Hacker News new | past | comments | ask | show | jobs | submit login
Ask HN: How do you read Academic Papers?
140 points by milesf on Mar 22, 2015 | hide | past | web | favorite | 71 comments
I have never really consciously taken the time to learn how to read academic papers, because it never occurred to me it's something I needed.

I replied to a tweet today suggesting people ought to take a course how to read them, and got a great reply from Glenn Vanderberg on a starting point:

https://twitter.com/glv/status/579411305347489792

Does anyone else have any thoughts or opinions about this? I have a hard time learning things. Maybe this insight is part of the reason why.




I have spent a large portion of my adult life finding and reading and understanding scientific papers. The advice offered in the tweet is good, so I won't restate it - but I will offer a couple of my own:

* Print the paper out and make notes on it by hand (this has a small mention in one of the articles) - this technique got me through university, I tried every software package imaginable but in the end having a hard copy forced me to deal with sheer amount of reading I had to do. It also allowed me to freely make notes, add sticky notes etc. Since then I have done this with any collection of papers I want to get my head around.

* Try to comprehend something on every read through - it doesn't have to be big, just something - whether it is the sample makeup or part of the methodology or the conclusions etc. You don't need to understand these in any particular order, but ensure to revise your understanding as you become more familiar with the paper.

* Most papers are useless (to you at the time you are reading them) - the sad truth about research is that 90% of the stuff you devote yourself to understanding will be wrong, outdated or not useful to what you are working on. It is very hard to pick out useful papers with nothing to go on but titles, and abstract and citations. As you get more used to the field it becomes easier and familiar names, authors and institutions can guide you, but even close to a decade after reading my first paper I still probably only manage a 10% hit rate when conducting research (but hey - 10% of a lot of papers is still a lot of papers!)


I'll second your first point about hard copies. It's one thing to skim a PDF on the screen to see if it might be worth my time, but if I really want to grok a paper I'm marking up a hardcopy.

This has also made me appreciative of authors who put in the effort to make sure that their graphs and diagrams reduce well to grayscale. Instead of referring to the red line versus the green line, they'll use labels, marks, different cross hatch types, etc. (Don't get me wrong, good color is still very nice to have too!) It's also led me to making it a point to print off drafts of my own submissions on a B&W laser printer and make sure that my figures and captions are still understandable. I expect that this makes it easier for any colorblind readers as well, whatever type of colorblindness they may have.


> * Most papers are useless (to you at the time you are reading them) - the sad truth about research is that 90% of the stuff you devote yourself to understanding will be wrong, outdated or not useful to what you are working on.

This is very important advice. Do not assume that just because it's a published research paper it is valuable, correct, or useful. In fact, especially in applied CS, I found that authors will sometimes make what looks like an intentional effort to obfuscate the methods so that the paper is publishable (peer reviewers will not try to reproduce the results anyway), but the methods are not implementable, or at least not easily.

This makes sense when you think about the competition in academia, authors doing consulting work for companies, or intending to start businesses of their own.


Usually the idea is bunk and they are trying to obscure that, more often than that the idea is great and they are trying to protect their IP for a startup.

If you know something is possible, then it is not impossible to replicate results even with obscure directions about it. Except for Paxos, you really need good instructions at that point (that guy should get an award).


Hah. I'm pretty dumb myself. That'll teach me. (I'm leaving the link, though. Great paper.)

[1]: http://research.microsoft.com/en-us/um/people/lamport/pubs/p...


Leslie Lamport is a man. You could argue something about Barbara Liskov's Viewstamped Replication, though, I guess.


Whoops. Thanks, I messed up pretty badly, didn't I.


It's an easy mistake to make. It's interesting, though, because there are at least three important women who were involved in related work at the same time -- I'm thinking of Liskov, Dwork, and Lynch.


> Try to comprehend something on every read through - it doesn't have to be big, just something - whether it is the sample makeup or part of the methodology or the conclusions etc. You don't need to understand these in any particular order, but ensure to revise your understanding as you become more familiar with the paper.

Lately I've been reading "How to Read a Book" by Adler and van Doren [1], which systematises this. It applies to papers just as well as books. Get the 1972 edition, as the 1940 edition is quite a bit less readable.

[1] https://en.wikipedia.org/wiki/How_to_Read_a_Book


> * Most papers are useless (to you at the time you are reading them)

I would say that most papers are worthless. The publish or perish mentality in academia leads to more noise and less signal. You really have to fail fast on papers, find the seminal ones and the few under looked gems related to your topic that can hopefully be fished out via Google (though word of mouth is important).

