Making all research data accessible, discoverable, and usable

Globus Labs is a research group led by Prof. Ian Foster and Dr. Kyle Chard that spans the Computation Institute, Department of Computer Science, and Math and Computer Science Division at the University of Chicago and Argonne National Laboratory. Our modest goal is to realize a world in which all research data are reliably, rapidly, and securely accessible, discoverable, and usable. To this end, we work on a broad range of research problems in data-intensive computing and research data management. Our work is made possible by much-appreciated support from the National Science Foundation, National Institutes of Health, Department of Energy, National Institute of Standards and Technology, and other sources, and in addition to computer science, engages fields as diverse as materials science, biology, archaeology, climate policy, and social sciences. We work closely with the team developing the Globus research data management platform, who often challenge us to think bigger—and sometimes implement our less crazy ideas.


FEATURED PROJECTS

We are developing methods to automatically extract scientific facts buried in scientific publications

Parallel scripting in Python

We are developing methods to automate the provisioning of cloud computing infrastructure


RECENT NEWS

Ian attended the 2018 Workshop on Clusters, Clouds, and Data for Scientific Computing (CCDSC) in Lyon, France. He presented on the computer systems challenges raised by the rise of deep learning in science, and technologies that Globus Labs is developing to address those challenges.

The Whole Tale team is holding a Workshop on Tools and Approaches for Publishing Reproducible Research on September 13-14, 2018 at the Big Ten Center in Chicago.

Ian Foster attended JupyterCon, The Official Jupyter Conference. He presented a talk about Scaling collaborative data science with Globus and Jupyter.

Tyler Skluzacek will be giving a talk at eScience 2018, this October in Amsterdam for his accepted paper: Skluma: An extensible metadata extraction pipeline for disorganized data.