1. There is a file rank.tsv (334 kb) that displays for every course on an hourly basis the rankings
2. There is a file vote.tsv (113 kb) that contains for every course on an hourly basis (between may 20th and today) the number of votes the course did acquire. The period of time for vote.tsv is so short since the votes have only been available in the html files during this time. 

Skimming the data with my eyes there are already some facts that make me very curious for a deeper data analysis:

1. Some courses gained several hundred votes within a short period of time (usually only 2 or 3 hours) whereas most courses (especially those gaining such a large amount of votes) often stayed far under 1000 votes at all. 
2. Also it is interesting to see how much variation has been going on in the last couple of days. 
3. Also I haven’t crawled the views of the Youtube videos of the courses and even now after observing the following I did not take a snapshot of them it is interesting that there is such a large difference in conversion rate. Especially the top courses seem to have much more votes than they have views of the application video. Where some really high class and outstanding applications like the ones from Chrstian Spannagel (Math) or  Oliver Vornberger (Algorithms and data structures) have two or three times as many views on Youtube as votes. Especially they have about the same amount of views on Youtube as the top voted courses.

I am pretty sure there are some more interesting facts and maybe someone else has collected a better data set over the complete periode of time and including Youtube snapshots as well as Facebook and Twitter mentions.
Since I have been asked several times already: here are the final rankings to download and also as a table in the blog post:

• source code
• slides from the talk
• data sets
• proposal
• cypher benchmark

was great talking here and again we are open source, open data and so on. So if you have suggestions or want to contribute feel free. Just do it or contact us. We are really looking forward to meet some other hackers that just want to go geek and change the world
the video of the talk can be found here:

• Open Access and bycott of Elsevier
• Why education is a human right
• Open Education a sucess story for africa

Even though this development is very good to see I am not happy about how the following discussion is going on about models how to fulfill the goals from the royal society.

Neelie Kroes from the European comission posted a really nice answer!

I am glad to see this step forward. After my successful submission of Graphity and reading the copyright form of IEEE which I had to sign I really did have concerns publishing my work with them.
I am still considering not submitting to big journals and conferences anymore but just publishing on my universities website, my blog and/or on open preprint archives.

take aways

Decentralized design that is very important. and if you talk about the centralized system like DNS vs internet there are really social issiues that you are talking about.
look at the way w3c builds standards it is hard. It is a huge organizsation and they have to work together but also be split into modular groups.

IDEA 1 Mobile web apps

he gave a lot of interesting insights about the freedom of programmers and the limits of programing models. (Closely related to my thoughts on query languages for graph data bases) He says the easier the programming model the more you restrict people the more they can achieve.
But he also points out there is an ongoing battle between being universal and just being a tool like a refrigerator. he didn’t say it directly but if you listented carefully to him he says don’t support closed blocked systems like apple or even android but make it a open html5 javascript webapp. You decide weather the web stays open. I think he really sees how the mobile web is beeing more and more closed.
He warns: Make open mobile web apps. Join the working groups for setting the standards…
takeaway: VERY VERY important and interesting part of his speech! especially for typology.
My Question: “As a company building something. if it is open you don’t get as much benefit (like access to the friendship graph) as in the case of going for android / iphone systems…”

IDEA2 Standards centralized vs decentralized – power

He talked about the importance of having the same standards for the low level like html, http, and so on. He compares this to people not speeaking the same language. He says it is cool to build all the cool stuff ontop of it.
He says it is hard to build the standards but also important because it made it possible for the web to scale and win against goopher and other things from the early web.
he says the standards are great because anyone can build something on it. it contributes to be decentralized. You don’t have to ask anyone to build something.
My Question: “SPDY seems better / faster and more mature than http. but of course it is hard to change a running system. ”
Value of being completely independend (decentralized) vs the value of working together in a common way (centralized) and this is a huge fight.

