cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/wg49h59y6m.2
title: Raccoons optimally forage for information: exploration exploitation tradeoffs in innovation

description: Cognitive Task Description:
In May-July 2020, captive raccoons housed at the United States Department of Agriculture National Wildlife Research Center were presented with a multi-access puzzle box (MAB). The MAB had 3 solutions at three difficulty levels: easy, medium, and hard. The raccoons were first given the easy solutions and allowed to explore and solve as many solutions on the MAB as they chose, but were only given one food reward per trial. Once the raccoon successfully solved one solution 15 times it was moved to the next difficulty level, until it was presented with all three difficulty levels. 

Hypotheses:
The design of this task allowed us to determine whether exploration-exploitation tradeoffs applied to innovative problem solving. First, we asked whether raccoons would innovate solutions on the MAB even while known solutions were still available and solvable. We then asked whether their innovation rates would follow an exploration-exploitation tradeoff when solution difficulties changed.

We found that, as predicted by optimal foraging theory, raccoons will tradeoff between exploration and exploitation in a problem solving context. Raccoons are more likely to explore when presented with easier problems and more likely to exploit known solutions were presented with more difficult problems.
DOI: 10.17632/2s4mw96gh4
title: Codes for the article “The impact of financial crises on industrial growth: lessons from the last 40 years”

description: These codes use the original sources of data to add in replicating the Economic Analysis and Policy article (R&R February 2026). I also included the Stata dataset files and the original Excel files necessary to replicate all the empirical exercises. The appendix contains all the robustness exercises.
DOI: 10.17632/2s4mw96gh4.1
title: Codes for the article “The impact of financial crises on industrial growth: lessons from the last 40 years”

description: These codes use the original sources of data to add in replicating the Economic Analysis and Policy article (R&R February 2026). I also included the Stata dataset files and the original Excel files necessary to replicate all the empirical exercises. The appendix contains all the robustness exercises.
DOI: 10.34740/kaggle/dsv/14857264
title: Shaukat Khanum Cancer Hospital Lahore (2020-2025)
DOI: 10.17632/462w8xb695
title: Supplementary Data: Multi-Stage Leak Localization Results for Water Distribution Networks Under Parameter Uncertainty

description: This dataset contains comprehensive localization results from all stages of the multi-stage clustering algorithm presented in the manuscript "Pressure Gradient Driven Multi-Stage Leak Localization for Multiple Simultaneous Leaks in Water Distribution Networks Under Uncertainty."

Detailed results for three water distribution networks (Net 2, Net 3, and Net 4) across multiple leak scenarios, including:

Stage-by-stage cluster identification and refinement
Leaky group predictions at each localization stage
Visual representations of leak localization progression
Complete results for all tested scenarios with four concurrent leaks under simultaneous nodal demand and pipe roughness uncertainties up to 25%
DOI: 10.17632/462w8xb695.1
title: Supplementary Data: Multi-Stage Leak Localization Results for Water Distribution Networks Under Parameter Uncertainty

description: This dataset contains comprehensive localization results from all stages of the multi-stage clustering algorithm presented in the manuscript "Pressure Gradient Driven Multi-Stage Leak Localization for Multiple Simultaneous Leaks in Water Distribution Networks Under Uncertainty."

Detailed results for three water distribution networks (Net 2, Net 3, and Net 4) across multiple leak scenarios, including:

Stage-by-stage cluster identification and refinement
Leaky group predictions at each localization stage
Visual representations of leak localization progression
Complete results for all tested scenarios with four concurrent leaks under simultaneous nodal demand and pipe roughness uncertainties up to 25%
DOI: 10.15468/dl.k4vm6n
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crocidura nicobarica"
 ]
}

