Almost all journals have an RSS feed. I just subscribe to a dozen or so major journals. Add a web feed reader as well you can skim through them easily, or save up the more interesting ones for later.
I actually just manually check arxiv every morning for the new submissions in my field. It's like getting in the habit of browsing reddit except with a lot less cute animal pictures (maybe because I'm not in biology).
ArXiv has email search alerts. I subscribe to a few topics, they are well formatted plain text digests.
I also have a few ScienceDirect search alerts set up, that come in once every few weeks typically with 1-5 papers.
And Google Scholar, if you use it and you are logged in with an account, learns from your search history and suggests new papers for you to read. It's relatively good.
Like spystath menrionned, all journals have an RSS Feeds stream or more, so I use RSS Feeds with my webapplication https://www.feedsapi.org/ to receive curated alerts in realtime (many of our users have this as use-case as well).
You can also use the rss feeds with a service like IFTTT or Zapier to set up an alert system.
A lot of groups have a journal club/ article aggregator. Try to start one with your colleagues if there is none.
Google scholar alerts are also a good option if your field has nice keywords.
There should be something like reddit for academic papers. With upvotes and what not. But I guess it takes people longer to read a paper than to read reddit content.
It's a neat idea, but I would want identity verification - only upvotes from people well-versed in the field should "count", precisely so it doesn't become Reddit. Which means you would have a chicken-and-egg problem when the service got started and few experts were on it yet.
That's actually something we are working on. We are working with verified researchers in the field (industry and academic) to help surface good papers and foster an open discussion.
There are websites that try to do that (e.g. scirate) but the problem is that people doesn't participate much. The problem is not the website, but convincing academics to comment and vote (academics are typically starved of time, and reading a paper and writing a good coment is not easy...)
It would be nice if someone solved the problem and managed to create a working one, though.
What about collecting tweets from verified researchers? It could help getting to critical mass. You could even consider papers tweeted by researchers that are followed by your known researchers, and so on, with the right weighting (something resembling pagerank).
With the right weighting this could really boost the size and quality of your dataset.
Right now we are working on helping the community surface information and working with verified researchers to build their "curated" lists for different topics.
Perhaps you could get an ordering based only on the upvotes from your friends, and maybe friends of friends with a lesser weight. Maybe also include upvotes from strangers whose past voting pattern is similar to yours. Maybe one can construct some PageRank-esque structure, whose votes should be weighted heavily and whose not, in the light of your own voting history.
Reddit doesn't allow subreddits to limit who can moderate posts or comments except by taking the subreddit private and limiting the membership.
It's actually a bit of a major pain, particularly for smaller public subreddits.
Reddit's moderation system in general is just hugely problematic. It kind of works, but it really doesn't, and has received very little love.
The first question for any such system should be "what is your goal?". Reddit serves popularity relatively well. Accuracy, relevance, information: rather less so.
Some non-brief thoughts on that from a few years back:
Great writeup. Too much of these web "platforms" and use the word loosely with air-quotes, don't support the level of compositional delegation that could/would enable what you are looking for w/o having to make your own platform.
The only thing I can suggestion w/o understanding your needs on more than a superficial level would be to create bots that have admin access, that attach "flair" that denotes rank that the bot uses to move stories around, etc. Network effects and availability still make sites such as reddit very attractive.
I have always wanted a multidimensional discussion, so that joke posts and memes automatically diverge from the current hyperplane of the discussion.
What Reddit does offer, and woeprks fairly well, is moderation tools and teams sufficient to scale out pretty well.
A bigger problem is that conversation sinply doesn't scale well, something old-timers have been realising for a while. I've got a Dave Winer quote somewhere to tthat effect, and was rereading Shiky's "A Group is its Own Worst Enemy" which suggest what I'm increasingly concluding: with the right people, from 2-3 through maybe 50-100 people can actually discuss something. More than that and it's broadcast or a large number of comingled side conversations.
I'm coming to appreciate Wordpress and blogging platforms' capabilities, and sheer size. There's a ton of blogged content out there, it's mostly that finding and commenting on it is challenging.
Another element that's lacking is filtering tools, for which I think randomness and/or community ought play a larger role -- filtering content up through smaller groups.
Also both implicit measures and known trusted quality "roots" (vetters / editors).
In the Reddit model, it would be nice to have sub-sub-reddits, where a splinter group can discussion a facet. For example, given a Redis subreddit, there could be a Lua-Redis sub-sub-reddit with a smaller audience and whose best posts bubbled up to the parent. I find that a smaller but more active community is preferable over a larger anonymous, passive one. People are quicker to help each other out, share w/o feeling stupid and don't blend into the background, keeping snark and insult to minimum.
