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When scientific citations go rogue: Uncovering ‘sneaked references’



theconversation.com – Lonni Besançon, Assistant Professor in Data Visualization, Linköping – 2024-07-09 07:22:58

Science is a of collaboration that depends on accurate citations.

AlexRaths/iStock via Getty Images

Lonni Besançon, Linköping University and Guillaume Cabanac, Institut de Recherche en Informatique de Toulouse

A researcher working alone – apart from the world and the rest of the wider scientific community – is a classic yet misguided image. Research is, in reality, built on continuous exchange within the scientific community: First you understand the work of others, and then you share your findings.


Reading and writing articles published in academic journals and presented at conferences is a central part of being a researcher. When researchers write a scholarly article, they must cite the work of peers to context, detail sources of inspiration and explain differences in approaches and results. A positive citation by other researchers is a key measure of visibility for a researcher's own work.

But what happens when this citation system is manipulated? A recent Journal of the Association for Information Science and Technology article by our team of academic sleuths – which includes information scientists, a computer scientist and a mathematician – has revealed an insidious method to artificially inflate citation counts through metadata manipulations: sneaked references.

Hidden manipulation

People are becoming more aware of scientific publications and how they work, their potential flaws. Just last year more than 10,000 scientific articles were retracted. The issues around citation gaming and the harm it causes the scientific community, including damaging its credibility, are well documented.

Citations of scientific work abide by a standardized referencing system: Each reference explicitly mentions at least the title, authors' names, publication year, journal or conference name, and page numbers of the cited publication. These details are stored as metadata, not visible in the article's text directly, but assigned to a digital object identifier, or DOI – a unique identifier for each scientific publication.


References in a scientific publication allow authors to justify methodological choices or present the results of past studies, highlighting the iterative and collaborative nature of science.

However, we found through a encounter that some unscrupulous actors have added extra references, invisible in the text but present in the articles' metadata, when they submitted the articles to scientific databases. The result? Citation counts for certain researchers or journals have skyrocketed, even though these references were not cited by the authors in their articles.

Chance discovery

The investigation began when Guillaume Cabanac, a professor at the University of Toulouse, wrote a post on PubPeer, a website dedicated to postpublication peer , in which scientists discuss and analyze publications. In the post, he detailed how he had noticed an inconsistency: a Hindawi journal article that he suspected was fraudulent because it contained awkward phrases had far more citations than downloads, which is very unusual.

The post caught the attention of several sleuths who are now the authors of the JASIST article. We used a scientific search engine to look for articles citing the initial article. Google Scholar found none, but Crossref and Dimensions did find references. The difference? Google Scholar is likely to mostly rely on the article's main text to extract the references appearing in the bibliography section, whereas Crossref and Dimensions use metadata provided by publishers.


A new type of fraud

To understand the extent of the manipulation, we examined three scientific journals that were published by the Technoscience Academy, the publisher responsible for the articles that contained questionable citations.

Our investigation consisted of three steps:

  1. We listed the references explicitly present in the HTML or PDF versions of an article.

  2. We compared these lists with the metadata recorded by Crossref, discovering extra references added in the metadata but not appearing in the articles.

  3. We checked Dimensions, a bibliometric platform that uses Crossref as a metadata source, finding further inconsistencies.

In the journals published by Technoscience Academy, at least 9% of recorded references were “sneaked references.” These additional references were only in the metadata, distorting citation counts and giving certain authors an unfair advantage. Some legitimate references were also lost, meaning they were not present in the metadata.

In addition, when analyzing the sneaked references, we found that they highly benefited some researchers. For example, a single researcher who was associated with Technoscience Academy benefited from more than 3,000 additional illegitimate citations. Some journals from the same publisher benefited from a hundred additional sneaked citations.


