cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/y8v9h96hc7.1
title: Framed to be forgotten; UK Media framing of Africa's forgotten crises

description: This is a partial dataset from our broader research that aimed to identify some of the dominant frames in UK newspaper coverage of Africa’s forgotten crises in DRC and CAR and further examine the role this framing plays in perpetuating their status as forgotten crises. Research Question (RQ) 1: What are the dominant frames in UK newspaper coverage of the humanitarian crises in DRC and CAR? RQ 2: How does this framing perpetuate CAR and DRC’s status as forgotten crises? 

Data Collection and Analysis Overview

This study employed a mixed-methods content analysis to examine how UK national newspapers frame Africa’s forgotten humanitarian crises—specifically those in the Democratic Republic of Congo (DRC) and Central African Republic (CAR)—between 2016 and 2020. Using LexisNexis, we sourced 120 news articles from The Guardian, The Independent, Daily Mail, and The Telegraph, focusing on keywords such as “Congo humanitarian crisis,” “DRC refugees,” and “CAR crisis.” These four newspapers were selected to represent a spectrum of political orientations and influence within the UK media landscape.

Data and Coding

The unit of analysis was the full newspaper article. We developed a detailed codebook incorporating 34 variables across three categories: unit identification, salience markers, and frame identifiers. A mixed deductive-inductive coding approach was adopted—drawing from existing literature to identify priori frames (e.g., blame, advocacy, human interest) and allowing for emergent frames (e.g., afro-pessimism, western-centrism). Intercoder reliability was tested on 25% of the sample, with Krippendorff’s alpha reaching 0.89 after codebook revisions, ensuring consistency and validity in coding.

Key Findings

Quantitative analysis revealed five dominant frames across the dataset: episodic (80%), violence (78.3%), western-centric (51.7%), blame (47%), and afro-pessimism (47%). These were more prevalent in right-leaning papers. Notably, there was a weak but significant correlation between the use of western-centric and afro-pessimistic frames. A qualitative analysis of selected articles further showed how framing choices—such as metaphor, omission of historical context, and prioritisation of Western actors—reinforce hierarchies of life and place, casting distant sufferers as passive, violent, or alien.

Interpretation and Relevance

The data suggests that Western media framing plays a critical role in the invisibilisation of certain humanitarian crises like DRC and CAR. By favouring episodic, violent, or decontextualised narratives, and foregrounding Western perspectives, these publications contribute to a hierarchy of suffering that limits public empathy and solidarity. This framing not only affects media representation but may also influence humanitarian prioritisation and global solidarity.
DOI: 10.15468/dl.crdzm6
title: Occurrence Download

description: A dataset containing 4175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Ireland",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Meles meles (Linnaeus, 1758)"
 ]
}

 The dataset includes 4175 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011851-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/w7j3pdjkgx.1
title: Dataset and Code of A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 reanalysis data

description: These dataset and DL model are used in the study: "A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 reanalysis data".
The netCDF format (.nc) dataset comes from ERA5. The model parameters and ERA5 exported data of the four CNN models are stored in four folders respectively.
Since the data file is too large, I will only upload the data of 41x41 experiment for now, and the data of the other three experiments will be uploaded later.
DOI: 10.17632/mwbfshbw5x
title: Football 5 Substitutions Dataset

description: Football dataset consisting of goals, passes and all events from matches after a 5 substitution was introduced.

Original source of the data: StatsBomb

https://github.com/statsbomb/open-data/tree/master/data
DOI: 10.17632/mwbfshbw5x.1
title: Football 5 Substitutions Dataset

description: Football dataset consisting of goals, passes and all events from matches after a 5 substitution was introduced.

