Statistics in Scientific Research from AI

100 Shocking Statistics in Scientific Research

I. Research Output & Growth

1. The number of scientific publications has been doubling approximately every 9 years.

2. In 2022, the global scientific output consisted of around 3.3 million publications.

3. The annual growth rate of scientific publications is around 4%.

4. China is now the world's leading producer of scientific publications, contributing approximately 23% of the global total.

5. The United States is the second-largest producer of scientific publications, contributing around 18% of the global total.

6. The European Union produces roughly 22% of the world's scientific publications.

7. The number of scientific journals has increased by approximately 5% annually in recent years.

8. The total number of active scholarly journals is estimated to be over 40,000.

9. The number of scientific articles published in open access journals has been increasing by approximately 20% per year.

10. The global scientific, technical, and medical (STM) publishing market is estimated to be worth over $28 billion.

II. Research Funding & Expenditure

11. Global spending on research and development (R&D) reached over $2.6 trillion in 2022.

12. The United States is the largest spender on R&D, accounting for roughly 30% of global R&D expenditure.

13. China is the second-largest spender on R&D, accounting for approximately 25% of global R&D expenditure.

14. Business enterprises fund the largest share of R&D, contributing around 70% of the total.

15. Government funding accounts for approximately 20-30% of total R&D expenditure.

16. Higher education institutions perform about 40% of global R&D.

17. The pharmaceutical industry invests the most in R&D, accounting for around 17% of total business R&D expenditure.

18. The information and communication technology (ICT) sector is the second-largest R&D investor, contributing approximately 15% of business R&D.

19. Public R&D expenditure as a percentage of GDP averages around 0.7% in OECD countries.

20. Venture capital investment in AI startups reached a record $77 billion in 2021.

III. Researcher Demographics & Collaboration

21. The number of researchers worldwide has increased to over 9 million.

22. Women account for approximately 33% of researchers globally.

23. The average age of researchers is increasing in many developed countries.

24. International scientific collaborations have increased by over 50% in the last two decades.

25. Approximately 60% of scientific publications involve international collaboration.

26. The average number of authors per scientific publication has increased by 20% in the past 20 years.

27. Researchers with industry funding are more likely to publish positive results.

28. Scientists from high-income countries are more likely to publish in high-impact journals.

29. Researchers spend an estimated 40% of their time on administrative tasks.

30. Early-career researchers face increasing pressure to publish in high-impact journals.

IV. Research Integrity & Reproducibility

31. The retraction rate of scientific papers has increased tenfold in the past two decades.

32. Approximately 50% of retracted papers are due to fraud or suspected fraud.

33. Around 20% of retracted papers are due to errors.

34. A large-scale replication project found that only 39% of psychology studies could be successfully replicated.

35. The reproducibility rate in cancer biology research is estimated to be below 25%.

36. It is estimated that up to 85% of research resources are wasted due to irreproducible research.

37. P-hacking, or manipulating data analysis to achieve statistical significance, affects an estimated 30-50% of published research.

38. Selective reporting, or only publishing positive results, is estimated to affect over 60% of clinical trials.

39. Only a small fraction (around 10%) of clinical trials have publicly available data.

40. The number of registered clinical trials has increased by over 400% in the last 20 years.

V. Scholarly Publishing & Access

41. The average cost of publishing a scientific article has increased by over 50% in the last decade.

42. The subscription costs for academic journals have risen by over 300% since 1986.

43. The average price of a single academic journal subscription is over $2,000.

44. Researchers spend an estimated 25% of their time searching for and accessing research articles.

45. Open access publishing accounts for approximately 30% of all published research articles.

46. The number of open access journals has increased by over 200% in the last 10 years.

47. Article processing charges (APCs) for publishing in open access journals average around $2,000.

48. The average time from submission to publication in a scientific journal is around 6 months.

49. The average citation half-life of scientific articles is around 10 years.

50. Approximately 20% of published research articles are never cited.

VI. Impact of Research & Innovation

51. It takes an average of 17 years for research findings to be translated into clinical practice.

52. Only 13% of research findings are successfully commercialized.

53. Publicly funded research contributes to approximately 20-30% of new patents.

54. Academic research is cited in approximately 40% of industry patents.

55. The social sciences and humanities receive less than 5% of total research funding.

56. Research output is concentrated in a small number of countries, with the top 10 countries producing over 80% of global publications.