We wanted our results to be externally validated, so we posted our study as a preprint, informed both Crossref and Dimensions of our findings and gave them a link to the preprinted investigation. Dimensions acknowledged the illegitimate citations and confirmed that their database reflects Crossref's data. Crossref also confirmed the extra references in Retraction Watch and highlighted that this was the first time that it had been notified of such a problem in its database. The publisher, based on Crossref's investigation, has taken action to fix the problem.

Implications and potential solutions

Why is this discovery important? Citation counts heavily influence research , academic promotions and institutional rankings. Manipulating citations can to unjust decisions based on false data. More worryingly, this discovery raises questions about the integrity of scientific impact measurement systems, a concern that has been highlighted by researchers for years. These systems can be manipulated to foster unhealthy competition among researchers, tempting them to take shortcuts to publish faster or achieve more citations.

To combat this practice we suggest several measures:

  • Rigorous verification of metadata by publishers and agencies like Crossref.

  • Independent audits to ensure data reliability.

  • Increased transparency in managing references and citations.

This study is the first, to our knowledge, to a manipulation of metadata. It also discusses the impact this may have on the evaluation of researchers. The study highlights, yet again, that the overreliance on metrics to evaluate researchers, their work and their impact may be inherently flawed and wrong.


Such overreliance is likely to promote questionable research practices, including hypothesizing after the results are known, or HARKing; splitting a single set of data into several papers, known as salami slicing; data manipulation; and plagiarism. It also hinders the transparency that is key to more robust and efficient research. Although the problematic citation metadata and sneaked references have now been apparently fixed, the corrections may have, as is often the case with scientific corrections, happened too late.

This article is published in collaboration with Binaire, a blog for understanding digital issues.

This article was originally published in French.The Conversation

Lonni Besançon, Assistant Professor in Data Visualization, Linköping University and Guillaume Cabanac, Professeur des universités, Institut de Recherche en Informatique de Toulouse

This article is republished from The Conversation under a Creative Commons license. Read the original article.


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Storytelling strategies make communication about science more compelling



theconversation.com – Emma Frances Bloomfield, Associate Professor of Communication Studies, University of Nevada, Las Vegas – 2024-07-11 07:26:49
A story that includes characters and focuses on what people care about can stand up to misinformation.
SDI Productions/E+ via Getty Images

Emma Frances Bloomfield, University of Nevada, Las Vegas

As a science communication scholar, I've always supported vaccination and trusted medical experts – and I still do. As a new mom, however, I've been confronting new-to-me emotions and concerns while weighing decisions about my son's .

Vaccines are incredibly effective and have minimal risks of side effects. But I began to see why some parents may hesitate because of the flood of content, especially online, about potential vaccine risks.

Part of what makes vaccine misinformation persuasive is its use of storytelling. Antivaccine advocates share powerful personal experiences of childhood illnesses or alleged vaccine side effects. It is rare, however, for scientists to use the same storytelling strategies to counter misinformation.


In my book “Science v. Story: Narrative Strategies for Science Communicators, I explore how to use stories to talk in a compelling way about controversial science topics, vaccination. To me, stories contain characters, action, sequence, scope, a storyteller, and content to varying degrees. By this definition, a story could be a book, a article, a social media post, or even a conversation with a friend.

While researching my book, I found that stories about science tend to be broad and abstract. On the other hand, science-skeptical stories tend to be specific and concrete. By borrowing some of the strategies of science-skeptical stories, I argue that evidence-backed stories about science can better compete with misinformation.

To make science's stories more concrete and engaging, it's important to put people in the story, explain science as a , and include what people care about.

woman and man with arms around each other looking at burned out house site
Stories hit home more when they include human characters and not just forces of nature.
VladTeodor/iStock via Getty Images Plus

Put people in the story

Science's stories often lack characters – at least, human ones. One easy way to make better stories is to include scientists making discoveries or performing experiments as the characters.

Characters can also be people affected by a scientific topic, or interested in learning more about it. For example, stories about climate change can include examples of people feeling the effects of more extreme weather , such as the devastating impacts of California wildfires on local communities.