Also, if you publish, try not to be part of the problem.


> The publish or perish mentality in academia leads to more noise and less signal.

Perhaps a Google-like ranking would help. The more cited a paper is (in the bibliography), the more it might not be noise. Then again, I don't like the idea that what is popular is also the best.


One of the tricks to getting your paper in (in many, not all, fields) is to cite as many PC papers as possible. It can turn into one big citation circle jerk, or at least an echo chamber.


Do you know google scholar? That's basically what it's for. Might as well be added to the list of advices: searching for the important papers with google scholar or CiteSeerX.


Touché. I have heard of it but never used it since I'm not required by my work to read scientific papers.


That was actually inspiration for Google's PageRank algorithm: http://en.wikipedia.org/wiki/PageRank#History


As someone who has read almost 50 academic papers in the last 6 months (MSc in Computer Science, we read a paper before class and then analyze it with the lecturer) I agree unanimously with the parent.

Above making notes, I find that challenging the material in the paper is key to understanding. For example, authors may make claims without sufficient data to support their arguments or they might not explain a particular anomaly in their results. There are nuances in papers everywhere, try to pick these these out as you read them.

Secondly, you will at times read papers which assume a particular level of prior background knowledge. For example, I've had to read papers on rateless codes (Spinal Codes), indoor passive radar and full duplex radio with zero background in electrical engineering or physics. 6 months ago I would look at some of the equations and think "nope, not even going to try and understand that". This is bad, don't do it. If you don't understand the equation at first glance (and I would be surprised if you did) break it down piece by piece. Annotate what each variable/Greek letter represents, how it fits together with their explaination of the equation and how it might fit a trend shown in their results. Sometimes you might find that you have to do extensive Googling to understand whole papers, don't worry, in my experience this is perfectly normal.

Finally, if you don't understand something, don't continue reading. Try to understand it before progressing. Only continue reading if you absolutely cannot get it. If you find that a lack of understanding of an earlier part of the paper starts to limit your understanding of later parts, stop! Go back and re-read the earlier parts. Quite often, material later in the paper can adequately explain concepts earlier on.

Best of luck!


I personally would recommend against making notes on the print out. I'd rather go with keeping your notes separately in a way you can easily categorize and search your notes.


That's kind of like asking, "How do you learn how to code?"

The answer is to do it. Do it often, do it a lot, over and over. The linked tweet provided great advice, as do other resources and comments in this thread. But the bottom line is that you learn by doing, and trying hard.

I remember reading the same section of a paper at least 30 times, no exaggeration. I would read it, then go and look up the topic online, then read it again, then go do something else, and then read it again. Over and over for days. i parsed every symbol and word of every line. And then one day I was elucidated. I finally understood it, and it was a feeling of awesome communion with the gods.

I'm not sure if there's a short cut, but I'll be monitoring this thread along with you in the hopes of finding one. Do what the others said and print the paper out, and take a pencil and highlighter to it. Look up terms you don't know online and be diligent in your use of search engines. Just stay at it, and don't give up!

As an afterthought, most modern computer science papers have a graphic or figure on the second or third page that does a good job of explaining the idea visually. Older papers tend not to have this, or it's buried deeper in the paper. Look for this key graphic and use it as a guide or framework to understand the rest of what's being said.


Academic papers can seem incredibly intimidating but you have to remember: they're not written to be readable to the casual reader unlike articles on the web.

Journals have strict page limits so there's no space to introduce domain specific terminology or give a brief tutorial on the topic. They only have enough room to tersely describe directly relevant background material and will assume you are knowledgable in the domain.

Read the abstract then the conclusion. If it sounds like you want to understand the rest of the paper after that, be prepared to look up terminology, read citations, follow tutorials and reread the paper multiple times to really understand it. Don't be put off if you can barely understand it the first time.


Reading the abstract and the conclusion is the most important part of reading and selecting academic papers. Especially in the beginning.

If you read the abstract and the conclusion you should have a good sense of what this paper is about, what it is trying to prove, and perhaps also a sense of how they prove this. These are the primary factors for you to see if this paper is worthwhile to you.


Agreed.

I read the abstract, if it seems relevant, then I flip to the conclusion, if it still seems relevant then I skim read it through, then print it out if it looks like something I need to dig deeper into.

