”
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1, dated using <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with nodes contain the mean and variance of tsdate's posterior distribution on node time. To access these values, we can use:</p> <pre><code>import json node = ts.node(10000) metadata_dict = json.loads(node.metadata) print("The mean of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["mn"])) print("The variance of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["vr"]))</code></pre> <p>Age estimates for each variant site can be derived from the mean of the age estimates of the upper and lower bounding nodes of the oldest mutation associated with a site. tsdate includes <a href="https://tsdate.readthedocs.io/en/latest/python-api.html?highlight=sites_time_from_ts#tsdate.sites_time_from_ts">a function to find the age estimates of all sites in the tree sequence</a>:</p> <pre><code>import tsdate site_times = tsdate.sites_time_from_ts(ts, node_selection='arithmetic')</code></pre> <p>This returns a numpy array which has a length equal to the number of sites.</p> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
Aulikara genealogy
<p>Figure 5 in</p> <p><em>To engrave his virtues on the disc of the moon… Inscriptions of the Aulikaras and Their Associates</em></p> <p>Dániel Balogh, 2019</p> <p>Genealogies of Aulikara and associated ruling houses</p> <p>Approximate dates shown in CE on left and ME on right, datable inscriptions labelled on far right. Rulers whose reign is mentioned in an inscription are shown in shaded fields, connected by dotted lines to the inscriptions mentioning them. The reigns of rulers in white fields are estimated, assuming 20 years per generation. Solid vertical lines indicate father-to-son descent, dotted lines show uncertain descent</p>
Fig. 3. Genealogical network for the combined 16S in Introduction Of An Indochinese Freshwater Crab Sayamia Germaini (Crustacea: Brachyura: Gecarcinucidae) To Taiwan: Morphological And Molecular Evidence
Fig. 3. Genealogical network for the combined 16S rRNA and COI haplotypes observed within the clades of Sayamia germaini (collected from Taiwan, Vietnam and Cambodia) and S. sexpunctata (Malaysia). The ancestral haplotype, or root of the network, is indicated by a square. Unlabelled nodes indicate inferred haplotypes not found in the sampled populations
"Do you want to know who you are?" The rise of genetic ancestry testing and the search for genealogies: an anonymized survey from Sweden
<p>Full, anonymized survey data on genetic genealogy, ancestry and identity conducted by the Swedish Genealogical Society as part of a research project funded by the HERA joint research program "Uses of the Past"</p>
Fig. 2. Genealogical relationships among 11 cytochrome oxidase subunit 1 in Population genetics of Oligonychus perseae (Acari: Tetranychidae) collected from avocados in Mexico and California
Fig. 2. Genealogical relationships among 11 cytochrome oxidase subunit 1 (COI) haplotypes detected in Oligonychus perseae populations in California, Mexico, and Costa Rica. Additional congeneric and outgroup sequences were retrieved from GenBank. Maximum likelihood tree constructed from a 305 base pair section of COI using PhyML. Support (aLRT) for major branches is shown.
Fig. 3. Genealogical network for the 557 in Fig. 5 in Fig. 1 in Fig. 20. A-E in Establishment of A New Bornean Genus of Gecarcinucidae (Crustacea: Brachyura), with Descriptions of Five New Species.
Fig. 3. Genealogical network for the 557-bp 16S haplotypes observed within Tubuca arcuata, constructed with TCS. The ancestral haplotype is indicated by square. Green and red circles mean the haplotypes of the N and W clades, respectively; and the square and circle in purple represent the haplotypes that are shared by N and W clades. Unlabelled hatches and node indicate inferred haplotypes not found in the sampled population.
Fig. 4. Genealogical network for the 658 in Fig. 5 in Fig. 1 in Fig. 20. A-E in Establishment of A New Bornean Genus of Gecarcinucidae (Crustacea: Brachyura), with Descriptions of Five New Species.
Fig. 4. Genealogical network for the 658-bp COI haplotypes observed within Tubuca arcuata, constructed with TCS. The ancestral haplotypes of the N and W clades are indicated by squares. Unlabelled hatches and nodes indicate inferred haplotypes not found in the sampled population.
Hyperspectral Image of a 6 Metre Genealogical Roll as a Video
<p>This video scrolls down the length of the 6 metre genealogical roll, whilst also passing through the wavelength range of the hyperspectral image. The image is made from multiple hyperspectral scans of a medieval genealogical roll tracing the lineage of England’s Plantagenent rulers back to Adam and Eve (<a href="https://ucldigitalpress.co.uk/Book/Article/2/9/18/">https://ucldigitalpress.co.uk/Book/Article/2/9/18/</a>).</p> <p>More info on the imaging pipeline is available here <a href="https://zenodo.org/record/1312942#.W7yAVGhKhPa">https://zenodo.org/record/1312942#.W7yAVGhKhPa</a>.</p> <p>Images courtesy of Special Collections, UCL Library Services.</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
COJO ARG variants from "Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits"
<p>These are COJO ARG variants accompanying the manuscript "Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits". For more details, view the README.md file and refer to our manuscript.</p>
Simulated genomes from manuscript "On the Genes, Genealogies and Geographies of Quebec"
<p>Tree sequences of simulated whole genomes of 1.4M present day individuals with at least four grandparents linked to the BALSAC French-Canadian pedigree.<br> <br> Although the tree sequences have been censored to remove personal identifying information, we have included temporal (decade) and spatial (latitude and longitude) information for the 1.4M samples and their ~2M genealogically recoded genetic ancestors.</p>
Estimating waiting distances between genealogy changes under a multi-species extension of the sequentially Markov coalescent
Open the record for dataset details and reuse information.