IDEA 3 Trust

Ananomous are not quite sure what they are fighing for. is it complete anarchie or fight against corruption. is it the fight against certain goverments or against goverments in general. again. central vs decentral.
Very technical part of his talk that I unfortunatly could not really follow. The main idea was clear. It was about trust, the role of social networks and the methods that could build and propagate trust.
He hopes decentralized trust systems can be found but the battle is not over. (Remark from me: Of course Google+ is better than facebook but both systems are centralized I guess there needs to be low level standards like http to olve the problem but they are hard to implement.) He already said that the semantic web people tried to do it but haven’t successed yet.

IDEA4 openess especially in UK

he talks a lot about open goverment data.
“I spend a lot of time to governments talking to governments to publish data on the web. But a lot of people are pushing back trying to hold back data an maintain power.”
he says it is your responsability to ask parties before the election to have a commitment on making government data open.
open licences. Companies complain about open licenses since they want to use it but not open up their own data.
As a member of the pirate party I can only emphisize on his statements!

Idea 5 Privacy

hes says there are three different forms of privacy:
1.) Of ourse having the shop remembering me and my shoe size is common practise and nice for me. That is one way of privacy. of course those companies if they want to have a good relation with me will not give away my data.
2.) you never know what data will be out there in the future. It might be possible that anonymous data now will become transparent through other data sets coming up in the future…
3.) invasion / tracking / sells to highest bidder / or to the goverment or whoever asks. In the first days of the web this was hard routers couldn’t do this but now it is impossible. But in my oppinion tbl was talking about the facebook’s and googles of this world.
he says this is as dynamite. If someone wants to use this data against you. you will be toasted.
And you cannot stop this. because there are institutions that collet this information and once it is collected it is not save by definition.
he basically says that you cannot collect data about users by default unless you have a similar powershare of “executive legislative and judicative” if this is not happening we should not allow anyone to make such data tracking.
We have to spend 90% time to do cool stuff on the web be innovative. but 10% of the time we have to deal with theses issues otherwise the web will be locked down at some time.
It is about blocking, spamming, twitter bombing, data stealing and so own. This is a huge challange and a big risk of our open web! It is not only the open market depending on it but also democracy and human discourse that depends on it. “I call that net neutrality”
“cispn” americans do this in trying to controll the web and close communication…. go out and look for this. This things happen quick go out and defend the internet and fight for it. This is a duty we have to do in the 10 % of our time.
“I want you to see discussing these things. Think about what you are leaving for the next generation of this. i am happy to do this in the next web conferences but I really want to see ”

questions

on distributed decission making:
How do we move from hirarchical system like our goverments are to a decentralized system which is possible due to all the connections that we have. We should be able to
people naturally don’t go out an break these boundaries of locallity. (there is also a youtube paper on this) Social networking site should rather suggest to spread friends in stead of building those communities of you have 81 friends in common. go out and meet people that are far away from you. use the connections that are being made. (what is the macroscopic effect of this little change in microscoping behaviour)
I think we should do research on new democratic systems like wikipedia (or in my opinion: pirate party) there was the story that people who didn’t vote for barack obama. It turned out those who wouldn’t vote for him didn’t because they couldn’t imagine to have a black president. There was a high correlation of those people and those who have never worked together with people from different ethnical beackground. so go out and spread friendship.
Idea: liquid feedback for W3C as a working group!
question on openess an facebook and the request on tbl’s thoughts
TBL why would you build an app on facebook or a closed world if there is still the open jungle out there. Peopole already asked my that question on netscape and internet explorer. They always ask me this if monoplies rise up.

Funny/interesing Quotes by Tim Berners Lee:

“There is only one person that has been to all web conferences and that is me”
“I recieved a mail recently saying: We had to do a project on an inventor and we decided to do it on you because you are not dead!”
“Values that made the web possible: Openess, concencious about openess, transparency, privacy,”
“If you have questions it is much more interesting for me. Because I have heard myself talking before.”
“Please develop HTML5 mobile web apps rater than native mobile apps!”

