cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.xksn02vp9
title: Vanishing twins, spared cohorts, and the difference in birthweight between the frailest White and Black infants in the United States

description: Infants born before completing 26 weeks of gestation account for less than
 1% of live births in the United States but more than 40% of infant deaths.
 The rate of these “periviable” births among non-Hispanic (NH) Blacks
 exceeds 4 times that among NH Whites. Among periviable infants, small
 males die most frequently. The mean birthweight of NH White periviable
 male singletons persistently exceeds that of their NH Black counterparts.
 The scientific literature includes no explorations of mechanisms that
 could explain this disparity in birth weight. We offer, and test, the
 argument that survivors of the vanishing twin syndrome, a phenomenon in
 which the slower-growing fetus of a twin pair dies in utero, contribute to
 the disparity. Among male periviable singleton births from 288 monthly
 conception cohorts (1/95 through 12/2018), we found an average NH White
 advantage of 30 grams (759g versus 729g). Consistent with our argument,
 however, cohorts with relatively few survivors of the vanishing twin
 syndrome showed no disparity.
description: # Vanishing twins, spared cohorts, and the difference in birthweight
 between the frailest White and Black infants in the United States
 [https://doi.org/10.5061/dryad.xksn02vp9](https://doi.org/10.5061/dryad.xksn02vp9) ## Description of the data and file structure The data are time series for 288 months beginning January 1995 and ending December 2018. The variable dictionary is as follows. ### Variable Dictionary tperimwN=count of periviable male Nhwhite twins tperimwN=count of periviable male NHwhite twins tperifbN=count of periviable female NHblack twins tperifbN=count of periviable female NHblack twins  \_\_ spmumwbw=mean birthweight for singleton nhwhite males spmdmwbw=median birthweight for singleton nhwhite males spp1mwbw= first percentile birthweight for singleton nhwhite males spp5mwbw =fifth percentile birthweight for singleton nhwhite males spmufwbw=mean birthweight for singleton nhwhite females spmdfwbw=median birthweight for singleton nhwhite females spp1fwbw= first percentile birthweight for singleton nhwhite females spp5fwbw =fifth percentile birthweight for singleton nhwhite females \_\_ spmumbbw=mean birthweight for singleton nhblack males spmdmbbw=median birthweight for singleton nhblack males spp1mbbw= first percentile birthweight for singleton nhblack males spp5mbbw =fifth percentile birthweight for singleton nhblack males spmufbbw=mean birthweight for singleton nhblack females spmdfbbw=median birthweight for singleton nhblack females spp1fbbw= first percentile birthweight for singleton nhblack females spp5fbbw =fifth percentile birthweight for singleton nhblack females ## Sharing/Access information Data were provided to us by the US National Center of Health Statistics (NCHS), Division of Vital Statistics.   ## Code/Software NA
DOI: 10.15468/dl.4u4d3e
title: Occurrence Download

description: A dataset containing 697 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euploea klugii"
 ]
}

 The dataset includes 697 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003895-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.j6q573nnc
title: A public mid-density genotyping platform for cultivated Blueberry

description: Small public breeding programs have many barriers to adopting technology,
 particularly creating, and using genetic marker panels for genomic-based
 decisions in selection. Here we report the creation of a DArTag panel of
 3,000 loci distributed across the tetraploid blueberry genome for use in
 molecular breeding and genomic prediction. The creation of this marker
 panel brings cost-effective and rapid genotyping capabilities to public
 and private breeding programs. The open access provided by this platform
 will allow genetic data sets generated on the marker panel to be compared
 and joined across projects, institutions, and countries. This genotyping
 resource has the power to make routine genotyping a reality for any
 breeder of blueberry.
description: The blueberry 3K DArTag panel was created from whole-genome skim
 sequencing (WGS) of 31 cultivated blueberry accessions focused on elite
 North American breeding lines. A total of 600K SNPs were discovered in the
 WGS. A high-confidence set of 10K SNPs was then identified using the
 following criteria: 1) not located within 5 bp from an indel; 2) QUAL
 &gt; 30; 3) minimum and maximum read depths of 20 and 1500,
 respectively; 4) at each heterozygous site, at least one read supporting
 the reference allele and two reads supporting the alternative allele; 5)
 no missing genotype per SNP position; 6) with a minor allele frequency
 greater than 0.25; 7) not located in transposable elements or within 1 Kb
 of chromosome termini; 8) even genomic distribution and mostly located in
 genic regions. The 10K SNPs were submitted for QC to DArT (Diversity
 Arrays Technology Pty Ltd, www.diversityarrays.com), from which, a 3K SNP set was selected. Additionally, a few experimentally validated SNPs were also force-included in the panel.
description: # A public mid-density genotyping platform for cultivated Blueberry
 [https://doi.org/10.5061/dryad.j6q573nnc](https://doi.org/10.5061/dryad.j6q573nnc) The blueberry 3K DArTag panel contains 3K marker loci evenly distributed throughout the blueberry genome. DArT generates genotyping results in several formats, among which the MADC format (missing allele discovery count) provides all the microhaplotypes (54-81 bp) discovered based on amplicons for the 3K marker loci. These microhaplotypes contain target SNPs per assay design as well as off-target SNPs, which are present in flanking amplicon sequences. BI created a microhaplotype database for the 3K blueberry marker loci. ## Description of the data and file structure This project consists of the following datasets: 1. **20200819-BI-Blueberry_10K_SNPs_forDArT_3K.txt: **The 3K DArTag panel probe information, which contains the target SNP coordinates, their 180 bp flanking sequences, and reference genome used for the SNP discovery 2. **blueberry_allele_db_v017.fa**: The microhaplotype database v17 contains microhaplotypes detected from genotyping more than 9,000 blueberry samples using the 3K DArTag panel.
DOI: 10.5061/dryad.9p8cz8wqf
title: Fish microbiota repel ovipositing mosquitoes

