Success.ai’s Beauty & Cosmetics Data for Cosmetics, Beauty & Wellness Professionals Worldwide delivers a powerful dataset tailored to connect businesses with key stakeholders in the global beauty and wellness industries. Covering professionals such as product developers, brand managers, wellness coaches, and salon owners, this dataset provides verified work emails, phone numbers, and actionable professional insights.

With access to over 700 million verified global profiles and detailed insights from 170 million professional datasets, Success.ai ensures your outreach, marketing, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is ideal for businesses aiming to lead in the competitive beauty and wellness market.

Why Choose Success.ai’s Beauty & Cosmetics Data?

1. Verified Contact Data for Effective Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in cosmetics, skincare, beauty services, and wellness industries.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and improving communication efficiency.

2. Comprehensive Global Coverage

   • Includes profiles of beauty and wellness professionals from regions such as North America, Europe, Asia-Pacific, and emerging markets.
   • Gain insights into global trends in cosmetics innovation, wellness services, and beauty product demand.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, professional roles, and market developments.
   • Stay aligned with the fast-paced nature of the beauty and wellness industry to identify opportunities and maintain relevance.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data for all business initiatives.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the beauty, cosmetics, and wellness industries worldwide.
• 170M+ Professional Datasets: Access verified contact information and detailed insights into industry leaders and innovators.
• Business Insights: Understand market trends, product innovations, and consumer preferences driving the beauty industry.
• Decision-Maker Contacts: Engage with CEOs, brand managers, product developers, and wellness leaders driving growth and innovation.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key players, including beauty brand executives, salon owners, skincare experts, and wellness influencers.
   • Access data on career histories, certifications, and industry expertise to target the right professionals effectively.

2. Advanced Filters for Precision Targeting

   • Filter professionals by industry focus (cosmetics, wellness, skincare), geographic location, or job function.
   • Tailor campaigns to align with specific market segments, such as luxury cosmetics, wellness services, or mass-market beauty products.

3. Global Trend Insights and Market Data

   • Leverage data on emerging beauty trends, wellness innovations, and skincare demands across regions.
   • Refine product development, marketing campaigns, and customer engagement strategies based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with beauty and wellness professionals.

Strategic Use Cases:

1. Marketing and Brand Outreach

   • Design targeted campaigns to promote beauty products, wellness services, or skincare innovations to industry professionals.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct engagement.

2. Product Development and Innovation

   • Utilize market insights to guide product development and align offerings with consumer demands in cosmetics, beauty, and wellness sectors.
   • Collaborate with product developers and brand managers to refine product lines or launch new offerings.

3. Sales and Partnership Development

   • Build relationships with wellness professionals, salon owners, and beauty distributors seeking innovative tools or products.
   • Present co-branding opportunities, supply chain partnerships, or new market expansion strategies to key decision-makers.

4. Market Research and Competitive Analysis

   • Analyze beauty and wellness trends, consumer preferences, and emerging niches to refine business strategies.
   • Benchmark against competitors to identify gaps, growth opportunities, and high-demand product categories.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality beauty and wellness data at competitive prices, ensuring strong ROI for your marketing, sales, and produc...

Health Check Software Market Outlook

The global Health Check Software market size is projected to experience a robust growth with a Compound Annual Growth Rate (CAGR) of 12.5% from 2024 to 2032. The market size was valued at approximately USD 1.2 billion in 2023 and is anticipated to reach around USD 3.2 billion by 2032. Key growth factors driving this market include the increasing emphasis on preventative healthcare, advancements in digital technology, and the rising demand for efficient health management solutions.

A significant growth factor for the Health Check Software market is the increasing global focus on preventative healthcare. Governments and healthcare providers are recognizing the benefits of early detection and intervention, which not only improve patient outcomes but also reduce healthcare costs in the long run. Health check software solutions enable continuous monitoring and early diagnosis of diseases, which is crucial in managing chronic conditions and preventing severe health complications.

Advancements in digital technology and artificial intelligence are also accelerating the growth of the Health Check Software market. Developments in AI and machine learning algorithms have enhanced the capabilities of health check software, making it possible to provide more accurate and personalized health assessments. These technologies enable the analysis of large datasets to identify patterns and predict potential health risks, thereby offering proactive healthcare solutions.

