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This file contains the semi-structured interview protocol developed to support the qualitative validation of the BiogAI-RMF framework. The protocol was designed to explore participants’ professional experiences and perspectives concerning risk management in wastewater treatment and biogas production, organisational readiness for artificial intelligence, data availability and quality, governance, explainability, user trust, implementation barriers, and the practical integration of the proposed framework. The questions were informed by the study’s conceptual model, survey findings and wider literature. The semi-structured format ensured consistency across interviews while allowing relevant issues to be explored in greater depth. Interviews were conducted with professionals possessing relevant operational, technical, managerial, regulatory, safety or digital expertise.

This file contains the cleaned, coded and analysis-ready survey dataset used for the empirical validation of the BiogAI-RMF framework. The dataset was derived from the original Microsoft Forms export and includes anonymised responses from professionals working across wastewater treatment, biogas production, engineering, risk management, health and safety, environmental management, regulation and digital technologies. Data preparation included consistency checks, removal of unusable or incomplete responses where applicable, standardisation of variable names, coding of categorical responses and conversion of Likert-scale responses into numerical values. The file supports descriptive analysis, reliability and validity assessment, partial least squares structural equation modelling, regression analysis and fuzzy logic modelling. No directly identifying personal information is included.

AraVADEmo is a corpus of Arabic tweets annotated for discrete emotion categories (based on Ekman's basic emotions) and Valence–Arousal–Dominance (VAD) dimensions. The corpus was built by collecting Arabic-language tweets via the X API using emotion-indicative seed phrases, then manually annotating each tweet with a final emotion label and VAD ratings. Please note that we only release the tweet ID along with annotations, in compliance with X's Terms of Service, which restrict distribution of tweet content. Dataset Summary: - Total annotated tweets: 8,081 - Language: Arabic (mixed dialects; collection restricted to lang:ar) - Collection window: October – December, 2023 - Collection method: X API v2 (search_recent_tweets via Tweepy), keyword/seed-phrase search - Annotation scheme: 7-class discrete emotion label + 3-dimensional VAD categorical label: low / medium / high, positive / neutral / negative) The file includes the following columns: - tweet_id: The tweet's unique X identifier - class: Final annotated emotion label (1: Happiness, 2: Anger, 3: Surprise, 4: Fear, 5: Sadness, 6: Disgust, 7: Neutral) - Valence: positive / neutral / negative - Arousal: low / medium / high - Dominance: low / medium / high

This file contains the original survey-response dataset exported directly from Microsoft Forms for the empirical validation of the BiogAI-RMF framework. The dataset includes anonymised responses collected from professionals with relevant experience in wastewater treatment, biogas production, engineering, risk management, health and safety, environmental management, regulation, and digital technologies. The file is provided in its original exported structure and has not been cleaned, recoded, transformed, or statistically processed. It is included to maintain a transparent record of the collected data and to enable comparison with the accompanying cleaned and coded dataset. Direct personal identifiers have been removed or excluded before publication.

This repository contains a high-fidelity teleoperated robotic manipulation dataset designed to support imitation learning, multi-view perception, and autonomous agricultural manipulation tasks—specifically tomato plant pruning and harvesting. The data captures real-world robot-plant interactions collected in a controlled biocell greenhouse chamber under consistent lighting conditions. Data Collection & Hardware Setup The dataset was generated using a dual-manipulator setup in a leader–follower configuration consisting of two MyCobot M5-280 robotic manipulators. As an operator teleoperated the leader robot (actor), the follower robot (imitator) mirrored its movements. Dataset Structure & Specifications The dataset comprises 100 total demonstrations sampled at a temporal frequency of 20 Hz (0.05-second intervals). Each individual demonstration consists of 300 temporally aligned timesteps containing synchronized visual and kinematic data streams: • Kinematic Data: 6 synchronized joint angles from the actor (leader) robot and 6 synchronized joint angles from the imitator (follower) robot. • Visual Data: Multi-view RGB camera streams captured simultaneously from three distinct viewpoints focusing on the agricultural interaction area. • Cultivar Variations: The dataset spans multiple tomato cultivars, including Celebrity, Beefsteak, and Big Boy Hybrid. Target Applications This data is structured to train and evaluate learning-based robotic frameworks, including self-supervised representation learning models (such as DINO) and policy learning frameworks (such as Conditional Diffusion Policies) for agricultural automation.

