New features of Delicious

October 19, 2009

Delicious is the best known social bookmarking site out there. Recently it added some new features, some of these might also be useful to add to an open repository like DSpace.

whatsnew_fresh

The first new feature is “Fresh” content, this is what Delicious likes to call a combination of Delicious bookmarks and Twitter conversations. The “Fresh” content page shows the most recently bookmarked sites and any related tweets. This gives the user a better idea of what others thinks about the bookmark and why it was bookmarked.

whatsnew_share

Another new feature is the possibility to share a newly added bookmark with your friends from Delicious, Twitter or via e-mail. This feature makes it a lot easier and faster to tell your friends about some exciting new link you found. It would be nice to do the same when a user would upload a paper or thesis, this would generate a lot more traffic to the publication. This also makes it easier for people to share those publication and to be kept up-to-date about the newest publications of a colleague. Both of the previous features seem to let social networks work together even more closely, in my opinion this is something all social networks should try to do.

The final feature of delicious I would like to highlight are the subscriptions on a tag. In a way this is the same as following someone on twitter, it makes it easier to gather information. This feature could easily be applied to publication of papers in an open repository, by showing the user a list of recently uploaded papers with a certain tag or within a certain discipline.

Advertisements

Storyboard: Visualization of Mendeley user data

October 18, 2009

One of the options for my thesis is to create a visualization, more specifically this might be a visualization of the user data from Mendeley. The last week I’v gathered some ideas and came up with this storyboard.

Thesis

First the user is shown a starting screen, showing the visualization the user is able to use. If the user chooses the “Trend Map” (1), the user is shown a map of the world. By selecting which information (tags or authors) the user wants to view, the map shows for the different locations on the map what is popular at these locations. This is shown by text appearing at the locations, the size of the text determines the popularity of the tag or author at the specific location. This is an easy way to see what are popular topics or authors at certain locations or parts of the world. The user is also allowed to filter the information that is shown, for example a user might only be interested in the trending tags in computer science.

When a user really wants more specific information about a certain city, it can type a city in the upper right box in the trend map. The user is redirected (2) to a detailed view of the trend within that city. Information like popular tags, authors, … is shown.

Another way to visualize trends is by showing the evolution of the popularity of a certain tag, author, … The way this is done, is by showing a map of the world and a time line(3). The map represents a certain period of time, by moving the slider time will pass on the map. Let’s say a user wants to known the origin of Web 2.0 research, all the user needs to do is filter on tag named web 2.0. The map will show the popularity of a tag in a certain region by coloring the region, the darker the more popular. Popularity of a tag might be the number of papers added to a library or number of papers created with this tag. By using the slider on the bottom of the screen the user is able to move through time and see the evolution of the web 2.0 tag.

The last visualization offered is a stack graph(4), it also show a evolutionary point of the user data. The difference with the map is that this focusses more on exploring data and comparing. For example, the user might want to know the upcoming disciplines in science by filtering on disciplines the user is shown a stacked graph of disciplines. Again the user is allowed to filter on the data let’s say for example a user wants to see all popular authors tagged with “Web 2.0” the users selects “Authors” and adds a filter on the tags “Web 2.0”.

Major inspiration sources were:

Any comments on this blog post are welcome.

Last.fm Explorer

October 15, 2009

This is a summary of the paper I read on Last.fm Explorer, this is an visualization tool that facilitates sophisticated data exploration with interactive controls to drill-down through hierarchical levels of data. The tool is heavily inspired on the “streamgraph” visualization of Lee Byron’s own Last.fm listening history. The visualization of Lee Byron had some shortcomings and the authors of this paper thought it did not realize the full potential of Last.fm’s data. The visualization of Lee Byron had a less-than-satisfactory experience: the graph is very wide and as it is a fixed rendered image, there is no way of seeing more data than what is immediately visible in the image.

