cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fyvg4tk88n
title: Uzbek Sentiment + Named Entity Recognition Dataset

description: This dataset contains 12,000 Uzbek (Latin script) sentences annotated for sentiment (positive/negative/neutral) and named entities (PER, ORG, LOC). Each record includes the raw sentence text, a numeric polarity score (-1/0/1) with the corresponding sentiment label, and a list of entity mentions aligned with their entity types. The dataset additionally provides emoji-related signals: extracted emojis, their position within the sentence, emoji sentiment, and a conflict flag indicating disagreement between text polarity and emoji polarity. Overall, the corpus includes 24,208 entity mentions and 4,548 emoji tokens (14 unique emojis). The dataset is intended for training and evaluation of sentiment classification, NER, and integrated pipelines such as “Sentiment → NER”, especially for low-resource Uzbek NLP and user-generated content analysis.
DOI: 10.17632/fyvg4tk88n.1
title: Uzbek Sentiment + Named Entity Recognition Dataset

description: This dataset contains 12,000 Uzbek (Latin script) sentences annotated for sentiment (positive/negative/neutral) and named entities (PER, ORG, LOC). Each record includes the raw sentence text, a numeric polarity score (-1/0/1) with the corresponding sentiment label, and a list of entity mentions aligned with their entity types. The dataset additionally provides emoji-related signals: extracted emojis, their position within the sentence, emoji sentiment, and a conflict flag indicating disagreement between text polarity and emoji polarity. Overall, the corpus includes 24,208 entity mentions and 4,548 emoji tokens (14 unique emojis). The dataset is intended for training and evaluation of sentiment classification, NER, and integrated pipelines such as “Sentiment → NER”, especially for low-resource Uzbek NLP and user-generated content analysis.
DOI: 10.17632/rxfk6t8f6x
title: RenSeg-KidneyCT

description: RenSeg-KidneyCT is a multi-class kidney CT dataset comprising three groups: Renal Calculi (annotated), Renal Carcinoma (annotated), and Normal Kidney cases (unannotated). The annotation of calculi and carcinoma regions was performed and verified by an experienced medical expert (nephrologist) to ensure clinical reliability. This dataset is intended for segmentation and classification research tasks in medical imaging and AI. The dataset was generated as part of the research presented in the article “RenSeg: Leveraging Unsupervised Segmentation Using Localization and Contour-Guided Quickshift for Renal Calculi and Carcinoma Segmentation and Classification.” Researchers using this dataset are kindly requested to cite the aforementioned article as the primary source of the data in any resulting publications, in addition to citing the dataset DOI.
DOI: 10.17632/nxpbdg6dpn
title: Dataset: Meta-analysis shows that mixed-species plantations improve soil physical and chemical properties

description: This dataset contains the data supporting the meta-analysis presented in the paper “Meta-analysis shows that mixed-species plantations improve soil physical and chemical properties.” The dataset comprises 9,959 paired observations extracted from 225 peer-reviewed studies worldwide, covering soil physical (e.g., bulk density, porosity, water-holding capacity) and chemical (e.g., pH, total and available C, N, P, and K) properties under mixed and monoculture plantations. Each record includes metadata on study location, climatic variables (mean annual temperature and precipitation), soil depth, stand characteristics, and tree functional traits. These data were used to quantify the overall and subgroup effects of mixed planting and to identify the main factors regulating soil physicochemical responses.
DOI: 10.17632/rxfk6t8f6x.2
title: RenSeg-KidneyCT

description: RenSeg-KidneyCT is a multi-class kidney CT dataset comprising three groups: Renal Calculi (annotated), Renal Carcinoma (annotated), and Normal Kidney cases (unannotated). The annotation of calculi and carcinoma regions was performed and verified by an experienced medical expert (nephrologist) to ensure clinical reliability. This dataset is intended for segmentation and classification research tasks in medical imaging and AI. The dataset was generated as part of the research presented in the article “RenSeg: Leveraging Unsupervised Segmentation Using Localization and Contour-Guided Quickshift for Renal Calculi and Carcinoma Segmentation and Classification.” Researchers using this dataset are kindly requested to cite the aforementioned article as the primary source of the data in any resulting publications, in addition to citing the dataset DOI.
DOI: 10.17632/nxpbdg6dpn.2
title: Dataset: Meta-analysis shows that mixed-species plantations improve soil physical and chemical properties

