Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

The Synthetic Data Generation Marketsize was valued at USD 288.5 USD Million in 2023 and is projected to reach USD 1920.28 USD Million by 2032, exhibiting a CAGR of 31.1 % during the forecast period.Synthetic data generation stands for the generation of fake datasets that resemble real datasets with reference to their data distribution and patterns. It refers to the process of creating synthetic data points utilizing algorithms or models instead of conducting observations or surveys. There is one of its core advantages: it can maintain the statistical characteristics of the original data and remove the privacy risk of using real data. Further, with synthetic data, there is no limitation to how much data can be created, and hence, it can be used for extensive testing and training of machine learning models, unlike the case with conventional data, which may be highly regulated or limited in availability. It also helps in the generation of datasets that are comprehensive and include many examples of specific situations or contexts that may occur in practice for improving the AI system’s performance. The use of SDG significantly shortens the process of the development cycle, requiring less time and effort for data collection as well as annotation. It basically allows researchers and developers to be highly efficient in their discovery and development in specific domains like healthcare, finance, etc. Key drivers for this market are: Growing Demand for Data Privacy and Security to Fuel Market Growth. Potential restraints include: Lack of Data Accuracy and Realism Hinders Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Abstract copyright UK Data Service and data collection copyright owner.

The Annual Survey of Hours and Earnings, 2020: Synthetic Data Pilot is a synthetic version of the Annual Survey of Hours and Earnings (ASHE) study available via Trusted Research Environments (TREs).

ASHE is one of the most extensive surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete. ASHE is available for research projects demonstrating public good to accredited or approved researchers via TREs such as the Office for National Statistics Secure Research Service (SRS) or the UK Data Service Secure Lab (at SN 6689). To access collections stored within TREs, researchers need to undergo an accreditation process.

Gaining access to data in a secure environment can be time and resource intensive. This pilot has created a low fidelity, low disclosure risk synthetic version of ASHE data, which can be made available to researchers more quickly while they wait for access to the real data.

The synthetic data were created using the Synthpop package in R. The sample method was used; this takes a simple random sample with replacement from the real values. The project was carried out in the period between 19th December 2022 and 3rd January 2023. Further information is available within the documentation.

User feedback received through this pilot will help the ONS to maximise benefits of data access and further explore the feasibility of synthesising more data in future.

The ASHE synthetic data contain the same variables as ASHE for each individual, relating to wages, hours of work, pension arrangements, and occupation and industrial classifications. There are also variables for age, gender and full/part-time status. Because ASHE data are collected by the employer, there are also variables relating to the organisation employing the individual. These include employment size and legal status (e.g. public company). Various geography variables are included in the data files. The year variable in this synthetic dataset is 2020.

This statistic shows the percentage of U.S. health care organizations that selected various potential opportunities to use artificial intelligence, as of 2017. According to the data, 34 percent said that they could use artificial intelligence for population health.

According to a 2023 survey of cybersecurity professionals, nearly 60 percent of respondents worldwide considered improved threat detection as the most significant benefit of incorporating artificial intelligence into their cybersecurity operations. Improved vulnerability management ranked second, according to 57 percent of respondents. Overall, over one-third of respondents saw the automation applications of AI as a way to ease talent shortage issues in cybersecurity operations.

Generative AI is everywhere

As a phenomenon that has taken the internet by storm, generative AI is increasingly being tested for business functions, including cybersecurity. Generative AI-powered solutions can search through vast amounts of data to identify abnormal behavior and detect malicious activity. Consequently, CEOs and IT professionals alike expect generative AI to be increasingly used to bolster cybersecurity, offering greater speed, accuracy, and cost-effectiveness.

The other side of the coin

Despite the security benefits of generative AI, there is the other side of the coin to account for, as the same advantages can also benefit hostile actors’ capabilities, such as phishing, malware development, and deepfakes. Looking forward, companies will have to adapt and stay up to speed so that generative AI does not end providing overall cyber advantage to attackers.