Characters can also be storytellers who are sharing their personal experiences. For example, I started this article with a brief discussion of my personal vaccine decisions. I was not a hidden or voiceless narrator, but someone sharing an experience that I hope others can relate to.

Explain science as a process

People often think of science as objective and unbiased. But science is actually a human practice that constantly involves choices, missteps and biases.

At the beginning of the pandemic, for example, the medical advice was not to mask. Scientists initially thought that masks didn't prevent transmission of the SARS-CoV-2 virus that causes COVID-19. However, after additional research, medical advice changed to support masking, providing the public with the most updated and accurate knowledge.

If you explain science as a process, you can walk people through the sequence of how science is done and why researchers reach certain conclusions. Science communicators can emphasize how science is conducted and why people should trust the process of science to provide the most accurate conclusions possible given the available information.


Include what people care about

Scientific topics are important, but they may not always be the public's most pressing concerns. In April 2024, Gallup found that “the quality of the environment” was one of the lowest-ranked priorities among people in the U.S. Of those polled, 37% said they cared a great deal about it. More immediate issues, such as (55%), crime and violence (53%), the (52%), and hunger and homelessness (52%) ranked much higher.

Stories about the environment could weave in connections to higher-priority topics to emphasize why the content is important. For example, stories can include information about how mitigating climate change can work hand in hand with improving the economy and creating jobs.

Medical provider faces woman and child, in discussion
A pediatrician is a science communicator, and so is a parent who talks about their own medical experiences.
SDI Productions/E+ via Getty Images

Telling science's stories

Scientists, of course, can be science communicators, but everyone can tell science's stories. When we share information online about health, or talk to friends and family about the weather, we contribute to information that circulates about science topics.

My son's pediatrician was a science communicator when she explained the vaccine schedule and ways to keep my son comfortable after receiving vaccines. I was a science communicator when I spoke to others about my decisions to fully vaccinate my son on the recommended schedule, and how he is now a healthy and happy 9-month-old.

When communicating about science topics, remember to borrow features from stories to strengthen your message. Think about all of a story's features – character, action, sequence, scope, storyteller and content – and how you might incorporate them into the topic. Everyone can find opportunities to strengthen their science communication, whether it's in their or in their everyday interactions with friends and family.The Conversation

Emma Frances Bloomfield, Associate Professor of Communication Studies, University of Nevada, Las Vegas


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AI supercharges data center energy use – straining the grid and slowing sustainability efforts



theconversation.com – Ayse Coskun, Professor of Electrical and Computer Engineering, Boston – 2024-07-11 07:26:28
A data center in Ashburn, Va., the heart of so-called Data Center Alley.
AP Photo/Ted Shaffrey

Ayse Coskun, Boston University

The artificial intelligence boom has had such a profound effect on big tech companies that their energy consumption, and with it their carbon emissions, have surged.

The spectacular success of large language models such as ChatGPT has helped fuel this growth in energy demand. At 2.9 watt-hours per ChatGPT request, AI queries require about 10 times the electricity of traditional Google queries, according to the Electric Power Research Institute, a nonprofit research firm. Emerging AI capabilities such as audio and generation are likely to add to this energy demand.

The energy needs of AI are shifting the calculus of energy companies. They're now exploring previously untenable options, such as restarting a nuclear reactor at the Three Mile Island power plant that has been dormant since the infamous disaster in 1979.


Data centers have had continuous growth for decades, but the magnitude of growth in the still-young era of large language models has been exceptional. AI requires a lot more computational and data storage resources than the pre-AI rate of data center growth could .

AI and the grid

Thanks to AI, the electrical grid – in many places already near its capacity or prone to stability challenges – is experiencing more pressure than before. There is also a substantial lag between computing growth and grid growth. Data centers take one to two years to build, while adding new power to the grid requires over four years.