description: The mere presence of predators causes prey organisms to display
 predation-avoidance strategies. Predator presence is often communicated
 through predator-released chemical signals. Ovipositing female mosquitoes
 of several species are repelled by unknown signals released from
 larvivorous fish. It was previously suggested that in many cases, a
 predator’s microbiota plays an important role in the release of these
 signals; however, this mechanism is still poorly understood. In this
 study, we looked into the effects of the microbiota originating from the
 larvivorous Gambusia affinis (Baird and Girard) on the oviposition
 behavior of gravid female mosquitoes. We used fish with altered microbiota
 and bacterial isolates in a set of outdoor mesocosm experiments to address
 this aim. We show that interference with the fish microbiota significantly
 reduces fish’s repellant effect. We further show that the bacterium
 Pantoea pleuroti, isolated from the skin of the fish, repels oviposition
 of Culex laticinctus (Edwards) and Culiseta longiareolata (Macquart)
 mosquitoes similarly to the way in which live fish repel them. Our results
 highlight the importance of bacteria in the interspecies interactions of
 their hosts. Furthermore, this finding may lead to the development of an
 ecologically friendly mosquito repellent, that may reduce the use of
 larvivorous fish for mosquito control.
description: # Fish microbiota repel ovipositing mosquitoes
 [https://doi.org/10.5061/dryad.9p8cz8wqf](https://doi.org/10.5061/dryad.9p8cz8wqf) The dataset includes data from 4 field experiments described in Figures 2-5. It includes the distribution of mosquito egg rafts from two species, *Culex laticinctus* and *Culiseta logiareolata*. Egg rafts were collected from pools as described in the method section. ## Description of the data and file structure Data describes the total number of egg rafts collected over all dates in each of the pools. Each pool is a combination of “block” and “treatment” variables. The other columns present the dependent variables, i.e., total number of egg rafts for each mosquito species. Experimental duration is presented at the bottom and consists of beginning and ending dates plus the total day count.
DOI: 10.5061/dryad.qfttdz0qq
title: Morphometric data for colonies of Evactinostella crucialis

description: One of the challenges for bryozoans is avoiding refiltering of water that
 has already had its plankton removed. Larger colonies develop colony-wide
 maculae-centered feeding currents to avoid refiltering water, and
 generally have elevated maculae (monticules). We hypothesize that height
 and spacing of monticules is inversely proportional to curvature of the
 colony surface. Larger flatter colonies should have higher and more
 closely spaced monticules. We compare two Permian palaeostomate bryozoans
 whose colonies form branches with elliptical cross-sections: the smaller
 and more elliptical cystoporate Evactinostella crucialis from Western
 Australia (n = 17) and the larger and flatter trepostome Tabulipora sp.
 from eastern North Greenland (n = 15). Using calipers and digital
 elevation models we measured curvature, monticule height, and number of
 monticules per area. Results indicate that Evactinostella branches are at
 least twice as curved as those of Tabulipora, their monticules are 1/2 the
 height of Tabulipora, and their monticules are 22% less densely spaced
 than those of Tabulipora. In Evactinostella colonies, surface curvature is
 inversely proportional to monticule height and spatial density which is
 not true for Tabulipora. Therefore, we conclude that the smaller and more
 curved the colony surface, the less the colony needs robust colony-wide
 feeding currents created by tall closely spaced monticules.
description: We measured branch width, branch thickness, and monticule height
 with digital calipers to the nearest 0.01 mm. Branch curvature in
 mm<sup>-1</sup> was calculated as ½ branch thickness/(½ branch
 width)<sup>2</sup>. Monticule spatial density in number per
 mm<sup>2</sup> was calculated as number of monticules per
 branch side/area of branch side. Using the Structure from Motion (SfM)
 method, we built digital elevation models (DEMs) of two branch
 surfaces. The DEMs were built in Agisoft’s Metashape Pro version 1.64 with
 0.1 mm resolution on the x and y coordinate axes and 0.01 mm resolution on
 the z (vertical axis). Topographic profiles through the colony surface
 were made with QGIS version 3.30 from monticule crest to monticule
 crest.
description: # Morphometric data for colonies of *Evactinostella crucialis* 
 Supplementary Table 1
 [https://doi.org/10.5061/dryad.qfttdz0qq](https://doi.org/10.5061/dryad.qfttdz0qq) Morphometric data for colonies of *Evactinostella crucialis* used in this study. ## Description of the data and file structure Standard Excel spreadsheet with colonies in rows and colony identification and morphometric characters in columns. Missing values are denoted by NA.
DOI: 10.5061/dryad.kkwh70sbq
title: PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models

