cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/h3f8pjbw73.2
title: UFTBESD: University of Frontier Technology, Bangladesh-Bangla Emotional Speech Dataset

description: UFTBESD (University of Frontier Technology, Bangladesh - Bangla Emotional Speech Dataset) is a Bangla language-based speech emotion recognition dataset developed to capture realistic emotional speech in everyday acoustic conditions. The dataset consists of 1400 speech-audio recordings collected from 100 native Bangla speakers aged between 19 and 60 years, with a balanced gender distribution (50 male and 50 female).

Each participant uttered two selected Bangla sentences, with each sentence spoken once (single trial) in seven emotional states: angry, disgust, fear, happy, neutral, sad, and surprise. Thus, the dataset contains 2 sentences × 7 emotions × 100 speakers = 1400 audio clips.

All recordings were collected using smartphone microphones in both indoor and outdoor environments. A significant portion of the data includes natural background noise, making the dataset suitable for real-world speech emotion recognition research. The audio files are stored in WAV format (44.1 kHz, 16-bit, mono). This release focuses on providing raw audio data, and detailed metadata will be added in future versions.

UFTBESD is intended to support the development and evaluation of Bangla speech emotion recognition systems and can be used with common machine learning and deep learning architectures such as CNN, LSTM, BiLSTM, and transformer-based models.
DOI: 10.57745/uxkguh
title: point_spacing_test_2025_0717_154617.mat
DOI: 10.57745/bb7nad
title: point_spacing_test_2025_0717_145423.mat
DOI: 10.57745/kidvxi
title: point_spacing_test_2025_0717_163709.mat
DOI: 10.57745/ai6uak
title: point_spacing_test_2025_0722_134046.mat
DOI: 10.57745/jgmyy0
title: point_spacing_test_2025_0722_124529.mat
DOI: 10.57745/6eoib8
title: point_spacing_test_2025_0722_133031.mat
DOI: 10.57745/ttykfp
title: point_spacing_test_2025_0722_135812.mat
DOI: 10.57745/ah1kcr
title: point_spacing_test_2025_0722_141116.mat
DOI: 10.15468/dl.pefae5
title: Occurrence Download

description: A dataset containing 1878439 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Himantopus mexicanus"
 ]
}

 The dataset includes 1878439 records from 300 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004697-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/x799hx
title: point_spacing_test_2025_0722_131111.mat
DOI: 10.57745/2occqw
title: point_spacing_test_2025_0722_130207.mat
DOI: 10.57745/spuept
title: point_spacing_test_2025_0722_131813.mat
DOI: 10.57745/5o5zbv
title: point_spacing_test_2025_0722_172140.mat
DOI: 10.57745/dfz8bt
title: point_spacing_test_2025_0722_171212.mat
DOI: 10.57745/0rgcpf
title: point_spacing_test_2025_0722_173132.mat
DOI: 10.57745/ghaqsv
title: point_spacing_test_2025_0722_173805.mat
DOI: 10.57745/cq8f8k
title: point_spacing_test_2025_0722_170537.mat
DOI: 10.57745/viwwpi
title: point_spacing_test_2025_0722_154821.mat
DOI: 10.57745/al89nn
title: point_spacing_test_2025_0722_144040.mat
DOI: 10.17632/2nhhdjx225.1
title: Data and scripts for longitudinal structural variant phylogenies in metastatic prostate cancer

description: This study evaluates whether structural variants (SVs) capture key evolutionary and resistance signals in mCRPC. We analyzed paired longitudinal tumor WGS (pre-BAT and on-BAT) with matched normals from COMBAT subjects treated with bipolar androgen therapy (BAT). SVs were called and genotyped, and SVCFit estimated structural-variant cellular fraction (SVCF), clustered SVs by prevalence, and inferred clonal phylogenies and clone proportions over time. Data show branched evolution and therapy-associated clonal shifts. In representative subjects, highly rearranged lineages contracted on BAT, while SV-defined clones expanded. Outputs include SV callsets, per-SV SVCF/CCF, cluster labels, trees, and figures for reuse.