 The dataset includes 9 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021839-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vgjbsr
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is BRA.21.224_2",
 "OccurrenceStatus is Present",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 17 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021838-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ffky4z8932
title: Scalable and Interoperable AIoT Ecosystems: Market-Driven Adoption of Large-Scale AI Models for Context-Aware Intelligent Infrastructure

description: The convergence of Artificial Intelligence of Things (AIoT) and large-scale artificial intelligence models is reshaping intelligent infrastructure across industries. While advances in deep learning, foundation models, and edge computing have expanded the technical capabilities of AIoT systems, large-scale adoption remains constrained by scalability, interoperability, and market readiness. This study investigates how scalable and interoperable AIoT ecosystems enable the market-driven adoption of large-scale AI models for context-aware intelligent infrastructure. A conceptual architecture is proposed that integrates cloud–edge–device intelligence, interoperable data standards, and modular AI services to support real-time contextual reasoning. Using a market-oriented analytical framework, the study examines adoption drivers, including cost efficiency, operational resilience, regulatory compliance, and ecosystem maturity. Synthetic scenario-based simulations are used to demonstrate how large-scale AI models enhance situational awareness, predictive decision-making, and adaptive automation in smart infrastructure environments. The findings highlight that interoperability and scalability are not merely technical requirements but strategic enablers that determine economic viability and long-term sustainability. This research contributes a systems-level perspective that bridges technical design with market dynamics, offering practical insights for policymakers, system architects, and industry stakeholders seeking to deploy large-scale, context-aware AIoT infrastructures.
DOI: 10.17632/ffky4z8932.1
title: Scalable and Interoperable AIoT Ecosystems: Market-Driven Adoption of Large-Scale AI Models for Context-Aware Intelligent Infrastructure

description: The convergence of Artificial Intelligence of Things (AIoT) and large-scale artificial intelligence models is reshaping intelligent infrastructure across industries. While advances in deep learning, foundation models, and edge computing have expanded the technical capabilities of AIoT systems, large-scale adoption remains constrained by scalability, interoperability, and market readiness. This study investigates how scalable and interoperable AIoT ecosystems enable the market-driven adoption of large-scale AI models for context-aware intelligent infrastructure. A conceptual architecture is proposed that integrates cloud–edge–device intelligence, interoperable data standards, and modular AI services to support real-time contextual reasoning. Using a market-oriented analytical framework, the study examines adoption drivers, including cost efficiency, operational resilience, regulatory compliance, and ecosystem maturity. Synthetic scenario-based simulations are used to demonstrate how large-scale AI models enhance situational awareness, predictive decision-making, and adaptive automation in smart infrastructure environments. The findings highlight that interoperability and scalability are not merely technical requirements but strategic enablers that determine economic viability and long-term sustainability. This research contributes a systems-level perspective that bridges technical design with market dynamics, offering practical insights for policymakers, system architects, and industry stakeholders seeking to deploy large-scale, context-aware AIoT infrastructures.
DOI: 10.15468/dl.96sqqn
title: Occurrence Download

description: A dataset containing 673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-5.27583 36.29359,-3.65225 36.62174,-3.94902 37.88876,-5.49914 37.62937,-5.27583 36.29359))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Genista hirsuta"
 ]
}

 The dataset includes 673 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021841-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/xxcv866gmw
title: Cognitive Business Autonomous Intelligence (CBAI): A GenAI‑Driven AIoT Framework for Unified Workforce Analytics, Predictive FinOps, and Circular Economy Marketplaces