description: We introduce PhyloJunction, a computational framework designed to
 facilitate the prototyping, testing, and characterization of evolutionary
 models. PhyloJunction is distributed as an open-source Python library that
 can be used to implement a variety of models, through its flexible
 graphical modeling architecture and dedicated model specification
 language. Model design and use are exposed to users via command-line and
 graphical interfaces, which integrate the steps of simulating,
 summarizing, and visualizing data. This paper describes the features of
 PhyloJunction – which include, but are not limited to, a general
 implementation of a popular family of phylogenetic diversification models
 – and, moving forward, how it may be expanded to not only include new
 models, but to also become a platform for conducting and teaching
 statistical learning.
description: # PhyloJunction: a computational framework for simulating, developing, and
 teaching evolutionary models
 [https://doi.org/10.5061/dryad.kkwh70sbq](https://doi.org/10.5061/dryad.kkwh70sbq) ## Supplementary files 1. A .pdf file (mendes_landis_24_supp.pdf - Zenodo) detailing the methods and results from analyses carried out to validate PhyloJunction; 2. A tarball (phylojunction_supp.tar.gz) with documentation (NOTES.md) and scripts necessary to reproduce the results presented in (1) above. ## Code/Software Documentation on how to install and use PhyloJunction can be found at [https://phylojunction.org](https://phylojunction.org). PhyloJunction's source code can be found on GitHub at [https://github.com/fkmendes/PhyloJunction/tree/main](https://github.com/fkmendes/PhyloJunction/tree/main).
DOI: 10.5061/dryad.rbnzs7hk6
title: Testing and training data sets for: A novel representation of time-resolved particle emissions from pyrolyzing wood

description: Biomass burning is responsible for emitting 90% of total primary organic
 aerosols into the atmosphere. Products from pyrolysis are released to the
 atmosphere when gas-phase reactions are unable to oxidize them, and even
 narrow windows of release can produce a large fraction of the overall
 particle emissions. This work introduces an approach to predict particle
 emission during high-emitting periods of biomass burning. We trained a
 regression-based machine learning model to predict particle emission using
 data from pyrolysis experiments covering seven wood types under controlled
 conditions. The model considered experimental mass-loss rate (MLR) of the
 wood, wood density, and heating conditions as features for prediction of
 measured particle-phase real-time emissions. After training, the
 experimental mass-loss rate was replaced with modeled MLR from a
 two-dimensional finite volume model of pyrolysis to predict particle
 emission rate using only wood properties and the boundary conditions of
 pyrolysis. The hybrid model explains 80% of the variance in particle
 emission and has comparable error metrics with the machine learning model
 that relies on experimental MLR. Errors are greatest during the initial
 transient where pyrolysis is occurring near the surface.
description: Data collection is described in the associated article and in a
 related article: M. Fawaz, A. Avery, T.B. Onasch, L.R. Williams, T.C.
 Bond, Technical note: Pyrolysis principles explain time-resolved organic
 aerosol release from biomass burning, Atmospheric Chemistry and Physics.
 21 (2021) 15605–15618.
description: # Testing and training data sets for "A novel representation of
 time-resolved particle emissions from pyrolyzing wood"
 [https://doi.org/10.5061/dryad.rbnzs7hk6](https://doi.org/10.5061/dryad.rbnzs7hk6) These files contain measured data from experiments in which solid wood biomass samples, considered thermally thick, were pyrolyzed at different temperatures under nitrogen gas. Mass loss from the wood samples was measured at one-second intervals along with emission rates of pyrolysis products. Further details, especially of the particulate measurement, are contained in the referenced papers. Testing and training data sets were separated using a k-fold split. ## Description of the data and file structure Two files are given PyroPredict_training_set.csv: Data set used to train the gradient boosting regressor machine learning algorithm PyroPredict_testing_set.csv: Data set used to test the algorithm's output Each file contains the following columns: MLR: Mass loss rate (g/s) CO: Carbon monoxide emission rate (g/s) CO2: Carbon dioxide emission rate (g/s) HC: Gaseous hydrocarbon emission rate (g/s) Location: Location within the wood identified as surface (0) or center (1) as described in the paper Density: Density of original sample (kg/m3) Temperature: Fixed or controlled temperature of pyrolysis PyOM: Emission rate of pyrolytic organic matter as partices (g/s)
DOI: 10.15468/dl.j9wu5t
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anchoa marinii",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 48 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003897-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fndeck
title: Occurrence Download

description: A dataset containing 485 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taractrocera maevius"
 ]
}