Often those lightbulb moments have only come for me after printing it out, despite reading it 4-5 times on readcube.


> They only have enough room to tersely describe directly relevant background material and will assume you are knowledgable in the domain.

I feel like it just is the dominant style; it doesn't have to be that way. Sometimes there are these gems, that are extraordinarily well written and bring new ideas in a gentle way. Some examples:

- Zhang, Cha, John C. Platt, and Paul A. Viola. "Multiple instance boosting for object detection." Advances in neural information processing systems. 2005.

- Gelman, Andrew, et al. Bayesian data analysis. Vol. 2. Chapman & Hall/CRC, 2014.

So, IMHO it can be done, but it is a talent.


>"Journals have strict page limits so there's no space to introduce domain specific terminology or give a brief tutorial on the topic."

This is a big problem, especially in the digital world we live in now. There should be no page-limits to publish a paper. Of course, I understand that you can't expect to have each paper introduce and re-explain every single word/topic/definition for a layman to get up to scratch, but there has to be a better middle ground than what we have now.


How to Read a Paper by S. Keshav

http://ccr.sigcomm.org/online/files/p83-keshavA.pdf


Useful: Literature Review Matrix mentioned there: https://d1b10bmlvqabco.cloudfront.net/attach/i7ax2kxrsnn3v5/...


The answer depends on why you're reading the papers, of course.

If it's only a dilettante's interest, then read it like you would read news: skim for a general sense, emphasizing the introduction, conclusion, and graphics.

For academics reading papers, the situation is (potentially) different. There are genuine professional consequences for failing to read or understand the material.

There are many approaches -- many good ideas here -- most of which are rehashing of basic study skills, but I would add one piece of generic advice.

Reading academic papers well takes a lot of time. Sometimes this is called active reading. If you are doing this type of reading, make sure you take something away from the reading. And by "take away" I mean "memorize", not index in Mandalay or outline in Evernote. Deliberately bolt each bit of information onto the edifice you have already built. Think about it in context. Wonder about it. Doubt it. Be skeptical. If you can't summarize the paper next week at a cocktail party, you're not reading, you're just filling out a bibliography.


> There are genuine professional consequences for failing to read or understand the material.

I wish they were more genuine and serious. I'm only a graduate student and I already can't count the number of reviews I've gotten where the reviewer clearly did not read the paper closely.


I have to disagree. IMHO most papers weren't written to be read with care. So you shouldn't waste your time on them. Skimming through is ok when you have to filter out the few papers that actually make a difference.


I don't think this can be true, except for experts. In the biography of Oppenheimer, American Prometheus, he was described as having an uncanny ability to flip through publications and understand their conclusion and significance in seconds. That description, or anything like it, certainly isn't true for me.

I would suggest that any paper that you perceive as a waste of time or not worth reading with care probably isn't worth reading at all. If it is the case that this sort of thing is the norm, as I know full well it is in some disciplines, then that says something important about what's being published in that discipline. It says nothing about what the best way to read and understand the latest pubs in a discipline with real research going on.


I try not to read primary research papers. Most of them are wrong[0]. Even the one's that are right are written for consumption by a very specialized community that I'm rarely a member of.

If I decide that i absolutely need to understand a particular paper, I start by finding a relevant review article. I look and see if the papers cited in the background section are also cited in a recent review of the state of the field. These are less likely to be flat out wrong, and they provide better context.

I try to read a paper with other people (preferably people who know the field) and we take turns trying to explain the figures and methods to each other.

But to reiterate: any given paper is most likely wrong and your default position should be persistent skepticism.

[0] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/


This is the way I learned to do it in grad school. There a lot of litterature out there and most chances are 97% is irrelevant to what you're interested in. So, a lot of "reading papers" is efficiently figuring out if it is interesting or not. You'll never realistically have time to read everything.

First read title and abstract. This is where you'll likely eliminate a lot of stuff.

Interestingly, I've found that the best point to go from there is the conclusion. A good conclusion will generally tell you the results (which is generally what interests you), plus a good summary of the motivation and approach/methods.

From there figure out what is interesting to you. Go through the background or motivation, specific areas of methodology you're interested in, or detailed analysis of the results.


Disclaimer: I spend a lot of time reading life sciences papers, which are mainly about experimental procedures and data rather than theory and which are almost universally frustrating, often misleading, wrong or actually just made up.

