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20 results for “distance matrix”
Distance matrix to newly build motorways in Slovakia
<p>This dataset includes distances from villages and cities within (mostly) central Slovakia to various motorway exits. Focus is on the R1 motorway. It includes car distances both in kilometers and in seconds. The dataset is intended for analysis on socio-economic development of settlements as a result of motorway expansion.</p> <p>Four distances to four different stages of building the motorways in Slovakia are present:</p> <ol> <li>useky1: link to Trnava built in 2000</li> <li>useky2: segments built between 1990 and 2000</li> <li>useky3: newest built segments of motorway (built after 2003, major parts in 2011)</li> <li>useky4: D1 motorway</li> </ol> <p>interactive map at https://epsilon.sk/mapa-dostupnosti/dialnice.html</p> <p> </p> <p>The dataset was created using OpenStreetMap data with OSRM routing engine.</p>
Table S1. Uncorrected p distance matrix
<p>Uncorrected p distance values for combined COI and 16S sequences from phylogenetic analysis of <em>Helminthoglypta</em> land snails (Mollusca: Gastropoda: Helminthoglyptidae) from northern California and southern Oregon, USA.</p> <p>Note: Specimen numbers marked with * were missing COI sequences.</p> <p>Table in both Excel (*.xlsx) and comma-separated value (*.csv) formats.</p> <p> </p>
Road distances and trip duration matrix for Brazilian municipalities
<p>This dataset presents a matrix with road distance and travel duration estimates for all the trips combination among the Brazilian municipalities. More details about the methodology are available <a href="https://rfsaldanha.github.io/data-projects/brazil_road_distances.html" target="_blank" rel="noopener">here</a>.</p> <p>This version was generated considering the road network at the OSRM project on May 2024.</p> <p><strong>Variable dictionary</strong></p> <ul> <li>orig: Code of the municipality of origin (IBGE 7-digits) </li> <li>dest: Code of the municipality of destiny (IBGE 7-digits) </li> <li>dist: Road distance of the shortest route, in meters</li> <li>dur: Travel time estimation, in minutes.</li> </ul> <p><strong>Files</strong></p> <ul> <li>dist_brasil.rds : R serialized object</li> <li>dist_brasil.parquet : Parquet format</li> <li>dist_brasil.zip : Compressed CSV file. Semi-colon ( ; ) field delimiter and point ( . ) as decimal separator</li> </ul>
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Figure. The phylogenetic tree showing the relationship among Brevibacillus parabrevis strains SA2.2 and TJ2.3, Bacillus licheniformis MG4.2, and their phylogenetically closest type strains. The GenBank accession numbers of the type strains and studied strains are shown following species names. Distance matrix was calculated by Kimura's 2-parameter model. The scale bar indicates 0.02 substitutions per nucleotide position. Alicyclobacillus pohliae AJ564766 served as an out-group. in Distribution of extracellular enzyme-producing bacteria in the digestive tracts of 4 brackish water fish species
Figure. The phylogenetic tree showing the relationship among Brevibacillus parabrevis strains SA2.2 and TJ2.3, Bacillus licheniformis MG4.2, and their phylogenetically closest type strains. The GenBank accession numbers of the type strains and studied strains are shown following species names. Distance matrix was calculated by Kimura's 2-parameter model. The scale bar indicates 0.02 substitutions per nucleotide position. Alicyclobacillus pohliae AJ564766 served as an out-group.
Total Ortholog Median Matrix (TOMM): an alternative unsupervised approach for phylogenomics based on evolutionary distance between protein coding genes
<p>The increasing number of available genomic data allowed the development of phylogenomic analytical tools. Current methods compile information from single gene phylogenies, whether based on topologies or multiple sequence alignments. Generally, phylogenomic analyses elect gene families or genomic regions to construct phylogenomic trees. Here, we presented an alternative approach for Phylogenomics, named TOMM (Total Ortholog Median Matrix), to construct a representative phylogram composed by amino acid distance measures of all pairwise ortholog protein sequence pairs from desired species inside a group of organisms. The procedure is divided two main steps, (1) ortholog detection and (2) creation of a matrix with the median amino acid distance measures of all pairwise orthologous sequences. We tested this approach within three different group of organisms: Kinetoplastida protozoa, hematophagous Diptera vectors and Primates. Our approach was robust and efficacious to reconstruct the phylogenetic relationships for the three groups. Moreover, novel branch topologies could be achieved, providing insights about some phylogenetic relationships between some taxa.</p>
Distance matrix for UNITE sequences
<p>Flattened and compressed version of 2 pairwise distance matrices corresponding to <a href="https://doi.org/10.15156/BIO/1264708">this</a> UNITE release.</p> <p>Can be imported in a pipeline for building an overarching fungal phylogeny for UNITE (<a href="https://github.com/luukromeijn/MDDB-phylogeny">Github source code</a>). </p> <p>Importing might take up to an hour, but this is still much quicker than the 36-hours pairwise distance calculation. </p> <p>The 6-mer Google distance is way more accurate than the w-metric, so we highly recommend using the 6-mer Google distance.</p> <p>More documentation can be found <a href="https://github.com/luukromeijn/MDDB-phylogeny#readme">here</a>.</p>
