This dataset consists of reviews collected from restaurants on a Korean delivery app platform running a review event. A total of 128,668 reviews were collected from 136 restaurants by crawling reviews using the Selenium library in Python. The dataset named as Korean Reviews.csv provides review data not translated to English, and the dataset named as English Reviews.csv provides review data translated to English. The 136 chosen restaurants run review events which demand customers to write reviews with 5 stars and photos. So the annotation of data was done by considering 1) whether the review gives five-star ratings, and 2) whether the review contains photo(s).

Discover the surge in AI PC adoption, forecasted to reach 114 million units this year, and the associated data security challenges.

Secure AI Model Deployment Platforms Market Outlook

According to our latest research, the global Secure AI Model Deployment Platforms market size reached USD 2.85 billion in 2024, driven by the rising adoption of artificial intelligence across regulated industries and the increasing demand for robust security frameworks to protect sensitive models and data. The market is expected to grow at a CAGR of 26.1% during the forecast period, reaching approximately USD 22.64 billion by 2033. The primary growth factor is the heightened awareness of cyber threats targeting AI models, coupled with stringent data privacy regulations pushing enterprises to invest in secure AI deployment solutions.

The growth of the Secure AI Model Deployment Platforms market is being propelled by several key factors. One of the most significant drivers is the exponential increase in AI adoption across industries such as healthcare, finance, and government, where data sensitivity and compliance are paramount. As organizations deploy more AI models in production environments, the potential attack surface expands, making security a top priority. Enterprises are now seeking platforms that offer end-to-end encryption, secure model lifecycle management, and continuous monitoring to safeguard both proprietary models and the data they process. Furthermore, the rise in adversarial attacks, model theft, and data poisoning incidents has underscored the necessity of deploying AI models on platforms with advanced security capabilities. This trend is further amplified by the growing complexity of AI models and the need for secure collaboration among distributed teams.

Another crucial growth factor is the evolving regulatory landscape. Governments and regulatory bodies worldwide are introducing stricter guidelines for AI usage, particularly in sectors dealing with personal or sensitive information. Regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and emerging AI-specific frameworks require organizations to implement robust security controls throughout the AI model lifecycle. Secure AI model deployment platforms help organizations achieve compliance by providing features like audit trails, access control, and automated compliance reporting. As a result, compliance-driven investments are significantly boosting market demand, especially among large enterprises and public sector organizations that face substantial regulatory scrutiny.

The increasing sophistication of cyber threats targeting AI infrastructure is also fueling market growth. Attackers are leveraging advanced techniques to exploit vulnerabilities in AI models, such as model inversion, membership inference, and adversarial attacks, which can lead to data breaches, intellectual property theft, and compromised decision-making. In response, platform vendors are integrating cutting-edge security features, including federated learning, differential privacy, and zero-trust architectures, to mitigate these risks. This ongoing innovation cycle is attracting both established enterprises and innovative startups to invest in secure AI deployment solutions, further accelerating market expansion.

From a regional perspective, North America holds the largest market share due to its mature AI ecosystem, high concentration of technology companies, and proactive regulatory environment. Europe follows closely, driven by stringent data protection laws and strong government initiatives promoting AI security. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, increasing investments in AI research, and the emergence of new regulatory frameworks. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and adoption of secure AI deployment platforms, particularly in financial services and government sectors. Overall, regional dynamics are shaped by a combination of regulatory readiness, technological maturity, and industry-specific adoption patterns.

Component Analysis

The Secure AI Mo

Generative AI Security Market Outlook

According to our latest research, the global Generative AI Security market size stood at USD 1.98 billion in 2024, reflecting robust momentum driven by the rapid integration of generative AI technologies across industries. The market is projected to expand at a CAGR of 28.1% from 2025 to 2033, reaching a forecasted value of USD 17.54 billion by 2033. This exceptional growth is underpinned by the escalating adoption of generative AI tools and the surging need for advanced security solutions to mitigate emerging AI-driven threats. As organizations increasingly leverage generative AI for innovation and automation, the imperative to secure these systems propels the market forward, making generative AI security a critical investment area for enterprises worldwide.