Original source of the data: StatsBomb

https://github.com/statsbomb/open-data/tree/master/data
DOI: 10.15468/dl.zzrt6b
title: Occurrence Download

description: A dataset containing 1 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey)) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey)) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey)) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey)) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey)) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey)) genuscount,
 COUNT(*) occurrences
FROM
 occurrence
WHERE
 occurrence.countrycode = 'RO'
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 3082263 OR occurrence.acceptedtaxonkey = 3082263 OR occurrence.kingdomkey = 3082263 OR occurrence.phylumkey = 3082263 OR occurrence.classkey = 3082263 OR occurrence.orderkey = 3082263 OR occurrence.familykey = 3082263 OR occurrence.genuskey = 3082263 OR occurrence.specieskey = 3082263)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.specieskey IS NOT NULL
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey

 The dataset's 1 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011843-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kfzyp9hnzb.1
title: MATLAB-Simulated Dataset for Automatic Modulation Classification in Wireless Fading Channels

description: This dataset contains feature-extracted numerical representations of five digital modulation schemes—BPSK, QPSK, 16-QAM, 64-QAM, and 256-QAM—generated under Rayleigh and Rician fading channels at different sampling frequencies (1 MHz, 10 MHz, 100 MHz, 500 MHz, and 1 GHz). The dataset consists of 10 CSV files, each corresponding to a unique combination of channel conditions and sampling rates. A separate folder contains MATLAB scripts for dataset generation, ensuring reproducibility and enabling customization according to user requirements.
The modulation signals were generated and transmitted through Rayleigh and Rician fading channels to simulate real-world wireless conditions. Each received signal underwent feature extraction through three primary approaches:
1. Signal Processing-Based Features – Extracted from both time and frequency domains, capturing statistical properties (mean, variance, skewness), spectral attributes (FFT-based spectral centroid, flatness, phase-based metrics), and signal measures (RSCR, PAPR, SCF). These features provide a comprehensive representation of modulation characteristics for classification and analysis.
2. Spectrogram-Based Features – Derived from the time-frequency representation of signals using STFT. Statistical measures (entropy, variance, skewness, kurtosis), spectral attributes (centroid, spread, flatness), and amplitude-based features (contrast, homogeneity, range) were extracted to quantify spectral energy distribution and signal consistency.
3. Image Processing-Based Features – Utilizing Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER), and Gray-Level Co-occurrence Matrix (GLCM) techniques to analyze spectrogram-based signal representations.
Each dataset file contains 202 extracted features, providing a high-dimensional numerical representation of modulation signals.
The dataset holds significant value across multiple domains, particularly in machine learning and deep learning, where it facilitates modulation classification using ML/DL models to develop robust classifiers for wireless communication systems. In wireless signal processing, researchers can utilize the extracted features for channel estimation, adaptive modulation, and interference analysis. Additionally, the dataset is highly relevant for cognitive radio and LTE/5G research, supporting spectrum sensing, signal recognition, and intelligent radio technologies. It also serves as a benchmarking resource for testing feature selection methods, dimensionality reduction techniques, and classification algorithms, making it a valuable asset for both academic and industrial applications.
DOI: 10.15468/dl.ysnbad
title: Occurrence Download

description: A dataset containing 16325 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Falco tinnunculus Linnaeus, 1758",
 "Year 2010-2025"
 ]
}

 The dataset includes 16325 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011854-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3kvpv6xt8h.1
title: Associations between intakes of food groups recommended by the Dietary Guidelines for Americans and iron status in subsets of the US population: a cross-sectional study

description: To determine how food groups recommended in the Dietary Guidelines for Americans relate to dietary iron intake and iron status in subsets of the US population, multivariate regressions were used to examine associations between Healthy Eating Index (HEI) total and component scores with dietary iron intake and iron status indicators [body iron, ferritin, soluble transferrin receptor, serum iron, transferrin saturation, hemoglobin, hematocrit) in males (12+ y, n=2420), females of reproductive age (12-49 y, n=9757), young children (2-5 y, n=4058), and toddlers (12-23 mo, n=1132) from NHANES 2003-18. Differences in body iron by quintiles of HEI total and composite scores were assessed with least-squared mean regressions. Covariate adjustments included age, ethnicity, body-mass-index, poverty-income-ratio, smoking, and supplemental iron intake. To explore specific foods contributing to associations, the top 3 foods consumed for each HEI component were identified in each analysis subset.
DOI: 10.17632/w3g3v35vxb.1
title: Supplementary Data for "Hormonal Intrauterine Devices Are Associated With Lower Long-Term Melasma Risk Compared to Combined and Progestin-Only Oral Contraceptives: a Population-Based Cohort Study."

