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  • Digital Twins in Health
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  • Authors: Ciocioc, Ion Valentin;

    This work studies the intersection of chromatics, mathematical algorithms, and innovative concepts such as grammatical geometry. Analyzes the impact of colors on language perception and introduces original mathematical formulas, extracted from the definitions of grammatical concepts, providing a deep and authentic approach to the study of language, which allows for a more precise interpretation of grammatical rules, thus facilitating systematic learning of the language. The concept of grammatical geometry, proposed in the pages of this book, opens new horizons in understanding the relationships between the elements of a sentence and how they interact. This geometry is not limited to visual representations but offers a solid theoretical framework for analyzing linguistic structures from various and multidimensional perspectives. Another remarkable aspect of this work is the presentation of the first chromatic map of grammar, which illustrates how different shades of colors can correspond to different grammatical functions and concepts. This map not only enriches the perspective on learning grammar but also serves as an innovative visual tool that helps associate colors with certain linguistic structures, thus facilitating the process of memorization and understanding. Due to the correlation of these four fields – algorithms, grammar, mathematics, and chromatics – the formula of metamorphosis has been identified, a new formula with potential applicability in other fields such as psychology, art, design, medicine, geology, biology, etc. This formula opens new perspectives for interdisciplinary explorations, contributing to a deeper understanding of the interaction between language and perception in various contexts, as well as within any discipline that generates the idea of metamorphosis.

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    This repository is used for archiving the classification of papers in the following article: Data: to share or not to share? A Semi-Systematic Literature Review in (rational) data sharing in inter-organizational systems.This archive is supporting the paper for the labels that have been given to the papers found in the Semi-Systematic Literature Review.The following categories and labels are possible in the classification of the literature:Category ["Paper"] - Labels: Summarized name(s) of the author(s).Category ["Knowledge dimension"] - Labels: ["Data", "Information", "Knowledge", "Data/information (paper is ambiguous on whether it is data or information that is shared)", "None", "Data/Knowledge (data is learned and transferred in to knowledge, the knowledge is shared)"]Category ["Type of industry"] - Labels: ["None", "Supply chain", "Healthcare", "Vehicles", "Agricultural", "Research", "Networks", "Automotive", "Innovation", "Smart Grid", "Social media", "R&D", "e-Governance", "Construction Sector", "Government-enterprise", "Manufacturing", "Power grid", "Smart Cities", "Personal data", "Cyber security", "E-commerce", "Maritime", "Online marketplaces", "Assembly", "Engineering", "Communities of Practice", "Knowledge Management Systems", "B2B commerce", "Fisheries", "Outsourcing", "High-tech firms", "Crisis", "e-Services", "Seaports", "Horticulture", "Data markets", "Media", "Cultural Heritage Institutions", "Fresh Products", "Knowledge market", "Ecological", "Ride Sharing", "Government", "Transit", "Medical", "Virtual Research Organization", "Energy", "Oil and gas", "Education"]Category ["Game theory approach"] - Labels: ["None", "Non-cooperative game", "Evolutionary", "Cooperative game", "Stackelberg", "Auction", "Unclear defined", "Diffusion kernels", "Markov game", "Negotiation", "(Non-)cooperative game", "Differential game", "Pricing", "Bayesian", "Contract theory", "Non-collusion", "Stochastic differential game", "Fisher’s market", "Hotelling", "Bargaining", "Access control"]Category ["Technologies of interest"] - Labels: ["blockchain", "None", "smart contracts", "federated learning", "internet of things", "machine learning", "cloud computing", "artificial intelligence", "ethereum", "5G", "data trust", "deep neural networks", "digital twins", "internet of (medical) things", "smart grid", "data governance", "artifical intelligence", "collaborative learning", "smart contract", "encryption", "data escrow", "NFT", "data mining", "semantic technologies", "transfer learning"]Category ["Level of trust"] - Labels: ["Calculus-Based Trust", "None", "security", "privacy", "integrity", "transparency", "authentication", "confidentiality", "traceability", "verification", "Knowledge-Based Trust", "privacy"]Category ["Type of contract"> - Labels: ["None", "Smart contracts", "Contract theory", "Linear wholesale price contract", "GMP and IPD contracts", "General contract", "Incentive contract", "Trust-embedded contract", "Wholesale price contract"]