As a recent report from the Electric Power Research Institute lays out, just 15 states contain 80% of the data centers in the U.S.. Some states – such as Virginia, home to Data Center Alley – astonishingly have over 25% of their electricity consumed by data centers. There are similar trends of clustered data center growth in other parts of the world. For example, Ireland has become a data center nation.

AI is a big impact on the electrical grid and, potentially, the climate.

Along with the need to add more power generation to sustain this growth, nearly all countries have decarbonization goals. This means they are striving to integrate more renewable energy sources into the grid. Renewables such as wind and solar are intermittent: The wind doesn't always blow and the sun doesn't always shine. The dearth of cheap, green and scalable energy storage means the grid faces an even bigger problem matching supply with demand.


Additional challenges to data center growth include increasing use of water cooling for efficiency, which strains limited fresh sources. As a result, some communities are pushing back against new data center investments.

Better tech

There are several ways the industry is addressing this energy crisis. First, computing hardware has gotten substantially more energy efficient over the years in terms of the operations executed per watt consumed. Data centers' power use efficiency, a metric that shows the ratio of power consumed for computing versus for cooling and other infrastructure, has been reduced to 1.5 on average, and even to an impressive 1.2 in advanced facilities. New data centers have more efficient cooling by using water cooling and external cool when it's available.

Unfortunately, efficiency alone is not going to solve the sustainability problem. In fact, Jevons paradox points to how efficiency may result in an increase of energy consumption in the longer . In addition, hardware efficiency gains have slowed down substantially, as the industry has hit the limits of chip technology scaling.

To continue improving efficiency, researchers are designing specialized hardware such as accelerators, new integration technologies such as 3D chips, and new chip cooling techniques.


Similarly, researchers are increasingly studying and developing data center cooling technologies. The Electric Power Research Institute report endorses new cooling methods, such as air-assisted liquid cooling and immersion cooling. While liquid cooling has already made its way into data centers, only a few new data centers have implemented the still-in-development immersion cooling.

a man wearing rubber gloves and a visor lowers a circuit board into a trough containing a liquid
Running computer servers in a liquid – rather than in air – could be a more efficient way to cool them.
Craig Fritz, Sandia National Laboratories

Flexible future

A new way of building AI data centers is flexible computing, where the key idea is to compute more when electricity is cheaper, more available and greener, and less when it's more expensive, scarce and polluting.

Data center operators can convert their facilities to be a flexible load on the grid. Academia and industry have provided early examples of data center demand response, where data centers regulate their power depending on power grid needs. For example, they can schedule certain computing tasks for off-peak hours.

Implementing broader and larger scale flexibility in power consumption requires innovation in hardware, software and grid-data center coordination. Especially for AI, there is much room to develop new strategies to tune data centers' computational loads and therefore energy consumption. For example, data centers can scale back accuracy to reduce workloads when AI models.

Realizing this vision requires better modeling and forecasting. Data centers can try to better understand and predict their loads and conditions. It's also important to predict the grid load and growth.


The Electric Power Research Institute's load forecasting initiative involves activities to help with grid planning and operations. Comprehensive monitoring and intelligent analytics – possibly relying on AI – for both data centers and the grid are essential for accurate forecasting.

On the edge

The U.S. is at a critical juncture with the explosive growth of AI. It is immensely difficult to integrate hundreds of megawatts of electricity demand into already strained grids. It might be time to rethink how the industry builds data centers.

One possibility is to sustainably build more edge data centers – smaller, widely distributed facilities – to bring computing to local communities. Edge data centers can also reliably add computing power to dense, urban regions without further stressing the grid. While these smaller centers currently make up 10% of data centers in the U.S., analysts project the market for smaller-scale edge data centers to grow by over 20% in the next five years.

