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2,848 results for “sequence data”
Fig. 4. A in New data on Thelohanellus nikolskii Achmerov, 1955 (Myxosporea, Myxobolidae) a parasite of the common carp (Cyprinus carpio, L.): The actinospore stage, intrapiscine tissue preference and molecular sequence
Fig. 4. A: Cross section of infected scales, stained with hematoxilin-eosin. The plasmodia (p) are filled with myxospores (s) and are surrounded by cartilaginous tissue (c) of the scales, covered by the epithelium layer (e). B: T. nikolskii myxospores from a plasmodium in the scale.
Fig. 4 in The neglected diversity: Description and molecular characterisation of Trypanosoma haploblephari Yeld and Smit, 2006 from endemic catsharks (Scyliorhinidae) in South Africa, the first trypanosome sequence data from sharks globally
Fig. 4. Bayesian Inference (BI)/Maximum Likelihood (ML) analysis showing the phylogenetic position of Trypanosoma haploblephari (Yeld and Smit, 2006) genotypes representing morphotypes A and B inferred from partial 18S rRNA gene sequences. Comparative sequences representing known Trypanosoma species, with Trypanosoma avium (KT728402) as outgroup, were obtained from GenBank. Tree topologies for both the BI and ML trees were identical; the nodal support values (BI/ML) are represented on the BI tree. Some branches have been shortened with each //= 0.04 substitutions per site.
Fig. 3 in The neglected diversity: Description and molecular characterisation of Trypanosoma haploblephari Yeld and Smit, 2006 from endemic catsharks (Scyliorhinidae) in South Africa, the first trypanosome sequence data from sharks globally
Fig. 3. Micrographs of Trypanosoma haploblephari (Yeld and Smit, 2006) morphotype A (A–C) and T. haploblephari morphotype B (D–F) in Giemsa-stained blood films of Haploblepharus pictus and Poroderma pantherinum, respectively. Blood stage with kinetoplast (k) and undulating membrane (μm) visible (A–C); slender forms (B, E); presence of a flagellum (f) in deeply stained individuals (C, F). Scale bar: 10 μm.
Fig. 2 in The neglected diversity: Description and molecular characterisation of Trypanosoma haploblephari Yeld and Smit, 2006 from endemic catsharks (Scyliorhinidae) in South Africa, the first trypanosome sequence data from sharks globally
Fig. 2. Line drawing of Trypanosoma haploblephari (Yeld and Smit, 2006) from the host Poroderma pantherinum (Slide HE18-18) next to a drawing of a red blood cell.
Supplemental data for: Evaluation of SARS-CoV-2 response at the University of North Carolina (UNC) at Charlotte using percent positivity data and viral genomic sequence data.
<p>Supplemental data for:</p> <p>Evaluation of SARS-CoV-2 response at the<br>University of North Carolina (UNC) at Charlotte<br>using percent positivity data and viral genomic<br>sequence data.</p> <p>Submitted to Biocarla 2024</p> <p>https://carla2024.org/portfolios/biocarla/</p> <p>Authors:</p> <p>Daniel Janies 1,2,3 [0000−0002−7890−9906], Shirish Yasa 1,2,3 [0000−0003−3217−4921],<br>Colby T. Ford 1,3,4 [0000−0002−7859−3622] Jannatul Ferdous 2,3 [0000−0003−3053−9616],<br>William Taylor 2,3 [0009−0000−6204−1172], April Harris 2,3 [0009−0009−2557−7926],<br>Sam Kunkleman 2,3 [0000−0002−2309−6418], Juan Bolanos 2,3, Kevin Lambirth 2,3 [0000-0002-6568-543X], Denis<br>Jacob Machado 1,2,3 [0000−0001−9858−4515], Cynthia Gibas 1,2,3 [0000−0002−1288−9543],<br>and Jessica Schlueter 1,2,3 [0000−0002−6490−0580]</p> <p>Affiliations:</p> <p>1) Center for Computational Intelligence to Predict Health and Environmental Risks<br>(CIPHER), University of North Carolina at Charlotte 28223, USA<br>Correspondence to: djanies@charlotte.edu<br>https://cipher.charlotte.edu<br>2) Department of Bioinformatics and Genomics, University of North Carolina at<br>Charlotte 28223, USA https://cci.charlotte.edu/departments/<br>department-of-bioinformatics-and-genomics/<br>3) College of Computing and Informatics, University of North Carolina at Charlotte<br>28223, USA https://cci.charlotte.edu<br>4) School of Data Science, University of North Carolina at Charlotte 28223, USA<br>https://sds.charlotte.edu<br>5) Division of Research, University of North Carolina at Charlotte 28223, USA<br>https://research.charlotte.edu/</p> <p> </p>
Sewage - deep sequencing data
Open the record for dataset details and reuse information.