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    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
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    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      Dataset . 2024
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      4TU.ResearchData | science.engineering.design
      Dataset . 2024
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      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
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    Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare. The Fusion of Technology and Life Sciences In Silico Methods: A New Frontier in Medicine The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials. Bridging the Gap with Computational Modeling Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles. The Impact on Clinical Research and Patient Care Enhancing Precision and Efficiency One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs. Promoting Medical Equitability The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy. Conclusion: The Future is Now The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.

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    Overview This repository contains a comprehensive dataset to assess cognitive states, workload, situational awareness, stress, and performance in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. It is based on an experimental study of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The study compared three different setups of human system interfaces in four human-in-the-loop (HITL) configurations, incorporating two alarm design formats (Prioritised vs non-prioritised) and three procedural guidance setups (e.g. one presenting paper procedures, one offering digitised screen-based procedures, and lastly an AI-based procedural guidance system). Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioural, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed tasks under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system (alarm display design, procedure format, AI support, interface design), communication, situational awareness, cognitive workload, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure The concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Perception, Understanding, and Projection, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. Calculated as the average of the score on perception, understanding and projection. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness. It is calculated using the equation U - (D - S). Situation Understanding (U) comprises Information Quantity, Information Quality, and Familiarity. Situation demand (D) includes the situation's Instability, Complexity, and Variability. At the same time, the Supply of attentional resources (S) comprises Arousal, Concentration, Division of Attention, and Spare Capacity. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety- critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in control room environments. Citation Please cite this article and dataset if you use this dataset in your research or publication. Amazu, C. W., Mietkiewicz, J., Abbas, A. N., Briwa, H., Perez, A. A., Baldissone, G., ... & Leva, M. C. (2024). Experiment Data: Human-in-the-loop Decision Support in Process Control Rooms. Data in Brief, 110170.

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    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      Dataset . 2024
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      Data sources: Datacite
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    Human-in-the-Loop Decision Support in Process Control Rooms Dataset Overview This repository contains a comprehensive dataset to assess cognitive states, workload, situational awareness, stress, and performance in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. It is based on an experimental study of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The study compared three different setups of human system interfaces in four human-in-the-loop (HITL) configurations, incorporating two alarm design formats (Prioritised vs non-prioritised) and three procedural guidance setups (e.g. one presenting paper procedures, one offering digitised screen-based procedures, and lastly an AI-based procedural guidance system). Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioural, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed tasks under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system (alarm display design, procedure format, AI support, interface design), communication, situational awareness, cognitive workload, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure The concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Perception, Understanding, and Projection, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. Calculated as the average of the score on perception, understanding and projection. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness. It is calculated using the equation U - (D - S). Situation Understanding (U) comprises Information Quantity, Information Quality, and Familiarity. Situation demand (D) includes the situation's Instability, Complexity, and Variability. At the same time, the Supply of attentional resources (S) comprises Arousal, Concentration, Division of Attention, and Spare Capacity. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety- critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in control room environments. Citation Please cite the article if you use this dataset in your research or publication.