Artificial Intelligence (AI), has become an integral part of today's technological world. With the ability to process and analyze data quickly, AI helps solve complex problems and provides endless benefits to humans. One of the main benefits of Artificial Intelligence is its ability to increase efficiency in various fields. In the industrial sector, Artificial Intelligence can be used to automate production processes, reduce the need for human labor, and increase productivity. In the health sector, Artificial Intelligence can be used to diagnose diseases more accurately and quickly, predict disease risks, and identify the most effective treatments. In the transportation sector, Artificial Intelligence can be used to optimize travel routes, reduce congestion, and improve road safety. Not only in business aspects, Artificial Intelligence also provides benefits in everyday life. The application of Artificial Intelligence in household devices, such as security monitoring and smart home systems, makes life more comfortable and safe. Artificial Intelligence is used in online applications and services, such as virtual assistants, which help users perform various tasks, such as scheduling meetings, searching for information, and providing recommendations based on user preferences. In addition, Artificial Intelligence has also made a significant contribution in the field of research and exploration. For example, in astronomy, Artificial Intelligence is used to analyze data from telescopes and look for patterns or rare events in the universe. In science, Artificial Intelligence is used to develop models and simulations that help understand complex phenomena such as climate change, biological evolution and human intelligence. However, like any other technology, Artificial Intelligence also has challenges and risks that need to be overcome. One of the main challenges is concern about data security and privacy. In using Artificial Intelligence, a lot of data is collected and processed, so it is important to maintain the security and confidentiality of user's personal information. In addition, there is also a risk that Artificial Intelligence may be used for unethical purposes, such as abuse of power or discrimination in decision making. In facing this challenge, it is important to develop appropriate policies and regulations to monitor and control the use of Artificial Intelligence. In addition, education and awareness of Artificial Intelligence technology is also important to prepare society to face changes and optimize the benefits of Artificial Intelligence. In addition to the many things described above, there are also other benefits. Namely Hack Instagram using Artificial Intelligence.

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.

This highly granular synthetic dataset created as an asset for the HDR UK Medicines programme includes information on 680 cancer patients over a period of three years. Includes simulated patient-related data, such as demographics & co-morbidities extracted from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to acute care process (readmissions, survival), primary diagnosis, presenting complaint, physiology readings, blood results (infection, inflammatory markers) and acuity markers such as AVPU Scale, NEWS2 score, imaging reports, prescribed & administered treatments including fluids, blood products, procedures, information on outpatient admissions and survival outcomes following one-year post discharge.

The data was generated using a generative adversarial network model (CTGAN). A flat real data table was created by consolidating essential information from various key relational tables (medications, demographics). A synthetic version of the flat table was generated using a customized script based on the SDV package (N. Patki, 2016), that replicated the real distribution and logic relationships.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. 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 & > 120 ITU bed capacity. 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”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and provide the real-data via application.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

Paper Accept by WACV 2024

[paper, supp] [arXiv]

Overview

Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages.

Description
------------
This dataset has been designed for land cover mapping and building change detection tasks.

File Structure and Content:
---------------------------
1. **1024.zip**:
  - Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.
  - `images` and `ss_mask` folders: Used for the land cover mapping task.
  - `images` folder: Post-event images for building change detection.
  - `small-pre-images`: Images with a minor off-nadir angle difference compared to post-event images.
  - `big-pre-images`: Images with a large off-nadir angle difference compared to post-event images.
  - `cd_mask`: Ground truth for the building change detection task.

2. **512-1.zip**, **512-2.zip**, **512-3.zip**:
  - Contains images of size 512x512 with a GSD of 0.3-0.6m.
  - `images` and `ss_mask` folders: Used for the land cover mapping task.
  - `images` folder: Post-event images for building change detection.
  - `pre-event` folder: Images for the pre-event phase.
  - `cd-mask`: Ground truth for building change detection.

Land Cover Mapping Class Grep Map:
----------------------------------
class_grey = {
  "Bareland": 1,
  "Rangeland": 2,
  "Developed Space": 3,
  "Road": 4,
  "Tree": 5,
  "Water": 6,
  "Agriculture land": 7,
  "Building": 8,
}

Reference

@misc{song2023syntheworld,
title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection},
author={Jian Song and Hongruixuan Chen and Naoto Yokoya},
year={2023},
eprint={2309.01907},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

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.