The first shortcoming of the visualization was it only shows one level of a users’ data: artists listened to. Music data typically has four levels of hierarchy that users understand:

  • genre
  • artist
  • album
  • track

Last.fm Explorer implementation

Last.fm Explorer attempts to resolve these issues through interaction like being able to filter the stacked graph of a single users’ tag listening data, viewing of different levels of hierarchy, … Last.fm Explorer allows a limited amount of social context by allowing comparisons between two users’ history in parallel. Last.fm Explorer also uses animation heavily to make changes in visualization state clear. Humans are much better at perceiving differences in the position of objects as opposed to length, this is applied by Last.fm Explorer by the use of multiple visualizations as well as interactively allowing a user to re-arrange the stacked graph.

To solve the problem of the limited overview and navigation in Lee Byron’s visualization they added a pair of arrowhead shaped handles on a slider bellow the main stack graph, allowing the user to adjust the left and right limits of the graph by data. To make this time slider less confusing, they draw a small version of the graph above the slider. An example of interactivity is when a user hovers over an object, the object is highlighted and a tooltip is shown. Another way they implemented interactivity is by allowing when clicking on a layer in the stacked graph this layer is switched with the bottommost layer, this allows for any layer to be viewed with a flat baseline for better perception of changes in value. Double- clicks makes the visualization filter based on the object clicked and switch to the next level down of hierarchical data, e.g. clicking on tag rock the visualization will show all artists of the genre rock. The filters applied by double-clicking can still be removed.

Results and discussion

In the results and discussion about the visualization they tell that people familiar with Last.fm’s data find the visualization exciting and interesting and I must agree on this fact. Another conclusion drawn is that stacked graph display seems to be a popular and accessible visualization for this kind of data but a drawback is the difficulty of comparing two different layers. A line graph doesn’t suffer form this problem but has other issues by allowing respositioning of single layers in the stacked graph they attempt to mitigate these problems. The line graph they implemented ran into several problems which limited its usefulness, it had the following problems:

  • clumping of data in the bottom of the graph, they tried to mitigate this by displaying it in a logarithmic display
  • logarithmic scale made it very complex and hard to view the graph
  • playcounts are integer numbers, it is not uncommon to have more than one node at the same point, this makes the graph difficult to read

The interactive performance was sometimes difficult to maintain, the main cause of this was the way Last.fm API processed requests and limitations of the Flash platform.

Future work

The paper also suggests some future improvements, including track lengths from the MusicBrainz database is one of those. This would enable the visualization to combine track length data with Last.fm’s listening data. Allowing visualization of the actual time spent listening to specific track, artists, and tags. At the moment doesn’t use the colors for the same elements in different visualizations, this is confusing and a significant shortcoming. As mentioned before the line graph suffers from a number of shortcomings.

In the future the visualization would like to implement the technique used by Sese.us  for synchronizing application state with unique arguments appended to the application’s URL. This allows for sharing the link of your visualization in a specific state with others, enabling more social exploration and discussion.

Opinion

When I first applied this visualization to my own user data of Last.fm I was amazed. There are so many things you can learn and discover from your own user data. This visualization is really user friendly this is because it offers great ways to interact with the visualization like one click feature to put a layer at the bottom of the visualization. In general this visualization is a great improvement of the visualization made by Lee Byron.

What I learned from this paper is that a good analysis of the data your visualizing and the functionality you want to offer is a must. I like the way this visualization approached the hierarchy of the music data, starting the visualization from the top and by applying filter allowing the user to go down in the hierarchy. Also the interactivity of a visualization can really improve the user experience but also the way you analyze the your own data.

The repositioning of a layer to me doesn’t solve the problem of comparing, maybe this could be solved by allowing users to select 2 or 3 layers and change to another visualization for example a line graph that might allow better comparison.

Researcher Profile

October 15, 2009

In this post I’ll give a summary of the paper about Researcher Profile I read and describe my opinion of the application related to my thesis.

