cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/p4kp5w3g7w.2
title: Co-immunization with two recombinant Newcastle disease viruses expressing ILTV glycoprotein B and H9N2 AIV HA confers cross-protection against three avian pathogens

description: File 1: Viral growth kinetics in BHK-21 and CEF cells. The cells were coinfected or individually infected with rTS-H9/rTS-gB at an MOI of 0.01. Viral titers in culture supernatants collected at 12-hour intervals were determined by IFA on the indicated cell lines.
File 2: The original image of Western blot analysis.BHK-21 or CEF cells were coinfected or individually infected with rTS-H9/rTS-gB at an MOI of 0.1. Western blot analysis of BHK-21 (F) and CEF (G) cell lysates harvested at 48 hpi. Proteins were detected using specific antibodies against HA (H9N2 AIV), gB (ILTV), and NDV nucleoprotein (NP). The mouse anti-actin polyclonal antibody served as a loading control.
File 3: Antibody titers against NDV, AIV and ILTV after immunization, and the date of survival rate, clinical symptoms, tissue viral load and virus shedding after challenging with NDV, AIV and ILTV in SPF chicks
File 4: Antibody titers against NDV, AIV and ILTV after immunization, and the date of survival rate, clinical symptoms, tissue viral load and virus shedding after challenging with NDV, AIV and ILTV in commercial chicks
File 5: The grouping situation of the test and the data of the virus shedding rate after challenge in SPF or commercial chicks
DOI: 10.17632/3ncczsrzh8
title: Adsorption Dataset

description: The dataset used to investigate the adsorption of organic pollutants by various adsorbent materials under the influence of different conditions.
DOI: 10.17632/3ncczsrzh8.1
title: Adsorption Dataset

description: The dataset used to investigate the adsorption of organic pollutants by various adsorbent materials under the influence of different conditions.
DOI: 10.17632/hzfn49c5yx
title: Wave erosion induced bank collapse and its impact on landslides: insights from model tests

description: Fig. 11 Soil pressure monitoring results in the horizontal direction
Fig. 12 Erosion rate of landslide soil by waves
Fig. 13 Erosion causes bank collapse and affects the stability of landslides. e) Model test results and numerical simulation results
DOI: 10.17632/hzfn49c5yx.1
title: Wave erosion induced bank collapse and its impact on landslides: insights from model tests

description: Fig. 11 Soil pressure monitoring results in the horizontal direction
Fig. 12 Erosion rate of landslide soil by waves
Fig. 13 Erosion causes bank collapse and affects the stability of landslides. e) Model test results and numerical simulation results
DOI: 10.17632/jdzpmbkdhp
title: Sand Dam Suitability Assessment Using Integrated Geospatial and Multi-Criteria Decision Analysis to Strengthen Water Security in Arid and Semi-Arid Regions: A Case of the Lower Awash Basin, Ethiopia

description: Water security is a critical issue in Ethiopia’s arid Lower Awash Basin due to increasing population and economic pressures on limited water resources. This study assesses the suitability of sand dam sites using a multi-criteria evaluation approach integrated with geospatial tools. Key biophysical factors slope, stream order, soil type, land use/land cover, runoff depth, and distance from roads were analyzed and weighted based on their influence on sand dam feasibility. Runoff in the basin ranges from 682 to 934 mm annually, with 69% of the area receiving between 820 and 930 mm. About 47.4% of the land features gentle slopes favorable for dam construction. Despite this, only 4.78% of the basin shows moderate to high suitability for sand dams. Soil analysis identified Fluvisols and Regosols as favorable, while Leptosols—covering 43% of the area—were unsuitable. Rangelands, covering 71.6%, showed moderate potential, while water bodies and flooded vegetation (2.3%) were highly favorable. Locations more than 4 km from roads, representing 79.6% of the area, were deemed safer for dam sites. Overall, just 1.2% of the basin is highly suitable for sand dams. These findings highlight the need for localized, data-driven planning to strengthen water security in arid regions.
DOI: 10.17632/jdzpmbkdhp.1
title: Sand Dam Suitability Assessment Using Integrated Geospatial and Multi-Criteria Decision Analysis to Strengthen Water Security in Arid and Semi-Arid Regions: A Case of the Lower Awash Basin, Ethiopia