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    Human-in-the-Loop Decision Support in Process Control Rooms Dataset Overview This repository contains a comprehensive dataset to assess cognitive states, workload, situational awareness, stress, and performance in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. It is based on an experimental study of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The study compared three different setups of human system interfaces in four human-in-the-loop (HITL) configurations, incorporating two alarm design formats (Prioritised vs non-prioritised) and three procedural guidance setups (e.g. one presenting paper procedures, one offering digitised screen-based procedures, and lastly an AI-based procedural guidance system). Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioural, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed tasks under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system (alarm display design, procedure format, AI support, interface design), communication, situational awareness, cognitive workload, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure The concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Perception, Understanding, and Projection, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. Calculated as the average of the score on perception, understanding and projection. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness. It is calculated using the equation U - (D - S). Situation Understanding (U) comprises Information Quantity, Information Quality, and Familiarity. Situation demand (D) includes the situation's Instability, Complexity, and Variability. At the same time, the Supply of attentional resources (S) comprises Arousal, Concentration, Division of Attention, and Spare Capacity. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety- critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in control room environments. Citation Please cite the article if you use this dataset in your research or publication.

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    Human-in-the-Loop Decision Support in Process Control Rooms Dataset Overview This repository contains a comprehensive dataset aimed at assessing cognitive states, workload, situational awareness, stress, and fatigue in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. Additionally, it incorporates data from a simulation of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The primary objective of this dataset is to compare the performance and safety outcomes of different groups of participants exposed to varying decision support tools. These tools include alarm prioritization, paper-based vs. digitized screen-based procedures, and an AI recommendation system. Statistical analysis was performed to compare the outcomes among the groups. Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioral, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed functions under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system, alarm design, procedure format, AI support, communication, situational awareness, cognitive workload, interface design, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure he concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Monitoring, Planning, and Intervention, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness in different simulated control room scenarios. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety-critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in the simulated control room environments. Citation If you use this dataset in your research or publication, please cite the article.

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    Human-in-the-Loop Decision Support in Process Control Rooms Dataset Overview This repository contains a comprehensive dataset aimed at assessing cognitive states, workload, situational awareness, stress, and fatigue in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. Additionally, it incorporates data from a simulation of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The primary objective of this dataset is to compare the performance and safety outcomes of different groups of participants exposed to varying decision support tools. These tools include alarm prioritization, paper-based vs. digitized screen-based procedures, and an AI recommendation system. Statistical analysis was performed to compare the outcomes among the groups. Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioral, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed functions under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system, alarm design, procedure format, AI support, communication, situational awareness, cognitive workload, interface design, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure he concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Monitoring, Planning, and Intervention, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness in different simulated control room scenarios. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety-critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in the simulated control room environments. Citation If you use this dataset in your research or publication, please cite the article.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2024
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    Authors: Manghi, Paolo; Atzori, Claudio; Bardi, Alessia; Baglioni, Miriam; +14 Authors

    This dataset contains metadata records of publications, research data, software and projects relevant for the research community in Virtual Twins in health.The dump contains the records available in the OpenAIRE Gateway on Digital Twins in Health of the EDITH CSA project of the European Commission (grant agreement n. 101083771). Records are identified via full-text mining and inference techniques applied to the OpenAIRE Graph.The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating thousands of scholarly data sources world-wide. The dump consists of a tar archive containing gzip files with one json per line.Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.10519297. The CSV file contains the links to the full-texts of the publications available in the gateway that OpenAIRE could download and use.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
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    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
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      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Evans, Stephanie; Naylor, Nichola R.; Fowler, Tom; Hopkins, Susan; +1 Authors

