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Posts Tagged ‘data visualization’

Summer’s finally here, and with it comes another milestone for 2dSearch: alternative visualisations. The default ‘Nested’ view has its strengths, but it isn’t to everyones liking. So we’ve added two completely new ways to view, understand and optimise your searches:

  • Tree View, which uses the metaphor of the family tree, with a root node at the top and successive generations of children below
  • Inline View, which maps hierarchical structure onto physical structure with groups aligned a common midline, allowing a traditional left-to-right ‘Boolean string’ reading

And of course you still have the Nested View, which maps hierarchical structure onto a series of nested containers. And all of this for free! There’s lots more details in our Medium post, but for the best experience just try them out for yourself.

Think outside the search box: https://www.2dsearch.com/

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Just published the following video on Youtube, which complements last week’s piece on the topic of How to debug and optimise Boolean strings. As always, comment & feedback welcome.

Struggling with complex Boolean searches? Editing Boolean strings is inefficient & error prone.

With 2dSearch you can visualize your search in 2D, then drag & drop blocks to optimise and refine. Use automated query suggestions to refine your search. Works with Bing, Google, Google Scholar, PubMed, TRIP, Epistemonikos and more. When you’re done, you can save & share your own collection of search templates and best practices.

Think outside the search box: https://www.2dsearch.com/

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Just published the following video on Youtube, Twitter et al. Sharing here for completeness!

Not seeing the results you expected? Debugging complex searches can be a frustrating experience. Don’t waste time editing Boolean strings: find and fix errors the visual way. Visualise your search in 2D, switch parts on & off, enable and disable terms…experiment and optimise, then save and share.

Think outside the search box: https://www.2dsearch.com/

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Most of us are familiar Google Scholar: a freely available subset of Google that indexes the world’s scholarly literature across a range of disciplines. With its database of over 389 million documents including articles, citations and patents, it has become an indispensable resource for scholars and researchers across the globe. Which is why we recently added Google Scholar integration to 2dSearch, thereby offering a tool of immediate utility to anyone wishing to search the world’s scientific literature in a systematic manner.

Now, we’d always known that despite its extensive index, searching GS is subject to the ‘secret sauce’ of Google’s search algorithms, and that this can compromise the ability of users to formulate sophisticated, reproducible searches. But what we perhaps didn’t realise was just how limited that support is. In particular, GS seems to support only 1 set of synonyms (i.e. a substring with terms separated by OR). For example, the following search string would seem to parse correctly:

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If you were tasked with designing a universal framework for search query formulation, where would you start? Well, you could start from a command-line paradigm. After all, that’s the approach adopted by most query builders and ‘advanced search’ forms. But convention aside, is that really the best place to start? Personally, I am not convinced – IMHO command line approaches reflect the days when searches were conducted via remote terminals to subscription databases, and in that respect, they represent the past, not the future. Moreover, using Boolean strings to articulate complex information needs suffers from a number of fundamental shortcomings, in particular regarding scalability, efficiency and transparency. So what’s the alternative?

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A couple of days ago I published a piece on Medium called “Searching PubMed just got smarter“, in which I talked about how we’ve added PubMed integration to 2dSearch, thereby offering a tool of immediate utility to anyone wishing to search MEDLINE in a systematic manner. The service has only been live a week or two, but the post did seem to strike a chord with a number of people. And some of that commentary got me thinking about various issues around reproducibility (with regard both to scientific research and to searching methodologies). But it also raised an issue around transparency, and the degree to which the formalisms we use to express information needs can scale to accommodate new use cases and requirements.

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A couple of days ago I published a piece on Medium called “Towards a universal language for search“, in which I talked about how we’ve added Google integration to 2dSearch, thereby allowing a user to apply the same search strategy (or Boolean string) to search multiple databases. Of course, this may not matter much to your ‘average web searcher’, but for some professions, this is a big thing. Now the point isn’t so much about Google (or Bing for the matter), that just happens to be the instance we have used to illustrate the concept. And crucially, the point isn’t about query languages either (in the programmatic sense) – important though they are, converting a user’s information need into a API call is a different problem.

Instead, what we’re contemplating here is the prospect of a universal framework for information needs. 

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