The dataset is a synthetic cohort for use for the VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge. The dataset was generated using Synthea, a tool created by MITRE to generate synthetic electronic health records (EHRs) from curated care maps and publicly available statistics. This dataset represents 147,451 patients developed using the COVID-19 module. The dataset format conforms to the CSV file outputs. Below are links to all relevant information. PrecisionFDA Challenge: https://precision.fda.gov/challenges/11 Synthea hompage: https://synthetichealth.github.io/synthea/ Synethea GitHub repository: https://github.com/synthetichealth/synthea Synthea COVID-19 Module publication: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531559/ CSV File Format Data Dictionary: https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, this introduces many challenges, especially when managing confidential clinical data. The aim of this 1 hr virtual workshop is to provide participants with knowledge about what synthetic data is, methods to create synthetic data, and the 2023 Pediatric Sepsis Data Challenge. Workshop Agenda: 1. Introduction - Speaker: Mark Ansermino, Director, Centre for International Child Health 2. "Leveraging Synthetic Data for an International Data Challenge" - Speaker: Charly Huxford, Research Assistant, Centre for International Child Health 3. "Methods in Synthetic Data Generation." - Speaker: Vuong Nguyen, Biostatistician, Centre for International Child Health and The HIPpy Lab This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Charly Huxford: Leveraging Synthetic Data for an International Data Challenge presentation and accompanying PowerPoint slides. Vuong Nguyen: Methods in Synthetic Data Generation presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Objective(s): The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset. The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C). Data Description: Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Data dictionary for the synthetic training dataset containing 148 variables. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant.

The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.

The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions.

A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.

At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

Patient-specific cranial implants are used to repair bone defects in the human skull after trauma or previous surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is both time-consuming and expensive. Recent advances in additive manufacturing (AM) made the in-hospital or in-operation room (in-OR) fabrication of personalized implants feasible. However, the design of implants is still manually performed by external manufacturer. To facilitate an optimized surgical workflow, fast and automatic implant design is highly desirable. Data-driven approaches, such as deep learning, show great potential towards automatic implant design. However, a considerable amount of data is needed to train such algorithms which is, especially in the medical domain, often a bottleneck. Therefore, we present a data set containing CT scans of healthy skulls from 24 patients, in which we injected various artificial cranial defects, resulting in 240 data pairs. In addition, we provide the 240 corresponding implants for these data pairs. With our collection, we also disclose a toolbox for processing our data, providing users wanting to work on automatic cranial implant design a solid base for their research.Toolbox Link:https://github.com/Jianningli/SciDataPlease use the following citation if you use the data in your work:J. Li, et al. Head CT Collection for Patient-specific Craniofacial Implant (PSI) Design. Figshare, 2020.J. Li, et al. Synthetic Skull Bone Defects for automatic Patient-specific Craniofacial Implant Design. Sci. Data, 2020.The datasets can be viewed with StudierFenster:www.studierfenster.atPlease see also our AutoImplant Challenge for cranial implant design:https://autoimplant.grand-challenge.org/

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Synthetic Biology Market Size 2024-2028

The synthetic biology market size is forecast to increase by USD 55.6 billion at a CAGR of 34.1% between 2023 and 2028.

Synthetic biology, the engineering of genetic material to create new biological functions, is gaining momentum due to its potential applications in various industries, particularly in healthcare. The ability to manipulate genetic codes at the DNA level holds immense promise for the development of treatments and cures for diseases such as sickle cell anemia and cystic fibrosis. However, the market faces several challenges. Proof of concept for many applications is still in its infancy, and regulatory hurdles loom large.
Similarly, synthetic organisms, including bacteria and yeast, are increasingly being used for biofuel production and biomaterials. Safety concerns and ethical use are paramount, as is ensuring compliance with complex regulatory frameworks. Moreover, deciphering intricate biological pathways and editing bacterial genomes require advanced technological capabilities. Despite these challenges, the potential benefits in addressing diseases such as cancer make it a promising area of research and development.

What will be the Size of the Synthetic Biology Market During the Forecast Period?