    Abstract Background Asymptomatic SARS-CoV-2 testing of hospitalised patients began in April-2020, with twice weekly healthcare worker (HCW) testing introduced in November-2020. Guidance recommending asymptomatic testing was withdrawn in August-2022. Assessing the impact of this decision from data alone is challenging due to concurrent changes in infection prevention and control practices, community transmission rates, and a reduction in ascertainment rate from reduced testing. Computational modelling is an effective tool for estimating the impact of this change. Methods Using a computational model of SARS-CoV-2 transmission in an English hospital we estimate the effectiveness of several asymptomatic testing strategies, namely; (1) Symptomatic testing of patients and HCWs, (2) testing of all patients on admission with/without repeat testing on days 3 and 5–7, and (3) symptomatic testing plus twice weekly asymptomatic HCW testing with 70% compliance. We estimate the number of patient and HCW infections, HCW absences, number of tests, and tests per case averted or absence avoided, with differing community prevalence rates over a 12-week period. Results Testing asymptomatic patients on admission reduces the rate of nosocomial SARS-CoV-2 infection by 8.1–21.5%. Additional testing at days 3 and 5–7 post admission does not significantly reduce infection rates. Twice weekly asymptomatic HCW testing can reduce the proportion of HCWs infected by 1.0-4.4% and monthly absences by 0.4–0.8%. Testing asymptomatic patients repeatedly requires up to 5.5 million patient tests over the period, and twice weekly asymptomatic HCW testing increases the total tests to almost 30 million. The most efficient patient testing strategy (in terms of tests required to prevent a single patient infection) was testing asymptomatic patients on admission across all prevalence levels. The least efficient was repeated testing of patients with twice weekly asymptomatic HCW testing in a low prevalence scenario, and in all other prevalence levels symptomatic patient testing with regular HCW testing was least efficient. Conclusions Testing patients on admission can reduce the rate of nosocomial SARS-CoV-2 infection but there is little benefit of additional post-admission testing. Asymptomatic HCW testing has little incremental benefit for reducing patient cases at low prevalence but has a potential role at higher prevalence or with low community transmission. A full health-economic evaluation is required to determine the cost-effectiveness of these strategies.

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    Collection . 2024
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    Data sources: Datacite
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    Collection . 2024
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    Data sources: Datacite
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129 Research products
  • Authors: Ciocioc, Ion Valentin;

    This work studies the intersection of chromatics, mathematical algorithms, and innovative concepts such as grammatical geometry. Analyzes the impact of colors on language perception and introduces original mathematical formulas, extracted from the definitions of grammatical concepts, providing a deep and authentic approach to the study of language, which allows for a more precise interpretation of grammatical rules, thus facilitating systematic learning of the language. The concept of grammatical geometry, proposed in the pages of this book, opens new horizons in understanding the relationships between the elements of a sentence and how they interact. This geometry is not limited to visual representations but offers a solid theoretical framework for analyzing linguistic structures from various and multidimensional perspectives. Another remarkable aspect of this work is the presentation of the first chromatic map of grammar, which illustrates how different shades of colors can correspond to different grammatical functions and concepts. This map not only enriches the perspective on learning grammar but also serves as an innovative visual tool that helps associate colors with certain linguistic structures, thus facilitating the process of memorization and understanding. Due to the correlation of these four fields – algorithms, grammar, mathematics, and chromatics – the formula of metamorphosis has been identified, a new formula with potential applicability in other fields such as psychology, art, design, medicine, geology, biology, etc. This formula opens new perspectives for interdisciplinary explorations, contributing to a deeper understanding of the interaction between language and perception in various contexts, as well as within any discipline that generates the idea of metamorphosis.