The Australian Priority Investment Approach to Welfare (PIA) policy initiative was established as part of the 2015-16 Budget, following a comprehensive review of Australia’s welfare system. The initiative uses data analysis to identify groups at risk of long-term welfare dependence. As part of the PIA, in September 2016, the Minister for Social Services announced a plan to allow limited public access to PIA data. \r \r A synthetic version of the PIA data has been created to provide access to administrative data for general users while maintaining the privacy of individuals. The synthetic PIA data relates to individuals who have made a claim for, are receiving or have received payments or services administered under social security law. This includes benefit types such as Aged Pension, Youth Allowance, Newstart and Disability Support Pension. The synthetic data contains a limited number of variables suitable for research, while maintaining the privacy and confidentiality of individuals. The synthetic dataset has been created by applying a privacy-preserving algorithm on the original PIA data. This process results in each person’s true data being modified such that the overall group data very closely represents that of the original dataset, yet no one individual’s data remains in the synthetic dataset. That is, the dataset is a combination of synthetic records that, when combined, reflect the shape of the original dataset. \r \r The synthetic PIA data contains a series of point-in-time quarterly snapshots dated from July 2001 to June 2015. This results in 56 separate quarters of administrative data. Each quarter includes 31 variables (listed below) that are consistent across all quarters. There are approximately 5 million individual records represented in each quarter. \r \r The synthetic PIA data is available via free registration at Dataverse: https://dataverse.ada.edu.au/dataverse/pia_synthetic. \r \r

Snapshot img

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

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform 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 million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

Get a glance at the Data Science Platform Industry report of share of various segments. Request Free Sample

The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request F

Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.

Despite a growing consensus among scholars that substance abuse treatment is effective in reducing offending, strict eligibility rules have limited the impact of current models of therapeutic jurisprudence on public safety. This research effort was aimed at providing policy makers some guidance on whether expanding this model to more drug-involved offenders is cost-beneficial. Since data needed for providing evidence-based analysis of this issue were not readily available, micro-level data from three nationally representative sources were used to construct a 40,320 case synthetic dataset -- defined using population profiles rather than sampled observation -- that was used to estimate the benefits of going to scale in treating drug involved offenders. The principal investigators combined information from the NATIONAL SURVEY ON DRUG USE AND HEALTH, 2003 (ICPSR 4138) and the ARRESTEE DRUG ABUSE MONITORING (ADAM) PROGRAM IN THE UNITED STATES, 2003 (ICPSR 4020) to estimate the likelihood of drug addiction or dependence problems and develop nationally representative prevalence estimates. They used information in the DRUG ABUSE TREATMENT OUTCOME STUDY (DATOS), 1991-1994 (ICPSR 2258) to compute expected crime reducing benefits of treating various types of drug involved offenders under four different treatment modalities. The project computed expected crime reducing benefits that were conditional on treatment modality as well as arrestee attributes and risk of drug dependence or abuse. Moreover, the principal investigators obtained estimates of crime reducing benefits for all crimes as well as select sub-types. Variables include age, race, gender, offense, history of violence, history of treatment, co-occurring alcohol problem, criminal justice system status, geographic location, arrest history, and a total of 134 prevalence and treatment effect estimates and variances.

If Artificial Intelligence (AI) still sounds like science fiction to some, it can already be found in all aspects of life. From automatic cash registers to airport security, AI is now slowly taking over the e-commerce business.

This graph represents the ranking of the the perceived beneficial uses of AI in French online retail businesses in 2019. For over 90 percent, the ability for e-marchants to use Artificial Intelligence (AI) would involve the possibility of aggregating a large amount of data (number of page views, previous purchases, etc.) and to make predictive purchase analysis more efficient and in turn, increase the turnover (82 percent). AI would not just be benefitial for consumers, but with Internet solutions moving at a high pace, new technologies come with cyberthreat opportunities. For France, AI could serve as an opportuniy to ensure cybersecurity within companies.