Researcher Profile is a Facebook for sharing research information within a community of collaborating researchers. The application offers visualizations on how researchers belonging to the same group collaborate. They chose to make a Facebook application for several reasons:

  • integration into an existing social network rather than developing a brand new social system (basic functions like forums, comments, … can be reused)
  • Facebook from all studied social networks had the best API: more stable and more functionalities
  • more development tools available for Facebook

The goal of the application is to allow researchers within the same domain (=Facebook group) to share and compare their research profile consisting of publications, events organised or attended, research projects involved in, … The application also allow importing of bibliographic references from a BibTeX file. There is also the implementation of tags to facilitate retreivel of information. The application offers to visualise all collaborations between different members of the research community in different ways and according to different viewpoints:

  • geographical viewpoint (Google Maps showing where an event takes place)
  • collaborative viewpoint (who is working with who)
  • viewpoint of a single individual’s collaborations(collaborations in which a single individual is involved)
  • evolutionary viewpoint: enables group members to see whether the activity of their community increases or decreases over time)
  • gender or age viewpoint

The paper also suggest some improvements to the application one these is the synchronization with digital libraries such as DBLP, the ACM Portal, … in order to facilitate importing new data without too much effort.

Opinion

This is a interesting approach to a research community, the visualization in the application are nice especially the viewpoint of the collaboration are very useful to researchers. Some of the application don’t seem that useful to me like the gender viewpoint. This application was developed without an existing community and that makes it quite different from the start point in my thesis. But the functionalities offered in this application would certainly be of added value to the open repository of DSpace.

Another fact in this paper I agree with is the fact that it is not wise to not develop a new social network but to take advantage of existing social network, instead of implementing a new social network in DSpace it might be easier to implement Facebook support into DSpace allowing users of DSpace to integrate their Facebook profile in the open repository. For example when a user uploads a paper a message is sent to his or her Facebook profile, Twitter Profile, … stating that a paper was uploaded. Comments made on the message would be shown in the profile of the researcher in DSpace. This would allow the user to publish his profile across DSpace. Allowing synchronization with digital libraries is also a great idea, it offers more information gathering and a bigger web presence of the user. One should realize that their are a lot of great online tools for science, working together with the most used tools will make DSpace a more attractive platform for researchers. Another route one could take is developing a Facebook Application for DSpace, this has the disadvantage of being another application which needs to be maintained but also installed by the user.

Scientific American on Science 2.0

October 15, 2009

In 2008 Scientific American published an article on Science 2.0, in this post I’ll try to recapture some of the main points of this article. The article start of by pointing out that Web 2.0 has influenced institutions like journalism, marketing, … by allowing users to publish, edit and collaborate with online information. Science could be next in line.

Openness

Critiquiquing, suggesting, sharing ideas and data is the heart of science, it is a powerful tool for correcting errors, building on colleagues’ work and fashioning new knowledge. Classic peer-reviewed papers are important but are not collaborative beyond that. Web 2.0 could open up a much richer dialogue! An example is open notebook science where a classic paper show the result, an open notebook allows people to see the research in more detail ( things someone tries but didn’t work out, … ). Some of the advantages of this open access are:

  • more collaboratite and therefore more productive
  • efficiency
  • faster development
  • competitiveness

Critique

Critics are afraid of the risk that comes with the openness like people copying or exploiting the work of others and even gain credit or patents for the work of others. In some fields of science patents, promotion and tenure can hinge on being the first to publsh a new discovery, so putting your work online might not be a good idea.

Success stories

Next the article tells us about some success stories. The write tells us that scientists have built up their knowledge about the world by “crowdsourcing” the contributions of many researchers and then refining that knowledge through open debate. Web 2.0 fits perfectly with the way science works, it’s just a matter of time before the transition will happen.

OpenWetWare

The article starts with the example of a wiki based on the same software as Wikipedia, called OpenWetWare. It is a collaborative website that can be edited by any one. It started of as a project to keep two labs up-to-date. Soon they discovered it was also a convient way to place posts about what they were learning about lab techniques (how-to’s, …). A side effect was that this information became available to the world and soon people who were searching information with Google found out about the website and started contributing. After a while enough people joined and dynamically evolving class sites where created, to share information. Another benefit mentioned in the article was it’s use in laboratory management, where it is hard to keep up with what your own team members are doing and organizing information. OpenWetWare is a solution for this problem and also allows people to access it from anywhere. Lately OpenWetWare has been used for a lot of sites offering some nice features like posting jobs, meetings, … May 2007 OpenWetWare got a grant to transform the platform into a selfsustaining community independent of its current base at M.I.T. and also to support creation of a generic version of OpenWetWare.