The rising demand for efficient health management solutions among corporate enterprises is another key driver of market growth. Many organizations are investing in health check software to monitor and improve the health and wellness of their employees. This not only helps in reducing absenteeism and boosting productivity but also demonstrates the companyÂ’s commitment to employee well-being, which can enhance corporate reputation and employee satisfaction.

The integration of Healthcare Compliance Software into the health check ecosystem is becoming increasingly vital as regulatory requirements continue to evolve. This type of software ensures that healthcare providers adhere to the necessary legal and ethical standards, safeguarding patient data and maintaining the integrity of healthcare services. By automating compliance processes, healthcare organizations can focus more on patient care while minimizing the risk of legal issues. Furthermore, Healthcare Compliance Software helps in streamlining audits and reporting, making it easier for organizations to demonstrate their adherence to regulations. As the healthcare landscape becomes more complex, the role of compliance software in ensuring smooth operations cannot be overstated.

Regionally, North America is expected to dominate the Health Check Software market during the forecast period. The regionÂ’s growth can be attributed to the presence of advanced healthcare infrastructure, high adoption of digital health technologies, and a strong emphasis on preventative healthcare. Additionally, supportive government policies and significant investments in healthcare IT are further propelling the market growth in North America.

Component Analysis

The Health Check Software market is segmented by components into software and services. The software segment is the primary driver of market growth, driven by the increasing adoption of digital health solutions. Health check software includes various applications that facilitate the monitoring, diagnosing, and management of health conditions. These applications are designed to integrate with existing healthcare systems, making it easier for healthcare providers and patients to access and utilize health data efficiently.

The services segment, which includes implementation, training, and maintenance services, is also crucial for the market. As more organizations and healthcare providers adopt health check software, the demand for services that ensure smooth implementation and operation of these software solutions is rising. Maintenance services are particularly important to ensure that the software is up-to-date and functioning correctly, preventing any disruptions in health monitoring and management processes.

The integration of advanced technologies such as AI and machine learning in health check software is also enhancing the capabilities of these solutions. AI-driven health

The AI Training Dataset In Healthcare Market size was valued at USD 341.8 million in 2023 and is projected to reach USD 1464.13 million by 2032, exhibiting a CAGR of 23.1 % during the forecasts period. The growth is attributed to the rising adoption of AI in healthcare, increasing demand for accurate and reliable training datasets, government initiatives to promote AI in healthcare, and technological advancements in data collection and annotation. These factors are contributing to the expansion of the AI Training Dataset In Healthcare Market. Healthcare AI training data sets are vital for building effective algorithms, and enhancing patient care and diagnosis in the industry. These datasets include large volumes of Electronic Health Records, images such as X-ray and MRI scans, and genomics data which are thoroughly labeled. They help the AI systems to identify trends, forecast and even help in developing unique approaches to treating the disease. However, patient privacy and ethical use of a patient’s information is of the utmost importance, thus requiring high levels of anonymization and compliance with laws such as HIPAA. Ongoing expansion and variety of datasets are crucial to address existing bias and improve the efficiency of AI for different populations and diseases to provide safer solutions for global people’s health.

This dataset is about book subjects. It has 3 rows and is filtered where the books is The global market for pig health and nutrition products. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Mental Health in Tech Survey

Understanding Employee Mental Health Needs in the Tech Industry

By Stephen Myers [source]

About this dataset

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. Mental health is an issue that affects all people of all ages, genders and walks of life. The prevalence of these issues within the tech industry–one that places hard demands on those who work in it–is no exception. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector.–and what kinds of resources they rely upon to find help–so that more can be done to create a healthier working environment for all.

This dataset tracks key measures such as age, gender and country to determine overall prevalence, along with responses surrounding employee access to care options; whether mental health or physical illness are being taken as seriously by employers; whether or not anonymity is protected with regards to seeking help; and how coworkers may perceive those struggling with mental illness issues such as depression or anxiety. With an ever-evolving landscape due to new technology advancing faster than ever before – these statistics have never been more important for us to analyze if we hope remain true promoters of a healthy world inside and outside our office walls

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How to use the dataset

In this dataset you will find data on age, gender, country, and state of survey respondents in addition to numerous questions that assess an individual's mental state including: self-employment status, family history of mental illness, treatment status and access or lack thereof; how their mental health condition affects their work; number of employees at the company they work for; remote work status; tech company status; benefit information from employers such as mental health benefits and wellness program availability; anonymity protection if seeking treatment resources for substance abuse or mental health issues ; ease (or difficulty) for medical leave for a mental health condition ; whether discussing physical or medical matters with employers have negative consequences. You will also find comments from survey participants.

To use this dataset effectively: - Clean the data by removing invalid responses/duplicates/missing values - you can do this with basic Pandas commands like .dropna() , .drop_duplicates(), .replace(). - Utilize descriptive statistics such as mean and median to draw general conclusions about patterns of responses - you can do this with Pandas tools such as .groupby() and .describe(). - Run various types analyses such as mean comparisons on different kinds of variables(age vs gender), correlations between different features etc using appropriate statistical methods - use commands like Statsmodels' OLS models (.smf) , calculate z-scores , run hypothesis tests etc depending on what analysis is needed. Make sure you are aware any underlying assumptions your analysis requires beforehand !
- Visualize your results with plotting libraries like Matplotlib/Seaborn to easily interpret these findings! Use boxplots/histograms/heatmaps where appropriate depending on your question !

Research Ideas

• Using the results of this survey, you could develop targeted outreach campaigns directed at underrepresented groups that answer “No” to questions about their employers providing resources for mental health or discussing it as part of wellness programs.
• Analyzing the employee characteristics (e.g., age and gender) of those who reported negative consequences from discussing their mental health in the workplace could inform employer policies to support individuals with mental health conditions and reduce stigma and discrimination in the workplace.
• Correlating responses to questions about remote work, leave policies, and anonymity with whether or not individuals have sought treatment for a mental health condition may provide insight into which types of workplace resources are most beneficial for supporting employees dealing with these issues

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redi...

Description The Post-Pandemic Remote Work Health Impact 2025 dataset presents a comprehensive, global snapshot of how remote, hybrid, and onsite work arrangements are influencing the mental and physical health of employees in the post-pandemic era. Collected in June 2025, this dataset aggregates responses from a diverse workforce spanning continents, industries, age groups, and job roles. It is designed to support research, data analysis, and policy-making around the evolving landscape of work and well-being.

This dataset enables in-depth exploration of:

• The prevalence of mental health conditions (e.g., anxiety, burnout, PTSD, depression) across different work setups.
• The relationship between work arrangements and physical health complaints (e.g., back pain, eye strain, neck pain).
• Variations in work-life balance, social isolation, and burnout levels segmented by demographic and occupational factors.
• Salary distributions and their correlation with health outcomes and job roles.

By providing granular, anonymized data on both subjective (self-reported) and objective (hours worked, salary range) factors, this resource empowers data scientists, health researchers, HR professionals, and business leaders to:

• Identify risk factors and protective factors for employee well-being. Benchmark health impacts across industries and regions.
• Inform organizational policy and future-of-work strategies.

| Column Name Description Example Values | | | Survey_Date Date when the survey response was submitted (YYYY-MM-DD) 2025-06-01 Age Age of the respondent (in years) 27, 52, 40 Gender Gender identity of the respondent Female, Male, Non-binary, Prefer not to say Region Geographical region of employment Asia, Europe, North America, Africa, Oceania Industry Industry sector of the respondent Technology, Manufacturing, Finance, Healthcare Job_Role Specific job title or function Data Analyst, HR Manager, Software Engineer Work_Arrangement Primary work mode Onsite, Remote, Hybrid Hours_Per_Week Average number of hours worked per week 36, 55, 64 Mental_Health_Status Primary self-reported mental health condition Anxiety, Burnout, Depression, None, PTSD Burnout_Level Self-assessed burnout (categorical: Low, Medium, High) High, Medium, Low Work_Life_Balance_Score Self-rated work-life balance on a scale of 1 (poor) to 5 (excellent) 1, 3, 5 Physical_Health_Issues Self-reported physical health complaints (semicolon-separated if multiple) Back Pain; Eye Strain; Neck Pain; None Social_Isolation_Score Self-rated social isolation on a scale of 1 (none) to 5 (severe) 1, 2, 5 Salary_Range Annual salary range in USD $40K-60K, $80K-100K, $120K+ | --- | | | |

CompanyData.com, (BoldData), is your gateway to verified global business intelligence. Our Healthcare Company Database provides in-depth, accurate data on 2.5 million organizations across the healthcare industry—from hospitals and clinics to pharmaceutical companies, biotech firms, and medical equipment suppliers. Every record is sourced from official trade registers and healthcare authorities, ensuring regulatory compliance and unmatched data quality.

We deliver comprehensive company profiles enriched with key firmographics, industry classifications, ownership structures, executive contact details, emails, direct phone numbers, and mobile data. Updated regularly and quality-checked against official sources, our healthcare data empowers organizations to make informed decisions across critical functions—from KYC verification and compliance to targeted sales campaigns, healthcare market analysis, CRM enrichment, and AI model development.

To suit every workflow, we offer flexible delivery solutions including custom bulk files, self-service platform access, real-time API integrations, and on-demand enrichment services. Whether you're scaling a B2B marketing strategy or building healthcare analytics tools, our datasets are ready to plug into your operations.

With coverage of over 380 million verified companies across all industries and regions, CompanyData.com (BoldData) offers the global reach and industry precision that modern organizations demand. Tap into our healthcare data solutions to discover new opportunities, reduce risk, and power smarter business growth across the global health economy.

Big Data Analytics In Healthcare Market size is estimated at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

Dataset for review of global mental health research in the construction industry

Healthcare Cloud Based Analytics Market Outlook

The global healthcare cloud based analytics market size was valued at approximately USD 14.8 billion in 2023, and it is anticipated to reach around USD 54.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.7% from 2024 to 2032. One of the primary growth factors influencing this market is the increasing demand for data-driven decision-making processes in healthcare settings to enhance patient outcomes and operational efficiency.

One significant growth factor for the healthcare cloud based analytics market is the rapid digital transformation within the healthcare sector. The transition from paper-based systems to electronic health records (EHRs) and the adoption of telehealth services are driving the need for sophisticated analytics solutions that can process vast amounts of healthcare data. The accessibility and scalability offered by cloud-based solutions make them particularly attractive for healthcare providers looking to leverage patient data for better diagnostic and treatment outcomes.

Moreover, the rising focus on personalized medicine and the need for population health management are propelling the demand for healthcare cloud based analytics. Personalized medicine requires the analysis of large datasets to understand individual patient profiles and predict responses to treatments. Similarly, population health management aims to improve health outcomes by analyzing data to identify trends and intervene proactively. Cloud-based analytics platforms provide the necessary computational power and flexibility to handle these complex data requirements efficiently.

The cost-efficiency of cloud based solutions compared to traditional on-premises systems is another crucial growth driver. Healthcare organizations are under constant pressure to reduce operational costs while improving patient care quality. Cloud-based analytics solutions eliminate the need for significant upfront investments in hardware and software while offering the benefits of scalable resources and reduced IT maintenance costs. This financial advantage is particularly appealing to small and medium-sized healthcare providers who may have limited budgets for technology investments.

The integration of Business Intelligence in Healthcare is transforming the way data is utilized to improve patient care and streamline operations. By employing BI tools, healthcare organizations can analyze vast datasets to uncover insights that drive better decision-making. These tools enable healthcare providers to track patient outcomes, optimize resource allocation, and enhance overall operational efficiency. The ability to visualize data through dashboards and reports allows for a deeper understanding of patient trends and organizational performance, ultimately leading to improved healthcare delivery and patient satisfaction.

From a regional perspective, North America currently holds the largest market share in the healthcare cloud based analytics market, driven by advanced healthcare infrastructure and high adoption rates of digital healthcare technologies. However, regions like Asia Pacific are expected to witness the highest growth rates during the forecast period. Factors such as increasing healthcare expenditures, growing awareness about the benefits of healthcare analytics, and supportive government initiatives are contributing to the market expansion in these regions.

Component Analysis

The healthcare cloud based analytics market can be segmented by component into software and services. The software segment includes various analytics platforms and tools designed to process and analyze healthcare data. These software solutions are essential for enabling healthcare providers to harness the power of big data and derive actionable insights. As the volume of healthcare data continues to grow exponentially, the demand for robust and scalable analytics software solutions is expected to increase significantly. Innovations in artificial intelligence and machine learning are also enhancing the capabilities of these software solutions, making them more effective in predictive analytics and decision support.

Cloud Computing in Healthcare is revolutionizing the way healthcare data is stored, accessed, and analyzed. By leveraging cloud technology, healthcar

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs) and the rising need for efficient health information management (HIM) systems. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors: the increasing digitization of healthcare, the growing demand for data-driven insights to improve patient care and operational efficiency, and the expanding adoption of cloud-based solutions offering scalability and accessibility. Pharmaceutical companies and academic/research institutions are significant drivers, leveraging these systems for drug discovery, clinical trials management, and advanced research initiatives. However, challenges such as data security concerns, high implementation costs, and the need for robust interoperability between different systems pose restraints to market growth. The market is segmented by software type (EHR, HIM) and application (pharmaceutical companies, academic institutions, others), providing diverse opportunities for specialized vendors. Geographic expansion continues, with North America and Europe currently holding significant market share, but growth is anticipated across Asia-Pacific and other regions as healthcare infrastructure modernizes. The competitive landscape is dynamic, with established players like NextGen Healthcare and emerging companies like Pabau and EHR Your Way vying for market share. The success of individual vendors depends on factors including the scalability of their solutions, the depth of their data analytics capabilities, and the strength of their customer support network. The market's trajectory is heavily influenced by government regulations regarding data privacy and interoperability, the ongoing evolution of healthcare technology, and the increasing focus on personalized medicine. Further growth is likely to be seen in areas such as AI-powered diagnostics, predictive analytics, and advanced data visualization tools integrated within medical databases.

United States Health Insurance: Premium Per Member Per Month data was reported at 364.000 USD in Sep 2024. This stayed constant from the previous number of 364.000 USD for Jun 2024. United States Health Insurance: Premium Per Member Per Month data is updated quarterly, averaging 262.000 USD from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 364.000 USD in Sep 2024 and a record low of 178.000 USD in Sep 2013. United States Health Insurance: Premium Per Member Per Month data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

Definition and descriptive statistics of variables, n = 8227.

Overview :

This dataset contains detailed information on a wide variety of vegetables from different regions across the world. Each entry includes data on the vegetable's category, color, seasonality, origin, nutritional value, pricing, availability, shelf life, storage requirements, growing conditions, health benefits, and common varieties. The dataset is structured to facilitate research and data analysis, offering insights into agricultural trends, nutritional science, and market dynamics. Ideal for use in academic research, market analysis, and agricultural studies.

Vegetable dataset Columns Details :

1. Vegetable ID: Unique identifier for each vegetable entry.
2. Name: Common name of the vegetable (e.g., Carrot, Broccoli).
3. Scientific Name: Scientific or botanical name of the vegetable.
4. Category: Type of vegetable (e.g., Root, Leafy, Fruit, Tubers).
5. Color: Color of the vegetable (e.g., Orange, Green).
6. Season: Season(s) when the vegetable is typically harvested (e.g., Spring, Summer).
7. Origin: Geographic origin or region where the vegetable is commonly grown.
8. Nutritional Value: Key nutritional information (e.g., calories, vitamins, minerals per 100g).
9. Price: Average market price per unit or weight.
10. Availability: Availability status (e.g., Year-round, Seasonal).
11. Shelf Life: Average shelf life in days.
12. Storage Requirements: Specific storage conditions (e.g., Refrigeration, Dry, Cool place).
13. Growing Conditions: Ideal growing conditions (e.g., Soil type, Water requirements, Sunlight).
14. Health Benefits: Notable health benefits or uses.
15. Common Varieties: Different varieties or types of vegetables.

Global In-memory database market is expected to revenue of around USD 36.21 billion by 2032, growing at a CAGR of 19.2% between 2024 and 2032.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2031, growing at a CAGR of 13.92% during the forecast period 2024-2031.

Global Real World Evidence Solutions Market Drivers

The market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:

Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations. Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE. Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions. Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records. Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development. Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences. Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

United States Health Insurance: Enrollment data was reported at 271.000 USD mn in Sep 2024. This records an increase from the previous number of 269.000 USD mn for Jun 2024. United States Health Insurance: Enrollment data is updated quarterly, averaging 225.000 USD mn from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 278.000 USD mn in Jun 2023 and a record low of 174.000 USD mn in Jun 2012. United States Health Insurance: Enrollment data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

The U.S. market for medical devices is the largest in the world. At an estimated $83 billion in 2006, this market represents nearly half the global total and is growing at 6 percent annually ? about double the rate of U.S. GDP. With the advent of microprocessors, miniaturization of electronic circuits, wired and wireless digital networking, and new materials and manufacturing processes, older generations of mechanical and analog electromechanical devices used in patient diagnosis, monitoring, and treatment have largely been replaced by devices and systems based on information technologies across the diverse array of contemporary medical devices...