cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.3d120
title: Data from: Objective choice of phylogeographic models

description: Phylogeography seeks to discover the evolutionary processes that have
 given rise to organismal and genetic diversity. This requires explicit
 hypotheses (i.e., models) to be evaluated with genetic data in order to
 identify those hypotheses that best explain the data. In recent years,
 advancements in the model-based tools used to estimate phylogeographic
 parameters of interest such as gene flow, divergence time, and
 relationships among groups have been made. However, given the complexity
 of these models, available methods can typically only compare a handful of
 possible hypotheses, requiring researchers to specify in advance the small
 set of models to consider. Without formal quantitative approaches to model
 selection, researchers must rely on their intuition to formulate the model
 space to be explored.We explore the adequacy of intuitive choices made by
 researchers during the process of data analysis by reanalyzing 20
 empirical phylogeographic datasets using PHRAPL, an objective tool for
 phylogeographic model selection. We show that the best models for most
 datasets include both gene flow and population divergence parameters, and
 that species tree methods (which do not consider gene flow) tend to be
 overly simplistic for many phylogeographic systems. Objective approaches
 to phylogeographic model selection offer an important complement to
 researcher intuition.
description: dryadCommand files for
 replicating the PHRAPL analyses described in the
 manuscript.
DOI: 10.7284/166520
title: Meteorological Station data collected during research cruise AR85-01 using a Vaisala WXT520 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/kvz3jkyjt3
title: Dataset: Machine Learning applied to Neuroimaging

description: Research Context
Machine learning and deep learning techniques have become increasingly important for analyzing complex neuroimaging and electrophysiological data in the study of Alzheimer’s disease and mild cognitive impairment. These approaches enable the identification of subtle structural, functional, and signal-based patterns that are difficult to detect using traditional analytical methods, supporting early diagnosis and biomarker discovery.

What the Dataset Contains
This dataset compiles structured information extracted from peer-reviewed studies that apply machine learning and deep learning methods to neuroimaging and electroencephalography data for Alzheimer’s disease, mild cognitive impairment, and cognitively normal populations. The dataset includes curated metadata describing imaging modalities, machine learning algorithms, sample characteristics, validation strategies, main findings, and reported biomarkers.
The included studies span multiple data modalities, including structural magnetic resonance imaging, resting-state functional magnetic resonance imaging, positron emission tomography, and electroencephalography, enabling cross-modal comparison of machine learning performance and biomarker relevance.

Notable Insights
Machine learning models applied to neuroimaging data can reliably differentiate Alzheimer’s disease from mild cognitive impairment and cognitively normal controls.
Functional connectivity alterations, particularly within default mode and frontoparietal networks, are recurrent biomarkers in functional magnetic resonance imaging–based studies.
Structural magnetic resonance imaging studies consistently identify hippocampal and temporal lobe atrophy as key discriminative features.
Electroencephalography-based deep learning approaches demonstrate promising performance for non-invasive classification across cognitive stages.
Model performance varies substantially depending on feature extraction methods and validation strategies, highlighting the importance of methodological transparency.

Data Interpretation and Intended Use
The dataset is designed for meta-analytical research, methodological comparison of machine learning approaches, and educational purposes. Researchers can use this dataset to:
Conduct systematic reviews and meta-analyses of machine learning performance in neuroimaging.
Compare biomarkers across imaging modalities.
Evaluate the impact of validation strategies on reported classification accuracy.
Support reproducibility and secondary analyses in computational neuroscience research.

Data Acquisition Summary
All data were manually extracted from peer-reviewed publications retrieved from major scientific databases and curated into a standardized structure compatible with Mendeley Data. Each study is documented through structured summary files and a consolidated dataset table, ensuring traceability, consistency, and reproducibility.
DOI: 10.17632/kvz3jkyjt3.1
title: Dataset: Machine Learning applied to Neuroimaging

description: Research Context
Machine learning and deep learning techniques have become increasingly important for analyzing complex neuroimaging and electrophysiological data in the study of Alzheimer’s disease and mild cognitive impairment. These approaches enable the identification of subtle structural, functional, and signal-based patterns that are difficult to detect using traditional analytical methods, supporting early diagnosis and biomarker discovery.