Trashing

But some fears remain, the article mentions an example of someone using OpenWetWare. At first the person kept all her post private afraid someone would trash published pages. But OpenWetWare has some built-in safeguards, every user has to be registered and established that they belong to a legitimate research organization. Even if you get trashed the system is able to perform a rollback.

Getting scooped

Another concern is getting scooped and losing the credit. This fear often keeps scientists from even discussing their unpublished work too freely, much less posting it on the Internet. As opposed to what people think the Web offers better protection than traditional journals. Every publication on a wiki gets a time stamp that proves you were the first. The article even suggest that this fear factor might drive open science: in journals your work won’t appear for another 6 or 9 months, on the web it is publish right away. Another benefit of a time stamp on every post is being able to track the contributions of every person.

Unsolved problems

Some problems might hold someone from publishing online, like the concern of the privacy of persons that were part of a research test. Also a journal might insist on copyrighting test and visuals, so pre-publishing online won’t be allowed. It still isn’t clear if a patent office will accept a wiki posting as proof of priority.

The more the better

The article also mentions a case in which a lot of people participated in a research, because of this search engines could index what they were doing and got discovered by people offering help from another part of the world.

Blogophobia

Scientist have been slow to adopt blogging. The whole point of blogging is getting ideas out there quickly, even at the risk of being wrong or incomplete. For a scientist this is a tough jump to make, because in the process of publishing a paper words are chosen carefully, … A benefit of blogging is that it is a good medium for brainstorming and discussion. Yet again some young scientists who are struggling to get tenure this might look dangerous because making a wrong impression could have some consequences. Out of fear pseudonyms are often used .

Credit problem

Some people might not participate on blogs because time spent with the online community is time not spent on cranking out that next publication. Scientist don’t blog because they get no credit, this credit problem is one of the biggest barriers tom many aspect of Science 2.0. The article explains that nobody believes that a scientist’s only contribution is from the papers he or she publishes, a good scientist also gives talks at conferences, shares ideas, takes a leadership role in the community, … Publication where the only thing one could measure, this has changed with a lot of this information going online and thus being able to measure it.

The payoff of collaboration

Acceptance of the measures requires a big change in academic culture.  The current technologies’ potential should be able to move researchers away from an obsessive focus on priority and publication. We should focus on openness and community instead these were the hallmarks of science. Great efforts are being made like Nature Network, Connotea, ..

Science Hour with Leo Laporte & Dr. Kiki

September 29, 2009

Just recently Dr. Jason Hoyt of Mendeley and Pete Binfield of PLoS ONE where invited to the podcast Science Hour with Leo Laporte & Dr. Kiki. During this one hour podcast they discussed scientific publishing on the internet. Some interesting topics were discussed, I will summarize them in this blogpost.

Why is publishing important in science.

Currently their is an attitude of “publish or perish”, which means their is pressure to publish to get or maintain a career in academia. A consequence is that people want to publish first and sometimes this causes fraud or a publication with false data. To avoid this their is the concept of scientific publishing, it contains the following phases:

  • records premise of a discovery (first to publish = guy who thought it up)
  • certification (peer-review, analysis of correctness),
  • dissemination,
  • archiving

One would think that this would be easy using the internet as a tool, we don’t need a publishing company any more to publish. But a problem arises how do we give credibility to a publication, is peer-review a correct way to do this?

Commenting

PLoS ONE tried adding a commenting system, this had a surprising result. Commenting on article was possible by annotation on text or sentence, star rating on article, …

The problem was that is wasn’t very well used, only a few comment. The discussion took place in a broader community like on blogs, chat, …

Traditional publishing

Another problem is that traditionally a researcher would write an iteration paper, receive comment on it and write a new one. If we look at the current technology this seems ridiculous. The result or conclusion of your paper isn’t as important any more, the first worry should be to get your data out in the world. Often publication don’t even contain the full data.

Another tradition in publishing is in peer-review, the current technology seems to require a quicker way to put your paper online and review it. What happens now at PLoS ONE is that they check if it contains good science. Other aspects like impact factor is decided by community.

At the same time I think this is a problem because some scientist are critical about online communities and might not think their is a high value to the online determined impact factor. This is why online tools should prove that the systems they use are just as good in determining values like impact factor as the old system.

