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Top 100 Journal Publications In The World: Global Rankings

Discover the pinnacle of academic publishing with our definitive list of the Top 100 Journal Publications in the World.

This carefully curated selection spans a wide array of disciplines, offering insights into the journals that lead in innovation, research impact, and scholarly influence.

Based on authoritative metrics and citation data from Google Scholar, this guide serves as an invaluable resource you to contribute to and stay informed on the cutting edge of their fields.

Top 100 Journal Publications In The World

How to determine a journal’s rankings.

Determining the value of a journal in the dense forest of academic publishing can be quite the task, especially with the myriad of scientific disciplines vying for your attention.

Impact Factor

A good starting point is to look at the journal’s impact factor.

This metric, often used as an indicator of a journal’s prestige and influence, reflects the average number of citations received by articles published in the journal. 

High-impact journals like those in the “Journal of the American Academy of Sciences” or “Springer’s Environmental Research” often boast impressive impact factors, signaling their importance in their respective fields.

Databases – Google Scholar, Scopus

Another critical aspect to consider is the journal’s ranking within international databases like Google Scholar or Scopus.

These platforms aggregate data on academic journals and provide rankings based on various metrics, including citation counts and impact factors.

A journal listed in the top 100 according to Google Scholar rank, for instance, is likely to be a high-quality publication respected by scholars worldwide. Google uses the H5-index score to rank journals.

It’s essential to dive into these rankings and understand the criteria behind them, as they offer insights into a journal’s reputation and the impact of its research output.

Top 100 Journal Publications In The World (Google Scholar)

Google Scholar, a widely used resource for academic literature, provides a comprehensive ranking of journals based on metrics such as citation counts and impact factors. Here, we explore the top 100 journal publications in the world according to the latest Google Scholar rankings (at the time of writing). We also look into their h5-index scores, fields, and a brief overview of each.

• Nature:  Score: 467 – Field: Multidisciplinary Nature is a flagship journal renowned for publishing some of the most pioneering research across all scientific disciplines. Its high score reflects its broad influence and the groundbreaking nature of its articles.
• The New England Journal of Medicine (NEJM): Score: 439 – Field: Medicine NEJM is considered one of the most prestigious medical journals, known for its rigorous peer review process and for publishing high-impact research that often shapes global health policies and practices.
• Science: Score: 424 – Field: Multidisciplinary As a peer-reviewed academic journal, Science is celebrated for its highly cited papers across diverse scientific fields, reflecting its role in advancing knowledge and fostering scientific discourse.
• IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Score: 422 – Field: Computer Science CVPR is a highly regarded conference in the field of computer vision, with its proceedings being pivotal for researchers and professionals looking to stay abreast of the latest developments in the field.
• The Lancet: Score: 368 – Field: Medicine The Lancet’s long-standing reputation for publishing high-quality medical research makes it a go-to source for clinicians and researchers alike, covering a wide array of medical disciplines.
• Nature Communications: Score: 349 – Field: Multidisciplinary This open-access journal is known for facilitating the exchange of ideas and findings across all areas of science, ensuring broad visibility of research through its accessible format.
• Advanced Materials: Score: 326 – Field: Materials Science With a focus on cutting-edge research in materials science, Advanced Materials is instrumental for those exploring the design and applications of new materials.
• Cell: Score: 316 – Field: Biology Cell’s high impact factor is testament to its role in publishing significant discoveries in biology, particularly those that advance understanding of cell structure and function.
• Neural Information Processing Systems (NeurIPS): Score: 309 – Field: Computer Science As a premier conference, NeurIPS is at the forefront of new advancements in machine learning and artificial intelligence, showcasing high-quality research that pushes the boundaries of the field.
• International Conference on Learning Representations (ICLR): Score: 303 – Field: Computer Science ICLR has rapidly become a key venue for the dissemination of cutting-edge research in deep learning and neural networks, reflecting the dynamic nature of the field.
• JAMA (Journal of the American Medical Association): Score: 286 – Field: Medicine JAMA is recognized for its influential research articles that span the spectrum of medicine, aiming to inform clinical practice and health policy.
• Science of The Total Environment: Score: 273 – Field: Environmental Science This journal provides an interdisciplinary platform for research on the environment, tackling issues from pollution and contamination to sustainable practices.
• Nature Medicine: Score: 268 – Field: Biomedical Science Focused on groundbreaking research in health and disease, Nature Medicine is a key resource for understanding the biomedical implications of scientific discoveries.
• Proceedings of the National Academy of Sciences (PNAS): Score: 268 – Field: Multidisciplinary PNAS publishes high-quality research across the physical, biological, and social sciences, making it a cornerstone of the scientific community.
• Angewandte Chemie International Edition: Score: 266 – Field: Chemistry Known for its high-quality papers in all areas of chemistry, this journal is essential for chemists seeking comprehensive insights into the field’s latest developments.
• Chemical Reviews: Score: 264 – Field: Chemistry This journal stands out for its in-depth review articles that synthesize current knowledge and trends in chemistry, serving as invaluable resources for researchers and educators alike.
• International Conference on Machine Learning (ICML): Score: 254 – Field: Computer Science ICML is a leading conference focusing on the latest in machine learning research, offering insights into theoretical foundations and practical applications.
• Chemical Society Reviews: Score: 248 – Field: Chemistry With its focus on review articles, this journal provides a critical overview of the major developments in the chemical sciences, guiding researchers through the vast landscape of the field.
• Journal of Cleaner Production: Score: 246 – Field: Environmental Science & Technology This journal addresses the environmental and sustainability challenges in production and consumption, promoting research that leads to cleaner and more efficient practices.
• Nucleic Acids Research: Score: 238 – Field: Molecular Biology Specializing in DNA and RNA research, this journal is pivotal for those in the field of genetics and molecular biology, publishing high-impact studies on the structure, function, and applications of nucleic acids.

