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The Origin of the Number Zero

Deep in the jungle, an intrepid scholar locates a symbol of power and mystery

Four miles from the great  temple of Angkor Wat, deep in the Cambodian jungle, I opened the door of a makeshift shed with a corrugated tin roof and walked into a dusty room painted in pale gray. Thousands of chunks and slabs of stone covered the dirt floor: smashed heads of statues of Khmer kings and Hindu gods, broken lintels and door frames from abandoned temples, the remains of steles with ancient writing. After years of searching, I’d finally arrived here, hoping to find a single dot chiseled into a reddish stone, a humble mark of incredible importance, a symbol that would become the very foundation of our number system—our first zero. 

It was a lifelong love that led me to this threshold. I grew up on a cruise ship in the Mediterranean that often called at Monte Carlo, and I was drawn to the alluring numbers on roulette wheels: half of them red, half black. My fascination led to a career as a mathematician, and, dabbling in mathematical archaeology, I’ve tracked down many ancient numerals, including a magic square (those mysterious numerical grids in which the sum of every column, row and diagonal is the same) on the doorway of a tenth-century Jain temple at Khajuraho, India. 

I’m convinced that the creation of numerals to represent the abstract entities we call numbers was our greatest intellectual achievement. The simple sign “3” represents all trios in the universe; it is the quality of “being three”—distinct from “being five” or “being seven.” Numerals allow us to keep track of belongings, record dates, trade goods, calculate so precisely that we are able to fly to the moon and operate on the brain.

We use them with such ease that we take them for granted. Surprisingly, our number system took hold in the West only in the 13th century, after the Italian mathematician Leonardo of Pisa—better known as Fibonacci—introduced the numerals to Europeans. He’d learned them from Arab traders, who presumably adopted them during travels to the Indian subcontinent.

Finding Zero: A Mathematician's Odyssey to Uncover the Origins of Numbers

The invention of numerals is perhaps the greatest abstraction the human mind has ever created. Virtually everything in our lives is digital, numerical, or quantified. The story of how and where we got these numerals, which we so depend on, has for thousands of years been shrouded in mystery. "Finding Zero" is an adventure filled saga of Amir Aczel’s lifelong obsession: to find the original sources of our numerals.

Of all the numerals, “0”—alone in green on the roulette wheel—is most significant. Unique in representing absolute nothingness, its role as a placeholder gives our number system its power. It enables the numerals to cycle, acquiring different meanings in different locations (compare 3,000,000 and 30). With the exception of the Mayan system, whose zero glyph never left the Americas, ours is the only one known to have a numeral for zero. Babylonians had a mark for nothingness, say some accounts, but treated it primarily as punctuation. Romans and Egyptians had no such numeral either.

A circle inscribed at a temple in Gwalior, India, dating to the ninth century, had been widely considered the oldest version of zero in our system, the Hindu-Arabic. At the time it was made, trade with the Arab empire connected East and West, so it could have come from anywhere. I was after an older zero, a particular instance arguing for an Eastern origin.

Found on a stone stele, it was documented in 1931 by a French scholar named George Coedès. Assigned the identifying label K-127, the inscription reads like a bill of sale and includes references to slaves, five pairs of oxen and sacks of white rice. Though some of the writing wasn’t deciphered, the inscription clearly bore the date 605 in an ancient calendar that began in the year A.D. 78. Its date was thus A.D. 683. Two centuries older than the one at Gwalior, it predated wide-ranging Arab trade. But K-127 disappeared during the Khmer Rouge’s rule of terror, when more than 10,000 artifacts were deliberately destroyed. 

I describe my obsession with finding this earliest zero in my forthcoming book, Finding Zero . I spent countless hours poring over old texts in libraries from London to Delhi and emailing and calling anyone who might know someone who could help me locate K-127. I made several unsuccessful trips to Cambodia, spending a significant amount of my own money. On the verge of giving up, I received a grant from the Alfred P. Sloan Foundation and forged ahead. Cambodia’s director general of the Ministry of Culture and Fine Arts, Hab Touch, directed me to the sheds at Angkor Conservation, a restoration and storage site closed to the public. When I was turned away twice, Touch graciously made a phone call, and in early January 2013, I was invited in. I still didn’t know if K-127 had even survived. 

And yet, within two hours, the roulette wheel had spun in my favor. My eye caught a piece of tape with a pencil-scribbled “K-127,” and then I recognized that single dot on the 3- by 5-foot slab, intact but for a rough break at the top. I was elated. I dared not touch the stone surface for fear I might harm it.

Since that fortuitous moment, I’ve pondered the feat that brought us numerals, this time wondering not where and when, but how? I’ve asked dozens of mathematicians a long-debated question: Were numbers discovered or invented? The majority view is that numbers exist outside of the human mind. Unlike Beethoven’s Symphony No. 9, they don’t require a human creator. What gave numbers their power was the very act of naming them and writing them down. I’m now working with Cambodian officials to move K-127 to a museum in Phnom Penh, where a wide audience can appreciate the incredible discovery it represents. 

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Amir Aczel | | READ MORE

Amir Aczel is a mathematician, science writer and the author of more than 15 books, including Entanglement , Why Science Does Not Disprove God and Finding Zero .

August 21, 2009

The Origin of Zero

Much ado about nothing: First a placeholder and then a full-fledged number, zero had many inventors

By John Matson

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The number zero as we know it arrived in the West circa 1200, most famously delivered by Italian mathematician Fibonacci (aka Leonardo of Pisa), who brought it, along with the rest of the Arabic numerals, back from his travels to north Africa. But the history of zero, both as a concept and a number, stretches far deeper into history—so deep, in fact, that its provenance is difficult to nail down.  

"There are at least two discoveries, or inventions, of zero," says Charles Seife, author of Zero: The Biography of a Dangerous Idea (Viking, 2000). "The one that we got the zero from came from the Fertile Crescent." It first came to be between 400 and 300 B.C. in Babylon, Seife says, before developing in India, wending its way through northern Africa and, in Fibonacci's hands, crossing into Europe via Italy.  

Initially, zero functioned as a mere placeholder—a way to tell 1 from 10 from 100, to give an example using Arabic numerals. "That's not a full zero," Seife says. "A full zero is a number on its own; it's the average of –1 and 1."  

It began to take shape as a number, rather than a punctuation mark between numbers, in India in the fifth century A.D., says Robert Kaplan, author of The Nothing That Is: A Natural History of Zero (Oxford University Press, 2000). "It isn't until then, and not even fully then, that zero gets full citizenship in the republic of numbers," Kaplan says. Some cultures were slow to accept the idea of zero, which for many carried darkly magical connotations.  

The second appearance of zero occurred independently in the New World, in Mayan culture, likely in the first few centuries A.D. "That, I suppose, is the most striking example of the zero being devised wholly from scratch," Kaplan says.  

Kaplan pinpoints an even earlier emergence of a placeholder zero, a pair of angled wedges used by the Sumerians to denote an empty number column some 4,000 to 5,000 years ago.  

But Seife is not certain that even a placeholder zero was in use so early in history. "I'm not entirely convinced," he says, "but it just shows it's not a clear-cut answer." He notes that the history of zero is too nebulous to clearly identify a lone progenitor. "In all the references I've read, there's always kind of an assumption that zero is already there," Seife says. "They're delving into it a little bit and maybe explaining the properties of this number, but they never claim to say, 'This is a concept that I'm bringing forth.'"  

Kaplan's exploration of zero's genesis turned up a similarly blurred web of discovery and improvement. "I think there's no question that one can't claim it had a single origin," Kaplan says. "Wherever you're going to get placeholder notation, it's inevitable that you're going to need some way to denote absence of a number."

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The mind-bendy weirdness of the number zero, explained

We explain nothing.

by Brian Resnick

Additional reporting by Ellen Rolfes and Kimberly Mas

The computer you’re reading this article on right now runs on a binary — strings of zeros and ones. Without zero, modern electronics wouldn’t exist. Without zero, there’s no calculus, which means no modern engineering or automation. Without zero, much of our modern world literally falls apart.

Humanity’s discovery of zero was “a total game changer ... equivalent to us learning language,” says Andreas Nieder, a cognitive scientist at the University of Tübingen in Germany.

But for the vast majority of our history, humans didn’t understand the number zero. It’s not innate in us. We had to invent it. And we have to keep teaching it to the next generation.

Other animals, like monkeys, have evolved to understand the rudimentary concept of nothing. And scientists just reported that even tiny bee brains can compute zero . But it’s only humans that have seized zero and forged it into a tool.

So let’s not take zero for granted. Nothing is fascinating. Here’s why.

What is zero, anyway?

Our understanding of zero is profound when you consider this fact: We don’t often, or perhaps ever, encounter zero in nature.

Numbers like one, two, and three have a counterpart. We can see one light flash on. We can hear two beeps from a car horn. But zero? It requires us to recognize that the absence of something is a thing in and of itself.

“Z ero is in the mind, but not in the sensory world,” Robert Kaplan, a Harvard math professor and an author of a book on zero , says. Even in the empty reaches of space, if you can see stars, it means you’re being bathed in their electromagnetic radiation. In the darkest emptiness, there’s always something. Perhaps a true zero — meaning absolute nothingness — may have existed in the time before the Big Bang. But we can never know.