description: The convergence of Artificial Intelligence of Things (AIoT) and Generative AI (GenAI) represents a paradigm shift toward autonomous enterprise systems. Current implementations remain siloed: AIoT provides assistive insights while GenAI operates in cognitive domains, disconnected from real-time physical operations. This paper introduces Cognitive Business Autonomous Intelligence (CBAI)—a holistic framework integrating GenAI-powered autonomous agents with pervasive AIoT sensor networks to create self-managing enterprise ecosystems. CBAI enables simultaneous optimization across three critical domains: (1) autonomous workforce analytics through real-time biometric sensing and dynamic task allocation; (2) predictive financial operations (FinOps) via continuous AIoT data streams for forecasting and risk mitigation; and (3) tokenized circular economy marketplaces for automated material reuse and sustainability compliance. We evaluate CBAI using a multi-agent simulation environment benchmarking against conventional ERP systems. Results demonstrate: workforce productivity improvements of 15–30%, financial forecast error reductions of 10–25%, and material reuse increases of 20–35%. A Blockchain-based governance layer ensures transparency, auditability, and ethical compliance. CBAI establishes both a technical architecture and research agenda for next-generation autonomous enterprises that simultaneously optimize economic, human, and environmental outcomes
DOI: 10.17632/xxcv866gmw.1
title: Cognitive Business Autonomous Intelligence (CBAI): A GenAI‑Driven AIoT Framework for Unified Workforce Analytics, Predictive FinOps, and Circular Economy Marketplaces

description: The convergence of Artificial Intelligence of Things (AIoT) and Generative AI (GenAI) represents a paradigm shift toward autonomous enterprise systems. Current implementations remain siloed: AIoT provides assistive insights while GenAI operates in cognitive domains, disconnected from real-time physical operations. This paper introduces Cognitive Business Autonomous Intelligence (CBAI)—a holistic framework integrating GenAI-powered autonomous agents with pervasive AIoT sensor networks to create self-managing enterprise ecosystems. CBAI enables simultaneous optimization across three critical domains: (1) autonomous workforce analytics through real-time biometric sensing and dynamic task allocation; (2) predictive financial operations (FinOps) via continuous AIoT data streams for forecasting and risk mitigation; and (3) tokenized circular economy marketplaces for automated material reuse and sustainability compliance. We evaluate CBAI using a multi-agent simulation environment benchmarking against conventional ERP systems. Results demonstrate: workforce productivity improvements of 15–30%, financial forecast error reductions of 10–25%, and material reuse increases of 20–35%. A Blockchain-based governance layer ensures transparency, auditability, and ethical compliance. CBAI establishes both a technical architecture and research agenda for next-generation autonomous enterprises that simultaneously optimize economic, human, and environmental outcomes
DOI: 10.17632/ymv8dgxggj
title: AIoT-Driven Management Strategies: Enhancing Decision-Making, Operations, and Sustainability in Modern Enterprises: A Conceptual Business Model Analytics

description: AIoT-Driven Management Strategies: Enhancing Decision-Making, Operations, and Sustainability in Modern Enterprises: A Conceptual Business Model Analytics
DOI: 10.17632/ymv8dgxggj.1
title: AIoT-Driven Management Strategies: Enhancing Decision-Making, Operations, and Sustainability in Modern Enterprises: A Conceptual Business Model Analytics

description: AIoT-Driven Management Strategies: Enhancing Decision-Making, Operations, and Sustainability in Modern Enterprises: A Conceptual Business Model Analytics
DOI: 10.17632/ggx7vjz29t
title: Stress, anxiety, and depression among health sciences university students

description: • High prevalence of anxiety (64.5%), depression (52%) and stress (47.2%) was found.
• Sleeping ≥7 hours per day protects against stress, anxiety and depression symptoms.
• Good family relationship is a protective factor against stress and depression.
• Cigarette smoking increases the risk of anxiety and depression in students.
• TikTok use is associated with a higher risk of depressive symptoms.
DOI: 10.17632/ggx7vjz29t.1
title: Stress, anxiety, and depression among health sciences university students

description: • High prevalence of anxiety (64.5%), depression (52%) and stress (47.2%) was found.
• Sleeping ≥7 hours per day protects against stress, anxiety and depression symptoms.
• Good family relationship is a protective factor against stress and depression.
• Cigarette smoking increases the risk of anxiety and depression in students.
• TikTok use is associated with a higher risk of depressive symptoms.
DOI: 10.15468/dl.af625w
title: Occurrence Download

description: A dataset containing 2736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-5.27583 36.29359,-3.65225 36.62174,-3.94902 37.88876,-5.49914 37.62937,-5.27583 36.29359))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Halimium atriplicifolium"
 ]
}