I hope that soon reading papers will be supplanted by automated aggregation and parsing of content, and eventually almost real time dissemination of data and results. Frankly, encasing scientific knowledge in papers is now beyond a joke. Why we are still relying on parchment based technology and pre-enlightenment publishing norms drives me nuts. Examples:

1. Genomics papers that have a table with thousands of rows printed in a PDF in the appendix. Almost willfully annoying.

2. Two page papers in top life sciences journals that have 100 page appendices which are not searchable or indexed.

3. Papers that are heavily criticised in letters, but none of this is obvious when looking at the original paper HTML page, the abstract or the PDF.

4. Long clinical trials that are published repeatedly over 20 years, where the previous interim data updates are locked away in journal archives that requires extra payment or a subscription to JSTOR.

5. Papers with 10 supplementary tables, all single file excel spreadsheets.

6. Papers are submitted for publication 12 months before they are actually published. Bad luck if you started working on the same thing 6 months ago.

7. If I want to show a table from your paper in a presentation, I have to manually snapshot from the PDF and paste it on my slide.

8. Because the only way to disseminate data is with a paper, important trial results can be given as a presentation at a conference, and not become available until published much later. Until then, the only record of these results are your memory if you were there, a pay-walled recording of the presentation, a drug company press release, or sandwiched between banner ads on a 'medical news' site.

9. Old papers get ignored, because they just aren't readily available. Life science researchers regularly 'discover' something that was published in the 1970s.

10. It is impossible to read all these papers. One part of my research field easily has 10000 papers or more that could be justifiably relevant. Biology has now reached the absurd tipping point where it is easier and cheaper in terms of labour costs to do an experiment yourself rather than thoroughly survey the literature.

Hopefully computer science papers aren't like this.

EDIT: grammar


In life sciences, just read the figures. Papers are full of exaggerations, overgeneralizations and other self-aggrandizing claims. AFAIK it's not like that in physics, and CS is different altogether. Anyone who has tried to do research to create a model based on biological data can attest how hard it is to find reliable meaningful consensus values in the literature.


I think one important thing to note is the hourglass format of most scientific papers: statement of the broad problem and general importance of the question at hand, gradually more specific identification of the current challenge/contribution, down to the methods/model and results, and then widening to look at the implications and relationship of the new findings to the rest of the field.

Where you focus on the paper depends on what you want: if you are just learning the central questions and theoretical views in the field then the beginning and end are more useful; as you gain an understanding of the field you may be more interested in the particular advances in a given paper and may devote more attention to the middle.

At least in several of the fields I read regularly (linguistics, cognitive science, NLP, and developmental psychology) there are three key paragraphs that tend to have the most useful information: 1) the abstract (duh!) 2) the last paragraph of the introduction (this usually has the most concise statement of the motivation) 3) the first paragraph of the discussion (this usually recaps whatever advances they make in a usable way; actual results sections often contain a level of detail that isn't very memorable after a few days).

Citations are shorthand for entire sets of ideas--they're a means by which whole arguments can be referred to very concisely. One of the challenges in reading a new literature is in learning what set of citations are commonly reused and how, especially because the conventionalized citation of a paper isn't necessarily what the author expected it to be known for, nor what you might take from a paper on a first reading (or any number of readings). For this reason I highly recommend reading papers with inline full citations (Chomsky, 1965) rather than numeric/ indexical citations that you have to look up [1].

Finally, mock-reviewing a paper is a fantastic way to make sure you're being honest about how much of the paper you understand. Another technique at higher grad school levels is mock-reviewing a paper from someone else's (=prominent academic's) perspective--which imposes the dual constraint of understanding the paper at hand as well as the theoretical perspective behind someone else's critique.


Its the citation chain game! When you first start reading papers in a new field you essentially have to read every citation present to understand whats going on. As you accrue knowledge you'll need to read fewer citations and you'll understand more of the jargon. That, to me, is the primary challenge.

It took me about 2 years before I could read a paper end to end and understand it. Of course by that point I realized that reading most papers was a waste of time. You often only need the figures, conclusion and methods section.


Like anything else, figure out why you're reading a paper, what are your goals?

If you're reading for expanding your knowledge in a field, then a good area to start is by reading well known dissemination in the field in past decade or so. So, say, you're into datastrucures, read the paper that brought Red-black tress by Rudolf Bayer. This is also a great route for somebody reading for the first time. Well written papers are less off-putting. Most papers out there are very poorly written.

