cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.k1d8c
title: Data from: Optimizing stimulation and analysis protocols for neonatal fMRI

description: The development of brain function in young infants is poorly understood.
 The core challenge is that infants have a limited behavioral repertoire
 through which brain function can be expressed. Neuroimaging with fMRI has
 great potential as a way of characterizing typical development, and
 detecting abnormal development early. But, a number of methodological
 challenges must first be tackled to improve the robustness and sensitivity
 of neonatal fMRI. A critical one of these, addressed here, is that the
 hemodynamic response function (HRF) in pre-term and term neonates differs
 from that in adults, which has a number of implications for fMRI. We
 created a realistic model of noise in fMRI data, using resting-state fMRI
 data from infants and adults, and then conducted simulations to assess the
 effect of HRF of the power of different stimulation protocols and analysis
 assumptions (HRF modeling). We found that neonatal fMRI is most powerful
 if block-durations are kept at the lower range of those typically used in
 adults (full on/off cycle duration 25-30s). Furthermore, we show that it
 is important to use the age-appropriate HRF during analysis, as mismatches
 can lead to reduced power or even inverted signal. Where the appropriate
 HRF is not known (for example due to potential developmental delay), a
 flexible basis set performs well, and allows accurate post-hoc estimation
 of the HRF.
description: cusack_fmriFor each of five neonates, there is an fMRI timeseries (raw data) and a manually drawn grey matter mask. The adult data from this paper are already available from the fc1000 dataset.
DOI: 10.5061/dryad.51bp263
title: Data from: Microevolutionary selection dynamics acting on immune genes of the green veined white butterfly, Pieris napi

description: Insects rely on their innate immune system to successfully mediate complex
 interactions with their microbiota, as well as the microbes present in the
 environment. Previous work has shown that components of the canonical
 immune gene repertoire evolve rapidly and have evolutionary
 characteristics originating from interactions with fast-evolving
 microorganisms. Although these interactions are likely to vary among
 populations, there is a poor understanding of the microevolutionary
 dynamics of immune genes, especially in non-Dipteran insects. Here we use
 the full set of canonical insect immune genes to investigate
 microevolutionary dynamics acting on these genes between and among
 populations by comparing three allopatric populations of the Green Veined
 White butterfly, Pieris napi (Lepidoptera, Pieridae). Immune genes showed
 increased genetic diversity compared to genes from the rest of the genome
 and various functional categories exhibited different types of signatures
 of selection, at different evolutionary scales, presenting a complex
 pattern of selection dynamics. Signatures of balancing selection were
 identified in 10 genes, and 17 genes appear to be under positive
 selection. Genes involved with the cellular arm of the immune response as
 well as the Toll pathway appear to be enriched among our outlier loci,
 regardless of functional category. This suggests that the targets of
 selection might focus upon an entire pathway, more than on functional
 subsets across pathways. Our microevolutionary results are similar to
 previously observed macroevolutionary patterns from diverse taxa,
 suggesting that either the immune system is robust to dramatic differences
 in life history and microbial communities, or that diverse microbes exert
 similar selection pressures.
description: Piebra_SP_k55b2000_MESPAnapiDraft genome of Pieris brassicae, scaffolded using MESPA.Pool-Seq libraries of the 3 P. napi populationsPool-Seq libraries of Spain: Aiguamolls, Sweden: Abisko, and Sweden: Kullaberg. Stored as NCBI bioproject PRJNA449143.command line scriptBioinformatics commands used to analyse the data.commandline.txtPnapi_FstEach row represents FST per base pair for all informative sites.Pnapi_TajD_piEach row represents pi and Tajima's D per gene, for all immune genes in the assembly.MK_gene_alignmentsMK gene alignments for significant genes. This is a zipped archive containing fasta files.
DOI: 10.5061/dryad.st8f5n7
title: Data from: Machine learning to classify animal species in camera trap images: applications in ecology