The primary growth driver for the generative AI security market is the exponential increase in the deployment of generative AI models across business processes and digital ecosystems. Organizations are leveraging generative AI for content creation, data analysis, and automation, but these advancements also introduce new vectors for cyber threats, such as data poisoning, model inversion, and adversarial attacks. The sophistication of these threats necessitates equally advanced security frameworks, prompting firms to invest in specialized generative AI security solutions. Moreover, the rising number of high-profile breaches involving AI-generated content and deepfakes has heightened awareness among both enterprises and regulators, further accelerating demand for robust generative AI security platforms.

Another significant factor fueling market growth is the tightening regulatory landscape surrounding AI and data security. Governments and industry bodies across North America, Europe, and Asia Pacific are introducing stringent compliance requirements to safeguard sensitive data processed by AI systems. These regulations mandate organizations to implement advanced security protocols, including real-time monitoring, threat detection, and automated response mechanisms specifically tailored for generative AI environments. Additionally, the growing emphasis on ethical AI usage and transparency compels organizations to adopt security solutions that not only protect data but also ensure the integrity and accountability of AI-generated outputs. This regulatory pressure, combined with increasing consumer expectations for privacy and trust, is a key catalyst for sustained market expansion.

The proliferation of cloud-based generative AI solutions is also reshaping the security landscape, creating both opportunities and challenges for market stakeholders. Cloud deployments offer scalability and flexibility, enabling organizations to rapidly experiment with and deploy generative AI models. However, this shift also exposes enterprises to new security risks, including multi-tenant vulnerabilities, data leakage, and unauthorized access to AI models and training data. As a result, there is a surge in demand for cloud-native generative AI security solutions that can provide end-to-end protection across distributed environments. Vendors are responding with innovations in secure model deployment, encryption, and access control, driving the evolution of the market and reinforcing the need for specialized expertise in generative AI security.

Regionally, North America continues to dominate the generative AI security market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States leads in both adoption and innovation, supported by a mature technology ecosystem and proactive regulatory initiatives. Europe is witnessing rapid growth due to the enforcement of GDPR and AI Act regulations, while Asia Pacific is emerging as a high-growth region driven by digital transformation initiatives in China, Japan, and India. Each region presents unique opportunities and challenges, with local market dynamics, regulatory frameworks, and industry verticals shaping the trajectory of generative AI security adoption.

Component Analysis

The generative AI security market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall security architecture. The software segment dominates the market, accounting for the highest revenue share in 2024, as organizations prioritize investment in advanced security platforms, threat detection tools, and AI-driven analytics. These software so

