For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. But, at least in the area of text analysis (AKA content analysis, or natural language processing), the old limits are crumbling
In the latest of its monthly series of interdisciplinary microsites addressing important public issues, SAGE Publishing is offering free access to a suite academic articles that focus on research about big data through November 15.
How do firms transform big data and why do firms differ in their abilities to create value from big data? in a research article that tries to find answers to these questions. Jing Zeng and Keith Glaister find “it is not the data itself, or individual data scientists, that generate value creation opportunities. Rather, value creation occurs through the process of data management.”
Current debates in higher education policy have drawn attention to the significant impacts of marketization, metrics, and performance management on the sector. Ben Williamson argues that a restructuring of the data infrastructure is shaping these HE trends.
Making data available for other researchers to find, use, reuse, ultimately makes research more efficient and effective. Yet despite policies that encourage and require data sharing, researchers in the UK and US report lower percentages of data sharing than average. Grace Baynes suggests researchers be given incentives, expert support, and training to make it easy to share data.
The recent revelation that Cambridge Analytica was able to acquire the Facebook data led to a surge of interest and questions around what companies do with people’s data. Amidst all of this, little attention has been paid to the feelings of those whose data are used, shared, and acted upon.
The underlying conceit of economist and data scientist Seth Stephens-Davidowitz’s work, whether in his new book, on the op-ed pages of the New York Times or in the classroom, or on campus, is that people’s search activity on a search engine reveals much more about them than do surveys, polls, or other social media
In a keynote address delivered to the London Info International conference, Ziyad Marar, president of global publishing for SAGE Publishing, outlines the intersection between big data and social science research. He notes that social and behavioral researchers have seen some opportunities as beyond their grasp, and that SAGE is working to bridge that gap.
The applications of big data provide a very mixed picture about its uses and abuses, in government, academe and private industry. And while where you stand on the net impact depends, as the cliche goes, on where you sit, a panel at the recent ESRC Festival of Social Science came out qualitatively optimistic about the future.
Where sharing industry information was once thought of as “giving away the farm,” it has since grown into its role as a commonplace technique capable of generating big results.
Social Science Space will publish the winning essays, runners-up and eight shortlisted pieces from the most recent ESRC writing competition in the next few weeks. Here we present “Better healthcare with deep data,” an essay from Alsion Harper detailing some of the concepts she’s observed with the use of endoscopy in a retirement hotspot.
When looking at big data, says computational social scientist Gary King, “The data itself isn’t likely to be particularly useful; the question is whether you can make it useful.” In this Social Science Bites podcast, he explains more about the importance of data analysis.