For journals from position 21-100, we list them down below:

• European Conference on Computer Vision : Score: 238 – Field: Computer Vision
• Advanced Energy Materials : Score: 236 – Field: Energy Materials
• Journal of the American Chemical Society : Score: 235 – Field: Chemistry
• IEEE Access : Score: 233 – Field: Multidisciplinary Engineering
• Advanced Functional Materials : Score: 230 – Field: Materials Science
• IEEE/CVF International Conference on Computer Vision : Score: 228 – Field: Computer Vision
• Renewable and Sustainable Energy Reviews : Score: 226 – Field: Energy & Environment
• ACS Nano : Score: 220 – Field: Nanoscience
• BMJ : Score: 218 – Field: Medicine
• Physical Review Letters : Score: 216 – Field: Physics
• International Journal of Molecular Sciences : Score: 215 – Field: Molecular Sciences
• Journal of Clinical Oncology : Score: 213 – Field: Oncology
• AAAI Conference on Artificial Intelligence : Score: 212 – Field: Artificial Intelligence
• Science Advances : Score: 212 – Field: Multidisciplinary Science
• PLoS ONE : Score: 212 – Field: Multidisciplinary Science
• Frontiers in Immunology : Score: 212 – Field: Immunology
• Scientific Reports : Score: 210 – Field: Multidisciplinary Science
• Circulation : Score: 206 – Field: Cardiology
• Chemical Engineering Journal : Score: 206 – Field: Chemical Engineering
• Energy & Environmental Science : Score: 205 – Field: Environmental Science
• Applied Catalysis B: Environmental : Score: 205 – Field: Environmental Science
• International Journal of Environmental Research and Public Health : Score: 201 – Field: Public Health
• The Lancet Oncology : Score: 198 – Field: Oncology
• Journal of the American College of Cardiology : Score: 193 – Field: Cardiology
• Meeting of the Association for Computational Linguistics (ACL) : Score: 192 – Field: Computational Linguistics
• Nutrients : Score: 189 – Field: Nutrition & Dietetics
• Nature Genetics : Score: 188 – Field: Genetics
• Morbidity and Mortality Weekly Report : Score: 186 – Field: Public Health
• Applied Energy : Score: 186 – Field: Energy
• Nature Biotechnology : Score: 185 – Field: Biotechnology
• Sustainability : Score: 185 – Field: Sustainability
• Nano Energy : Score: 184 – Field: Energy
• Joule : Score: 183 – Field: Energy
• Journal of Materials Chemistry A : Score: 183 – Field: Materials Chemistry
• Nature Materials : Score: 180 – Field: Materials Science
• IEEE Transactions on Pattern Analysis and Machine Intelligence : Score: 179 – Field: Computer Science
• ACS Applied Materials & Interfaces : Score: 179 – Field: Materials Science
• Nature Energy : Score: 177 – Field: Energy
• ACS Catalysis : Score: 177 – Field: Catalysis
• The Lancet Infectious Diseases : Score: 176 – Field: Infectious Diseases
• Conference on Empirical Methods in Natural Language Processing (EMNLP) : Score: 176 – Field: Natural Language Processing
• Journal of Business Research : Score: 173 – Field: Business
• Gastroenterology : Score: 172 – Field: Gastroenterology
• European Heart Journal : Score: 171 – Field: Cardiology
• IEEE Internet of Things Journal : Score: 171 – Field: Internet of Things
