cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.jc21t36
title: Data from: Novel Fourier quadrature transforms and analytic signal representations for nonlinear and non-stationary time series analysis

description: The Hilbert transform (HT) and associated Gabor analytic signal (GAS)
 representation are well-known and widely used mathematical formulations
 for modeling and analysis of signals in various applications. In this
 study, like the HT, to obtain quadrature component of a signal, we propose
 novel discrete Fourier cosine quadrature transforms (FCQTs) and discrete
 Fourier sine quadrature transforms (FSQTs), designated as Fourier
 quadrature transforms (FQTs). Using these FQTs, we propose sixteen Fourier
 quadrature analytic signal (FQAS) representations with following
 properties: (1) real part of eight FQAS representations is the original
 signal and imaginary part of each representation is FCQT of real part, (2)
 imaginary part of eight FQAS representations is the original signal and
 real part of each representation is FSQT of imaginary part, (3) like the
 GAS, Fourier spectrum of the all FQAS representations has only positive
 frequencies, however unlike the GAS, real and imaginary parts of FQAS
 representations are not orthogonal. The Fourier decomposition method (FDM)
 is an adaptive data analysis approach to decompose a signal into a set
 Fourier intrinsic band functions. This study also proposes new
 formulations of the FDM using discrete cosine transform with GAS and FQAS
 representations, and demonstrate its efficacy for improved
 time-frequency-energy representation and analysis of many real-life
 nonlinear and non-stationary signals.
description: MATLABCodeOfFDM_DCT_DFT_FIR_FSASJuly2018
DOI: 10.5061/dryad.3p9b4c2
title: Data from: The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction

description: Objective: Alzheimer’s disease (AD) is a severe neurodegenerative disorder
 and has become a global public health problem. Intensive research has been
 conducted for AD. But the pathophysiology of AD is still not elucidated.
 Disease comorbidity often associates diseases with overlapping patterns of
 genetic markers. This may inform a common etiology and suggest essential
 protein targets. US Food and Drug Administration (FDA) Adverse Event
 Reporting System (FAERS) collects large-scale post-marketing surveillance
 data that provide a unique opportunity to investigate disease
 co-occurrence pattern. We aim to construct a heterogeneous network that
 integrates disease comorbidity network from FAERS with protein-protein
 interaction to prioritize the AD risk genes using network-based ranking
 algorithm. Materials and Methods: We built a disease comorbidity network
 (DCN) based on indication data from FAERS using association rule mining.
 DCN was further integrated with protein-protein interaction network. We
 used random walk with restart ranking algorithm to prioritize AD risk
 genes. Results: We evaluated the performance of our approach using AD risk
 genes curated from genetic association studies. Our approach achieved an
 area under a receiver operating characteristic curve (AUROC) of 0.770. Top
 500 ranked genes achieved 5.53-fold enrichment for known AD risk genes as
 compared to random expectation. Pathway enrichment analysis using top
 ranked genes revealed that two novel pathways, ERBB and coagulation
 pathways, might be involved in AD pathogenesis. Conclusion: We
 innovatively leveraged FAERS, a comprehensive data resource for FDA
 post-market drug safety surveillance, for large-scale AD comorbidity
 mining. This exploratory study demonstrated the potential of
 disease-comorbidities mining from FAERS in AD genetics discovery.
description: DCN_PPI
 networkThis folder contains
 three files listed below. 1. DCN_PPI_net.txt: The heterogeneous network
 file that contains 19,398 nodes (1,538 disease nodes and 17,860 gene
 nodes) and 1,401,358 edges.  Format: UMLS_ID or gene symbol|UMLS_ID or
 gene symbol|weight.  Note: The edge weigh is set to 1.0 since the network
 is undirected and unweighted. 2. disUMLS_name.txt: A disease node mapping
 file from UMLS ID name to disease concept name . Format:
 UMLS_ID|disease_name 3. AD_novel_genes.csv: A file that contains novel AD
 risk genes we predicted from DCN_PPI network.  Format:
 Rank,Genenetwork and
 predictions.zip
DOI: 10.57745/ayoyxs
title: EBSD_data.zip

description: :unav
DOI: 10.57745/duuqs1
title: readme_thermodynamic_modelling.txt

description: :unav
DOI: 10.5061/dryad.2kb6fh4
title: Data from: The consequences of an introgression event