description: Water security is a critical issue in Ethiopia’s arid Lower Awash Basin due to increasing population and economic pressures on limited water resources. This study assesses the suitability of sand dam sites using a multi-criteria evaluation approach integrated with geospatial tools. Key biophysical factors slope, stream order, soil type, land use/land cover, runoff depth, and distance from roads were analyzed and weighted based on their influence on sand dam feasibility. Runoff in the basin ranges from 682 to 934 mm annually, with 69% of the area receiving between 820 and 930 mm. About 47.4% of the land features gentle slopes favorable for dam construction. Despite this, only 4.78% of the basin shows moderate to high suitability for sand dams. Soil analysis identified Fluvisols and Regosols as favorable, while Leptosols—covering 43% of the area—were unsuitable. Rangelands, covering 71.6%, showed moderate potential, while water bodies and flooded vegetation (2.3%) were highly favorable. Locations more than 4 km from roads, representing 79.6% of the area, were deemed safer for dam sites. Overall, just 1.2% of the basin is highly suitable for sand dams. These findings highlight the need for localized, data-driven planning to strengthen water security in arid regions.
DOI: 10.17632/ctx8j3dm3w
title: VR Acceptance

description: The dataset was derived from a quasi-experimental, single-arm study conducted between May and June 2025, to examine the acceptance of virtual reality (VR) among older adults in Serdang, Malaysia. A total of 22 retired participants (five males and 17 females), aged between 60 and 73 years (M = 65.6), were recruited using convenience sampling. All participants had attained at least a diploma-level education and met the eligibility criteria of being aged 60 years or older, free from chronic conditions that could interfere with VR use, and willing to participate in the study. Each participant used the Meta Quest 2 VR system for two sessions of 30 minutes each, over a two-week period, engaging with five applications designed to promote physical activity, stimulate cognitive function, and provide entertainment. Data on VR acceptance were collected using a validated 17-item questionnaire adapted from previous research, rated on a five-point Likert scale ranging from strongly disagree to strongly agree, and organized into six subscales: Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Subjective Norms, User Experience, and Intention to Use. The instrument demonstrated strong validity and reliability and was translated into Malay using a forward–backward translation process to ensure linguistic and cultural equivalence. Ethical approval was granted by the Ethics Committee for Research Involving Human Subjects, Universiti Putra Malaysia, and written informed consent was obtained from all participants. The dataset, anonymized to protect confidentiality, is stored in SPSS format with clearly labelled variables corresponding to the subscales and is available under a CC BY 4.0 license.
DOI: 10.17632/ctx8j3dm3w.1
title: VR Acceptance

description: The dataset was derived from a quasi-experimental, single-arm study conducted between May and June 2025, to examine the acceptance of virtual reality (VR) among older adults in Serdang, Malaysia. A total of 22 retired participants (five males and 17 females), aged between 60 and 73 years (M = 65.6), were recruited using convenience sampling. All participants had attained at least a diploma-level education and met the eligibility criteria of being aged 60 years or older, free from chronic conditions that could interfere with VR use, and willing to participate in the study. Each participant used the Meta Quest 2 VR system for two sessions of 30 minutes each, over a two-week period, engaging with five applications designed to promote physical activity, stimulate cognitive function, and provide entertainment. Data on VR acceptance were collected using a validated 17-item questionnaire adapted from previous research, rated on a five-point Likert scale ranging from strongly disagree to strongly agree, and organized into six subscales: Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Subjective Norms, User Experience, and Intention to Use. The instrument demonstrated strong validity and reliability and was translated into Malay using a forward–backward translation process to ensure linguistic and cultural equivalence. Ethical approval was granted by the Ethics Committee for Research Involving Human Subjects, Universiti Putra Malaysia, and written informed consent was obtained from all participants. The dataset, anonymized to protect confidentiality, is stored in SPSS format with clearly labelled variables corresponding to the subscales and is available under a CC BY 4.0 license.
DOI: 10.17632/pgsy36rrwt
title: North Atlantic BSR and landslide prediction machine learning data set

description: This data set includes machine learning training and prediction results of BSR and landslide prediction.
DOI: 10.17632/pgsy36rrwt.1
title: North Atlantic BSR and landslide prediction machine learning data set

description: This data set includes machine learning training and prediction results of BSR and landslide prediction.
DOI: 10.17632/pfnn94tfsj
title: Weakening Effect of Graphite on the High-Temperature Dislocation Creep of Olivine–Graphite Aggregates

description: This data set is about the raw data on deformation of olivine-graphite aggregates at confining pressures of ~300 MPa and temperatures of 1423- 1523K in a Paterson gas-medium apparatus. This data set can be used to inverstigate the dislocation creep of olivine-graphite aggregates and weakening effects of graphite. Results show that increasing graphite content leads to progressive weakening of olivine–graphite aggregates, accompanied by systematic variations in rheological parameters.
DOI: 10.17632/pfnn94tfsj.1
title: Weakening Effect of Graphite on the High-Temperature Dislocation Creep of Olivine–Graphite Aggregates

description: This data set is about the raw data on deformation of olivine-graphite aggregates at confining pressures of ~300 MPa and temperatures of 1423- 1523K in a Paterson gas-medium apparatus. This data set can be used to inverstigate the dislocation creep of olivine-graphite aggregates and weakening effects of graphite. Results show that increasing graphite content leads to progressive weakening of olivine–graphite aggregates, accompanied by systematic variations in rheological parameters.
DOI: 10.17632/4w6ry5rfxs
title: Code Mixed Dataset (Indonesian-English)

description: The code-mixed dataset comprising Indonesian-English bilingual text, designed to support sentiment analysis and POS tagging tasks. This dataset was compiled from diverse sources, including 22 applications available on the Google Play Store, yielding a total of 42,145 data points. The dataset is strategically partitioned into three subsets to facilitate different phases of model training and evaluation: a pretrained language model corpus (29,529 data points), a sentiment analysis corpus (11,791 data points), and a POS tagging corpus (1,825 data points). To enhance the linguistic diversity and robustness of the pretrained corpus, additional data were incorporated from Indonesian Wikipedia and English Wikipedia, ensuring a broad representation of code-mixed linguistic patterns.

Sentiment Analysis Corpus
The sentiment analysis corpus, consisting of 19,767 annotated data points, is specifically tailored for classifying emotions expressed in code-mixed text. This corpus was annotated into two primary categories—positive and negative—reflecting the binary sentiment classification task. The data exhibit a natural mix of Indonesian and English, often featuring colloquial expressions, abbreviations, and syntactic blending, such as "Sudah update, sudah clear cache, masih aja lemot. Sorry to say dear Shopee, very bad user experience," which captures the real-world complexity of code-mixed communication. Preprocessing steps, including tokenization with BERT vocab, lowercase conversion, and removal of special characters, were applied to standardize the dataset while preserving its linguistic nuances.

POS Tagging Corpus
The POS tagging corpus comprises 11,184 data points, tokenized into 185,424 individual tokens, and is annotated with a detailed set of 14 POS categories: ADJ (adjective), ADP (preposition), ADV (adverb), AUX (auxiliary), CCONJ (coordinating conjunction), DET (determiner), NOUN (noun), NUM (number), PART (particle), PRON (general pronoun), PROPN (proper noun), PUNCT (punctuation), SCONJ (subordinating conjunction), and VERB (verb). This corpus includes examples of language switching, such as "Aku suka this app because of its features," which challenges traditional POS tagging models due to the interplay of Indonesian and English syntax. Similar preprocessing techniques were applied, ensuring consistency across the dataset while maintaining the integrity of bilingual dependencies.
DOI: 10.15468/dl.xw98sd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capra hircus",
 "Country is Micronesia (Federated States of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026634-250811113504898/datasets/export for details.
DOI: 10.17632/4w6ry5rfxs.1
title: Code Mixed Dataset (Indonesian-English)

description: The code-mixed dataset comprising Indonesian-English bilingual text, designed to support sentiment analysis and POS tagging tasks. This dataset was compiled from diverse sources, including 22 applications available on the Google Play Store, yielding a total of 42,145 data points. The dataset is strategically partitioned into three subsets to facilitate different phases of model training and evaluation: a pretrained language model corpus (29,529 data points), a sentiment analysis corpus (11,791 data points), and a POS tagging corpus (1,825 data points). To enhance the linguistic diversity and robustness of the pretrained corpus, additional data were incorporated from Indonesian Wikipedia and English Wikipedia, ensuring a broad representation of code-mixed linguistic patterns.

Sentiment Analysis Corpus
The sentiment analysis corpus, consisting of 19,767 annotated data points, is specifically tailored for classifying emotions expressed in code-mixed text. This corpus was annotated into two primary categories—positive and negative—reflecting the binary sentiment classification task. The data exhibit a natural mix of Indonesian and English, often featuring colloquial expressions, abbreviations, and syntactic blending, such as "Sudah update, sudah clear cache, masih aja lemot. Sorry to say dear Shopee, very bad user experience," which captures the real-world complexity of code-mixed communication. Preprocessing steps, including tokenization with BERT vocab, lowercase conversion, and removal of special characters, were applied to standardize the dataset while preserving its linguistic nuances.

POS Tagging Corpus
The POS tagging corpus comprises 11,184 data points, tokenized into 185,424 individual tokens, and is annotated with a detailed set of 14 POS categories: ADJ (adjective), ADP (preposition), ADV (adverb), AUX (auxiliary), CCONJ (coordinating conjunction), DET (determiner), NOUN (noun), NUM (number), PART (particle), PRON (general pronoun), PROPN (proper noun), PUNCT (punctuation), SCONJ (subordinating conjunction), and VERB (verb). This corpus includes examples of language switching, such as "Aku suka this app because of its features," which challenges traditional POS tagging models due to the interplay of Indonesian and English syntax. Similar preprocessing techniques were applied, ensuring consistency across the dataset while maintaining the integrity of bilingual dependencies.
DOI: 10.17632/pzx6cwx3k9
title: Optical genome mapping for complex and cryptic chromosomal rearrangements detection in unsolved adverse pregnancy couples

description: Regarding the provision of raw sequencing data for the detection of cryptic chromosomal translocations and complex rearrangements in three families using OGM technology.
DOI: 10.17632/pzx6cwx3k9.1
title: Optical genome mapping for complex and cryptic chromosomal rearrangements detection in unsolved adverse pregnancy couples

description: Regarding the provision of raw sequencing data for the detection of cryptic chromosomal translocations and complex rearrangements in three families using OGM technology.
DOI: 10.17632/gxsdcjn4h7
title: Shortwave imaginary refractive index of alumina aerosols.

description: This repository contains the following datasets and supporting materials:

1) Alumina_Refractive_Index_1nm.csv

Description: Complex refractive index (n and k) at 1 nm resolution.
Wavelength Range: 10–100,000 nm. Refractive indices between 120–6500 nm are derived from Urbach-tail fits of photoacoustic spectrometry (PAS) data; values outside this range are from Tropf and Thomas (1997).
Columns:
• wavelength (nm)
• k — imaginary part of refractive index
• k_25 — 25th percentile of k from PAS-derived fits
• k_75 — 75th percentile of k from PAS-derived fits


2) Alumina_Refractive_Index_PAS.csv

Description: Imaginary refractive index (k) retrieved from in situ measurements and Mie theory inversions (Sumlin et al., 2016). Absorption and scattering coefficients were measured with integrated photoacoustic spectrometers (Upadhyay et al., 2025), and particle number size distributions with a scanning mobility particle sizer and an aerodynamic aerosol classifier.
Columns:
• particle_type — spherical α-phase, irregular α-phase, irregular γ-phase
• wavelength (nm) — 375, 405, 532, 721, and 1047 nm.
• N — number of data points
• k_25 — 25th percentile
• k_median — median value
• k_75 — 75th percentile


3) Alumina_Refractive_Index_EELS.csv

Description: Imaginary refractive index (k) measured for individual particles using STEM–EELS (Scanning Transmission Electron Microscopy–Electron Energy Loss Spectroscopy) of spherical α-phase alumina particles.
Columns:
• wavelength (nm)
• k_1 to k_10 — k values for ten individual particles

4) EELS_Anlysis (folder)

• Contains raw EELS spectra (subfolder: particles) and processed data (subfolder: data).
• A PowerPoint presentation (EELS_SI.pptx) provides a comprehensive analysis of the EELS measurements, including an exemplar particle with a corresponding HAADF image, the extraction of the refractive index (k) via Kramers–Kronig analysis (KKA), an assessment of the influence of zero-loss peak (ZLP) tail cutoff on the derived optical constants, and the identification of oxygen-vacancy defects through the evaluation of the imaginary component of the refractive index.
• A separate codes folder includes the analysis scripts used to process the EELS data and generate figures.
DOI: 10.17632/gxsdcjn4h7.1
title: Shortwave imaginary refractive index of alumina aerosols.

description: This repository contains the following datasets and supporting materials:

1) Alumina_Refractive_Index_1nm.csv

Description: Complex refractive index (n and k) at 1 nm resolution.
Wavelength Range: 10–100,000 nm. Refractive indices between 120–6500 nm are derived from Urbach-tail fits of photoacoustic spectrometry (PAS) data; values outside this range are from Tropf and Thomas (1997).
Columns:
• wavelength (nm)
• k — imaginary part of refractive index
• k_25 — 25th percentile of k from PAS-derived fits
• k_75 — 75th percentile of k from PAS-derived fits