How to get the respected scientists online.

At the moment younger scientists are forced to publish in traditional way because of pressure to get a grant, tenure track, … An other reason is social reluctance the way career structure of academics is setup.

At the moment I think this seems to be a social problem, which might solve over time. But might be helped by putting some journals online only. Of course again the issue of online value of a paper should be addressed.

Too much information

A lot of people are publishing, will we be able to find the right information? Traditionally journals would filter the information flow, disadvantage was that some good articles weren’t discovered. Filtering information online is done by an algorithm, Mendeley has another approach. Mendeley allows articles to be discovered at an individual level, lower impact articles can still be found.

When data is published online the find ability and speed of searching is a lot higher than in the traditional way. Another way to find your way in the large number of publications is by tagging.

Impact factor

Another problem with online publication is the impact factor, users with a high impact factor are those who already have established an impact factor. This forces younger people to go to places where they can get an high impact factor because of the academic pressure to publish. They end up in the old model where they want to get published in an journal with an high impact factor.

Mendeley wants to get rid of this by introducing impact factor at the article level, when doing this the article itself gets examined. To do this Mendeley got some good algorithms.

Article level metrics

An interesting point made in this podcast is about article level metrics. PLoS ONE is pioneering in article level metrics. This is an indicator on an article containing: number of citations, number of social bookmarks, number of blogposts about the article, … Recently they introduced usage data this is information like number of downloads, page views, … Their seems to be a reluctance to make this level of data open. You can refer to these metrics as social media metrics.

To me this is very useful data as it gives you an overview of impact and allows other analysis on the data. But in the podcast they mention we should also focus on standardization so that in the end we have the same metrics everywhere.

Conclusion

I think this podcast contained some relevant discussion it made me familiar with the “publish or perish” tradition. A lot of problems to get people to use online publication seems to involve getting people out of this tradition. The solution isn’t clear at the moment but as people in the podcast suggest, the focus should be more on the article level. To do this we need to find a way to give a value to article based on their impact factor, citations, … As my thesis will involve an open repository I think it could be a nice feature to at some kind of impact factor or article level metrics to the papers. This would offer users a good way to discover new content. It also offers them a way to decide what they think is of great value for science for example by a ranking system, commenting, …

Michael Nielsen about The Future of Science

September 29, 2009

In this following blogpost I’ll try to summarize a blogpost I read about “The future of Science”. This blogpost contains a lot of information, I’m going to focus on the parts that would be of use when I’ll have to implement social features myself.

In his blogplost Michael Nielsen starts with describing the history of science and research. The system of publication hasn’t changed much the last 300 years. But the internet offers us a great way to handle science more openly and a sort of online memory. He predicts that the way we do science will change more in the next 20 years than in the past 300 years.

Part I

In the first part he explains some of the benefits of internet for science, problems, … .

How can the internet benefit science?

It could improve the way we do science by either

  • Online tools as a way of expanding the range of scientific knowledge that can be shared with the world. Example: physics preprint arXiv who allow researchers to share preprints of their papers without a long delay and more recently ResearchGate that allows to share papers based on self-archiving agreements.
  • A change to the process and scale of creative collaboration itself, a change enabled by social software such as wikis, online forums, and their descendants.

Failure of science online

He also show some examples where the idea of online science has failed, an example closest to my thesis is the failure of online comment sites. The example he gives is: Nature’s 2006 trial of open commentary on papers undergoing peer review at Nature. This was not a success because most of the people wanted to read reviews of the papers but not comment on the papers.

One of the reasons he points out is that there are few incentives for people to write such comments.

Achieving extreme openness in science

In the examples mentioned by Michael Nielsen failures are all caused by a surprising reluctance to share knowledge that could be useful to others. He tells us we should aim to open scientific culture where as much information as possible is moved out of people’s heads and labs, onto the network, and into tools which can help us structure and filter the information. This means everything – data, scientific opinions, questions, ideas, folk knowledge, workflows, and everything else – the works. Information not on the network can’t do any good. To achieve a kind of extreme openness.

By this he means: making many more types of content available than just scientific papers, not just the end result but also the creative process.