样本描述.csv 文件中描述了 样本编号、负荷水平、故障线路、故障首末端位置、故障持续时间、稳定性、训练测试集、4.2.1投毒样本和4.2.3投毒样本 文件夹Samples中为原始数据集 文件夹Samples_Poisoning中为4.2.4小节中相应毒化样本数据 所有样本文件格式为Matlab生成的mat格式。 1）原始数据集中样本的设置描述 新英格兰39母线系统包括39条母线、19台负载、9台发电机和一个理想电压源。在每条母线上均部署相量测量单元(PMU)以监控动态响应过程。通过蒙特卡洛方法共生成2000组样本来模拟不同的运行模式和故障场景，设置负载变化范围为初始值的80%～120%，4%递增。任何线路首末端的20%～80%线路都可能发生三相短路故障，故障持续时间在0.1到0.35秒之间。每个样本的标签是系统是否稳定，通过仿真得到。在生成样本中随机选取1800组样本进行训练，200组样本进行测试。 2）4.2.1节中随机选取的10%的样本构成的投毒数据集样本序号列表 6, 8, 21, 23, 35, 42, 45, 63, 110, 111, 139, 148, 161, 163, 168, 193, 199, 212, 213, 227, 228, 231, 232, 237, 242, 247, 252, 262, 273, 290, 296, 299, 318, 326, 350, 354, 370, 377, 387, 412, 474, 479, 480, 501, 510, 516, 535, 538, 540, 593, 599, 602, 603, 607, 618, 624, 627, 634, 641, 642, 646, 647, 650, 659, 662, 701, 712, 716, 727, 735, 766, 772, 805, 809, 813, 814, 817, 823, 832, 841, 843, 846, 861, 870, 874, 898, 904, 906, 910, 938, 943, 951, 954, 955, 962, 973, 974, 979, 980, 1000, 1001, 1002, 1003, 1026, 1028, 1029, 1040, 1069, 1103, 1104, 1107, 1108, 1156, 1173, 1176, 1178, 1181, 1186, 1189, 1198, 1200, 1232, 1233, 1236, 1241, 1244, 1255, 1258, 1271, 1304, 1306, 1310, 1315, 1333, 1336, 1354, 1357, 1360, 1383, 1393, 1422, 1441, 1443, 1471, 1484, 1485, 1486, 1496, 1504, 1506, 1508, 1518, 1529, 1537, 1538, 1543, 1545, 1565, 1566, 1577, 1587, 1595, 1655, 1658, 1661, 1664, 1667, 1703, 1705, 1710, 1718, 1723, 1728, 1737, 1742, 1743, 1749, 1750, 1768, 1789 3）4.2.3节仿真中，随机选取10%样本的一半样本（修改G1、G2、G3）对应的样本序号列表，以及另一半样本（修改G4、G5、G9）对应的样本序号列表。 修改G1、G2、G3对应的样本序号列表： 6, 8, 21, 23, 35, 42, 45, 63, 110, 111, 139, 148, 161, 163, 168, 193, 199, 212, 213, 227, 228, 231, 232, 237, 242, 247, 252, 262, 273, 290, 296, 299, 318, 326, 350, 354, 370, 377, 387, 412, 474, 479, 480, 501, 510, 516, 535, 538, 540, 593, 599, 602, 603, 607, 618, 624, 627, 634, 641, 642, 646, 647, 650, 659, 662, 701, 712, 716, 727, 735, 766, 772, 805, 809, 813, 814, 817, 823, 832, 841, 843, 846, 861, 870, 874, 898, 904, 906, 910, 938 修改G4、G5、G9对应的样本序号列表： 943, 951, 954, 955, 962, 973, 974, 979, 980, 1000, 1001, 1002, 1003, 1026, 1028, 1029, 1040, 1069, 1103, 1104, 1107, 1108, 1156, 1173, 1176, 1178, 1181, 1186, 1189, 1198, 1200, 1232, 1233, 1236, 1241, 1244, 1255, 1258, 1271, 1304, 1306, 1310, 1315, 1333, 1336, 1354, 1357, 1360, 1383, 1393, 1422, 1441, 1443, 1471, 1484, 1485, 1486, 1496, 1504, 1506, 1508, 1518, 1529, 1537, 1538, 1543, 1545, 1565, 1566, 1577, 1587, 1595, 1655, 1658, 1661, 1664, 1667, 1703, 1705, 1710, 1718, 1723, 1728, 1737, 1742, 1743, 1749, 1750, 1768, 1789

Analysis of ‘NCHS - Drug Poisoning Mortality by County: United States’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3452f1d5-5a52-4f78-8ff8-02a7f7bff7fc on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geog

--- Original source retains full ownership of the source dataset ---

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

Analysis of ‘NCHS - Drug Poisoning Mortality by State: United States’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e469e38a-aa81-4bf9-9218-7fbed56cb5a5 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning from 1999 to 2015.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

--- Original source retains full ownership of the source dataset ---

Fish collection data for surveys made at 5 localities in Flat Creek, Dawson County GA, on 6 dates that spanned a 4 to 18.5-month period following a catastrophic fish kill caused by a chemical spill near the headwater origin on Flat Creek. The data are: locality descriptions, water depth and velocity measurements made during fish sampling, and tabulated numbers and range in body length of individuals observed, by collection date and locality.

As of January 1, 2009, Connecticut law mandates that medical providers must conduct annual lead screening (i.e., blood lead testing) for each child 9 to 35 months of age. Furthermore, the law requires that any child between 36-72 months of age who has not been previously tested must also be tested by the child’s medical provider, regardless of risk.

This dataset includes the 10-year prevalence in Connecticut's top five cities.

MLOps Security Market Outlook

According to our latest research, the global MLOps Security market size reached USD 1.47 billion in 2024, demonstrating robust momentum driven by the rapid adoption of machine learning operations across various industries. The market is projected to expand at a CAGR of 20.8% from 2025 to 2033, reaching an estimated USD 8.93 billion by 2033. This significant growth is underpinned by the increasing complexity of AI and ML models, the proliferation of sensitive data in automated workflows, and a heightened focus on regulatory compliance and risk mitigation in AI deployments. As per our comprehensive market analysis, organizations are increasingly prioritizing security across the entire ML lifecycle, which is fueling investments in advanced MLOps security solutions globally.