Supplementary data to the African Yam Bean Whole Genome Sequencing Project ENA Project_ID:PRJEB57813
<p>The first chromosome-scale assembly of the African yam bean, Sphenostylis stenocarpa (Hochst. ex. A. Rich.) Harms, an original African tuberous legume producing both pods and protein-rich tubers.</p> <p>ENA Project_ID: <strong>PRJEB57813</strong></p> <p>Genome Assembly Accession: <strong>GCA_963425845</strong></p>
Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)
<p>Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).</p> <p>My thesis was structured along the following objectives:</p> <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>
Linked collectors and determiners for: Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity.
Natural history specimen data linked to collectors and determiners held within, "Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a2afe874-9ec7-4101-8f63-da98506a340b">https://bionomia.net/dataset/a2afe874-9ec7-4101-8f63-da98506a340b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a2afe874-9ec7-4101-8f63-da98506a340b">https://gbif.org/dataset/a2afe874-9ec7-4101-8f63-da98506a340b</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: New species and phylogenetic relationships of the spider genus Coptoprepes using morphological and sequence data (Araneae: Anyphaenidae).
Natural history specimen data linked to collectors and determiners held within, "New species and phylogenetic relationships of the spider genus Coptoprepes using morphological and sequence data (Araneae: Anyphaenidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8ceb6188-a9e2-4687-b929-47e5f380ed70">https://bionomia.net/dataset/8ceb6188-a9e2-4687-b929-47e5f380ed70</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8ceb6188-a9e2-4687-b929-47e5f380ed70">https://gbif.org/dataset/8ceb6188-a9e2-4687-b929-47e5f380ed70</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Combining target enrichment and Sanger sequencing data to clarify the systematics of the diverse Neotropical butterfly subtribe Euptychiina (Nymphalidae, Satyrinae).
Natural history specimen data linked to collectors and determiners held within, "Combining target enrichment and Sanger sequencing data to clarify the systematics of the diverse Neotropical butterfly subtribe Euptychiina (Nymphalidae, Satyrinae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba">https://bionomia.net/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba">https://gbif.org/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov..
Natural history specimen data linked to collectors and determiners held within, "A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a>. Formatted as a Frictionless Data package.