description: The spread of adaptive alleles is fundamental to evolution, and in theory,
 this process is well‐understood. However, only rarely can we follow this
 process—whether it originates from the spread of a new mutation, or by
 introgression from another population. In this issue of Molecular Ecology,
 Hanemaaijer et al. (2018) report on a 25‐year long study of the mosquitoes
 Anopheles gambiae (Figure 1) and Anopheles coluzzi in Mali, based on
 genotypes at 15 single‐nucleotide polymorphism (SNP). The species are
 usually reproductively isolated from each other, but in 2002 and 2006,
 bursts of hybridization were observed, when F1 hybrids became abundant.
 Alleles backcrossed from A. gambiae into A. coluzzi, but after the first
 event, these declined over the following years. In contrast, after 2006,
 an insecticide resistance allele that had established in A. gambiae spread
 into A. coluzzi, and rose to high frequency there, over 6 years (~75
 generations). Whole genome sequences of 74 individuals showed that A.
 gambiae SNP from across the genome had become common in the A. coluzzi
 population, but that most of these were clustered in 34 genes around the
 resistance locus. A new set of SNP from 25 of these genes were assayed
 over time; over the 4 years since near‐fixation of the resistance allele;
 some remained common, whereas others declined. What do these patterns tell
 us about this introgression event?
description: Introgression with
 background selection SI v2Mathematica notebook,
 containing calculations used to estimate effective population size, and
 simulations of the introgression event.
DOI: 10.57745/qloxgt
title: readme_EBSD.txt

description: :unav
DOI: 10.57745/bxj1k3
title: readme_WDS_maps.txt

description: :unav
DOI: 10.57745/ojsqat
title: Thermodynamic_modelling.zip

description: :unav
DOI: 10.57745/eoahoh
title: Compositional_WDS_maps.zip

description: :unav
DOI: 10.5061/dryad.d4q57s5
title: Data from: Outlier SNPs detect weak regional structure against a background of genetic homogeneity in the Eastern Rock Lobster, Sagmariasus verreauxi

description: Genetic differentiation is characteristically weak in marine species
 making assessments of population connectivity and structure difficult.
 However the advent of genomic methods have increased genetic resolution,
 enabling studies to detect weak, but significant population
 differentiation within marine species. With an increasing number of
 studies employing high resolution genome-wide techniques, we are realising
 the connectivity of marine populations is often complex and quantifying
 this complexity can provide an understanding of the processes shaping
 marine species genetic structure and to inform long-term, sustainable
 management strategies. This study aims to assess the genetic structure,
 connectivity and local adaptation of the Eastern Rock Lobster (Sagmariasus
 verreauxi), which has a maximum pelagic larval duration of 12 months and
 inhabits both subtropical and temperate environments. We used 645 neutral
 and 15 outlier SNPs to genotype lobsters collected from the only two known
 breeding populations and a third episodic population — encompassing S.
 verreauxi’s known range. Through examination of the neutral SNP panel, we
 detected genetic homogeneity across the three regions, which extended
 across the Tasman Sea encompassing both Australian and New Zealand
 populations. We discuss differences in neutral genetic signature of S.
 verreauxi and a closely-related, co-distributed rock lobster, Jasus
 edwardsii, determining a regional pattern of genetic disparity between the
 species, which have largely similar life histories. Examination of the
 outlier SNP panel detected weak genetic differentiation between the three
 regions. Outlier SNPs showed promise in assigning individuals to their
 sampling origin and may prove useful as a management tool for species
 exhibiting genetic homogeneity.
description: Full SNP panel (756
 SNPs)756 SNPs that were
 identified for Sagmariasus verreauxi in a genpop file
 format.FullSNPPanel.txtNeutral SNP Panel645 neutral SNPs identified for Sagmariasus verreauxi in a structure file format.NeutralSNPs.struOutlier SNP Panel15 outlier SNPs identified for Sagmariasus verreauxi in a structure file format.OutlierSNPs.struMigrate-N input fileInput file containing 50 loci for Migrate-n analysis between NSW and NZ.infile.50Loci_NSWvsNZ
DOI: 10.5061/dryad.23bd3ds
title: Data from: On the causes of rapid diversification in the Páramos: Isolation by ecology and genomic divergence in Espeletia

description: How diversity arises and what is the relative role of allopatric and
 ecological divergence are among the most persistent questions in evolution
 and ecology. Here, we assessed whether ecological divergence has enhanced
 the diversification of the Neotropical alpine plant complex Espeletia,
 also known as frailejones. This genus has one of the highest
 diversification rates ever reported and is distributed in the world’s
 fastest evolving biodiversity hotspot, the Páramo (Neotropical alpine
 grasslands at elevations of c. 2800–4700 m). Our goal was to determine
 whether ecology plays a role in divergence within the Espeletia complex by
 quantifying genome-wide patterns of ecological divergence. We
 characterized 162 samples of the three most common and contrasting
 ecotypes (distinct morphotypes occupying particular habitats) co-occurring
 in six localities in the northern Andes using Genotyping by Sequencing.
 Contrasting ecotypes were caulescent cloud forest populations, caulescent
 populations from wind-sheltered and well-irrigated depressions and
 acaulescent populations from wind-exposed drier slopes. We found high
 polymorphism with a total of 1,273 single nucleotide polymorphisms (SNPs)
 that defined the relationships among nine genetic clusters. We quantified
 allelic associations of these markers with localities and habitats using
 18 different general and mixed-effects statistical models that accounted
 for phylogenetic distance. Despite that these models always yielded more
 SNPs associated with the localities, markers associated with the habitat
 types were recovered too. We found strong evidence for
 isolation-by-distance (IBD) across populations despite rampant gene flow,
 as expected for plant groups with limited seed dispersal. Contrasts
 between populations of different habitat types showed that an
 isolation-by-environment (IBE) trend emerged and masked the IBD signal.
 Maximum likelihood estimation of the number of migrants per generation
 (Nem) among ecotypes confirmed the IBE pattern. This result illustrates
 the importance of mountains’ environmental variation at a local scale in
 generating rapid morphological radiations and maintaining multiple
 adaptations in a fast-evolving ecosystem like the Páramo.
description: Data analysis pipeline
 configuration files and cleaned datasetDRYAD.zip
DOI: 10.5061/dryad.0k90c0v
title: Data from: Approaches to integrating genetic data into ecological networks

description: As molecular tools for assessing trophic interactions become common,
 research is increasingly focused on the construction of interaction
 networks. Here we demonstrate three key methods for incorporating DNA data
 into network ecology and discuss analytical considerations using a model
 consisting of plants, insects, bats and their parasites from the Costa
 Rican dry forest. The simplest method involves the use of Sanger
 sequencing to acquire long sequences to validate or refine field
 identifications, for example of bats and their parasites, where one
 specimen yields one sequence and one identification. This method can be
 fully quantified and resolved and these data resemble traditional
 ecological networks. For more complex taxonomic identifications, we target
 multiple DNA loci e.g. from a seed or fruit pulp sample in faeces. These
 networks are also well resolved but gene targets vary in resolution and
 quantification is difficult. Finally for mixed templates such as faecal
 contents of insectivorous bats we use DNA metabarcoding targeting two
 sequence lengths (157bp, 407bp) of one gene region and a MOTU, BLAST and
 BIN association approach to resolve nodes. This network type is complex to
 generate and analyse and we discuss the implications of this type of
 resolution on network analysis. Using these data we construct the first
 molecular-based network of networks containing 3304 interactions between
 762 nodes of 8 trophic functions and involving parasitic, mutualistic, and
 predatory interactions. We provide a comparison of the relative strengths
 and weaknesses of these data types in network ecology.
description: BatsSequences and
 identifications for all bats.ParasitesSequences and provisional ID for all parasitesSeed 2 MatKMatK sequences from seedsSeed 2 RbcLRbcL sequences for seedsSeed 2 TrnHTrnH sequences for seeds157bp region157 region with primers clipped, dereplicated and singleton haplotypes revoved. All sequences identified to bat species.407bp regionSequences from 405bp region.SeeIDBatCOdesCodes for bat ID to identify seed sequences to individual bat.
DOI: 10.5061/dryad.69161bg
title: Data from: Cultural revolutions reduce complexity in the songs of humpback whales

