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.
88
datasets available to search
ShareScore release 0.9.0
Dataset results
88 results for “phylogenetic datasets”
Distribution dataset of the 140 Chinese mountain floras and dated phylogenetic tree
Open the record for dataset details and reuse information.
Ultraconserved element data for phylogenomic analysis and four-gene Sanger dataset for phylogenetic analysis of Tenkana, a new genus of plexippine jumping spider (Salticidae, Plexippini, Plexippina)
Open the record for dataset details and reuse information.
Concatenated amino acid (AA) phylogenetic dataset of nuclear gene orthologs for Ephydroidea (Diptera)
Open the record for dataset details and reuse information.
Phylogenetic dataset of clade Benthamidia of Cornus for biogeographic analysis of big-bracted dogwoods
Open the record for dataset details and reuse information.
Implementing large genomic SNP datasets in phylogenetic network reconstructions: a case study of particularly rapid radiations of cichlid fish
Open the record for dataset details and reuse information.
Getting to know our biomonitor neighbours: urban lichens and allied fungi of Edmonton, Alberta, Canada: Phylogenetic Datasets
Open the record for dataset details and reuse information.
Original dataset, coded matrix, and Bayesian phylogenetic trees of three Late Ordovician brachiopod genera (Atrypida: Anazygidae)
Open the record for dataset details and reuse information.
The dataset of five chloroplast regions used in: A contribution to Gymnosphaera (Cyatheaceae) in mainland Asia: Two new species, reinstatement of Cyathea bonii, and their phylogenetic positions
Open the record for dataset details and reuse information.
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
<p>Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.</p>
FIGURE 2. Phylogenetic results. A, Maximum likelihood tree from COI dataset rooted with Ophelia limacina. B, Maximum likelihood tree from ITS1 in Validation of three sympatric Thoracophelia species (Annelida: Opheliidae) from Dillon Beach, California using mitochondrial and nuclear DNA sequence data
FIGURE 2. Phylogenetic results. A, Maximum likelihood tree from COI dataset rooted with Ophelia limacina. B, Maximum likelihood tree from ITS1 dataset rooted according to the result for the COI dataset. Support values are shown as jackknife from parsimony analysis and bootstrap from maximum likelihood respectively separated by /. * indicates 100% values for each support measure.
Dataset and code for the manuscript "Plant indirect interactions reduce species richness but increase phylogenetic diversity"
<p>This Zenodo repository contains the original data set and code for replicating the result published in the paper "<strong>Plant indirect interactions reduce species richness but increase phylogenetic diversity</strong>"</p><p> </p><ol><li>The "BD.xlsx" data frame contains the original data. The first column, "ID," is an ID for each one of the patches (i.e., vegetation units containing one or more individuals for the same or different species separated from other patches by bare ground). The second and third columns, "localidad" and "suelo", inform about the location where patches are found. The "species" column identifies the different species present at each patch.</li><li> The "Comb1.xlsx" data frame contains the possible combination of 4 species for each ID (concatenation of "localidad" and "suelo") for all the species found in the sampling. Besides "ID", there is one column for each of the species included in interactions ("spA", "spB", "spC", "spD") that contains the name of the species.</li><li>The "phylo.xlsx" data frame contains the taxonomic information of the species found during the sampling. For each species recorded in column "species" we assign its genus (column "genus") and family (column "family")</li><li> "ALL_IN.xlsx" is a data frame containing for each "ID" (proxy of location), the species involved in interactions ("spA", "spB", "spC" & "spD"), the "interaction sign" indicating if a given interaction is positive or negative (results based on simulation) and "Order" indicating the number of species involved in the interactions(from two to four).</li><li>"Code.R" provides the R code necessary to obtain the results. As statistics is based on simulation, every run can provide slightly different results, although differences do not affect interpretation. Please note that running time can be elevated depending on the computer used.</li></ol><p> </p>
FIGURE. Phylogenetic tree based on RAxML analyses of a combined LSU, ITS and SSU dataset. Bootstrap support values for ML and MP equal to or greater than 75% and PP value greater than 0.95 are in thickened. Ex-type isolates are in bold, and new taxa are indicated in red. The tree is rooted with Atractospora aquatica (S-1297) and A. aquatica (MFLU 18–2322). in Conlarium sichuanense sp. nov., on Ficus virens from Sichuan Province, China
FIGURE. Phylogenetic tree based on RAxML analyses of a combined LSU, ITS and SSU dataset. Bootstrap support values for ML and MP equal to or greater than 75% and PP value greater than 0.95 are in thickened. Ex-type isolates are in bold, and new taxa are indicated in red. The tree is rooted with Atractospora aquatica (S-1297) and A. aquatica (MFLU 18–2322).
