"data portal"

User Stories

  • Materials Science Artificial Intelligence (AI) Project Supports Data Service Initiative

    Argonne National Lab

    Data Science and Learning Division (DSL) scientists have undertaken a project to develop a lab service for interactive, scalable, reproducible data science, leveraging machine learning methods to reduce simulation costs and increase data quality and value for researchers.

  • Accelerating Research at UCSD with Globus

    UCSD

    Researchers are now able to reliably transfer and share large data sets, and even share protected data with ease. With the increase in Globus managed endpoints, researchers at UC San Diego can easily transfer and share data both internally and with researchers outside UC San Diego.

  • Building a Genomic Data Repository Using the Globus Platform

    New Zealand eScience Infrastructure (NeSI)

    A few years ago NeSI adopted Globus as their de facto national data transfer platform for research.

  • Building an Advanced Crop Analytics Portal

    Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF)

    Used Globus to build portal which will provide open access to huge datasets that will guide breeding decisions, facilitate collaboration, and allow unprecedented data sharing

  • Building a portal for the Human BioMolecular Atlas Program

    University of Pittsburgh

    The University of Pittsburgh together with the Pittsburgh Supercomputing Center are one of five funded components contributing to the infrastructure for the NIH-funded Human BioMolecular Atlas Program (HuBMAP), which relies heavily on Globus for software infrastructure to build a frictionless research platform.

  • DLHub: Supporting Machine Learning in Materials Science

    University of Chicago, Argonne National Laboratory

    The Data and Learning Hub for Science (DLHub) serves as an automated facilitator and interconnection point for ML models and associated data transformation and analysis tools. It allows researchers to describe and publish such tools in ways that support discovery and reuse; run published tools over the network (with tools executed on a scalable hosted infrastructure); and link models, other tools, and data sources into complete ML/AI pipelines that can themselves be published, discovered, and run. DLHub relies on Globus services for data management.