A scalable CRISPR-Cas9 gene editing system facilitates CRISPR screens in the malaria parasite Plasmodium berghei - sequencing data
<p>This holds raw sequencing data, and extracted sgRNA counts </p>
Data from: Koe: Web-based software to classify acoustic units and analyse sequence structure in animal vocalisations
<p>1. Classifying acoustic units is often a key step in studying repertoires and sequence structure in animal communication. Manual classification by eye and ear remains the primary method, but new tools and techniques are urgently needed to expedite the process for large, diverse datasets.</p> <p>2. Here we introduce <i>Koe</i>, an application for classifying and analysing animal vocalisations. <i>Koe</i> offers bulk-labelling of units via interactive ordination plots and unit tables, as well as visualisation and playback, segmentation, measurement, data filtering/exporting and new tools for analysing repertoire and sequence structure—in an integrated environment.</p> <p>3. We demonstrate <i>Koe</i> with a real-world case study of New Zealand bellbird <i>Anthornis melanura</i> songs from an archipelago metapopulation. Having classified 21,500 units in <i>Koe</i>, we compare repertoires and sequence structure between sites and sexes.</p> <p>4. <i>Koe</i> is web-based (koe.io.ac.nz) and easy to use, making it ideal for collaboration, education and citizen science. By enabling large-scale, high-resolution classification and analysis of animal vocalisations, <i>Koe</i> expands the possibilities for bioacoustics research.</p>
Code and data associated with Christiansen et al. 2021 "Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing"
<p>All code and data input and output files (except reference genome and raw sequencing data) needed to reproduce the results of Christiansen et al. 2021 as released on <a href="https://github.com/notothen/radpilot">https://github.com/notothen/radpilot</a> alongside journal publication. See published paper:</p> <p>Christiansen, H., Heindler, F.M., Hellemans, B. <em>et al.</em> Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing. <em>BMC Genomics</em> <strong>22, </strong>625 (2021). <a href="https://doi.org/10.1186/s12864-021-07917-3">https://doi.org/10.1186/s12864-021-07917-3</a></p>
Accurate annotation of protein coding sequences with IDTAXA - Training Data
<p>Training data used to test IDTAXA, HMMER, and BLAST performance of classification of amino acid and nucleotide sequences.</p>
Generation of transcriptional novelty by transposable element insertions in Arabidopsis, RNAseq Control Condition Sequencing Data
<p><strong>Arabidopsis stranded 150 bp paired end RNA sequencing data (Illumina) of plants that were grown under control conditions for the manuscript "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong></p> <p><strong><strong>Plant growth conditions</strong></strong></p> <p>Sequenced F4 seeds were sterilized for 10 minutes in 10% bleach, rinsed, and stratified at 4°C for four days in the dark before being sown on 0.5x Murashige & Skoog media (Du<em>schefa cat# M0222</em>) and transferred to growth chambers under long day conditions (16h of light at 24°C followed by 8h of darkness at 21°C; 20 seeds per plate, 6 replicate plates). Ten days after sowing, plants were subjected to 6°C for 24 hours and control plants were returned to normal long day growing conditions for 24 hours before harvesting (3 replicate plates per condition).</p> <p><strong><strong>RNA extraction and sequencing</strong></strong></p> <p>Seedlings were harvested and RNA extractions were done on pools of 5 plants. RNA extractions were performed for 3 biological replicate samples for each line in each condition (n=96) using the Macherey-Nagel NucleoSpin RNA kit (cat# 740955.50). Samples were sent to Novogene for Illumina 150bp paired-end sequencing using a stranded poly-A library.</p> <p><strong>RNAseq sample descriptions of the plants grown under control conditions</strong></p> <p>wt_control: wild-type plants.</p> <p>wtHS_control: wild-type plants that have been submitted to heat stress in a previous generation.</p> <p>wtAZ_control: wild-type plants that have been submitted to epigenetic drug treatments (alpha-amanitin and zebularine) in a previous generation.</p> <p>htLine#: plants carrying additional <em>ONSEN</em> transposable element insertions.</p> <p>Files description: Forward and reverse strand RNA seq data are combined in one file. The numbering at the end ("_1") denominates the biological replicate number.</p>