description: Supplementary Data
DOI: 10.15468/dl.ycjagr
title: Occurrence Download

description: A dataset containing 3320 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Antarctica",
 "TaxonKey is Euphausia superba Dana, 1850",
 "Year 2005-2025"
 ]
}

 The dataset includes 3320 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011855-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/5tulma
title: ANAT.zip

description: Contains the anatomical MRI files ending by "_N4.nii.gz"
DOI: 10.57745/ffpbcf
title: ABDOMINAL_MUSCLES_MASK.zip

description: Contains the segmentation masks of rectus and materal muscles, files ending by "mask_clean.nii.gz"
DOI: 10.57745/kkahlu
title: Morpho_data.tab

description: :unav
DOI: 10.57745/sq2yfh
title: MUSCLES_PARCELS.zip

description: Contains the segmentation masks of the parcellisation, files ending by "parcel.nii.gz"
DOI: 10.57745/wu0cmw
title: 0_README.md

description: :unav
DOI: 10.57745/z0boyl
title: 0_README.pdf

description: :unav
DOI: 10.15468/dl.gyab8u
title: Occurrence Download

description: A dataset containing 1 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey)) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey)) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey)) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey)) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey)) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey)) genuscount,
 COUNT(*) occurrences
FROM
 occurrence
WHERE
 occurrence.countrycode = 'RO'
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 3020791 OR occurrence.acceptedtaxonkey = 3020791 OR occurrence.kingdomkey = 3020791 OR occurrence.phylumkey = 3020791 OR occurrence.classkey = 3020791 OR occurrence.orderkey = 3020791 OR occurrence.familykey = 3020791 OR occurrence.genuskey = 3020791 OR occurrence.specieskey = 3020791)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.specieskey IS NOT NULL
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey

 The dataset's 1 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011845-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7pn4pwj3td
title: Gut Microbiome Profiles in CB2R-Knockout and Wild-Type Mice With and Without Salmonella Infection

description: This dataset examines how genetic deletion of cannabinoid receptor 2 (CB2R) impacts gut microbiome composition during Salmonella enterica serovar Typhimurium infection. CB2R is a G protein-coupled receptor involved in regulating immune responses and intestinal homeostasis. Its expression in immune cells and the gastrointestinal tract suggests a potential role in shaping host-microbiota interactions.

To investigate whether CB2R deficiency alters microbial dynamics in a way that influences susceptibility to infection, we compared CB2R knockout (CB2R-KO) mice with wild-type (WT) littermate controls in an oral Salmonella challenge model. Fecal samples were collected at two time points: pre-infection (baseline) and four days post-infection. Samples were subjected to shallow shotgun metagenomic sequencing (1×150 bp, Illumina). Raw reads were filtered using Vsearch to remove low-quality sequences and short fragments. Taxonomic classification was performed with Kraken2, and processed data were analyzed using R (phyloseq, ALDEx2, microbiome packages).

Alpha diversity analysis showed no significant difference in species richness or evenness between genotypes. However, CB2R-KO mice exhibited distinct shifts in microbial composition following infection. These findings suggest that CB2R contributes to maintaining a balanced gut microbial ecosystem and that its absence may promote dysbiosis during enteric infection, potentially increasing host susceptibility.
DOI: 10.17632/7pn4pwj3td.1
title: Gut Microbiome Profiles in CB2R-Knockout and Wild-Type Mice With and Without Salmonella Infection

description: This dataset examines how genetic deletion of cannabinoid receptor 2 (CB2R) impacts gut microbiome composition during Salmonella enterica serovar Typhimurium infection. CB2R is a G protein-coupled receptor involved in regulating immune responses and intestinal homeostasis. Its expression in immune cells and the gastrointestinal tract suggests a potential role in shaping host-microbiota interactions.