• Nature Nanotechnology : Score: 170 – Field: Nanotechnology
• Environmental Pollution : Score: 170 – Field: Environmental Science
• The Astrophysical Journal : Score: 169 – Field: Astrophysics
• Environmental Science & Technology : Score: 169 – Field: Environmental Science
• Frontiers in Psychology : Score: 169 – Field: Psychology
• Immunity : Score: 168 – Field: Immunology
• Sensors : Score: 168 – Field: Sensors & Electronics
• Annals of Oncology : Score: 166 – Field: Oncology
• ACS Energy Letters : Score: 166 – Field: Energy
• Journal of Hazardous Materials : Score: 166 – Field: Environmental Science
• IEEE Communications Surveys & Tutorials : Score: 164 – Field: Telecommunications
• Nature Neuroscience : Score: 164 – Field: Neuroscience
• Gut : Score: 164 – Field: Gastroenterology
• Molecular Cancer : Score: 164 – Field: Cancer Research
• Molecules : Score: 164 – Field: Chemistry
• Small : Score: 164 – Field: Nanoscience
• Clinical Infectious Diseases : Score: 163 – Field: Infectious Diseases
• Nature Methods : Score: 163 – Field: Methods in Life Sciences
• Accounts of Chemical Research : Score: 163 – Field: Chemistry
• IEEE Transactions on Industrial Informatics : Score: 162 – Field: Industrial Informatics
• Physical Review D : Score: 161 – Field: Physics
• Bioresource Technology : Score: 161 – Field: Bioenergy and Bioproducts
• American Economic Review : Score: 160 – Field: Economics
• Cell Metabolism : Score: 160 – Field: Metabolism
• Monthly Notices of the Royal Astronomical Society : Score: 160 – Field: Astronomy
• Chemosphere : Score: 160 – Field: Environmental Science
• Blood : Score: 158 – Field: Hematology
• Cell Reports : Score: 158 – Field: Cell Biology
• Nano Letters : Score: 158 – Field: Nanoscience
• Advanced Science : Score: 158 – Field: Multidisciplinary Science
• Journal of High Energy Physics : Score: 158 – Field: High Energy Physics
• Nature Reviews Immunology : Score: 157 – Field: Immunology
• Technological Forecasting and Social Change : Score: 157 – Field: Social Sciences & Technology
• Frontiers in Microbiology : Score: 155 – Field: Microbiology
• Water Research : Score: 155 – Field: Water Science & Engineering

Prestigious Academic Journals By Ranking and Citation

This list of Top 100 Journal Publications represent the zenith of scholarly communication, embodying excellence across diverse scientific and academic fields.

This list not only highlights the journals that set the benchmark for quality and impact but also serves as a beacon for researchers aiming for the highest standards in their work.

As the landscape of academic publishing continues to evolve, these publications will undoubtedly play a pivotal role in shaping the future of research and knowledge dissemination.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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Experts call for global genetic warning system to combat the next pandemic and antimicrobial resistance

Scientists champion global genomic surveillance using latest technologies and a “One Health” approach to protect against novel pathogens like avian influenza and antimicrobial resistance, catching epidemics before they start.