Data release: Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration
<p>Source data used in the publication: Berger et al. (2021) - Provisional title: 'Whole-genome sequencing of <em>Schistosoma mansoni</em> reveals extensive diversity with limited selection despite mass drug administration'. These data were used to generate all figures used in the publication and all files are organised and labelled specifically to run with the custom code that uses these data can be found at: http://doi.org/10.5281/zenodo.4975908. </p> <p><br> <strong>File descriptions:</strong></p> <p><strong>SOURCE DATA.zip - All source data for all figures. </strong></p> <p><strong>Figure 1b:</strong></p> <ul> <li>supplementary_data_9.txt - Metadata</li> </ul> <p><strong>Figure 2a&b:</strong></p> <ul> <li>207_PCA.eigenvec - PCA eigenvectors</li> <li>207_PCA.eigenval - PCA eigenvalues</li> </ul> <p><strong>Figure 2c:</strong></p> <ul> <li>autosomes.mdist - PLINK distance matrix used to build the neighbour joining phylogeny</li> </ul> <p><strong>Figure 2d:</strong></p> <ul> <li>all.pi.pixy.schools.txt - Nucleotide diversity results for each school subpopulation.</li> </ul> <p><strong>Figure 2e:</strong></p> <ul> <li>autosomes.dxy.5kb.schools.txt - Autosomal D<sub>XY</sub> results between school subpopulations. </li> <li>autosomes.fst.5kb.schools.txt - Autosomal F<sub>ST</sub> results between school subpopulations.</li> </ul> <p><strong>Figure 2f:</strong></p> <ul> <li>admixture_all.txt - ADMIXTURE results for each sample and population sizes, column 1 represents number of populations (K), columns 3-8 represent admixture values for each population. </li> </ul> <p><strong>Figure 3a, Supplementary figure 10a:</strong></p> <ul> <li>sfs.csv - Site frequency spectra (allelic proportions at each frequency bin) for each school. </li> </ul> <p><strong>Figure 3b:</strong></p> <ul> <li>TD.all.txt - Tajima's D values calculated in 5 kb windows for each school subpopulation. </li> </ul> <p><strong>Figure 4a, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGE.IHS.ihs.out.100bins.norm.txt.zip - Normalised iHS scores for the Mayuge district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4b, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.TORORO.IHS.ihs.out.100bins.norm.txt.zip -<strong> - </strong>Normalised iHS scores for the Tororo district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4c, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGEvsTORORO.xpehh.xpehh.out.norm.txt.zip - - Normalised XP-EHH scores between Mayuge and Tororo parasite populations.</li> </ul> <p><strong>Figure 4d, Supplementary figures 13-18:</strong></p> <ul> <li>MAYUGE_TORORO_2000.windowed.weir.txt.zip - F<sub>ST</sub> values calculated between Mayuge and Tororo populations in 2kb windows. </li> </ul> <p><strong>Figure 4e, Supplementary figures 12a&c:</strong></p> <ul> <li>MAYUGE_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Mayuge populations. </li> <li>TORORO_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Kocoge populations (Tororo district).</li> </ul> <p><strong>Figure 5a:</strong></p> <ul> <li>all.pi.treat.fix.txt.zip - Nucleotide diversity results for each treatment subpopulation</li> </ul> <p><strong>Figure 5b</strong></p> <ul> <li>autosomes.dxy.5kb.treatment.txt - <strong> </strong>- Autosomal D<sub>XY</sub> results between clearance phenotype subpopulations. </li> <li>autosomes.fst.5kb.treatment.txt<strong> </strong>- Autosomal F<sub>ST</sub> results between clearance phenotype subpopulations. </li> </ul> <p><strong>Figure 5c:</strong></p> <ul> <li>fst.windows.2kb.treatment.txt.zip - F<sub>ST</sub> values for comparisons between different treatment groups (Pre-treatment, post-treatment (good clearers), post-treatment (poor clearers))</li> </ul> <p><strong>Figure 5d: </strong></p> <ul> <li>assoc_err_binary.txt.zip - Results of binary trait association between miracidia sampled from hosts with good clearance phenotypes (where treatment appeared to be highly effective) and miracidia isolated post-treatment from hosts with poor clearance phenotypes (where miracidia are potentially derived from parasites that survived treatment.