To investigate whether CB2R deficiency alters microbial dynamics in a way that influences susceptibility to infection, we compared CB2R knockout (CB2R-KO) mice with wild-type (WT) littermate controls in an oral Salmonella challenge model. Fecal samples were collected at two time points: pre-infection (baseline) and four days post-infection. Samples were subjected to shallow shotgun metagenomic sequencing (1×150 bp, Illumina). Raw reads were filtered using Vsearch to remove low-quality sequences and short fragments. Taxonomic classification was performed with Kraken2, and processed data were analyzed using R (phyloseq, ALDEx2, microbiome packages).

Alpha diversity analysis showed no significant difference in species richness or evenness between genotypes. However, CB2R-KO mice exhibited distinct shifts in microbial composition following infection. These findings suggest that CB2R contributes to maintaining a balanced gut microbial ecosystem and that its absence may promote dysbiosis during enteric infection, potentially increasing host susceptibility.
DOI: 10.17632/ddh9bkh3tk
title: Hybrid evolutionary-exact optimization method for the bi-objective design-for-control of water distribution networks

description: This MATLAB dataset contains network properties and problem characteristics for the case study networks 
- pescara
- Net25
- BWFL
used to illustrate the optimal WDN valve placement and control problem in the paper titled "Hybrid evolutionary-exact optimization method for the bi-objective design-for-control of water distribution networks" (submitted for publication in Water Resources Research).
DOI: 10.17632/ddh9bkh3tk.1
title: Hybrid evolutionary-exact optimization method for the bi-objective design-for-control of water distribution networks

description: This MATLAB dataset contains network properties and problem characteristics for the case study networks 
- pescara
- Net25
- BWFL
used to illustrate the optimal WDN valve placement and control problem in the paper titled "Hybrid evolutionary-exact optimization method for the bi-objective design-for-control of water distribution networks" (submitted for publication in Water Resources Research).
DOI: 10.15468/dl.p8au6c
title: Occurrence Download

description: A dataset containing 1 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey)) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey)) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey)) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey)) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey)) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey)) genuscount,
 COUNT(*) occurrences
FROM
 occurrence
WHERE
 occurrence.countrycode = 'RO'
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 2888728 OR occurrence.acceptedtaxonkey = 2888728 OR occurrence.kingdomkey = 2888728 OR occurrence.phylumkey = 2888728 OR occurrence.classkey = 2888728 OR occurrence.orderkey = 2888728 OR occurrence.familykey = 2888728 OR occurrence.genuskey = 2888728 OR occurrence.specieskey = 2888728)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.specieskey IS NOT NULL
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey

 The dataset's 1 records were derived from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011847-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pzn5866378.1
title: Neurologistics Simulation Code: Neuro-Inspired Forecasting of Ethical Decision-Making in Sustainable Supply Chains

description: This dataset contains the full Python codebase used in the study "Neurologistics: A Neuro-Inspired Simulation for Forecasting Ethical Decision-Making in Sustainable Supply Chains." The Neurologistics framework models how biologically inspired cognitive traits—empathy, stress tolerance, and impulse control—influence ethical trade-offs in logistics operations under pressure from emerging technologies (e.g., AI optimization, autonomous delivery, ESG dashboards).
The simulation produces 15,000 agent-based decisions across three ethical scenarios: speed vs. emissions, cost vs. labor rights, and efficiency vs. sustainability. It includes modules for simulation execution, econometric analysis, structural equation modeling, robustness checks, and visualizations. Several files simulate learning mechanisms and compare agent behavior across time, enabling the study of ethical adaptation.
The code supports generating figures in the manuscript and allows replication of all reported findings.
DOI: 10.17632/6fks2gk7k7.1
title: Micro irrigation adoption India

description: The data was collected from four states to examine farmers' adoption of micro-irrigation. It consists of two datasets: one with Likert-scale responses from farmers and another with quantitative data on adoption and its intensity.
DOI: 10.15468/dl.fvpm8v
title: Occurrence Download

description: A dataset containing 41348 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is greater than or equal to 1981",
 "Year is less than or equal to 2010",
 "HasCoordinate is true",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "VerbatimScientificName is Agrostis capillaris",
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 41348 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011857-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20347/wias.data.11
title: Research data to Ch. 4 "Machine-learning based multigrid methods" of the dissertation "Turbulence modeling and blood flow: impacts and alternatives"