Safeguarding peer review to ensure quality at scale

Making scientific research open has never been more important. But for research to be trusted, it must be of the highest quality. Facing an industry-wide rise in fraudulent science, Frontiers has increased its focus on safeguarding quality.

Scientists call for urgent action to prevent immune-mediated illnesses caused by climate change and biodiversity loss

Climate change, pollution, and collapsing biodiversity are damaging our immune systems, but improving the environment offers effective and fast-acting protection.

Baby sharks prefer being closer to shore, show scientists

marine scientists have shown for the first time that juvenile great white sharks select warm and shallow waters to aggregate within one kilometer from the shore.

Puzzling link between depression and cardiovascular disease explained at last

It’s long been known that depression and cardiovascular disease are somehow related, though exactly how remained a puzzle. Now, researchers have identified a ‘gene module’ which is part of the developmental program of both diseases.

Air pollution could increase the risk of neurological disorders: Here are five Frontiers articles you won’t want to miss this Earth Day

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Opening health for all: 7 Research Topics shaping a healthier world

We have picked 7 Research Topics that tackle some of the world's toughest healthcare challenges. These topics champion everyone's access to healthcare, life-limiting illness as a public health challenge, and the ethical challenges in digital public health.

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Find top journals in a research field: a step-by-step guide

Joanna Wilkinson

This blog about how to find top journals is part of our Research Smarter series. Download our cheat sheet, which brings together top tips for finding relevant journals, papers, and authors in your field, sign up for our webinar on the same topic or read the related blog posts for each, here .

Finding relevant journals can be a challenging task for researchers both new and experienced. The myriad tools and technologies can be difficult to navigate. This is frustrating when many databases vary in the reliability, relevancy and organization of their data. Beyond that, the rise of open access, preprint repositories and transparent peer review (to name a few) have had a transformative impact on academic journals in recent years. It has reshaped the way journals are shared, used and valued by scholars across all disciplines.

Web of Science™ delivers unique journal insight at any given time. The comprehensive and complete citation network provides all the data you need to understand a journal’s value and impact. Web of Science Core Collection™ titles are all carefully selected for quality. Furthermore, the tools and features we outline in this guide help you quickly and accurately assess them for your own research.

Journal Citation Reports™: Discover quality research journals

Journal Citation Reports  (JCR) is the most powerful product for journal intelligence. It provides researchers with a definitive list and guide to discover and select the most appropriate journals to read and publish findings. JCR delivers a rich array of publisher-independent data, metrics and analysis throughout tens of thousands of journal profile pages. The research community powers these pages via meaningful citation connections.

Researchers and organizations use this comprehensive and trusted subscription-based product to confidently find and assess journals. Head over to JCR now or watch the video below to learn more.

“Journal Citation Reports is the most powerful product for journal intelligence.”

Master Journal List: Quickly assess top journals

Master Journal List (MJL) is a free and trusted resource that supports researchers to find and compare reputable journals, books and conference proceedings indexed in Web of Science. MJL is not as comprehensive as Journal Citation Reports. However, its profile pages help researchers assess the quality of individual journals at a glance. Each journal page lists its publication details and Web of Science coverage and journal metrics. It also as lists open access and peer review characteristics. A range of filters are available to sort and group results, and you can also take advantage of EndNote’s free Manuscript Matcher tool to find the best match for your manuscript. Watch the video below to learn more.

“Master Journal List profile pages help researchers assess the quality of individual journals at a glance.”

Web of Science: Explore influential and emerging journals

The Analyze Results feature in Web of Science identifies the journals publishing the greatest number of papers on any given topic. It helps you dig deep into your specific subject area and identify any trends. Furthermore, it reveals all the influential and emerging journals that can be further analyzed using the products above. The Analyze Results feature can also find trends among the top institutions, funding agencies and authors publishing research. Learn more about Analyze Results here,  in the video below, or visit  the Web of Science search page to try this out today.

Stay connected

Interested in discovering more tips to really understand your research field? You can d ownload our cheat sheet , which brings together top tips for finding relevant journals, papers and authors in your field, or sign up for our webinar on the same topic. You can also read the related blog posts in our Research Smarter series, here .

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Top 100 Journal Publications in the World 2024

Highest impact factor journals in the world

The top scientific journals are a reliable source of information on the latest developments in engineering, science, and technology. The editors and peer-reviewers of the journal ensure that the published articles are original, accurate, and reliable. Every research scholar, academician, and scientist aspires to publish their research findings in the top journal publishers namely Science Magazine, Nature, The New England Journal of Medicine, and Physical Review Letters. In this article, iLovePhD compiled the most influential journals in the world related to medical, engineering, and natural sciences.

iLovePhD compiled the top 100 scientific journals of 2024 based on Google Scholar citations. Key areas include medical, engineering, and natural sciences. Prestigious journals like Nature, NEJM, and Science dominate due to their impact, quality, and interdisciplinary reach. Conferences are also prominent for their field-specific influence and innovation. This independent list is a guide for researchers and academicians aiming to publish impactful work.

List of Top 100 Journals Google Scholar Ranking

Google Scholar is a widely used platform for academic research and citation analysis. It provides a ranking system that measures the impact and influence of scholarly journals based on the number of citations received by their articles. The following list represents the top 100 journals based on the Google Scholar ranking.

The top 100 most influential and prestigious academic journals in 2024 – According to Google Scholar Rank are as follows.

Source: Google Scholar

10 Reasons Behind Top 100 Journals 2024

• Impact: Journals have a significant impact on their respective fields.
• Quality of Research: Publishes high-quality, influential research.
• Global Reach: Recognized internationally for its contributions.
• Interdisciplinarity: Addresses multidisciplinary topics, fostering collaboration.
• Innovation: Pioneers in introducing and advancing new concepts and methodologies.
• Citation Metrics: Frequently cited by other researchers, indicating influence.
• Open Access: Journals embracing open access to promote the dissemination of knowledge.
• Field Leadership: Plays a central role in shaping and leading its field.
• Peer Review: Maintains rigorous peer-review processes to ensure research quality.
• Conference Impact: For conference entries, significant impact and contributions in the respective conference area.

I hope this article will help you to find the Top 100 journals related to medical, engineering, and natural sciences in the World.

Copyright Infringement Disclaimer:

The above list consists of the top 100 journals based on the Google Scholar ranking. These rankings are determined by the number of citations received by articles published in these journals. The list includes renowned publications such as Nature, The New England Journal of Medicine, and Science, which cover a wide range of scientific disciplines. It also includes conferences and specific fields of study, such as computer vision, machine learning, and chemistry. While efforts have been made to ensure the accuracy of the rankings, users are encouraged to verify the information independently, as the rankings may change over time. The provided copyright infringement disclaimer clarifies that the list is for informational purposes only and does not claim ownership or authorship of the mentioned journals. It also highlights that the use of the list is at the user’s own risk, and iLovePhD.com is not liable for any errors or damages arising from its use.

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Hi, about 1 year ago I did a journal ranking of PubMed journals for 2020 (all of them by month). This was based on total articles. Now that Medline is scripted I was thinking of doing a 2021 ranking but with a medline percent added. Its another way to look at medical journals. Its doable because PubMed owns the field and it would be difficult to do another topic mapping (WOS, Scopus, etc.) Its also interesting to see the differences between those in my ranking and this list. I think looking at both help give the full picture. However, using the journal name without the ID is problematic for many reasons. Is it possible to add the journal ID? I also, recently pulled out 1.8 million lines of text from PubMed abstracts to see what the top sources were for Systematic Reviews. Would like to see some articles breaking out data like this because not many (anyone) is doing it.

How is the impact factor calculated and why are they not in order of highest to lowest impact factor?

Thankyou so Much

Good one. Thanks for sharing

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The countries leading the world in scientific research

The 'teenies' decade saw China overtaking the US in the amount of scientific research it publishes. Image:  Unsplash/Ibrahim Boran

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• In 2018, more than 2.5 million science and engineering articles were published globally.
• China has displaced the U.S. as the world's top research publisher in science and engineering.

The U.S. National Science Foundation (NSF) has released data showing that 2,555,959 science and engineering (S&E) articles were published around the world in 2018, a considerable increase on the 1,755,850 recorded a decade ago. Global research output in that sector has grown around 4 percent annually over the past ten years and China's growth rate is notable as being twice the world average. While the U.S. led the way in 2008, it has now been displaced as the world's top S&E research publisher by China.