I think this is a good goal but not realistic because like in all other fields, a creative process is something individual and people are even less eager to share this kind of information. But in my opinion we should offer the tools for this creative process which will offer a way to work online and once they do the creative process online they might be willing to share it.

How can we open up scientific culture?

Michael Nielsen tells us that to create an open scientific culture that embraces new online tools, two challenging tasks must be achieved:

  1. build superb online tools;
  2. and cause the cultural changes necessary for those tools to be accepted.

A mistake that is often made is to focus on building tools, with cultural change an afterthought. He tells us to develop such tools requires a rare combination of strong design and technical skills, and a deep understanding of how science works.

The second task seems to be the most difficult, Michael Nielsen suggest for the people building the new online tools to also develop and boldly evangelize ways of measuring the contributions made with the tools. This is an interesting way of looking at building tools because if using a tool is not recognized in the scientific community why even bother spending time using it.

A success story is the arXiv and SPIRES story in which not long after the arXiv began, a citation tracking service called SPIRES decided they would extend their service to include both arXiv papers and conventional journal articles. Because of the quality of the service it is now used by hiring committees to evaluate candidates.

Looking at this success story and the two tasks of Michael Nielsen I think a good online tool start with good quality. But it doesn’t stop there it should be able the measure the contributions made by the tool!

Part II

In the second part he starts of with the problem of collaboration. Often people have we a small group of trusted collaborators with whom we exchange questions and ideas when we are stuck. Unfortunately, most of the time even those collaborators aren’t that much help. They may point us in the right direction, but rarely do they have exactly the expertise we need.

The question he asks is whether it is possible to scale up this conversational model, and build an online collaboration market to exchange questions and ideas, a sort of collective working memory for the scientific community? An extremely demanding creative culture already exists which shows that such a collaboration market is feasible – the culture of free and open source software.

Next the author describes two embryonic examples which suggest that collaboration markets for science may be valuable. One of the examples is FriendFeed, it can be used for collaboration. But a problem like a lot of online tools is again, the lack of widely accepted metrics to measure contribution.

To end Michael Nielsen gives some examples of inefficiences in the current system of collaboration. Like scientists having problems in a project that is outside their field but could easily be solved by a colleague at the other side of the world. This problem can be solved by using the internet, but another problem comes to mind. For scientist to work there has to be a great deal of mutual trust. An ideal collaboration would have elements like metrics of contributions, impact of work, archiving of contributions, …

Opinion

It is clear that the author is trying to say that their is a lack of metrics of contributions and impact of work in the current tools available. I agree with the author on this point because looking at some of the social networks earlier this information is present. Having more information about how many times a paper is cited or a is mentioned in a science blog could give a great indication of the impact of a work. Creating a value for online work in science would in my opinion be a great incentive for scientist to share content online. One could see this as a challenge for the future.

It is a strange fact that often social networks like Facebook and Linked in are used to check if someone is capable for a job. But when it comes to science their is no equivalent, again their is no value to the online work of a scientist.

Another thing he points out is the difficulty of getting the scientist out of their old culture of publishing, for this reason tools that are developed should have an even higher quality and usability to convince people to use it.

Mendeley the Last.fm for academia

September 24, 2009

Mendeley was created with a minimalized social aspect in mind, this is because often scientist are afraid of social networks. By offering a good organizing tool scientist might be more eager to start using the software and the social features that come with it.

Mendeley is based upon the Last.fm model, it exists of a desktop and a web application. Simply put the desktop application allows you to organize your research files like you would organize your music files. You can create collections, rename, search, tag, …

The application has a science scrobbler which extracts metadata from your files and add it to Mendeley’s database. This speeds up the process of analyzing data like citation. To make the life of a researchers easier Mendeley also offers plug-ins for Word and OpenOffice which allow the user to automatically generate reference lists.

Mendeley is not all about social networking but it’s got some great social features. The desktop application recommends related papers to the ones in your library. And if you choose to share your research, Mendeley offers some nice social features. You can share what you are reading and recommendations. Next to these features Mendeley offers collaboration. Once you share a collection of papers  you can collaborate: you can edit, annotate(commenting) and insert citations to a document.