If you're trying to extend the field, then it helps scouting what interests you in extremely recent, interesting works. You can use sites like arxiv to find edgy works done in past month of so, and then drill down the references to understand where they came from. You can then work on improving that work.

So it depends on what you're trying to do. For software, I usually use GoodReader on iPad. Nothing fancy, but it annotates, stores all my research papers and organizes them. On computer, give Zotero a try.

If you're at a university, remember to catalog papers you read. It's so much easier now with things like Dropbox. After you get out of the college, you will need to pay huge amounts of money for them, so it behooves to maintain a steady collection safe somewhere where you can always look back to. So catalog what you read properly. Don't get overwhelmed and start losing the PDFs.


As far as deciding whether to read a paper in depth, the advice I've heard is to read the abstract, and if you're still interested read the conclusion. If you're still interested after that, dig into the detail.

Don't let the "academic" format and style fool you. There's plenty of BS churned out in academia, though if you're not used to reading papers it may not be apparent at first.


Besides the S. Keshav's paper on how to read papers "How to Read a Paper", take a look at the section "Reading Research Papers" by Margo Seltzer in "CS261: Graduate Operating Systems (2014)" http://www.eecs.harvard.edu/cs261/notes/intro.html


In addition to the other advice, I'd recommend starting with the "seminal" articles in topic of interest – articles that are heavily cited by other researchers – before moving on to something more fine-grained. Even though I've read a fair amount of academic literature, it'd be difficult for me to pull an article off of arXiv and understand what's going on, since much of academic writing is building off of prior work and requires context. Seminal works are usually written more clearly because the author is trying to introduce a new idea.

Google scholar makes it a bit easier to identify seminal works, as the results return a count for "cited by". You'll see that some are cited thousands of times by other researchers. Plus, these are typically the most interesting articles to read either way.


A related approach is to also start with review/survey papers in a given field. Beside giving you a more digestible overview, they'll also cite the papers describing important results that one might want to read for the details.


With great skepticism. (I worked for years in academia and have published many papers.)


A related question: How do you access academic papers?

I've got a list of papers I'd like to read on the Church-Turing Thesis and Quantum Computing, but I'm just a working freelance programmer with no access to a research library. For instance how do I get this one?:

K. Steiglitz (1988), Two non-standard paradigms for computation: Analog machines and cellular automata, in Performance Limits in Communication Theory and Practice, Proceedings of the NATO Advanced Study Institute, Il Ciocco, Castelvecchio Pascoli, Tuscany, Italy, July 7-19, 1986, J. K. Skwirzynski, ed., Kluwer Academic Publishers, pp. 173-192.


There is a reddit for precisely this purpose, I believe it is r/scholar

Mind you I have no first hand experience with this, usually just going to author's site is good enough for most recent authors.


1) You will stand up and scream with delight when you realized that the things which were once impossibly convoluted are not comprehensible ( yes this even applies to abstracts )

2) Be cognizant of absorbtion / attention: Its easy to get stuck glossy-eyed and pacing through the lines

3) As stated elsewhere in here: They can be filled with LOTS of crap -- keep this in mind and work on attenuating your bullshit-detection.


Read the abstract a couple of times. Internalize every sentence. Then you can start reading the rest of the paper. The abstract is meant to be easy to read and it'll help you form an outline of the paper in your head before you start reading. This will help you stay on track while you read the rest of the paper as it's pretty easy to get lost in the middle somewhere.


Not that it's perfect, but http://mendeley.com/ is the best tool I've seen for organizing and annotating papers. If you don't have a reasonable way of staying organized, then no matter how you read academic papers, you'll wind up forgetting what you read eventually.


In addition, the bulk paper renaming feature has been an invaluable time saver to me.

Throughout the years I've collected thousands of papers, many still unread, but my 'must read' ('must skim' mostly) folder currently houses several hundred of them and I can't imagine how much time I would've wasted by manually renaming using the template {Author} - {Year} - {Title}.

Mendeley is free, but a paid alternative is Papers (http://www.papersapp.com/), which used to be Mac-only and the reason I ended up discovering and using Mendeley. Haven't personally tried it, but heard very good things about it.

My paper reading selection process consists in reading the abstract and the conclusion. Quite a few papers have disappointing conclusions despite attention-grabbing titles. If all is sound and promising, I keep it and depending on other factors, I decide when to read it in its entirety and/or where to store it for later use.