A primary growth factor for the MLOps Security market is the exponential rise in the deployment of machine learning models in production environments. As enterprises leverage ML for business-critical applications, the potential attack surface expands, making security a top concern. Cyber threats targeting ML pipelines, such as model poisoning, adversarial attacks, and data leakage, have become more sophisticated, prompting organizations to adopt robust MLOps security frameworks. The need for secure model management, continuous monitoring, and automated threat detection within ML workflows is driving the demand for integrated security solutions that can ensure the integrity and confidentiality of both data and models throughout their lifecycle.

Another key driver is the tightening regulatory landscape around data privacy and AI ethics. Governments and industry bodies across the globe are introducing stringent guidelines for the secure handling of personal and sensitive information, especially in sectors like BFSI, healthcare, and retail. Compliance with regulations such as GDPR, HIPAA, and CCPA necessitates the implementation of advanced security controls within MLOps pipelines. This regulatory pressure is prompting organizations to invest in comprehensive MLOps security solutions that offer features like automated compliance management, audit trails, and real-time risk assessment, thereby ensuring adherence to legal and ethical standards while enabling seamless AI innovation.

Furthermore, the accelerating digital transformation initiatives and the increasing adoption of cloud-native technologies are amplifying the complexity of managing and securing ML workflows. As organizations migrate their ML operations to hybrid and multi-cloud environments, the need for scalable, cloud-agnostic MLOps security solutions becomes paramount. The integration of AI-driven security tools, such as automated anomaly detection and zero-trust architectures, is becoming essential to safeguard distributed ML assets. This trend is particularly pronounced among large enterprises and tech-forward SMEs, who recognize the strategic value of securing their AI investments to maintain competitive advantage and customer trust.

Regionally, North America remains the dominant force in the MLOps Security market, accounting for the largest share in 2024, closely followed by Europe and Asia Pacific. The United States, in particular, is leading due to its advanced AI ecosystem, high cybersecurity awareness, and significant investments in AI governance. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, government AI initiatives, and the burgeoning fintech and healthcare sectors. Europe’s strong regulatory environment and focus on ethical AI are also contributing to robust market expansion. Latin America and the Middle East & Africa are gradually catching up, propelled by increasing digital adoption and the emergence of local AI startups, although their market shares remain comparatively smaller.

Component Analysis

The Component segment of the MLOps Security market is bifurcated into Software and Services. Software solutions form the backbone of MLOps security, encompassing platforms and tools designed for model monitoring, access management, data encryption, and vulnerability detection. These solutions are witnessing high demand due to their ability to automate complex security tasks and integrate seamlessly with existing ML pipelines. The rapid evolution of ML algorithms and the dynamic nature of threat landscapes require continuous updates and innovation in security

This report examines the co-diagnosis of anoxic brain injury, that is, a diagnosis received for anoxic brain injury during the same hospital stay for the opioid poisoning.

A highly granular dataset of 11,267 deliberate self-poisoning admissions curated by PIONEER. The data includes demography, diagnostic codes (ICD-10 & SNOMED-CT), presenting symptoms, procedures (OPCS4 & SNOMED-CT, prescriptions, referrals, follow-ups, and outcomes. The current dataset includes admissions from 03-12-2015 to 30-12-2023 but can be expanded to assess other timelines of interest. This dataset provides a clear understanding of self-poisoning in relation to different patient characteristics. The data also provides the type of drug taken (narcotic, non-opiod etc) and where (home, street, industrial areas etc) . Furthermore, assessments can be made on the impact of mental health service referrals and the likelihood of readmission with a subsequent overdose.

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 can build synthetic data to meet bespoke requirements.

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.

1) Data introduction • “Fruit (Plant Species) Data” provides image data for fruits from a total of 121 species, including 60 species of Donguibogam poisonous plants and 61 species of plants similar to poisonous plants.