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    ZENODO
    Dataset . 2024
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    Data sources: Datacite
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      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    This repository is used for archiving the classification of papers in the following article: Data: to share or not to share? A Semi-Systematic Literature Review in (rational) data sharing in inter-organizational systems.This archive is supporting the paper for the labels that have been given to the papers found in the Semi-Systematic Literature Review.The following categories and labels are possible in the classification of the literature:Category ["Paper"] - Labels: Summarized name(s) of the author(s).Category ["Knowledge dimension"] - Labels: ["Data", "Information", "Knowledge", "Data/information (paper is ambiguous on whether it is data or information that is shared)", "None", "Data/Knowledge (data is learned and transferred in to knowledge, the knowledge is shared)"]Category ["Type of industry"] - Labels: ["None", "Supply chain", "Healthcare", "Vehicles", "Agricultural", "Research", "Networks", "Automotive", "Innovation", "Smart Grid", "Social media", "R&D", "e-Governance", "Construction Sector", "Government-enterprise", "Manufacturing", "Power grid", "Smart Cities", "Personal data", "Cyber security", "E-commerce", "Maritime", "Online marketplaces", "Assembly", "Engineering", "Communities of Practice", "Knowledge Management Systems", "B2B commerce", "Fisheries", "Outsourcing", "High-tech firms", "Crisis", "e-Services", "Seaports", "Horticulture", "Data markets", "Media", "Cultural Heritage Institutions", "Fresh Products", "Knowledge market", "Ecological", "Ride Sharing", "Government", "Transit", "Medical", "Virtual Research Organization", "Energy", "Oil and gas", "Education"]Category ["Game theory approach"] - Labels: ["None", "Non-cooperative game", "Evolutionary", "Cooperative game", "Stackelberg", "Auction", "Unclear defined", "Diffusion kernels", "Markov game", "Negotiation", "(Non-)cooperative game", "Differential game", "Pricing", "Bayesian", "Contract theory", "Non-collusion", "Stochastic differential game", "Fisher’s market", "Hotelling", "Bargaining", "Access control"]Category ["Technologies of interest"] - Labels: ["blockchain", "None", "smart contracts", "federated learning", "internet of things", "machine learning", "cloud computing", "artificial intelligence", "ethereum", "5G", "data trust", "deep neural networks", "digital twins", "internet of (medical) things", "smart grid", "data governance", "artifical intelligence", "collaborative learning", "smart contract", "encryption", "data escrow", "NFT", "data mining", "semantic technologies", "transfer learning"]Category ["Level of trust"] - Labels: ["Calculus-Based Trust", "None", "security", "privacy", "integrity", "transparency", "authentication", "confidentiality", "traceability", "verification", "Knowledge-Based Trust", "privacy"]Category ["Type of contract"> - Labels: ["None", "Smart contracts", "Contract theory", "Linear wholesale price contract", "GMP and IPD contracts", "General contract", "Incentive contract", "Trust-embedded contract", "Wholesale price contract"]

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    4TU.ResearchData
    Dataset . 2024
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    4TU.ResearchData
    Dataset . 2024
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    4TU.ResearchData | science.engineering.design
    Dataset . 2024
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    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2024
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      4TU.ResearchData | science.engineering.design
      Dataset . 2024
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      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare. The Fusion of Technology and Life Sciences In Silico Methods: A New Frontier in Medicine The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials. Bridging the Gap with Computational Modeling Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles. The Impact on Clinical Research and Patient Care Enhancing Precision and Efficiency One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs. Promoting Medical Equitability The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy. Conclusion: The Future is Now The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.

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    Overview This repository contains a comprehensive dataset to assess cognitive states, workload, situational awareness, stress, and performance in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. It is based on an experimental study of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The study compared three different setups of human system interfaces in four human-in-the-loop (HITL) configurations, incorporating two alarm design formats (Prioritised vs non-prioritised) and three procedural guidance setups (e.g. one presenting paper procedures, one offering digitised screen-based procedures, and lastly an AI-based procedural guidance system). Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioural, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed tasks under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system (alarm display design, procedure format, AI support, interface design), communication, situational awareness, cognitive workload, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure The concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Perception, Understanding, and Projection, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. Calculated as the average of the score on perception, understanding and projection. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness. It is calculated using the equation U - (D - S). Situation Understanding (U) comprises Information Quantity, Information Quality, and Familiarity. Situation demand (D) includes the situation's Instability, Complexity, and Variability. At the same time, the Supply of attentional resources (S) comprises Arousal, Concentration, Division of Attention, and Spare Capacity. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety- critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in control room environments. Citation Please cite this article and dataset if you use this dataset in your research or publication. Amazu, C. W., Mietkiewicz, J., Abbas, A. N., Briwa, H., Perez, A. A., Baldissone, G., ... & Leva, M. C. (2024). Experiment Data: Human-in-the-loop Decision Support in Process Control Rooms. Data in Brief, 110170.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite