Data mining

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.
Data mining Science policy
Data access: Vast possibilities and inequities
September 27, 2017
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In our ongoing series on data mining to inform policy, we are giving the topic of data access its own post because of the implications it had for the success or failure of our case studies. The simple reality is that you can’t mine data that don’t exist (or that may as well not exist when they are functionally or realistically impossible to access). As a result, access is particularly important since it underpins the rest of the work in a data mining project. Let’s tease this topic out a little, shall we?
Data mining Open access Science policy
Data mining: Open access policies and outcomes
September 20, 2017
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During our data mining project for the European Commission, one of the case studies we undertook to test our framework to guide data mining for policy research explored open access (OA) publications in the European context. Specifically, the question we aimed to tackle was whether institutional OA policies have an effect on the share of an institution’s papers available in OA, and if so, to what degree. An answer to this question would provide actionable advice for institutions that are looking to increase the availability of their research. Here’s what we found.
Data mining Science policy
Data mining: Exploring the connection between innovation, growth and prosperity
September 13, 2017
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In the most recent post in our ongoing data mining blog series, we explored the effect on innovation of research collaboration across disciplinary and sectoral boundaries. That topic was worth exploring because beliefs that such collaborations are effective levers to promote innovation are foundational to many policy choices, and there is scant evidence available to determine whether these levers work or not (and how powerful they are). The present post will take that line of exploration one step further: we usually promote innovation as a way to drive social and/or economic prosperity, creating “jobs and growth,” often with some qualification about these developments being “inclusive,” “smart,” or “sustainable,” or helping out “the middle class.” Such approaches have been particularly emphasized since the Financial Crisis a decade ago. The purpose of this blog post—and the case study on which it is based—is to explore the relationship between innovation and growth, especially for small and fast-growing firms.
Data mining Science policy
Data mining: Cross-boundary collaboration and innovation
September 6, 2017
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In our data mining project for the European Commission, two of the six case studies treated levers for promoting innovation, and we’ll start to tease those apart here. In brief, collaboration across disciplinary and sectoral boundaries is believed to promote innovation, while innovation in turn is believed to support broader economic and social prosperity. Even […]
Data mining Science policy
Budgets, timelines and expectations: The influence of project novelty
August 30, 2017
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In the previous post in our ongoing series on data mining for policy, I touched on the importance of organizational context for successful management of these projects. In today’s installment, we dig into novelty and the pressure it exerts on the budgets, timelines and expectations of the project. Novelty is an interesting beast. Alongside the […]
Data mining Science policy
Data mining: Organizational context matters
August 23, 2017
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The previous posts in this series on data mining to inform policy have covered our initial technical framework and two of its further developments. In this post, I present some of the project management lessons we learned over the course of the data mining project, which are largely drawn from the two less successful case […]
Data mining Science policy
Data mining: The value of a scoping phase
August 16, 2017
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In previous posts in our data mining series, we laid out our initial technical framework for guiding data mining projects, then supplemented that with plug-ins to facilitate its use for R&I policy research specifically. These plug-ins helped to overcome the challenge of applying a generic framework to a specific thematic area. However, there was another […]
Data mining Science policy
Data mining: Technical framework plug-ins for the R&I context
August 9, 2017
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In my previous post, I outlined the initial technical framework developed by Science-Metrix in the course of the data mining project for the European Commission documented in this blog series. This initial data mining framework—strongly inspired by existing frameworks—provided a solid foundation on which to build. However, to support data mining in a policy context […]