This file contains the survey instrument developed to support the empirical validation of the BiogAI-RMF framework for wastewater treatment and biogas production. The questionnaire examines risk factors, effective risk management, artificial intelligence adoptability, performance outcomes, and the perceived usability and integration of the proposed framework. It was designed for professionals working in wastewater treatment, anaerobic digestion, biogas production, engineering, operations, maintenance, health and safety, environmental management, regulation, risk management, and digital technologies. Items use a five-point Likert scale. The instrument supports descriptive analysis, reliability and validity assessment, partial least squares structural equation modelling, regression analysis, and fuzzy logic modelling. The survey was administered electronically through Microsoft Forms, participation was voluntary, and responses were collected anonymously. The instrument is provided to support methodological transparency, reproducibility, and future research in AI-enabled risk management for wastewater and biogas systems.

This file provides the replication data and code for the study titled “Tail Risk Connectedness and Systemic Volatility in the Cryptocurrency Market: Evidence from a Value-at-Risk Framework.” It enables full reproduction of the empirical results reported in the paper.

This dataset integrates six data sources to support machine-learning research on Robusta coffee price forecasting in Vietnam: (1) daily domestic Robusta coffee prices at five provinces in the Central Highlands, (2) local weather observations, (3) ICE London Robusta coffee futures prices, (4) USD/VND exchange rates, (5) world crude oil prices (Brent, WTI), and (6) Vietnamese retail fuel prices. Coverage spans 1 January 2020 to 22 May 2026 (5,445 daily records, 25 variables, zero missing values after preprocessing). This dataset accompanies the manuscript "Benchmarking Machine Learning Models for Multivariate Robusta Coffee Price Forecasting: A Multi-Source Dataset and Feature Ablation Study" (submitted to ISDS 2026). Please cite the manuscript (once published) if you use this dataset.

瓦楞纸板真实订单数据

BanFakeMM is a large-scale, detector-guided multimodal Bengali fake news benchmark comprising 7,040 authentic news articles (with images) collected from eight Bangla newspapers across eleven topical categories, together with five progressively challenging adversarial datasets constructed via a five-round, detector-in-the-loop framework using Gemini 2.5 Flash Lite and Imagen 3/4. Each benchmark dataset used for detector training/evaluation is balanced with 1,000 real and 1,000 fake samples (2,000 total), split 80/10/10 into train/val/test: - AdversarialGen (1,000 fake sampled from a larger pool of 3,500 generated articles): fully LLM-generated fake news (text + image) across four categories (clickbait, hoax, propaganda, misleading) - LinguisticAttack (1,000 fake): real news perturbed via antonym, quantifier, and mixed mutations while preserving style - AdversarialEdit (1,000 fake): coordinated text-image manipulation with localized image editing - RandomSwap (1,000 fake): authentic text paired with a randomly swapped image from an unrelated article - SemanticSwap (1,000 fake): authentic text paired with a swapped image from a semantically similar (but factually distinct) article Every sample includes an image and a CSV row with schema (article_id, source, category, title, content, published_date, image_path, label, fake_type). This benchmark supports research on adversarially robust multimodal fake news detection, cross-modal consistency modeling, image forensics, and low-resource multilingual NLP. It was used to train/evaluate five detection architectures (BanglaBERT, Feature Late Fusion, TACMA, CMCF, and CMCGF) described in the accompanying paper "A Progressive Adversarial Benchmark and Cross-Modal Consistency Guided Detection for Bengali Multimodal Fake News Detection."