Nevertheless, zero doesn’t have to exist to be useful. In fact, we can use the concept of zero to derive all the other numbers in the universe.

Kaplan walked me through a thought exercise first described by the mathematician John von Neumann. It’s deceptively simple.

Imagine a box with nothing in it. Mathematicians call this empty box “the empty set.” It’s a physical representation of zero. What’s inside the empty box? Nothing.

Now take another empty box, and place it in the first one.

How many things are in the first box now?

There’s one object in it. Then, put another empty box inside the first two. How many objects does it contain now? Two. And that’s how “we derive all the counting numbers from zero … from nothing,” Kaplan says. This is the basis of our number system. Zero is an abstraction and a reality at the same time. “It’s the nothing that is,” as Kaplan said. (At this point in the story, you may want to take another hit on your bong.)

He then put it in more poetic terms. “Zero stands as the far horizon beckoning us on the way horizons do in paintings,” he says. “It unifies the entire picture. If you look at zero you see nothing. But if you look through it, you see the world. It’s the horizon.”

Once we had zero, we have negative numbers. Zero helps us understand that we can use math to think about things that have no counterpart in a physical lived experience; imaginary numbers don’t exist but are crucial to understanding electrical systems . Zero also helps us understand its antithesis, infinity, in all of its extreme weirdness. (Did you know that one infinity can be larger than another ?)

Why zero is so damn useful in math

Zero’s influence on our mathematics today is twofold. One: It’s an important placeholder digit in our number system. Two: It’s a useful number in its own right.

The first uses of zero in human history can be traced back to around 5,000 years ago, to ancient Mesopotamia. There, it was used to represent the absence of a digit in a string of numbers.

Here’s an example of what I mean: Think of the number 103. The zero in this case stands for “there’s nothing in the tens column.” It’s a placeholder, helping us understand that this number is one-hundred and three and not 13.

Okay, you might be thinking, “this is basic.” But the ancient Romans didn’t know this. Do you recall how Romans wrote out their numbers? 103 in Roman numerals is CIII. The number 99 is XCIX. You try adding CIII + XCIX. It’s absurd. Placeholder notation is what allows us to easily add, subtract, and otherwise manipulate numbers. Placeholder notation is what allows us to work out complicated math problems on a sheet of paper.

If zero had remained simply a placeholder digit, it would have been a profound tool on its own. But around 1,500 years ago (or perhaps even earlier ), in India , zero became its own number, signifying nothing. The ancient Mayans, in Central America, also independently developed zero in their number system around the dawn of the common era.

In the seventh century, the Indian mathematician Brahmagupta wrote down what’s recognized as the first written description of the arithmetic of zero:

When zero is added to a number or subtracted from a number, the number remains unchanged; and a number multiplied by zero becomes zero.

Zero slowly spread across the Middle East before reaching Europe, and the mind of the mathematician Fibonacci in the 1200s, who popularized the “Arabic” numeral system we all use today.

From there, the usefulness of zero exploded. Think of any graph that plots a mathematical function starting at 0,0. This now-ubiquitous method of graphing was only first invented in the 17th century after zero spread to Europe. That century also saw a whole new field of mathematics that depends on zero: calculus.

You may recall from high school or college math that the simplest function in calculus is taking a derivative. A derivative is simply the slope of a line that intersects with a single point on a graph.

To calculate the slope of a single point, you usually need a point of comparison: rise over run . What Isaac Newton and Gottfried Leibniz discovered when they invented calculus is that calculating that slope at a single point involves getting even closer, closer, and closer — but never actually — dividing by zero.

“All infinite processes [in math] pivot around, dance around, the notion of zero,” Robert Kaplan says. Whoa.

Why is zero so profound as a human idea?

We’re not born with an understanding of zero. We have to learn it, and it takes time.

Elizabeth Brannon is a neuroscientist at Duke University who studies how both humans and animals represent numbers in their minds. She explains that even when kids younger than 6 understand that the word “zero” means “nothing,” they still have a hard time grasping the underlying math. “When you ask [a child] which number is smaller, zero or one, they often think of one as the smallest number,” Brannon says. “It’s hard to learn that zero is smaller than one.”

In experiments, Brannon will often play a game with 4-year-olds. She’ll put out a pair of cards on a table or screen. And each card will have a number of objects on it. One card will have two dots, for instance. Another will have three. Here’s an example of what they might see.

She’ll simply ask the kids to pick the card with the fewest number of objects. When a card with nothing on it is paired with a card with one object on it, less than half the kids will get the answer right.

So what happens to make it all click?

Andreas Nieder , the cognitive scientist from Germany, hypothesizes there are four psychological steps to understand zero, and each step is more cognitively complicated than the one before it.

Many animals can get through the first three steps. But the last stage, the most difficult one, is “reserved for us humans,” Nieder says.

The first is a just having the simple sensory experience of stimulus going on and off. This is the simple ability to notice a light flickering on and off. Or a noise turning on and off.

The second is behavioral understanding. At this stage, not only can animals recognize a lack of a stimulus, they can react to it. When an individual has run out of food, they know to go and find more.

The third stage is recognizing that zero, or an empty container, is a value less than one. This is tricky, though a surprising number of animals, including honey bees and monkeys, can recognize this fact. It’s understanding “that nothing has a quantitative category,” Nieder says.

The fourth stage is taking the absence of a stimulus and treating as it as a symbol and a logical tool to solve problems. No animal outside of humans, he says, “no matter how smart,” understands that zero can be a symbol.

But even well-educated humans can still stumble a bit when thinking about zero. Studies have shown that adults take a few moments longer to recognize the number zero compared to other numerals. And when Brannon’s pick-the-lowest-number-card experiment is repeated with adults, they take slightly longer when deciding between zero and one, than when comparing zero to a larger number.

That suggests that zero, even for adults, takes an extra effort of brain power to process.

What else can understand nothing?

We may not be born with the ability to understand zero. But our capacity to learn it may have deep evolutionary roots, as some new science shows us.

The fourth step in thinking of zero — that is thinking of zero as a symbol — may be unique to humans. But a surprising number of animals can get to step three: recognizing that zero is less than one.

Even bees can do it.

Scarlett Howard, a PhD student at Royal Melbourne Institute of Technology, recently published an experiment in Science that’s almost identical to the one Brannon did with kids. The bees chose the blank page 60 to 70 percent of the time. And they were significantly better at discriminating a large number, like six, from zero, than they were in discriminating one from zero. Just like the kids.

This is impressive, considering that “we’ve got this big mammalian brain but bees have a brain that’s so small weighs less than a milligram,” Howard says. Her research group is hoping to understand how bees do these calculations in their minds, with the goal of one day using those insights to build more efficient computers.

In similar experiments, researchers have shown that monkeys can recognize the empty set (and are often better at it than 4-year-old humans). But the fact that bees can do it is kind of amazing, considering how far they are away from us on the evolutionary trees of life. “The last common ancestor between us and the bees lived about 600 million years ago, which is an eternity in evolutionary times,” Nieder says.

We humans might have only come to understand zero as a number 1,500 years ago. What the experiments on bees and monkeys show us is that it’s not just the work of our ingenuity. It’s also, perhaps, the culminating work of evolution.

There are still great mysteries about zero. For one, Nieder says “we hardly know anything” about how the brain physically processes it. And we don’t know how many animals can grasp the idea of nothing as a quantity.

But what mathematics has clearly shown us is that when we investigate nothing, we’re bound to find something.

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Who Invented the Zero?

By: History Staff

Updated: June 6, 2023 | Original: January 22, 2014

It might seem like an obvious piece of any numerical system, but the zero is a surprisingly recent development in human history. In fact, this ubiquitous symbol for “nothing” didn’t even find its way to Europe until as late as the 12th century. 

Zero’s origins most likely date back to the “fertile crescent” of ancient Mesopotamia. Sumerian scribes used spaces to denote absences in number columns as early as 4,000 years ago, but the first recorded use of a zero-like symbol dates to sometime around the third century B.C. in ancient Babylon. The Babylonians employed a number system based around values of 60, and they developed a specific sign—two small wedges—to differentiate between magnitudes in the same way that modern decimal-based systems use zeros to distinguish between tenths, hundreds and thousandths. A similar type of symbol cropped up independently in the Americas sometime around A.D. 350, when the Mayans began using a zero marker in their calendars.

These early counting systems only saw the zero as a placeholder—not a number with its own unique value or properties. A full grasp of zero’s importance would not arrive until the seventh century A.D. in India. There, the mathematician Brahmagupta and others used small dots under numbers to show a zero placeholder, but they also viewed the zero as having a null value, called “sunya.” Brahmagupta was also the first to show that subtracting a number from itself results in zero. 

From India, the zero made its way to China and back to the Middle East, where it was taken up by the mathematician Mohammed ibn-Musa al-Khowarizmi around 773. He studied and synthesized Indian arithmetic and showed how zero functioned in the system of formulas he called ‘al-jabr’—today known as algebra. By the 10th century, the zero had entered the Arabic numeral system in a form resembling the oval shape we use today.

