Peer reviews of your life science research products with BenchWise

Screen Shot 2013-01-30 at 6.53.13 PMFellow postdocs and graduate student of Standford have put together an interesting online tool. It started by the ascertainment that a lot of scientist are tired of knowing nothing about the quality of products before buying and testing them. This applies in particular to antibodies, used in mainly bio-related fields to detect specific parts of proteins, sugars or lipids. Antibodies are known to not always “work” for certain applications, and for reasons often unknown. Testing by trial and error, by purchasing similar antibodies from various suppliers is the only way to go. That’s where BenchWise kicks in.

BenchWise is platform that help you find peer reviews of life science research products, mainly antibodies for now. The goal it to help scientists find the right tools fast. For now the site has a list of hundred of antibodies, targeting different antigens and produced by various manufacturers  The users can give feedback for specific antibodies, for example explain if it works properly for certain application (wester blots vs immunofluorescence). Each item can also be discussed.

This is addressing a clear gap in the kind of information that is available to scientists. It is extremely easy to get reviews of the latest iphone, but so much more difficult to get a real idea of the quality and properties of the “for science only” reagents. I hope this will catches on and help save many tax-payer dollars.

Scholrly goes live – search and connect with the experts

Scholrly_logoI came across Scholrly the other day while browsing through twitter. Although the bright green logo was an eye-catcher, it’s their innovative approach to literature-search that got me really interested. I spoke with Corbin Pon, co-founder of Scholrly to know a bit more.

During his undergrad at Georgia Tech, Corbin did a bit of research in academic labs. He quickly understood that there is a lack of good online tools for researchers, and came to the conclusion that web2.0 technologies could help solve challenges of modern research. One thing that striked him in particular was how difficult it could be for researchers to identify and connect with specialists that are outside of their field of expertise. Since multidisciplinary project are becoming the norm and connections between fields of science are more frequent, there is a clear need a better way to connect scientists.From this observation a team was assembled and Scholrly was born.

Scholrly is a search engine that puts the emphasis on people, more than on articles. Type in keywords, and along with the publications, profiles of important authors are also displayed.


The screen is divided into two halves, one for the publications and the other for identifying the major actors in the field investigated.

We want the user to reach out to the researchers, to email them” says Corbin. And indeed the tool is geared towards building connections and relationships between scientists. Scholrly automatically gathers information about the authors such as publications, affiliations, co-authors and impact-metric. Most importantly Scholrly will provide the contact information you need to, follow-up on an article, ask for a reagents or start new collaborations.

The site has been in private beta testing for more than a year, with around a thousand users providing them with feedback. Since January, Scholrly is live and entered a public beta testing phase. The search engine is optimized for the computer science field, but will soon fully integrate other fields. Corbin explained that this step by step development is essential since it is important to consider particularities in the culture of different fields.

Thinking ahead, Corbin explains that they are developing several potential business plans to sustain their venture. They see their main value as being the connections created between users. They also hope to attract a new and slightly more lucrative audience than professors, graduate students and postdocs. Companies, in particular pharmaceutical and biotechs are turning more and more towards academic research to find their next blockbuster products. Scholrly will connect them with researchers and help them find innovative solutions to their problems. A service very close to what tech-transfer offices are doing in many universities and research institutes.

Scholrly is up against giants like Google and Thomson Reuters, but comes with a fresh look and a new approach. They realize there’s a brand new space for online tools for researchers, ready to be explored. Lets wish them good luck for this new year to come!

Crowdsourcing scientific skills: Kaggle and data modeling

kaggle-logo-transparent-300Kaggle is not exactly a newcomer but it is an excellent example of how the web 2.0 can boost science and help solve scientific problems.

Kaggle harvests the power of crowdsourcing to solve problems in need of data modeling. Predictive models are everywhere, they help predict various phenomena, from customer behaviors to bird migration.  However there is no general rule for designing such models, and they often end up being optimized by trial and error. So it seems the field is well suited for the massive amounts of work-hours crowdsourcing can provide.

Kaggle asks participants to develop predictive models to help resolve problems that have been submitted by companies ( GE, Allstate, Merck, Ford…) and other organisations (universities, governmental organisations…). Tens to hundreds of different models can then be compared, and the best is chosen as the winner.

Turning work into a game is a common startegy to motivate participation, however it is interesting to see that Kaggle pushes the sport analogy quite far. The terms “player”, “competition” and “winner” are often used.  And a winner there is, with the creator of the most optimized models usually rewarded with hundreds if not millions of dollars.

Founded in 2010, the company has successfully raised millions of dollars and major companies are coming onboard with their own data to be modeled, convinced by a series of successful projects. A great example how to put brilliant minds (with some free time on their hands) to collaborative working!