Along with converting data centers into flexible and controllable loads, innovating in the edge data center may make AI's energy demands much more sustainable.The Conversation

Ayse Coskun, Professor of Electrical and Computer Engineering, Boston University


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What do storm chasers really do? Two tornado scientists take us inside the chase and tools for studying twisters



theconversation.com – Yvette Richardson, Professor of Meteorology, Senior Associate Dean for Undergraduate Education, Penn – 2024-07-11 07:25:05
Scientists in a truck outfitted with instruments race toward a storm.
National Severe Storms Lab/NOAA

Yvette Richardson, Penn State and Paul Markowski, Penn State

Storm-chasing for science can be exciting and stressful – we know, because we do it. It has also been essential for developing 's understanding of how tornadoes form and how they behave.

In 1996 the movie “Twister” brought storm-chasing into the public imagination as scientists played by Helen Hunt and Bill Paxton raced ahead of tornadoes to deploy their sensors and occasionally got too close. That inspired a generation of atmospheric scientists.

With the new movie “Twisters” coming out on July 19, 2024, we've been getting questions about storm-chasing – or storm intercepts, as we call them.


Here are some answers about what scientists who do this kind of fieldwork are up to when they race off after storms.

A tornado near Duke, Oklahoma, with a wheat field blowing in the foreground.
Scientists with the National Severe Storms Lab ‘intercepted' this tornado to collect data using mobile radar and other instruments on May 24, 2024.
National Severe Storms Lab

What does a day of storm-chasing really look like?

The morning of a chase day starts with a good breakfast, because there might not be any chance to eat a good meal later in the day.

Before out, the team looks at the weather conditions, the National Weather Service computer forecast models and outlooks from the National Oceanic and Atmospheric Administration's Storm Prediction Center to determine the target.

Our goal is to figure out where tornadoes are most likely to occur that day. Temperature, moisture and winds, and how these change with height above the ground, all provide clues.

There is a “hurry up and wait” cadence to a storm chase day. We want to get into position quickly, but then we're often waiting for storms to develop.

A radar image shows a storm cell with a hook at the back suggesting a tornado could form.
A ‘hook echo' on radar, typically a curl at the back of a storm cell, is one sign that a tornado could form. The hook reflects precipitation wrapping around the back side of the updraft.
National Severe Storms Lab

Storms often take time to develop before they're capable of producing tornadoes. So we watch the storm carefully on radar and with our eyes, if possible, staying well ahead of it until it matures. Often, we'll watch multiple storms and look for signs that one might be more likely to generate tornadoes.

Once the mission scientist declares a deployment, everyone scrambles to get into position.

We use a lot of different instruments to track and measure tornadoes, and there is an art to determining when to deploy them. Too early, and the tornado might not form where the instruments are. Too late, and we've missed it. Each instrument needs to be in a specific location relative to the tornado. Some need to be deployed well ahead of the storm and then stay stationary. Others are car-mounted and are driven back and forth within the storm.

A row of seven minivans, SUVS and jeeps with racks on top holding the sorts of instruments one might see in a weather station.
Vehicle-mounted equipment can act as mobile weather stations known as mesonets. These were used in the VORTEX2 research . Dozens of scientists, including the authors, succeeded in recording the entire life cycle of a supercell tornado during VORTEX2 in 2009.
Yvette Richardson

If all goes well, team members will be concentrating on the data coming in. Some will be launching weather balloons at various distances from the tornado, while others will be placing “pods” containing weather instruments directly in the path of the tornado.

A whole network of observing stations will have been set up across the storm, with radars collecting data from multiple angles, photographers capturing the storm from multiple angles, and instrumented vehicles transecting key of the storm.

Not all of our work is focused on the tornado itself. We often target areas around the tornado or within other parts of the storm to understand how the rotation forms. Theories suggest that this rotation can be generated by temperature variations within the storm's precipitation region, potentially many miles from where the tornado forms.