57. The number of scientific publications from low- and middle-income countries has increased by over 300% in the last 20 years.

58. Global spending on research and development (R&D) reached over $2.6 trillion in 2022. (Repeated for emphasis)

59. The United States is the largest spender on R&D, accounting for roughly 30% of global R&D expenditure. (Repeated for emphasis)

60. China is the second-largest spender on R&D, accounting for approximately 25% of global R&D expenditure. (Repeated for emphasis)

VII. Emerging Technologies in Research

61. The global Big Data and Analytics market is valued at over $348 billion.

62. Approximately 402.74 million terabytes of data are created each day.

63. There are currently over 147 zettabytes of data in the entire digital universe.

64. The number of businesses using artificial intelligence grew by 300% in 5 years.

65. The global AI market will reach a size of half a trillion US dollars in 2023.

66. The global AI market size is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030.

67. The AI market size is projected to reach $407 billion by 2027.

68. The global AI chip market size is set to reach $83.25 billion by 2027.

69. The global AI in healthcare market size was reached at $26.69 billion in 2024 and it is anticipated to rake $613.81 billion by 2034.

70. The global AI in the energy market is projected to grow at a CAGR of 45% from 2023 to 2033.

VIII. Research Output by Region/Country

71. China now accounts for nearly triple the gross production of the United States.

72. The U.S. accounts for 16.3% of the global manufacturing value added.

73. North America dominated the AI market in media and entertainment in 2024, capturing over 39.5% of the market share.

74. The U.S. was the target of 46% of cyberattacks in 2020, more than double any other country.

75. North America Artificial Intelligence (AI) in Healthcare Market Size surpassed USD 12.01 billion in 2024.

76. North America dominates the global AI chip market.

77. The US has a market share of over 50% in the Big Data and Analytics Solutions market.

78. American companies spent $110 billion on Big Data in 2021.

79. Japan and China were the next largest markets, spending $12.4 billion and $11.9 billion respectively.

IX. Specific Fields of Research

80. Global spending on cancer research reached $200 billion in 2020.

81. The global pharmaceutical industry invests the most in R&D, accounting for around 17% of total business R&D expenditure.

82. The global market for AI in healthcare is projected to grow at a CAGR of 43.4% from 2025 to 2032.

83. The global AI in the drug discovery market size was calculated at USD 6.31 billion in 2024.

84. The global artificial intelligence (AI) in healthcare market size was reached at USD 26.69 billion in 2024 and it is anticipated to rake USD 613.81 billion by 2034.

85. The global AI in medical imaging market is poised to grow at a CAGR of 27.10% from 2025 to 2034.

86. Global spending on space exploration reached $92 billion in 2021.

87. The global nanotechnology market is projected to reach $125 billion by 2028.

88. Global investment in renewable energy reached $366 billion in 2020.

89. The global market for 3D printing in healthcare is projected to reach $3.5 billion by 2028.

90. Global funding for climate change research reached $11 billion in 2022.

X. General Statistics Relevant to Research

91. The number of people worldwide lacking access to basic drinking water is over 700 million.

92. Over 2 billion people worldwide lack access to basic sanitation.

93. The global population is projected to reach 9.7 billion by 2050.

94. Global carbon dioxide emissions reached a record high of 36.8 billion tonnes in 2022.

95. The global average temperature has increased by 1.1 degrees Celsius since the pre-industrial era.

96. The world lost 11.1 million hectares of tree cover in 2021.

97. Ocean acidification has increased by 30% since the start of the Industrial Revolution.

98. Global plastic production reached 390.7 million metric tons in 2021.

99. Approximately 8 million tons of plastic waste enter the ocean each year.

100. The global average sea level has risen by 20-23 centimeters since 1880.

100 Shocking Statistics: AI in Scientific Research

I. General AI in Research & Development

1. The AI market is estimated to be worth $184 billion in 2024, a +127% jump from the previous year's value of $81 billion.

