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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.
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.
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).
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.
(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.
Earlier quoted context omitted.
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.
It would be nice if someone solved the problem and managed to create a working one, though.
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.
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.
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.