description: This dataset contains the data supporting the meta-analysis presented in the paper “Meta-analysis shows that mixed-species plantations improve soil physical and chemical properties.” The dataset comprises 9,959 paired observations extracted from 225 peer-reviewed studies worldwide, covering soil physical (e.g., bulk density, porosity, water-holding capacity) and chemical (e.g., pH, total and available C, N, P, and K) properties under mixed and monoculture plantations. Each record includes metadata on study location, climatic variables (mean annual temperature and precipitation), soil depth, stand characteristics, and tree functional traits. These data were used to quantify the overall and subgroup effects of mixed planting and to identify the main factors regulating soil physicochemical responses.
DOI: 10.17632/v28n8x6hgf
title: Supplementary Materials

description: This supplementary dataset supports the research paper titled “Optimizing Asphalt Compaction: Intelligent Compaction Roller Frequency and Machine Learning Prediction of In-Place Density.” The data used in this study are provided in the dataset files associated with this record.
DOI: 10.17632/v28n8x6hgf.1
title: Supplementary Materials

description: This supplementary dataset supports the research paper titled “Optimizing Asphalt Compaction: Intelligent Compaction Roller Frequency and Machine Learning Prediction of In-Place Density.” The data used in this study are provided in the dataset files associated with this record.
DOI: 10.34740/kaggle/dsv/14624947
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17632/hd96j49fnz
title: Financial Reports of SEC 20-F and 20-F/A Submitters (2015 - 2025)

description: For academic research in accounting and / or auditing, the financial reports are considered to be of paramount importance. In accordance with the US regulations, entities which have they securities traded on US public markets needs to fill in the forms (which includes financial reports) to Securities and Exchange Commission (SEC). Thanks to SEC policy of open access, those financial reports are available through its EDGAR database. However, because of the nature of the financial reports, some of the data are provided in the unstructured form - for example, data about the financial framework used for the compilation of the reports (i. e. whether US GAAP, IFRS or other framework has been used) or the data about the auditing company engaged in auditing of the report. This dataset provided such information in a structured form (including the name of the submitter of the form, end of the period for which the form is provided, the source address for which the financial report is available, type of the accounting framework (US GAAP, IFRS, other) and the name of the respective auditor. The dataset is focused on the forms 20-F and 20-F/A only - that is, for submitters which have their securities traded at US securities markets, but are themselves operating outside the USA.
DOI: 10.17632/hd96j49fnz.1
title: Financial Reports of SEC 20-F and 20-F/A Submitters (2015 - 2025)

description: For academic research in accounting and / or auditing, the financial reports are considered to be of paramount importance. In accordance with the US regulations, entities which have they securities traded on US public markets needs to fill in the forms (which includes financial reports) to Securities and Exchange Commission (SEC). Thanks to SEC policy of open access, those financial reports are available through its EDGAR database. However, because of the nature of the financial reports, some of the data are provided in the unstructured form - for example, data about the financial framework used for the compilation of the reports (i. e. whether US GAAP, IFRS or other framework has been used) or the data about the auditing company engaged in auditing of the report. This dataset provided such information in a structured form (including the name of the submitter of the form, end of the period for which the form is provided, the source address for which the financial report is available, type of the accounting framework (US GAAP, IFRS, other) and the name of the respective auditor. The dataset is focused on the forms 20-F and 20-F/A only - that is, for submitters which have their securities traded at US securities markets, but are themselves operating outside the USA.
DOI: 10.17632/44p9gbbhph
title: Data: Cross-Segment Credit Contagion Analysis

description: Dataset used in the empirical analysis.
DOI: 10.17632/44p9gbbhph.1
title: Data: Cross-Segment Credit Contagion Analysis

description: Dataset used in the empirical analysis.
DOI: 10.17632/cbxtmct7k8
title: Data: Financial uncertainty, climate risks, and sustainable investments

description: Dataset used in the empirical analysis.
DOI: 10.17632/cbxtmct7k8.1
title: Data: Financial uncertainty, climate risks, and sustainable investments

description: Dataset used in the empirical analysis.
DOI: 10.17632/t9jctkzymk
title: Data: Asymmetric impact of global uncertainty on emerging market sovereign credit risk

description: Dataset used in the empirical analysis.
DOI: 10.17632/t9jctkzymk.1
title: Data: Asymmetric impact of global uncertainty on emerging market sovereign credit risk

description: Dataset used in the empirical analysis.
DOI: 10.17632/c42dvj7j65
title: Impacto de la comunicación en Instagram del MIMP en jóvenes de 18 a 25 años Lima Metropolitana - Resultados de Análisis de contenido

