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Managing Complex Data with Globus DataSet Services

Explore how researchers can organize, share, and collaborate on complex and diverse datasets efficiently with Globus DataSet Services. Learn about tagging, grouping, and accessing data with ease.

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Managing Complex Data with Globus DataSet Services

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  1. DataSet Services in Globus Online Carl Kesselman University of Southern California

  2. How Do We Use Data? • Researcher’s questions often require many pieces of data to answer • Data elements are spread across: • Different locations, e.g. GO endpoints • Different file types (excel, txt, CDF, HDF, DICOM) • Ad hoc types and locations • Appropriate grouping of data can vary during investigation • Data need to be operated on as a unit • Shared, processed, copied, …

  3. Organizing Data Is a Challenge • Structure into directories using file and directory naming conventions • Hard to change once its done • Globus Transfer can help once we have done this • Capture data status in README files, … Managing complex heterogeneous data using ad hoc methods is too time-consuming, complex and error prone • Why can’t we manage our data like we manage our pictures and music?

  4. Photos as DataSets

  5. Introducing the DataSet • Provide a simple method for grouping together files based on use • Logical grouping to organize, reorganize, search and describe closer to how the data is used • Tag the data with characteristics that reflect its content • Capture as much existing information as we can • Tag data to reflect current status in investigation • Stage of processing, provenance, validation, .. • Share data sets for collaboration • Provide methods that operate on data sets • Copy, export, analyze, …

  6. Core Ideas • A dataset represents a collection of files • Datasets are first class citizens • Datasets are annotated with tags • Can be predefined or created on the fly • Files in GO endpoints can be added to dataset • Content remains in the endpoint • Basic characteristics are automatically extracted • Datasets are stored in per-project catalogs • Can define conventions for community • Fine grain access control for sharing and collaboration

  7. Globus DataSet Services • Builds on current GO services and new hosted services • tagging service (tagfiler) • Extensible metadata extraction service (InBox) • Accessible via UI or REST interface Globus Online APIs Dataset Services Globus Connect Sharing Service Transfer Service Globus Nexus (Identity, Group, Profile) Globus Toolkit

  8. Demonstration in Genomics • Exome analysis pipelines used by the Onel lab at U. Chicago • Compare gene sequence data from leukemia patients against the human genome project sequence data to investigate rare variants • More detailed description of science in next talk • Data from human and animal subjects • Phenotypic, genotypic and imaging data • DICOM, FASTQ, and VCF formatted files • Create datasets around patients and analyze • Create, annotate, share, and export

  9. Demonstration

  10. DataSet Services Wrapup • Paradigm shift in how researchers work with their data • Allow researcher to organize data in ways that make sense for what they are trying to do • Enhances sharing and collaboration • Data management for the small labs • Thanks to: Karl Czajkowski, Rob Schuler, Bryce Allen, RachanaAnanthakrishnan, Steve Tuecke

  11. Questions and Open Discussion

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