description: Much evidence for non-human culture comes from vocally learned displays,
 such as the vocal dialects and song displays of birds and cetaceans. While
 many oscine birds use song complexity to assess male fitness, the role of
 complexity in humpback whale (Megaptera novaeangliae) song is uncertain
 due to population-wide conformity to one song pattern. Although songs
 change gradually each year, the eastern Australian population also
 completely replaces their song every few years in cultural ‘revolutions’.
 Revolutions involve learning large amounts of novel material introduced
 from the western Australian population. We examined two measures of song
 structure, complexity and entropy, in the eastern Australian population
 over 13 consecutive years. These measures aimed to identify the role of
 complexity and information content in the vocal learning processes of
 humpback whales. Complexity was quantified at two hierarchical levels: the
 entire sequence of individual sound ‘units’; and the stereotyped
 arrangements of units which comprise a ‘theme’. Complexity increased as
 songs evolved over time but decreased when revolutions occurred. No
 correlation between complexity and entropy estimates suggests that changes
 to complexity may represent embellishment to the song which could allow
 males to stand out amidst population-wide conformity. The consistent
 reduction in complexity during song revolutions suggests a potential limit
 to the social learning capacity of novel material in humpback whales.
description: Allen et al -
 Proceedings B Supplementary Data - Raw DataThis file contains the
 raw song transcriptions for all songs used in every study year
 (2002-2014), the data used to create the complexity measures, and the data
 used to create the individuality metric.
DOI: 10.5061/dryad.3f9r8r6
title: Data from: Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

description: Implantable-cardioverter defibrillators (ICD) detect and terminate
 life-threatening ventricular tachyarrhythmia with electric shocks after
 they occur. This puts patients at risk if they are driving or in a
 situation where they can fall. ICD’s shocks are also very painful and
 affect a patient’s quality of life. It would be ideal if ICDs can
 accurately predict the occurrence of ventricular tachyarrhythmia and then
 issue a warning or provide preventive therapy. Our study explores the use
 of ICD data to automatically predict ventricular arrhythmia using heart
 rate variability (HRV). A 5 minute and a 10 second warning system are both
 developed and compared. The participants for this study consist of 788
 patients who were enrolled in the ICD arm of the Sudden Cardiac Death –
 Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular
 heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed
 and different HRV features are extracted. Machine learning algorithms,
 including random forests (RF) and support vector machines (SVM), are
 trained on these features to classify the two groups of rhythms in a
 subset of the data comprising the training set. These algorithms are then
 used to classify rhythms in a separate test set. This performance is
 quantified by the area under the curve (AUC) of the ROC curve. Both RF and
 SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean
 AUC of 0.87-0.88 for 10-second prediction; an AUC over 0.8 typically
 warrants further clinical investigation. Our work shows that moderate
 classification accuracy can be achieved to predict ventricular
 tachyarrhythmia with machine learning algorithms using HRV features from
 ICD data. These results provide a realistic view of the practical
 challenges facing implementation of machine learning algorithms to predict
 ventricular tachyarrhythmia using HRV data, motivating continued research
 on improved algorithms and additional features with higher predictive
 power.
description: SCD-HeFT Heart Rate
 Variability Data Recorded by ICDspublic_SCD-HeFT_ICD_Data.zip
DOI: 10.6084/m9.figshare.31157938
title: Integrated Unified Field

description: 1. Core Identity Metadata
Title: Absolute Force: Mathematical Nodal-Hardening Constant (0.92) and Topographical Midbrain Integration.
Authors: Jamie Davis.
Item Type: Dataset (Select from the dropdown menu to encompass mixed data types).
License: CC BY 4.0 (Creative Commons Attribution 4.0 International).
2. Comprehensive Description
Copy and paste this into the "Description" field to link the physics to the biology:
This publication archives the discovery of Absolute Force as the architect of spatial reality. It establishes the 0.92 Nodal-Hardening Constant where fluid frequency field patterns (1000x1000 resolution) transition into rigid Dimensional Walls.
Biological Interface: The record identifies the Inferior Colliculus and Superior Colliculus as the primary neural receivers for Sianoic Resonance generated at this threshold. This integration proves a unified link between environmental geometric laws and human midbrain anatomy.
Integrity Verification: All data transitions are secured with SHA-256 cryptographic signatures to ensure immutability and accountability.
3. Keywords and Categories
Keywords: Absolute-Force, Dimensional-Walls, Sianoic-Resonance, Nodal-Hardening, SHA256-Verification, Geometric-Constraint, Midbrain-Mapping, Bio-Geometric-Integration.
Categories:
Mathematical physics not elsewhere classified
Algebraic structures in mathematical physics
Biological physics
Neurosciences
4. Structural Requirements (Files)
Primary Data: Upload the field simulation datasets and the midbrain topography maps.
Documentation: Include a README.txt file explaining the SHA-256 verification process and how to replicate the 0.92 constant results.
References: Use the "Related materials" or "References" section to paste the DOI of any of your prior related work.
5. Final Submission Actions
Reserve/Check DOI: Ensure the identifier matches 10.6084/m9.figshare.31157827.
Save/Publish: Click the "Save changes" button, then select "Publish item" to make the record permanent.
DOI: 10.6084/m9.figshare.31157938.v1
title: Integrated Unified Field