description: Motion‐activated cameras (“camera traps”) are increasingly used in
 ecological and management studies for remotely observing wildlife and are
 amongst the most powerful tools for wildlife research. However, studies
 involving camera traps result in millions of images that need to be
 analysed, typically by visually observing each image, in order to extract
 data that can be used in ecological analyses. We trained machine learning
 models using convolutional neural networks with the ResNet‐18 architecture
 and 3,367,383 images to automatically classify wildlife species from
 camera trap images obtained from five states across the United States. We
 tested our model on an independent subset of images not seen during
 training from the United States and on an out‐of‐sample (or
 “out‐of‐distribution” in the machine learning literature) dataset of
 ungulate images from Canada. We also tested the ability of our model to
 distinguish empty images from those with animals in another out‐of‐sample
 dataset from Tanzania, containing a faunal community that was novel to the
 model. The trained model classified approximately 2,000 images per minute
 on a laptop computer with 16 gigabytes of RAM. The trained model achieved
 98% accuracy at identifying species in the United States, the highest
 accuracy of such a model to date. Out‐of‐sample validation from Canada
 achieved 82% accuracy and correctly identified 94% of images containing an
 animal in the dataset from Tanzania. We provide an r package (Machine
 Learning for Wildlife Image Classification) that allows the users to (a)
 use the trained model presented here and (b) train their own model using
 classified images of wildlife from their studies. The use of machine
 learning to rapidly and accurately classify wildlife in camera trap images
 can facilitate non‐invasive sampling designs in ecological studies by
 reducing the burden of manually analysing images. Our r package makes
 these methods accessible to ecologists.
description: raw_labels
DOI: 10.5061/dryad.40hs5
title: Data from: Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved metazoan primer reveals species-rich and reservoir-specific communities

description: Freshwater habitats are of high conservation value and provide a wide
 range of ecosystem services. Effective management requires regular
 monitoring. However, conventional methods based on direct observation or
 specimen collection are so invasive, expensive and labour-intensive that
 frequent monitoring is uncommon. Here, we test whether the evaluation of
 environmental DNA (eDNA) from water based on a simple protocol can be used
 for assessing biodiversity. We use universal metazoan primers for
 characterizing water eDNA across horizontal and vertical spatial
 dimensions in two reservoirs with known species diversity for two key
 taxa. eDNA obtained directly from 42 samples × 15 ml water
 (total = 630 ml) per reservoir yielded DNA signatures for more than 500
 metazoan species, of which 105 could be identified to species/genus based
 on DNA barcodes. We show that eDNA can be used to assign each water sample
 to its reservoir of origin, and that eDNA outperforms conventional survey
 methods in single-sample richness comparisons, while revealing evidence
 for hundreds of unknown species that are undetected by conventional
 bioassessment methods. eDNA also confirms the presence of a recently
 discovered invasive snail species and provides evidence for the continued
 survival of a rare native species of goby not sighted in that habitat
 since 2007. eDNA thus promises to be a useful addition to the
 bioassessment toolbox for freshwater systems.
description: Bedok ngsfilter
 fileDemultiplexing reference
 file for Bedok Reservoir dataset, containing primer tag assignments. For
 use with the ngsfilter step of the obitools pipeline.SI_Bedok_obitools-ngsfilter.txtPandan ngsfilter fileDemultiplexing reference file for Pandan Reservoir dataset, containing primer tag assignments. For use with the ngsfilter step of the obitools pipeline.SI_Pandan_obitools-ngsfilter.txtAligned sequence set used for objective clusteringFinal aligned COI sequence set comprising the combined Bedok and Pandan datasets, used for MOTU delimitation via objective clustering.BKPN_metazoa_pullout_align.faBedok obitools outputCleaned Bedok dataset after obitools filtering.BK_water_amp_ali_assigned_uniq_10_300_clean.faPandan obitools outputCleaned Pandan dataset after obitools filtering.PN_water_amp_ali_assigned_uniq_10_300_clean.fa
DOI: 10.5061/dryad.5207q
title: Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling

description: Remote sensing techniques offer an opportunity to improve biodiversity
 modeling and prediction worldwide. Yet, to date, the weather-station based
 WorldClim dataset has been the primary source of temperature and
 precipitation information used in correlative species distribution models.
 WorldClim consists of grids interpolated from in situ station data
 recorded primarily from 1960 to 1990. Those datasets suffer from uneven
 geographic coverage, with many areas of Earth poorly represented. Here, we
 compare two remote sensing data sources for the purposes of biodiversity
 prediction: MERRA climate reanalysis data and AMSR-E, a pure remote
 sensing data source. We use these data to generate novel temperature-based
 bioclimatic information and to model the distributions of 20 species of
 vertebrates endemic to four regions of South America: Amazonia, the
 Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic
 datasets derived from MERRA and AMSR-E information with in situ station
 data, and contrast species distribution models based on these two products
 to models built with WorldClim. Surface temperature estimates provided by
 MERRA and AMSR-E showed warm temperature biases relative to the in situ
 data fields, but the reliability of these datasets varied in geographic
 space. Species distribution models derived from the MERRA data performed
 equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic
 Forest) than models built with the WorldClim data. In contrast, the
 performance of models constructed with the AMSR-E data was similar to
 (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of
 models built with WorldClim data. Whereas this initial comparison assessed
 only temperature fields, efforts to estimate precipitation from remote
 sensing information hold great promise; furthermore, other environmental
 datasets with higher spatial and temporal fidelity may improve upon these
 results.
description: Bioclimatic grids -
 MERRA4 Temperature-based
 bioclimatic grids created from MERRAmerra_4tempbioclim_rastersforcomparison_southamerica.zipBioclimatic grids - AMSR-E4 Temperature-based bioclimatic grids created from AMSR-Easmre_4tempbioclim_rastersforcomparison_southamerica.zipBioclimatic grids - Worldclim4 Temperature-based WorldClim bioclimatic grids for comparisonsworldclim_4tempbioclim_rastersforcomparison_southamerica.zipUncompared bioclimatic grids - MERRA15 additional bioclimatic grids created from MERRAmerra_other15bioclim_rasters_southamerica.zip
DOI: 10.5061/dryad.9q1n4
title: Data from: Genomic islands of divergence are not affected by geography of speciation in sunflowers.

description: Genomic studies of speciation often report the presence of highly
 differentiated genomic regions interspersed within a milieu of weakly
 diverged loci. The formation of these speciation islands is generally
 attributed to reduced inter-population gene flow near loci under divergent
 selection, but few studies have critically evaluated this hypothesis.
 Here, we report on transcriptome scans among four recently diverged pairs
 of sunflower (Helianthus) species that vary in the geographical context of
 speciation. We find that genetic divergence is lower in sympatric and
 parapatric comparisons, consistent with a role for gene flow in eroding
 neutral differences. However, genomic islands of divergence are numerous
 and small in all comparisons, and contrary to expectations, island number
 and size are not significantly affected by levels of interspecific gene
 flow. Rather, island formation is strongly associated with reduced
 recombination rates. Overall, our results indicate that the functional
 architecture of genomes plays a larger role in shaping genomic divergence
 than does the geography of speciation.
description: HA412_trinity_noAltSplice_400bpminfasta file. Reference transcriptome described in the study and used for all alignments.ordered_transcriptome_trinity.txtThis is the genetic map used to position genes and SNPs. Column 1 refers to the contig name, column 2 to the chromosome number and column 3 to the position in centimorgansann_pet_fst_mapSNP table for H. annuus - H. petiolaris comparison. This table also contain Fst ("Fst" column) values for each SNP and the position ("map_centiMorgan") and chromosome number ("LG") were SNPs are found.deb_arg_fst_mapSNP table for H.debilis - H. argophyllus comparison. This table also contain Fst ("Fst" column) values for each SNP and the position ("map_centiMorgan") and chromosome number ("LG") were SNPs are found.pet_arg_fst_mapSNP table for H. petiolaris -H. argophyllus comparison. This table also contain Fst ("Fst" column) values for each SNP and the position ("map_centiMorgan") and chromosome number ("LG") were SNPs are found.ann_deb_fst_mapSNP table for H. annuus - H. debilis comparison. This table also contain Fst ("Fst" column) values for each SNP and the position ("map_centiMorgan") and chromosome number ("LG") were SNPs are found.coverage_per_gene_individualsColumn 1 is the name of the reference contig. Column 2, the length of the reference. Column 3 and after, the number of sequences aligedn to that particular contig (calculated using samtools idxstats).
DOI: 10.5061/dryad.689d2
title: Data from: The Mouse Universal Genotyping Array: from substrains to subspecies