I'm trying an experiment (and am way behind schedule) at /r/MKaTS and /r/MKaTH along these lines. There's a private and a public subreddit, one for more closed discussion, one for more open. The idea is to build these out.
Using flair, you can get something like the related-subtopic discussion. See /r/dredmorbius (a solo bloggy effort) or any of the big subs with flaired discussion (/r/AskHistorians or /r/AskScience) for examples -- you can look at the full sub, or dive into a specific flair's topics.
A significant problem with Reddit is that establishing these structures is difficult. Setting up post flair -- the names, the styles, the sidebar search, etc. -- is a major PITA. FSM help you should you want to revise the scheme later.
And you're still stuck with the problem that it's not possible to filter out a flair to report only posts above some arbitrary cutoff (you can sort by "best" or "top"), not that the moderation system gives you any particularly good mechanism for doing that in the first place.
Reddit (as with many discussion systems) is a bit too focused on the now and not sufficiently on the good. I'm particularly annoyed that it's not possible to revisit old posts for discussion (the six month comment freeze), a feature of G+ which actually turned out to be really useful.
There's also the whole Notifications dynamic which ... simply doesn't work well. Yes, you see if someone's mentioned your name, specifically, but you can't get a general notification of discussion on a post (unless you've specifically subscribed to it, and that only for 48 hours). That's utterly unworkable for larger discussions, but works well for small ones.
In addition to the important conferences proceedings, it's common for researchers to work in a very narrow subfield where everybody knows everybody. They keep seeing each other at various events where they discuss their ongoing work.
(1) I manually check the proceedings of the important conferences in my subfield when they come out.
(2) I check my field's arXiv every other day or so.
(3) Google Scholar alerts me of papers that it thinks will interest me, based on my own papers, and it's very useful. Most of what it shows me is in fact interesting for me, and it sometimes catches papers from obscure venues that I wouldn't see otherwise. The problem is that you need to have papers published for this to work, and also, it's only good for stuff close to your own work, not that much for expanding horizons - (1), (2) and Google Scholar search are better for that.
It provides basically a subset (maybe around a third) of the same interesting papers that I see in conference proceedings, but it provides them earlier (typically 1 month to 1 year earlier). This can be important as my field (NLP) is quite fast-paced.
Just to give a concrete example, this paper (which was a relevant read for me) was published in TACL in July this year but was available in arXiv since February: https://arxiv.org/abs/1602.01595
I'm in CS (at the intersection of PL/compilers/HPC), and I've never heard of anyone in my field doing that. In fact, the only papers I've read on arxiv have been ones linked on HN.
I'm in a similar intersection (hi!), and same goes for me. I want to change that, though. I have started publishing tech reports (I work in an industry research lab) whenever I submit a paper for review. I'm tired of work being stuck in endless review cycles, not public and not referenceable. Were I still in academia, I would submit to arxiv, and I have even recommended this to grad students.
At least for theoretical CS and cryptography, Crypto ePrint and arXiv often have more detailed full versions of the paper. These are often invaluable for understanding proofs and other important details.
Yep, this is what I do, except that security papers don't make it to arXiv so I also keep an eye on twitter (I have followed a bunch of academic security people) and a couple subreddits (/r/ReverseEngineering, /r/REMath, and /r/systems). It's not ideal, but it works out okay.
None of them are a substitute for a proper related work search when I'm writing up a paper though, this is just to keep current on what the trends and interests of the community are.
I have set up several Google Scholar alerts for articles. It works extremely well. I also follow everyone I can in my field on Twitter. My field is evolutionary biology.
Yes. I have an account there. Saw either in their newsletter or on their site recently, that they say some X0 million people (researchers) are using it.
I made a simple service for myself (http://paperfeed.io) which is a feed of all the new papers in journals I care about. I can "star" papers for reading later. Works extremely well for my habits.
You're welcome to try it (not sure if the signup workflow still works; let me know). I'll be happy to hear your feedback.
Edit: you can upvote papers, and they'll float to the top just like on HN.
This might be off topic but would you mind sharing how you wrote the website and if you have any tutorial that you can recommend? I want to design something extremely similar for a different application but I do not have much knowledge in web development (I am more experience in programming for numerical and data analysis). I figure this might be a good project to get my feet wet. Thanks!