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Synthetic biology, a revolutionary field that combines engineering principles with biology, has gained significant traction in various industries. This sector encompasses DNA sequencing, synthesizing, and manipulating organisms to produce desired outcomes. While the market presents numerous opportunities, it also faces challenges related to biosafety, biosecurity, and ethical concerns. DNA sequencing and synthesizing play a pivotal role. The ability to read and write genetic information has led to advancements in gene-editing technologies, RNA development, and therapeutic genome editing. These innovations have significant implications for pharmaceutical and biotechnology companies, particularly in healthcare verticals. Modern Meadow and Bolt Threads, for instance, have made strides in creating animal-free leather and textiles using synthetic organisms. Biomolecules and medical devices are other areas where synthetic biology is making a mark. Despite the potential benefits, biosafety, biosecurity, and ethical concerns pose challenges to the market. Ensuring the safe handling and containment of synthetic organisms is crucial to prevent unintended consequences.
Moreover, biosecurity concerns arise from the potential misuse of these organisms for malicious purposes. Ethical concerns center around the creation and use of synthetic organisms, particularly those that mimic human or animal life. Multiplexed diagnostics and cellular recording are emerging applications. Multiplexed diagnostics allow for the simultaneous detection of multiple diseases or genetic markers, offering improved accuracy and efficiency. Cellular recording enables the monitoring of cellular processes in real-time, providing valuable insights for drug discovery and genome engineering. The synthetic DNA market is expected to grow significantly due to its applications in gene therapy, gene editing, and industrial production. RNA development is another area of focus, with potential applications In therapeutics and vaccine development. Drug discovery and genome engineering are also key areas of investment, as these technologies offer the potential for creating targeted therapies and treating genetic diseases.

How is this Synthetic Biology Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Healthcare
  Industrial
  Food and agriculture
  Others


Product

  Oligonucleotides
  Enzymes
  Cloning technology kits
  Xeno-nucleic acids
  Chassis organism


Technology

  NGS Technology
  PCR Technology
  Genome Editing Technology
  Bioprocessing Technology
  Other Technologies


Geography

  North America

    US


  Europe

    Germany
    UK
    France


  Asia

    China


  Rest of World (ROW)

By Application Insights

The healthcare segment is estimated to witness significant growth during the forecast period. The market, which involves the buying behavior of synthesizing and manipulating DNA sequences to create synthetic organisms, is experiencing notable growth In the healthcare sector. Synthetic biology's clinical applications offer innovative solutions to address various health issues and enhance medical treatments' efficacy. These applications span diagnostics and therapeutics, with the potential to construct molecular tissues, develop novel medicines and vaccines, and design advanced diagnostics. The recent introduction o

Background: Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias toward the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterized by under-diagnosis.Methods: Study 1: We created synthetic data, produced to mimic structured EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic.Results: Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR.Conclusions: The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult to find. Our simple model made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrate the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missing diagnoses in EHR data.

This synthetic datset is about lung diseases and visits to Asia. It was introduced in Lauritzen and Spiegelhalter (1988).

Task: The dataset can be used to study causal discovery algorithms.

Summary:

• Size of dataset: 5,000 x 6
• Task: Causal Discovery Problem
• Data Type: Binary Data
• Dataset Scope: Standalone Dataset
• Ground Truth: Known Graph
• Temporal Structure: Static Data
• License: CC0 (generated for bnlearn)
• Missing Values: No Missing Values

Missingness Statement: There are no missing values.

Features:

• D: Dyspnoea (yes / no)
• T: Tuberculosis (yes / no)
• L: Lung cancer (yes / no)
• B: Bronchitis (yes / no)
• A: Visit to Asia (yes / no)
• S: Smoking (yes / no)
• X: Chest X-ray (yes / no)
• E: Tuberculosis versus lung cancer/bronchitis (yes / no)

Files:

• asia.csv: dataset
• ground_truth.csv: DAG used for data generation (Lauritzen and Spiegelhalter (1988)).
• asia.bif: Bayesian Network from (Scutari (2010), License CC BY-SA 3.0). The network was used for data generation in Lauritzen and Spiegelhalter (1988).