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In 2008, the U.S. published 394,979 S&E articles in peer-reviewed journals, ahead of China's 249,049. China's rapid growth rate saw it publish 528,263 articles last year, ahead of U.S. output totalling 422,808. India came third in 2018 with 135,788. The European Union's collective output came to 622,000 research articles, meaning it accounted for almost a quarter of the global total. China made up 20.67 percent while the U.S. accounted for 16.54 percent.

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Google Scholar reveals its most influential papers for 2021

Early clinical observations of COVID-19 and its mortality risk factors among the most cited output, while a five-year-old AI paper continues to command attention.

Examples of using SSD, an object-detection algorithm described in a highly cited artificial intelligence paper. Credit: Wei Liu et al. European Conference on Computer Vision (2016)

24 August 2021

Wei Liu et al. European Conference on Computer Vision (2016)

Examples of using SSD, an object-detection algorithm described in a highly cited artificial intelligence paper.

COVID-19-related papers have eclipsed artificial intelligence research in the annual listing of the most highly-cited publications in the Google Scholar database. The most highly cited COVID-19 paper, published in The Lancet in early 2020, has garnered more than 30,000 citations to date (see below for paper summary).

But, in the database of almost 400 million academic papers and other scholarly literature, even it fell a long way short of the most highly cited paper of the last five years, ‘Deep Residual Learning for Image Recognition’, published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition by a team from Microsoft in 2016.

The five-year-old paper’s astonishing ascendancy continues, from 25,256 citations in 2019 to 49,301 citations in 2020 to 82,588 citations in 2021. We wrote about it last year here .

The 2021 Google Scholar Metrics ranking tracks papers published between 2016 and 2020, and includes citations from all articles that were indexed in Google Scholar as of July 2020. Google Scholar is the largest database in the world of its kind.

Below we describe selections from Google Scholar’s most highly-cited articles for 2021. COVID-19 research dominated new arrivals in the list, but we’re also featuring a popular AI paper from 2016, and research that provides an economical shortcut to seeing patterns of human genetic variation, also from 2016.

See our coverage of the 2019 and 2020 lists.

‘Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China’

30,529 citations

Published in February 2020, this is one of the earliest papers to describe the clinical characteristics of COVID-19. It was authored by researchers in China and doctors working in hospitals in Wuhan, the city where COVID-19 was first detected in late 2019.

The team, from institutions such as the Jin Yin-tan Hospital in Wuhan and China-Japan Friendship Hospital in Beijing, reviewed the clinical and nursing reports, chest X-rays and lab results of the first 41 COVID-19 patients. They noted that the novel virus acts similarly to SARS and MERS, in that it causes pneumonia, but is different in that it seldom manifests as a runny nose or intestinal symptoms.

The final sentences of the paper call for robust and rapid testing, because of the likelihood of the disease spreading out of control:

“Reliable quick pathogen tests and feasible differential diagnosis based on clinical description are crucial for clinicians in their first contact with suspected patients. Because of the pandemic potential of 2019-nCoV, careful surveillance is essential to monitor its future host adaption, viral evolution, infectivity, transmissibility, and pathogenicity.”

The paper has been referenced or cited in almost 100 policy documents to date , including several released by the World Health Organization on topics such as mask-wearing and clinical care of patients with severe symptoms .

‘Clinical Characteristics of Coronavirus Disease 2019 in China’

New England Journal of Medicine

19,656 citations

Published online in February 2020, this study was a retrospective review of medical records for 1,099 COVID-19 cases reported to the National Health Commission of the People's Republic of China between 11 December 2019 and 29 January 2020.

The team, which included almost 40 researchers from China from institutions such as the Guangzhou Medical University in Guangzhou and Wuhan Jinyintan Hospital in Wuhan, accessed electronic medical records from 552 hospitals in mainland China to summarise exposure risk, signs and symptoms, laboratory and radiologic findings related to COVID-19 infection.

The study garnered a lot of media attention based on the evidence it put forward that men might be more severely impacted by disease – 58% of the patient cohort were male.