This repository comprises the source data and computational workflows supporting "Longitudinal structural variant phylogenies define tumor evolution under therapeutic selection pressure in metastatic prostate cancer" by Liu et. al. (2026). 

1. Clinical Cohort Data (COMBAT). 
-- annotsv: Functional SV annotations generated via AnnotSV (Geoffroy et. al. 2018).
-- SVCFit_output: SV annotated with inferred SVCF, phasing, zygosity and overlapping CNVs (BED format).
-- cluster_result.csv: clusters of deduplicated SVs based on inferred SVCF.
-- sv2cluster.csv: clusters of all SVs based on inferred SVCF.
-- samp_pur.csv: Purity of all processed tumor samples estimated by FACETS (Shen et. al. 2016).
-- pair.bed: Matched pre- and on-BAT biopsy samples. 
* The raw sequencing data and germline variant calls used in the current study are available from the corresponding author upon reasonable request. These are not publicly available due to informed consent restrictions.

2. Synthetic Benchmarking Data (VISOR_benchmark) 
-- truth: Ground truth BED files defining spiked-in SVs: exact breakpoints , SV types (deletions, duplications, homozygosity).
-- depth: Test of the impact of variable breakpoint depth for SVCF estimation.
-- facet: CNV profiles generated via FACETS across five distinct experimental conditions to model heterogeneous tumor mixtures.
-- svclone_rdata: Serialized R objects containing subclonal deconvolution results (SVClone/ccube).
-- SV_vcfs: SV calls from Manta (Chen et. al. 2016). 
-- SNP: SNP calls from GATK4 (McKenna et. al. 2010). 

3. In Silico Validation (Prostate_mixture) Controlled admixtures of prostate cancer reads designed to evaluate sensitivity in polyclonal environments
-- prostate_vcfs: SV call sets for 3-, 4-, and 5-clone mixtures (3m, 4m, 5m) by Manta.
-- facet: CNV profiles generated via FACETS across all clone mixtures.
-- SNP: SNP calls from GATK4 across all clone mixtures.

4. Scripts
-- COMBAT: Clinical pipelines for alignment, consensus SV calling (integrating Manta, Delly, GRIDSS, and SURVIVOR), and functional annotation.
-- VISOR: Scripts for synthetic genome generation, read simulation, and benchmarking execution.
-- Prostate_mixture: Utilities for BAM mixing, interval splitting, and downstream processing.
DOI: 10.57745/0enfho
title: point_spacing_test_2025_0722_151946.mat
DOI: 10.57745/9co7qq
title: point_spacing_test_2025_0722_162637.mat
DOI: 10.57745/odaed3
title: point_spacing_test_2025_0722_160028.mat
DOI: 10.57745/jvc9iv
title: point_spacing_test_2025_0722_150841.mat
DOI: 10.57745/hjzyuk
title: point_spacing_test_2025_0722_094917.mat
DOI: 10.57745/zfsjqt
title: point_spacing_test_2025_0722_105950.mat
DOI: 10.57745/wfcmiz
title: point_spacing_test_2025_0722_104559.mat
DOI: 10.57745/gkz1vc
title: point_spacing_test_2025_0722_122019.mat
DOI: 10.57745/lgpl0s
title: point_spacing_test_2025_0722_123247.mat
DOI: 10.57745/xvdazx
title: point_spacing_test_2025_0722_111037.mat
DOI: 10.17632/32xts2zxdc
title: Psoriatic arthritis (PsA) clinical lipidomics dataset with hidden laboratory workflow artifacts

description: This dataset provides plasma lipidomic profiles from a cross-sectional cohort of patients with psoriatic arthritis (PsA) and healthy controls matched by age, sex, and BMI. Plasma samples were collected at University Hospital Frankfurt and the German Red Cross Blood Transfusion Service in Frankfurt, Germany, and analyzed on high-resolution mass spectrometry platforms (Thermo Fisher Q Exactive and Sciex QTRAP 6500+) using both targeted and untargeted lipid assays.