description: El presente estudio tuvo como objetivo analizar el impacto del contenido publicado en la cuenta oficial de Instagram del Ministerio de la Mujer y Poblaciones Vulnerables (MIMP) en el posicionamiento institucional entre mujeres jóvenes de 18 a 25 años residentes en Lima. La investigación adoptó un enfoque metodológico mixto, de tipo aplicada y nivel descriptivo, utilizando como una de las técnicas el análisis de contenido. En el presente documento se adjuntan los resultados obtenidos para 241 publicaciones de Instagram analizadas correspondientes al primer bimestre del 2024 y primer bimestre del 2025.
DOI: 10.17632/c42dvj7j65.1
title: Impacto de la comunicación en Instagram del MIMP en jóvenes de 18 a 25 años Lima Metropolitana - Resultados de Análisis de contenido

description: El presente estudio tuvo como objetivo analizar el impacto del contenido publicado en la cuenta oficial de Instagram del Ministerio de la Mujer y Poblaciones Vulnerables (MIMP) en el posicionamiento institucional entre mujeres jóvenes de 18 a 25 años residentes en Lima. La investigación adoptó un enfoque metodológico mixto, de tipo aplicada y nivel descriptivo, utilizando como una de las técnicas el análisis de contenido. En el presente documento se adjuntan los resultados obtenidos para 241 publicaciones de Instagram analizadas correspondientes al primer bimestre del 2024 y primer bimestre del 2025.
DOI: 10.17632/zt6byhck38
title: Impacts of Off-Road Vehicle Traffic on Soil Degradation, Plant Biodiversity, and Soil erosion ...

description: This study assessed the effects of ORV activity on soil physicochemical properties, plant diversity, runoff dynamics, and road-scale soil erosion in forest ecosystems
DOI: 10.17632/zt6byhck38.1
title: Impacts of Off-Road Vehicle Traffic on Soil Degradation, Plant Biodiversity, and Soil erosion ...

description: This study assessed the effects of ORV activity on soil physicochemical properties, plant diversity, runoff dynamics, and road-scale soil erosion in forest ecosystems
DOI: 10.17632/p2d6cm5kgj
title: Books

description: Research on science, technology and philosophy.
DOI: 10.17632/p2d6cm5kgj.20
title: Books

description: Research on science, technology and philosophy.
DOI: 10.17632/6h2k5jnvbz
title: Life Cycle Inventory data for bubble tea packaging design alternatives

description: This dataset contains the raw Life Cycle Inventory (LCI) data supporting the research article "Optimizing Bubble Tea Packaging Sustainability in Taiwan: A Life Cycle Assessment and Multi-Objective Optimization Approach for Circular Economy Integration" submitted to Resources, Conservation & Recycling.
DOI: 10.17632/6h2k5jnvbz.1
title: Life Cycle Inventory data for bubble tea packaging design alternatives

description: This dataset contains the raw Life Cycle Inventory (LCI) data supporting the research article "Optimizing Bubble Tea Packaging Sustainability in Taiwan: A Life Cycle Assessment and Multi-Objective Optimization Approach for Circular Economy Integration" submitted to Resources, Conservation & Recycling.
DOI: 10.17632/s7mfvnbk8z
title: Effect of aqueous extract of doum (Hyphaene thebaica L.) fruit on the physicochemical, microbiological, antioxidant, antimicrobial, and sensory properties of set-type yogurt

description: Table S1. Sensory evaluation of set-type yogurt containing doum fruit aqueous extract (DFAE) over 21 days of refrigerated storage
DOI: 10.17632/s7mfvnbk8z.1
title: Effect of aqueous extract of doum (Hyphaene thebaica L.) fruit on the physicochemical, microbiological, antioxidant, antimicrobial, and sensory properties of set-type yogurt

description: Table S1. Sensory evaluation of set-type yogurt containing doum fruit aqueous extract (DFAE) over 21 days of refrigerated storage
DOI: 10.17632/2wjs6mbt48.2
title: Natural Language Industrial Control

description: Intelligent Industrial Automation Platform: Integration of Edge AI, IoT, and Cloud Supervision
Abstract
This project represents a modern approach to industrial automation, bridging the gap between traditional PLC (Programmable Logic Controller) systems and advanced AI/IoT technologies. It consists of a distributed architecture featuring an intelligent edge node (Raspberry Pi), a central coordination server, and a high-performance React-based HMI (Human-Machine Interface). The system allows for real-time control, data logging, and novel 'Natural Language Control' capabilities, enabling users to interact with industrial hardware using conversational AI