Beginning of our Product Requirement Document

We propose to create a new website for the scientific community which brings together people which are reading the same paper. The basic idea is to mix the functionality of a Q&A platform (like MathOverflow) with a paper database (like arXiv). We follow a strict openness principal by making available the source code and the data we collect.
We start with an analysis how the internet is currently used in different fields and explain the shortcomings. The actual product description can be found under the section “Basic idea”. At the end we present an overview over the websites which follow a similar approach.
This document – as well as the whole project – is work in progress. We are happy about any kind of comments or other contributions.

The distribution of scientific knowledge

Every scientist hast to stay up to date with the developments in his area of research. The basic sources for finding new information are:

• Conferences
• Research Seminars
• Journals
• Preprint-servers (arXiv)
• Review Databases (MathSciNet, Zentralblatt, …)
• Q&A Sites (MathOverflow, StackOverflow, …)
• Blogs
• Social Networks (Twitter, Google+)
• Bibliograhpic Databases (Mendeley, nNode, Medline, etc. )

Every community has found its very own way of how to use this tools.

Mathematics by Heinrich Hartmann – Oxford:

To stay up to date with recent developments I check arxiv.org on a daily basis (RSS feed) participate in mathoverflow.net and search for papers over Google Scholar or MathSciNet. Occasionally interesting work is shared by people in my Google+ circles. In general the speed of pure mathematics is very slow. New research often builds upon work which has been out for a few years. To stay reasonably up to date it is enough to go to conferences every 3-5 months.
I read many papers on myself because I am the only one at the department who does research on that particular topic. We have a reading class where we read papers/lecture notes which are relevant for more people. Usually they are concerned with introductions to certain kinds of theory. We have weekly seminars where people talk about their recently published work. There are some very active blogs by famous mathematicians, but in my area blogs play virtually no role.

Computer Science by René Pickhardt – Uni Koblenz

In Computer Science topics are evolving but also changing very quickly. It is always important to have both an overview of upcoming technologies (which you get from tech blogs) as well as access to current research trends.
Since the speed in computer science is so fast and the review process in Journals often takes much time our main source of information and papers are conferences and twitter.

• Usually conference papers are distributed digitally to participants. If one is interested in those papers google queries like “conference name year papers” are frequently used. Sites like http://www.sciweavers.org/ host and aggregate preprints of papers and organize them by conference.
• The general method to follow a conference that one is not attending is to follow the hashtag of the conference on Twitter. In general Twitter is the most used tool to share distribute and find information not only for papers but also for the above mentioned news about upcoming technologies.

Another rich source for computer scientists is, of course, the related work of papers and google scholar. Especially useful is the method of finding a very influential paper with more than 1000 citations and find newer papers that quote this paper containing a certain keyword which is one of the features of google scholar.
The main problem in computer science is not to find a rare paper or idea but rather to filter the huge amount of publications and also bad publications and also keep track of trends. In this way a system that ranks and summarize papers (not only by abstract and citation counts) would help me a lot to select what related work of a paper I should read!

Psychology by Elisa Scheller – Uni Freiburg

As a psychologist/neuroscientist, I receive recommendations for scientific papers via google scholar alerts or science direct alerts (http://www.sciencedirect.com/); I receive alerts regarding keywords or regarding entire journal issues. When I search for a certain publication, I use pubmed.org or scholar.google.com. This can sometimes be kind of annoying, as I receive multiple alerts from different sources; but I guess it is the best way to stay up to date regarding recent developments. This is especially important in my field, as we feel a big amount of “publication pressure”; I work on a method which is considered as “quite fancy” at the moment, so I also use the alerts to make sure nobody has published “my” experiment yet.
Sometimes a facebook friend recommends a certain publication or a colleague points me to it. Most of the time, I read articles on my own, as I am the only person working on this specific topic at my institution. Additionally, we have a weekly journal club where everyone in turn presents work which is related to our focus of research, e.g. a certain part of the human brain. There is also a weekly seminar dedicated to presentations about ongoing projects.
Blogs (e.g. mindhacks.com, http://neuroskeptic.blogspot.com/) can be a source to get an overview about recent developments, but I have to admit I use them mainly for work-related entertainment.
All in all, it is easy to stay up to date using alerts from different platforms;  the annoying part of it is the flood of emails you receive and that you are quite often alerted to articles that don’t fit your interests (no matter how exact you try to specify your keywords).