An illustration shows a thunderstorm cloud with an updraft with a smaller downdraft behind it. Both are spinning. A spinning football indicates the type of spin.
Formation of a tornado: Changes in wind speed and direction with altitude, known as wind shear, are associated with horizontal spin, similar to that of a football. As this spinning is drawn into the storm's updraft, the updraft rotates. A separate air stream descends through a precipitation-driven downdraft and acquires horizontal spin because of temperature differences along the air stream. This spinning air can be tilted into the vertical and sucked upward by the supercell's updraft, contracting the spin near the ground into a tornado.
Paul Markowski/Penn State

Through all of this, the teams stay in contact using text messages and software that allows us to see everyone's position relative to the latest radar images. We're also watching the forecast for the next day so we can plan where to go next and find hotel rooms and, hopefully, a late dinner.

What do all those instruments tell you about the storm?

One of the most important tools of storm-chasing is weather radar. It captures what's with precipitation and winds above the ground.

We use several types of radars, typically attached to trucks so we can move fast. Some transmit with a longer wavelength that helps us see farther into a storm, but at the cost of a broader width to their beam, resulting in a fuzzier picture. They are good for collecting data across the entire storm.

Smaller-wavelength radars cannot penetrate as far into the precipitation, but they do offer the high-resolution view necessary to capture small-scale phenomena like tornadoes. We put these radars closer to the developing tornado.

An inside look at some of the mobile systems and tools scientists use in storm-chasing, including how team members monitor storms in real time.

We also monitor wind, air pressure, temperature and humidity along the ground using various instruments attached to moving vehicles, or by temporarily deploying stationary arrays of these instruments ahead of the approaching storm. Some of these are meant to be hit by the tornado.


Weather balloons provide crucial data, too. Some are designed to ascend through the atmosphere and capture the conditions outside the storm. Others travel through the storm itself, measuring the important temperature variations in the rain-cooled air beneath the storm. Scientists are now using drones in the same way in parts of the storm.

Symbols show the paths of over 70 balloon-borne probes that the authors' team launched into a supercell thunderstorm. The probes, carried by the wind, mapped the temperature in the storm's downdraft region, which can be a critical source of rotation for tornadoes. Luke LeBel/Penn State

All of this gives scientists insight into the processes happening throughout the storm before and during tornado development and throughout the tornado's lifetime.

How do you stay safe while chasing tornadoes?

Storms can be very dangerous and unpredictable, so it's important to always stay on top of the radar and watch the storm.

A storm can cycle, developing a new tornado downstream of the previous one. Tornadoes can change direction, particularly as they are dying or when they have a complex structure with multiple funnels. Storm chasers know to look at the entire storm, not just the tornado, and to be on alert for other storms that might sneak up. An escape plan based on the storm's expected motion and the road network is essential.

In 1947, the Thunderstorm Project was the first large-scale U.S. scientific study of thunderstorms and the first to use radar and airplanes. Other iconic projects followed, including ones that deployed a Totable Tornado Observatory, or Toto, which inspired the ‘Dorothy' instrument in the movie ‘Twister.'

Scientists take calculated risks when they're storm chasing – enough to collect crucial data, but never putting their teams in too much danger.


It turns out that driving is actually the most dangerous part of storm-chasing, particularly when roads are wet and visibility is poor – as is often the case at the end of the day. During the chase, the driving danger can be compounded by erratic driving of other storm chasers and traffic jams around storms.

What happens to all the data you collect while storm-chasing?

It would be nice to have immediate eureka moments, but the results take time.

After we collect the data, we spend years analyzing it. Combining data from all the instruments to get a complete picture of the storm and how it evolved takes time and patience. But data on the wind, temperature, relative humidity and pressure from many different angles and instruments allows us to test theories about how tornadoes develop.

Although the analysis process is slow, the discoveries are often as exciting as the tornado itself.The Conversation

Yvette Richardson, Professor of Meteorology, Senior Associate Dean for Undergraduate Education, Penn State and Paul Markowski, Distinguished Professor of Meteorology, Penn State


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