This study addresses the heterogeneity of Breast Cancer (BC) by employing a Conditional Probabilistic Diffusion Model (CPDM) to synthesize Magnetic Resonance Images (MRIs) based on multi-omic data, including gene expression, copy number variation, and DNA methylation. The lack of paired medical images and genomics data in previous studies presented a challenge, which the CPDM aims to overcome. The well-trained CPDM successfully generated synthetic MRIs for 726 TCGA-BRCA patients, who lacked actual MRIs, using their multi-omic profiles. Evaluation metrics such as Frechet’s Inception Distance (FID), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) demonstrated the CPDM’s effectiveness, with an FID of 2.02, an MSE of 0.02, and an SSIM of 0.59 based on the 15-fold cross-validation. The synthetic MRIs were used to predict clinical attributes, achieving an Area Under the Receiver-Operating-Characteristic curve (AUROC) of 0.82 and an Area Under the Precision-Recall Curve (AUPRC) of 0.84 for predicting ER+/HER2+ subtypes. Additionally, the MRIs served to accurately predicted BC patient survival with a Concordance-index (C-index) score of 0.88, outperforming other baseline models. This research demonstrates the potential of CPDMs in generating MRIs based on BC patients’ genomic profiles, offering valuable insights for radiogenomic research and advancements in precision medicine. The study provides a novel approach to understanding BC heterogeneity for early detection and personalized treatment.

IMPORTANT: This deposit contains a range of supplementary material related to the deposit of the SIPHER Synthetic Population for Individuals, 2019-2021 (https://doi.org/10.5255/UKDA-SN-9277-1). See the shared readme file for a detailed description describing this deposit. Please note that this deposit does not contain the SIPHER Synthetic Population dataset, or any other Understanding Society survey datasets.

The lack of a centralised and comprehensive register-based system in Great Britain limits opportunities for studying the interaction of aspects such as health, employment, benefit payments, or housing quality at the level of individuals and households. At the same time, the data that exist, is typically strictly controlled and only available in safe haven environments under a “create-and-destroy” model. In particular when testing policy options via simulation models where results are required swiftly, these limitations can present major hurdles to coproduction and collaborative work connecting researchers, policymakers, and key stakeholders. In some cases, survey data can provide a suitable alternative to the lack of readily available administrative data. However, survey data does typically not allow for a small-area perspective. Although special license area-level linkages of survey data can offer more detailed spatial information, the data’s coverage and statistical power might be too low for meaningful analysis.

Through a linkage with the UK Household Longitudinal Study (Understanding Society, SN 6614, wave k), the SIPHER Synthetic Population allows for the creation of a survey-based full-scale synthetic population for all of Great Britain. By drawing on data reflecting “real” survey respondents, the dataset represents over 50 million synthetic (i.e. “not real”) individuals. As a digital twin of the adult population in Great Britain, the SIPHER Synthetic population provides a novel source of microdata for understanding “status quo” and modelling “what if” scenarios (e.g., via static/dynamic microsimulation model), as well as other exploratory analyses where a granular geographical resolution is required

As the SIPHER Synthetic Population is the outcome of a statistical creation process, all results obtained from this dataset should always be treated as “model output” - including basic descriptive statistics. Here, the SIPHER Synthetic Population should not replace the underlying Understanding Society survey data for standard statistical analyses (e.g., standard regression analysis, longitudinal multi-wave analysis). Please see the respective User Guide provided for this dataset for further information on creation and validation.

This research was conducted as part of the Systems Science in Public Health and Health Economics Research - SIPHER Consortium and we thank the whole team for valuable input and discussions that have informed this work.

THE PROBLEM: There is strong evidence that the social and economic conditions in which we grow, live, work and age determine our health to a much larger degree than lifestyle choices. These social determinants of health, such as income, good quality homes, education, or work, are not distributed equally in society, which leads to health inequalities. However, we know very little about how specific policies influence the social conditions to prevent ill health and reduce health inequalities. Also, most social determinants of health are the responsibility of policy sectors other than health, which means policymakers need to promote health in ALL their policies if they are to have a big impact on health. SIPHER will provide new scientific evidence and methods to support such a shift from health policy to healthy public policy.

OUR POLICY FOCUS: We are working with four policy partner organisations at local, regional, and national level to tackle their above-average chronic disease burden and persistent health inequalities: Sheffield City Council, Greater Manchester Combined Authority, the Scottish Government and Public Health Scotland. We will focus on three jointly agreed policy priorities for good health: - Inclusive Economies - Public Mental Health - Providing affordable, good quality housing

OUR COMPLEX SYSTEMS SCIENCE APPROACH: Each of the above policy areas is a complex political system with many competing priorities, where policy choices in one sector (e.g., housing) can have large unintended effects in others (e.g., poverty). There is often no correct solution because compromises between different outcomes require value judgements. This means that to assess the true benefits and costs of a policy in relation to health, policy effects and their interdependencies need to be assessed across a wide range of possible outcomes. However, no policymaker has knowledge of the whole system and future economic and political developments are uncertain. Ongoing monitoring of expected and unexpected effects of policies and other system changes...

The Data Annotation and Collection Services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse sectors. The market, estimated at $10 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $45 billion by 2033. This significant expansion is fueled by several key factors. The surge in autonomous driving initiatives necessitates high-quality data annotation for training self-driving systems, while the burgeoning smart healthcare sector relies heavily on annotated medical images and data for accurate diagnoses and treatment planning. Similarly, the growth of smart security systems and financial risk control applications demands precise data annotation for improved accuracy and efficiency. Image annotation currently dominates the market, followed by text annotation, reflecting the widespread use of computer vision and natural language processing. However, video and voice annotation segments are showing rapid growth, driven by advancements in AI-powered video analytics and voice recognition technologies. Competition is intense, with both established technology giants like Alibaba Cloud and Baidu, and specialized data annotation companies like Appen and Scale Labs vying for market share. Geographic distribution shows a strong concentration in North America and Europe initially, but Asia-Pacific is expected to emerge as a major growth region in the coming years, driven primarily by China and India's expanding technology sectors. The market, however, faces certain challenges. The high cost of data annotation, particularly for complex tasks such as video annotation, can pose a barrier to entry for smaller companies. Ensuring data quality and accuracy remains a significant concern, requiring robust quality control mechanisms. Furthermore, ethical considerations surrounding data privacy and bias in algorithms require careful attention. To overcome these challenges, companies are investing in automation tools and techniques like synthetic data generation, alongside developing more sophisticated quality control measures. The future of the Data Annotation and Collection Services market will likely be shaped by advancements in AI and ML technologies, the increasing availability of diverse data sets, and the growing awareness of ethical considerations surrounding data usage.

The Medical Synthetic Sutures market has emerged as a critical segment within the healthcare industry, catering to a variety of surgical needs by providing strong, reliable, and biocompatible materials for wound closure. Synthetic sutures are primarily used in surgeries to aid in tissue approximation, ensuring wound

These are synthetically generated unit and area level population and sample data that can be used for testing model-based unit-level small area methods. To prevent disclosure issues the datasets have been generated by repeated (Monte-Carlo) sampling of real EU-SILC (Survey of Income and Living Conditions) data in Austria. The data include geographical identifies and can be used for fitting unit-level (Battese-Harter and Fuller type) models and area level models (Fay-Herriott- type) models. The datasets are part of the R package emdi. Examples of the use of the data can be found in the emdi manual available via https://cran.r-project.org/web/packages/emdi/emdi.pdf and in Kreutzmann et al. (2019)

Kreutzmann, A. K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (2019). The R package emdi for the estimation and mapping of regional disaggregated indicators. Journal of Statistical Software, 91(7). https://doi.org/10.18637/jss.v091.i07

Reliable statistics are crucial for policy relevant research. Small Area Estimation (SAE) methods generate robust reliable and consistent statistics at geographical scales for which survey data are either non-existent or too sparse to provide direct estimates of acceptable accuracy. The last decade has seen a rapid increase in the use of SAE. Statistical agencies and Governmental organisations are actively developing their own suites of estimates. In the UK the Office for National Statistics (ONS) has responded to user demands by producing estimates of average household income for wards and using SAE to answer queries from local authorities, policy advisers and government departments. The Welsh Assembly Government (WAG) is actively seeking to develop capacity for SAE. Public Health England produces SAEs of adolescent smoking and chronic kidney disease. Initial demands for small area statistics are now shifting to requirements for more complex statistics that extend beyond averages and proportions to encompass estimates of statistical distributions, multidimensional indicators (e.g. inequality and deprivation indicators) and methods for replacing the Census and adjusting Census results for undercount. These developing requirements pose significant methodological and applied real-world challenges. These challenges are deepened by different methodological approaches to SAE remaining largely unconnected, locked in disciplinary silos. The technical presentation of SAE also impedes more widespread uptake by social scientists and understanding by users. The proposed programme of work aims to (a) develop novel SAE methodologies to better serve the needs of users and producers of SAE (b) bridge different methodological approaches to SAE, (c) apply SAE for answering substantive questions in the social sciences and (d) 'Mainstream' SAE within the quantitative social sciences through the creation of methodologically comprehensive and accessible resources. The project comprises three work packages of methodological innovative research designed to deepen the understanding of SAE and achieve the aforementioned aims. The project will capitalise on a cross-disciplinary research team drawn together through an NCRM methodological network and reflecting a large part of the SAE expertise in the UK. Through long-standing collaborations with national and international agencies in the UK, Mexico and Brazil, which are placed at the centre of the project, we enjoy access to individual level secondary survey and Census data. Collaboration with key SAE users will ensure that the project remains relevant to user needs and that methodologies are used for expanding the set of small area statistics currently available. The involvement of international experts ensures the quality and relevance of the research. Substantive outputs will include SAEs of attributes of interest to users, including income, inequality, deprivation, health, ethnicity and a realistic pseudo-Census dataset for use by other researchers. The project will advance knowledge across disciplines in the social sciences including social statistics, applied economics, human geography and sociology. It will additionally impact on the production of official and Census statistics. The project is committed to adding value to NCRM's training and capacity building activities by developing new resources.

The SimTool is a toolset to simulate soft tissue deformation during resection surgery. The proposed toolset is an assimilation of 3D packages in Python and Unreal Engine 4 (UE4) with Nvidia Flex integration, which make it possible to adapt the dataset for more applications. The SynBench is a synthetic definable object benchmark that includes the ground truth between two point sets as well as various challenges, namely, deformation levels, noise, outlier, and data incompleteness.

The Synthetic Retinol market has emerged as a vital segment within the skincare industry, driven by the increasing demand for effective anti-aging products and skin health solutions. Synthetic retinol, a derivative of vitamin A, is revolutionizing skincare formulations by providing users with the benefits of traditi

This synthetic dataset represents alarm message system for patient monitoring. It was introduced in Beinlich et al. (1989).

Task: The dataset collection can be used to study causal discovery algorithms.

Summary:

• Size of collection: 20,000 x 37
• Task: Causal Discovery Problem
• Data Type: Categorical Data
• Dataset Scope: Standalone Dataset
• Ground Truth: Known Graph
• Temporal Structure: Static Data
• License: CC0 (generated for bnlearn)
• Missing Values: No Missing Values

Missingness Statement: There are no missing values.

Collection:

The alarm dataset contains the following 37 variables:

• CVP: Central venous pressure (LOW / NORMAL / HIGH)
• PCWP: Pulmonary capillary wedge pressure (LOW / NORMAL / HIGH)
• HIST: History (TRUE / FALSE)
• TPR: Total peripheral resistance (LOW / NORMAL / HIGH)
• BP: Blood pressure (LOW / NORMAL / HIGH)
• CO: Cardiac output (LOW / NORMAL / HIGH)
• HRBP: Heart rate / blood pressure (LOW / NORMAL / HIGH)
• HREK: Heart rate measured by an EKG monitor (LOW / NORMAL / HIGH)
• HRSA: Heart rate / oxygen saturation (LOW / NORMAL / HIGH)
• PAP: Pulmonary artery pressure (LOW / NORMAL / HIGH)
• SAO2: Arterial oxygen saturation (LOW / NORMAL / HIGH)
• FIO2: Fraction of inspired oxygen (LOW / NORMAL)
• PRSS: Breathing pressure (ZERO / LOW / NORMAL / HIGH)
• ECO2: Expelled CO2 (ZERO / LOW / NORMAL / HIGH)
• MINV: Minimum volume (ZERO / LOW / NORMAL / HIGH)
• MVS: Minimum volume set (LOW / NORMAL / HIGH)
• HYP: Hypovolemia (TRUE / FALSE)
• LVF: Left ventricular failure (TRUE / FALSE)
• APL: Anaphylaxis (TRUE / FALSE)
• ANES: Insufficient anesthesia/analgesia (TRUE / FALSE)
• PMB: Pulmonary embolus (TRUE / FALSE)
• INT: Intubation (NORMAL / ESOPHAGEAL / ONESIDED)
• KINK: Kinked tube (TRUE / FALSE)
• DISC: Disconnection (TRUE / FALSE)
• LVV: Left ventricular end-diastolic volume (LOW / NORMAL / HIGH)
• STKV: Stroke volume (LOW / NORMAL / HIGH)
• CCHL: Catecholamine (NORMAL / HIGH)
• ERLO: Error low output (TRUE / FALSE)
• HR: Heart rate (LOW / NORMAL / HIGH)
• ERCA: Electrocauter (TRUE / FALSE)
• SHNT: Shunt (NORMAL / HIGH)
• PVS: Pulmonary venous oxygen saturation (LOW / NORMAL / HIGH)
• ACO2: Arterial CO2 (LOW / NORMAL / HIGH)
• VALV: Pulmonary alveoli ventilation (ZERO / LOW / NORMAL / HIGH)
• VLNG: Lung ventilation (ZERO / LOW / NORMAL / HIGH)
• VTUB: Ventilation tube (ZERO / LOW / NORMAL / HIGH)
• VMCH: Ventilation machine (ZERO / LOW / NORMAL / HIGH)