New technologies in e-commerce

Another implication of Artificial Intelligence for e-commerce is the Visual Search, a technology that involves recognizing a product through a picture. This technology is starting to finds it's place on social media selling platforms. Likewise, with the new Voice Search feature through smartphones or indepedent Voice Assistant, AI helps to better understand the customer's spoken phrases in context, interpret them and redeem the right product from the e-merchant. Furthermore, AI would help set up learning mechanims to enlarge the virtuous circle of shopping personalization, even though around 40 percent of the French population expressed being absolutely not interested in virtual personal assistant systems to buy on the internet (e. g. through Google Home, Alexa from Amazon...). In terms of personalization of the online shopping experience, most of French online shoppers expressed their wishes to make the payment process easier and to stay informed about promotions and offers.

AI downfalls and prediction

AI is seeing a rather high level of trust from the consumers' side. Big companies have jumped on the same wagon, supported through a rise in research investments in the past few years. For the year 2023 the predicted AI investments was to attain 1.3 billion euros.

However, the implementation of AI is not self-evident for many businesses, as they state a lack of technological skills to choose and maintain the right AI solution (75 percent). The high cost of this modern applied science was named another downside to it's implementation.

The general feeling is that AI applied to e-commerce would increase the sites' profits while making the client experience better and the client happier. However, in order to achieve personalized experiences companies need to collect their client's data and how much of the French population would be willing to share their information?

People are epxressing their issues with giving away their personal data and are absolutely sure that big players such as Facebook, Amazon or Google are using that data. Some report that even though they have a problem with data collection, they still agree to it in order to gain personal profit from it. For the data introverts, around 20 percent would even renounce being part of the technology developement, even if it meant they had to pay more.

This article evaluates the effects of two educational policies implemented in the Brazilian state of Ceará. The first was a tax incentive (TI) for mayors to improve municipal education. Under this policy, municipal tax transfers were conditioned on educational achievement. The second was a program to offer educational technical assistance (TA) to municipalities. The impact of these policies was estimated by employing the synthetic control method to create a synthetic Ceará not affected by TI and TA. When the two policies were combined, the results were consistent with a 12 percent increase in Portuguese test scores in primary education and a 6.5 percent increase in lower secondary education. There were similar increases in mathematics test scores; however, these were not statistically significant. This study also investigates the impact of educational interventions on upper secondary schools, which, despite not being directly affected by the new policies, received better-prepared students from lower secondary schools. The findings show no effect on this level of education, highlighting the need for debate on how to extend the benefits of educational policies to upper secondary schools, as well as to other Brazilian states. This research is the first to analyze the impacts of the policies in Ceará on primary, lower secondary, and upper secondary schools using data from 1995 to 2019.

Models for egocentric 3D and 4D reconstruction, including few-shot interpolation and extrapolation settings, can benefit from having images from exocentric viewpoints as supervision signals. No existing dataset provides the necessary mixture of complex, dynamic, and multi-view data. To facilitate the development of 3D and 4D reconstruction methods in the autonomous driving context, we propose a Synthetic Ego--Exo Dynamic 4D (SEED4D) dataset. SEED4D encompasses two large-scale multi-view synthetic urban scene datasets. Our static (3D) dataset encompasses 212k inward- and outward-facing vehicle images from 2k scenes, while our dynamic (4D) dataset contains 16.8M images from 10k trajectories, each sampled at 100 points in time with egocentric images, exocentric images, and LiDAR data. This Kaggle repository contains a subset of those data.

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American AI Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative call for Federal agencies to prioritize AI research and development (R&emp;D) investments, enhance access to high-quality cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards for AI, and provide education and training opportunities to prepare the American workforce for the new era of AI. In support of the American AI Initiative, this National AI R&emp;D Strategic Plan: 2019 Update defines the priority areas for Federal investments in AI R&emp;D. This 2019 update builds upon the first National AI R&emp;D Strategic Plan released in 2016, accounting for new research, technical innovations, and other considerations that have emerged over the past three years. This update has been developed by leading AI researchers and research administrators from across the Federal Government, with input from the broader civil society, including from many of America’s leading academic research institutions, nonprofit organizations, and private sector technology companies. Feedback from these key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also calling for greater attention to making AI trustworthy, to partnering with the private sector, and other imperatives.