System Architecture
The project is divided into three main components:

1. Intelligent Edge Controller (ServidorPlc)
Hosted on a Raspberry Pi, this component acts as the "Soft PLC".

Technology: Node.js, Express, Socket.io, Node-OPCUA.
Functionality:
Hardware Interface: Direct actuation (PWM, Digital I/O) and sensing (ADC communication).
Control Loops: Implements PI/PID algorithms and automatic system identification (ARX models).
AI Integration: Processes natural language prompts (via OpenAI/Local LLMs) to generate control commands dynamically.
Connectivity: MQTT and WebSockets for real-time telemetry.
2. Supervisory Interface / HMI (Plc)
A web-based SCADA/HMI application for monitoring and control.

Technology: React, Vite, CoreUI, Redux, Cesium (Resium), Vega-Lite.
Features:
Digital Twin & GIS: Uses CesiumJS for 3D geospatial visualization of sensor nodes.
Data Visualization: Real-time charts (Vega-Lite) for sensor data (temperature, level, flow).
Command Center: Interface to send voice/text commands to the PLC.
Dashboard: CoreUI-based administrative panels for user management and configuration.
3. Backend & Data Layer (ServidorPc)
(Contextual) A central server or database handler for long-term storage and aggregation.

Technology: Node.js, PostgreSQL.
Functionality:
Data persistence for historical analysis.
Centralized authentication and user management.
Key Features
Automatic Model Identification: The system can automatically perturb the plant, record response data, and fit ARX (AutoRegressive with Exogenous input) models (Order 1-3) to mathematically represent the system dynamics.
Generative AI Control: Users can issue commands like "Maintain level at 50% indefinitely" or "Identification test with steps of 5 seconds," which are parsed into executable machine code.
Remote Telemetry: Real-time streaming of process variables (Setpoints, Process Values, Control Outputs).
Extensibility: Modular Node.js architecture allows easy addition of new protocols (Modbus, OPC UA) or AI models.
DOI: 10.17632/k6wm46g4tw
title: Data set for the paper entitled "CRISPR-enabled genetic screens identify synthetic lethal targets across frequently altered cancer drivers" by Desjardins, Bowlan, et al. (2026)

description: We undertook a series of genome-wide CRISPR screens using functionally validated isogenic cell lines and mapped SL interactions across 15 genetic alterations characteristic of diseases with high incidence and unmet clinical need: FBXW7, CCNE1, CDK12, ARID1A, KMT2D, DNMT3A, TET2, KEAP1, STK11, IDH1, SF3B1, SRSF2, U2AF1, chromosome 18q loss, and chromosome 13q loss. The data deposited here make the raw read count files available for all CRISPR screens conducted in the study.
DOI: 10.17632/k6wm46g4tw.1
title: Data set for the paper entitled "CRISPR-enabled genetic screens identify synthetic lethal targets across frequently altered cancer drivers" by Desjardins, Bowlan, et al. (2026)

description: We undertook a series of genome-wide CRISPR screens using functionally validated isogenic cell lines and mapped SL interactions across 15 genetic alterations characteristic of diseases with high incidence and unmet clinical need: FBXW7, CCNE1, CDK12, ARID1A, KMT2D, DNMT3A, TET2, KEAP1, STK11, IDH1, SF3B1, SRSF2, U2AF1, chromosome 18q loss, and chromosome 13q loss. The data deposited here make the raw read count files available for all CRISPR screens conducted in the study.
DOI: 10.17632/3gms2fvs93
title: Synthetic Clustered Multi-Task Dataset

description: Data are generated with a mixing parameter $\omega = 0.9$. Input features $\mathbf{x}$ are sampled uniformly from the hypercube $[-1,1]^{d_x}$, where $d_x = 5$ denotes the input dimensionality.
DOI: 10.17632/3gms2fvs93.1
title: Synthetic Clustered Multi-Task Dataset

description: Data are generated with a mixing parameter $\omega = 0.9$. Input features $\mathbf{x}$ are sampled uniformly from the hypercube $[-1,1]^{d_x}$, where $d_x = 5$ denotes the input dimensionality.
DOI: 10.17632/ydmpkd52kr
title: Multimodal Dataset for Detecting AI-Generated Deceptive Fashion Product Representations on Pinterest

