Data Science - Information Matters

July 24, 2025 Shalini Urs

The Commons of Science—Why It Takes a Village: Christine Borgman on Collaboration, Curation, and the Invisible Infrastructure of Knowledge

This article examines the evolution of scientific knowledge infrastructures through the influential work of Christine L. Borgman, Distinguished Research Professor at UCLA. Framed around the concept of science as a commons, it traces a three-decade transformation—from digital libraries in the 1990s to cyberinfrastructure in the 2000s, culminating in today’s sociotechnical framing of knowledge infrastructures. Borgman’s scholarship highlights how data acquire value not in isolation, but through complex systems of people, practices, tools, and institutions that enable their curation, sharing, and reuse.

Featured Professional Development

November 14, 2024 Junwan Liu

To Succeed, Find a Career Partner

Super-partnerships exist between scholars connected within densely-knit collaboration networks. Understanding how such relationships affect scholars’ careers is of great importance. In this paper, focusing on the longitudinal aspects of scientific collaboration, we analyze collaboration profiles from the egocentric perspective and use analytic extreme value thresholds to identify super-partners. We explore the characteristics of super-partners and the added value of a long-term commitment, which provides quantitative insights into the effect on scientific collaboration associated with close collaboration.

Featured Original

July 7, 2023 Najeem Olawale Adelakun

Navigating Challenges and Future Trends in Sentiment Analysis for Investment Decision Making

Sentiment analysis provides insights that guide investment decisions by analysing and interpreting sentiment from textual data sources such as news articles, social media posts, and financial reports. However, using sentiment analysis to make financial decisions has its own set of issues and constraints.

Featured Original

June 14, 2023 Bob Glushko

Seven Ways That Data Science Projects Fail

The pragmatic value of data science for solving business problems has made it a rival or replacement for information science from an industry perspective. I reviewed numerous data science projects and interviewed numerous data science experts to understand the factors that make projects successful, but this work also revealed—by counterfactual reasoning and some confessions from the experts—why some data science projects fail. I identified seven causes of failure and I explain here how “information science thinking” can prevent or lessen these problems in data science projects.

Featured Translation

June 1, 2023 Fred Ye

The Differences and Linkages between Data Science and Information Science

Data Science and Information Science are two distinct yet interconnected fields which underpin the structures of our societies, economies, and technologies, offering unique methodologies to interpret the vast landscapes of data and information that define our world.

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April 20, 2023 Karen Wickett

Critical Data Modeling: Tools to Trace Oppression in Information Systems

Critical data modeling uses tools from data modeling and systems analysis to analyze information systems, connecting social critiques to the structure of digital information systems and data objects.

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September 23, 2022 Haihua Chen

Why Does Data Quality Matter? How to Evaluate and Improve Data Quality for Machine Learning Systems

With data as the infrastructure, and machine learning and deep learning as the technology foundations of building AI systems combined, they largely determine the performance, fairness, robustness, reliability, and scalability of AI systems.

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June 23, 2022 Hao Xu

What Should We Teach? A Human-Centered Data Science Graduate Curriculum Model Design for iField Schools

As a discipline with solid multidisciplinary elements, there are numerous approaches to developing data science teaching methods and curricula, with a general emphasis on computation or statistics.

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January 1, 2022 Yunhe Feng

Unifying Telescope and Microscope: A Multi-lens Framework with Open Data for Modeling Emerging Events

When investigating a phenomenon or an event, we think choosing a data source can be seen as choosing a lens for observations in physical sciences; a telescope and a microscope both allow us to observe, but two very different worlds.