Analysis code and data for "End-to-end study of the host galaxy and genealogy of GW170817 with BPASS"
<p>This folder contains the code and data required to reproduce all figures and values presented in the study titled "End-to-end study of the host galaxy and genealogy of GW170817 with BPASS". </p> <p>The running of the data analysis jupyter notebooks will require the installation of the python package "hoki" v1.7 and the download of the BPASS models that are already publically available. The README.md file contains all the information regarding the dependencies of this directory. </p> <p>Should you need assistance please email hfstevance@gmail.com</p>
Genealogical Forest Files for Unified (Wohns 2022)
<p>Genealogical forest files for the 1000 Genomes phase 3 autosomes. Converted using <a href="https://github.com/lukashuebner/gfkit">gfkit</a> from the <code>.trees</code> files available <a href="../records/5495535">here</a>.</p>
Genealogical Forest Files for Simons Genome Diversity Project
<p>Genealogical forest files for the 1000 Genomes phase 3 autosomes. Converted using <a href="https://github.com/lukashuebner/gfkit">gfkit</a> from the <code>.trees</code> files available <a href="../records/3052359">here</a>.</p>
Genealogical Forest Files for Thousand Genome Project
<p>Genealogical forest files for the 1000 Genomes phase 3 autosomes. Converted using <a href="https://github.com/lukashuebner/gfkit">gfkit</a> from the <code>.trees</code> files available <a href="../records/3051855">here</a>.</p>
Results of quantitative genetic sensitivity analysis performed on reconstructed pedigrees based on large-scale genealogies
Open the record for dataset details and reuse information.
Data from: Phylogenomics provides new insight into evolutionary relationships and genealogical discordance in the reef-building coral genus Acropora
Understanding the genetic basis of reproductive isolation is a long-standing goal of speciation research. In recently diverged populations, genealogical discordance may reveal genes and genomic regions that contribute to the speciation process. Previous work has shown that conspecific colonies of Acropora that spawn in different seasons (spring and autumn) are associated with highly diverged lineages of the phylogenetic marker PaxC. Here, we used 10 034 single-nucleotide polymorphisms to generate a genome-wide phylogeny and compared it with gene genealogies from the PaxC intron and the mtDNA Control Region in 20 species of Acropora, including three species with spring- and autumn-spawning cohorts. The PaxC phylogeny separated conspecific autumn and spring spawners into different genetic clusters in all three species; however, this pattern was not supported in two of the three species at the genome level, suggesting a selective connection between PaxC and reproductive timing in Acropora corals. This genome-wide phylogeny provides an improved foundation for resolving phylogenetic relationships in Acropora and, combined with PaxC, provides a fascinating platform for future research into regions of the genome that influence reproductive isolation and speciation in corals.
Data from: Genealogy and palaeodrainage basins in Yunnan Province: phylogeography of the Yunnan spiny frog, Nanorana yunnanensis (Dicroglossidae)
Historical drainage patterns adjacent to the Qinghai-Tibetan Plateau differed markedly from those of today. We examined the relationship between drainage history and geographic patterns of genetic variation in the Yunnan spiny frog, Nanorana yunnanensis, using approximately 981 base pairs of mitochondrial DNA (mtDNA) partial sequences from encoding genes ND1 and ND2, and intervening areas including complete tRNA Ile, tRNA Gln, and tRNA Met. Two null hypotheses were tested, including (1) that genetic patterns do not correspond to the development of drainage systems and (2) that populations had been stable and not experienced population expansion, bottlenecking and selection. Genealogical analyses identified three, major, well-supported maternal lineages, each of which had two sublineages. These divergent lineages were completely concordant with six geographical regions. Genetic structure and divergence were strongly congruent with historical rather than contemporary drainage patterns. Most lineages and sublineages were formed through population fragmentation events during the rearrangement of paleodrainage basins in the early Pliocene and early Pleistocene. Sympatric lineages occurred only in localities at the boundaries of major drainages, likely reflecting secondary contact of previously allopatric populations. Extensive population expansion probably occurred early in the Middle Pleistocene accompanying dramatic climatic oscillations.
Data from: Differential introgression causes genealogical discordance in host races of Acrocercops transecta (Insecta: Lepidoptera)
Recently diverged populations often exhibit incomplete reproductive isolation, with a low level of gene flow continuing between populations. Previous studies have shown that, even under a low level of gene flow, genetic divergence between populations can proceed at the loci governing local adaptation and reproductive isolation but not at other neutral loci. A leaf-mining moth, Acrocercops transecta, consists of Juglans- and Lyonia-associated host races. The two host races differ in host preferences of ovipositing females and in larval adaptation to host plants but mate readily in the laboratory, producing fertile hybrids. The Juglans and Lyonia races are often sympatric in the wild, implying that gene introgression could occur in nature between the two host races. We tested this hypothesis by combining phylogenetic analyses with coalescent simulations, focusing on mitochondrial genes (COI and ND5) and the nuclear Tpi, Per and Ldh genes located on the Z-chromosome. The mitochondrial genes clearly distinguished the Lyonia race from the Juglnas race, whereas the Tpi, Per and Ldh genealogies did not reflect the two host races. Coalescent simulations indicated gene flow at the three Z-linked genes in both directions, whereas there was no introgression in the mitochondrial genes. The lack of introgression in mitochondrial genes suggests that female host preference is the primary force leading to the bifurcation of maternally inherited loci. Thus, the results show that a low level of gene flow coupled with the inflexible female host preference differentiates histories of divergence between maternally and biparentally inherited genes in this host race system.
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.