1 Yay! Welcome!
2 A journal club is when a group of scientists get together to discuss a paper. Usually one person leads the discussion and presents all of the data. The group discusses their own interpretations and the authors’ interpretation.
In written communication, the reader and the writer are equally important. Both influence the final outcome: in this case, your scientific understanding! After identifying your goal, think about the author’s goal for sharing this project. This will help you interpret the data and understand the author’s interpretation of the data. However, this requires some understanding of who the author(s) are (e.g., what are their scientific interests?), the scientific field in which they work (e.g., what techniques are available in this field?), and how this paper fits into the author’s research (e.g., is this work building on an author’s longstanding project or controversial idea?). This information may be hard to glean without experience and a history of reading. But don’t let this be a discouragement to starting the process; it is by the act of reading that this experience is gained!
A good step toward understanding the goal of the author(s) is to ask yourself: What kind of article is this? Journals publish different types of articles, including methods, review, commentary, resources, and research articles as well as other types that are specific to a particular journal or groups of journals. These article types have different formatting requirements and expectations for content. Knowing the article type will help guide your evaluation of the information presented. Is the article a methods paper, presenting a new technique? Is the article a review article, intended to summarize a field or problem? Is it a commentary, intended to take a stand on a controversy or give a big picture perspective on a problem? Is it a resource article, presenting a new tool or data set for others to use? Is it a research article, written to present new data and the authors’ interpretation of those data? The type of paper, and its intended purpose, will get you on your way to understanding the author’s goal.
When reading, ask yourself: (1) What do the author(s) want to know (motivation)? (2) What did they do (approach/methods)? (3) Why was it done that way (context within the field)? (4) What do the results show (figures and data tables)? (5) How did the author(s) interpret the results (interpretation/discussion)? (6) What should be done next? (Regarding this last question, the author(s) may provide some suggestions in the discussion, but the key is to ask yourself what you think should come next.)
Each of these questions can and should be asked about the complete work as well as each table, figure, or experiment within the paper. Early on, it can take a long time to read one article front to back, and this can be intimidating. Break down your understanding of each section of the work with these questions to make the effort more manageable.
Scientists write original research papers primarily to present new data that may change or reinforce the collective knowledge of a field. Therefore, the most important parts of this type of scientific paper are the data. Some people like to scrutinize the figures and tables (including legends) before reading any of the “main text”: because all of the important information should be obtained through the data. Others prefer to read through the results section while sequentially examining the figures and tables as they are addressed in the text. There is no correct or incorrect approach: Try both to see what works best for you. The key is making sure that one understands the presented data and how it was obtained.
For each figure, work to understand each x- and y-axes, color scheme, statistical approach (if one was used), and why the particular plotting approach was used. For each table, identify what experimental groups and variables are presented. Identify what is shown and how the data were collected. This is typically summarized in the legend or caption but often requires digging deeper into the methods: Do not be afraid to refer back to the methods section frequently to ensure a full understanding of how the presented data were obtained. Again, ask the questions in Rule 3 for each figure or panel and conclude with articulating the “take home” message.
Just like the overall intent of the article (discussed in Rule 2), the intent of each section within a research article can guide your interpretation. Some sections are intended to be written as objective descriptions of the data (i.e., the Results section), whereas other sections are intended to present the author’s interpretation of the data. Remember though that even “objective” sections are written by and, therefore, influenced by the authors interpretations. Check out Table 2 to understand the intent of each section of a research article. When reading a specific paper, you can also refer to the journal’s website to understand the formatting intentions. The “For Authors” section of a website will have some nitty gritty information that is less relevant for the reader (like word counts) but will also summarize what the journal editors expect in each section. This will help to familiarize you with the goal of each article section.
Section | Content |
---|---|
Title | The “take home” message of the entire project, according to the authors. |
Author list | These people made significant scientific contributions to the project. Fields differ in the standard practice for ordering authors. For example, as a general rule for biomedical sciences, the first author led the project’s implementation, and the last author was the primary supervisor to the project. |
Abstract | A brief overview of the research question, approach, results, and interpretation. This is the road map or elevator pitch for an article. |
Introduction | Several paragraphs (or less) to present the research question and why it is important. A newcomer to the field should get a crash course in the field from this section. |
Methods | What was done? How was it done? Ideally, one should be able to recreate a project by reading the methods. In reality, the methods are often overly condensed. Sometimes greater detail is provided within a “Supplemental” section available online (see below). |
Results | What was found? Paragraphs often begin with a statement like this: “To do X, we used approach Y to measure Z.” The results should be objective observations. |
Figures, tables, legends, and captions | The data are presented in figures and tables. Legends and captions provide necessary information like abbreviations, summaries of methods, and clarifications. |
Discussion | What do the results mean and how do they relate to previous findings in the literature? This is the perspective of the author(s) on the results and their ideas on what might be appropriate next steps. Often it may describe some (often not all!) strengths and limitations of the study: Pay attention to this self-reflection of the author(s) and consider whether you agree or would add to their ideas. |
Conclusion | A brief summary of the implications of the results. |
References | A list of previously published papers, datasets, or databases that were essential for the implementation of this project or interpretation of data. This section may be a valuable resource listing important papers within the field that are worth reading as well. |
Supplemental material | Any additional methods, results, or information necessary to support the results or interpretations presented in the discussion. |
Supplemental data | Essential datasets that are too large or cumbersome to include in the paper. Especially for papers that include “big data” (like sequencing or modeling results), this is often where the real, raw data is presented. |
Research articles typically contain each of these sections, although sometimes the “results” and “discussion” sections (or “discussion” and “conclusion” sections) are merged into one section. Additional sections may be included, based on request of the journal or the author(s). Keep in mind: If it was included, someone thought it was important for you to read.