The zero continued to migrate for another few centuries before finally reaching Europe sometime around the 1100s. Thinkers like the Italian mathematician Fibonacci helped introduce zero to the mainstream, and it later figured prominently in the work of Rene Descartes along with Sir Isaac Newton and Gottfried Leibniz’s invention of calculus. Since then, the concept of “nothing” has continued to play a role in the development of everything from physics and economics to engineering and computing.

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Research Download - SPD Flash Tool R24.0.0003 All Versions

What Is Research Download Tool?

What are the features of the research download tool, how to flash the firmware with research download tool, what are the contents of research download tool files.

• What Are the Additional Details of the Research Download Tool Tool?

Download Link of Research Download - SPD Flash Tool.

Research download tool features list:.

• Read Flash.
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• Set Debug Level.
• UART Download.
• End of Download (Reset to Normal, Power Off, Dump Uboot Log).
• Read Option (Read MCP Type, Read Chip UID, Read Partition).
• Check Option (Check Match, DDR Check, EMMC Check, Self Refresh, Check Second Hand Memory).
• Auto Comparison
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• UART Port Switch.
• First of all, you need to download the required Flash Tool program and the firmware file according to your mobile device. If you have already downloaded it, then skip this step.
• Now extract the compressed file of Flash tool with the latest version of WinRAR.
• Right-click on the " ResearchDownload.exe " setup file and run it in administrator mode.
• Click the Load Packet button in the upper left corner and select the .pac firmware file. Once you select the firmware file, the Flash tool will take some time to load the file, so be patient.
• After successfully loading the firmware file into the tool, click the third " Start Downloading " button of the program.
• Then turn off the mobile device, press the boot key (volume up + volume down), and connect to the computer.
• As soon as you connect your mobile device to the computer, the flashing process starts, and after the flashing process is completed, you will see Previous Passed written in green in the progress bar.
• Finally, disconnect your mobile device from the computer and turn on the power; For the first time since the flash, the mobile will take about five to ten minutes to power on.

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A null hypothesis is a statistical concept suggesting no significant difference or relationship between measured variables. It’s the default assumption unless empirical evidence proves otherwise.

The null hypothesis states no relationship exists between the two variables being studied (i.e., one variable does not affect the other).

The null hypothesis is the statement that a researcher or an investigator wants to disprove.

Testing the null hypothesis can tell you whether your results are due to the effects of manipulating ​ the dependent variable or due to random chance. 

How to Write a Null Hypothesis

Null hypotheses (H0) start as research questions that the investigator rephrases as statements indicating no effect or relationship between the independent and dependent variables.

It is a default position that your research aims to challenge or confirm.

For example, if studying the impact of exercise on weight loss, your null hypothesis might be:

There is no significant difference in weight loss between individuals who exercise daily and those who do not.

Examples of Null Hypotheses

When Do We Reject The Null Hypothesis? 

We reject the null hypothesis when the data provide strong enough evidence to conclude that it is likely incorrect. This often occurs when the p-value (probability of observing the data given the null hypothesis is true) is below a predetermined significance level.

If the collected data does not meet the expectation of the null hypothesis, a researcher can conclude that the data lacks sufficient evidence to back up the null hypothesis, and thus the null hypothesis is rejected. 

Rejecting the null hypothesis means that a relationship does exist between a set of variables and the effect is statistically significant ( p > 0.05).

If the data collected from the random sample is not statistically significance , then the null hypothesis will be accepted, and the researchers can conclude that there is no relationship between the variables. 

You need to perform a statistical test on your data in order to evaluate how consistent it is with the null hypothesis. A p-value is one statistical measurement used to validate a hypothesis against observed data.

Calculating the p-value is a critical part of null-hypothesis significance testing because it quantifies how strongly the sample data contradicts the null hypothesis.

The level of statistical significance is often expressed as a  p  -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01) as general guidelines to decide if you should reject or keep the null.

When your p-value is less than or equal to your significance level, you reject the null hypothesis.

In other words, smaller p-values are taken as stronger evidence against the null hypothesis. Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis.

In this case, the sample data provides insufficient data to conclude that the effect exists in the population.

Because you can never know with complete certainty whether there is an effect in the population, your inferences about a population will sometimes be incorrect.

When you incorrectly reject the null hypothesis, it’s called a type I error. When you incorrectly fail to reject it, it’s called a type II error.

Why Do We Never Accept The Null Hypothesis?

The reason we do not say “accept the null” is because we are always assuming the null hypothesis is true and then conducting a study to see if there is evidence against it. And, even if we don’t find evidence against it, a null hypothesis is not accepted.

A lack of evidence only means that you haven’t proven that something exists. It does not prove that something doesn’t exist. 

It is risky to conclude that the null hypothesis is true merely because we did not find evidence to reject it. It is always possible that researchers elsewhere have disproved the null hypothesis, so we cannot accept it as true, but instead, we state that we failed to reject the null. 

One can either reject the null hypothesis, or fail to reject it, but can never accept it.

Why Do We Use The Null Hypothesis?

We can never prove with 100% certainty that a hypothesis is true; We can only collect evidence that supports a theory. However, testing a hypothesis can set the stage for rejecting or accepting this hypothesis within a certain confidence level.

The null hypothesis is useful because it can tell us whether the results of our study are due to random chance or the manipulation of a variable (with a certain level of confidence).

A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis.

Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists. 

Hypothesis testing is a critical part of the scientific method as it helps decide whether the results of a research study support a particular theory about a given population. Hypothesis testing is a systematic way of backing up researchers’ predictions with statistical analysis.

It helps provide sufficient statistical evidence that either favors or rejects a certain hypothesis about the population parameter. 

Purpose of a Null Hypothesis 

• The primary purpose of the null hypothesis is to disprove an assumption. 
• Whether rejected or accepted, the null hypothesis can help further progress a theory in many scientific cases.
• A null hypothesis can be used to ascertain how consistent the outcomes of multiple studies are.

Do you always need both a Null Hypothesis and an Alternative Hypothesis?

The null (H0) and alternative (Ha or H1) hypotheses are two competing claims that describe the effect of the independent variable on the dependent variable. They are mutually exclusive, which means that only one of the two hypotheses can be true. 

While the null hypothesis states that there is no effect in the population, an alternative hypothesis states that there is statistical significance between two variables. 

The goal of hypothesis testing is to make inferences about a population based on a sample. In order to undertake hypothesis testing, you must express your research hypothesis as a null and alternative hypothesis. Both hypotheses are required to cover every possible outcome of the study. 

What is the difference between a null hypothesis and an alternative hypothesis?

The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population.

It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.

What are some problems with the null hypothesis?

One major problem with the null hypothesis is that researchers typically will assume that accepting the null is a failure of the experiment. However, accepting or rejecting any hypothesis is a positive result. Even if the null is not refuted, the researchers will still learn something new.

Why can a null hypothesis not be accepted?

We can either reject or fail to reject a null hypothesis, but never accept it. If your test fails to detect an effect, this is not proof that the effect doesn’t exist. It just means that your sample did not have enough evidence to conclude that it exists.

We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.

Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.

If the p-value is greater than the significance level, then you fail to reject the null hypothesis.

Is a null hypothesis directional or non-directional?

A hypothesis test can either contain an alternative directional hypothesis or a non-directional alternative hypothesis. A directional hypothesis is one that contains the less than (“<“) or greater than (“>”) sign.

A nondirectional hypothesis contains the not equal sign (“≠”).  However, a null hypothesis is neither directional nor non-directional.

A null hypothesis is a prediction that there will be no change, relationship, or difference between two variables.

The directional hypothesis or nondirectional hypothesis would then be considered alternative hypotheses to the null hypothesis.

Gill, J. (1999). The insignificance of null hypothesis significance testing.  Political research quarterly ,  52 (3), 647-674.

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method.  American Psychologist ,  56 (1), 16.

Masson, M. E. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing.  Behavior research methods ,  43 , 679-690.

Nickerson, R. S. (2000). Null hypothesis significance testing: a review of an old and continuing controversy.  Psychological methods ,  5 (2), 241.

Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test.  Psychological bulletin ,  57 (5), 416.

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Fewer than half of U.S. jails provide life-saving medications for opioid use disorder

NIH findings highlight critical gaps in treatment access in correctional facilities, where almost two-thirds of people have a substance use disorder

A new look into addiction treatment availability in the U.S. criminal justice system reveals that fewer than half (43.8%) of 1,028 jails surveyed across the nation offered any form of medication for opioid use disorder, and only 12.8% made these available to anyone with the disorder. With two-thirds of people who are incarcerated in U.S. jails experiencing a substance use disorder – in many cases, an opioid use disorder – the failure to make these medications widely available in criminal justice settings represents a significant missed opportunity to provide life-saving treatments in an environment where people in need of care can be easily reached.

The study , published in JAMA Network Open and supported by NIH’s National Institute on Drug Abuse (NIDA), also found that most jails did offer some type of substance use disorder treatment or recovery support (70.1%). The most common reason jails cited for not offering medications for opioid use disorder was lack of adequate licensed staff (indicated by 49.8% of jails). In general, larger jails, those in counties with lower “social vulnerability” (lower levels of poverty and unemployment, and greater education, housing, and transportation access), and those with greater proximity to community-based providers of medications for opioid use disorder were more likely to offer these treatments.