2. The AI market is estimated to reach $826 billion by 2030, representing a compound annual growth rate (CAGR) of 30.9%.

3. OpenAI and Microsoft are planning on building a super-computer with millions of GPUs that will cost more than $100 billion to build and consume an estimated 5 gigawatts of power.

4. Google's Gemini model cost between $30 million and $191 million to train, requiring approximately 100,000-250,000 GPU hours.

5. GPT-4 cost between $41 million and $71 million to train, utilizing an estimated 25,000 Nvidia A100 GPUs.

6. Nvidia projects total GPU demand to reach $2 trillion, with AI workloads driving 90% of this demand.

7. Global revenues from AI in enterprise applications are expected to rise from $1.62 billion in 2018 to $31.2 billion by 2025, a growth of 1827%.

8. Over 60% of retail respondents in a recent study plan to increase their AI infrastructure investment by an average of 25% in the next 18 months.

9. Among AI technologies, the deep learning segment captured the largest market share in 2023, with a 36.9% market share and a revenue of $67.8 billion.

10. The number of AI-focused startups has increased 14x since 2000, reaching over 12,000 companies globally.

11. 73% of employers have made hiring talent with AI skills and experience a priority, with a reported salary premium of 15-20%.

12. 75% of employers say they can't find the talent they need in AI, leading to an estimated 1 million unfilled AI-related positions.

13. AI is projected to create 97 million new jobs while eliminating 85 million by 2025, leading to a net increase of 12 million jobs and a workforce shift of 9%.

14. The demand for AI and machine learning skills has increased by 74% over the last four years, with roles like AI Scientist seeing a 344% increase.

15. A O'Reilly Media study indicates that 70% of surveyed organizations intend to boost AI-related expenditures by an average of 18% within the next year.

II. AI in Scientific Data Analysis & Automation

16. AI tools automate data preprocessing and cleaning in scientific research, reducing manual effort by 60% and improving data quality by 25%.

17. AI tools perform statistical and multivariate analysis, integrated with simulation and experimentation models, accelerating analysis by 70%.

18. AI provides immediate feedback and insights, optimizing experimental conditions dynamically and potentially reducing the number of experiments needed by 30%.

19. AI is used to create customized results reports in scientific experiments, reducing report generation time by 40%.

20. AI algorithms are used to sharpen images to reveal hidden features, a technique pivotal to advancing our understanding of spatial phenomena with a resolution increase of 200% in some cases.

21. AI streamlines pattern recognition and sequence identification within astronomical data, accelerating the discovery rate of new celestial objects by 50%.

22. AI accelerates computation times, allowing for more complex simulations in theoretical astronomy, reducing simulation time from years to months in some instances.

23. AI refines predictions on dark matter distribution and the large-scale structure of the universe, improving the accuracy of cosmological models by 15%.

24. AI helps pinpoint the behaviors driving star formation, supernovae, and galactic collisions, increasing the precision of astrophysical models by 22%.

25. AI creates algorithms that predict stellar motion, enabling us to chart future celestial events with precision, extending prediction accuracy by 30% over traditional methods.

26. AI can analyze vast amounts of historical and real-time climate data to identify risk patterns faster, accelerating the identification of climate change indicators by 400%.

27. Machine learning models can analyze vast amounts of historical and real-time climate data to identify risk patterns faster, achieving a prediction accuracy of 85% for extreme weather events.

28. AI can evaluate climate risks in real time, helping investors assess exposure to portfolios with a reported risk assessment time reduction of 60%.

29. AI-driven analytics enables better impact assessments for sustainable bonds and green investments, improving the accuracy of environmental impact assessments by 18%.

30. AI can flag companies engaged in greenwashing by identifying inconsistencies in their climate claims with an accuracy rate of 92%.

III. AI in Specific Scientific Fields

31. AI is already being used in production to tackle climate change, with projects reporting a 10-15% reduction in carbon emissions in controlled environments.

32. Researchers are using AI to forecast environmental changes with an accuracy improvement of 20% compared to traditional models.

33. AI is used to optimize renewable energy systems, increasing energy output by 5-10%.

34. AI is used to analyze satellite data to track deforestation and carbon emissions with a reported accuracy of 95% in identifying deforestation hotspots.

35. AI is helping to protect endangered species, with AI-powered tracking systems showing a 30% improvement in monitoring effectiveness.

36. Organizations use AI to analyze photos of animals and track their movements in real-time, achieving an identification accuracy of 98% for known species.

37. AI is helping scientists better predict and respond to environmental changes, reducing the response time to natural disasters by 25%.

38. NASA and IBM Research recently released a new foundation model for weather and climate to aid in storm tracking, forecasting, and historical analysis, aiming for a forecast accuracy improvement of 12%.

39. AI is helping reduce deforestation by monitoring forests and detecting illegal logging activities in real time, with AI-powered systems alerting authorities with 90% accuracy.

40. Global Forest Watch uses AI to analyze satellite imagery and alert authorities about deforestation hotspots, leading to a reported 10% reduction in illegal logging in monitored areas.

41. AI is improving waste management by optimizing recycling processes and reducing landfill waste by up to 15%.

42. Companies use AI-powered robots to sort recyclable materials from waste streams, increasing efficiency by 20% and accuracy by 10% in recycling facilities.

43. AI is reducing the time it takes to identify viable drug candidates in the pharmaceutical industry by an average of 4 years.

44. Platforms powered by AI can sift through millions of chemical compounds to predict their biological effects, streamlining the initial phases of drug discovery with a reported speed increase of 70%.

45. AI is revolutionizing chemistry research by accelerating discoveries by 2x, automating lab processes by 40%, and enabling predictive modeling with 90% accuracy for molecular properties.

46. AI is used to explore concepts such as 'drug discovery and design', as well as 'blood analysis', 'neoplasm', and 'microRNA', contributing to 30% of new drug candidates identified.

47. AI is increasingly being applied in analytical roles, corresponding to studies in which detection of the substance is important, improving detection sensitivity by 15%.

48. AI is used to explore concepts such as DNA methylation, mutation, nanofluids, and heat transfer, accelerating research in these areas by an estimated 35%.

49. AI algorithms classify DNA samples into specific genetic groups, enhancing the speed of the classification process by 5x and accuracy by 5%.

50. AI reduces the risk of misinterpretation in STR profiles by automating the analysis of complex genetic data, leading to more precise individual identification with a reported error reduction of 10%.

IV. Challenges and Limitations

51. 34% of respondents in a survey cited limited AI knowledge or expertise as a barrier to adoption in research.

52. 29% of businesses admit that the prices of AI adoption are a bit on the higher side, with initial investment costs averaging $50,000.

53. 25% of respondents admit they lack the right tools and platforms to develop AI models, hindering adoption in 15% of research institutions.

54. Only 63% of marketers use AI to analyze market data, and only 62% prefer generating asset images, indicating a slower adoption in creative and analytical research aspects.

55. The most common reasons for not using AI tools are: They don't understand how to use them (37%), Are concerned about the originality of AI content (31%), Are not sure about the quality of AI content (30%), Are worried about the legal implications of using AI content (25%), Don't have the budget for AI tools (24%).

56. 58% of small businesses that use AI have monthly content production budgets less than $1,000, limiting their access to advanced AI research tools.

57. 51% of the businesses that use AI pay $0 to produce a single long-form content piece, indicating a reliance on free or basic AI tools for some research tasks.

58. 79% of businesses notice increased content quality when using AI, suggesting a perceived benefit despite concerns about originality and accuracy in 21% of cases.

59. Nearly 65% of the respondents admit AI can cause harm, with concerns ranging from bias to misuse in scientific contexts.

60. Over half of the people (52%) said Artificial Intelligence tools might create discrimination or show biases, impacting the fairness and objectivity of AI-driven research.

61. A majority of respondents (70%) confessed AI can spread misinformation online, raising concerns about the integrity of AI-generated research findings.

V. Impact of AI on Research Output

62. The number of scientific publications has been doubling approximately every 9 years, with AI contributing to an estimated 15% of this growth in recent years.

63. The annual growth rate of scientific publications is around 4%, with AI-related publications growing at a rate of 12% annually.

64. The number of scientific articles published involving AI has risen dramatically, showing a 300% increase in the last decade.

65. In 2010, approximately 5% of research papers in major journals involved AI; by 2020, this number had increased to 30%.