2) Alumina_Refractive_Index_PAS.csv

Description: Imaginary refractive index (k) retrieved from in situ measurements and Mie theory inversions (Sumlin et al., 2016). Absorption and scattering coefficients were measured with integrated photoacoustic spectrometers (Upadhyay et al., 2025), and particle number size distributions with a scanning mobility particle sizer and an aerodynamic aerosol classifier.
Columns:
• particle_type — spherical α-phase, irregular α-phase, irregular γ-phase
• wavelength (nm) — 375, 405, 532, 721, and 1047 nm.
• N — number of data points
• k_25 — 25th percentile
• k_median — median value
• k_75 — 75th percentile


3) Alumina_Refractive_Index_EELS.csv

Description: Imaginary refractive index (k) measured for individual particles using STEM–EELS (Scanning Transmission Electron Microscopy–Electron Energy Loss Spectroscopy) of spherical α-phase alumina particles.
Columns:
• wavelength (nm)
• k_1 to k_10 — k values for ten individual particles

4) EELS_Anlysis (folder)

• Contains raw EELS spectra (subfolder: particles) and processed data (subfolder: data).
• A PowerPoint presentation (EELS_SI.pptx) provides a comprehensive analysis of the EELS measurements, including an exemplar particle with a corresponding HAADF image, the extraction of the refractive index (k) via Kramers–Kronig analysis (KKA), an assessment of the influence of zero-loss peak (ZLP) tail cutoff on the derived optical constants, and the identification of oxygen-vacancy defects through the evaluation of the imaginary component of the refractive index.
• A separate codes folder includes the analysis scripts used to process the EELS data and generate figures.
DOI: 10.15468/dl.mwgneq
title: Occurrence Download

description: A dataset containing 237 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.3087 10.34048,-73.3087 10.34048,-73.30869 10.34126,-73.30875 10.34204,-73.30887 10.34281,-73.30906 10.34357,-73.30932 10.34431,-73.30965 10.34502,-73.31003 10.34571,-73.31048 10.34636,-73.31098 10.34696,-73.31153 10.34753,-73.31213 10.34804,-73.31277 10.3485,-73.31346 10.3489,-73.31417 10.34924,-73.31491 10.34952,-73.31568 10.34974,-73.31646 10.34989,-73.31725 10.34997,-73.31805 10.34998,-73.31884 10.34993,-73.31962 10.34981,-73.3204 10.34962,-73.32115 10.34936,-73.32187 10.34904,-73.32257 10.34866,-73.32323 10.34822,-73.32385 10.34773,-73.32442 10.34719,-73.32494 10.3466,-73.32541 10.34596,-73.32582 10.34529,-73.32616 10.34459,-73.32645 10.34386,-73.32667 10.34311,-73.32682 10.34234,-73.3269 10.34156,-73.32691 10.34078,-73.32686 10.34,-73.32673 10.33923,-73.32654 10.33847,-73.32628 10.33773,-73.32596 10.33701,-73.32557 10.33633,-73.32513 10.33568,-73.32463 10.33507,-73.32407 10.33451,-73.32347 10.334,-73.32283 10.33354,-73.32215 10.33314,-73.32143 10.33279,-73.32069 10.33251,-73.31993 10.3323,-73.31914 10.33215,-73.31835 10.33207,-73.31756 10.33205,-73.31677 10.33211,-73.31598 10.33223,-73.31521 10.33242,-73.31446 10.33268,-73.31373 10.333,-73.31303 10.33337,-73.31238 10.33381,-73.31176 10.33431,-73.31119 10.33485,-73.31067 10.33544,-73.3102 10.33607,-73.30979 10.33674,-73.30944 10.33745,-73.30916 10.33818,-73.30894 10.33893,-73.30879 10.3397,-73.3087 10.34048))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 237 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026635-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5ph4d654pr
title: Data for: Scale-habitat differentiation effect of fragmentation on multiple ecosystem functions in urban remnant forests

description: This data set contains all data used for analyzing the effect of fragmentation on multiple ecosystem functions in urban remnant forests. It includes plant information, soil physiochemical and microbial data and ecosystem functional data in 60 interior and edge samples in 30 remnant forest patches.