 The dataset includes 2736 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021842-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/np2m98rrfg
title: Heterogeneous welfare costs of perceived credit constraints in West Africa

description: Financial inclusion policies typically treat demand-side credit barriers as being interchangeable. Using double/debiased machine learning on 16,957 households across four WAEMU countries (HSHLS 2018–2021), we show that perceived high interest rates, absence of credit institutions, and lack of collateral significantly reduce per capita consumption.
DOI: 10.17632/np2m98rrfg.1
title: Heterogeneous welfare costs of perceived credit constraints in West Africa

description: Financial inclusion policies typically treat demand-side credit barriers as being interchangeable. Using double/debiased machine learning on 16,957 households across four WAEMU countries (HSHLS 2018–2021), we show that perceived high interest rates, absence of credit institutions, and lack of collateral significantly reduce per capita consumption.
DOI: 10.17632/hbgh48kpj2
title: AIoT, CIoT, and QIoT Paradigms and Their Applications in Business and Automation: An Comprehensive Approach.

description: The convergence of Artificial Intelligence of Things (AIoT), Cognitive Internet of Things (CIoT), and Quantum Internet of Things (QIoT) marks a new paradigm in business automation, transforming decision-making, operational efficiency, and predictive intelligence. This paper provides an integrated analysis of these three paradigms and their impact on business and industrial ecosystems. AIoT leverages machine learning and edge intelligence to enhance IoT-driven predictive analytics, enabling real-time supply chain optimization, smart manufacturing, and dynamic customer personalization. CIoT introduces cognitive computing and context-awareness into IoT, allowing systems to understand, learn, and adapt autonomously, which drives innovation in smart offices, healthcare, and human–machine collaboration. QIoT extends this frontier by integrating quantum communication, cryptography, and optimization, offering unparalleled computational power and security for large-scale, data-intensive business processes. Drawing on a mixed-method approach combining dataset simulation, case studies, and literature synthesis, this study identifies core drivers, challenges, and opportunities of AIoT, CIoT, and QIoT adoption in business automation. Case studies highlight real-world applications, including AIoT in supply chain resilience, CIoT in workplace productivity, and QIoT in financial risk modeling. Findings suggest that while AIoT is currently the most mature and widely deployed, CIoT is expanding in adaptive enterprise applications, and QIoT—though emerging—holds disruptive potential for the future of automation and secure business operations. The paper concludes with strategic recommendations and a future blueprint for integrating AIoT, CIoT, and QIoT into sustainable, intelligent, and competitive business ecosystems.
DOI: 10.17632/hbgh48kpj2.1
title: AIoT, CIoT, and QIoT Paradigms and Their Applications in Business and Automation: An Comprehensive Approach.

description: The convergence of Artificial Intelligence of Things (AIoT), Cognitive Internet of Things (CIoT), and Quantum Internet of Things (QIoT) marks a new paradigm in business automation, transforming decision-making, operational efficiency, and predictive intelligence. This paper provides an integrated analysis of these three paradigms and their impact on business and industrial ecosystems. AIoT leverages machine learning and edge intelligence to enhance IoT-driven predictive analytics, enabling real-time supply chain optimization, smart manufacturing, and dynamic customer personalization. CIoT introduces cognitive computing and context-awareness into IoT, allowing systems to understand, learn, and adapt autonomously, which drives innovation in smart offices, healthcare, and human–machine collaboration. QIoT extends this frontier by integrating quantum communication, cryptography, and optimization, offering unparalleled computational power and security for large-scale, data-intensive business processes. Drawing on a mixed-method approach combining dataset simulation, case studies, and literature synthesis, this study identifies core drivers, challenges, and opportunities of AIoT, CIoT, and QIoT adoption in business automation. Case studies highlight real-world applications, including AIoT in supply chain resilience, CIoT in workplace productivity, and QIoT in financial risk modeling. Findings suggest that while AIoT is currently the most mature and widely deployed, CIoT is expanding in adaptive enterprise applications, and QIoT—though emerging—holds disruptive potential for the future of automation and secure business operations. The paper concludes with strategic recommendations and a future blueprint for integrating AIoT, CIoT, and QIoT into sustainable, intelligent, and competitive business ecosystems.
DOI: 10.17632/9gjrxcmgk4
title: Bibliometric Dataset (Educational Transformation Through Extended Reality)