Next to the desktop application which allows you to synchronize your library at every computer, there is a web application. The online access offers you to create your own profile but most importantly it allows you to manage your library, view statistics about trends in Mendeley’s database, your library and your  publications. The statistics are visualized mostly like they are on a Last.fm profile, trends are easy to spot. The online account makes it possible for the user to sync citations from CiteULike. There is a webimporter to import documents from a large amount of websites, this is of course a great research tool.

Mendeley seems to be a great success, I think this success is mainly because of the scrobbling model from Last.fm. Offering scientists a great and easy way to organize their research will make them more eager to use the software. This also means they might be triggered to use some of the social features offered. It also seems to be a fast way to add papers to a repository, when we look at open repositories people are hesitant to upload their papers because of the complexity or high effort rate to do this. The desktop application makes this process easier but it helps the researcher in his activities. Mendeley tries to addresses the problem of getting researchers to share but doesn’t offer any groundbreaking social features.

Overview of social features in Science 2.0 platforms

September 14, 2009

It’s been a while since I my first blog post but I’m still alive and I’d like to tell you about some of things I’ve done lately. During the last week I’ve started with the first part of my literature study. I’ve been looking at some of the Science 2.0 platforms which try to introduce online social networks for sharing papers, ideas, … . I’d like to give you an overview of the social features they offer in this blog post. I’ve done this to let me get a better overview of social features that might be useful to add to repository software.

In my next blog posts I will be tackling some other aspects of Science 2.0 platforms like desktop applications, online citation managers, … I’d like to review all of these to get some knowledge of the social features they might offer or could inspire me to create.

To start of here’s a list of the sites I’ve been looking at:

Profiles and groups

Almost all of the web based Science 2.0 platforms out there offer the user a way to create his own profile, nothing new there. The researcher is allowed to create his own profile with information like the institution he’s working at, research field, … This offers people in the network to get to know who they are interacting with, but also offers an opportunity for to link people with same interests from over the whole world.

Some of the sites offer a process to complete your profile by importing contacts, … and some even automatically recognize friends and propose them this is done by Epernicus. Important with this last feature is that your profile is as complete as possible. This all sounds great but still someone can act on behalf of someone else, some of the platforms try preventing this by only allowing people with mail addresses from schools, universities, …

An other basic social feature is joining, creating, … of groups this allows for interaction. Groups can be made public or private.

Recommendations

When uploading or looking at a document almost all sites offer recommendations for the user. Another part of recommendations is being able to recommend documents to others and to save those recommendations.

Collaborations

In offering a user ways to share documents working together on a document seems to be a natural next step. But this is a service that is harder to implement than the previous social features. Next to being more difficult there are also more ways to do this.

Documents and groups

A more classic approach taken by some sites is allowing a user to upload documents to his own map and share these with others. Others can in their turn edit, upload and share the document again. This is most of the times related with a group of users, this approach doesn’t seem to be the easiest.

Wiki’s

Most people are already familiar with wiki’s and it’s often used by groups of researchers in labs. So some of the sites just implement wiki’s into their site as a collaboration services. The benefits are that is a very well known principle and easy to implement.

Google Doc system

Some sites even go as far as implementing their own Google Doc like system, as for development this seems a very intensive process. On the other side the system of Google Doc’s is a very good one, allowing people to work on the same document.

When it comes to collaboration an approach like a Wiki seems the best offering an easy way to put a lot of information in a structured way online. Of course with the arrival of Google Wave this might all change.

Visualization

Visualizations in the sites doesn’t seem to get a lot of attention, possibly because they don’t see it as an immediately productive tool for the researches. At first hand this might be so because it’s not a thing we associate with doing research, but it might offer some interesting results. This is shown by Academia.edu and Sci Link, the last one offers a tree of science in which visually maps relationships between people.

Your own dashboards

Personalization is one of the social features offered by some of the Science 2.0 and this is likely best done by a personal dashboard. It allows the user a easy way to access specific services of the site and community. The development of own widgets is sometimes even allowed.

Blogging

Another way of offering interaction is of course offering a blog for every user, this is mainstream in all the services I’ve seen like profiles and groups.

