cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rsd53mk7kc.1
title: Bangladeshi Local Fish Dataset with length measurement in inch

description: This dataset contains images of 17 distinct fish species commonly found in Bangladesh. The data has been collected to support research in computer vision, deep learning, and fisheries science. It can be used for tasks such as image classification, object detection, species recognition, and conservation studies.

Source : 
All fish samples were collected from Bangladesh, covering different regions and local markets. Each species is labeled with its Bangladeshi local name.

Dataset Contents 
- Total Classes: 17 (2915 images).
- Each class corresponds to a distinct fish species. 
- Images are stored in JPEG/PNG format. 
- Resolution: varies, but most images are clear and properly labeled.
 
Fish Classes (Bangladeshi Local Names) : 
1. Rui (Labeo rohita) 
2. Aier (Long-whiskered Catfish)
3. Bhetki (Barramundi or Asian Sea Bass) 
4. Pangas (Pangasius pangasius) 
5. Tilapia (Oreochromis niloticus) 
6. Carp (Cyprinus carpio)
7. Shing (Heteropneustes fossilis) 
8. Magur (Clarias batrachus) 
9. Sor-Puti (Puntius sophore) 
10.Common Carp (Cyprinus carpio) 
11.Datina (yellowfin seabream) 
12. Bata (Labeo bata) 
13. ladyfish Kokila((Xenentodon cancila) 
14. Mirror carp(Cyprinus carpio) 
15. Ilish (Tenualosa ilisha) 
16. Nola (Eleutheronema tetradactylum) 
17. Chingri (Macrobrachium rosenbergii / Prawn)
DOI: 10.15468/dl.bdhthy
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Oligostachyum spongiosum"
 ]
}

 The dataset includes 49 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005795-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9xg5wrr3cg
title: GZSL

description: HCLF Dataset.
DOI: 10.17632/9xg5wrr3cg.1
title: GZSL

description: HCLF Dataset.
DOI: 10.5281/zenodo.17699895
title: sparkly-sparkle/data: v2.0

description: rice milling data

45 dataset

concatenated dataset with additional variables
DOI: 10.5281/zenodo.17700199
title: sparkly-sparkle/data: v2.0

description: rice milling data

45 dataset

concatenated dataset with additional variables
DOI: 10.17632/3f8rfrx42h
title: Code _ China’s Technological Rise and Its Global Welfare Effects

description: Replication code of the project "China’s Technological Rise and Its Global Welfare Effects"
DOI: 10.17632/3f8rfrx42h.3
title: Code _ China’s Technological Rise and Its Global Welfare Effects

description: Replication code of the project "China’s Technological Rise and Its Global Welfare Effects"
DOI: 10.6084/m9.figshare.30694838
title: Dataset for Study on Media Exposure and AI-Related Attitudes (Online Survey, N = 299)

description: This dataset is part of a project investigating how media narratives shape public attitudes toward AI-assisted healthcare in China. The project consists of two studies: (1) a content analysis of Chinese media coverage of AI-assisted healthcare (N = 1,632) and (2) an online survey examining how media exposure relates to individuals’ attitudes toward AI-assisted healthcare (Study 2, N = 299). The dataset uploaded here corresponds to Study 2, which focuses on the relationship between media exposure and public attitudes. This dataset enables researchers to explore how media exposure is associated with positive attitudes toward AI-assisted healthcare, including both direct effects and indirect effects through reduced risk perception. It also allows examination of how personal experience moderates these relationships, as heavier users of AI-assisted healthcare show a weaker link between media exposure and positive attitudes. Together, these data provide empirical support for understanding how media narratives, individual experience, and evolving state policy interact within China’s rapidly developing AI-healthcare landscape.
DOI: 10.6084/m9.figshare.30694838.v1
title: Dataset for Study on Media Exposure and AI-Related Attitudes (Online Survey, N = 299)

description: This dataset is part of a project investigating how media narratives shape public attitudes toward AI-assisted healthcare in China. The project consists of two studies: (1) a content analysis of Chinese media coverage of AI-assisted healthcare (N = 1,632) and (2) an online survey examining how media exposure relates to individuals’ attitudes toward AI-assisted healthcare (Study 2, N = 299). The dataset uploaded here corresponds to Study 2, which focuses on the relationship between media exposure and public attitudes. This dataset enables researchers to explore how media exposure is associated with positive attitudes toward AI-assisted healthcare, including both direct effects and indirect effects through reduced risk perception. It also allows examination of how personal experience moderates these relationships, as heavier users of AI-assisted healthcare show a weaker link between media exposure and positive attitudes. Together, these data provide empirical support for understanding how media narratives, individual experience, and evolving state policy interact within China’s rapidly developing AI-healthcare landscape.
DOI: 10.17632/49ytg7gh2w
title: GZSL EHA

description: HCLF dataest
DOI: 10.17632/49ytg7gh2w.1
title: GZSL EHA

description: HCLF dataest
DOI: 10.15468/dl.fw4kaa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-119.4801 34.9466, -119.4795 34.94667, -119.4794 34.94697, -119.4794 34.94725, -119.4796 34.94758, -119.4797 34.94761, -119.4798 34.94765, -119.4799 34.94771, -119.4801 34.94773, -119.4801 34.94752, -119.4806 34.94737, -119.4804 34.94666, -119.4801 34.9466))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005796-251120083545085/datasets/export for details.
DOI: 10.17632/xypv74wsjv
title: Bioinformatics-Based Investigation of the Mechanisms by Which Gastrodia Elata and Dauricine Enhance Hypoxia Adaptation

description: Initial data for Bioinformatics-Based Investigation of the Mechanisms by Which Gastrodia Elata and Dauricine Enhance Hypoxia Adaptation
DOI: 10.17632/xypv74wsjv.1
title: Bioinformatics-Based Investigation of the Mechanisms by Which Gastrodia Elata and Dauricine Enhance Hypoxia Adaptation

description: Initial data for Bioinformatics-Based Investigation of the Mechanisms by Which Gastrodia Elata and Dauricine Enhance Hypoxia Adaptation
DOI: 10.17632/728w25w77c
title: Chronic sleep restriction and physical activity

description: This data is for a study titled: Chronic sleep restriction and physical activity: Effects on motor function, growth, and hormone levels in young rats.
DOI: 10.17632/728w25w77c.1
title: Chronic sleep restriction and physical activity

description: This data is for a study titled: Chronic sleep restriction and physical activity: Effects on motor function, growth, and hormone levels in young rats.
DOI: 10.5281/zenodo.15635249
title: Transcriptomic atlas in Pisum sativum

description: We integrated a total of 149 publicly available RNA-seq libraries from 7 international studies (see atlas_info). These transcriptomes were generated from 10 different pea varieties and covered a wide range of biological conditions, including a comprehensive collection of plant organs, various modalities of abiotic stress (mineral nutrition, water supply and temperature) and biotic interactions (nodule). The raw expression data from the source RNA-seq libraries were re-assembled to the reference genome (Kreplak et al., 2019) and the mean expression value was computed between biological replicates produced in individual studies (see atlas_info), thus providing a transcriptomic atlas of the 44.756 genes in the pea genome across 81 biological conditions. 

Method : RNA-seq data (sequenced reads and fastq files) generated from P. sativum were downloaded from the Sequence Read Archive publicly available at NCBI (Bioproject numbers listed in table info) using SRAtools v3.0.1 (SRA toolkit). A fastp v0.22.0 (Chen et al., 2018) analysis was performed to trim the adapters and filter out reads with a low-quality score, followed by a quality assessment performed using FastQC v0.12.1 (Babraham Bioinformatics). The RNA-seq reads were mapped to the P. sativum reference genome v1a (Kreplak et al., 2019) using STAR v2.7.10b (Dobin et al., 2012). Gene expression table counts were generated using FeatureCounts v2.0.1 (Liao et al., 2013) and normalized by median ratio using the DESeq2 R-package. The transcriptomic atlas comprises expression data of 44.756 genes of the pea genome across 81 biological conditions.
DOI: 10.5281/zenodo.15635248
title: Transcriptomic atlas in Pisum sativum

description: We integrated a total of 149 publicly available RNA-seq libraries from 7 international studies (see atlas_info). These transcriptomes were generated from 10 different pea varieties and covered a wide range of biological conditions, including a comprehensive collection of plant organs, various modalities of abiotic stress (mineral nutrition, water supply and temperature) and biotic interactions (nodule). The raw expression data from the source RNA-seq libraries were re-assembled to the reference genome (Kreplak et al., 2019) and the mean expression value was computed between biological replicates produced in individual studies (see atlas_info), thus providing a transcriptomic atlas of the 44.756 genes in the pea genome across 81 biological conditions. 

Method : RNA-seq data (sequenced reads and fastq files) generated from P. sativum were downloaded from the Sequence Read Archive publicly available at NCBI (Bioproject numbers listed in table info) using SRAtools v3.0.1 (SRA toolkit). A fastp v0.22.0 (Chen et al., 2018) analysis was performed to trim the adapters and filter out reads with a low-quality score, followed by a quality assessment performed using FastQC v0.12.1 (Babraham Bioinformatics). The RNA-seq reads were mapped to the P. sativum reference genome v1a (Kreplak et al., 2019) using STAR v2.7.10b (Dobin et al., 2012). Gene expression table counts were generated using FeatureCounts v2.0.1 (Liao et al., 2013) and normalized by median ratio using the DESeq2 R-package. The transcriptomic atlas comprises expression data of 44.756 genes of the pea genome across 81 biological conditions.
DOI: 10.17632/rr23ct8kb7
title: Simulation Study on the Enhanced Decarbonization of Flue Gas in Three New Types of Spouted Beds data

description: Under the dual-carbon strategic framework, this study explores the application of spouted bed technology in CO₂ capture to develop efficient and low-carbon solutions. Addressing inadequate gas-solid radial mixing in conventional spouted beds, we propose three enhanced configurations: a spouted bed with longitudinal vortex generators (SB with LVG), an integral multi-jet spouted-fluidized bed (IMJSFB), and a swirl blade nozzle-equipped spouted bed (SB with SBN). Numerical simulations systematically investigated multiphase flow dynamics, water vaporization, and decarbonization processes. The chemical reaction processes of multiple acidic gases in CSB were also investigated. Results demonstrate that all intensification structures significantly improve particle agitation and water vaporization efficiency, ranked as SB with SBN &gt; IMJSFB &gt; SB with LVG &gt; CSB. Through optimized gas-solid interaction and enhanced heat/mass transfer, these modified systems achieve superior semi-dry flue gas decarbonization efficiencies of 75.13% (SB with LVG), 79.77% (IMJSFB), and 82.59% (SB with SBN), compared to 72.46% in CSB. The presence of reduced the decarbonization efficiencies to 47.7%, representing a decrease of 34.17%.
DOI: 10.17632/rr23ct8kb7.1
title: Simulation Study on the Enhanced Decarbonization of Flue Gas in Three New Types of Spouted Beds data

description: Under the dual-carbon strategic framework, this study explores the application of spouted bed technology in CO₂ capture to develop efficient and low-carbon solutions. Addressing inadequate gas-solid radial mixing in conventional spouted beds, we propose three enhanced configurations: a spouted bed with longitudinal vortex generators (SB with LVG), an integral multi-jet spouted-fluidized bed (IMJSFB), and a swirl blade nozzle-equipped spouted bed (SB with SBN). Numerical simulations systematically investigated multiphase flow dynamics, water vaporization, and decarbonization processes. The chemical reaction processes of multiple acidic gases in CSB were also investigated. Results demonstrate that all intensification structures significantly improve particle agitation and water vaporization efficiency, ranked as SB with SBN &gt; IMJSFB &gt; SB with LVG &gt; CSB. Through optimized gas-solid interaction and enhanced heat/mass transfer, these modified systems achieve superior semi-dry flue gas decarbonization efficiencies of 75.13% (SB with LVG), 79.77% (IMJSFB), and 82.59% (SB with SBN), compared to 72.46% in CSB. The presence of reduced the decarbonization efficiencies to 47.7%, representing a decrease of 34.17%.
DOI: 10.17632/6yfr2vjwz2
title: Ethnobotanical Data in Forests of Zegie Peninsula and Nearby Monastries

description: Hypothesis of Research and Data Overview:
This dataset presents an ethnobotanical study concerning traditional ecological knowledge and the use of medicinal plants by communities in the Zegie Peninsula and nearby monastic islands of Ethiopia. The hypothesis for this research is that these communities possess a detailed traditional knowledge of plant uses, especially medicinal ones, which would vary with different demographic groups and geographical locations. Data collection entailed structured interviews with 260 informants across several sites, covering demographic profiles, plant species utilization, and traditional healing practices.

Data Collection Methodology:

Data collection was based on systematic ethnobotanical surveys from [insert collection dates]. Interviews, guided field walks, and botanical specimen collection were conducted. Collected voucher specimens were deposited with identity numbers (AW24001-AW24120) for botanical verification. The informants were chosen by purposive and snowball sampling from three main sites representing the Peninsula-Ura, Mehal Zegie, and Yiganda-and the Islands-Daga Estifanos, Kebran Gebriel, and Enton Eyesus-representative occupational groups, including monks (51.5%), coffee growers, carpenters, and other community members.
Key Findings and Data Interpretation:
These data represent a rich pharmacopoeia of 120 plant species belonging to 56 families used for medicinal purposes, and the most represented families are Asteraceae, Fabaceae, and Rutaceae. Leaves are predominantly used in medication (43%), followed by roots (22%), and the most frequent route of administration is oral (58%). The data provide evidence of a sophisticated treatment protocol, ranging from common diseases such as malaria and gastrointestinal disorders to chronic diseases and spiritual illnesses. The large proportion of informants that belong to monastic institutions indicates that religious institutions are important repositories of traditional ecological knowledge. From the demographic analysis, knowledge is distributed among age groups as follows: 18-35 years, 27.7%; 36-55 years, 49.2%; above 56 years, 23.1%, while gender distribution is as follows: male, 83.1%, female, 16.9%. Data Structure and Usage: The dataset consists of four complementary files: (1) Demographic survey data with informant characteristics; (2) Plant species, listed according to their uses, such as medicinal, food, fuelwood, and construction, among others; (3) Detailed traditional medicinal uses, considering the mode of preparation; and (4) Botanical inventory with taxonomic information. This dataset allows for comparative ethnobotanical analysis, biodiversity conservation planning, pharmacological screening, and interpretation of traditional knowledge transmission patterns. The voucher numbers can be used to cross-reference herbarium specimens for verification.
DOI: 10.17632/6yfr2vjwz2.1
title: Ethnobotanical Data in Forests of Zegie Peninsula and Nearby Monastries

description: Hypothesis of Research and Data Overview:
This dataset presents an ethnobotanical study concerning traditional ecological knowledge and the use of medicinal plants by communities in the Zegie Peninsula and nearby monastic islands of Ethiopia. The hypothesis for this research is that these communities possess a detailed traditional knowledge of plant uses, especially medicinal ones, which would vary with different demographic groups and geographical locations. Data collection entailed structured interviews with 260 informants across several sites, covering demographic profiles, plant species utilization, and traditional healing practices.

Data Collection Methodology:

Data collection was based on systematic ethnobotanical surveys from [insert collection dates]. Interviews, guided field walks, and botanical specimen collection were conducted. Collected voucher specimens were deposited with identity numbers (AW24001-AW24120) for botanical verification. The informants were chosen by purposive and snowball sampling from three main sites representing the Peninsula-Ura, Mehal Zegie, and Yiganda-and the Islands-Daga Estifanos, Kebran Gebriel, and Enton Eyesus-representative occupational groups, including monks (51.5%), coffee growers, carpenters, and other community members.
Key Findings and Data Interpretation:
These data represent a rich pharmacopoeia of 120 plant species belonging to 56 families used for medicinal purposes, and the most represented families are Asteraceae, Fabaceae, and Rutaceae. Leaves are predominantly used in medication (43%), followed by roots (22%), and the most frequent route of administration is oral (58%). The data provide evidence of a sophisticated treatment protocol, ranging from common diseases such as malaria and gastrointestinal disorders to chronic diseases and spiritual illnesses. The large proportion of informants that belong to monastic institutions indicates that religious institutions are important repositories of traditional ecological knowledge. From the demographic analysis, knowledge is distributed among age groups as follows: 18-35 years, 27.7%; 36-55 years, 49.2%; above 56 years, 23.1%, while gender distribution is as follows: male, 83.1%, female, 16.9%. Data Structure and Usage: The dataset consists of four complementary files: (1) Demographic survey data with informant characteristics; (2) Plant species, listed according to their uses, such as medicinal, food, fuelwood, and construction, among others; (3) Detailed traditional medicinal uses, considering the mode of preparation; and (4) Botanical inventory with taxonomic information. This dataset allows for comparative ethnobotanical analysis, biodiversity conservation planning, pharmacological screening, and interpretation of traditional knowledge transmission patterns. The voucher numbers can be used to cross-reference herbarium specimens for verification.
DOI: 10.15468/dl.kcahdf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-119.8883 35.04544, -119.8879 35.04615, -119.8892 35.04659, -119.8897 35.04634, -119.8899 35.04615, -119.89 35.04578, -119.8901 35.04506, -119.8883 35.04544))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005797-251120083545085/datasets/export for details.
DOI: 10.17632/rb3bzv9bnm
title: AI-Detector Multi-Language Evaluation Dataset

description: This dataset includes the data relevant to an evaluation of 12 AI-detector tools' performance metrics. The dataset consists of three parts: AI-generated texts in three languages (English, Ukrainian and Russian); Real texts in three languages (English, Ukrainian and Russian); Results and relevant code. The AI-generated texts is a collection of news texts, generated by 48 LLMs divided by 21 folders with the LLM's creator names. Each subfolder is divided by relevant LLM and, where applicable, relevant model versions. Each LLM generated 3 texts in each language and the results are placed in relevant .odt files. For example, AI-Generated Texts/01_OpenAI/GPT/GPT-3.5/News/En-RandomNews-500words.odt is a path to three AI-generated news texts, generated in English by OpenAI's CPT-3.5. The real texts is a collection of news texts, retreived from 9 news agencies from 3 years: 2018, 2019, and 2024. Each year includes three mentioned languages each representing 3 news agencies operating in those languages with each file including 3 news articles from that agency of that year as .odt files. For example: Real texts/2018/EN/BBC_2018.odt represents three articles from the year 2018 by BBC, written in English. Results&Code includes the Raw Data and Metrics .xlsx table, which is divided into three parts: AI texts, which includes the detectors' predictions on the AI-generated texts, Real texts, which includes the detectors' predictions on the real news articles, and metrics, which includes and all relevant calculated values. The majority of the metrics are calculated inside the table using Excel's capabilities, but ROC AUC, AP and Log Loss are calculated using Python code, located in the relevant folder. The folder includes Main.py - the code used for calculating the metrics, using the Scikit-learn library, definitions.py a supplementary code with each detectors' responses, used in Main.py, PR_curves and ROC_curves folders, and out.txt with the results of the calculations, which are generated by Main.py. The execution of Main.py can be done in the following way: py .\Calculator.py &gt; out.txt. You will need to have scikit-learn, numpy and matplotlib installed.