What the Dataset Contains
This dataset compiles structured information extracted from peer-reviewed studies that apply machine learning and deep learning methods to neuroimaging and electroencephalography data for Alzheimer’s disease, mild cognitive impairment, and cognitively normal populations. The dataset includes curated metadata describing imaging modalities, machine learning algorithms, sample characteristics, validation strategies, main findings, and reported biomarkers.
The included studies span multiple data modalities, including structural magnetic resonance imaging, resting-state functional magnetic resonance imaging, positron emission tomography, and electroencephalography, enabling cross-modal comparison of machine learning performance and biomarker relevance.

Notable Insights
Machine learning models applied to neuroimaging data can reliably differentiate Alzheimer’s disease from mild cognitive impairment and cognitively normal controls.
Functional connectivity alterations, particularly within default mode and frontoparietal networks, are recurrent biomarkers in functional magnetic resonance imaging–based studies.
Structural magnetic resonance imaging studies consistently identify hippocampal and temporal lobe atrophy as key discriminative features.
Electroencephalography-based deep learning approaches demonstrate promising performance for non-invasive classification across cognitive stages.
Model performance varies substantially depending on feature extraction methods and validation strategies, highlighting the importance of methodological transparency.

Data Interpretation and Intended Use
The dataset is designed for meta-analytical research, methodological comparison of machine learning approaches, and educational purposes. Researchers can use this dataset to:
Conduct systematic reviews and meta-analyses of machine learning performance in neuroimaging.
Compare biomarkers across imaging modalities.
Evaluate the impact of validation strategies on reported classification accuracy.
Support reproducibility and secondary analyses in computational neuroscience research.

Data Acquisition Summary
All data were manually extracted from peer-reviewed publications retrieved from major scientific databases and curated into a standardized structure compatible with Mendeley Data. Each study is documented through structured summary files and a consolidated dataset table, ensuring traceability, consistency, and reproducibility.
DOI: 10.5061/dryad.2hj75
title: Data from: Theory, practice, and conservation in the age of genomics: the Galápagos giant tortoise as a case study

description: Hgh-throughput DNA sequencing allows efficient discovery of thousands of
 single nucleotide polymorphisms (SNPs) in non-model species. Population
 genetic theory predicts that this large number of independent markers
 should provide detailed insights into population structure, even when only
 a few individuals are sampled. Still, sampling design can have a strong
 impact on such inferences. Here, we use simulations and empirical SNP data
 to investigate the impacts of sampling design on estimating genetic
 differentiation among populations that represent three species of
 Galápagos giant tortoises (Chelonoidis spp.). Though microsatellite and
 mitochondrial DNA analyses have supported the distinctiveness of these
 species, a recent study called into question how well these markers
 matched with data from genomic SNPs, thereby questioning decades of
 studies in non-model organisms. Using &gt;20,000 genome-wide SNPs from
 30 individuals from three Galápagos giant tortoise species, we find
 distinct structure that matches the relationships described by the
 traditional genetic markers. Furthermore, we confirm that accurate
 estimates of genetic differentiation in highly structured natural
 populations can be obtained using thousands of SNPs and 2-5 individuals,
 or hundreds of SNPs and 10 individuals, but only if the units of analysis
 are delineated in a way that is consistent with evolutionary history. We
 show that the lack of structure in the recent SNP-based study was likely
 due to unnatural grouping of individuals and erroneous genotype filtering.
 Our study demonstrates that genomic data enable patterns of genetic
 differentiation among populations to be elucidated even with few samples
 per population, and underscores the importance of sampling design. These
 results have specific implications for studies of population structure in
 endangered species and subsequent management decisions.
description: polymorphic_snps_pbl_cru_vaVCF file of the polymorphic loci among 30 individuals from three Galápagos giant tortoise species. Data are derived from ddRAD sequencing. PBL corresponds to Chelonoidis becki, CRU corresponds to C. porteri, and VA corresponds to C. vandenburghi.nomiss3.vcf
DOI: 10.7284/166530
title: Multibeam Echosounder data collected during research cruise AR85-01 using a Kongsberg EM712 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5061/dryad.b8d23
title: Data from: Nowhere to hide: the impact of linear disturbances on the spatial dynamics of predator and prey in a large mammal system