Road distances and trip duration matrix for USA counties
<p>This dataset presents a matrix with road distance and travel duration estimates for all trip combinations among USA counties. More details about the methodology are available <a href="https://rfsaldanha.github.io/data-projects/usa_road_distances.html" target="_blank" rel="noopener">here</a>.</p> <p>This version was generated considering the road network at the OSRM project in October 2024.</p> <p><strong>Variable dictionary</strong></p> <ul> <li>orig: County FIPS code of origin</li> <li>dest: County FIPS code of destiny</li> <li>dist: Road distance of the shortest route, in meters</li> <li>dur: Travel time estimation, in minutes.</li> </ul> <p><strong>Files</strong></p> <ul> <li>dist_usa.rds : R serialized object</li> <li>dist_usa.parquet : Parquet format</li> <li>dist_usa.csv : CSV file. Colon (, ) field delimiter and point ( . ) as decimal separator</li> <li>uscounties.csv : CSV file with counties names, codes, and other information, from the <a href="https://simplemaps.com/data/us-counties" target="_blank" rel="noopener">Simple Maps database</a>.</li> </ul>
Matrix of minimum distance between toponyms (rivers and settlements/towns) in Poland
<p>The matrix of distances between 205 rivers and 30,700 towns and villages in Poland published in CSV format.</p> <p> </p> <p>More details in the paper: R.Szczepanek, 2023, A Deep Learning Model of Spatial Distance and Named Entity Recognition (SD-NER) for Flood Marks Text Classification, Water.</p>
Total Ortholog Median Matrix (TOMM): an alternative unsupervised approach for phylogenomics based on evolutionary distance between protein coding genes
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Occupation distance matrix
<p>The data provides a distance measure between different ISCO 08 occupations. It was used in a academic paper titled <em>Specific and general skills: Concepts, dimensions, and measurements, </em>which was published in Competition & Change (Vol. 24, issue 1). The research was financed by the Research Council of Lithuania (grant No. S-MOD-17-20). For more information see: <a href="http://www.visionary.lt/industrial-change-and-skills">http://www.visionary.lt/industrial-change-and-skills</a></p> <p> </p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
WGSUniFrac experiment1 tree pairwise distance matrix
<p>Pairwise distance matrix generated from filtered nodes of gg_13_5 99_otus tree from Greengenes.</p>
Ranked distance matrix for WGSUniFrac experiments - Grinder with matched primer
<p>Distance matrix for generating 16s and WGS grinder inputs.</p>
intraspecific the to correspond. outgroups bold: grey in in Values; retzii. ) pb Microphis 605 ( : alignment red in; . I nov . subunit sp oxydase arrakisae c Microphis cytochrome: blue the in; on torrentius based) Microphis p-distances: green uncorrected in; (nicoleae matrix distance Microphis : Pairwise yellow In. . 2 divergence TABLE in A new freshwater pipefish species (Syngnathidae: Microphis) from the Sunda shelf islands, Indonesia
intraspecific the to correspond. outgroups bold: grey in in Values; retzii. ) pb Microphis 605 ( : alignment red in; . I nov . subunit sp oxydase arrakisae c Microphis cytochrome: blue the in; on torrentius based) Microphis p-distances: green uncorrected in; (nicoleae matrix distance Microphis : Pairwise yellow In. . 2 divergence TABLE
ANI distance matrix of 25412 KOs generated using sourmash
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TABLE 3. Distance matrix from nucleotide sequences for COI, 16 S in A new species of stauromedusa, Calvadosia festivala (Cnidaria: Staurozoa: Kishinouyeidae) from India
<p><b>TABLE 3.</b> Distance matrix from nucleotide sequences for COI, 16S, and 18S alignments from <i>Calvadosia</i> species closely related to <i>Calvadosia festivala</i> n. sp. “−”: represents absence of data for comparison (COI not available for <i>C. tasmaniensis</i> and <i>C. corbini</i>).</p><table><tbody><tr><th>Species</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></tbody><tbody><tr><th>1. <i>C. tasmaniensis</i></th><td>COI: 0.0000 16S: 0.0000 18S: 0.0000</td><td></td><td></td><td></td><td></td></tr><tr><th>2. <i>C. lewisi</i></th><td>COI: − 16S: 0.0945 18S: 0.0017</td><td>COI: 0.0000 16S: 0.0000 18S: 0.0000</td><td></td><td></td><td></td></tr><tr><th>3. <i>C. corbini</i></th><td>COI: − 16S: 0.0985 18S: 0.0017</td><td>COI: − 16S: 0.1266 18S: 0.0038</td><td>COI: 0.0000 16S: 0.0000 18S: 0.0000</td><td></td><td></td></tr><tr><th>4. <i>C. festivala</i> n. sp.</th><td>COI: − 16S: 0.1400 18S: 0.0000</td><td>COI: 0.2215 16S: 0.1629 18S: 0.0014</td><td>COI: − 16S: 0.1430 18S: 0.0014</td><td>COI: 0.0000 16S: 0.0000 18S: 0.0000</td><td></td></tr><tr><th>5. <i>Calvadosia</i> sp. Moorea</th><td>COI: − 16S: 0.1917 18S: 0.0085</td><td>COI: 0.2506 16S: 0.2197 18S: 0.0070</td><td>COI: − 16S: 0.21009 18S: 0.0045</td><td>COI: 0.2158 16S: 0.2193 18S: 0.0083</td><td>COI: 0.0000 16S: 0.0000 18S: 0.0000</td></tr></tbody></table>
Distance matrix showing the distance between twenty-three eight most similar sequences from NCBI GenBank.
<p>Distance matrix showing the distance between twenty-eight most similar sequences from NCBI GenBank.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.