cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.3bh66/1
title: 1 km2 data file

description: Data file of vegetation vertical structure and physical, soil, socioeconomic, urban form, and landscape structure variables at 1 km2 spatial resolution across Brisbane, Australia.
DOI: 10.5061/dryad.3bh66/3
title: Spatial autoregressive model (SAR) R script for 1 ha analysis

description: R script of spatial autoregressive models (SAR) and model selection at a 1 ha spatial resolution
DOI: 10.5061/dryad.3bh66/4
title: Spatial Autoregressive Model (SAR) R script for 1 km2 analysis

description: R script of spatial autoregressive models (SAR) and model selection at a 1 km2 spatial resolution
DOI: 10.5061/dryad.3bh66/5
title: Custom R Spatial autoregressive model (SAR) functions

description: Custom R functions for creation of multiple spatial autoregressive (SAR) models, diagnostics for model evaluation, calculation of Nagelkerke-R2 values, and plotting of partial residual results.
DOI: 10.5061/dryad.51db2/1
title: All Supplemental Info

description: All supplementary tables as worksheets in Excel Workbook, and supplementary methods in MS Word file.
DOI: 10.5061/dryad.51db2/2
title: CheckDrosMetExpression

description: R script to compare R. pomonella expression data to gene expression during early pupal morphogenesis in Drosophila melanogaster.
DOI: 10.5061/dryad.51db2/3
title: DEbtHostRacesAndDrosEarlyMetGenes

description: Annotation (flybase ids) for transcripts significantly differentially regulated between Rhagoletis host races also &gt;= 2-fold differentially regulated between 0 and 24 hours post pupariation in Drosophila melanogaster (see accompanying R script)
DOI: 10.5061/dryad.83k00/1
title: L.montandoni VCF's

description: Variant calling files (VCF) for L. montandoni individuals. Archive consists of 2886 VCF files - one for each individual (39) times each gene (74)
DOI: 10.5061/dryad.83k00/2
title: L.vulgaris VCF's

description: Variant calling files (VCF) for L. vulgaris individuals. Archive consists of 3330 VCF files - one for each individual (45) times each gene (74)
DOI: 10.5061/dryad.83k00/3
title: Poly dataset

description: Alignments of sequenced markers used to calculate polymorphism and differentiation, so called Poly dataset in the publication. Archive consists of 74 fasta files. Sequence names consist of marker name, individual name (correspond to Table S1) and allele.
DOI: 10.5061/dryad.83k00/4
title: ABC dataset

description: Fasta file containing alignment of sequences used to calculate observed summary statistics. So called ABC dataset in the publication.
DOI: 10.5061/dryad.83k00/5
title: Cleaned fasta files

description: ABC dataset was obtained from these files by subsampling random gene copy per population. Archive consists of 66 cleaned (no gaps, no alignment columns with missing data, no individuals with more than 10% missing data) fasta files.
DOI: 10.5061/dryad.87rc8/1
title: Ornithodolite data

description: This file contains the Ornithodolite data from gulls soaring over two sites that border Swansea Bay.
DOI: 10.5061/dryad.87rc8/2
title: Flight paths for CFD modelling

description: This workbook contains two worksheets that detail (i) wind data (collected from balloon ascents) and (ii) summary data from birds gliding over hotels that border Swansea Bay. These data were used to estimate the wind fields around these flight paths, when combined with CFD model outputs. This file is also described in the README file for the Ornithodolite data.
DOI: 10.5061/dryad.87rc8/3
title: WindData

description: The excel file "WindData" includes the 2D (averaged wind vector components) along the Y,Z axis. Data are available for each session where the positions of gulls soaring over the seafront hotels were recorded.
DOI: 10.5061/dryad.87rc8/4
title: Glide_polar

description: This contains the MATLAB code to estimate the glide polar for a fixed wing gull.
DOI: 10.5061/dryad.d2tr2/1
title: data for the paper

description: This data source contained all of the result data in the paper. Please see the readme file to see the description of each dataset.
DOI: 10.5061/dryad.d4r50/10
title: Stacks catalogue output

description: Raw output data from Stacks catalogue - includes alleles, SNPs and RAD tags called from Stacks reference mapping pipeline.
DOI: 10.5061/dryad.d4r50/8
title: Stacks raw genotypes

description: Raw genotypes from Stacks calling pipeline.
DOI: 10.5061/dryad.d4r50/9
title: Convert stacks raw output to Rqtl

description: Utility tool to convert stacks raw output to Rqtl input
DOI: 10.5061/dryad.g549v/1
title: Supplementary Figure 1