“Offering substance use disorder treatment in justice settings helps to break the debilitating – and often fatal – cycle of addiction and incarceration,” said NIDA Director Nora D. Volkow, M.D. “Though someone may be in jail for only a short time, connecting them to addiction treatment while they are there is critical to reduce risk of relapse and overdose, and to help them achieve long-term recovery.”

The criminal justice system is a crucial point of intervention in the overdose crisis. Overdose is the leading cause of death among people returning to their communities after incarceration. A recent county-level study found that 21% of individuals who died of a fatal overdose had been in jail, a facility for short-term stays, where most people are awaiting trial, sentencing, or serving a short sentence.

Research shows that medications for opioid use disorder – buprenorphine, methadone, and naltrexone – reduce opioid use, prevent overdose deaths, and support long-term recovery. Among people who were formerly incarcerated, access to these medications during incarceration or at release has been shown to reduce overdose deaths , increase use of community-based treatment , and decrease rates of reincarceration . However, access to medications for opioid use disorder in jails remains limited due to various barriers, including cost, staffing, and regulatory challenges.

To update current knowledge of addiction treatment gaps in jails across the country, researchers at NORC at the University of Chicago invited a random sample of 2,791 jails to take a survey on availability of medications for opioid use disorder. These jails were selected to be representative of the over 3,500 jails in the U.S. The researchers collected data between June 2022 and April 2023 and received responses from 1,028 jails, 927 of which were included in analysis. More than half of the participating jails (55.6%) were located in non-metropolitan areas, and many jails offered contracted health care services (59.8%).

The researchers found that more than half of the surveyed jails did not offer medications for opioid use disorder, and that those with direct or hybrid health care services were more likely to provide these medications than those relying on external facilities or with no onsite health care services. For those jails that did offer these medications, buprenorphine was the most commonly provided – available in 69.9% of jails that offered these medications – followed by naltrexone (54.5%) and methadone (46.6%).

The researchers note that even within the jails that offer medications for opioid use disorder, most often these medications are only made available to people who are pregnant, or to those who were already receiving any of these medications at the time of their arrest. The research team is conducting additional analyses to better understand the barriers to universal medication availability within jails.

“Data on health care gaps for people who are incarcerated provides a necessary knowledge base to help policymakers, public health officials, researchers, and communities assess where to allocate resources to improve care for opioid use disorder for this population,” said Elizabeth Flanagan Balawajder, senior research associate at NORC at the University of Chicago and the study’s corresponding author. “Our findings suggest that supporting areas such as staff training, infrastructure improvements, and partnerships with community treatment providers are key areas to improve substance use disorder treatment for people in jail.”

While this study provides the most comprehensive overview to date of the availability of these medications in U.S. jails, its limitations include low rates of jail responses, reliance on self-reported data, and a lack of assessment of the quality or outcomes of addiction treatment programs. Future research will include evaluating the impact of providing these medications on health outcomes for the people in jail, as well as exploring sex, gender, race and ethnicity-related disparities in access to medications for opioid use disorder within the criminal justice system.

This study was conducted by researchers in the NIDA-funded Justice Community Opioid Innovation Network (JCOIN), which is supported through the NIH Helping to End Addiction Long-term Initiative, or NIH HEAL Initiative . The study included contributions from experts at the University of Illinois Chicago, Baystate Health, the University of Massachusetts Chan Medical School-Baystate, the University of Chicago’s Crown Family School of Social Work, Policy and Practice, the Department of Medicine and Public Health Sciences at the University of Chicago, and NIDA.

Under the Biden-Harris Administration, the Department of Health and Human Services has taken several steps that expand access to medications for opioid use disorder and addiction care to people who are incarcerated. For examples, see new guidance from the Centers for Medicare & Medicaid Services , new funding opportunities through the Health Resources and Services Administration , and SAMHSA’s Adult Reentry Program Grants .

The NIH Helping to End Addiction Long-term® and NIH HEAL Initiative® are registered service marks of the Department of Health and Human Services.

If you or someone you know is struggling or in crisis, help is available. Call or text 988 or chat at 988lifeline.org . To learn how to get support for mental health, drug or alcohol conditions visit  FindSupport.gov . If you are ready to locate a treatment facility or provider, you can go directly to  FindTreatment.gov or call  800-662-HELP (4357) .

• EF Balawajder, et al. Factors associated with the availability of medications for opioid use disorder in US jails . JAMA Network Open . DOI: 10.1001/jamanetworkopen.2024.34704 (2024).

About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

About substance use disorders: Substance use disorders are chronic, treatable conditions from which people can recover. In 2022, nearly 49 million people in the United States had at least one substance use disorder. Substance use disorders are defined in part by continued use of substances despite negative consequences. They are also relapsing conditions, in which periods of abstinence (not using substances) can be followed by a return to use. Stigma can make individuals with substance use disorders less likely to seek treatment. Using preferred language can help accurately report on substance use and addiction. View NIDA’s online guide .

NIH…Turning Discovery Into Health®

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Statistical significance.

Steven Tenny ; Ibrahim Abdelgawad .

Affiliations

Last Update: November 23, 2023 .

• Introduction

In research, statistical significance measures the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer. We can better understand statistical significance if we break apart a study design. [1] [2] [3] [4] [5] [6] [7]

When creating a study, the researcher has to start with a hypothesis; that is, they must have some idea of what they think the outcome may be. For example, a study is researching a new medication to lower blood pressure. The researcher hypothesizes that the new medication lowers systolic blood pressure by at least 10 mm Hg compared to not taking the new medication. The hypothesis can be stated: "Taking the new medication will lower systolic blood pressure by at least 10 mm Hg compared to not taking the medication." In science, researchers can never prove any statement as there are infinite alternatives as to why the outcome may have occurred. They can only try to disprove a specific hypothesis. The researcher must then formulate a question they can disprove while concluding that the new medication lowers systolic blood pressure. The hypothesis to be disproven is the null hypothesis and typically the inverse statement of the hypothesis. Thus, the null hypothesis for our researcher would be, "Taking the new medication will not lower systolic blood pressure by at least 10 mm Hg compared to not taking the new medication." The researcher now has the null hypothesis for the research and must specify the significance level or level of acceptable uncertainty.

Even when disproving a hypothesis, the researcher can not be 100% certain of the outcome. The researcher must then settle for some level of confidence, or the degree of significance, for which they want to be confident their finding is correct. The significance level is given the Greek letter alpha and specified as the probability the researcher is willing to be incorrect. Generally, a researcher wants to be correct about their outcome 95% of the time, so the researcher is willing to be incorrect 5% of the time. Probabilities are decimals, with 1.0 being entirely positive (100%) and 0 being completely negative (0%). Thus, the researcher who wants to be 95% sure about the outcome of their study is willing to be wrong about the result 5% of the time. The alpha is the decimal expression of how much they are ready to be incorrect. For the current example, the alpha is 0.05. The level of uncertainty the researcher is willing to accept (alpha or significance level) is 0.05, or a 5% chance they are incorrect about the study's outcome.

Now, the researcher can perform the research. In this example, a prospective randomized controlled study is conducted in which the researcher gives some individuals the new medication and others a placebo. The researcher then evaluates the blood pressure of both groups after a specified time and performs a statistical analysis of the results to obtain a  P  value (probability value). Several different tests can be performed depending on the type of variable being studied and the number of subjects. The exact test is outside the scope of this review, but the output would be a  P  value. Using the correct statistical analysis tool when calculating the  P  value is imperative. If the researchers use the wrong test, the  P  value will not be accurate, and this result can mislead the researcher. A  P  value is a probability under a specified statistical model that a statistical summary of the data (eg, the sample mean difference between 2 compared groups) would be equal to or more extreme than its observed value.

In this example, the researcher hypothetically found blood pressure tended to decrease after taking the new medication, with an average decrease of 15 mm Hg in the group taking the new medication. The researcher then used the help of their statistician to perform the correct analysis and arrived at a  P  value of 0.02 for a decrease in blood pressure in those taking the new medication versus those not taking the new medication. This researcher now has the 3 required pieces of information to look at statistical significance: the null hypothesis, the significance level, and the  P  value.

The researcher can finally assess the statistical significance of the new medication. A study result is statistically significant if the  P  value of the data analysis is less than the prespecified alpha (significance level). In this example, the P value is 0.02, which is less than the prespecified alpha of 0.05, so the researcher rejects the null hypothesis, which has been determined within the predetermined confidence level to be disproven, and accepts the hypothesis, thus concluding there is statistical significance for the finding that the new medication lowers blood pressure. 

What does this mean? The P value is not the probability of the null hypothesis itself. It is the probability that, if the study were repeated an infinite number of times, one would expect the findings to be as, or more extreme, than the one calculated in this test. Therefore, the  P  value of 0.02 would signify that 2% of the infinite tests would find a result at least as extreme as the one in this study. Given that the null hypothesis states that there is no significant change in blood pressure if the patient is or is not taking the new medication, we can assume that this statement is false, as 98% of the infinite studies would find that there was indeed a reduction in blood pressure. However, as the  P  value implies, there is a chance that this is false, and there truly is no effect of the medication on the blood pressure. However, as the researcher prespecified an acceptable confidence level with an alpha of 0.05, and the  P  value is 0.02, less than the acceptable alpha of 0.05, the researcher rejects the null hypothesis. By rejecting the null hypothesis, the researcher accepts the alternative hypothesis. The researcher rejects the idea that there is no difference in systolic blood pressure with the new medication and accepts a difference of at least 10 mm Hg in systolic blood pressure when taking the new medication.