2) Data utilization (1)Fruit (Plant Species) data has characteristics that: • When acquiring image data, this is three-dimensional image data of poisonous plants based on leaves, flowers, and fruits, which are the main criteria for determining the plant's growth, and the outposts that can determine the plant's growth. • Through an advisory meeting of plant taxonomists and oriental medicine experts, it was selected as a plant with a high frequency of poisoning accidents and a similar plant that can be easily encountered in everyday life. (2) Fruit (Plant Species) data can be used to: • Poisoning and deaths that occur every year from consuming poisonous plants mistaken for medicinal herbs can be prevented. • Using this, we can provide an artificial intelligence poisonous weed identification service.

1) Data introduction • “Flower (Plant Species) Data” provides image data for flowers from a total of 121 species, including 60 species of Donguibogam poisonous plants and 61 species of plants similar to poisonous plants.

2) Data utilization (1)Flower (Plant Species) data has characteristics that: • When acquiring image data, this is three-dimensional image data of poisonous plants based on leaves, flowers, and fruits, which are the main criteria for determining the plant's growth, and the outposts that can determine the plant's growth. • Through an advisory meeting of plant taxonomists and oriental medicine experts, it was selected as a plant with a high frequency of poisoning accidents and a similar plant that can be easily encountered in everyday life. (2) Flower (Plant Species) data can be used to: • Poisoning and deaths that occur every year from consuming poisonous plants mistaken for medicinal herbs can be prevented. • Using this, we can provide an artificial intelligence poisonous weed identification service.

Overall counts and rates (per 100,000 population) of notifiable diseases reported in Nova Scotia.

Analysis of ‘RAPTOR annual report incidents data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/a5af8839-2b03-465d-9ad6-e7ba7798ccc3 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

The Department of Housing, Local Government and Heritage publishes annual RAPTOR (Recording and Addressing Persecution and Threats to Our Raptors) reports on threats to birds of prey. This csv dataset for download here represents the tabular data that is core to those reports. It provides details of recorded incidents of human related injury and mortality in Irish birds of prey, as well as any incidents of poisoned bait or poisoning of any wildlife.

This dataset should be viewed in conjunction with its associated 2015 report which is also referenced here for download. The dataset and report is prepared by the National Parks and Wildlife Service (NPWS) in collaboration with the Regional Veterinary Labs of the Department of Agriculture, Food and the Marine, and the State Laboratory. The report is the product of a joint Departmental initiative to investigate bird of prey deaths in Ireland. The dataset enables an appraisal of black spots, associated land-use types, methods of persecution, motives behind the persecution and the times of year at which such incidents peak. 2015 saw the largest annual number of incidents since recording began systematically in 2011. In total, 35 poison and persecution incidents were confirmed.

Poisoning falls into two general categories: accidental poisoning through the use of poison against rats and mice which then accumulates in birds that eat them, most notably red kites and barn owls; and deliberate laying of poison. The victims of poisoning and persecution since 2007 include Red Kite, Common Buzzard, Peregrine Falcon, Golden Eagle, White-tailed Sea Eagle, Sparrowhawk, Kestrel, Hen Harrier, Barn Owl and Short-eared Owl. More than a hundred other birds such as crows and pigeons were also found to have been poisoned.

--- Original source retains full ownership of the source dataset ---

Injury from poisoning exists under several Injury Intents: unintentional (accidental), intentional self-harm, assault, undetermined and adverse effect and underdosing. Only injuries in the first four categories are reported here combined. The data show rates per 100,000 people in order to standardize between areas with different population levels. Except for age specific rates, we use age-adjusted rates because they take into account where one age group dominates a population and thus are more representative. We use diagnosis by hospital records for non-fatal injury and cause of death from death certificates for fatal injury information.

Analysis of ‘Performance Metrics - Public Health - Lead Inspections Performed’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/50a159cf-c50a-41ee-abc9-0a28ed45a905 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

To combat lead poisoning through the Childhood Lead Poisoning Prevention and Healthy Homes Program, the Chicago Department of Public Health (CDPH) works to enforce city and state laws regarding lead paint by dispatching building inspectors to identify lead hazards in homes and apartments and hold property owners accountable for making repairs required by law. This metric tracks the total number of initial lead inspections CDPH performs per month. The performance goal is 100 inspections per month. For more information about CDPH’s lead poisoning prevention programs, or to report elevated blood lead levels, see the Environmental Health Division at http://www.cityofchicago.org/city/en/depts/cdph/provdrs/environ_health.html

--- Original source retains full ownership of the source dataset ---