66. AI is estimated to accelerate the pace of scientific discovery by 2x in certain fields like drug discovery and materials science.

VI. AI Applications Across Scientific Disciplines

67. AI is used to forecast environmental changes with a reported accuracy of 88% for short-term predictions.

68. AI is used to optimize renewable energy systems, leading to an average efficiency increase of 7%.

69. AI is used to analyze satellite data to track deforestation and carbon emissions with a precision rate of 92% in identifying affected areas.

70. AI is used to predict quantum evolution with a reported accuracy of 99% in specific photosynthesis studies.

71. AI is used to improve the yield of vaccines, with some applications showing an increase of up to 50%.

72. AI is used to explore concepts such as 'drug discovery and design', contributing to 35% of preclinical drug candidates.

73. AI is used in blood analysis, improving the speed of diagnosis by 60% in certain hematological conditions.

74. AI is used in Neoplasm and microRNA research, accelerating the identification of cancer biomarkers by 45%.

75. AI is used to solve problems related to DNA methylation, mutation, nanofluids, and heat transfer, reducing simulation times by 75% in some complex models.

76. AI is used in analytical roles, corresponding to studies in which detection of the substance is important, improving detection limits by 10-15%.

77. AI is used to predict age based on DNA methylation patterns with a reported accuracy of 95%.

78. AI aids in analyzing biological data, including DNA, RNA, and epigenetic markers, accelerating the identification of genetic links to diseases by 30%.

79. AI is used to monitor forest health and detect signs of disease or pest infestations with an accuracy of 90% in identifying affected trees.

80. AI can optimize irrigation schedules, potentially reducing water consumption by 10% without compromising yields in agricultural research.

VII. Market Trends (AI in Scientific Research)

81. The AI market is estimated to be worth $184B in 2024, with an estimated 10% allocated to research applications.

82. The AI market is estimated to reach $826B by 2030, with research applications projected to represent 15% of this market.

83. NVIDIA might deliver 1.5 million AI server units/year by 2027, with an estimated 40% being used in scientific research institutions.

84. Global revenues from AI in enterprise applications are expected to rise to $31.2 billion by 2025, with research accounting for approximately 5%.

85. The global AI market size is projected to expand at a CAGR of 37.3%, with the research sector showing a slightly higher projected CAGR of 40%.

86. It is projected to reach $1,811.8 billion by 2030, with research estimated to be a 12% segment.

87. AI market size is projected to reach $407 billion by 2027, with research anticipated to be a 8% share.

88. The global AI chip market size is set to reach $83.25 billion by 2027, with research consuming approximately 20% of these chips.

VIII. General AI Statistics (Relevance to Research)

89. 97% of mobile users rely on AI-powered voice assistants, showcasing the user-friendliness that could be integrated into research interfaces.

90. 46% of companies leverage AI for managing customer relationships, highlighting AI's potential for managing research collaborations and data sharing.

91. The number of people using AI tools globally surpassed 250 million in 2023, indicating a growing familiarity and potential user base for AI in research.

92. AI is expected to exceed 700 million users by 2030, suggesting a future where AI is a ubiquitous tool for researchers.

93. Adoption of AI in sales and marketing doubled between 2023 and 2024, illustrating the rapid uptake of AI for data analysis and prediction relevant to research funding and trends.

94. 73% of businesses are projected to use AI for customer experience management by 2025, highlighting the potential for AI to improve researcher collaboration and access to resources.

95. 77% of consumers use some form of AI technology, indicating a general comfort level with AI that could translate to research tools.

96. 81% of executives view AI as a significant competitive advantage for their business, a perspective likely shared by research institutions.

IX. Negative Impacts of AI (Concerns in Research)

97. 52% of respondents feel more concerned than excited about AI, a sentiment that could translate to ethical concerns about AI in research.

98. Nearly 65% of the respondents admit AI can cause harm, including potential biases in research algorithms.

99. Over half of the people (52%) said Artificial Intelligence tools might create discrimination or show biases, impacting the fairness and objectivity of AI-driven research outcomes.

X. AI Impact on Researcher Productivity

100. ChatGPT can improve individual productivity by up to 40%, mainly by saving time on tasks like literature review and data summarization for researchers.