Comments

A user is able to comment on blogs, documents, events, … this allows for interaction on all fields.

News feeds

Offering a user a quick view of what is going on in the community or research field can be done by offering a news feed. This feed can be generated by the service that looks at the profile of the user and gets information the user might be interested in. The choice of what a user might be interested in needs to be right, because a user doesn’t want to see a feed with useless information. Offering a user to integrate news feeds from other websites is also an option that is implemented by some sites.

Academia.edu seems to have focused on news feeds in it’s development, it offer feeds about events, published papers, … Building up a good profile is important in this case. But this approach gives a very personal feeling to the service. Ologeez more or less does the same thing and collects information from groups, wiki’s, … a user is interested in and shows them in a MyWorld page, which gives the user an overview of recent activities.

Another interesting feature of Academia.edu is the Twitter-like following system which allows a user to follow someone and to receive information about them in the news feeds. Yet another Twitter-like feature is implemented by SciLink, SciLink allows for you to share with your friends which paper you are reading at the moment.

Events

As mentioned above, users are interested in events. Some sites take a Last.FM like approach allowing users to look up events, join and review events. Next to events ResearchGATE allows for users to plan meetings.

Managing citations

Some sites offer users to manage their citations, users are able to share them with others, to access them online, … 2Collab takes a different approach than the previously mentioned systems, it is build around bookmarks like Delicious. It let’s people share, manage, discuss, … bookmarks they make.

Conclusion

Most of these social networks try to do more or less the same thing, putting some of their focuses differently, some might concentrate on collaboration and others on searching and sharing of documents. All offer a social network around this all, but there still seems to be little to no integration with existing services like Twitter, Facebook, … which all have a larger audience.

The social features I’d like to see added to repository software should make adding and editing papers, journals, … easier. So managing your citations is a helpful feature for anyone writing his paper.

An easy way to collaborate online would certainly increase activity from users of the repository software. It offers researchers to work together over a greater distance and a place to meet. It’s also easier to have a place to collaborate and publish at the same time. Visualizations offer some great insight to researchers about their papers, people they work with, … but the question I’m likely to ask is, will people frequently use this feature?

News feeds are offered by some of the social networks are interesting and recent developments in this area have been made. Like the lifestreaming service offered by FriendFeed which allows users to share content from different services like Flickr, Delicious, … would likely be a good social feature to share information between users. It gives the user a quick view of what is going on in the community, an easy way to share, …

Other social features like dashboard, event, comments, … are common to most social networks scientific or not. People who’d like to use a social network will most like expect them to be available. Feedback on this post (things I haven’t mentioned or skipped over to quickly) are welcome in the comments.

In my next blog post I will talk about some Science 2.0 platforms that use applications on the desktop instead of a website.

Report: First meeting

July 16, 2009

At the first meeting with my supervisors a few things were explained about the thesis and the communication with my supervisors. A good way to keep my supervisors informed was to maintain a blog. In my blog posts I could tell about the progress in my work like the papers I’m reading, any ideas, … This is an easy way to write some of the text of my final documentation of the thesis and my supervisors are informed about what I’m up to.

To begin with my supervisors explained the idea behind the thesis. At the moment @mire is using Dspace to enable researches to share papers with each other and the rest of the world. In my thesis I will have to add some social features to Dspace, of course there are a lot of possibilities. A few propositions were:

  • Automatic recommendations services: when sharing or viewing a paper a few papers will be recommended, having same topic or other related areas.
  • Citation analysis: who is referring to who?
  • Comment: sharing opinions on a paper with the author
  • Collaboration: enabling different persons to collaborate on a paper. Google Wave could be a new solution to this issue.
  • Information visualisation: a visual way to analyse links between papers, … e.g. tag cloud.
  • Content: Current users often don’t like adding document, because it’s to complex for them. Can this be made easier? Could access to the papers be made easier?
  • Personalization:  for easier and faster access to content a personalized home page could be a solution.

In the end a choice needs to be made from these options and possible other ideas. To make this choice easier a study has to be made of the current situation in research 2.0 and the social features. This study needs to be finished at the start of the acadamic year. Any ideas and feedback are welcome in the comments.