SciCurve: revealing life science’s curves

Screen Shot 2014-09-25 at 11.00.18 AMAfter the recent launch of Sciencescape, here is  another startup pledging to help us cope with the enormous amount of data and literature at our disposal. SciCurve uses PubMed‘s library of 23 million references to generate visually pleasing graphs and curves that helps you grasp trends in literature.  It comes with three main functionalities

Observing trends for your field of study.

For academic researchers, knowing about the publication trends in a field is fundamental when writing a review or a grant proposal. Is this field new? Has is been prolific in the 70s and is coming back? Or is it the latest hot field and is in exponential growth? SciCurve saves you the trouble of going through PubMed results and manually copy/pasting the number of search results. Simply enter one or more keyword and it will display a timeline of the number of publications and citation counts over the past 14 years.

Her's an exemple of teh trend function, showing an increasing number of publication since 2000, which is followed with an exponentially increasing number of citations.

The number of publications and citations for the keyword “biomaterials”. The raising  publication count since 2000 is followed by an increase in the number of citations.

 Exploring research networks.

One particularly challenging task when exploring the literature, is to quickly find the key papers, on which most of the field is based. Manually going through papers and tracing back the original work is time-consuming, and the large amount of information gathered could end up be confusing. SciCurve helps you analyse the literature automatically and helps you understand its findings by generating representations of how paper are interconnected.

The network tab displays a map of the network of publications based on citation relationships. Important papers tend to form nodes from which radiates the papers citing that work. Key papers, and the articles citing them can then easily be identified.

Example of a networks graph, with Biomaterials as a keyword.

Example of a network graph, with “biomaterials” as a keyword.

Second, the map function places on a map top papers of the field represented by circles of size proportional to their relevance. SciCurve clusters similar papers together, naturally point-out relevant sub-fields. This is particularly useful to understand what a field might entail.

Example of a map view with the keyword "Biomaterials" Clusters form show that sub-categories such as collagen, surface and engineering are prominent.

Example of a map view with the keyword “biomaterials”. Clusters reveals  sub-fields such as collagen, surface and engineering.

Finding who drives the scientific endeavour?

SciCurve also automatically generates author profile pages, including a map of their most frequent co-authors and a list of their publication. Wondering who are the most prolific authors in your field? SciCurve can identify the key authors in a particular field and map them out as a function of their publication record.

A map of the most published authors in the field of biomaterials

A map of the most published authors in the field of biomaterials

The graphs and maps, but not the raw data, can be downloaded in the free version of SciCurve. An enterprise version, with more advanced functionalities is offered for a fee. SciCurve also supports Zotero and Mendeley integration which allows you to easily export  references to your favorite citation management tools.

Beyond a rather obvious usefulness for academics and R&D scientists, this sort of tool could be interesting for general practitioners and other medical specialists that do not have the time to grasp the latest trends in medical research. Publishers and research databases could also use this approach to improve their search engines by providing a more data-rich and more visual experience to their users.

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