However, as Sharon Begley reported for STAT , “It’s possible the apparent sex imbalance reflects patterns of travel and contacts that make men more likely to be exposed to carriers of the virus, not any inherent biological differences. It’s also possible the apparent worse disease severity in men could skew the data.”

A paper published in JAMA around the same time by researchers in the United States reported that, among hospitalized patients, there is “a slight predominance of men”.

A Nature Communications meta-analysis , published in December 2020, looked at 92 studies covering more than three million patients and concluded that, while males and females appeared to be susceptible to infection, men were 2.84 times more likely to be end up in intensive care and 1.39 times more likely to die from the disease.

‘Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study’

17,047 citations

Published in March 2020, The Lancet described this study as the first time researchers have examined risk factors associated with severe symptoms and death in hospitalised or deceased patients. Of the 191 patients studied, 137 were discharged from hospital and 54 died.

The study, by researchers from hospitals in China, also presented new data on viral shedding – information that informed early understanding of how the virus spreads and can be detected over the cause of infection.

“The extended viral shedding noted in our study has important implications for guiding decisions around isolation precautions and antiviral treatment in patients with confirmed COVID-19 infection,” said co-lead author, Bin Cao, from the China-Japan Friendship Hospital and Capital Medical University in Beijing.

“However, we need to be clear that viral shedding time should not be confused with other self-isolation guidance for people who may have been exposed to COVID-19 but do not have symptoms, as this guidance is based on the incubation time of the virus.”

‘A Novel Coronavirus from Patients with Pneumonia in China, 2019’

The New England journal of medicine

16,194 citations

On 31 December 2019, the Chinese Center for Disease Control and Prevention (China CDC) dispatched a rapid response team to accompany health authorities in Hubei province and Wuhan city in conducting COVID-19 investigations.

This study, published in January 2020, reported the results of that investigation, including the clinical features of the pneumonia of two patients.

Described by Jose Manuel Jimenez-Guardeño, a researcher in the Department of Infectious Diseases at King's College London , UK and colleagues in an article for The Conversation as “the article that released this virus to the world”, the paper details how the virus was isolated from patients with pneumonia in Wuhan in cell cultures.

“In fact, actual photographs of SARS-CoV-2 were shown to the world for the first time here,” say Jimenez-Guardeño and his co-authors .

The study authors urged that more epidemiologic investigations were needed in order to characterize transmission modes, reproduction intervals and other characteristics of the virus to inform strategies to control and stop its spread.

‘SSD: Single Shot MultiBox Detector’

European Conference on Computer Vision

15,368 citations

A change of pace from recent COVID-19 studies, this paper, led by Wei Liu from the University of North Carolina at Chapel Hill and published in 2016, remains one of the most highly cited in the field of artificial intelligence (AI). It describes a new method for detecting objects in images or video footage using a single deep neural network – a set of AI algorithms inspired by the neurological processes that fire in the human cerebral cortex.

The approach, called the Single Shot MultiBox Detector, or SSD, has been described as faster than Faster R-CNN – another object detection technology that was described in a very highly cited paper published in 2015 ( see our coverage here ).

SSD works by dividing the image into a grid, with each grid cell responsible for detecting objects within that part of the image. As the name indicates, the network is able to identify all objects within an image in a single pass, allowing for real-time analysis.

SSD is now one of a handful of object detection technologies that are now available. YOLO (You Only Look Once) is a similar single-shot object detection algorithm, whereas R-CNN and Faster R-CNN use a two-step approach , which involves first identifying the regions where objects might be, and then detecting them.

‘Analysis of protein-coding genetic variation in 60,706 humans’

7,696 citations

Led by Monkol Lek from the University of Sydney in Australia and Daniel MacArthur from the Broad Institute of MIT and Harvard University , this 2016 paper presents an open-access catalogue of more than 60,000 human exome sequences (exomes are the coding portions of genes) from people of European, African, South Asian, East Asian, and Latinx ancestry.

The collection was compiled as part of the Exome Aggregation Consortium project, run by an international group of researchers with a focus on exome sequencing. As exomes only make up about 2% of the human genome , the approach has been praised for being able to highlight patterns of genetic variation, including known disease-related variants, in a more cost-effective way than whole-genome sequencing.