The dataset is organized into four comma-separated values (CSV) files containing Box-Cox-transformed and imputed lipidomics values, corresponding back-transformed measurements on the original scale, detailed clinical and analytical metadata including PsA status, ESOM-based classes, sex, and assay-specific batch identifiers, and variable-level descriptions for all 292 lipids.

Across all files, 292 plasma lipid species are included, named according to LIPID MAPS classification and standardized lipid nomenclature. The variables span major lipid classes such as carnitines, ceramides, glycerophospholipids, sphingolipids, glycerolipids, fatty acids, sterols and esters, and endocannabinoids.

Because the dataset contains an embedded batch structure, it is not intended for deriving new biological conclusions. Instead, it serves as a resource for methodological research on data quality control, batch effect identification, evaluation of preprocessing pipelines, and assessment of how sampling and processing parameters influence analytical robustness.

The dataset consists of four CSV files:

"PsA_lipids_BC.csv"
Contains the primary lipidomics data matrix with 107 subjects and Box-Cox-transformed, outlier-cleaned, and imputed plasma lipid measurements for 292 lipids.

"PsA_lipids_orig_values.csv"
Contains the same subjects and variables as above, but values are back-transformed to the original measurement scale following outlier removal and imputation, preserving identical identifiers and structure.

"PsA_classes.csv"
Provides 107 rows and 12 columns of metadata linking each subject to clinical (PsA vs. control), ESOM-based, and gender classifications, as well as six assay-specific batch identifiers. Columns 11 and 12 include the sampling date and weekday for control samples, enabling investigation of temporal or workflow-related effects.

"readme.csv"
Contains 292 rows (matching the number of lipid variables) and 7 columns describing each lipid at the individual variable level: "variable_name" (lipid identifier), "unit" ("arbitrary" for screening = peak area relative to class-specific internal standard; "ng/mL" or "pg/mL" for targeted), "class_name" (e.g., "Fatty acids", "Lysophosphatidylcholine"), "class_code" (e.g., "FA", "LPC"), "analytical_method_category" ("Lipid screening" or "Lipid targeted"), "LLOQ", and "ULOQ" (quantification limits; NA for screening lipids).
DOI: 10.57745/1fekkm
title: point_spacing_test_2025_0722_102643.mat
DOI: 10.57745/q0kdit
title: point_spacing_test_2025_0721_181410.mat
DOI: 10.17632/32xts2zxdc.3
title: Psoriatic arthritis (PsA) clinical lipidomics dataset with hidden laboratory workflow artifacts

description: This dataset provides plasma lipidomic profiles from a cross-sectional cohort of patients with psoriatic arthritis (PsA) and healthy controls matched by age, sex, and BMI. Plasma samples were collected at University Hospital Frankfurt and the German Red Cross Blood Transfusion Service in Frankfurt, Germany, and analyzed on high-resolution mass spectrometry platforms (Thermo Fisher Q Exactive and Sciex QTRAP 6500+) using both targeted and untargeted lipid assays.

The dataset is organized into four comma-separated values (CSV) files containing Box-Cox-transformed and imputed lipidomics values, corresponding back-transformed measurements on the original scale, detailed clinical and analytical metadata including PsA status, ESOM-based classes, sex, and assay-specific batch identifiers, and variable-level descriptions for all 292 lipids.

Across all files, 292 plasma lipid species are included, named according to LIPID MAPS classification and standardized lipid nomenclature. The variables span major lipid classes such as carnitines, ceramides, glycerophospholipids, sphingolipids, glycerolipids, fatty acids, sterols and esters, and endocannabinoids.

Because the dataset contains an embedded batch structure, it is not intended for deriving new biological conclusions. Instead, it serves as a resource for methodological research on data quality control, batch effect identification, evaluation of preprocessing pipelines, and assessment of how sampling and processing parameters influence analytical robustness.

