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The Most Cited Science Papers of All Time
The Most Cited Science Papers of All Time
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0:00
This is the most popular science of all time!
0:04
Makes sense that SciShow is covering it.
0:06
Pop sci is kind of our thing.
0:08
But I mean something different when I say this video is about the most popular science ever.
0:13
I mean it’s the most popular science according to scientists.
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…Based on data!
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Here are the most cited publications of all time, from five big fields of science.
0:23
And I bet you’ve never even heard of them.
0:26
[♪ INTRO]
0:29
In April of 2025, a paper published in the journal “Nature” ranked academic
0:34
publications based on how many other papers referenced their work with a citation,
0:38
going back to the beginning of their databases.
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So we went through the list and compiled the number one
0:43
most cited papers from five different fields.
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And we’re going to count down to the most cited one, starting with materials science.
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The fifth most cited paper on our list has 52,000 citations.
0:56
It’s all about a legend in the world of materials science: graphene.
1:00
Yes, one of the most popular publications in the
1:03
world describes a material you probably know very little about.
1:06
You’re more likely to be familiar with its cousin, graphite.
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The stuff in pencils.
1:11
But graphene is actually much cooler.
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It’s a two dimensional version of graphite.
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It’s literally one atom thick, with its carbon atoms arranged
1:20
in a flat array of hexagons, like a drawing of a honeycomb pattern.
1:24
And that shape is so bonkers that back in the 1940s, many scientists didn’t believe it
1:30
could physically exist, since a material that thin would be extremely unstable.
1:34
But in 2004, researchers managed to make this impossibly thin carbon sheet IRL.
1:40
That’s when graphene really made a name for itself.
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And it turned out that honeycomb shape was a game changer, making it way more functional.
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Materials get people’s attention when they have specific properties that make them useful,
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like their ability to react with light or electricity, their lightness, or their strength.
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And graphene happens to check a lot of those boxes.
2:01
Graphene is an incredibly useful material because of its conductivity, thinness, and stability.
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Let’s start with conductivity.
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That quality gets people, and electrons, going because pretty much anything
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involving electricity needs a conductor, or a material that electrons can flow through.
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Usually conductors are metal, since the structure
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of metals already has plenty of space for its electrons to all move together.
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It’s like a crowd of marathon runners running along the same race course.
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But metal has some limitations.
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And graphene can do the same thing while being made entirely of carbon.
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The secret is its thin honeycomb atoms.
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That orientation lets electrons skate across the surface without interference from other atoms.
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So it’s a great conductor.
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But it’s also thin enough to be used in applications like nearly
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transparent coatings and smartphone screens.
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And it’s strong enough to reinforce the parts
2:53
of a tennis racket that take the most force when hitting the ball.
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Because of all the uses graphene has today, the paper has garnered
3:00
a lot of attention from scientists over the years.
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And that’s what makes this graphene review the most popular publication in materials science.
3:07
Now, you might expect that as this list goes on, the papers will get older.
3:11
They’ll have had more time to accumulate citations, after all.
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But sometimes, a new topic bursts onto the scene with so much enthusiasm behind it
3:19
that it quickly moves up the ranks and surpasses everything that came before.
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That’s what happened for the next entry in our list:
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the 2015 paper “Deep Residual Learning for Image Recognition”.
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This paper has more than 116,000 citations.
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And while it’s not the first publication to
3:37
describe how deep learning could be used in the context of images,
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it became a landmark paper in this fast-growing field by proposing a much easier way forward.
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It suggested an improvement to the way we build image models.
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And that’s wildly popular because we see the world in images.
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So, naturally, they make up a lot of the world’s data.
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Think about all of the images we take of space to explore it,
3:59
and the images we take of organs in the human body to diagnose people.
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Overall, our brains are pretty good at understanding what’s going on in those images.
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…But not so good at doing that for thousands of images, at least not fast.
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And that’s where computing for images comes in.
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Image recognition AIs are sort of modeled after the way our brains work,
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but can interpret images significantly faster.
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They’re layered models known as neural networks,
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which “learn” patterns from lots and lots of examples.
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Starting with an image, each layer does a little math and passes information down the
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line to the next layer, and the next, until it generates the output we want.
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For example, a label for objects in the image.
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And models with lots of layers tend to perform better on tasks like image
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recognition because of how complex images are.
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There’s so much to learn that having all those
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layers gives them more opportunity to identify various details.
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But sometimes, when networks have tons of layers,
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they can lose track of some information on the way.
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Each layer of the network is transforming information.
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With, say, 100 layers, it gets to be like a game of telephone.
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To address that problem, the most popular paper in computer science
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proposes grouping the model's layers into chunks.
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Now the model can take shortcut paths from one chunk to the next.
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And a model that uses these shortcuts also still
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maintains the direct path of information through each layer.
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We’ve just added another mechanism to make sure nothing gets lost.
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They call this method residual learning.
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And while image recognition isn’t its only use,
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it’s especially helpful for complex images that require really deep neural networks.
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For applications like self-driving cars and
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identifying tumors on scans, we don’t want our neural networks losing any information.
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And those innovative uses for deep residual learning just keep coming,
5:49
which is why this paper is the most popular in all of computer science.
5:52
Image recognition is all around us.
5:55
But not all of the top cited papers have their utility right in the title.
5:59
One example is the most-cited paper in physics,
6:02
titled “Generalized Gradient Approximation Made Simple”.
6:06
Spoiler alert, it was not made simple.
6:09
But enough physicists seem to have understood its value,
6:12
because it's racked up more than 174,000 citations.
6:16
That’s because this paper made it easier to solve the huge problem
6:20
of describing our tiniest components; the things that make up everything.
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We’re talking about atoms.
6:26
They’re hard to describe because they’re just so dang small!
