User type: Researcher
Alex Gates is an Assistant Professor at the School of Data Science in the University of Virginia. He’s a computational social science and network scientist whose work unpicks how interconnectedness shapes our lives.
His research group at the University of Virginia uses data to understand more about how society works. The group includes Assistant Professor Jess Reia and PhD student Beau LeBlond. They’re currently working on a project exploring how government uses scholarly research, to gauge how academia can better serve the government population.
How they use Overton: a policy discovery and assessment tool
The latest paper from Alex and his group examines the relationship between academic research and public policy in the context of General Data Protection Regulation (GDPR). They set out to understand how government organises and uses academic research, to work out how to identify research that’s relevant to policy.
They used Overton to find policy citations of research related to GDPR and to analyse patterns of interaction between government entities and academia.
“This couldn’t have been done without the citation data – super useful”
Their work has a strong practical focus. The Overton data helps them shed light on the complex interface between scholarship and government, and provides valuable insights that can improve evidence-based policymaking.
Key features of Overton for the University of Virginia
Coverage
Alex, Beau and Jess stress the importance of citation data provided by Overton, describing it as “super useful” for their research. They note the comprehensiveness of the database, especially appreciating Overton’s ability to incorporate new sources promptly, expanding the coverage of the data – “We noticed that something wasn’t in there and you guys pulled it in immediately”.
Much of their work involves aggregating the appropriate dataset, so being able to access all the policy information in a centralised place saved huge amounts of time. Without Overton they would have had to narrow their scope, and look at specific countries. Accessing Overton enabled them to conduct large-scale analyses that would otherwise be challenging to accomplish.
We definitely could not have done something at this scale without the work already put in by Overton
Beau leBlond
DOI search
Overton users can search for a list of DOIs (unique identifiers assigned to specific academic outputs) to find policy documents that cite them. Overton’s utilisation of DOIs facilitates easy linking to other datasets, streamlining the research process for the Virginia team and enabling seamless integration with other scholarly resources. He said that “being able to link these institutions and entities across multiple platforms using a universal ID is really helpful”
Topics
Overton’s topic classification feature enables the team to single out results to those solely related to GDPR, allowing them to filter their results to a targeted group, making their dataset easier to analyse. Topics represent the main themes of the policy – Overton’s taxonomy is built by analysing the phrases and entities used in the document and comparing them to pages in wikipedia, to match them to common subjects. Overton uses Wikipedia for topics and MediaTopics to classify subject areas to policy documents. Find out more about topics, entities and subject areas in Overton here.
“The topics are great”
Bespoke data set
The team received a custom dataset from Overton, tailored to their research needs. Getting the data in this way helps them analyse it within their own systems. Data snapshots give you access to the data in a machine readable format which enables you to import it into your own business intelligence system or database. Find out more about data snapshots.
Alex’s research insights
Their case study on general data protection regulation is part of a wider effort to find what research may have influenced government departments and agencies in their decision making processes.
They used Overton to find all the papers cited in government policy related to GDPR, and mapped these onto a dataset from Open Alex, (a catalogue of scholarly papers, researchers, journals and institutions) to create two interconnected networks – a government citation network and an academic citation network.
They then undertook network analysis to understand the relationship between the different data points and see how information flows between academia and government.
One of the key findings of the analysis was the development of a co-citing analysis network, which revealed patterns of when and where policy documents cite the same academic literature. Notably, the analysis showed that the United States tends to cite academic documents differently from the rest of the world, with a noticeable insular trend.
The research also highlighted an exponential increase in the likelihood of government citation if an academic document is already cited by other academics. This indicates the importance of the academic community as a whole in shaping policy decisions.
Their work shows how Overton’s data can facilitate nuanced analysis of the research-policy interface, with implications for policy and knowledge production in regulatory domains like GDPR.
Are you a bibliometric scholar interested in policy? Contact us here and get free access to the Overton database to help with your research.