description: Rapid landscape alteration associated with human activity is currently
 challenging the evolved dynamical stability of many predator-prey systems
 by forcing species to behaviorally respond to novel environmental stimuli.
 In many forested systems, linear features (LFs) such as roads, pipelines
 and resource exploration lines (i.e. seismic lines) are a ubiquitous form
 of landscape alteration that have been implicated in altering
 predator-prey dynamics. One hypothesized effect is that LFs facilitate
 predator movement into and within prey refugia, thereby increasing
 predator-prey spatial overlap. We evaluated this hypothesis in a large
 mammal system, focusing on the interactions between boreal woodland
 caribou (Rangifer tarandus caribou) and their two main predators, wolves
 (Canis lupus) and black bears (Ursus americanus), during the calving
 season of caribou. In this system, LFs extend into and occur within
 peatlands (i.e. bogs and nutrient-poor fens), a habitat type highly used
 by caribou due to its refugia effects. Using resource selection analyses,
 we found that LFs increased predator selection of peatlands. Female
 caribou appeared to respond by avoiding LFs and areas with high LF
 density. However, in our study area most caribou cannot completely avoid
 exposure to LFs and variation in female response had demographic effects.
 In particular, increasing proportional use of LFs by females negatively
 impacted survival of their neonate calves. Collectively, these results
 demonstrate how LFs can reduce the efficacy of prey refugia. Mitigating
 such effects will require limiting or restoring LFs within prey refugia,
 though the effectiveness of mitigation efforts will depend upon spatial
 scale, which in turn will be influenced by the life history traits of
 predator and prey.
description: Caribou GPS Location
 Datacsv file containing GPS
 location data of boreal caribou. Note that data has been cleaned as per
 the methods outlined in the Journal of Animal ecology
 paper.AllCaribouDataSpatialOverlapAnalysis.csvWolf GPS Location Datacsv file containing GPS location data of wolves from northeast British Columbia. Note that data have been cleaned as per the methods outlines in the Journal of Animal Ecology paper.All_Wolf_Data_2012_15_clean.csvBlack Bear GPS Location Datacsv file containing GPS location data of black bears in northeast British Columbia. Data have been cleaned as per the methods outlined in the Journal of Animal Ecology paper.All_Bear_Data_no2D_greaterthan5.csv
DOI: 10.7284/166497
title: Radiometer data collected during research cruise AR85-01 using a Eppley RAD instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/166498
title: Radiometer data collected during research cruise AR85-01 using a Biospherical QSR-2150 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5061/dryad.6737v
title: Data from: Multiple scaling behavior and nonlinear traits in music scores