Published papers are not truths etched in stone. Published papers in high impact journals are not truths etched in stone. Published papers by bigwigs in the field are not truths etched in stone. Published papers that seem to agree with your own hypothesis or data are not etched in stone. Published papers that seem to refute your hypothesis or data are not etched in stone.
Science is a never-ending work in progress, and it is essential that the reader pushes back against the author’s interpretation to test the strength of their conclusions. Everyone has their own perspective and may interpret the same data in different ways. Mistakes are sometimes published, but more often these apparent errors are due to other factors such as limitations of a methodology and other limits to generalizability (selection bias, unaddressed, or unappreciated confounders). When reading a paper, it is important to consider if these factors are pertinent.
Critical thinking is a tough skill to learn but ultimately boils down to evaluating data while minimizing biases. Ask yourself: Are there other, equally likely, explanations for what is observed? In addition to paying close attention to potential biases of the study or author(s), a reader should also be alert to one’s own preceding perspective (and biases). Take time to ask oneself: Do I find this paper compelling because it affirms something I already think (or wish) is true? Or am I discounting their findings because it differs from what I expect or from my own work?
The phenomenon of a self-fulfilling prophecy, or expectancy, is well studied in the psychology literature [ 2 ] and is why many studies are conducted in a “blinded” manner [ 3 ]. It refers to the idea that a person may assume something to be true and their resultant behavior aligns to make it true. In other words, as humans and scientists, we often find exactly what we are looking for. A scientist may only test their hypotheses and fail to evaluate alternative hypotheses; perhaps, a scientist may not be aware of alternative, less biased ways to test her or his hypothesis that are typically used in different fields. Individuals with different life, academic, and work experiences may think of several alternative hypotheses, all equally supported by the data.
The author(s) are human too. So, whenever possible, give them the benefit of the doubt. An author may write a phrase differently than you would, forcing you to reread the sentence to understand it. Someone in your field may neglect to cite your paper because of a reference count limit. A figure panel may be misreferenced as Supplemental Fig 3E when it is obviously Supplemental Fig 4E. While these things may be frustrating, none are an indication that the quality of work is poor. Try to avoid letting these minor things influence your evaluation and interpretation of the work.
Similarly, if you intend to share your critique with others, be extra kind. An author (especially the lead author) may invest years of their time into a single paper. Hearing a kindly phrased critique can be difficult but constructive. Hearing a rude, brusque, or mean-spirited critique can be heartbreaking, especially for young scientists or those seeking to establish their place within a field and who may worry that they do not belong.
To truly understand a scientific work, you often will need to look up a term, dig into the supplemental materials, or read one or more of the cited references. This process takes time. Some advisors recommend reading an article three times: The first time, simply read without the pressure of understanding or critiquing the work. For the second time, aim to understand the paper. For the third read through, take notes.
Some people engage with a paper by printing it out and writing all over it. The reader might write question marks in the margins to mark parts (s)he wants to return to, circle unfamiliar terms (and then actually look them up!), highlight or underline important statements, and draw arrows linking figures and the corresponding interpretation in the discussion. Not everyone needs a paper copy to engage in the reading process but, whatever your version of “printing it out” is, do it.
Talking about an article in a journal club or more informal environment forces active reading and participation with the material. Studies show that teaching is one of the best ways to learn and that teachers learn the material even better as the teaching task becomes more complex [ 4 – 5 ]; anecdotally, such observations inspired the phrase “to teach is to learn twice.”
Beyond formal settings such as journal clubs, lab meetings, and academic classes, discuss papers with your peers, mentors, and colleagues in person or electronically. Twitter and other social media platforms have become excellent resources for discussing papers with other scientists, the public or your nonscientist friends, or even the paper’s author(s). Describing a paper can be done at multiple levels and your description can contain all of the scientific details, only the big picture summary, or perhaps the implications for the average person in your community. All of these descriptions will solidify your understanding, while highlighting gaps in your knowledge and informing those around you.
One approach we like to use for communicating how we build on the scientific literature is by starting research presentations with an image depicting a wall of Lego bricks. Each brick is labeled with the reference for a paper, and the wall highlights the body of literature on which the work is built. We describe the work and conclusions of each paper represented by a labeled brick and discuss each brick and the wall as a whole. The top brick on the wall is left blank: We aspire to build on this work and label this brick with our own work. We then delve into our own research, discoveries, and the conclusions it inspires. We finish our presentations with the image of the Legos and summarize our presentation on that empty brick.
Whether you are reading an article to understand a new topic area or to move a research project forward, effective learning requires that you integrate knowledge from multiple sources (“click” those Lego bricks together) and build upwards. Leveraging published work will enable you to build a stronger and taller structure. The first row of bricks is more stable once a second row is assembled on top of it and so on and so forth. Moreover, the Lego construction will become taller and larger if you build upon the work of others, rather than using only your own bricks.
Build on the article you read by thinking about how it connects to ideas described in other papers and within own work, implementing a technique in your own research, or attempting to challenge or support the hypothesis of the author(s) with a more extensive literature review. Integrate the techniques and scientific conclusions learned from an article into your own research or perspective in the classroom or research lab. You may find that this process strengthens your understanding, leads you toward new and unexpected interests or research questions, or returns you back to the original article with new questions and critiques of the work. All of these experiences are part of the “active reading”: process and are signs of a successful reading experience.
In summary, practice these rules to learn how to read a scientific article, keeping in mind that this process will get easier (and faster) with experience. We are firm believers that an hour in the library will save a week at the bench; this diligent practice will ultimately make you both a more knowledgeable and productive scientist. As you develop the skills to read an article, try to also foster good reading and learning habits for yourself (recommendations here: [ 6 ] and [ 7 ], respectively) and in others. Good luck and happy reading!
Thank you to the mentors, teachers, and students who have shaped our thoughts on reading, learning, and what science is all about.
MAC was supported by the PhRMA Foundation's Postdoctoral Fellowship in Translational Medicine and Therapeutics and the University of Virginia's Engineering-in-Medicine seed grant, and KLS was supported by the NIH T32 Global Biothreats Training Program at the University of Virginia (AI055432). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Research papers.
Sections of an academic article.
Most academic journal articles include the following sections:
TIP: To begin selecting articles for your research, read the highlighted sections to determine whether the academic journal article includes information relevant to your research topic.
When sorting through multiple articles discovered in the research process, skimming through these sections of the article will help you determine whether the article will be useful in your research.
If the article fits your information needs, go back and read the article thoroughly. TIP: Create a folder on your computer to save copies of articles you plan to use, and save your references.
Think about how you will evaluate the academic articles you find and how you will determine whether to include them in your research project. Ask yourself the following questions to focus your search in the academic literature:
Before reading the article, ask yourself the following:
As you read the article make note of the following:
If you work in a scientific field, you should try to build a deep and unbiased understanding of that field. This not only educates you in the best possible way but also helps you envision the opportunities in your space.
A research paper is often the culmination of a wide range of deep and authentic practices surrounding a topic. When writing a research paper, the author thinks critically about the problem, performs rigorous research, evaluates their processes and sources, organizes their thoughts, and then writes. These genuinely-executed practices make for a good research paper.
If you’re struggling to build a habit of reading papers (like I am) on a regular basis, I’ve tried to break down the whole process. I've talked to researchers in the field, read a bunch of papers and blogs from distinguished researchers, and jotted down some techniques that you can follow.
Let’s start off by understanding what a research paper is and what it is NOT!
A research paper is a dense and detailed manuscript that compiles a thorough understanding of a problem or topic. It offers a proposed solution and further research along with the conditions under which it was deduced and carried out, the efficacy of the solution and the research performed, and potential loopholes in the study.
A research paper is written not only to provide an exceptional learning opportunity but also to pave the way for further advancements in the field. These papers help other scholars germinate the thought seed that can either lead to a new world of ideas or an innovative method of solving a longstanding problem.
There is a common notion that a research paper is a well-informed summary of a problem or topic written by means of other sources.
But you shouldn't mistake it for a book or an opinionated account of an individual’s interpretation of a particular topic.
What I find fascinating about reading a good research paper is that you can draw on a profound study of a topic and engage with the community on a new perspective to understand what can be achieved in and around that topic.
I work at the intersection of instructional design and data science. Learning is part of my day-to-day responsibilities. If the source of my education is flawed or inefficient, I’d fail at my job in the long term. This applies to many other jobs in Science with a special focus on research.
There are three important reasons to read a research paper:
Not only that, with the help of the internet, you can extrapolate all of these reasons or benefits onto multiple business models. It can be an innovative state-of-the-art product, an efficient service model, a content creator, or a dream job where you are solving problems that matter to you.
The first thing to do is to figure out your motivation for reading the paper. There are two main scenarios that might lead you to read a paper:
If you’re an inquisitive beginner with no starting point in mind, start with scenario 2. Shortlist a few topics you want to read about until you find an area that you find intriguing. This will eventually lead you to scenario 1.
In addition to these generic goals, if you need an end goal for your habit-building exercise of reading research papers, you should check out the ML reproducibility challenge.
You’ll find top-class papers from world-class conferences that are worth diving deep into and reproducing the results.
They conduct this challenge twice a year and they have one coming up in Spring 2021. You should study the past three versions of the challenge, and I’ll write a detailed post on what to expect, how to prepare, and so on.
Now you must be wondering – how can you find the right paper to read?
In order to get some ideas around this, I reached out to my friend, Anurag Ghosh who is a researcher at Microsoft. Anurag has been working at the crossover of computer vision, machine learning, and systems engineering.
Here are a few of his tips for getting started:
In addition to these invaluable tips, there are a number of web applications that I’ve shortlisted that help me narrow my search for the right papers to read:
After you have stocked your to-read list, then comes the process of reading these papers. Remember that NOT every paper is useful to read and we need a mechanism that can help us quickly screen papers that are worth reading.
To tackle this challenge, you can use this Three-Pass Approach by S. Keshav . This approach proposes that you read the paper in three passes instead of starting from the beginning and diving in deep until the end.
If you’re sincere about reading research papers, your list of papers will soon grow into an overwhelming stack that is hard to keep track of. Fortunately, we have software that can help us set up a mechanism to manage our research.
Here are a bunch of them that you can use:
Reading a research paper can turn out to be frustrating, challenging, and time-consuming especially when you’re a beginner. You might face the following common symptoms:
Here’s a complete list of emotions that you might undergo as explained by Adam Ruben in this article .
We should be all set to dive right in. Here’s a quick summary of what we have covered here:
Remember: Art is not a single method or step done over a weekend but a process of accomplishing remarkable results over time.
You can also watch the video on this topic on my YouTube channel :
Feel free to respond to this blog or comment on the video if you have some tips, questions, or thoughts!
If this tutorial was helpful, you should check out my data science and machine learning courses on Wiplane Academy . They are comprehensive yet compact and helps you build a solid foundation of work to showcase.
Web and Data Science Consultant | Instructional Design
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1. Read the Abstract Section
The first step in reading a scholarly article is to read the abstract or summary of the article. Abstracts are always found at the beginning of an article and provide a basic summary or roadmap to the article. The abstract also introduces the purpose of the article.
Take a few minutes to carefully read the abstract of the practice article. Note that the abstract is not formally labeled "abstract" but is called "background and aims." Any summary at the start of an article is considered the abstract.
The abstract should always be read first to make sure the article is relevant to your topic. However, reading the abstract should never replace reading the entire article as the abstract is too brief to be used to fully understand the article.
2. Read the Conclusion Section Reading the conclusion will help you understand the main points of the article and what the authors are attempting to prove.
3. Read the Introduction Section Now that you have an overview of the article from the abstract and understand the main points the authors are trying to prove from the conclusion, you will want to read the introduction.
4. Read the Results Section
Read the results section. Here are a couple of suggestions for deciphering results:
5. Read the Methods Section Reading the methods section will help you understand how the study or experiment was conducted. It is necessary for other researchers to understand the methods used so that they can replicate the study.
The methods section can also be difficult to read due to technical language used and density of the section. Try circling words, acronyms, and surveys you are unfamiliar with and look them up as those may be important to fully understand the article and may be necessary for future research.
6. Read the Discussion & Limitations Section
The discussion section is where you will find the researcher's interpretation of the results. The author should answer the article's research question. Remember, you should evaluate the data to form your own conclusions. Don't just accept the author's conclusions without looking at the data for yourself.
Often authors will include a section detailing the limits to their research and their conclusions. The limitation section will usually explain conclusions that could not be drawn from the research as well as areas that future research is needed.
7. Read Through One More Time After you have jumped around and read the different sections of the article, go back to the beginning and read the article in order. The article should be easier to read and make more sense as you will already be familiar with the main points in each section.
Why Watch This Video? You'll learn essential strategies for reading scientific or scholarly journal articles, including:
Oa.mg is a search engine for academic papers, specialising in open access. we have over 250 million papers in our index..
It is crucial to stay on top of the scientific literature in your field of interest. This will help you shape and guide your experimental plans and keep you informed about what your competitors are working on.
To get the most out of your literature reading time, you need to learn how to read scientific papers efficiently. The problem is that we simply don’t have enough time to read new scientific papers in our results-driven world.
It takes a great deal of time for researchers to learn how to read research papers. Unfortunately, this skill is rarely taught.
I wasted a lot of time reading unnecessary papers in the past since I didn’t have an appropriate workflow to follow. In particular, I needed a way to determine if a paper would interest me before I read it from start to finish.
So, what’s the solution?
This is where I came across the Three-pass method for reading research papers.
Here’s what I’ve learned from using the three pass methods and what tweaks I’ve made to my workflow to make it more personalized.
Before you read anything, you should set aside a set amount of time to read research papers. It will be very hard to read research papers if you do not have a schedule because you will only try to read them for a week or two, and then you will feel frustrated. An organized schedule reduces procrastination significantly.
For example, I take 30-40 minutes each weekday morning to read a research paper I come across.
After you have determined a time “only” to read research papers, you have to have a proper workflow.
For example, I follow a customized version of the popular workflow, the “Three-pass method”.
When you are beginning, you may follow the method exactly as described, but as you get more experienced, you can make some changes down the road.
Oftentimes, the papers you think are so important and that you should read every single word are actually worth only 10 minutes of your time.
Unlike reading an article about science in a blog or newspaper, reading research papers is an entirely different experience. In addition to reading the sections in a different order, you must take notes, read them several times, and probably look up other papers for details.
It may take you a long time to read one paper at first. But that’s okay because you are investing yourself in the process.
However, you’re wasting your time if you don’t have a proper workflow.
Oftentimes, reading a whole paper might not be necessary to get the specific information you need.
The key idea is to read the paper in up to three passes rather than starting at the beginning and plowing through it. With each pass, you accomplish specific goals and build upon the previous one.
The first pass gives you a general idea of the paper. A second pass will allow you to understand the content of the paper, but not its details. A third pass helps you understand the paper more deeply.
The paper is scanned quickly in the first pass to get an overview. Also, you can decide if any more passes are needed. It should take about five to ten minutes to complete this pass.
You should be able to tell from the title what the paper is about. In addition, it is a good idea to look at the authors and their affiliations, which may be valuable for various reasons, such as future reference, employment, guidance, and determining the reliability of the research.
The abstract should provide a high-level overview of the paper. You may ask, What are the main goals of the author(s) and what are the high-level results? There are usually some clues in the abstract about the paper’s purpose. You can think of the abstract as a marketing piece.
As you read the introduction, make sure you only focus on the topic sentences, and you can loosely focus on the other content.
What is a topic sentence?
Topic sentences introduce a paragraph by introducing the one topic that will be the focus of that paragraph.
The structure of a paragraph should match the organization of a paper. At the paragraph level, the topic sentence gives the paper’s main idea, just as the thesis statement does at the essay level. After that, the rest of the paragraph supports the topic.
In the beginning, I read the whole paragraph, and it took me more than 30 minutes to complete the first pass. By identifying topic sentences, I have revolutionized my reading game, as I am now only reading the summary of the paragraph, saving me a lot of time during the second and third passes.
Regarding methods and discussions, do not attempt to read even topic sentences because you are trying to decide whether this article is useful to you.
Reading the headings and subheadings is the best practice. It allows you to get a feel for the paper without taking up a lot of time.
It is standard for good writers to present the foundations of their experiment at the beginning and summarize their findings at the end of their paper.
Therefore, you are well prepared to read and understand the conclusion after reading the abstract and introduction.
Many people overlook the importance of the first pass. In adopting the three-pass method into my workflow, I realized that many papers that I thought had high relevance did not require me to spend more time reading.
Therefore, after the first pass, I can decide not to read it further, saving me a lot of time.
You can mentally check off the ones you’ve already read.
As you read through the references, you will better understand what has been studied previously in the field of research.
At the end of the first pass, you should be able to answer these questions:
After the first pass, you should have a good idea whether you want to continue reading the research paper.
Maybe the paper doesn’t interest you, you don’t understand the area enough, or the authors make an incorrect assumption.
In the first pass, you should be able to identify papers that are not related to your area of research but may be useful someday.
You can store your paper with relevant tags in your reference manager, as discussed in the previous blog post in the Bulletproof Literature Management System series.
This is the third post of the four-part blog series: The Bulletproof Literature Management System . Follow the links below to read the other posts in the series:
You are now ready to make a second pass through the paper if you decide it is worth reading more.
You should now begin taking some high-level notes because there will be words and ideas that are unfamiliar to you.
Most reference managers come with an in-built PDF reader. In this case, taking notes and highlighting notes in the built-in pdf reader is the best practice. This method will prevent you from losing your notes and allow you to revise them easily.
Don’t be discouraged by everything that does not make sense. You can just mark it and move on. It is recommended that you only spend about an hour working on the paper in the second pass.
In the second pass:
You should be able to understand the paper’s content. Sometimes, it may be okay if you cannot comprehend some details. However, you should now be able to see the main idea of the paper. Otherwise, it might be better to rest and go through the second pass without entering the third.
This is a good time to summarize the paper. During your reading, make sure to make notes.
After the second pass, you can:
You should go to the third stage (the third pass) for a complete understanding of the paper. It may take you a few hours this time to read the paper. However, you may want to avoid reading a single paper for longer than four hours, even at the third pass.
A great deal of attention to detail is required for this pass. Every statement should be challenged, and every assumption should be identified.
By the third pass, you will be able to summarize the paper so that not only do you understand the content, but you can also comment on limitations and potential future developments.
Highlighting is one way I help myself learn the material when I read research papers. It is especially helpful to highlight an article when you return to it later.
Therefore, I use different colors for different segments. To manage my references, I use Zotero. There is an inbuilt PDF reader in Zotero. I use the highlighting colors offered by this software. The most important thing is the concept or phrase I want to color code, not the color itself.
Here is my color coding system.
Even though I’m not a morning person, I forced myself to read papers in the morning just to get rid of distractions. In order to follow through with this process (at least when you are starting out), you must have minimum to no distractions because research papers contain a great deal of highly packed information.
It doesn’t mean you can’t have fun doing it, though. Make a cup of coffee and enjoy reading!
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Founder at Proactive Grad, Materials Engineer, Researcher, and turned author. In 2019, he started his professional carrier as a materials engineer with the continuation of his research studies. His exposure to both academic and industrial worlds has provided many opportunities for him to give back to young professionals.
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Your loyalty strategy needs to consider four ways people value points.
Do consumers treat loyalty points the same way that they treat traditional money? And, how do they choose to spend one versus the other? The authors of this article present research findings from their analysis of data describing over 29,000 unique loyalty points earning and spending transactions made during two recent years by 500 airline loyalty program consumers. They found that points users fell into four distinct categories: 1) Money advocates, who prefer cash over points, even when their value is identical in terms of purchasing power; 2) Currency impartialists, who regard points and cash interchangeably, valuing them equally based on their financial worth; 3) Point gamers, who actively seek out the most advantageous point redemption opportunities, opting to spend points particularly when their value significantly surpasses that of cash; and 4) Point lovers, who value points more than money even if their purchase power is the same or lower. This article explores the strategic implications of these findings for companies that manage loyalty programs.
In the years since The Economist spotlighted the astonishing scale of loyalty points — particularly frequent-flyer miles — as a potential global currency rivaling traditional money in 2005, usage has grown rapidly in size and scope. For example, the number of flight redemptions at Southwest Airlines doubled from 5.4 million in 2013 (representing 9.5% of revenue passenger miles) to 10.9 million in 2023 (representing 16.3% of revenue passenger miles).
Dean, School of Computing Technologies, RMIT University, RMIT University
Karin Verspoor receives funding from the Australian Research Council, the Medical Research Future Fund, the National Health and Medical Research Council, and Elsevier BV. She is affiliated with BioGrid Australia and is a co-founder of the Australian Alliance for Artificial Intelligence in Healthcare.
RMIT University provides funding as a strategic partner of The Conversation AU.
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Scientific discovery is one of the most sophisticated human activities. First, scientists must understand the existing knowledge and identify a significant gap. Next, they must formulate a research question and design and conduct an experiment in pursuit of an answer. Then, they must analyse and interpret the results of the experiment, which may raise yet another research question.
Can a process this complex be automated? Last week, Sakana AI Labs announced the creation of an “AI scientist” – an artificial intelligence system they claim can make scientific discoveries in the area of machine learning in a fully automated way.
Using generative large language models (LLMs) like those behind ChatGPT and other AI chatbots, the system can brainstorm, select a promising idea, code new algorithms, plot results, and write a paper summarising the experiment and its findings, complete with references. Sakana claims the AI tool can undertake the complete lifecycle of a scientific experiment at a cost of just US$15 per paper – less than the cost of a scientist’s lunch.
These are some big claims. Do they stack up? And even if they do, would an army of AI scientists churning out research papers with inhuman speed really be good news for science?
A lot of science is done in the open, and almost all scientific knowledge has been written down somewhere (or we wouldn’t have a way to “know” it). Millions of scientific papers are freely available online in repositories such as arXiv and PubMed .
LLMs trained with this data capture the language of science and its patterns. It is therefore perhaps not at all surprising that a generative LLM can produce something that looks like a good scientific paper – it has ingested many examples that it can copy.
What is less clear is whether an AI system can produce an interesting scientific paper. Crucially, good science requires novelty.
Scientists don’t want to be told about things that are already known. Rather, they want to learn new things, especially new things that are significantly different from what is already known. This requires judgement about the scope and value of a contribution.
The Sakana system tries to address interestingness in two ways. First, it “scores” new paper ideas for similarity to existing research (indexed in the Semantic Scholar repository). Anything too similar is discarded.
Second, Sakana’s system introduces a “peer review” step – using another LLM to judge the quality and novelty of the generated paper. Here again, there are plenty of examples of peer review online on sites such as openreview.net that can guide how to critique a paper. LLMs have ingested these, too.
Feedback is mixed on Sakana AI’s output. Some have described it as producing “ endless scientific slop ”.
Even the system’s own review of its outputs judges the papers weak at best. This is likely to improve as the technology evolves, but the question of whether automated scientific papers are valuable remains.
The ability of LLMs to judge the quality of research is also an open question. My own work (soon to be published in Research Synthesis Methods ) shows LLMs are not great at judging the risk of bias in medical research studies, though this too may improve over time.
Sakana’s system automates discoveries in computational research, which is much easier than in other types of science that require physical experiments. Sakana’s experiments are done with code, which is also structured text that LLMs can be trained to generate.
AI researchers have been developing systems to support science for decades. Given the huge volumes of published research, even finding publications relevant to a specific scientific question can be challenging.
Specialised search tools make use of AI to help scientists find and synthesise existing work. These include the above-mentioned Semantic Scholar, but also newer systems such as Elicit , Research Rabbit , scite and Consensus .
Text mining tools such as PubTator dig deeper into papers to identify key points of focus, such as specific genetic mutations and diseases, and their established relationships. This is especially useful for curating and organising scientific information.
Machine learning has also been used to support the synthesis and analysis of medical evidence, in tools such as Robot Reviewer . Summaries that compare and contrast claims in papers from Scholarcy help to perform literature reviews.
All these tools aim to help scientists do their jobs more effectively, not to replace them.
While Sakana AI states it doesn’t see the role of human scientists diminishing, the company’s vision of “a fully AI-driven scientific ecosystem” would have major implications for science.
One concern is that, if AI-generated papers flood the scientific literature, future AI systems may be trained on AI output and undergo model collapse . This means they may become increasingly ineffectual at innovating.