Biomedical Research by Johanna Goldmann – MIT

In the biological sciences, in research at the bench – communication is one of the most fundamental tools a scientist can have. Communication with other scientist may open up the possibilities of new collaborations, can lead to a completely new view point of a known question, the integration and expansion of methods as well as allowing a scientist to have a good understanding of what is known, what is not known and what other people have – both successfully and unsuccessfully – tried to investigate.
Yet communication is something that is currently very much lacking in academic science – lacking to the extent that most scientist will agree hinders the progress of research. Nonetheless the lack of communication and the issues it brings with it is something that most scientists will have accepted as a necessary evil – not knowing how to possibly change it.
Progress is only reported in peer-reviewed journals – many which are greatly affected not only but what is currently “sexy” in research but also by politics and connections and the “publish or perish” pressure. Due to the amount of this pressure in publishing in journals and the amount of weight the list of your publications will have upon any young scientists chances of success, scientist tend also to be very reluctant in sharing any information pre-publication.
Furthermore one of the major issues is that currently there really is no way of publishing or communicating either negative results or minor findings, which causes may questions or methods to be repeatedly investigated as well as a loss of information.
Given how much social networks and the internet has changed communication as well as the access to information over the past years – there is a need for this change to affect research and communication in the life science and transform the way we think not only about solving and approaching research questions we gather but the information and insights we gain as a whole.

Philosophy by Sascha Benjamin Fink – Uni Osnabrück

The most important source of information for philosophers is http://philpapers.org/. You can follow trends going on in your field of interest. Philpapers has a list of almost all papers together with their abstracts, keywords and categories as well as a link to the publisher. Additional information about similar papers is displayed.
Every category of papers is managed by some editor. For each category it is possible to subscribe to a newsletter. In this way once per month I will be informed about current publications in journals related to my topic of interest. Every User is able to create an account and manage his literature and the papers of his he is interested in.
Other research and information exchange methods among philosophers consist of mailing lists, reading clubs and Blogs. Have a look at David Chalmers blog list. Blogs are also becoming more and more important. Unfortunately they are usually on general topics and discussing developments of the community (e.g. Leiter’s Blog, Chalmers’ Blog and Schwitzgebel’s Blog).
But all together I still think that for me a centralized service like Philpapers is my favourite tool because it aggregates most information. If I don’t hear about it on Philpapers usually it is not that important. I think among Philosophers this platform – though incomplete – seems to be the standard for the next couple of years.

Problems

As a scientist it is crucial to be informed about the current developments in the research area. Abstracting from the reports above we divide the tasks roughly into the following stages.

1. Finding and filtering new publications:

• What is happening right now? What are the current hot topics my area? What are current trends? (→ Check arXiv/Twitter)
• Did a friend of mine write something? Did a “big shot” write something?
  (→ Check meta information: title, authors)
• Are my colleagues excited about a new development? (→ Talk to them.)

2. Getting more information about a given paper:

• What is actually done in a given paper? Is it relevant for me? Is it really new? Is it a breakthrough? (→ Read abstracts. Find a good readable summary/review.)
• Judge the quality of a paper: Is it correct? Is it well written?
  ( → Where is it published, if at all? Skim through content.)

Finally there is a fundamental decision: Shall I read the whole paper, or not? which leads us to the next task.