 The dataset includes 485 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003898-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.4xgxd25ht
title: Original genotype data of 159 wheat samples

description: This dataset contains all 55K SNP original genotype data from 159 wheat
 samples used in the study, including SNP site IDs, chromosomes and
 positions, allele information, and genotype information for each material
 at each SNP site.
description: # Data of 159 original wheat genotypes
 This dataset contains
 genotype data of a total of 53063 SNP loci from 159 wheat samples,
 including 4003 SNP loci that cannot be determined on the chromosome or
 position. ## Description of data and file structure Each column
 of this original dataset includes SNP site ID, chromosome location,
 position on chromosome, allele A, allele B, and wheat material. When
 conducting quality control on the dataset, first filter out SNP sites that
 cannot determine the chromosome and location, and then modify the name of
 each column to: rs #, alleles, chrome, pos, strand, assembly #, center,
 protLSID, asseyLSID, panelLSID, Qccode, wheat material. Among them,
 "rs #", "chrome", and "pos" are SNP site
 IDs, chromosomes, and positions on chromosomes; The "alleles"
 column is formed by combining allele A and allele B and separating them
 with "/"; "Strand" columns are all represented by
 "+"; The columns "assembly #", "center",
 "protLSID", "asseyLSID", "panelLSID", and
 "Qccode" are all represented by "NA"; The wheat
 material remains unchanged. After modifying the original data,
 high-quality loci can be obtained through minimum allele frequency (MAF)
 filtering in VCFtools. ##
DOI: 10.5061/dryad.brv15dvh0
title: Young and older adult vowel categorization responses

description: Age-related changes in auditory processing may reduce physiological coding
 of acoustic cues, contributing to older adults’ difficulty perceiving
 speech in background noise. This study investigated whether older adults
 differed from young adults in patterns of acoustic cue weighting for
 categorizing vowels in quiet and in noise. All participants relied
 primarily on spectral quality to categorize /Ꜫ/ and /æ/ sounds in both
 listening conditions. However, relative to young adults, older adults
 exhibited greater reliance on duration and less reliance on spectral
 quality. These results suggest that aging alters patterns of perceptual
 cue weights that may influence speech recognition abilities.
description: Data were collected via Gorilla Experiment Builder and hosted on
 Prolific to obtain participant responses. Data was downloaded from Gorilla
 and R was used to remove the columns in the dataframe that were not
 necessary for analysis.  
description: # Young and older adult vowel categorization responses
 [https://doi.org/10.5061/dryad.brv15dvh0](https://doi.org/10.5061/dryad.brv15dvh0) On each trial, participants heard a stimulus and clicked a box on the computer screen to indicate whether they heard "SET" or "SAT." Responses of "SET" are coded as 0 and responses of "SAT" are coded as 1. The continuum steps, from 1-7, for duration and spectral quality cues of the stimulus on each trial are named "DurationStep" and "SpectralStep," respectively. Group (young or older adult) and listening condition (quiet or noise) information are provided for each row of the dataset.
DOI: 10.21227/vqhg-tp72
title: "Non-Invasive-Cancer-Detection-Using-Breath-Volatile-Organic-Compounds-and-Support-Vector-Machine"

description: "This dataset provides a synthetic collection of breath-based volatile organic compound (VOC) measurements for the non-invasive detection of cancer using machine learning techniques. The data were generated based on reported concentration ranges of clinically relevant VOC biomarkers, including acetone, isoprene, ethanol, formaldehyde, benzene, toluene, and methane, which are known to be associated with metabolic alterations in cancer patients. In addition to VOC features, demographic variables such as age, sex, and smoking status are included to enhance classification performance.The dataset consists of 1,000 samples, balanced between healthy individuals and cancer cases, and is formatted in CSV to ensure compatibility with common data analysis frameworks. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is employed as the baseline classification model, demonstrating the suitability of the dataset for supervised learning, feature analysis, and performance evaluation using standard biomedical metrics.This dataset is intended for research and educational purposes, particularly in the areas of non-invasive cancer diagnosis, breath analysis, gas sensor data processing, and machine learning model development. It can be used to benchmark classification algorithms, evaluate feature relevance, and support the design of breath-based diagnostic systems."