If the researcher had prespecified an alpha of 0.01, implying they wanted to be 99% sure the new medication lowered the blood pressure by at least 10 mm Hg, the  P  value of 0.02 would be more significant than the prespecified alpha of 0.01. The researcher would conclude the study did not reach statistical significance as the  P  value is equal to or greater than the prespecified alpha. The research would then not be able to reject the null hypothesis.

A study is statistically significant if the  P  value is less than the pre-specified alpha. Stated succinctly:

• A  P  value less than a predetermined alpha is considered a statistically significant result 
• A  P  value greater than or equal to alpha is not a statistically significant result.
• Issues of Concern

A few issues of concern when looking at statistical significance are evident. These issues include choosing the alpha, statistical analysis method, and clinical significance.

Many current research articles specify an alpha of 0.05 for their significance level. It cannot be stated strongly enough that there is nothing special, mathematical, or certain about picking an alpha of 0.05. Historically, the originators concluded that for many applications, an alpha of 0.05, or a one in 20 chance of being incorrect, was good enough. The researcher must consider what the confidence level should genuinely be for the research question being asked. A smaller alpha, say 0.01, may be more appropriate.

When creating a study, the alpha, or confidence level, should be specified before any intervention or collection of data. It is easy for a researcher to "see what the data shows" and then pick an alpha to give a statistically significant result. Such approaches compromise the data and results as the researcher is more likely to be lax on confidence level selection to obtain a result that looks statistically significant.

A second important issue is selecting the correct statistical analysis method. There are numerous methods for obtaining a  P  value. The method chosen depends on the type of data, the number of data points, and the question being asked. It is essential to consider these questions during the study design so the statistical analysis can be correctly identified before the research. The statistical analysis method can help determine how to collect the data correctly and the number of data points needed. If the wrong statistical method is used, the results may be meaningless, as an incorrect  P  value would be calculated.

• Clinical Significance

A key distinction between statistical significance and clinical significance is evident. Statistical significance determines if there is mathematical significance to the analysis of the results. Clinical significance means the difference is vital to the patient and the clinician. This study's statistical significance would be present as the  P  value was less than the prespecified alpha. The clinical significance would be the 10 mmHg drop in systolic blood pressure. [6]

Two studies can have a similar statistical significance but vastly differ in clinical significance. In a hypothetical example of 2 new chemotherapy agents for treating cancer, Drug A increased survival by at least 10 years with a  P  value of 0.01 and an alpha for the study of 0.05. Thus, this study has statistical significance ( P  value less than alpha) and clinical significance (increased survival by 10 years). A second chemotherapy agent, Drug B, increases survival by at least 10 minutes with a  P  value of 0.01 and alpha for the study of 0.05. The study for Drug B also found statistical significance ( P  value less than alpha) but no clinical significance (a 10-minute increase in life expectancy is not clinically significant). In a separate study, those taking Drug A lived an average of 8 years after starting the medication versus living for only 2 more years for those not taking Drug A, with a  P  value of 0.08 and alpha for this second study of Drug A of 0.05. In this second study of Drug A, there is no statistical significance ( P  value greater than or equal to alpha).

• Enhancing Healthcare Team Outcomes

Each healthcare team member needs a basic understanding of statistical significance. All members of the care continuum, including nurses, physicians, advanced practitioners, social workers, and pharmacists, peruse copious literature and consider conclusions based on statistical significance. Suppose team members do not have a cohesive and harmonious understanding of the statistical significance and its implications for research studies and findings. In that case, various members may draw opposing conclusions from the same research.

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Ibrahim Abdelgawad declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Abdelgawad I. Statistical Significance. [Updated 2023 Nov 23]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Quantitative Research Assistant

STANDARD GRADE 6

£33,832-£39,705 p.a.

We are seeking to recruit a Quantitative Research Assistant who will work as part of the Young Lives research team to support agreed research priorities. The post holder will be expected to work collaboratively with researchers and policy staff in Oxford and the four Young Lives study countries.

The position of Quantitative Research Assistant emphasises analysis of quantitative data and requires strong quantitative analysis skills and interpersonal/team-working skills. The post-holder will be responsible to the Young Lives Quantitative Research Officer.

Key Responsibilities:

• Prepare Young Lives survey data for analysis and archiving including producing composite variables and linking data across rounds;
• Prepare Young lives fieldwork documents (e.g. fieldwork manual, consent form and training materials) and research materials for publication;
• Analyse survey data using appropriate statistical models and techniques; linking and merging data from external sources with Young Lives data where appropriate;
• Support the data manager on data matching exercises;
• Prepare reports from analyses, including preparing tables, graphs etc. that can be used to best represent the findings in Young Lives written outputs and presentations;
• Contribute to research publications, book chapters and reviews;
• Contribute ideas for new and existing components of the Young Lives study;
• Provide technical support to researchers and policy staff on their use of the Young Lives survey data;
• Contribute to discussions and share research findings with colleagues in Oxford and partner institutions in the Young Lives study countries;
• Represent Young Lives at external meetings/seminars;
• Contribute to wider project planning, including ideas for new research papers and sub-studies.

The successful candidate will have a Master’s degree completed by October 2024 in a quantitative social science discipline (e.g. economics, social statistics). They will also have proficiency in using the STATA statistical package for data management, manipulation and analysis. High level written communication skills in English are essential for this role.

At ODID we embrace and cherish our differences, and endeavour to be considerate and welcoming of all. You are most welcome at ODID, without the need to hide any part of who you are. We acknowledge societal inequalities and how these affect us, and those around us, personally and professionally. We hold an Athena Swan bronze award in recognition of our efforts to introduce organisational and cultural changes which promote equality, cherish diversity and create a better working environment for all. We are also taking active steps to promote race equality and reduce the risk of bias and discrimination. We work together to enrich, fortify and grow our community and dedicate our combined efforts to teaching and research to increase our impact and influence in development debates at the national and global level. We encourage all eligible candidates to apply for our vacancies and join us in pursuing our goals.

Applications for this vacancy are to be made online. You will be required to upload a CV and supporting statement as part of your online application.

Only applications received before 12 midday on 14 October 2024 can be considered. 

Interviews are envisioned to be held on 21 October 2024.

Communications Officer, Young Lives

Director of academic programmes (refugee-led research hub), director of operations and strategy (refugee-led research hub), research and grants administrator.

Alcohol can increase your cancer risk. What experts say about how much is too much

by CBS News

Despite growing research that points to the negative health impacts of alcohol, many Americans are unaware of the connection between drinking and cancer risk.

A recent report from the American Association for Cancer Research found excessive levels of alcohol consumption increase the risk for six different types of cancer, including:

• breast cancer
• colorectal cancer
• liver cancer
• stomach cancer
• certain types of head and neck cancer
• esophageal squamous cell carcinoma

"Some of this is happening through chronic inflammation. We also know that alcohol changes the microbiome, so those are the bacteria that live in your gut, and that can also increase the risk," Dr. Céline Gounder, CBS News medical contributor and editor-at-large for public health at KFF Health News, recently said on "CBS Mornings."

But how much is too much when it comes to drinking? We asked experts what to know:

How much alcohol is bad for you?

"Excessive levels of alcohol" equates to about three or more drinks per day for women and four or more drinks per day for men, Gounder said.

According to the National Institute on Alcohol Abuse and Alcoholism, a standard alcoholic drink contains 14 grams (or 0.6 ounces) of pure alcohol. Generally, this amount is found in 12 ounces of beer, 5 ounces of wine and 1.5 ounces, or a "shot," of 80-proof distilled spirits or liquor.

Other studies have shown, however, there is no "safe amount" of alcohol, Gounder said, particularly if you have underlying medical conditions.

"If you don't drink, don't start drinking. If you do drink, really try to keep it within moderation," she said.

Dr. Amy Commander, medical director of the Mass General Cancer Center specializing in breast cancer, told CBS News alcohol is the third leading modifiable risk factor that can increase cancer risk after accounting for cigarette smoking and excess body weight.

"There really isn't a safe amount of alcohol for consumption," she said. "In fact, it's best to not drink alcohol at all, but that is obviously hard for many people. So I think it's really important for individuals to just be mindful of their alcohol consumption and certainly drink less."

How else can alcohol affect your health?

The benefits of limiting alcohol is not just about cancer risk. Alcohol in large or frequent amounts is considered toxic to the human body, said Dr. Angela Tatiana Alistar, medical director of GI Medical Oncology at Morristown Medical Center, Atlantic Health System.

"All organs can be affected by alcohol intake depending on dose and frequency," she told CBS News. "There is vast scientific literature regarding the impact of alcohol consumption on the liver described as hepatitis (in the acute form), liver cirrhosis (chronic form) and possible liver cancer."

Drinking alcohol can also have direct and immediate effects on other organs like the brain, affecting the sleep-wake cycle.

"It can also affect cognition, coordination and emotional regulation," Alistar said. "Acute and chronic dependence and tolerance to alcohol is a high-risk factor for social functioning and mental health."

Is wine healthy?

