Tuesday, November 17, 2015

Something cool made with our API

Very cool


For 20 years, the Smithsonian/NASA Astrophysics Data System (ADS) has kept all professional astronomers worldwide up-to-date via their digital library of 12 million records which provides links to ScienceDirect and other platforms for full-text retrieval. The ADS maintains relationships with all major publishers and offers users access to four million full-text article links with some of those links originating in 40 full-text Elsevier journals on ScienceDirect.
In order to increase visibility of - and encourage linking to – their subscribed full-text (especially articles written by NASA researchers), NASA had the idea to add thumbnails of graphics appearing within the article to the abstract view of a publication. To do this, they turned to the ScienceDirect Object retrieval and Object search APIs to mine the images and then linked them to the corresponding articles on ScienceDirect. Until now, the ADS has been able to implement this feature for 32,000 publications.

A view of the ADS abstract page
ADS abstract page

A view of of the ADS graphics page with thumbnails linking to the full-text of the article
ADS thumbnails page

“My experience with the ScienceDirect API was exemplary. A well-designed API with a very efficient and friendly support team to back it up!”
- Edwin Henneken, IT Specialist for the Smithsonian/NASA Astrophysics Data System, employed at the Smithsonian Astrophysical Observatory in Cambridge, Massachusetts.

The redesigned ADS remains in beta release and can be easily accessed while more infornation about the ADS in general, is also available.

Example of ScienceDirect article page with images
ScienceDirect homepage

ScienceDirect APIs are designed to help developers retrieve and integrate full-text content from publications on ScienceDirect into their websites or applications. Visit the ScienceDirect API page to learn more, watch videos and get started.

API text mining videos

Friday, November 13, 2015

Boson Higgs

According wikipedia "On 4 July 2012, the discovery of a new particle with a mass between 125 an was announced; physicists suspected that it was the Higgs boson"

However, scholar returns results from 1960 and 1990, which is 22 years before the Scientific discovery. One result is from Elsevier

ScienceDirect returns fresher and relevant results,

Thursday, November 12, 2015

Instantaneous Recommendation: real time suggestions for your Academic Library

One of my most favorite features shipped during the last round is a form of instantaneous recommendations. This feature suggests in real time new relevant papers as soon as my library is updated.

So suppose that I add a few papers about deep learning to my library and that this is the first time I have papers about this research topic in my library.

The suggestions are immediately updated and I see papers about Deep Neural Networks for speech recognition, Convolutional Networks, and LCVSR

and relevant papers published by Yann LeCun

I believe that this feature is useful to explore a subject if you are not familiar with the topic, and to make sure that your next paper has a solid "Related Works" section where the most important papers for your research activity are mentioned.

Wednesday, November 11, 2015

Stats is bigdata

Feature: Stats
If you are a published author, Mendeley’s “Stats” feature provides you with a unique, aggregated view of how your published articles are performing in terms of citations, Mendeley sharing, and (depending on who your article was published with) downloads/views. You can also drill down into each of your published articles to see the statistics on each item you have published. This powerful tool allows you to see how your work is being used by the scientific community, using data from a number of sources including Mendeley, Scopus, NewsFlo, and ScienceDirect.
Stats gives you an aggregated view on the performance of your publications, including metrics such as citations, Mendeley readership and group activity, academic discipline and status of your readers, as well as any mentions in the news media – helping you to understand and evaluate the impact of your published work. With our integration with ScienceDirect, you can find information on views (PDF and HMTL downloads), search terms used to get to your article, geographic distribution of your readership, and links to various source data providers.
Please keep in mind that Stats are only available for some published authors whose works are listed in the Scopus citation database. To find out if your articles are included, just visit www.mendeley.com/stats and begin the process of claiming your Scopus author profile. If not, please be patient as we work further on this feature.

Tuesday, November 10, 2015

Satisfying the exploratory search needs : poster query {dyscalculia}

{dyscalculia} is severe difficulty in making arithmetical calculations, as a result of brain disorder.This is scientific term for a cognitive problem associated to 3%-6% of the world population. Therefore, many people are interested in better understanding the topic.

Google Scholar returns Elsevier content from 1992 and 1985 and from Wiley 1996.

Undoubtedly, Science made significant progress in the last 9 years but this progress is not easily found in Google Scholar for this query.

ScienceDirect finds fresh Elsevier's content for {dyscalculia} including books, and articles. All the results are from 2015, and 2016 (pre-print)

Monday, November 9, 2015

New research features on Mendeley.com - Recommends

(posted in http://blog.mendeley.com/academic-features/new-research-features-on-mendeley-com/ 

Mendeley’s Data Science team have been working to crack one of the hardest “big data” problems of all: How to recommend interesting articles that users might want to read? For the past six months they have been working to integrate 6 large data sets from 3 different platforms to create the basis for a recommender system. These data sets often contain tens of millions of records each, and represent different dimensions which can all be applied to the problem of understanding what a user is looking for, and providing them with a high-quality set of recommendations.

With the (quite literally) massive base data set in place, the team then tested over 50 different recommender algorithms against a “gold standard” (which was itself revised five times for the best possible accuracy). Over 500 experiments have been done to tweak our algorithms so they can deliver the best possible recommendations. The basic principle is to combine our vast knowledge of what users are storing in their Mendeley libraries, combined with the richness of the citation graph (courtesy of Scopus), with a predictive model that can be validated against what users actually did. The end result is a tailored set of recommendations for each user who has a minimum threshold of documents in their library.

We are happy to report that two successive rounds of qualitative user testing have indicated that 80% of our test users rated the quality of their tailored recommendations as “Very good” (43%) or “Good” (37%), which gives us confidence that the vast majority of Mendeley reference management users will receive high-quality recommendations that will save them time in discovering important papers they should be reading.

For those who are new to Mendeley, we have made it easy for you to get started and import your documents – simply drag-and-drop your papers, and get high-quality recommendations.

On our new “Suggest” page you’ll be getting improved article suggestions, driven by four different recommendation algorithms to support different scientific needs:
  • Popular in your discipline – Shows you the seminal works, for all time, in your field
  • Trending in your discipline – Shows you what articles are popular right now in your discipline
  • Based on the last document in your library – Gives you articles similar to the one you just added
  • Based on all the documents in your library – Provides the most tailored set of recommended articles by comparing the contents of your library with the contents of all other users on Mendeley.
Suggestions you receive will be frequently recalculated and tailored to you based on the contents of your library, making sure that there is always something new for you to discover. This is no insignificant task, as we are calculated over 25 million new recommendations with each iteration. This means that even if you don’t add new documents to your library, you will still get new recommendations based on the activity of other Mendeley users with libraries similar to yours.

To find your recommended articles, check out www.mendeley.com/suggest and begin the discover new papers in your field!