description: Simulation procedure for dropping RAD-seq data by mutation-disruption in the program simrrls.
DOI: 10.5061/dryad.g549v/2
title: Supplementary Figure 2

description: The impact of missing data on quartet informativeness for simulated data (a-f) and the empirical Viburnum data set (g). This is an extension of Fig. 1. Data were simulated on three topologies, a balanced tree, an imbalanced tree, and the Viburnum topology with branch lengths scaled by penalized likelihood, and the outgroup removed. The number of loci that are quartet informative for each split is shown under each tree. In the absence of missing data all 1,000 simulated loci are informative about every edge (a-f; black circles). Under mutation-disruption (a-c) quartet information is lost faster in double-digest data (light grey) than in single-digest data (dark grey), and its effect varies depending on tree shape (see description in Fig. 1). Data simulated at low sequencing coverage (d-f) had either 50% (dark grey) or 80% (light grey)
of data randomly missing. Here the effect of tree shape is more pronounced. Nearly all information is recovered across the deepest splits in the balanced topology (d) due to its hierarchical redundancy, but no data is recovered in the imbalanced topology (e) which does not increase in hierarchical redundancy across deeper edges. The empiricalViburnum topology is relatively balanced, and data simulated on this topology (c, f) appears similar to that simulated on the balanced topology (a, d). The true distribution of quartet informativeness recovered in the Viburnum RAD-seq data set (g) is similar to the expectation when data were simulated on this topology under low sequencing coverage (f).
DOI: 10.5061/dryad.g549v/3
title: Supplementary Figure 3

description: The effect of two forms of mutation-disruption in causing allelic dropout in simulations. Single digest (restriction recognition site length = 4, 6, or 8 bp; grey) and double digest (ezyme1 recognition length = 4, 6, or 8, and enzyme2 recognition length = 4; black) differ in the number of loci retained. (a) When only mutations occurring within cut sites cause disruption (dashed lines) longer cutters recover fewer data than short cutters. When only mutations giving rise to new cut sites within sequences cause disruption (solid lines) shorter cutters recover fewer data than long cutters. In both cases, the double digest data recover fewer loci than single digest, due to the greater opportunity for disruption. (b) When both forms of disruption cause dropout simultaneously the length of a single cutter (4, 6, or 8 bp) has little effect on the amount of data loss, while adding a second independent cutter causes the rate of mutation-disruption to approximately double.
DOI: 10.5061/dryad.g549v/4
title: Supplementary Figure 4

description: Scatterplots of the number of shared loci among quartets of sampled individuals in ten empirical data sets and their relationship with two predictor variables: phylogenetic distance and log median number of input reads. All values are mean-standardized
DOI: 10.5061/dryad.g549v/5
title: Supplementary Figure 5

description: Histograms of sequencing depth (coverage) in clusters recovered by pyrad across ten empirical data sets. In each, the sample with the fewest excluded low depth clusters (loci at depth &lt;6X; green) is shown. The proportion of low coverage loci varies greatly across data sets with respect to total sequencing effort and the evenness of sequencing (Table 1).
DOI: 10.5061/dryad.g549v/6
title: Supplementary Table 1

description: Archived locations of raw data files for ten empirical RAD-seq data sets. Jupyter notebooks containing the code and assembly statistics for each assembled data set are available in the online repository: https://github.com/dereneaton/RADmissing.
DOI: 10.5061/dryad.g549v/7
title: Supplementary Table 2

description: Sequence read archive metadata for bioproject accession PRJNA299402 -- Viburnum RAD sequences.
DOI: 10.5061/dryad.g549v/8
title: Supplementary Figure 6

description: Maximum likelihood phylogeny of Viburnum inferred from the full (a) and half (b) min4 data sets, and (c) a species tree constructed by quartet-joining with quartets inferred from the full min4 SNP alignment.
DOI: 10.5061/dryad.gm061/1
title: Rodent Bartonella Infection Data (Species-level Bartonella identification)

description: Bartonella infection status of all blood samples taken from Wood Mice and Bank Voles at Manor Wood (MW) at 3 sites in northwest England during 2011 and 2012 - Maresfield and Gordale (MFG) and Rode Hall (RH). Where "NUM.pITS.BANDS" is &gt;1, this indicates that multiple bands were present on the gel following a genus-specific Bartonella PCR, thus indicating coinfection. Bartonella species identity were determined by size of the PCR amplicon. Presence or absence of each of 7 Bartonella species is indicated in separate columns. "HOST.ID" is a unique number given to each individual rodent using a subcutaneous microchip. "BLOOD.NO" identifies unique blood samples.
DOI: 10.5061/dryad.gm061/2
title: Rodent Bartonella Sequence Data