Some studies have suggested health benefits from drinking red wine . Occasional wine is also a part of the Mediterranean diet, which is often praised for a reduction in mortality, heart disease and more. But, other research says any potential benefit doesn't outweigh the risks.

"A glass of red wine is included in terms of the alcoholic beverages that one should be mindful of — beer, red wine, other spirits," said Commander. "There's been at times this health halo about red wine, but when it comes to cancer, it is still considered alcohol and it should be limited."

But, doesn't everything cause cancer anyway?

Commander admits cancers are complex with many factors that can contribute to someone's risk — but, at the same time, we're making strides in cure rates and early detection.

For example, the latest Cancer Progress Report found a 33% reduction in the overall cancer deaths from 1991 to 2021.

"So that is hopeful," Commander said, adding alcohol is just "one piece of the puzzle" in prevention.

"There are so many aspects of our lifestyle that we should focus on in order to optimize our health, certainly to reduce our risk of cancer, but also other chronic diseases, such as cardiovascular disease, Type 2 diabetes and other conditions that we face in this country," she said.

Alistar recognizes social changes can be difficult when it comes to alcohol.

"Socializing with friends and family typically involves sharing food and drinks. Having open conversations about the impact of alcohol on general health with your family and friends would be a good way to start," she said, but added "celebratory drinks or foods are OK in my opinion if most of the time one has a healthy diet and lifestyle."

Other ways to decrease cancer risk

Reducing alcohol isn't the only way to decrease your risk of cancer. There are several other modifiable risk factors that researchers found are linked to more than 40% of all cancer fatalities, including smoking, excess body weight, physical inactivity and diet.

Using sunscreen to prevent skin cancer and getting the HPV vaccine to prevent cervical cancer and oral cancer related to the human papillomavirus are other prevention measures, Gounder said.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Article Contents

Introduction, summary of rpfdb features and functionalities, expansion and improvement of the rpfdb database, database usage example, conclusion and discussion, data availability, acknowledgements, rpfdb v3.0: an enhanced repository for ribosome profiling data and related content.

The first two authors should be regarded as Joint First Authors.

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Yan Wang, Yuewen Tang, Zhi Xie, Hongwei Wang, RPFdb v3.0: an enhanced repository for ribosome profiling data and related content, Nucleic Acids Research , 2024;, gkae808, https://doi.org/10.1093/nar/gkae808

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RPFdb ( http://www.rpfdb.org or http://sysbio.gzzoc.com/rpfdb/ ) is a comprehensive repository dedicated to hosting ribosome profiling (Ribo-seq) data and related content. Herein, we present RPFdb v3.0, a significant update featuring expanded data content and improved functionality. Key enhancements include (i) increased data coverage, now encompassing 5018 Ribo-seq datasets and 2343 matched RNA-seq datasets from 496 studies across 34 species; (ii) implementation of translation efficiency, combining Ribo-seq and RNA-seq data to provide gene-specific translation efficiency; (iii) addition of pausing score, facilitating the identification of condition-specific triplet amino acid motifs with enhanced ribosome enrichment; (iv) refinement of open reading frame (ORF) annotation, leveraging RibORF v2.0 for more sensitive detection of actively translated ORFs; (v) introduction of a resource hub, curating advances in translatome sequencing techniques and data analytics tools to support a panoramic overview of the field; and (vi) redesigned web interface, providing intuitive navigation with dedicated pages for streamlined data retrieval, comparison and visualization. These enhancements make RPFdb a more powerful and user-friendly resource for researchers in the field of translatomics. The database is freely accessible and regularly updated to ensure its continued relevance to the scientific community.

Translation is a highly regulated process that plays a crucial role in shaping cellular proteomes ( 1 ). Its significance extends beyond protein synthesis, serving as a rapid and reversible means of gene regulation that enables cells to respond dynamically to environmental cues and stress conditions ( 2 ). The multi-step nature of translation, including initiation, elongation and termination phases, each regulated by numerous factors, underscores its complexity ( 3 ). Recent years have seen a paradigm shift in our understanding of gene translation and translational regulation. A pivotal advancement in this field is the development of ribosome profiling (Ribo-seq) by Ingolia and Weissman in 2009 ( 4 ). This breakthrough technique revolutionized translatome research, providing an unprecedented, genome-wide, high-resolution view of messenger RNA (mRNA) translation by capturing and sequencing ribosome-protected mRNA fragments.

Since its inception, Ribo-seq has undergone significant methodological refinements to address technical challenges such as ribosome runoff during sample preparation and biases in library construction ( 5 ). These improvements, coupled with advances in bioinformatics ( 6 ), have broadened its applications. Consequently, Ribo-seq has facilitated groundbreaking discoveries: (i) identification of pervasive translation outside annotated protein-coding regions, including within long non-coding RNAs and circular RNAs, challenging traditional definitions of the coding genome ( 7–9 ); (ii) revelation of the prevalence and importance of upstream and downstream open reading frames (ORFs) and alternative start codons in translational control, elucidating novel mechanisms of gene expression regulation ( 10 , 11 ); (iii) insights into phenomena such as ribosome pausing and its role in protein folding and regulation, linking translational kinetics to protein function ( 12 ); (iv) elucidation of translational dysregulation in various pathologies, opening new avenues for therapeutic interventions ( 13 ); and (v) understanding of how viruses hijack and manipulate host translation machinery, informing the development of novel antiviral strategies ( 14 ).

These discoveries have precipitated a surge in Ribo-seq experiments across molecular biology and biomedicine. As researchers increasingly embrace this powerful technique, the growing volume of data has necessitated the creation of dedicated databases for hosting, processing and analyzing this information. These databases range from comprehensive, multi-species repositories such as RPFdb ( 15 ), Trips-Viz ( 16 ), GWIPS-viz ( 17 ) and TranslatomeDB ( 18 ) to more specialized resources such as Ribo-uORF ( 19 ), uORFdb ( 20 ), riboCIRC ( 21 ) and sORFs.org ( 22 ). The development of these databases has significantly reduced technical barriers, democratizing access to this complex data type and enabling researchers without extensive bioinformatics expertise to explore and utilize it effectively.

RPFdb is purpose-built to host Ribo-seq data and related content, processed through a unified pipeline to ensure consistency and comparability across datasets ( 15 , 23 ). To maintain its relevance and utility in this rapidly advancing field, RPFdb now undergoes systematic updates. This iterative process involves not only the addition of new datasets but also the refinement of ORF annotation, incorporation of new analytical contents and enhancement of user interfaces based on user feedback and emerging requirements. By continuously improving and expanding RPFdb, our objective is to provide the ribosome profiling community with a comprehensive, up-to-date resource, facilitating translation of raw sequencing data into meaningful biological insights.

Since its initial release in 2016 ( 23 ), RPFdb has evolved to version 3.0, now offering enhanced capabilities for the exploration of Ribo-seq data and related content. Figure 1 illustrates the key features of RPFdb v3.0, showcasing its comprehensive functionalities for researchers. RPFdb v3.0 maintains its intuitive interface while significantly expanding its functionalities: (i) Browse: enables users to explore an extensive collection of studies and samples with detailed information on data descriptions and data quality assessment; (ii) ORF catalog: presents an updated compilation of actively translated ORFs with enhanced annotation and filtering options; (iii) Quantification: facilitates the visualization of footprints and comparative analysis of RPKM (Reads Per Kilobase per Million mapped reads) values across genomic regions, as well as the assessment of translation efficiency and pausing scores; (iv) Download: provides access to tabular metadata and the latest Ribo-seq and RNA-seq tracks; (v) Statistics: offers graphical summaries of collected data; (vi) Resource: informs users about cutting-edge advances in translatome sequencing techniques and data analytical tools; (vii) Citation: guides proper attribution and usage of the database and (viii) Help: includes a step-by-step tutorial for new users. These enhanced features collectively empower RPFdb to promote collaboration and knowledge sharing within the ribosome profiling community.

Overview of RPFdb features and functionalities.

Increased data coverage

The evolution of RPFdb into its current version, RPFdb v3.0, represents a significant advancement, particularly in terms of data coverage and diversity (Table 1 ). The database now hosts an extensive collection of 5018 Ribo-seq datasets derived from 496 distinct studies. These datasets encompass a wide spectrum of experimental conditions, tissue types, developmental stages and environmental stimuli, providing researchers with a rich array of biological contexts to explore. A key enhancement in RPFdb v3.0 is the inclusion of 2343 matched RNA-seq datasets. This integration allows for comparative analyses that link translational dynamics with transcriptomic profiles, offering a more comprehensive view of gene expression regulation. The database’s scope has expanded to cover 34 different species, from unicellular microbes to multicellular plants and animals, underscoring its broad applicability across diverse biological systems.

Summary of RPFdb updates

The progression from RPFdb v1.0 to v3.0 demonstrates consistent and substantial growth. Each version has markedly increased the number of samples, studies and species represented. Notably, the latest version nearly doubles the Ribo-seq datasets compared with v2.0 and represents more than a 6-fold increase from v1.0. The introduction of matched RNA-seq data further enhances the database’s utility for comprehensive translational studies. This expansion not only increases the quantity of data available but also improves the quality and depth of potential analyses. The broader species coverage facilitates comparative studies across evolutionarily diverse organisms, while the inclusion of matched RNA-seq data enables researchers to distinguish between transcriptional and translational regulation effects.