Files:

• alarm.csv: dataset
• alarm.bif: Bayesian Network from (Scutari (2010), License CC BY-SA 3.0). The network was used for data generation in Beinlich et al. (1989).

Background. Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT).

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Data with the activities performed by citizens in a day, with the corresponding locations, exposure and risk for the fifth scenario population. It is composed by the following variables: id (citizen identifier), cell_id (the identifier for each cell in the grid, activities related to two or more cells are coding as the concatenation of the cell_id of all the included cells), order, Activity, Duration, DateStart, DateEnd, Sex, Age, Asthma, Diabetes, High Blood Pressure, Pulmonar Disease, Heart Disease, Anxiety, Smoke, Alcohol (binary epidemiological variables, if the value is 1 the citizen suffer the affection), concentration (PM2.5 concentration in μg/m3), exposure (PM2.5 expsure in μg * min/m3), level, mortality relative risk. File: ./data/sequence_data/sintetic_data_v6_scenario5.csv. (CSV)

The data collection and labeling market is experiencing robust growth, fueled by the escalating demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033), reaching approximately $75 billion by 2033. This expansion is primarily driven by the increasing adoption of AI across diverse sectors, including healthcare (medical image analysis, drug discovery), automotive (autonomous driving systems), finance (fraud detection, risk assessment), and retail (personalized recommendations, inventory management). The rising complexity of AI models and the need for more diverse and nuanced datasets are significant contributing factors to this growth. Furthermore, advancements in data annotation tools and techniques, such as active learning and synthetic data generation, are streamlining the data labeling process and making it more cost-effective. However, challenges remain. Data privacy concerns and regulations like GDPR necessitate robust data security measures, adding to the cost and complexity of data collection and labeling. The shortage of skilled data annotators also hinders market growth, necessitating investments in training and upskilling programs. Despite these restraints, the market’s inherent potential, coupled with ongoing technological advancements and increased industry investments, ensures sustained expansion in the coming years. Geographic distribution shows strong concentration in North America and Europe initially, but Asia-Pacific is poised for rapid growth due to increasing AI adoption and the availability of a large workforce. This makes strategic partnerships and global expansion crucial for market players aiming for long-term success.

Data with the activities performed by citizens in a day, with the corresponding locations, exposure and risk for the third scenario population. It is composed by the following variables: id (citizen identifier), cell_id (the identifier for each cell in the grid, activities related to two or more cells are coding as the concatenation of the cell_id of all the included cells), order, Activity, Duration, DateStart, DateEnd, Sex, Age, Asthma, Diabetes, High Blood Pressure, Pulmonar Disease, Heart Disease, Anxiety, Smoke, Alcohol (binary epidemiological variables, if the value is 1 the citizen suffer the affection), concentration (PM2.5 concentration in μg/m3), exposure (PM2.5 expsure in μg * min/m3), level, mortality relative risk. File: ./data/sequence_data/sintetic_data_v6_running.csv. (CSV)