</li> </ul> <p><strong>Figure 5e:</strong></p> <ul> <li>assoc_err_linear.txt.zip - - Results of linear regression genome-wide association study with the ERR estimates for all 198 samples, using the mean of the posterior ERR estimates from Crellen et al. (2016) as a quantitative trait.</li> </ul> <p><strong>Supplementary figure 1:</strong></p> <ul> <li>median.coverage.txt - Normalised depth of read coverage (column 4) calculated in 25 kb windows (columns 2&3) across all samples for all chromosomes (column 1).</li> </ul> <p><strong>Supplementary figure 2a-f: </strong></p> <ul> <li>cohort.genotyped.txt.zip - <strong> </strong>- Variant quality site values (used to inform variant site retention or removal). </li> </ul> <p><strong>Supplementary figure 2g:</strong></p> <ul> <li>hard_filtered.imiss.txt - Per sample variant missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 2h:</strong></p> <ul> <li>hard_filtered_filtindv.lmiss.txt.zip - Per site missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 3a, 4a, 4b:</strong></p> <ul> <li>prunedData.eigenvec - PCA eigenvectors</li> <li>prunedData.eigenval - PCA eigenvalues</li> </ul> <p><strong>Supplementary figure 3b:</strong></p> <ul> <li>pruned_data.mdist.csv - Distance matrix used as the basis for the neighbour joining phylogeny.</li> </ul> <p><strong>Supplementary figure 5:</strong></p> <ul> <li>cv_scores.txt - ADMIXTURE coefficient of variation scores (column 2) for each population size (1).</li> </ul> <p><strong>Supplementary figure 6:</strong></p> <ul> <li>*_SMC_SE.csv - SMC++ results (from 25 subsampled replicates) for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 7:</strong></p> <ul> <li>smcpp.csv - SMC++ results for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 8a-d</strong></p> <ul> <li>pi.per_host.txt.zip - Nucleotide diversity values for each host infrapopulation. </li> </ul> <p><strong>Supplementary Figure 9:</strong></p> <ul> <li>sexing.csv - inferred sex (based on differential read coverage over pseudoautosomal and Z-specific regions of the Z chromosome). </li> </ul> <p><strong>Supplementary Figure 10b:</strong></p> <ul> <li>sfs_res.csv - residuals for the SFS analysis in 3a/10a.</li> </ul> <p><strong>Supplementary Figure 11:</strong></p> <ul> <li>MAYUGE_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Mayuge population in 2kb windows. </li> <li>Tororo_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Tororo population in 2kb windows. </li> </ul> <p><strong>Supplementary Figures 13-18:</strong></p> <ul> <li>genes.bed - Coordinates of gene models (<em>S. mansoni </em>v7 annotation).</li> <li>KOCOGE_SITE_PI.sites.pi.txt.zip - Per site nucleotide diversity values</li> <li>MAYUGE_TORORO_sites.weir.fst.txt.zip - Per site F<sub>ST</sub> values between Mayuge and Tororo populations. </li> <li>coverage_5kb.windows.txt.zip - Per sample depth of read coverage in 5 kb windows. Columns 4,5,6 represent the median, mean and sstev of coverage for each 5kb window (columns 2&3) along each chromosome (column 1). </li> <li>median.sample.coverage.txt - Median chromosomal depth of read coverage for each sample. </li> </ul> <p><strong>Supplementary Figure 19:</strong></p> <ul> <li>kocoge_median.ld.txt.zip - <strong> </strong>- The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Kocoge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> <li>mayuge_median.ld.txt.zip - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Mayuge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> </ul> <p><strong>Misc files:</strong></p> <p>schools.list - List of samples and schools where they were sampled. </p> <p> </p>
Data and software associated with the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences"
<p>1. Data files and run logs produced for the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences".</p> <p>2. Software script versions used in the above.</p>
Fig. 3 in Evolution of Polyscias sect. Tieghemopanax (Araliaceae) based on nuclear and chloroplast DNA sequence data
Fig. 3. — Strict consensus of 10,000 most parsimonious trees resulting from the analysis of 58 ITS sequences; tree length = 725 steps; consistency index = 0.518; retention index = 0.734. Clades denoted by brackets are those referred to in text. Values along branches are bootstrap percentages. Placement of Polyscias elegans (discussed in text) is denoted by an asterisk. Labels for the Tieghemopanax group and other clades in "Polyscias sensu lato" follow PLUNKETT et al. (2001).
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.