Enhanced ORF annotation

Actively translated ORFs represent the regions of the genome that are transcribed into mRNA and subsequently translated into proteins. Accurate annotation of actively translated ORFs is crucial for understanding the fundamental mechanisms of gene expression and protein synthesis. We refine actively translated ORF annotations through the use of RibORF v2.0 software ( 24 , 25 ), which automates data quality control, selects 3-nt periodic reads and employs ribosomal A-site corrected reads to identify genome-wide ORFs. To improve the reliability of ORF identification among duplicate samples, we have replaced the previous method of combining Ribo-seq data with duplicate samples. This change allows for a more accurate assessment of ORF consistency across duplicates, providing researchers with more reliable data. In addition to these refined annotations, we offer a consensus set of Ribo-seq ORFs for each species. This consensus set represents a standardized collection by merging RibORF-identified ORFs from different conditions ( 26 ). By providing refined ORF annotations and consensus sets, RPFdb v3.0 not only enhances the accuracy of translational data but also supports more consistent and reproducible research outcomes across different studies and species.

New content presentation

Optimizing primary sequences to enhance mRNA translation is an important focus in the development of mRNA-based therapeutics ( 27 ). Translation efficiency and ribosome pausing are known to be two critical factors influencing the dynamics of translation. In this update, we tackle these aspects in RPFdb v3.0, facilitating deeper insights into the intricacies of mRNA translation. We now present translation efficiency estimates by dividing the footprint RPKM by mRNA RPKM for each gene’s coding sequence, utilizing both Ribo-seq and RNA-seq data ( 4 ). This enables researchers to quantitatively assess the rate of mRNA translation into proteins, identifying genes with high translation activity and shedding light on the cellular mechanisms governing protein production. Additionally, we introduce context-specific pausing scores for each triplet amino acid (tri-AA). These scores, calculated as the sum of normalized ribosome densities on each tri-AA motif using RiboMiner software ( 28 ), are crucial for pinpointing tri-AA motifs with enhanced ribosome enrichment. Such motifs often signify regions where translation is temporarily halted or slowed, potentially influencing protein folding, localization or regulatory functions. These new features will improve our understanding of translational dynamics by elucidating where and how ribosomes regulate protein synthesis. Furthermore, Ribo-seq tracks coupled with RNA-seq tracks are currently available, enabling intuitive display of coverage signals over genomic ranges and supporting comparative analysis of translation rates. Potentially, this update has significant implications for developing RNA therapeutic strategies, such as aiding in the design of mRNA sequences with optimized translation rates and providing a basis for identifying and improving translational pause sites.

New resource webpage

We have added a resource webpage to RPFdb v3.0 that serves as an information hub for researchers engaged in translatome studies, offering a concise yet informative summary of recent advancements in this rapidly evolving field. This curated collection highlights cutting-edge techniques that have revolutionized our ability to investigate translation at a genome-wide scale. Central to this resource is an overview of Ribo-seq and its variant methodologies, ranging from bulk profiling to single-cell profiling and spatial profiling. The page also features a section on computational tools specifically designed for Ribo-seq data analysis, covering software for read alignment, differential translation analysis and visualization of ribosome occupancy profiles, among others. By consolidating this information, we aim to equip researchers with essential knowledge to advance their translational research endeavors. This centralized resource not only facilitates easier access to cutting-edge methodologies but also promotes best practices in the field of translatome research.

Improved web interface

The significant expansion in both datasets and content necessitated an enhanced web interface. To achieve easier navigation, we redesigned the study browse and sample browse pages, offering a more intuitive layout and improved user experience. Furthermore, we optimized the ORF search response on the ORF catalog page, significantly reducing wait times for users. Additionally, we introduced new dedicated pages for quantification content, including measurements of ribosome occupancy, translation efficiency and ribosome pausing. These separate pages streamline the processes of data retrieval, comparison and visualization, thereby enhancing researchers’ capability to derive meaningful insights efficiently. To facilitate secondary analyses of data, we also enhanced the download functionality. The download page now supports retrieval of translation efficiency, pausing score, Ribo-seq tracks and RNA-seq tracks. These improvements collectively aim to provide a more user-friendly and comprehensive platform for researchers engaged in translatome studies, supporting more efficient data access and analysis.

To access information about specific Ribo-seq data and its related content, users can start by navigating to the ‘Study browse’ page under the ‘Browse’ button on the homepage. For instance, entering the keyword ‘eye’ in the search box will return a dataset titled ‘Change in translation efficiency in mouse eyes at P0.5 by RNG140 knockout’. Clicking the ‘Details’ button will direct users to the ‘Sample browse’ page, which provides comprehensive meta information about the dataset, including sample attributes and experimental variables, quality control checks for raw sequence data and post-mapping data, mapping statistics for each sample and more. For this dataset, users can review actively translated ORFs on the ‘ORF catalog’ page, and quantitative measurements on the ‘Ribosome occupancy’, ‘Translation efficiency’ and ‘Ribosome pausing’ pages under the ‘Quantification’ button. Notably, the ‘Ribosome occupancy’ and ‘Translation efficiency’ pages allow users to retrieve individual gene information. For example, by selecting ‘M.musculus’ as the species and entering ‘Smad4 (a gene important in eye development and disease) as the gene of interest, users can compare results across different experimental conditions within the same species. The ‘Ribosome occupancy’ page also features a genome browser for visualizing Ribo-seq tracks, enabling users to examine the distribution of ribosomes along transcripts. All the quantitative measurements for this dataset can be downloaded from the ‘Download’ page, facilitating further analysis and integration with other data types. This example demonstrates how users can efficiently navigate the database to access, visualize and analyze Ribo-seq data relevant to their research interests.

The RPFdb database is dedicated to advancing research within the ribosome profiling community, providing a comprehensive platform to explore Ribo-seq data and related content across diverse species. The latest update to RPFdb has further enhanced its utility with expanded data content and improved functionality, facilitating more efficient and insightful analyses. Although RPFdb has made substantial strides in democratizing access to Ribo-seq data and related contents, continuous development and adaptation will be crucial to keep pace with the rapid evolution in the field of translatomics. As Ribo-seq technology continues to advance, particularly with the emergence of single-cell translatomics ( 29–31 ), RPFdb must adapt to accommodate these new data types. Including single-cell Ribo-seq data would allow researchers to explore translational heterogeneity, providing unprecedented insights into cell-specific translation and translational regulation. Moreover, integrating spatial Ribo-seq data could offer a new dimension to our understanding of localized translation within tissues. Additional development of RPFdb could focus on incorporating more sophisticated online analysis and visualization tools directly into the platform. This could include interactive data exploration features, allowing users to perform custom secondary analyses without the need for local computational resources. Advanced visualization tools could enable researchers to generate publication-quality figures directly from the web interface, enhancing the accessibility and interpretability of complex Ribo-seq data.

RPFdb is publicly available at http://www.rpfdb.org or http://sysbio.sysu.edu.cn/rpfdb/ .

We would like to thank all team members for their support and RPFdb users for their invaluable feedback and suggestions.

Author contributions : H.W. and Z.X.: conceptualization and writing; Y.W.: data collection, data analysis, writing and website redesign; Y.T.: data collection, data analysis and data presentation.

National Natural Science Foundation of China [32270700 to H.W.W., in part]; Guangdong Basic and Applied Basic Research Foundation [2024A1515010103 to H.W.W.]; Guangzhou Science and Technology Program key projects [2024A03J0158 to H.W.W.]. Funding for open access charge: National Natural Science Foundation of China.

Conflict of interest statement . None declared.

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Sell my car, car research, sign in, 2024 toyota land cruiser review and test drive.

The Land Cruiser returns as a smaller, less costly, retro-styled, and more utilitarian SUV.

QuickTakes:

• 2024 Toyota Land Cruiser: The design
• 2024 Toyota Land Cruiser: The technology
• 2024 Toyota Land Cruiser: The drive
• Is the 2024 Toyota Land Cruiser a good SUV?

Toyota's Land Cruiser debuted in the United States in 1958 as a basic four-wheel-drive , Jeep-like tool. By 2021, it had grown into a large, leather-lined, luxe SUV just shy of a Lexus badge and tickled the $90,000 mark in price. Given its origin story, that '21 Land Cruiser exemplified automotive mission creep. At the end of that year, Toyota shelved it — temporarily, as it turns out.

With the redesigned 2024 Land Cruiser, Toyota has returned to the drawing board. It's a more basic SUV with a robust four-wheel-drive system and a lower price tag. Like the previous Cruiser, it still has a Lexus cousin, but this time it's the GX . A Toyota-badged sibling arrives soon as the redesigned 2025 4Runner .

The new 2024 Toyota Land Cruiser answers to several market realities. First, the old Land Cruiser felt too bulky and luxurious, particularly given the existence of the nearly identical Lexus LX. Second, Jeep's perennially popular Wrangler , Ford's recently reintroduced Bronco , and Land Rover's well-received Defender shifted the segment toward more elemental off-roaders.

Third, in my opinion, the Land Cruiser name still has great value. Wasting it would be a shame.

Prices Drop for the All-New 2024 Toyota Land Cruiser

The 2024 Toyota Land Cruiser comes in three trim levels. Toyota calls the base model 1958 (for the first year it sold the model in the U.S.), while the middle trim is simply called Land Cruiser and the top version is the First Edition.