3. Understanding a paper: Understanding a paper in depth can be a very time consuming and tedious process. The presentation is often very short and much knowledge is assumed from the reader. The notation choices can be bad, so that even the statements are hard to understand. In effect the paper is easily readable only for a very small circle of specialist in the area. If one is not in the lucky situation to belong to that circle, one usually applies the following strategies:

1. Lookup references. This forces you to process a whole tree of older papers which might be hard to read, and hard to get hold of. Sometimes it is worthwhile to consult a textbook to polish up fundamentals.
2. Finding additional resources. Is there a review? Is there a related video lecture or slides explaining the material in more detail? Is the author going to a conference in the near future, or even giving a seminar in the area?
3. Join forces. Find people thinking about the same paper: Has somebody at my department already read the paper, so that I can ask some questions? Is there enough interest to make a reading group, or more formally, run a seminar about that paper.
4. Contact the author. This a last resort. If you have struggled with understanding the paper for a very long time and really need/want to get it, you might eventually write an email to the author – who might respond, or not. Sometimes even errors are found! – and not published! An indeed, there is no easy way to publish “errata” anywhere on the net.

In mathematics most papers are not getting read though the end. One uses strategies 1 & 2 till one gets stuck and moves on to something more exciting. The chances of survival are much better with strategy 3 where one is committed putting a lot of effort in it over weeks.

4. Finding related work. Where to go from there? Is the paper superseded by a more recent development? Which are the relevant papers which the author builds upon? What are the historic influences? What are the founding ideas of the subject? Finding related work is very time consuming. It is easy to overlook things given that the references are often vast, and sometimes hard to get hold of. Getting information over citations requires often access to commercial databases.

Basic idea:

All researchers around the world are faced with the same problems and come up with their individual solutions. There are great synergies in bringing these people together with an online platform! Most of the addressed problems are solved with a paper centric service which allows you to…

• …get to know other readers of the paper.
• …exchange with the other readers: ask questions, write comments, reviews.
• …share the gained insights with the community.
• …ask questions about the paper.
• …discuss the paper.
• …review the paper.

We want to do that with a new mixture of a traditional Q&A system like StackExchange or MathOverflow with a paper database and social features. The key features of this system are as follows:

Openness: We follow a strict openness principle. The software will be developed in open source. All data generated on this site will be under a creative commons license (like Wikipedia) and will be made available to the community in form of database dumps or an API (open data).

We use two different types of content sites in our system: Papers and Discussions.

Paper sites. A paper site is dedicated to a single publication. And has the following features:

1. Paper meta information
   – show title, author, abstract, journal, tags
   – leave a comment
   – write a review (with wiki option)
   – vote up/down
2. Paper resources
   – show pdfs, slides, notes, video lectures, etc.
   – add a resource
3. Related Work
   – show the reference-tree and citations in an intelligent way.
4. Discussions:
   – show related discussions
   – start a new discussion
5. Social features
   – bookmark
   – share on G+, twitter

The point “Related Work” deserves some further explanation. The citation graph offers a great deal more information than just a list of references. Together with the user generated content like votes and the individual paper bookmarks and social graph one has a very interesting data set which can be harvested. We want this point at least view with respect to: Popularity/Topics/Read by Friends. Later on one could add more sophisticated, even graphical views on this graph.

Discussion sites. A discussion looks more like a traditional QA-question, with the difference, that each discussion may have related (many) papers. A discussion site contains:

1. Discussion meta information (title, author, body)
2. Discussion content
3. Related papers
4. Voting
5. Follow/Bookmark

Besides the content sides we want to provide the following features:

News Stream. This is the start page of our website. It will be generated from the network consisting of friends, papers and authors. There should be several modes like:

• hot: heavily discussed papers/discussions
• new papers: list new publications (filtered by tag, like arXiv feed)
• social: What did your friends do lately
• default: intelligent mix of recent activity that is relevant to the logged in user

Moreover, filter by tag should be always available.

Search bar:

• Searches contents of the site, but should also find papers on freely available databases (e.g. arXiv). Adding a paper should be very seamless process from there.
• Search result ranking uses vote and view information.
• Personalized search information. (Physicists usually do not want sociology results.)
• Auto completion on paper titles, author, discussions.

Social: (hard to implement, maybe for second version!)