description: Scopus database as of 18 November 2025 (Educational Transformation Through Extended Reality)
DOI: 10.17632/9gjrxcmgk4.1
title: Bibliometric Dataset (Educational Transformation Through Extended Reality)

description: Scopus database as of 18 November 2025 (Educational Transformation Through Extended Reality)
DOI: 10.17632/3yhbwr48sg
title: AIoT-Driven Business Forecasting Models in Emerging Markets: Multi-Sector Case Studies from Bangladesh

description: Emerging economies face significant challenges in business forecasting due to data fragmentation, limited analytics infrastructure, and reactive decision-making processes. This study investigates the application of Artificial Intelligence of Things (AIoT) systems to enhance business forecasting capabilities across multiple sectors in Bangladesh. We present a comprehensive AIoT framework that integrates real-time IoT sensor data with advanced machine learning algorithms to enable predictive analytics and intelligent decision-making. Through three longitudinal case studies spanning manufacturing, retail, and financial services sectors, we demonstrate the transformative potential of AIoT implementation. Results indicate substantial improvements in forecasting accuracy (average increase of 19%), operational efficiency (cost reduction of 10-15%), and sustainability metrics. The textile manufacturing case achieved 93% demand forecast accuracy (up from 75%) and 70% reduction in downtime. The retail chain reduced stockouts by 75% while improving forecast accuracy to 90%. The fintech application enhanced loan default prediction accuracy to 89% while reducing non-performing loans by 42%. These findings underscore AIoT's viability as a strategic enabler of predictive intelligence in resource-constrained emerging markets, offering actionable insights for business leaders, policymakers, and researchers focused on digital transformation in developing economies.
DOI: 10.17632/3yhbwr48sg.1
title: AIoT-Driven Business Forecasting Models in Emerging Markets: Multi-Sector Case Studies from Bangladesh

description: Emerging economies face significant challenges in business forecasting due to data fragmentation, limited analytics infrastructure, and reactive decision-making processes. This study investigates the application of Artificial Intelligence of Things (AIoT) systems to enhance business forecasting capabilities across multiple sectors in Bangladesh. We present a comprehensive AIoT framework that integrates real-time IoT sensor data with advanced machine learning algorithms to enable predictive analytics and intelligent decision-making. Through three longitudinal case studies spanning manufacturing, retail, and financial services sectors, we demonstrate the transformative potential of AIoT implementation. Results indicate substantial improvements in forecasting accuracy (average increase of 19%), operational efficiency (cost reduction of 10-15%), and sustainability metrics. The textile manufacturing case achieved 93% demand forecast accuracy (up from 75%) and 70% reduction in downtime. The retail chain reduced stockouts by 75% while improving forecast accuracy to 90%. The fintech application enhanced loan default prediction accuracy to 89% while reducing non-performing loans by 42%. These findings underscore AIoT's viability as a strategic enabler of predictive intelligence in resource-constrained emerging markets, offering actionable insights for business leaders, policymakers, and researchers focused on digital transformation in developing economies.
DOI: 10.6094/blnn/mitt/27.04.1
title: Anhänge zu Beitrag zur eulitoralen Käferfauna am nördlichen Bodenseeufer

description: Zwei Anhänge zum Artikel.
Textanhang mit einer Beschreibung der Käferarten Datenanhang mit den erhobenen Felddaten C