Dataset for the manuscript: Phylogenetic and functional constraints of plant facilitation rewiring. ECOLOGY
<p>The following directory contains the data necessary to replicate the results obtained in the manuscript entitled: <strong>Phylogenetic and functional constraints of plant facilitation rewiring</strong></p> <p>We provided two spreadsheets containing the data "Adult species data" and "Interactions data.xlsx". Each archive contains a readme textbox with detailed information on the variables included in the archives</p>
Figure 3. The Bayesian tree inferred from the dataset 107 in Phylogenetic relationships of Brachycera (Insecta: Diptera) inferred from mitochondrial genome sequences
Figure 3. The Bayesian tree inferred from the dataset 107-taxon_PCG_nt12 using PhyloBayes, under the site-heterogeneous mixture model (CAT-GTR). Node numbers show the posterior probability values (> 0.90). The insect pictures are provided by Yuqiang Xi.
Computational Phylogenetics and the Internal Structure of Pama-Nyungan: Dataset
<p>Dataset of cognate judgements on which Bowern and Atkinson (2012) was based. Note that this dataset has been largely superseded by subsequent work which adds more languages and forms.</p>
Phylogenetics: back to basics dataset
<p>This dataset is associated with the Galaxy Training Network tutorial "Phylogenetics: back to basics".</p> <p>It contains a subset of anolis lizard DNA sequences, from Jackman, Larson, de Queiroz & Losos (Phylogenetic Relationships and Tempo of Early Diversfication in Anolis Lizards; Systematic Biology 1999 48(2):254-285; <a href="https://doi.org/10.1080/106351599260283">https://doi.org/10.1080/106351599260283</a>). DNA sequences were obtained from Genbank.</p> <p>The sequences are from the mitochondrial NADH dehydrogenase subunit 2 gene (ND2) and five transfer RNA (tRNA) genes, with an average sequence length of 1419.109 and a range of [1329,1727]. </p> <p>We are using a small set of sequences because phylogenetic estimation on many sequences is computationally very intensive, and can take weeks of time even on a high-performance computer. It is not uncommon for a phylogenetic analysis to span hundreds, or even thousands, of taxa.</p> <p>Files in this record:</p> <ul> <li>anolis-raw.fst : FASTA file of raw, unaligned sequences</li> </ul> <p>This Galaxy Training Network tutorial ‘Phylogenetics: back to basics’ was developed by Professor Michael Charleston from the University of Tasmania in collaboration with Australian BioCommons. </p> <p>Contact: training@biocommons.org.au</p> <p>Version notes: anolis-raw.fst file updated to remove blank line at the beginning of the file. </p>
FIGURE 5. Maximum likelihood phylogenetic tree constructed with UCEs and exon loci dataset for the novel species P in A new species of Plumarella (Octocorallia: Calcaxonia: Primnoidae) from the Northeast Pacific, and the redescription of Plumarella longispina Kinoshita, 1908
FIGURE 5. Maximum likelihood phylogenetic tree constructed with UCEs and exon loci dataset for the novel species P. williamsi (in bold), the redescribed species P. longispina (in red), the related taxa and rooted to outgroup genera. ML bootstrap support values>70% are shown above branches.
Cambrian comb jellies from Utah illuminate the early evolution of nervous and sensory systems in ctenophores - Phylogenetic dataset
<p>Ctenophores are a group of predatory macroinvertebrates whose controversial phylogenetic position has prompted several competing hypotheses regarding the evolution of animal organ systems. Although ctenophores date back at least to the Cambrian, they have a poor fossil record due to their gelatinous bodies. Here, we describe two ctenophore species from the Cambrian of Utah, which illuminate the early evolution of nervous and sensory features in the phylum. Thalassostaphylos elegans has 16 comb rows, an oral skirt, and an apical organ with polar fields. Ctenorhabdotus campanelliformis has 24 comb rows, an oral skirt, an apical organ enclosed by a capsule and neurological tissues preserved as carbonaceous films. These are concentrated around the apical organ and ciliated furrows, which connect to a circumoral nerve ring via longitudinal axons. C. campanelliformis deviates from the neuroanatomy of living ctenophores, and demonstrates a substantial complexity in the nervous system of Cambrian ctenophores.</p>
Figure 14. Pruned maximum credibility Bayesian trees. Dataset C in Redescription of the skull of the Australian flatback sea turtle, Natator depressus, provides new morphological evidence for phylogenetic relationships among sea turtles (Chelonioidea)
Figure 14. Pruned maximum credibility Bayesian trees. Dataset C based on the matrix from Evers & Benson (2019). Dataset D based on matrix from Evers & Benson (2019) with added characters found in this study. Colours at nodes represent base of clades. Numbers at the node show posterior probability value of node.
Figure 13. Maximum credibility Bayesian trees. Dataset A in Redescription of the skull of the Australian flatback sea turtle, Natator depressus, provides new morphological evidence for phylogenetic relationships among sea turtles (Chelonioidea)
Figure 13. Maximum credibility Bayesian trees. Dataset A based on the matrix from Evers & Benson (2019). Dataset B based on matrix from Evers & Benson (2019) with added characters found in this study. Different colours at nodes represent base of clades. Numbers at the node show posterior probability value of node.
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.