Well it really depends on your situation but I guess the main question to ask is: Do you possess the necessary background to read the paper? It takes a lot of groundwork before you can read a paper and be confident you understood the gist of it.

I personally have to sit down with pen and paper and toy with key equations, maybe take out a book or two to make sure the author and I are agreeing on specific vocabulary, or even double check symbols used which can differ from what one is used to.

And keep in mind that completely novel, truly groundbreaking papers in a field are rare. Most of what I've read is iterations on very well known basic methods, a modification of this or that, some change to XYZ etc.

Just keep reading. And re-reading. And...


Accepting that I have to read a paper two or three times before I really understand it has been the biggest breakthrough I've had.

Usually this is because I just didn't have the background to understand the paper until I read it, but academic papers can be pretty poorly written. For example, a recent paper hinged upon something that I considered physically impossible - only in the last paragraph did it explain why it wasn't (they were even like "everyone is probably assuming this is impossible - here's why it's not". I really wish that had led with that as it was an overwhelmingly convincing argument.


If it is a case study often there is usually a problem definition/scope/abstract (it really depends on what type and who is publishing). I often start with that and then skip to the back to te conclusion. This gives me a bit if a road map of how and what to read. If I read beginning to end I often get lost in confusing technical academic jargon.

After doing that I think about all the questions I have. Then go forth and read it through keying in on the areas that may answer my questions.

I really like data and graphs so I usually spend lots if time playing around and testing theories that I may have as I analyze.


http://ccr.sigcomm.org/online/files/p83-keshavA.pdf - this paper is actually pretty insightful.


More literally, does anyone know of a way to physically read papers on a Kindle? I've never been able to reformat even simple two-column PDFs into a format suitable for ereaders. Calibre seems like a good start, but I get mangled results. I end up printing stuff out. Which is OK, but I'd like to have the option.

Even something as basic as trimming ridiculous margins seems rather difficult to accomplish. Is there a software package that'll make this easy?


I had good results with http://www.willus.com/k2pdfopt/.


I trim the margins using Briss and then read in landscape mode. Doesn't help in all cases.


Not start-to-finish, that's for sure. Or --- almost never.

If you're doing post-grad (or research) computer science, you should really think about learning to read the literature faster. I was very slow at this when I started, and I notice new grad students struggling at it.

A common problem is to try to read too deeply. You should get good at reading a paper for 1 minute, or 5 minutes, or 10 minutes.



Read the abstract. Then the figures. Then the introduction. Then the conclusion. If you're still in the paper, read the whole thing.


As seanwilson has pointed out already, academic papers are written to be intelligible to experts in the field. They're not meant to be tutorials for practitioners. So you can easily get stuck if you're reading a paper in a field in which you're not an expert.

I also see a lot of useful tips here such as making notes, rewriting parts of the paper, and so forth. I think these ideas only work when you're already getting something out of the paper. These tips are optimizers, they can help you get more out of a paper if you're already somewhat conversant in the field.

In theory, if you're not an expert, you can keep following the citation trail, read all the papers there, become an expert and finally read the paper you actually want to read. I don't think this actually works. If you're not a expert, the paper itself will seem like gibberish. You won't know what citations are actually important and what citations were just pushed in to please a reviewer. And even if you somehow do, the citations themselves will be just as difficult to read.

So what are the solutions? First, seek out accessible papers. There are a lot of tutorials that get published in magazines like the CACM and IEEE Spectrum. These are written with an explicit goal of being accessible to a technical non-expert audience, so you will get something out of these articles. Maybe if you read one such article, you'll get the big ideas in the article and then you can read the technical paper(s) that the article was based on. Since you already know the big ideas, the technical paper won't seem so daunting. And maybe now, you can follow up on a few citations.

The general trick is to find alternate sources that simplify and explain the paper and then read the paper. Alternate sources can be tutorial articles, slides, blog posts, textbooks, talks and so on. This is actually how people learn to read papers in grad school. First, you go to few a masters level classes where the professors teach from papers rather than textbooks but don't necessarily expect students to read and understand all the details in the paper. Next you go to a few seminar-type classes where professors and students discuss the nuances of a number of important papers. There's a lot of non-obvious spoonfeeding that happens: the professors use their expertise to pick papers that important and accessible, they guide the discussion towards the important parts of the papers, they point out flaws and discuss methodologies, suggest projects for the students to implement and learn from, and so on. Over time, the students absorb these lessons by osmosis and become good at picking out and reading papers themselves.