description: The identity of pITS variants detected in wood mice and bank voles sampled from 3 sites in Northwest England during 2011-2012 - Manor Wood (MW), Maresfield and Gordale (MFG) and Rode Hall (RH). "HOST.ID" is a unique number given to each rodent using a subcutaneous microchip. "BLOOD.NO." is a number that identifies each unique blood sample. Species identity of Bartonella was first determined by running PCR amplicons on an agarose gel. Most Bartonella species differ in length at this region of DNA and positive samples were grouped accordingly (size A = doshiae in BV and doshiae-like in WM, size B = grahamii, size C = taylorii, size D = birtlesii, size E = rochalimae-like in BV and BGA in WM). pITS band size is given for each sample, as multiple bands may have been sequenced from the same coinfected sample.
DOI: 10.5061/dryad.gm061/3
title: Flea Data

description: The species identity of fleas collected from Wood Mice and Bank Voles at 3 different sites in northwest England during 2012-2014 - Maresfield and Goredale (MFG), Rode Hall (RH) and Haddon Wood (HW). "FLEA.ID" is a unique identifier given to each flea collected. Information is given on whether DNA was extracted from each flea ("DNA.EXTRACTED.FROM.FLEA"), whether Bartonella DNA was detected in each flea ("BARTONELLA.DETECTED.IN.FLEA") and whether the Bartonella DNA was sequenced at the pITS region ("FLEA.BARTONELLA.SEQUENCED"), along with the pITS Bartonella variant detected in each flea ("BARTONELLA.VARIANT.IN.FLEA"). Information is also given about the rodent host from which each flea was collected, including "HOST.ID" (a unique identifier given to each rodent using a subcutaneous microchip), "HOST.BLOOD.NO." (the corresponding blood sample for each host if collected during the same sampling session), "BLOOD.BARTONELLA.PRESENT" (the infection status of host blood samples where available) and "BLOOD.BARTONELLA.VARIANT" (the pITS variant of any corresponding Bartonella-positive blood samples that were also sequenced at the pITS region).
DOI: 10.5061/dryad.j6fj6/1
title: Data collected from truck drivers

description: Screening for obstructive sleep apnea in truck drivers; frequency of driving accidents and near miss accidents.
DOI: 10.5061/dryad.jh65f/1
title: nutrients treeline

description: File containing data from C reserves and leaf nutrient concentrations of a treeline species (Nothofagus pumilio) in the southern Andes of Chile
DOI: 10.5061/dryad.m2h55/1
title: Data from Sperm competition trials, male reproductive organs and sperm measurements

description: Sheet1 contains behavioural data and paternity outcome from sperm competition trials; Sheet2 contains data from computer assisted sperm analysis; Sheet3 contains data on male reproductive organ weights; Sheet4 contains variable descriptions
DOI: 10.5061/dryad.m35k6/1
title: ModelSel_BRCR_2012_DynamicAll

description: AIC model selection table and associated coefficients for brown creeper 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/2
title: ModelSel_BRCR_2013_DynamicAll

description: AIC model selection table and associated coefficients for brown creeper 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/3
title: ModelSel_CBCH_2012_DynamicAll

description: AIC model selection table and associated coefficients for chestnut-backed chickadee 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/4
title: ModelSel_CBCH_2013_DynamicAll

description: AIC model selection table and associated coefficients for chestnut-backed chickadee 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/5
title: ModelSel_GCKI_2012_DynamicAll

description: AIC model selection table and associated coefficients for golden-crowned kinglet 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/6
title: ModelSel_GCKI_2013_DynamicAll

description: AIC model selection table and associated coefficients for golden-crowned kinglet 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/7
title: ModelSel_HAFL_2012_DynamicAll

description: AIC model selection table and associated coefficients for Hammond's flycatcher 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.m35k6/8
title: ModelSel_HAFL_2013_DynamicAll

description: AIC model selection table and associated coefficients for Hammond's flycatcher 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy.  Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt.
DOI: 10.5061/dryad.nc3f2/1
title: Specimen catalogue for the Minto Formation.

description: This file is a catalogue of all vertebrate specimens reported from the Minto Formation. Details include genus and species names, number of fossil material for each specimen and the lithological horizon, with reference to Ó Gogáin et al. 2016, that each specimen belongs to.
DOI: 10.5061/dryad.q9c54.1
title: Data from: Consensus forecasting of intertidal seagrass habitat in the Wadden Sea
DOI: 10.5061/dryad.q9c54.2/13.2
title: 4_gop: R-script for computing goodness of prediction

description: Goodness of Prediction is computed for all models based on random and spatial cross-validation. The gop of the consensus model is also computed. The script outputs the predictions of the best (lowest RMSE) models and latex tables.
DOI: 10.5061/dryad.q9c54.2/14.2
title: 5_ensemble: R-script for constructing the ensemble of predictions
DOI: 10.5061/dryad.q9c54.2/15.2
title: 6_consensus: R script to produce the consensus predictions

description: This script produces the consensus predictions (weighted average and weighted prediction variance) on the basis of the ensemble of predictions. It also adds the presence only data.
DOI: 10.5061/dryad.q9c54.2/16.2
title: 7_Boyce: R script for validation with presence-only data

description: Area-adjusted frequency of the presence of seagrass (“continuous Boyce index”) vs HSS (habitat suitability score) on the basis of a rolling mean.
DOI: 10.5061/dryad.q9c54.2/17.2
title: Presence only shapefile

description: A shapefile (epsg:3035) with observations/occurrences of seagrass. It is presence-only because absences are not systematically scored.
DOI: 10.5061/dryad.q9c54.2/18.2
title: sessionInfo

description: The output of the R-function sessionInfo() which shows the used versions of R and packages.
DOI: 10.5061/dryad.q9c54.2/19.2
title: Consensus habitat suitability scores (geotif; epsg:3035)

description: Consensus habitat suitability scores for littoral seagrasshabitat in the Wadden Sea.
DOI: 10.5061/dryad.q9c54.2/20.2
title: Spread in the habitat suitability scores (geotif; epsg:3035)

description: Spread in the habitat suitability scores for littoralseagrass habitat in the Wadden Sea.
DOI: 10.5061/dryad.q9c54.2/21.2
title: Reclassified habitat suitability scores (geotif; epsg:3035)

description: Reclassification (3 classes) of the consensus suitabilityscores for littoral seagrass habitat in the Wadden Sea.
DOI: 10.5061/dryad.r7s92/1
title: Supplementary figure 1

description: Rank abundance distributions for habitats at three taxonomic levels
DOI: 10.5061/dryad.r7s92/2
title: Supplementary figure 2

description: Evenness and species richness of the four habitats at three taxonomic levels.
DOI: 10.5061/dryad.r7s92/3
title: Supplementary figure 3

description: Distribution of p-values from Mantel test for Spearman correlation between dissimilarity matrices representing different taxonomic and numerical levels. A-C, Correlation between taxonomic levels at different numerical resolutions. D-F, Correlation between proportional abundance data and higher levels of numerical transformation. Filled points represent median p-values across 1000 subsampling iterations, empty points are outliers that lie beyond 1.5 times the interquartile range from the upper quartile.
DOI: 10.5061/dryad.s7m33/1
title: Msat Primer info for Bird Id in Insects

description: Information about microsatellite primers used to ID blood meals of vectors.
DOI: 10.5061/dryad.s7m33/2
title: MS Data Full

description: Raw data for manuscript. Separate tabs indicate corresponding experiments or data sets analyzed.
DOI: 10.5061/dryad.sm107/2
title: A collection of intrinsic disorder characterizations from eukaryotic proteomes

description: This is a sqlite3 file containing intrinsic disorder characterizations from ten eukaryotic proteomes. For detailed information regarding the content and the database schema, please refer to Vincent and Schnell, 2016.
DOI: 10.5061/dryad.tf71f/1
title: 2016_Richman_IpomopsisSecondaryRobbing

description: Contains metadata and all data reported in the publication
DOI: 10.5061/dryad.v7g7d/1
title: Table S1 - Barcoding data used in tree construction

description: Table S1: Codes for sequences used in the construction of the NJ tree for hoverflies (Diptera: Syrphidae).  See Figure S1 for the finished neighbour joining tree and the main text for the analytical procedures by which those sequences were processed.
DOI: 10.17632/27864x5sp6.1
title: An Efficient Method for the Design of a Look-Up-Table (LUT) using Reversible Fault Tolerant Gates

description: This data presents the design of a reversible fault tolerant architecture for LUT (Look-Up-Table). A new 6×6 fault tolerant reversible gate is introduced for the design of an efficient LUT. The proposed design achieves the improvement in terms of number of gates, Quantum cost and unit delay compared to the best known existing approach. The proposed LUT can be used to implement any combinational Boolean function with minimum number of transistors which satisfies the fault tolerant property.