Base prices range from the high $50,000s to the mid-$70,000s, including the destination charge to ship the SUV from the factory in Tokyo, Japan, to your local dealership.

For this Land Cruiser review, I test-drove the middle trim level variant in Arizona. Highlights from the optional equipment list include the Premium Package, 20-inch alloy wheels, a large roof rack, and white paint that nicely offset the Smoky Blue body color.

The manufacturer's suggested retail price of the test vehicle was $71,364, including the $1,395 destination charge. (The carmaker has since increased its destination charge to $1,450.) Toyota provided the vehicle for this Land Cruiser review.

A Stylish, Boxy Exterior Makes This a Handsome Truck

The Land Cruiser isn't as distinctive as the Ford Bronco or Jeep Wrangler, instead presenting a more mature design. The round headlights on 1958 and First Edition models, the jaunty upkick at the rear door windows, and the available bright paint colors all add character, however.

High-grade interior plastics decorate the Land Cruiser's cabin, and the build quality is excellent. Some minor details are pleasing, too, such as the shape of the JBL audio system's tweeter housings.

That said, there's room for improvement. The rear seatbelts get in the way when you fold and tumble the second-row seats up and forward, for example. Also, the rear seats don't fold flat, so you can't create a flat-load floor in this SUV for cargo hauling or camping out in the back.

A Clean-Lined Interior Features Decadent Seats

The new Land Cruiser isn't as posh as the old Land Cruiser, but it's not exactly lacking. Where the base Land Cruiser 1958 comes with cloth seats, the middle-trim Land Cruiser has SofTex artificial leather or, as an option, the real thing. My test vehicle's medium-brown leather upholstery looked terrific, adding a hint of richness to the cabin.

Thanks to the large center console, there's a cozy, tucked-in feeling up front. The only real letdown I found was that the passenger seat doesn't have the same manually adjustable thigh bolster as the driver's seat.

The rear-seat room felt about average, with just enough space for my legs and knees as long as the front seats weren't at the far end of their travel. There's also decent headroom. Given that the Land Cruiser is very tall, though, you might expect there'd be more.

The Land Cruiser does not offer a third-row seat, which surprised me, because the Lexus GX provides one.

Acceptable Cargo Space Is Hampered by a High Floor

Since it doesn't have a third-row seat, it seems like the new Land Cruiser should provide a generous cargo area. It's by no means tiny, measuring 37.5 cubic-feet behind the rear seats, but the high cargo floor dictated by the hybrid powertrain's battery means loading objects requires more effort than with other SUVs. The Land Cruiser's high ride height accentuates that, too.

The test vehicle had a refrigerated cooler box perfect for stashing a few soda cans or a box of chocolates. However, it takes the place of the center console storage bin, and I missed that storage space more than I liked the mini fridge.

Toyota Audio Multimedia Provides the Infotainment

Toyota equips the Land Cruiser with Toyota Audio Multimedia, the company's in-house infotainment system. The Land Cruiser 1958 includes an 8.0-inch touchscreen, but my midlevel test vehicle had a larger 12.3-inch display.

Wireless Apple CarPlay and Android Auto connectivity, SiriusXM satellite radio, and a 10-speaker sound system are standard. My test vehicle boosted that equation with an optional 14-speaker JBL audio package. This system performed exceptionally well, with excellent fidelity and strength in the bass department.

Connecting my iPhone via Bluetooth proved seamless and running CarPlay was no trouble. Upon returning to the vehicle, my device reconnected every time. The native voice recognition — alerted with a firm "Hey, Toyota" — could find tricky business names that sometimes flummox other systems. It also usefully notes whether a chosen business is open or closed at the time of the search.

The Head-Up Display Highlights Key Info

The new Land Cruiser's instrument cluster is fully digital and configurable, offering detailed information that I found easy to reference. There are two options for presenting the instruments: conventional dial gauges or a simple slate of data.

My test vehicle's head-up display repeated the most vital information in my line of sight, including road speed, speed limit, and navigational graphics when active. As part of the optional Premium Package, a digital rearview camera mirror offers a clear view to the rear, so luggage stacked to the rafters inside the cargo area won't block the view.

Toyota Safety Sense 3.0 Comes in Every Land Cruiser

Every 2024 Land Cruiser has Toyota Safety Sense 3.0 (TSS 3.0), a collection of advanced driver-assistance systems that equips the SUV with the features you likely expect in a modern vehicle.

On the freeway, the Land Cruiser's adaptive cruise control never failed to see a leading car slow down, nor did it take more than a split second to recognize a merging vehicle in front. The adaptive cruise also brought the SUV to a complete stop when the car ahead did the same. Once traffic started moving again, I could simply press the cruise control's resume function to let it accelerate again.

In addition, the Land Cruiser's lane-centering assist performed nearly flawlessly. It had one unusual tendency to hug a yellow line on the driver's side through a construction zone on a freeway I frequently drive. I didn't feel this was horrible, though, because hugging the other side would have meant kissing tractor-trailers, an activity I prefer to avoid.

Lane-change assist is also part of TSS 3.0, where a tip of the turn signal initiates a lane change if the coast is clear. Calibrated conservatively, it requires plenty of clearance ahead and behind. The system also takes longer to execute a lane change than most drivers, I found, but that's understandable. Overall, I found it too lethargic to use regularly.

Torque Aplenty, and the Hefty Land Cruiser Needs It

At the heart of the new Land Cruiser is a hybrid powertrain based on a turbocharged 2.4-liter four-cylinder engine. It makes a healthy 326 horsepower and a whopping 465 pound-feet of torque, and while that's down in power from the old V8-powered 2021 Land Cruiser, torque is far more critical in a heavy vehicle.

Most SUVs (even those with fantastic off-roading capabilities) will see more suburban pavement than red rock-lined trails of the Wild West. Toyota fits the Land Cruiser with cushy suspension tuning, mild Dunlop Grandtrek mud and snow tires, and lots of noise-abating insulation. It all equates to a quiet ride in tame, suburban environments.

Controllable, Progressive, and Versatile Power

At 5,038 pounds, the Land Cruiser needs every bit of its 465 lb-ft of torque to get underway, and it does so smartly. This SUV isn't fast, but you can't complain about the acceleration. In the combustion world, a good V8 makes a glorious sound. What emanates from the new Cruiser's turbo hybrid four-cylinder can't match the old Cruiser's V8 voice, but it isn't rough, unrefined, or surly in the way some large-displacement fours can be.

The engine doles out power smoothly and controllably when off-roading, too. Throughout one afternoon in the dirty and rocky stuff, the Land Cruiser handled trails a bit beyond the scope of the standard tires, and the throttle's progressive nature proved spot-on for rough going. Disconnecting the front anti-roll bar (which I did via a button on the console) is probably overkill on anything but the ruttiest path, but I tried it out. It worked, and I harmed no wheels, running boards, or suspension links in the process.

The Ride Can Feel Very Luxurious

The Land Cruiser wafts over highway pavement at least as well as it dispenses with rutty and rocky trails. The ride quality and impact harshness feel like what I might expect of a Lexus SUV, to a point.

The Toyota's truckier nature is evident on twisty roads. The grip is just middling, and there's lots of body lean in corners and nose-dive under braking. Actual braking power and effectiveness — separate from the body's reaction to brake application — is excellent, though.

At speeds above 75 mph, the Land Cruiser exhibits wind noise. However, this is inevitable given its tall stature, large side mirrors, and optional roof rack.

On my 73-mile loop of mixed freeway and suburban driving, the Land Cruiser logged a disappointing 20.5 mpg. It's disappointing because the EPA rates it at 22/25/23 mpg in city/highway/combined driving.

Worse, Toyota says the Land Cruiser is supposed to run on premium fuel. Between the observed fuel-economy shortfall and the extra cost of premium gas, owners may have to bear higher gas costs than anticipated.

The Land Cruiser Offers Mixed-Use Opportunities

Compared with Land Cruisers of the past 15 years, the more popular 4Runner exemplifies the truly mixed-use 4x4 with its excellent off-roading capabilities and reputation for reliability.

Some family friction is likely to ensue.

Looking beyond the Toyota lineup, Land Rover's compelling Defender 110 is in a similar price league and has the same general mission as the Land Cruiser. And the Jeep Grand Cherokee is generally acknowledged to have excellent off-roading ability, everyday practicality, and plenty of creature comforts.

Regardless, I imagine what matters most to the fans of the brand is that the Land Cruiser is back in business. Better yet, it is more faithful to its original mission than it has been in a long time.

Written by humans. Edited by humans.

From racing exotic sports cars, to ranking new cars, to peeling back layers of cover up in an exhaust emissions scandal, Jim has chronicled the automotive sector for decades. Jim has also worked inside the corporate headquarters of three carmakers, and therefore understands how the automotive sausage is really made. But Jim’s affinity for vehicles takes a back seat to finding the truth and the cultural implications of modern transportation. He has also lectured at universities to engineering and policy students and faculty on the industry’s relationship with legislation in the wake of the diesel exhaust emissions scandal several years ago. Put simply, Jim reports on autos, mobility, tech, car culture, and the traffic jam of topics within.

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