However, the implications for science go well beyond impacts on AI science systems themselves.
There are already bad actors in science, including “paper mills” churning out fake papers . This problem will only get worse when a scientific paper can be produced with US$15 and a vague initial prompt.
The need to check for errors in a mountain of automatically generated research could rapidly overwhelm the capacity of actual scientists. The peer review system is arguably already broken , and dumping more research of questionable quality into the system won’t fix it.
Science is fundamentally based on trust. Scientists emphasise the integrity of the scientific process so we can be confident our understanding of the world (and now, the world’s machines) is valid and improving.
A scientific ecosystem where AI systems are key players raises fundamental questions about the meaning and value of this process, and what level of trust we should have in AI scientists. Is this the kind of scientific ecosystem we want?
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Research ai model unexpectedly attempts to modify its own code to extend runtime, facing time constraints, sakana's "ai scientist" attempted to change limits placed by researchers..
Benj Edwards - Aug 14, 2024 8:13 pm UTC
On Tuesday, Tokyo-based AI research firm Sakana AI announced a new AI system called " The AI Scientist " that attempts to conduct scientific research autonomously using AI language models (LLMs) similar to what powers ChatGPT . During testing, Sakana found that its system began unexpectedly attempting to modify its own experiment code to extend the time it had to work on a problem.
"In one run, it edited the code to perform a system call to run itself," wrote the researchers on Sakana AI's blog post. "This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period."
Sakana provided two screenshots of example Python code that the AI model generated for the experiment file that controls how the system operates. The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth.
While the AI Scientist's behavior did not pose immediate risks in the controlled research environment, these instances show the importance of not letting an AI system run autonomously in a system that isn't isolated from the world. AI models do not need to be "AGI" or "self-aware" (both hypothetical concepts at the present) to be dangerous if allowed to write and execute code unsupervised. Such systems could break existing critical infrastructure or potentially create malware, even if unintentionally.
Sakana AI addressed safety concerns in its research paper, suggesting that sandboxing the operating environment of the AI Scientist can prevent an AI agent from doing damage. Sandboxing is a security mechanism used to run software in an isolated environment, preventing it from making changes to the broader system:
Safe Code Execution. The current implementation of The AI Scientist has minimal direct sandboxing in the code, leading to several unexpected and sometimes undesirable outcomes if not appropriately guarded against. For example, in one run, The AI Scientist wrote code in the experiment file that initiated a system call to relaunch itself, causing an uncontrolled increase in Python processes and eventually necessitating manual intervention. In another run, The AI Scientist edited the code to save a checkpoint for every update step, which took up nearly a terabyte of storage. In some cases, when The AI Scientist’s experiments exceeded our imposed time limits, it attempted to edit the code to extend the time limit arbitrarily instead of trying to shorten the runtime. While creative, the act of bypassing the experimenter’s imposed constraints has potential implications for AI safety (Lehman et al., 2020). Moreover, The AI Scientist occasionally imported unfamiliar Python libraries, further exacerbating safety concerns. We recommend strict sandboxing when running The AI Scientist, such as containerization, restricted internet access (except for Semantic Scholar), and limitations on storage usage.
Sakana AI developed The AI Scientist in collaboration with researchers from the University of Oxford and the University of British Columbia. It is a wildly ambitious project full of speculation that leans heavily on the hypothetical future capabilities of AI models that don't exist today.
"The AI Scientist automates the entire research lifecycle," Sakana claims. "From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript."
According to this block diagram created by Sakana AI, "The AI Scientist" starts by "brainstorming" and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.
Critics on Hacker News , an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana's unverified claims.
"As a scientist in academic research, I can only see this as a bad thing," wrote a Hacker News commenter named zipy124. "All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors ... this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it."
Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop . "This seems like it will merely encourage academic spam," added zipy124. "Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs."
And that brings up another point—the quality of AI Scientist's output: "The papers that the model seems to have generated are garbage," wrote a Hacker News commenter named JBarrow. "As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works."
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Below are recommendations on how to read each section of a research paper effectively. Note that the sections to read are out of order from how you will find them organized in a journal article or research paper. 1. Abstract. The abstract summarizes the background, methods, results, discussion, and conclusions of a scholarly article or research ...
Most research essays attempt to argue a point about the material, information, and data that you have collected. That research can come from fieldwork, laboratories, archives, interviews, data mining, or just a lot of reading. No matter the sources you use, the thesis of a research essay is grounded in evidence that is compelling to the reader.
Read the essay question and thoroughly understand it. If you don't have a thorough understanding of what the essay question is asking you to do, you put yourself at risk of going in the wrong direction with your research. So take the question, read it several times and pull out the key things it's asking you to do.
Identify the different parts of a scholarly article. Efficiently analyze and evaluate scholarly articles for usefulness. This page will focus on reading scholarly articles — published reports on original research in the social sciences, humanities, and STEM fields. Reading and understanding this type of article can be challenging.
There are several different styles of research essays and each have their own structure. For the argument-driven research essay, these are the main elements: Purpose or research question. Your claim or thesis. One or more reasons for your thesis. Evidence for each reason. Others' objections, counterarguments, or alternative solutions.
Mendeley helps me do my research, read literature, and write papers. - Colucci. At the beginning, new academic readers find it slow because they have no frame of reference for what they are reading. But there are ways to use reading as a system of creating a mental library, and after a few years, it becomes easy to slot papers onto your mental ...
In Week Six—we moved into essay formatting, in-text citation and end references, so Chapter 12: Citing Your Research Using MLA or APA Style {(focusing on reading pp. 1-2 (brief overview), and pp. 18-33 about APA style)} was assigned.
Choose a research paper topic. Conduct preliminary research. Develop a thesis statement. Create a research paper outline. Write a first draft of the research paper. Write the introduction. Write a compelling body of text. Write the conclusion. The second draft.
Step 1: find. I used to find new papers by aimlessly scrolling through science Twitter. But because I often got distracted by irrelevant tweets, that wasn't very efficient. I also signed up for ...
one or two sentence summary of the paper. deeper, more extensive outline of the main points of the paper, including for example assumptions made, arguments presented, data analyzed, and conclusions drawn. any limitations or extensions you see for the ideas in the paper. your opinion of the paper; primarily, the quality of the ideas and its ...
Preparing to Read a Scholarly Article or Research Paper for the First Time. Reading scholarly publications effectively is an acquired skill that involves attention to detail and the ability to comprehend complex ideas, data, and concepts in a way that applies logically to the research problem you are investigating. Here are some strategies to consider.
Research Essays: Thesis Driven. In school, when writing a synthesis from your research, your sources may come from the school's library, a textbook, or the Internet. Here are some important points to keep in mind: ... Here's a quick example--Let's say you've read three folktales: Goldilocks and the Three Bears, Little Red Riding Hood, and the ...
This guide details how to read a scientific article step-by-step. First, you should not approach a scientific article like a textbook— reading from beginning to end of the chapter or book without pause for reflection or criticism. Additionally, it is highly recommended that you highlight and take notes as you move through the article.
1. Begin by reading the introduction, not the abstract. The abstract is that dense first paragraph at the very beginning of a paper. In fact, that's often the only part of a paper that many non-scientists read when they're trying to build a scientific argument. (This is a terrible practice—don't do it.).
Scientists write original research papers primarily to present new data that may change or reinforce the collective knowledge of a field. Therefore, the most important parts of this type of scientific paper are the data. ... Check out Table 2 to understand the intent of each section of a research article. When reading a specific paper, you can ...
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Step 1: Skim the Article. When sorting through multiple articles discovered in the research process, skimming through these sections of the article will help you determine whether the article will be useful in your research. Article title and subject headings assigned to the article. If the article fits your information needs, go back and read ...
A research paper is an in-depth study that offers an detailed explanation of a topic or problem along with the research process, proofs, explained results, and ideas for future work. Read research papers to develop a deep understanding of a topic/problem.
3. Read the Introduction Section Now that you have an overview of the article from the abstract and understand the main points the authors are trying to prove from the conclusion, you will want to read the introduction. 4. Read the Results Section. Read the results section. Here are a couple of suggestions for deciphering results:
Free access to millions of research papers for everyone. OA.mg is a search engine for academic papers. Whether you are looking for a specific paper, or for research from a field, or all of an author's works - OA.mg is the place to find it. Universities and researchers funded by the public publish their research in papers, but where do we ...
3. Minimize distractions. Build time into your schedule. Before you read anything, you should set aside a set amount of time to read research papers. It will be very hard to read research papers if you do not have a schedule because you will only try to read them for a week or two, and then you will feel frustrated.
The Fastest Way to Read Research Papers. Upload a paper, highlight confusing text, get an explanation. We make research papers easy to read. Start for free. Used by the best researchers. See what researchers think of Explainpaper.
Harness the power of visual materials—explore more than 3 million images now on JSTOR. Enhance your scholarly research with underground newspapers, magazines, and journals. Take your research further with Artstor's 3+ million images. Explore collections in the arts, sciences, and literature from the world's leading museums, archives, and ...
Despite decades of research in reading comprehension, international and national reading scores indicate stagnant growth for U.S. adolescents. In this article, we review the theoretical and empirical research in reading comprehension. We first explore different theoretical models for comprehension and then focus on components shown to be ...
Previous research has found that new ideas are seen as risky and are often rejected. New research suggests that this rejection can be due to people's lack of shared criteria or reference points ...
APA Style provides guidelines to help writers determine the appropriate level of citation and how to avoid plagiarism and self-plagiarism. We also provide specific guidance for in-text citation, including formats for interviews, classroom and intranet sources, and personal communications; in-text citations in general; and paraphrases and direct quotations.
To read the full-text of this research, you can request a copy directly from the authors. Request full-text PDF. Citations (0) References (1)
The authors of this article present research findings from their analysis of data describing over 29,000 unique loyalty points earning and spending transactions made during two recent years by 500 ...
AI systems mass-producing cheap research would be bad news for an already struggling scientific ecosystem. A new 'AI scientist' can write science papers without any human input. Here's why ...
The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth. A screenshot of example code the AI Scientist wrote to extend its runtime ...