description: We present a statistical analysis of music scores from different composers
 using detrended fluctuation analysis. We find different fluctuation
 profiles that correspond to distinct auto-correlation structures of the
 musical pieces. Further, we reveal evidence for the presence of nonlinear
 auto-correlations by estimating the detrended fluctuation analysis of the
 magnitude series, a result validated by a corresponding study of
 appropriate surrogate data. The amount and the character of nonlinear
 correlations vary from one composer to another. Finally, we performed a
 simple experiment in order to evaluate the pleasantness of the musical
 surrogate pieces in comparison with the original music and find that
 nonlinear correlations could play an important role in the aesthetic
 perception of a musical piece.
description: SI-masterSupporting Information, here are the graphs for the fluctuation functions. The graphs are labeled as following: Palestrina Motets: PalestrinaMo-# Bach Musical Offering: BachMO-# Bach Well Tempered Clavier: BachWTK-# Haydn String Quartets: HaydnSQ-# Mozart Piano Sonatas: MozartPS-# Mozart String Quartets: MozartSQ-# Beethoven Fugues: BeethovenF-# Beethoven Early Quartets: BeethovenEQ-# Beethoven Late Quartets: BeethovenLQ-# Dvorak Humoresques: DvorakH-# Dvorak Silhouettes: DvorakS-# Dvorak Serenade for Strings: DvorakSt-# Shostakovich Preludes and Fugues: ShostakovichPF-# The number corresponds to the place in the full list of opus. Files corresponding to a scaling behavior over all s boxes (profile 1) are in Scaling.pdf, files corresponding to the functions showing a crossover (profiles 1 and 2) are in Crossovers.pdf, the files with the graps of profiles 4 and 5 are in NoScaling.pdf The values of the alpha's and R squared for the linear fitting in the fluctuations function are on the Tables: TableScaling.pdf corresponds to the functions following a power law and TableCrossovers.pdf to the functions with a crossover.InfoSeries.jl-masterRepository for every calculation of the DFA and the construction of the time series from the .csv file. The repository can also be consulted in GitHub: https://github.com/spiralizing/InfoSeries.jlSupplementary MaterialFull list of opus analyzed, details for the construction of the time series, individual DFA functions and their relation with the power spectra. Description of the survey.SupplementaryMaterial.pdfSM-TimeSeriesAll time series analyzed and the four tracks of the Survey. The time series were constructed as described in the Supplementary Material, from MIDIs downloaded from the web databases mentioned in references.
DOI: 10.17632/xbd3yn4kp2
title: Sustainable Stabilization of Expansive Soils Using Basalt Fibers: Multi-Criteria Optimization for Low-Carbon Subgrade Engineering

description: Test Data
DOI: 10.7284/166525
title: Speedlog data collected during research cruise AR85-01 using a Furuno DS-60 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/xbd3yn4kp2.1
title: Sustainable Stabilization of Expansive Soils Using Basalt Fibers: Multi-Criteria Optimization for Low-Carbon Subgrade Engineering

description: Test Data
DOI: 10.5061/dryad.6514r
title: Data from: Seeing spots: quantifying mother-offspring similarity and assessing fitness consequences of coat pattern traits in a wild population of giraffes (Giraffa camelopardalis)

description: Polymorphic phenotypes of mammalian coat coloration have been important to
 the study of genetics and evolution, but less is known about the
 inheritance and fitness consequences of individual variation in complex
 coat pattern traits such as spots and stripes. Giraffe coat markings are
 highly complex and variable and it has been hypothesized that variation in
 coat patterns most likely affects fitness by camouflaging neonates against
 visually hunting predators. We quantified complex coat pattern traits of
 wild Masai giraffes using image analysis software, determined the
 similarity of spot pattern traits between mother and offspring, and
 assessed whether variation in spot pattern traits was related to fitness
 as measured by juvenile survival. The methods we described could comprise
 a framework for objective quantification of complex mammal coat pattern
 traits based on photographic coat pattern data. We demonstrated that some
 characteristics of giraffe coat spot shape were likely to be heritable, as
 measured by mother-offspring regression. We found significant variation in
 juvenile survival among phenotypic groups of neonates defined by
 multivariate clustering based on spot trait measurement variables. We also
 found significant variation in neonatal survival associated with spot size
 and shape covariates. Larger spots (smaller number of spots) and
 irregularly shaped spots (smaller aspect ratio) were correlated with
 increased survival. These findings will inform investigations into
 developmental and genetic architecture of complex mammal coat patterns and
 their adaptive value.
description: Calf_Mother_Spots_Output_from_ImageJOutput from ImageJ 'analyze particles' procedure for 32 mother-calf giraffe pairs. M refers to mothers, C refers to calves, numbers identify mother-calf pairs.SpotSurvivalMARK input file for estimating calf survival as a function of spot trait covariates.InheritancePicsImage files used in giraffe coat pattern trait heritability analyses. M refers to mothers, C refers to calves, numbers identify mother-calf pairs.CalfSurvivalSpotsOutputFromImageJOutput from 'analyze particles' procedure applied to calf coat pattern trait images for calf survival analysis.
DOI: 10.5255/ukda-sn-858275
title: Open Play: A Longitudinal Dataset of Multi-Platform Video Game Digital Trace Data and Psychological Measures, 2022-2025