Not the OP, but this looks like an nginx-powered API (which may be coming through a reverse-proxy) that returns JSON, and Bootstrap 3 + KnockoutJS for the client side to render it all. That doesn't answer your questions about the OP's thought and design processes but maybe it'll give you something to read up.
Exactly. The API is written in Go (because I wanted to learn it), and there's a Postgres database behind it, and a background Go process that scans the journals for updates. I recently rewrote the client in React as a learning exercise, but haven't made the switch yet
If I restarted from scratch I would do the server-side in Python because there's just a lot more good libraries available.
Hmm, I've been thinking about learning Go or Rust since they've been almost on the front page of hacker news lately. Is it worth it or should I stick with Python?
It's always worth learning something new. I don't mean to sound like a dick when I say that, but it's true; build yourself a little API in Go with the help of https://gobyexample.com and see if it's right for you. We really cannot tell you if it will be worth your salt for your particular project. Structure your application in a manner that if you decide to throw it out and replace it with C# tomorrow, your client won't know the difference.
In my case, I really enjoy Go, but certainly not all the time. It has its place. You may find either that it's the best thing ever, or that you cannot stand how it does X, and Python does it so much better. Some comparisons are objective, but the things that make or break it for you may be subjective.
Great! How did you manage the different feeds? I did something similar for my field but its a nightmare since some journals violate the rss, or dump metadata in the feed (my shameless plug is http://sciboards.com)
I have slightly different parsers for each family of journals. I use the DOI to get the metadata where possible. Then I reformat to show title, authors and journal consistently. I also create a direct link to the PDF where possible because I prefer to get at the paper with a single click.
Just FYI, you should know about SHARE. It's an effort to create a free, open dataset of research activity across the research lifecycle. You can read more at
So, if you want to see a reddit for research, better news feeds, etc., it is the SHARE dataset that can provide that data. SHARE won't build all those things--we want to facilitate others in doing so. You can contribute at
The tooling is all free open source, and we're just finishing up work on v2. You can see an example search page http://osf.io/share, currently using v1. Some more info on the problem and our approach....
What is SHARE doing?
SHARE is harvesting, (legally) scraping, and accepting data to aggregate into a free, open dataset. This is metadata about activity across the research lifecycle: publications and citations, funding information, data, materials, etc. We are using both automatic and manual, crowd-sourced curation interfaces to clean and enhance what is usually highly variable and inconsistent data. This dataset will facilitate metascience (science of science) and innovation in technology that currently can't take place because the data does not exist. To help foster the use of this data, SHARE is creating example interfaces (e.g., search, curation, dashboards) to demonstrate how this data can be used.
Why is SHARING doing it?
The metadata that SHARE is interested in is typically locked behind paywalls, licensing fees, restrictive terms of service and licenses, or a lack of APIs. This is the metadata that powers sites like Google Scholar, Web of Science, and Scopus--literature search and discovery tools that are critical to the research process but that are incredibly closed (and often incredibly expensive to access). This means that innovation is exclusive to major publishers or groups like Google but is otherwise stifled for everyone else. We don't see theses, dissertations, or startups proposing novel algorithms or interfaces for search and discovery because the barrier of entry in acquiring the data is too high.
Hi. This looks really interesting. Unfortunately the results page after a search freezes the stock browser on my LG G3.
I've also read the front page, the about page, and your post several times, and I'm not exactly clear what you provide. I thought I'd do some searches to see the product made sense. A search for a field in interested in, arthritis, yielded zero results. Okay, so... no medical research? A search for "reddit" yielded results, and mentions of "providers". I'm not clear what providers are... is reddit a provider, or the research papers, or the publishers, or the researchers...?
I'll read more later when I'm not on mobile, maybe it will be clearer.
I'm starting a project related to analysing published research, so this is a field I'm very interested in. I hope SHARE can help in some way, and I'll definitely be keeping tabs on your work. Thanks for posting.
I know this question is probably a little off topic for this post but I'm very eager to get some kind of answer.
What should I be reading? I'm a computer science student, I want to go into a "Software Engineering" line of work. Are there any places to read up on related topics? I have yet to find something that interests my direct field of choice. Is there one on in academia writing about software?
I also like NLP and other interesting parts. Basically all practical software and their applications are things that interest me.
ICSE [1] and FSE [2] are the top software engineering research conferences. Skimming the titles/abstracts of their papers each year doesn't take long.
Also, they generally have industry or "in practice" tracks that have postmortems from the big software companies in case you want something more applied.