• Easily refer to users by @-syntax familiar from Twitter/Google+
• Maintain a friendship / trust graph
• Friendship recommendations
• Find friends from Google+ on the site

Benefits

Our proposed websites improves the above mentioned problems in the following ways.
1. Finding and filtering new publications:This step can be improved with even very little  community effort:

• Tell other people, that you are interested in the paper. Vote it up or leave a comment if you are very excited about it.

• Point out a paper to a colleague.

2. Getting more information about a given paper:

• Write a summary or review about a paper you have read or skimmed through. Maybe the introduction is hard to read or some results are not clearly stated.
• Can you recommend reading this paper? Vote it up!
• Ask a colleague for his opinion on the paper. Maybe he can write a summary?

Many reviews of new papers are already written. E.g. MathSciNet and Zentralblatt maintain a large database of Reviews which are provided by the community and are not freely available. Many authors would be much more happy to write them to an open system!
3. Understanding a paper:Here are the mayor synergies which we want to address with our project.

• Ask a question: Why is the author using this experimental method? How does Lemma 3.4 work? Why do I need this assumption? What is the intiution behind the “virtual truncation”? What implications does this work have?
• Start a discussion: (might involve more than one paper.) What is the difference of these two papers? Is there a reference explaining this more clearly? What should I read in advance to understand the theory?
• Add resources. Tell the community about related videos, notes, books etc. which are available on other sites.
• Share your notes. If you have discussed a paper in a reading class or seminar. Collect your notes or opinions and make them available for the community.
• Restate interesting statements. Tell the community when you have found a helpful result which is buried inside the paper. In that way Google may find it!

4. Finding related work. Having a well structured and easily navigable view on related papers simplifies the search a lot. The filtering benefits from the content generated by the users (votes) and individual information, like friends who have written/bookmarked a paper.

Similar Sites on the Web

There are several discussions in QA forum which are discussing precisely this problem:

• Quora.com: Where can I comment on sci papers?
• MathOverflow: Good websites for discussions of mathematical papers?
• MathOverflow: Is a free alternative to MathSciNet possible?
• MathOverflow: Errata-Database

We found three sites on the internet which follow a similar approach which we examined more carefully.
1. There is a social network which has most of our features implemented:

researchgate.net
“Connect with researchers, make your work visible, and stay current.”

The Economist has dedicated an article to them. It is essentially a facebook clone, with special features for scientist.

• Large, fast growing community. 1.4m +50.000/m. Mainly Biology and Medicine.
  (As Daniel Mietchen points out, the size might be misleading due to institutional accounts)
• Very professional Look and Feel. Company from Berlin, Germany, funded by VC. (48 People involved, 10 Jobs advertised)
• Huge Feature set:
• Profile site, Connect to friends
• News Feed
• Publication Database, Conference Finder, Jobmarket
• Every Paper its own page: with
• Voting up/down
• Comments
• Metadata (Title, Author, Abstract, Preveiw)
• Social Media (Share, Bookmark, Follow author)
• Organize Workgroups/Reading Classes.

Differences to our approach:

• Closed Data / Closed Source
• Very complex site which solves a lot of purposes
• Only very basic features on paper site: vote/comment.
• QA system is not linked well to paper database
• No MathML
• Mainly populated by undergraduates

2. Another website which comes reasonably close is:

http://www.sciweavers.org/

“an academic network that aggregates links to research paper preprints
then categorizes them into proceedings.”

• Includes a large collection of online tools for various purposes
• Have a big library of papers/software/datasets/conferences for computer science.
  Paper sites have:
• Meta information and preview
• Vote functionality and view statistics, tags
• Comments
• Related work
• Bookmarking
• Author information
• User profiles (no friendships)

Differences to our approach:

• Focus on computer science community
• Comment and Discussions are well hidden on paper sites
• No News stream
• Very spacious design

 
3. Another very similar site is:

journalfire.com – beta
“Share what your read – connect to colleagues – create journal clubs.”

