Posts Tagged: big data

Team diversity widget: how do you measure up?
December 6, 2017
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Collaboration and disciplinary diversity are hot topics in the science policy and research communities. At Science-Metrix, we've been working on multi-/inter-/trans-disciplinarity issues for a while now, and we figured that some of you might find it useful to have a tool you can use to take a quick measurement of the multidisciplinarity of your team. As part of our 15th anniversary celebrations, we've created a free widget that we’re sharing for just such a purpose. Any team can be measured—your research team in academia, a product team in your company, or even your Wednesday-night hockey team. In this post, we’ll explain what the disciplinarity widget does, how to interpret the measurements, and how you can use it yourself. We hope you enjoy the widget—a little birthday gift from us to you!
Data mining
Using data readiness levels to address challenges in data mining projects
October 11, 2017
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In a blog post from earlier this year, Neil Lawrence describes some challenges to data mining projects that are familiar to many working in the domain—our team definitely included! These challenges include the availability and quality of the data available for the project. Data scientists are often faced with very detailed expectations of budgets and timelines for a project but are provided with very little information at the outset regarding what data they will have to work with, making it difficult to determine whether a project’s outline is realistic. To begin addressing this problem, Lawrence lays out a very general taxonomy of “data readiness levels,” which provides useful language to help us identify and ultimately overcome these important challenges that currently hinder many data science projects.
Data mining Science policy
Data mining: revisiting our definition
October 4, 2017
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In our ongoing blog series on data mining for policy, we’ve been trying to synthesize a lot of information into short, bite-sized chunks for our audience. Invariably, well-intentioned as such efforts are, something valuable always ends up on the cutting room floor. In this case, we were a bit too hasty in providing the definition of data mining itself, which one of our readers followed up to ask about. Our initial definition was put together through literature review and our earliest experiences with data mining, but the opportunity to revisit that definition more recently has enabled us to uncover some further nuances that we hadn’t yet appreciated.