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.
2,326
datasets available to search
ShareScore release 0.9.0
Dataset results
2,326 results for “clusters”
Data from: Cluster analysis successfully identifies clinically meaningful knee valgus moment patterns: frequency of early peaks reflects sex-specific ACL injury incidence
Open the record for dataset details and reuse information.
Plasmodium falciparum genomic surveillance reveals spatial and temporal trends, association of genetic and physical distance, and household clustering
Open the record for dataset details and reuse information.
Data from: Factors that can affect the spatial positioning of large and small individuals in clusters of sit-and-wait predators
Open the record for dataset details and reuse information.
Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)
Open the record for dataset details and reuse information.
Data from: Recognizing pulses of extinction from clusters of last occurrences
Open the record for dataset details and reuse information.
Cluster Data for creating smart city typology
<p>A data set used to create a typology of smart city apps</p>
SVXplorer: three-tier approach to identification of structural variants via sequential recombination of discordant cluster signatures
<p>This repository contains the bgzipped variants calls in VCF format for CHM1, NA12878 and AJ trio dataset that are used in the SVXplorer manuscript. The names of the files contain the name of the sample (CHM1/NA12878/HG002/HG003/HG004), the name of the method (SVXplorer/DELLY/LUMPY/TIDDIT/TARDIS/MANTA) used to call the variants. There are three separate files for the DELLY calls which have the deletions, duplications and the inversion calls made by DELLY for each of the samples. For NA12878, there are two sets of calls, one for each of the libraries (ERR194147/SRR505885)</p> <p> </p>
Shapefiles of environmental clusters and biogeographic sector and subsectors of Egypt
<p>ESRI shapefile polygons of environmental clusters and biogeographic sectors and subsectors of all Egypt.</p> <p>For file descriptions, please refer to: Abdelaal, M., Fois, M., Fenu, G., & Bacchetta, G. (2020). Biogeographical characterisation of Egypt based on environmental features and endemic vascular plants distribution. <em>Applied Geography</em>, <em>119</em>, 102208. <a href="https://doi.org/10.1016/j.apgeog.2020.102208">https://doi.org/10.1016/j.apgeog.2020.102208</a></p>
Dataset: Water-flume model geometries of a tall-building cluster in Beijing
<p>This dataset contains CAD drawings (.stl files) of five geometry variations of a cluster of tall buildings in Beijing. The dimensions are in model scale (S1 = 1:2400; S2 = 1:4800) and have units of mm. The models were used in water-flume experiments at the University of Southampton in collaboration with the University of Reading (flow and concentration measurements).</p> <p>This repository contains:</p> <ol> <li>wf_geometry_readme.pdf : Meta information and description of data in 'wf_geometry.zip'</li> <li>wf_geometry.zip : model drawings (.stl files)</li> </ol>
Clustering and kernel density estimation for assessment of measurable residual disease by flow cytometry
<p>Flow cytometry raw data and supplementary table S1.</p>
Cluster analysis and its application in geochemistry
<p>This is the fourth session video recording of the Goldschmidt 2020 Virtual Workshop: Earth Science meets Data Science - Services & Systems, Policies & Procedures, Tools & Techniques for Geochemistry. Moderated by Shaunna Morrison (Carnegie Earth and Planets Laboratory)</p> <p>Github repository: <a href="https://github.com/wadesnoopy/Goldschmidt-2020-cluster">https://github.com/wadesnoopy/Goldschmidt-2020-cluster</a></p>
Animation of MS evolution in young star clusters
<p>The video shows the evolution of binary and single stellar models from 1Myr to 100Myr. </p>
Data for article "Tracking Charge Transfer to Residual Metal Clusters in Conjugated Polymers for Photocatalytic Hydrogen Evolution"
<p>Data presented in the publication "Tracking Charge Transfer to Residual Metal Clusters in Conjugated Polymers for Photocatalytic Hydrogen Evolution", published in the Journal of the American Chemical Society. The published article is available at <a href="https://doi.org/10.1021/jacs.0c06104">https://doi.org/10.1021/jacs.0c06104</a></p>
The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method
Inference of genetic clusters is a key aim of population genetics, sparking development of numerous analytical methods. Within these, there is a conceptual divide between finding de novo structure versus assessment of a priori groups. Recently developed, Discriminant Analysis of Principal Components (DAPC), combines discriminant analysis (DA) with principal component (PC) analysis. When applying DAPC, the groups used in the DA (specified a priori or described de novo) need to be carefully assessed. While DAPC has rapidly become a core technique, the sensitivity of the method to misspecification of groups and how it is being empirically applied, are unknown. To address this, we conducted a simulation study examining the influence of a priori versus de novo group designations, and a literature review of how DAPC is being applied. We found that with a priori groupings, distance between genetic clusters reflected underlying FST. However, when migration rates were high and groups were described de novo there was considerable inaccuracy, both in terms of the number of genetic clusters suggested and placement of individuals into those clusters. Nearly all (90.1%) of 224 studies surveyed used DAPC to find de novo clusters, and for the majority (62.5%) the stated goal matched the results. However, most studies (52.3%) omit key run parameters, preventing repeatability and transparency. Therefore, we present recommendations for standard reporting of parameters used in DAPC analyses. The influence of groupings in genetic clustering is not unique to DAPC, and researchers should consider their goal and which methods will be most appropriate.
