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.
70
datasets available to search
ShareScore release 0.9.0
Dataset results
70 results for “morphological homoplasy”
Figure 7 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 7. Agreement subtree with groups present with all concavities obtained with the Ratchet algorithm for parsimonious analyses using combined data: morphological matrix without gamete-related characters and molecular data (18S rRNA and 28S rRNA). Values above branches are bootstrap supports after 1000 replicates with a k-value of 16. Values under branches are Bremer relative supports with a k-value of 16.
Figure 5. Agreement subtree cladogram obtained with the Ratchet algorithm for parsimonious analyses using the complete morphological matrix without gamete-related characters. Values above branches are bootstrap supports after 1000 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 5. Agreement subtree cladogram obtained with the Ratchet algorithm for parsimonious analyses using the complete morphological matrix without gamete-related characters. Values above branches are bootstrap supports after 1000 replicates; values under branches are Bremer relative supports.
Figure 2 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 2. Different states (from 0 to 5) coded in the present study for the shape of the furcae (character 13; Table 2).
Figure 4 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 4. Different types of claws present amongst eutardigrades (A–N) and in the outgroup Echiniscidae (O). M modified from Pilato (1971). Dotted lines in F and G indicate right angles in Isohypsibius- and Hypsibius-type claws, respectively. Arrows in D and E indicate cuticular bars joining external and internal claws in Dactylobiotus and Macroversum, respectively. Arrows in L indicate claw position. PIII, third pair of legs. PIV, fourth pair of legs.
Data from: Homoplasy-based partitioning outperforms alternatives in Bayesian analysis of discrete morphological data
Bayesian analysis of morphological data is becoming increasingly popular mainly (but not only) because it allows for time-calibrated phylogenetic inference using relaxed morphological clocks and tip dating whenever fossils are available. As with molecular data, recent studies have shown that modeling among character rate variaton (ACRV) in morphological matrices greatly improves phylogenetic inference. In a likelihood framework this may be accomplished, for instance, by employing a hidden Markov model (HMM) to assign characters to rate categories drawn from a (discretized) Γ distribution and/or by partitioning datasets according to rate heterogeneity and estimating per-partition branch lengths, conditioned on a single topology. While the first approach is available in many phylogenetic analysis software, there is still no clear consensus on how to partition data, except perhaps in the simplest cases (e.g. "by codon" partitioning of coding sequences). Additionally, there is a trade-off between improvement in likelihood scores and the number of free parameters in the analysis, which rises quickly with the number of partitions. This trade-off may be dealt with by employing statistics that penalize overfitting of complex models, such as Akaike or Bayesian information criteria (AIC and BIC), or the more recently introduced stepping-stone (SS) method for marginal likelihood approximation. We applied the latter to three distinct matrices of discrete morphological data and demonstrated that sorting characters by homoplasy scores (obtained from implied weighting parsimony analysis) outperformed other partitioning strategies (anatomically-based and PartitionFinder2). The method was in fact so efficient in segregating characters by rates of evolution that no within-partition ACRV modeling was necessary, while among partition rate variation (APRV) was adequately accommodated by rate multipliers. We conclude that partitioning by homoplasy is a powerful and easy-to-implement strategy to address ACRV in complex datasets. We provide some guidelines focusing on morphological matrices, although this approach may be also applicable to molecular datasets.
Linked collectors and determiners for: Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera.
Natural history specimen data linked to collectors and determiners held within, "Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8f234792-c061-46de-8675-09a522d30a1c">https://bionomia.net/dataset/8f234792-c061-46de-8675-09a522d30a1c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8f234792-c061-46de-8675-09a522d30a1c">https://gbif.org/dataset/8f234792-c061-46de-8675-09a522d30a1c</a>. Formatted as a Frictionless Data package.
FIG. 4 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIG. 4. — Reconstruction of character evolution by mapping morphological characters onto the Bayesian tree inferred on rbcL gene. Geographical distribution is given for all the samples. Values at the nodes represent posterior probability, values <0.8 are not shown. Abbreviations: See Figure 3.
Data from: Homoplasy-based partitioning outperforms alternatives in Bayesian analysis of discrete morphological data
Open the record for dataset details and reuse information.
Figure 9 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 9. Majority rule phylogram that best fitted current Eutardigrada classification (Marley et al., 2011), obtained with PAUP for parsimonious analyses using the reduced morphological matrix, that is, without any homoplastic characters. Values above branches are parsimonious bootstrap supports after 1000 replicates. Values under branches are Bremer relative supports. Superfamilies with associated claw morphologies and families are indicated.
Figure 6 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 6. Maximum clade credibility phylogram obtained with Bayesian inference using the complete morphological matrix without gamete-related characters. Values above branches are posterior probabilities supports. Scale bar indicates nucleotide substitutions per site.
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.