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Let’s put it this way.
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When you sit at the top of a slide, we know that gravity will pull you down,
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even if you don’t get a push.
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You have high potential energy at the top of the slide,
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and will move toward a place where you have lower energy.
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But atoms probably don’t go down slides like we do.
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Although how cute would that be?!
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Little atoms going down the slides at the atom playground?
6:52
At scales that tiny, matter starts acting …weird.
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The mass of particles like electrons is almost nothing.
7:00
So if they were at the top of a teeny tiny slide,
7:03
we can’t say for sure that they’d get pulled down to the bottom.
7:06
And we can’t really ignore the way electrons in atoms move, because figuring that out is
7:11
critical to understanding how little things like drugs in our bodies work.
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So physicists came up with the density functional theory of quantum mechanics.
7:19
This theory lets us estimate what electrons are doing in an atom, and how much energy they have.
7:24
And from that, we can figure out how they interact outside the atom as well.
7:28
Based on the charge of an atom’s nucleus and on other atoms nearby,
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electrons might gather round an atom’s nucleus more densely or disperse.
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And we can figure out when electrons disperse
7:39
or crowd together using the density functional theory.
7:42
But these calculations were not simple, which is probably where this paper’s title came from.
7:48
Each molecule is its own unique environment.
7:51
So to model electron density accurately, we needed to account for its specific arrangement of atoms.
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That is, until this paper came along,
7:59
proposing a more streamlined way to approximate electron density.
8:03
Now, instead of figuring out the nuances of each environment,
8:06
we have an equation that depends only on physical constants.
8:09
That’s stuff like the speed of light, that won’t change for each situation.
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So that kind of calculation just got a lot easier to do.
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The outputs of these calculations have helped scientists understand
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how current moves through semiconductors,
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how to make better pharmaceuticals, even how to make more breathable fabric in your sneakers.
8:27
It might not seem as flashy as the pop science you’re used to, but there’s such a wide range
8:32
of applications that this publication is the most popular physics paper of all time.
8:37
We’re about halfway through our list of popular science.
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So it’s time for a quick ad break.
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Thanks to our Presidents of Science, binorthedrunkdwarf,
8:46
Charlie Stanley, and Harry Plumley for supporting this SciShow video!
8:50
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8:53
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8:58
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9:00
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9:04
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9:08
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9:13
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9:22
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9:29
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Presidents of Science and other patrons, who support us at patreon.com/SciShow.
9:40
You might have noticed by now that the science that stands the test
9:43
of time is the stuff that’s incredibly useful.
9:46
Like an optimized method for studying proteins that’s used,
9:49
almost unchanged from 1970 to this day.
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This paper has more than 250,000 citations with the catchy title,
9:58
“Cleavage of structural proteins during the assembly of the head of bacteriophage T4.”
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But, uh, that title isn’t what made it so popular.
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It racked up the citations because this paper introduces SDS-PAGE,
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a method used to separate proteins by their mass.
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Figuring out the size of molecules in our bodies is a major step in
10:18
characterizing those molecules so doctors and researchers know what they’re dealing with.
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If you’ve ever done a genetic testing kit, for example, you’re looking for a variety of genes.
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The DNA encoding different genes have different,
10:30
reliable sizes, which helps professionals identify them.
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But proteins aren’t as straightforward as DNA.
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Their charge and shape are a lot more variable.
10:40
So we need a little extra help to identify their size.
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That’s what sodium dodecyl sulfate, or SDS does.
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While this method was iterated on for a while, the most popular biology paper of all time is
10:53
the one that locked it in and demonstrated it on the protein envelope of a virus.
10:58
This version of SDS-PAGE is now a daily tool for labs diagnosing HIV, among other things.
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It separates proteins to help clinicians identify those associated with the virus.
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Which means that lifesaving techniques are the most popular biological science.
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As they should be.
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Finally, the most cited paper ever as of April 2025 is in the field often
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called “the central science.” It’s chemistry.
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This paper, published in 1951, has more than 350,000 citations.
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That’s because it describes a method that has
11:32
become the basis for many tools that scientists use every day.
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In labs across the world,
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researchers are constantly measuring how much protein is in their samples.
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Those could be samples of soil, blood, or even your protein shake.
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I mean, you need to know if you’re going to meet your macro goals for the day.
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Even though the method in this paper, called the Lowry assay,
11:52
is based on old technology, it’s very sensitive and provides consistent results.
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So we still use it, often to fill in the gaps where newer assays won’t work.
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And when I say “assay,” I just mean a lab test that tells you what’s in your sample.
12:05
This assay gets its utility from the Folin Phenol reagent, which is usually clear.
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But under certain conditions, it can become oxidized, and turn bright blue.
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The Lowry assay creates those oxidizing conditions by adding copper to a sample of protein.
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Then, with more protein present, more of the reagent turns blue,
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creating a more intense blue color in the solution when protein concentration is high.
12:28
The day when your science teacher brought out the
12:30
demonstration of color-changing solutions was always the best part of that class.
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And it’s even better when the color tells you something about a person or environment’s health.
12:39
So even in the days of self-driving cars and virtual doctors’ visits,
12:44
this old and simple chemistry trick is still everyone’s favorite thing in science.
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Maybe the most cited papers in history aren’t the most exciting discoveries to the average person.
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But just like the foundation of a building,
12:56
the foundations of science aren’t always eye-catching and newsworthy.
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Instead, they’re often solid and reliable,
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stepping stones for thousands of other scientists to build with.
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Whether they’re glamorous or not,
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these papers have been integral to creating the world we live in today.
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So now you can add us to the long list of people citing them.
13:16
[♪ OUTRO]
The Most Cited Science Papers of All Time - Video học tiếng Anh