Supplemental Material for Paper 'Taxonomy Extraction Using Knowledge Graph Embeddings and Hierarchical Clustering'
<p>Contains input data and gold standard for the non-expressive extraction task, as well as examples of extracted taxonomies for both the non-expressive and expressive cases. Extracted taxonomies can also be found at <a href="http://labowest.ca/sdb2020/">labowest.ca</a>.</p>
Figure 3 from: Wang L-J, Gao M-D, Sheng M-Y, Yin J (2020) Cluster analysis of karyotype similarity coefficients in Epimedium (Berberidaceae): insights in the systematics and evolution. PhytoKeys 161: 11-26. https://doi.org/10.3897/phytokeys.161.51046
Figure 3 Diagram of cluster analysis of karyotype similarity coefficients in 51 Epimedium taxa and two Vancouveria species.
Figure 2 from: Wang L-J, Gao M-D, Sheng M-Y, Yin J (2020) Cluster analysis of karyotype similarity coefficients in Epimedium (Berberidaceae): insights in the systematics and evolution. PhytoKeys 161: 11-26. https://doi.org/10.3897/phytokeys.161.51046
Figure 2 Mitotic metaphase chromosomes in 27 Epimedium taxa and two Vancouveria species. 25E. sagittatum26E. sagittatum var. glabratum27E. dolichostemon28E. truncatum29E. brevicornu30E. myrianthum31E. stellulatum32E. fargesii33E. elachyphyllum34E. koreanum35E. grandiflorum var. grandiflorum36E. grandiflorum var. thunbergianum37E. grandiflorum var. higoense38E. grandiflorum var. coelestre39E. sempervirens40E. sempervirens var. hypoglaucum41E. sempervirens var. multifoliolatum42E. trifoliatobinatum43E. diphyllum44E. cremeum45E. kitamuranum46E. setosum47E. alpinum48E. pubigerum49E. pinnatum subsp. colchicum50E. pinnatum cv. "Elegans" 51E. perralderianum52V. hexandra53V. chrysantha. Scale bars: 5 μm.
Figure 1 from: Wang L-J, Gao M-D, Sheng M-Y, Yin J (2020) Cluster analysis of karyotype similarity coefficients in Epimedium (Berberidaceae): insights in the systematics and evolution. PhytoKeys 161: 11-26. https://doi.org/10.3897/phytokeys.161.51046
Figure 1 Mitotic metaphase chromosomes in 24 Epimedium species. 1E. ecalcaratum2E. shuichengense3E. platypetalum4E. davidii5E. pauciflorum6E. flavum7E. ilicifolium8E. mikinorii9E. membranaceum10E. lishihchenii11E. acuminatum12E. wushanense13E. leptorrhizum14E. baojingense15E. chlorandrum16E. luodianense17E. pudingense18E. glandulosopilosum19E. pseudowushanense20E. franchetii21E. enshiense22E. sutchuenense23E. zhushanense24E. pubescens. Scale bars: 5 μm.
Supplementary material 1 from: Wang L-J, Gao M-D, Sheng M-Y, Yin J (2020) Cluster analysis of karyotype similarity coefficients in Epimedium (Berberidaceae): insights in the systematics and evolution. PhytoKeys 161: 11-26. https://doi.org/10.3897/phytokeys.161.51046
Table S1. Karyotype resemblance-near coefficients in 51 Epimedium taxa and two Vancouveria species
Data of "JGR-Cluster analysis of submicron particle number size distribution at SORPES station in Yangtze River Delta of East China"
<p>Data of "JGR-Cluster analysis of submicron particle number size distribution at SORPES station in Yangtze River Delta of East China"</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.