It has the following features:

• Comment on Papers. Activity feed (?). Follow articles.
• Host Journal Clubs. Create Events related to papers.
• Powerful search box fetching papers from Arxiv and Pubmed (slow)
• Social features on site: User profiles, friend finder (no fb/g+ integration yet)
• News feed – from subscribed papers and friends
• Easy paper import via Bookmarklet
• Good usability!! (but slow loading times)
• Private reading clubs cost money!

They are very skilled: Maintained by 3 PhD students/postdocs from Caltec and MIT.

Differences to our approach:

• Closed Data, Closed Source
• Also this site misses (currently) misses out ranking features
• Very Closed model – Signup required
• Weak Crowd sourcing: Cannot add Meta information

The site is still at its very beginning with little users. The project started in 2010 and did not gain much momentum since.

The other sites are roughly classified in the following categories:
1. Single people who are following a very similar idea:

• annotatr.appspot.com. Combines a metadata-base with the disqus plugin. You can comment but not rate. Good usability. Nice CSS. Good search function. No MathML. No related article suggestion. Maintained by two academics in private time. Hosted on Google Apps. Closed Source – Closed Data.
• r-Forum – a resource where mathematicians can collect record reviews, corrections of a resource (e.g. paper, talk, …). A simple Vanilla-Forum/Wiki with almost no content used by maybe 12 people in US. No automated Data import. No rating system.
• http://math-arch.org/ – Post comments to math papers. very bad usability – get even errors. Maintained by a group of russian programmers LogicSun. Closed Source – Closed Data.

Analysis: Although the principal idea to connect people reading papers is there. The implementation is very bad in terms of usability and even basic programming. Also the voting features are missed out.

2. (Semi) Professional sites.

• Public Libary of Science very professional, huge paper data base for mainly biology, medicine. Features full text papers, lots of interesting meta information including references. Has comment features (not very visible) and news stream on the start page.
  No QA features (+1, Ask question) on the site. Only published articles are on the site.
• Mendeley.com – Huge Bibliographic database with bookmarking and social features. You can organize reading groups in there, with comments and notes shared among the participants. Features a news stream with papers by friends. Nice import. Impressive fulltext data and Reference features.
  No QA features for paper. No comments for paper. Requires Signup to do anything useful.
• papercritic.com – Open review database. Connected to Mendely bibliographic libary. You can post reviews. No rating. No comments. Not open: Mendely is commercial.
• webofknowledge.com. Commercial academic citation index.
• zotero.org – features programm that runs inside a browser. “easy-to-use tool to help you collect, organize, cite, and share your research sources”

Analysis: The goal of all these tools is to simplify the reference management, by providing metadata like references, citations, abstracts, author profiles. Commenting features on the paper site are not there or not promoted.
3. Vaguely related sites which solve different problems:

• citeulike.org – Social bookmarking for papers. Closed Source – Open Data.
• http://www.scholarpedia.org. A peer reviewed open access encyclopedia.
• Philica.com Online Journal which publishes articles from any field along with its reviews.
• MathSciNet/Zentralblatt – Review database for math community. Closed Source – Commercial.
• http://f1000research.com/ – Online Journal with a public, post publish review process. “Open Science – Open Data – Open Review”
• http://altmetrics.org/manifesto/ as an emerging trend from the web-science trust community. Their goal is to revolutionize the review process and create better filters for scientific publications making use of link structures and public discussions. (Might be interesting for us).
• http://meta.wikimedia.org/wiki/WikiScholar – one of several ideas under discussion at Wikimedia as to a central repository for references (that are cited on Wikipedias and other Wikimedia projects)

Upshot of all this:

There is not a single site featuring good Q&A features for papers.

If you like our approach you can contact us or contribute on the source code find some starting documentation!
So the plan is to fork an open source question answer system and enrich it with the features fulfilling the needs of scientists and some social aspects which will eventually help to rank related work of a paper.
Feel free to provide us with feedback and wishes and join our effort!