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<p><a href="http://share-research.org" rel="nofollow">http://share-research.org</a><p>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<p><a href="https://github.com/CenterForOpenScience/share" rel="nofollow">https://github.com/CenterForOpenScience/share</a><p>The tooling is all free open source, and we're just finishing up work on v2. You can see an example search page <a href="http://osf.io/share" rel="nofollow">http://osf.io/share</a>, currently using v1. Some more info on the problem and our approach....<p>What is SHARE doing?<p>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.<p>Why is SHARING doing it?<p>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.