description: A major limitation to understanding digital technology use, and its potential psychological consequences, is the lack of sufficiently detailed, multidimensional, and accurate data. We present a dataset of 2.0K individuals’ video game play telemetry data from Nintendo Switch, Steam, and Xbox, paired with psychological measures across multiple dimensions of mental health, motivations, well-being, and cognitive ability. The data were collected under a preregistered design that included 12 weeks of survey data (thirty daily surveys, six biweekly surveys, three biweekly cognitive tests), and digital trace data for 43 months. Cleaned data include 1.5M hours of video game play across 10,475 titles, 23K responses to 14 survey instruments, and 2.9K attention ability measures to facilitate examining longitudinal associations between play behaviors and psychological functioning. Data and codebook are available under a CC0 (with supplemental reidentification clause) license at https://doi.org/10.5281/zenodo.17536656.
DOI: 10.57760/sciencedb.j00124.00308
title: Effect of Coating Asphalt Softening Point on the Pre-oxidation Pathway and Sodium Storage Performance of Derived Hard Carbon
title: 包覆沥青软化点对预氧化路径及其衍生硬碳储钠性能的调控研究

description: The dataset includes all data lists, Origin figures and corresponding data of the paper, Excel spreadsheets, as well as the cover figure and brief introduction.
description: The dataset includes all data lists, Origin figures and corresponding data of the paper, Excel spreadsheets, as well as the cover figure and brief introduction.
DOI: 10.57760/sciencedb.32053
title: Analysis data on rock masses, landslides and landform of the Batang fault and the Deqin-Zhongdian-Daju fault
title: Analysis data on rock masses, landslides and landform of the Batang fault and the Deqin-Zhongdian-Daju fault

description: The dataset includes the mean trace length (μ), the trace midpoint density (λ), the volumetric joint count (Jv), and Geological Strength Index (GSI) values at each measuring site of the Batang fault (BTF) and the Deqin-Zhongdian-Daju fault (DZDF). It also provides data on landslide density and mean slope gradient values at different distances from the fault core.
description: The dataset includes the mean trace length (μ), the trace midpoint density (λ), the volumetric joint count (Jv), and Geological Strength Index (GSI) values at each measuring site of the Batang fault (BTF) and the Deqin-Zhongdian-Daju fault (DZDF). It also provides data on landslide density and mean slope gradient values at different distances from the fault core.
DOI: 10.57760/sciencedb.32259
title: State Estimation for 2-D Markov Jump Systems with Multiple Observation Delays over Decoded-and-Forward Relay Channels
title: State Estimation for 2-D Markov Jump Systems with Multiple Observation Delays over Decoded-and-Forward Relay Channels

description: Using MATLAB R2023b, original datasets were generated via random functions. The proposed filtering algorithm was then applied to these data, and the tracking performance was subsequently demonstrated. 
description: Using MATLAB R2023b, original datasets were generated via random functions. The proposed filtering algorithm was then applied to these data, and the tracking performance was subsequently demonstrated.
DOI: 10.15468/dl.b3msa2
title: Occurrence Download

description: A dataset containing 940815 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Footprint WKT mismatch, Footprint WKT invalid, Country derived from coordinates, Continent coordinate mismatch, Continent derived from coordinates, Recorded date mismatch, Recorded date invalid, Taxon match higher rank, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, Depth unlikely, Elevation min max swapped, Modified date invalid, Identified date invalid, Multimedia URI invalid, References URI invalid, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status unparsable, Occurrence status inferred from individual count)",
 "OccurrenceStatus is Present",
 "TaxonKey is Festuca rubra",
 "Year 1950-2025"
 ]
}

 The dataset includes 940815 records from 1608 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007233-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.