There are a lot of papers on sentiment analysis if I recall correctly. I would look into literature on parsing and statistical analysis, a lot of big data stuff is related to that and there are a lot of books on big data. Very popular field to hire people for as well, a lot of big companies want people to massage their data into giving them useful avenues for money-making.
I'll suggest a minority position: If you feel the need to keep up at the bleeding edge of your field, your work is probably replaceable, i.e., if you didn't do it then someone else would do it a year later.
Instead, read more review papers and seminal papers in your field.
Ok but where do I find those for my field? I'd just like engaging material to read that will provide others insights into how I can do my job better then I currently can.
Honestly, I read hacker news for the noteworthy stuff. Otherwise, I ask people who are savvy in the domain what papers I should check out - a lot of the smarter people I've worked with are raving about new architectural approaches etc.
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[ 2.7 ms ] story [ 224 ms ] threadhttps://blog.acolyer.org/
Also, the more experienced researchers all seemed to be have many connections to other researches through which news propagated.
I scan the emails during weekly meeting.
I also have a few ScienceDirect search alerts set up, that come in once every few weeks typically with 1-5 papers.
And Google Scholar, if you use it and you are logged in with an account, learns from your search history and suggests new papers for you to read. It's relatively good.
- OSDI - SOSP - FAST - EuroSys - APSys - NSDI - SIGCOMM - ATC - ISMM - PLDI - VLDB
These days, accepted papers in specialized conferences are actually on mixed topics these days.. like you'll see security and file systems in SOSP
You can also use the rss feeds with a service like IFTTT or Zapier to set up an alert system.
It would be nice if someone solved the problem and managed to create a working one, though.
With the right weighting this could really boost the size and quality of your dataset.
Right now we are working on helping the community surface information and working with verified researchers to build their "curated" lists for different topics.
That would create an echo chamber. You need to know about research that challenges your assumptions.
Reddit doesn't allow subreddits to limit who can moderate posts or comments except by taking the subreddit private and limiting the membership.
It's actually a bit of a major pain, particularly for smaller public subreddits.
Reddit's moderation system in general is just hugely problematic. It kind of works, but it really doesn't, and has received very little love.
The first question for any such system should be "what is your goal?". Reddit serves popularity relatively well. Accuracy, relevance, information: rather less so.
Some non-brief thoughts on that from a few years back:
https://www.reddit.com/r/dredmorbius/comments/28jfk4/content...
The only thing I can suggestion w/o understanding your needs on more than a superficial level would be to create bots that have admin access, that attach "flair" that denotes rank that the bot uses to move stories around, etc. Network effects and availability still make sites such as reddit very attractive.
I have always wanted a multidimensional discussion, so that joke posts and memes automatically diverge from the current hyperplane of the discussion.
What Reddit does offer, and woeprks fairly well, is moderation tools and teams sufficient to scale out pretty well.
A bigger problem is that conversation sinply doesn't scale well, something old-timers have been realising for a while. I've got a Dave Winer quote somewhere to tthat effect, and was rereading Shiky's "A Group is its Own Worst Enemy" which suggest what I'm increasingly concluding: with the right people, from 2-3 through maybe 50-100 people can actually discuss something. More than that and it's broadcast or a large number of comingled side conversations.
I'm coming to appreciate Wordpress and blogging platforms' capabilities, and sheer size. There's a ton of blogged content out there, it's mostly that finding and commenting on it is challenging.
Another element that's lacking is filtering tools, for which I think randomness and/or community ought play a larger role -- filtering content up through smaller groups.
Also both implicit measures and known trusted quality "roots" (vetters / editors).
I'm coming to appreciate Wordp
As you mention, it is broadcast vs discussion.
I'm trying an experiment (and am way behind schedule) at /r/MKaTS and /r/MKaTH along these lines. There's a private and a public subreddit, one for more closed discussion, one for more open. The idea is to build these out.
Using flair, you can get something like the related-subtopic discussion. See /r/dredmorbius (a solo bloggy effort) or any of the big subs with flaired discussion (/r/AskHistorians or /r/AskScience) for examples -- you can look at the full sub, or dive into a specific flair's topics.
A significant problem with Reddit is that establishing these structures is difficult. Setting up post flair -- the names, the styles, the sidebar search, etc. -- is a major PITA. FSM help you should you want to revise the scheme later.
And you're still stuck with the problem that it's not possible to filter out a flair to report only posts above some arbitrary cutoff (you can sort by "best" or "top"), not that the moderation system gives you any particularly good mechanism for doing that in the first place.