The dataset consists of four CSV files:

"PsA_lipids_BC.csv"
Contains the primary lipidomics data matrix with 107 subjects and Box-Cox-transformed, outlier-cleaned, and imputed plasma lipid measurements for 292 lipids.

"PsA_lipids_orig_values.csv"
Contains the same subjects and variables as above, but values are back-transformed to the original measurement scale following outlier removal and imputation, preserving identical identifiers and structure.

"PsA_classes.csv"
Provides 107 rows and 12 columns of metadata linking each subject to clinical (PsA vs. control), ESOM-based, and gender classifications, as well as six assay-specific batch identifiers. Columns 11 and 12 include the sampling date and weekday for control samples, enabling investigation of temporal or workflow-related effects.

"readme.csv"
Contains 292 rows (matching the number of lipid variables) and 7 columns describing each lipid at the individual variable level: "variable_name" (lipid identifier), "unit" ("arbitrary" for screening = peak area relative to class-specific internal standard; "ng/mL" or "pg/mL" for targeted), "class_name" (e.g., "Fatty acids", "Lysophosphatidylcholine"), "class_code" (e.g., "FA", "LPC"), "analytical_method_category" ("Lipid screening" or "Lipid targeted"), "LLOQ", and "ULOQ" (quantification limits; NA for screening lipids).
DOI: 10.57745/jxdqge
title: point_spacing_test_2025_0721_174555.mat
DOI: 10.57745/gyeuhz
title: point_spacing_test_2025_0721_180210.mat
DOI: 10.57745/hef0ft
title: point_spacing_test_2025_0721_182722.mat
DOI: 10.57745/jqzj9y
title: point_spacing_test_2025_0721_183430.mat
DOI: 10.57745/zl8x5y
title: point_spacing_test_2025_0721_175601.mat
DOI: 10.57745/7zleqh
title: point_spacing_test_2025_0721_200654.mat
DOI: 10.57745/mybesc
title: point_spacing_test_2025_0721_190611.mat
DOI: 10.57745/ztaj9t
title: point_spacing_test_2025_0721_194125.mat
DOI: 10.57745/5zyfs4
title: point_spacing_test_2025_0721_202238.mat
DOI: 10.17632/rd8kt4brt5.1
title: Trancriptional profile of iGNs and iMNs after TDP-43 knockdown

description: Research Hypothesis
The study hypothesized that while TDP-43 loss-of-function is a near-universal hallmark of ALS, its molecular consequences—specifically regarding gene expression and cryptic splicing—are cell-type specific (glutamatergic induced neurons - iGNs versus cholinergic induced neurons - iMNs) . We further hypothesized that the presence of astrocytes (iAs) would modulate the detectable splicing repertoire of human induced neurons (iNs), providing a more clinically relevant representation of the disease environment.

What the Data Shows
Subtype-Specific Signatures: The data reveals distinct transcriptomic profiles for iGNs and iMNs. While TDP-43 depletion affected 92 genes in iMNs and 52 in iGNs, only 33 genes overlapped between the two subtypes.
Conserved Pathological Core: Despite low overall overlap, a conserved set of ALS-relevant targets—including STMN2, UNC13A, CAMK2B, and ATG4B—were consistently downregulated and mis-spliced across both populations.
Glial Influence: The inclusion of astrocytes (iNs+iAs) facilitated the detection of additional cryptic events, most notably in HDGFL2, which matched patient-derived cerebrospinal fluid data .
DOI: 10.57745/3ol5sr
title: point_spacing_test_2025_0721_204838.mat
DOI: 10.57745/apwzjd
title: point_spacing_test_2025_0721_203400.mat
DOI: 10.57745/7u4qlu
title: point_spacing_test_2025_0721_192605.mat
DOI: 10.57745/1sezpw
title: point_spacing_test_2025_0721_145222.mat
DOI: 10.57745/edev44
title: point_spacing_test_2025_0721_135426.mat
DOI: 10.57745/izqpr6
title: point_spacing_test_2025_0721_135426.mat
DOI: 10.57745/mmcxq5
title: point_spacing_test_2025_0721_151143.mat
DOI: 10.57745/olpha7
title: point_spacing_test_2025_0721_150014.mat
DOI: 10.57745/4t1v7j
title: point_spacing_test_2025_0721_152250.mat
DOI: 10.57745/x0n4ro
title: point_spacing_test_2025_0721_142404.mat
DOI: 10.57745/4eejfa
title: point_spacing_test_2025_0721_165638.mat
DOI: 10.57745/q7ztxw
title: point_spacing_test_2025_0721_164649.mat
DOI: 10.57745/cwetws
title: point_spacing_test_2025_0721_162423.mat
DOI: 10.57745/st0fnq
title: point_spacing_test_2025_0721_163816.mat
DOI: 10.57745/pcjano
title: point_spacing_test_2025_0721_170626.mat
DOI: 10.57745/mkhcyr
title: point_spacing_test_2025_0721_163042.mat
DOI: 10.57745/sviri5
title: point_spacing_test_2025_0721_171504.mat
DOI: 10.57745/jjge5y
title: point_spacing_test_2025_0717_182459.mat
DOI: 10.57745/djrghq
title: point_spacing_test_2025_0717_170539.mat
DOI: 10.57745/joowt1
title: point_spacing_test_2025_0717_181028.mat
DOI: 10.57745/mqnqwa
title: point_spacing_test_2025_0717_184024.mat
DOI: 10.17632/5s6n6tp2g9
title: Edible Wild Plants

description: The dataset comprises 10 classes and a total of 1,794 images of edible wild plants. The included species are common chickweed, common mallow, common poppy, dandelion, foxtail lily, knotweed, lamb's quarters, patience dock, plantain, shepherd's purse.
DOI: 10.57745/nqxerd
title: point_spacing_test_2025_0717_190518.mat
DOI: 10.17632/5s6n6tp2g9.1
title: Edible Wild Plants

description: The dataset comprises 10 classes and a total of 1,794 images of edible wild plants. The included species are common chickweed, common mallow, common poppy, dandelion, foxtail lily, knotweed, lamb's quarters, patience dock, plantain, shepherd's purse.
DOI: 10.57745/vluvy9
title: point_spacing_test_2025_0717_174315.mat
DOI: 10.57745/dh4awr
title: point_spacing_test_2025_0721_155426.mat
DOI: 10.57745/qbmswk
title: point_spacing_test_2025_0721_153556.mat
DOI: 10.57745/kp0p9s
title: point_spacing_test_2025_0721_154723.mat
DOI: 10.17632/ggwp65ms8m
title: Data related to Chinese audiences’ sense of cultural superiority when watching videos in which foreign internet influencers praise Chinese culture or display no explicit cultural orientation

description: Data Description: Illusory Cultural Confidence via Foreign Influencers’ Praise
This study explores Chinese netizens’ preference for foreign influencers’ pro-Chinese culture content, testing four hypotheses: H1a/H1b (viewing praising videos boosts cultural superiority regardless of face pursuit), H2 (face pursuit moderates this relationship), and H3 (cultural superiority stems from cultural inferiority). Adopting a mixed-methods design (approved by East China Normal University’s HR2-0388-2025), it recruited 200 valid participants (19-35 years old, 88.5% effective rate) split into high/low face pursuit groups (median split, mean scores 34.65 vs. 28.43) and further into experimental (praising videos) and control (neutral content) subgroups (50 each). Face consciousness was measured via Chan et al.’s (2009) scale (Cronbach’s α=0.80), and cultural superiority via the revised GENE Scale (α=0.764). The experiment included pre-test, 10-minute video exposure, post-test with attention checks, followed by 23 in-depth interviews. Results supported H1a/H1b: paired-samples t-tests showed significant post-test cultural superiority increases in experimental groups (high face: ΔM=-5.64, t=-7.49; low face: ΔM=-4.98, t=-6.73; p&lt;0.001), with a main effect of video type (F=81.84, p&lt;0.001). H2 was unsupported (three-way interaction F=2.06, p=0.152), and H3 was confirmed by interviews, where participants linked external praise-driven confidence to latent inferiority, describing it as “fleeting” and “hollow” versus enduring internally rooted confidence. Notable findings include the universal impact of praising content across face pursuit levels, a disconnect between individual face concerns and collective cultural affect, and the illusory nature of externally dependent confidence. The data indicates that foreign influencers’ praise triggers compensatory cultural superiority, reflecting unconsolidated cultural subjectivity shaped by history, media algorithms, and social emotions. For use, it informs cross-cultural communication research and cultural confidence strategies, with caveats about the university student sample’s homogeneity and the need to distinguish cultural superiority from genuine confidence. Genuine cultural confidence requires internal reflexivity over external validation, and future research should expand sample diversity.
DOI: 10.57745/tivsgc
title: point_spacing_test_2025_0721_172636.mat
DOI: 10.57745/j8pego
title: point_spacing_test_2025_0717_192103.mat
DOI: 10.57745/nosyr0
title: point_spacing_test_2025_0717_200627.mat
DOI: 10.17632/ggwp65ms8m.2
title: Data related to Chinese audiences’ sense of cultural superiority when watching videos in which foreign internet influencers praise Chinese culture or display no explicit cultural orientation

description: Data Description: Illusory Cultural Confidence via Foreign Influencers’ Praise
This study explores Chinese netizens’ preference for foreign influencers’ pro-Chinese culture content, testing four hypotheses: H1a/H1b (viewing praising videos boosts cultural superiority regardless of face pursuit), H2 (face pursuit moderates this relationship), and H3 (cultural superiority stems from cultural inferiority). Adopting a mixed-methods design (approved by East China Normal University’s HR2-0388-2025), it recruited 200 valid participants (19-35 years old, 88.5% effective rate) split into high/low face pursuit groups (median split, mean scores 34.65 vs. 28.43) and further into experimental (praising videos) and control (neutral content) subgroups (50 each). Face consciousness was measured via Chan et al.’s (2009) scale (Cronbach’s α=0.80), and cultural superiority via the revised GENE Scale (α=0.764). The experiment included pre-test, 10-minute video exposure, post-test with attention checks, followed by 23 in-depth interviews. Results supported H1a/H1b: paired-samples t-tests showed significant post-test cultural superiority increases in experimental groups (high face: ΔM=-5.64, t=-7.49; low face: ΔM=-4.98, t=-6.73; p&lt;0.001), with a main effect of video type (F=81.84, p&lt;0.001). H2 was unsupported (three-way interaction F=2.06, p=0.152), and H3 was confirmed by interviews, where participants linked external praise-driven confidence to latent inferiority, describing it as “fleeting” and “hollow” versus enduring internally rooted confidence. Notable findings include the universal impact of praising content across face pursuit levels, a disconnect between individual face concerns and collective cultural affect, and the illusory nature of externally dependent confidence. The data indicates that foreign influencers’ praise triggers compensatory cultural superiority, reflecting unconsolidated cultural subjectivity shaped by history, media algorithms, and social emotions. For use, it informs cross-cultural communication research and cultural confidence strategies, with caveats about the university student sample’s homogeneity and the need to distinguish cultural superiority from genuine confidence. Genuine cultural confidence requires internal reflexivity over external validation, and future research should expand sample diversity.
DOI: 10.57745/1i1uvm
title: point_spacing_test_2025_0717_195228.mat
DOI: 10.57745/hj4mwv
title: point_spacing_test_2025_0717_201541.mat
DOI: 10.57745/qkrnch
title: point_spacing_test_2025_0717_202353.mat