description: Genotyping microarrays are an important resource for genetic mapping,
 population genetics, and monitoring of the genetic integrity of laboratory
 stocks. We have developed the third generation of the Mouse Universal
 Genotyping Array (MUGA) series, GigaMUGA, a 143,259-probe Illumina
 Infinium II array for the house mouse (Mus musculus). The bulk of the
 content of GigaMUGA is optimized for genetic mapping in the Collaborative
 Cross and Diversity Outbred populations, and for substrain-level
 identification of laboratory mice. In addition to 141,090 single
 nucleotide polymorphism probes, GigaMUGA contains 2006 probes for copy
 number concentrated in structurally polymorphic regions of the mouse
 genome. The performance of the array is characterized in a set of 500
 high-quality reference samples spanning laboratory inbred strains,
 recombinant inbred lines, outbred stocks, and wild-caught mice. GigaMUGA
 is highly informative across a wide range of genetically diverse samples,
 from laboratory substrains to other Mus species. In addition to describing
 the content and performance of the array, we provide detailed probe-level
 annotation and recommendations for quality control.
description: Genotypes matrix
 (markers x samples)Genotypes matrix
 (markers x samples) as comma-separated values. First six headers are
 chromosome, marker name, genetic position (cM), physical position (bp, in
 build mm10/GRCm38 of the mouse reference genome), reference allele
 ("A1"), alternate allele ("A2"). Remaining column
 headers are sample names.refsamples.csv.zipGenotypes in PLINK binary format (*.bed; 1 of 3)Genotypes for reference samples in PLINK binary format. The PLINK format entails three separate files: *.bed (binary-encoded genotypes), *.bim (marker map) and *.fam (sample metadata).refsamples.bedGenotypes in PLINK binary format (*.bim; 2 of 3)Genotypes for reference samples in PLINK binary format. The PLINK format entails three separate files: *.bed (binary-encoded genotypes), *.bim (marker map) and *.fam (sample metadata).refsamples.bimGenotypes in PLINK binary format (*.fam; 3 of 3)Genotypes for reference samples in PLINK binary format. The PLINK format entails three separate files: *.bed (binary-encoded genotypes), *.bim (marker map) and *.fam (sample metadata).refsamples.famGenotypes as a compressed R data object (*.rds)Genotypes matrix (markers x samples) as an R data object for use with the `argyle` package (https://github.com/andrewparkermorgan/argyle). The object itself is just a matrix, with marker and sample metadata as attributes. For details see the `argyle` manuscript: Morgan AP (2016) argyle: an R package for analysis of Illumina genotyping arrays. G3 6: 281-286. http://www.g3journal.org/content/6/2/281refsamples.rds
DOI: 10.5061/dryad.768sd
title: Data from: Selection on male sex pheromone composition contributes to butterfly reproductive isolation

description: Selection can facilitate diversification by inducing character
 displacement in mate choice traits that reduce the probability of
 maladaptive mating between lineages. Although reproductive character
 displacement (RCD) has been demonstrated in two-taxa case studies, the
 frequency of this process in nature is still debated. Moreover, studies
 have focused primarily on visual and acoustic traits, despite the fact
 that chemical communication is probably the most common means of species
 recognition. Here, we showed in a large, mostly sympatric, butterfly
 genus, a strong pattern of recurrent RCD for predicted male sex pheromone
 composition, but not for visual mate choice traits. Our results suggest
 that RCD is not anecdotal, and that selection for divergence in male sex
 pheromone composition contributed to reproductive isolation within the
 Bicyclus genus. We propose that selection may target olfactory mate choice
 traits as a more common sensory modality to ensure reproductive isolation
 among diverging lineages than previously envisaged.
description: raw GC-MS data, traits
 matrices, and tree filesbacquet_brattström_et_al._-_selection_on_male_sex_pheromone_composition.zip
DOI: 10.5061/dryad.b835k
title: Data from: Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth

description: Among various advantages, their small size makes model organisms preferred
 subjects of investigation. Yet, even in model systems detailed analysis of
 numerous developmental processes at cellular level is severely hampered by
 their scale. For instance, secondary growth of Arabidopsis hypocotyls
 creates a radial pattern of highly specialized tissues that comprises
 several thousand cells starting from a few dozen. This dynamic process is
 difficult to follow because of its scale and because it can only be
 investigated invasively, precluding comprehensive understanding of the
 cell proliferation, differentiation, and patterning events involved. To
 overcome such limitation, we established an automated quantitative
 histology approach. We acquired hypocotyl cross-sections from tiled
 high-resolution images and extracted their information content using
 custom high-throughput image processing and segmentation. Coupled with
 automated cell type recognition through machine learning, we could
 establish a cellular resolution atlas that reveals vascular morphodynamics
 during secondary growth, for example equidistant phloem pole formation.
description: Sankar_et_al_data_file_1Archive of tables (tab-delimited) combining raw data for all features of all cells in all sections obtained from Col-0.Sankar_et_al_data_file_2Archive of tables (tab-delimited) combining raw data for all features of all cells in all sections obtained from Ler.Sankar_et_al_data_file_3Archive of tables (tab-delimited) of the normalized data used in machine learning for the Col-0 sections from data file 1, combined with the cell type assignment.Sankar_et_al_data_file_4Archive of tables (tab-delimited) of the normalized data used in machine learning for the Ler sections from data file 2, combined with the cell type assignment.
DOI: 10.5061/dryad.v6q0p
title: Data from: Comparison of taxon-specific versus general locus sets for targeted sequence capture for plant phylogenomics

description: Premise of the study: Targeted sequence capture can be used to efficiently
 gather sequence data for large numbers of loci, such as single-copy
 nuclear loci. Most published studies in plants have used taxon-specific
 locus sets developed individually for a clade using multiple genomic and
 transcriptomic resources. General locus sets can also be developed from
 loci that have been identified as single-copy and having orthologs in
 large clades of plants. Methods: We identify and compare a taxon-specific
 locus set and three general locus sets (COSII, APVO SSC, PPR) for targeted
 sequence capture in Buddleja (Scrophulariaceae) and outgroups. We evaluate
 their performance in terms of assembly success, sequence variability, and
 resolution and support of inferred phylogenetic trees. Results: The
 taxon-specific locus set had the most target loci. Assembly success was
 high for all locus sets in Buddleja samples. For outgroups, general locus
 sets had greater assembly success. Taxon-specific and PPR loci had the
 highest average variability. The taxon-specific dataset produced the best
 supported tree, but all datasets showed improved resolution over previous
 non-sequence capture datasets. Discussion: General loci can be a useful
 source of sequence capture targets, especially if multiple genomic
 resources are not available for a taxon.
description: Target locus sequences
 for probe designSet of sequences from
 Buddleja globosa of putative single-copy nuclear loci used for probe
 design. Includes taxon-specific loci (sequence name beginning with GRFT or
 XRLM) and COSII, APVO SSC, and PPR loci.Target_Locus_Set.fastaAssembly of Buddleja globosa genomeAssembly of Buddleja globosa genome (voucher: R.G. Olmstead 2010-46 [WTU]) from whole-genome shotgun Illumina sequencing. De novo assembly performed with CLC Genomics Server 5.0.2.Buddleja_globosa_Assembly.fastaBuddleja APVO SSC loci concatenated alignmentBuddleja-APVO-alignment.nexBuddleja COSII loci concatenated alignmentBuddleja-COSII-alignment.nexBuddleja PPR loci concatenated alignmentBuddleja-PPR-alignment.nexBuddleja taxon-specific loci concatenated alignmentBuddleja-taxon_specific-alignment.nex