If you're not going to grad school yourself, you'll have to recreate this process of slowly building expertise in a field by reading papers and comprehending them and trying to implement and recreate them. The key word is slowly because you're not going to become an expert in a day, or even a month. You'll have to keep going for a year or two and slowly you'll feel like a blind man who can see more and more of his surroundings everyday.


The linked page on the tweet offers great advice, the only two things I can add are:

* Take a sheet of paper and write down all definitions for easy reference

* If you are planning to use the learning from the paper in a production system, make sure you fully understand all stated (and unstated) assumptions the researchers made when presenting their results.


It depends on the difficulty of the field, but I think on average it takes about 30-80 articles to get into a divergent field. Review articles can help a lot, but you don't always get them for hotly moving fields.

I also think it helps a lot to be aware of popular experimental designs and statistical techniques.


Has anyone looked at ReadCube? I found it when searching for software to help manage research papers I needed to read. You can download papers through their client, annotate and get analysis on papers. https://www.readcube.com/


Like I am dating them. Visit them again and again to learn more about them and what makes them tick.


I've read thousands of papers. I read the abstract and the summary. Those tell me if I want to read the paper.

They also tell me how to write a paper because everyone else is doing the same thing. Tease in the abstract, please in the summary.


I think most people forget the first step: decide whether it's really worth reading the paper. Don't spend too much time on weak papers that won't get you anywhere. Unfortunately, most papers are "weak".


I don't for the most part. Don't want to deal with the paywalls and the content i need is generally also available in a more digested format already. i.e. My job doesn't require reading raw papers



With other people! Impenetrably obscure papers really unravel in a journal club. You can understand the main thrust of a paper much faster if you find a social setting for talking about it.


Reading is a skill. It improves with practice. Breadth helps with depth. It's ok not to grok things in their entirety. Enjoy what is grokked and the process of grilling.


First a disclaimer: I am the founder of Qiqqa.com, which I wrote while doing my PhD at the University of Cambridge, and without which, I quite literally would have failed to complete my PhD.

I believe the key statement that has been iterated over and over here is that only 10% of scientific papers are ever worth reading. And probably only 10% of the text of those 10% are worth reading. They are they 1%.

This means that you end up with hundreds (if not thousands) of papers that you think you might have to read, but have no way of knowing whether or not you should until you have read them. Chicken meet egg.

I built Qiqqa (and wrote a PhD) to solve this from two broad directions: using human intelligence and using artificial intelligence.

USING HUMAN INTELLIGENCE: While a load of people recommend printing out a paper to read it, I believe that that course leads to a lot of future pain. Highlighting and annotating while you read is absolutely important. It allows you to skim through a paper and highlight only the important stuff. Then you can come back later to properly read the important papers – only once you have skimmed enough papers to get a more general sense of the domain you are exploring. If you have printed out papers, it is very difficult to quickly regroup and reread your annotated papers. However, if you have highlighted your papers on your computer, laptop, tablet (or even phone while travelling from Cambridge to London), free products like Qiqqa (or www.pdfhighlights.com) offers you a simple annotation report to pull out all your annotations to not only remind you of what and where the important fact are, but also to let to jump straight to them to get reading immediately.

USING ARTIFICIAL INTELLIGENCE: An interesting side effect of reading more scientific research is that the more you read, the more you have left to read. Every paper you end up reading will make reference to another few papers that you probably should read; like The Magic Porridge Pot. To solve the problem of ‘what should I read next’ (the working title of my PhD), I built into Qiqqa the capability for it to ‘automatically read the papers for you’ using machine learning. To this end, when reading a paper, Qiqqa can recommend to you the most relevant papers to read using something called Topical PageRank which calculates the relevance of a paper to what you are reading not only by its similarity to what you are reading, but also by how well received (cited) that paper is in your community. Think of it as having your own personal Google where it biases results to satisfy your personal predilections. You can read about how it works at http://aclanthology.info/papers/topical-pagerank-a-model-of-....

Good luck with your research! Jimme


angrily while wondering why the two column style persists when most of the reading is being down on wide screen monitors.


I need a guide on how to read textbooks.




Applications are open for YC Summer 2019

Guidelines | FAQ | Support | API | Security | Lists | Bookmarklet | Legal | Apply to YC | Contact

Search: