cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24586143
title: Biogeography_analyses

description: To investigate the factors that may have influenced the distribution of <i>Ocymyrmex</i> species in southern Africa, we inferred their biogeographic histories using the R package BioGeoBEARS (BioGeography with Bayesian (and likelihood) Evolutionary Analysis in R Scripts) (Matzke, 2018). This likelihood-based model is designed to perform inferences of biogeographical histories on phylogenies and to compare different models of range evolution (Matzke, 2013a). The following biogeographical models were tested against our data to estimate the ancestral area of <i>Ocymyrmex</i> species: a Dispersal-Extinction-Cladogenesis (DEC) model, which allows the geographic range to change across a phylogeny through several events (Ree, 2005; Ree and Smith, 2008); a likelihood version of the Dispersal-Vicariance model (DIVALIKE) (Ronquist, 1997); and a likelihood version of the Bayesian Analysis of Biogeography (BAYAREA-LIKE), which is a Bayesian model that samples geographical history along phylogenetic branches, together with sampling parameter values (Landis et al., 2013). These models have free parameters specifying the rate of dispersal (<i>d</i> = range expansion) and extinction (<i>e</i> = range of contraction)<i> </i>along the phylogeny branches. At cladogenesis events, DEC assumes that one daughter lineage will always inherit the ancestral range if the ancestor lives in a single area; if the ancestor is widespread, one daughter lineage will live in a subset of that area, or one area will split off by vicariance. By contrast, DIVA only allows vicariance. BayArea assumes that no range evolution occurs at cladogenesis, and therefore the ancestral range is copied by both daughters (Matzke, 2013b). A founder event speciation model (+j) was also tested in combination with each base model.BioGeoBears requires a time calibrated tree and geographic distribution units as input files. <i>Ocymyrmex </i>distribution records were extracted from Iziko Museum’s Specify6 database (Specify version 6.7.01; Specify Collections Consortium | Software for Biological Collections and Samples). Given the tight habitat affinity of <i>Ocymyrmex</i>, species occurrences were assigned and coded according to the biomes defined by Dinerstein et al. (2017) (Appendix B: Table B2). The following biome regions were designated and used as biogeographical units: Tropical & subtropical moist broadleaf forests; Tropical & subtropical grasslands, savannas & shrublands; Flooded grasslands & savannas; Montane grasslands & shrublands; Mediterranean forests, woodlands & scrub; and Deserts & xeric shrublands. The dated phylogeny produced by MCMCTREE was pruned to exclude outgroups and was used as an input file. The Akaike information criterion (AIC) was used to select the best-fitting model. The maximum number of areas any species may occupy was set to five, as the <i>Ocymyrmex </i>species with the largest distribution range, <i>O. flaviventris</i>, can be found in five biomes. We followed guidelines in http://phylo.wikidot.com/biogeobears.
DOI: 10.15468/dl.rq3p5m
title: Occurrence Download

description: A dataset containing 4914 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ophrys bombyliflora Link"
 ]
}

 The dataset includes 4914 records from 153 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065425-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24586032
title: MCMCTREE

description: To assess the timing of historical processes that may have led to diversification in <i>Ocymyrmex</i>, a dated phylogeny for the genus was inferred using MCMCTREE v4.9j in the PAML package (Phylogenetic Analysis by Maximum Likelihood; (Yang, 2007)). Divergence ages were reconstructed using the 80% taxon completeness matrix, trimmed with 0.97 cut-off in SPRUCEUP v2020.2.19 (Borowiec, 2019), and the tree resulting from partitioned analysis of this data set as input topology (Appendix C). This tree was pruned to include only one representative of each <i>Ocymyrmex</i> species, and two outgroup taxa (<i>Nesomyrmex</i> <i>madecassus</i> and <i>Cardiocondyla thoracica</i>) were also removed prior to this analysis. Given the unknown fossil record of <i>Ocymyrmex</i>, secondary ages estimated by Blaimer et al. (2018) were used to calibrate two nodes in our tree. The split between <i>Ocymyrmex</i> and <i>Cardiocondyla</i> was calibrated with 46.5–71.9 Ma and the split between <i>Tetramorium </i>and the <i>Cataulacus</i> genus-group of which both <i>Ocymyrmex</i> and <i>Cardiocondyla</i> are part of (= root node) was calibrated with 60.1–84.9 Ma. Both calibration ranges were derived from 95% highest posterior density (HPD) intervals across four analyses from Blaimer et al. (2018) to account for uncertainty.We first estimated the Hessian gradient in MCMCTREE (set usedata = 3), then the estimates from the Hessian gradient were used to perform approximate likelihood estimation (usedata = 2). We performed four independent runs, with the following parameters: burnin was set to 500,000; sampling was performed every 100 iterations until 1,000,000 samples were gathered; the model was set to 7 (GTR); an independent rates model was applied (clock=2); and we used a single data partition. Convergence and effective sample size (ESS) were visualized in TRACER v1.7 (Rambaut et al., 2018).
DOI: 10.6084/m9.figshare.24586005
title: Coalescent_analyses

description: The potential effects of independent lineage sorting on our dataset were explored by performing coalescent analyses with ASTRAL-III v. 5.7.7 (Zhang et al., 2018). ASTRAL-III is a java program for estimating a species tree given a set of unrooted gene trees, each inferred from a different part of a genome. This approach aims to explain the genealogical discordances between gene trees and species trees which might be caused by incomplete lineage sorting (Zhang et al., 2018). Gene trees were reconstructed from 2055 UCE loci retained in the 70% dataset, 1235 UCE loci retained by 80% dataset and 88 UCE loci retained by the 90% dataset. We estimated gene trees using the following command in IQ-TREE version 2.1.2 (Minh et al., 2020): iqtree2 -S ALN_DIR --prefix loci -T AUTO.
DOI: 10.15468/dl.m6dp2h
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panicum inaequilatum Stapf & C.E.Hubb."
 ]
}

 The dataset includes 52 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065426-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24585954
title: Concatenated analyses_unpartitioned

description: The demultiplexed FASTQ data were cleaned and trimmed of adapters using Illumiprocessor v.2.0 (Faircloth, 2013), based on the package Trimmomatic (Bolger et al., 2014). Data processing was done through a series of scripts available in the PHYLUCE package v.1.7.1 (Faircloth, 2015). Trimmed reads were assembled into contigs using a wrapper script (phyluce_assembly_assemblo_trinity.py) and the program TRINITY (version trinityrnaseq_r20140717) (Grabherr et al., 2011). We used the PHYLUCE pipeline to identify and extract contigs containing UCE loci. Species-specific contig assemblies were aligned to a FASTA file of all enrichment baits using phyluce_assembly_match_contigs_to_probes.py (min_coverage=50, min_identity=80). A list of UCE loci shared across all taxa was generated by using phyluce_assembly_get_match_counts.py. This list was then used to create FASTA files for each UCE locus using phyluce_get_fastas_from_match_counts.py. All sequence data in these FASTA files were aligned using MAFFT (Katoh and Standley, 2013) through phyluce_seqcap_align.py (min. length =100, no trim) and trimmed using a wrapper script (get_gblocks_trimmed_alignment_from_untrimmed.py) for Gblocks (Castresana, 2000) with the following settings: b1=0.5, b2=0.5, b3=12, b4=7. After trimming, multiple subsets based on filtering UCE loci for different levels of taxon occupancy (70%, 80% and 90% taxon completeness) were created using phyluce_get_only_loci_with_min_taxa.py, and we generated statistics across all subsets using get_align_summary_data.py. Individual alignments of UCE loci for each subset were then concatenated into one nexus alignment file with phyluce_align_format_nexus_files_for_raxml.py script for subsequent phylogenetic analyses. SPRUCEUP v2020.2.19 (Borowiec, 2019) was used to remove poorly aligned sequences or sequence fragments. The matrices were trimmed based on the following cut-off values: 95%, 97%, 98% and 99%. For this study, all the analyses here are based on 97% and 98% cut-off values, as a 95% cut-off was too stringent, and a 99% cut-off did not trim outlier sequences sufficiently.
DOI: 10.15468/dl.yzzwhc
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ozoroa okavangensis R.Fern. & A.Fern."
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065427-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24585897
title: Concatenated analyses_partitioned

description: The demultiplexed FASTQ data were cleaned and trimmed of adapters using Illumiprocessor v.2.0 (Faircloth, 2013), based on the package Trimmomatic (Bolger et al., 2014). Data processing was done through a series of scripts available in the PHYLUCE package v.1.7.1 (Faircloth, 2015). Trimmed reads were assembled into contigs using a wrapper script (phyluce_assembly_assemblo_trinity.py) and the program TRINITY (version trinityrnaseq_r20140717) (Grabherr et al., 2011). We used the PHYLUCE pipeline to identify and extract contigs containing UCE loci. Species-specific contig assemblies were aligned to a FASTA file of all enrichment baits using phyluce_assembly_match_contigs_to_probes.py (min_coverage=50, min_identity=80). A list of UCE loci shared across all taxa was generated by using phyluce_assembly_get_match_counts.py. This list was then used to create FASTA files for each UCE locus using phyluce_get_fastas_from_match_counts.py. All sequence data in these FASTA files were aligned using MAFFT (Katoh and Standley, 2013) through phyluce_seqcap_align.py (min. length =100, no trim) and trimmed using a wrapper script (get_gblocks_trimmed_alignment_from_untrimmed.py) for Gblocks (Castresana, 2000) with the following settings: b1=0.5, b2=0.5, b3=12, b4=7. After trimming, multiple subsets based on filtering UCE loci for different levels of taxon occupancy (70%, 80% and 90% taxon completeness) were created using phyluce_get_only_loci_with_min_taxa.py, and we generated statistics across all subsets using get_align_summary_data.py. Individual alignments of UCE loci for each subset were then concatenated into one nexus alignment file with phyluce_align_format_nexus_files_for_raxml.py script for subsequent phylogenetic analyses. SPRUCEUP v2020.2.19 (Borowiec, 2019) was used to remove poorly aligned sequences or sequence fragments. The matrices were trimmed based on the following cut-off values: 95%, 97%, 98% and 99%. For this study, all the analyses here are based on 97% and 98% cut-off values, as a 95% cut-off was too stringent, and a 99% cut-off did not trim outlier sequences sufficiently.
DOI: 10.6084/m9.figshare.24585888
title: Phyluce_commands

description: Phyluce commands
DOI: 10.15468/dl.ejhdvh
title: Occurrence Download

description: A dataset containing 467 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Osbeckia nepalensis Hook."
 ]
}

 The dataset includes 467 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065428-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10899729
title: AIVFIARs AI-Verifiable Full Immersion Alternate Realities
DOI: 10.5281/zenodo.10899730
title: AIVFIARs AI-Verifiable Full Immersion Alternate Realities
DOI: 10.15468/dl.h4myvt
title: Occurrence Download

description: A dataset containing 248133 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vaccinium uliginosum L."
 ]
}

 The dataset includes 248133 records from 751 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065392-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh252022.07fu
title: SH252022.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252023.07fu
title: SH252023.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252024.07fu
title: SH252024.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252025.07fu
title: SH252025.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252026.07fu
title: SH252026.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252027.07fu
title: SH252027.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252028.07fu
title: SH252028.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252029.07fu
title: SH252029.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh252030.07fu
title: SH252030.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.