description: 1. Core Identity Metadata
Title: Absolute Force: Mathematical Nodal-Hardening Constant (0.92) and Topographical Midbrain Integration.
Authors: Jamie Davis.
Item Type: Dataset (Select from the dropdown menu to encompass mixed data types).
License: CC BY 4.0 (Creative Commons Attribution 4.0 International).
2. Comprehensive Description
Copy and paste this into the "Description" field to link the physics to the biology:
This publication archives the discovery of Absolute Force as the architect of spatial reality. It establishes the 0.92 Nodal-Hardening Constant where fluid frequency field patterns (1000x1000 resolution) transition into rigid Dimensional Walls.
Biological Interface: The record identifies the Inferior Colliculus and Superior Colliculus as the primary neural receivers for Sianoic Resonance generated at this threshold. This integration proves a unified link between environmental geometric laws and human midbrain anatomy.
Integrity Verification: All data transitions are secured with SHA-256 cryptographic signatures to ensure immutability and accountability.
3. Keywords and Categories
Keywords: Absolute-Force, Dimensional-Walls, Sianoic-Resonance, Nodal-Hardening, SHA256-Verification, Geometric-Constraint, Midbrain-Mapping, Bio-Geometric-Integration.
Categories:
Mathematical physics not elsewhere classified
Algebraic structures in mathematical physics
Biological physics
Neurosciences
4. Structural Requirements (Files)
Primary Data: Upload the field simulation datasets and the midbrain topography maps.
Documentation: Include a README.txt file explaining the SHA-256 verification process and how to replicate the 0.92 constant results.
References: Use the "Related materials" or "References" section to paste the DOI of any of your prior related work.
5. Final Submission Actions
Reserve/Check DOI: Ensure the identifier matches 10.6084/m9.figshare.31157827.
Save/Publish: Click the "Save changes" button, then select "Publish item" to make the record permanent.
DOI: 10.5061/dryad.t0g149j
title: Data from: Counting crows: flock structure and subgroup size variation in an urban population of crows

description: Social complexity arises from the formation of social relationships like
 social bonds and dominance hierarchies. In turn, these aspects may be
 affected by the degree of fission-fusion dynamics, i.e. changes in group
 size and composition over time. Whilst fission-fusion dynamics has been
 studied in mammals, birds have received comparably little attention,
 despite some species having equally complex social lives. Here, we
 investigated the influence of environmental factors on aspects of
 fission-fusion dynamics in a free-ranging population of carrion and hooded
 crows (Corvus corone ssp.) in the urban zoo of Vienna, Austria over a
 one-year period. We investigated i) the size and ii) spatio-temporal
 structure of the local flock, and iii) environmental influences on local
 flock and subgroup size. The local flock size varied considerably over the
 year, with fewest birds being present during the breeding season. The
 spatio-temporal structure of the local flock showed four distinct presence
 categories, of which the proportions changed significantly throughout the
 year. Environmental effects on both local flock and subgroup size were
 time of day, season, temperature and weather, with additional pronounced
 effects of the structure of the surroundings and age class on subgroup
 size. Our findings show environmental influences on party size at the
 local flock and subgroup level, as well as indications of structured party
 composition in respect to the four presence categories. These results
 suggest that environmental factors have significant effects on
 fission-fusion dynamics in free-ranging crows, thereby influencing social
 complexity.
description: CC_Dataoriginal data collected in the field, Manipulated in ESRI ArcGIS and R