Presented at a 2015 genomics conference, the catalogue encompasses 7.4 million genetic variants, which can be used to identify those connected to rare diseases. “Large-scale reference datasets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes,” Lek said when the paper was published.

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Lindsay Rechtman [1], Stephan Brenner [2], Marcienne Wright [2], Maggie Ritsick [1], Farhana Rahman [2], Moon Han [2], Jaime Raymond [2], Theodore Larson [2], D. Kevin Horton [2], Paul Mehta [2]

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This paper describes the analysis undertaken by the Agency for Toxic Substances and Disease Registry (ATSDR) to examine the impact of National ALS Registry funded ALS research activities. Since 2012, the National ALS Registry has funded 14 projects through extramural research grants. On average these funded projects produced 1-2 publications that were cited 114 times by other researchers. Analysis of the program found that Registry-funded research fills an essential gap in the overall ALS scientific community as it is difficult to prevent and treat a disease without a deeper understanding of its causes.

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Research on Aluminum Electrolysis from 1970 to 2023: A Bibliometric Analysis

• Aluminum: Eliminating GHG Emissions
• Published: 13 May 2024

Cite this article

• Jingkai Lin 1 ,
• Chang Liu 1 ,
• Aimin Liu 1 ,
• Zhongning Shi 1 ,
• Zhaowen Wang 1 ,
• Shaoyan Jiang 2 ,
• Gang Wang 3 &
• Fengguo Liu   ORCID: orcid.org/0000-0003-1526-3877 1  

20 Accesses

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The purpose of this work is to analyze the development direction and prospects in the field of aluminum electrolysis and to provide reference information for related research and industry personnel. The scientific papers on aluminum electrolysis published in Scopus database from 1970 to 2023 were collected. Bibliometric methods and knowledge mapping visualization software were used to analyze the papers. Both quantitative statistics and qualitative comparative analysis of global scientific papers on aluminum electrolysis were done in terms of annual paper trends, papers by major countries, authors, institutions, journals and research topics, respectively. The results showed that the number of published papers has had an increasing trend in recent years. The top three productive countries are China, Russia and the US, respectively. The top three productive institutions are Northeastern University, Central South University and Norwegian University of Science and Technology, respectively. TMS Light Metals is the publication with the most papers on aluminum electrolysis. The distribution of research results in the field of aluminum electrolysis was analyzed using a visual analysis chart so that scholars can determine the research trends and hot spots in the field of aluminum electrolysis.

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Acknowledgements

The authors would like to acknowledge support from the National Natural Science Foundation of China (52341402, 51804070), Fundamental Research Funds for the Central Universities (N2325017) and State Key Laboratory of Advanced Refractories Funds (SKLAR202007).

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Li, X., Lin, J., Liu, C. et al. Research on Aluminum Electrolysis from 1970 to 2023: A Bibliometric Analysis. JOM (2024). https://doi.org/10.1007/s11837-024-06596-1

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GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators

Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully exploit the rich information in the diverse N-best hypotheses, making them less optimal for translation tasks that require a single, high-quality output sequence. In this paper, we propose a new generative paradigm for translation tasks, namely GenTranslate, which builds upon LLMs to generate better results from the diverse translation versions in N-best list. Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the rich information in N-best candidates to generate a higher-quality translation result. Furthermore, to support LLM finetuning, we build and release a HypoTranslate dataset that contains over 592K hypotheses-translation pairs in 11 languages. Experiments on various speech and machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that our GenTranslate significantly outperforms the state-of-the-art model.

• Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
• HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
• It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition
• Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition

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AI Is Everybody’s Business

This briefing presents three principles to guide business leaders when making AI investments: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects. The principles are supported by the MIT CISR data monetization research, and the briefing illustrates them using examples from the Australia Taxation Office and CarMax. The three principles apply to any kind of AI, defined as technology that performs human-like cognitive tasks; subsequent briefings will present management advice distinct to machine learning and generative tools, respectively.

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Author Barb Wixom reads this research briefing as part of our audio edition of the series. Follow the series on SoundCloud.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

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MIT CISR wishes to thank all of our associate members for their support and contributions.

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MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.