Reddit (as with many discussion systems) is a bit too focused on the now and not sufficiently on the good. I'm particularly annoyed that it's not possible to revisit old posts for discussion (the six month comment freeze), a feature of G+ which actually turned out to be really useful.
There's also the whole Notifications dynamic which ... simply doesn't work well. Yes, you see if someone's mentioned your name, specifically, but you can't get a general notification of discussion on a post (unless you've specifically subscribed to it, and that only for 48 hours). That's utterly unworkable for larger discussions, but works well for small ones.
Is something like that for papers on the arXiv.
(2) I check my field's arXiv every other day or so.
(3) Google Scholar alerts me of papers that it thinks will interest me, based on my own papers, and it's very useful. Most of what it shows me is in fact interesting for me, and it sometimes catches papers from obscure venues that I wouldn't see otherwise. The problem is that you need to have papers published for this to work, and also, it's only good for stuff close to your own work, not that much for expanding horizons - (1), (2) and Google Scholar search are better for that.
For example, I usually log in to the ACM site and go to my SIGs and see what's new there. I've never thought about visiting arXiv.
Just to give a concrete example, this paper (which was a relevant read for me) was published in TACL in July this year but was available in arXiv since February: https://arxiv.org/abs/1602.01595
None of them are a substitute for a proper related work search when I'm writing up a paper though, this is just to keep current on what the trends and interests of the community are.
The one place where one could actually use a "Follow" button for other people...there isn't one. Classic.
You're welcome to try it (not sure if the signup workflow still works; let me know). I'll be happy to hear your feedback.
Edit: you can upvote papers, and they'll float to the top just like on HN.
If I restarted from scratch I would do the server-side in Python because there's just a lot more good libraries available.
In my case, I really enjoy Go, but certainly not all the time. It has its place. You may find either that it's the best thing ever, or that you cannot stand how it does X, and Python does it so much better. Some comparisons are objective, but the things that make or break it for you may be subjective.
http://share-research.org
So, if you want to see a reddit for research, better news feeds, etc., it is the SHARE dataset that can provide that data. SHARE won't build all those things--we want to facilitate others in doing so. You can contribute at
https://github.com/CenterForOpenScience/share
The tooling is all free open source, and we're just finishing up work on v2. You can see an example search page http://osf.io/share, currently using v1. Some more info on the problem and our approach....
What is SHARE doing?
SHARE is harvesting, (legally) scraping, and accepting data to aggregate into a free, open dataset. This is metadata about activity across the research lifecycle: publications and citations, funding information, data, materials, etc. We are using both automatic and manual, crowd-sourced curation interfaces to clean and enhance what is usually highly variable and inconsistent data. This dataset will facilitate metascience (science of science) and innovation in technology that currently can't take place because the data does not exist. To help foster the use of this data, SHARE is creating example interfaces (e.g., search, curation, dashboards) to demonstrate how this data can be used.
Why is SHARING doing it?
The metadata that SHARE is interested in is typically locked behind paywalls, licensing fees, restrictive terms of service and licenses, or a lack of APIs. This is the metadata that powers sites like Google Scholar, Web of Science, and Scopus--literature search and discovery tools that are critical to the research process but that are incredibly closed (and often incredibly expensive to access). This means that innovation is exclusive to major publishers or groups like Google but is otherwise stifled for everyone else. We don't see theses, dissertations, or startups proposing novel algorithms or interfaces for search and discovery because the barrier of entry in acquiring the data is too high.
I've also read the front page, the about page, and your post several times, and I'm not exactly clear what you provide. I thought I'd do some searches to see the product made sense. A search for a field in interested in, arthritis, yielded zero results. Okay, so... no medical research? A search for "reddit" yielded results, and mentions of "providers". I'm not clear what providers are... is reddit a provider, or the research papers, or the publishers, or the researchers...?
I'll read more later when I'm not on mobile, maybe it will be clearer.
I'm starting a project related to analysing published research, so this is a field I'm very interested in. I hope SHARE can help in some way, and I'll definitely be keeping tabs on your work. Thanks for posting.
What should I be reading? I'm a computer science student, I want to go into a "Software Engineering" line of work. Are there any places to read up on related topics? I have yet to find something that interests my direct field of choice. Is there one on in academia writing about software?
I also like NLP and other interesting parts. Basically all practical software and their applications are things that interest me.
Also, they generally have industry or "in practice" tracks that have postmortems from the big software companies in case you want something more applied.
[1] http://2016.icse.cs.txstate.edu/
[2] http://www.cs.ucdavis.edu/fse2016/
Instead, read more review papers and seminal papers in your field.