description: This dataset contains a curated collection of fashion gown product listings collected from Pinterest, designed to support research on AI-generated content, deceptive product representations, and multimodal signaling in visually mediated electronic markets. The dataset integrates visual, textual, and contextual features for each listing, enabling joint analysis of image aesthetics, linguistic framing, and engagement-based platform dynamics. The data were collected from publicly accessible Pinterest pins linking to gown product listings during a defined sampling period. Listings were identified using keyword-based and category-based queries related to formal gowns and fashion apparel, and were restricted to content intended for commercial or promotional purposes. Only publicly visible pins and associated metadata were collected; no private user data were accessed.
Each observation corresponds to a unique product listing and includes one or more associated images, descriptive text, and engagement metrics (e.g., saves, likes). Listings were subsequently labeled as AI-generated deceptive or non-deceptive (human-created or verifiable) based on a multi-stage verification procedure combining visual plausibility assessment, textual grounding analysis, and external validation of product existence or seller credibility. Ambiguous cases were excluded to preserve label reliability.

Data structure and variables
For each listing, the dataset includes:
Visual features extracted from product images, capturing aesthetic complexity, texture regularity, and structural consistency relevant to fashion imagery
Textual features derived from listing descriptions, including measures of abstraction, affective language, and informational specificity
Contextual and control variables, such as price, seller tenure (where available), and basic listing metadata
Engagement indicators, reflecting platform-level user interaction with the listing
Binary labels indicating whether the listing was classified as AI-generated deceptive or non-deceptive
DOI: 10.17632/ydmpkd52kr.1
title: Multimodal Dataset for Detecting AI-Generated Deceptive Fashion Product Representations on Pinterest

description: This dataset contains a curated collection of fashion gown product listings collected from Pinterest, designed to support research on AI-generated content, deceptive product representations, and multimodal signaling in visually mediated electronic markets. The dataset integrates visual, textual, and contextual features for each listing, enabling joint analysis of image aesthetics, linguistic framing, and engagement-based platform dynamics. The data were collected from publicly accessible Pinterest pins linking to gown product listings during a defined sampling period. Listings were identified using keyword-based and category-based queries related to formal gowns and fashion apparel, and were restricted to content intended for commercial or promotional purposes. Only publicly visible pins and associated metadata were collected; no private user data were accessed.
Each observation corresponds to a unique product listing and includes one or more associated images, descriptive text, and engagement metrics (e.g., saves, likes). Listings were subsequently labeled as AI-generated deceptive or non-deceptive (human-created or verifiable) based on a multi-stage verification procedure combining visual plausibility assessment, textual grounding analysis, and external validation of product existence or seller credibility. Ambiguous cases were excluded to preserve label reliability.

Data structure and variables
For each listing, the dataset includes:
Visual features extracted from product images, capturing aesthetic complexity, texture regularity, and structural consistency relevant to fashion imagery
Textual features derived from listing descriptions, including measures of abstraction, affective language, and informational specificity
Contextual and control variables, such as price, seller tenure (where available), and basic listing metadata
Engagement indicators, reflecting platform-level user interaction with the listing
Binary labels indicating whether the listing was classified as AI-generated deceptive or non-deceptive
DOI: 10.17632/3tvnm3yhmp
title: coasts-4071064

description: Nutrient and oxygen data (24-hour cycle) from interdune lagoons in Veracruz (Mexico)
DOI: 10.17632/3tvnm3yhmp.2
title: coasts-4071064

description: Nutrient and oxygen data (24-hour cycle) from interdune lagoons in Veracruz (Mexico)
DOI: 10.17630/4236f630-5f85-45c3-a0ed-b07f49b414c3
title: Whodunit? Determining the source and eruptive characteristics of unidentified volcanic eruptions from ice and sediment core archives (thesis data)

description: Research data underpinning the thesis of Helen M. Innes. The supplementary dataset consists of:

Readme_furtherinformation.txt
.txt document containing description of supplementary files. 

1HI_TephraGeochemicalAnalysis_All 
.xlsx file containing all tephra geochemical analyses.

2HI_TUNU2013_SisotopeData_Ch3
.xlsx file containing sulfur isotope data.

3HI_IceCoreSulfateData_Ch4
.xlsx file containing ice core sulfate concentrations and VSSI calculation.

4HI_Grainsize_Distributions_Ch5
.xlsx file containing grainsize analysis data.

The data files are embargoed until 14/02/2029