Practically FAIR 2023 Workshop

FAIR Data Principles have emerged as a convenient way to describe the practice of offering data sufficiently publicly available and in usable enough formats that others can use it to either confirm research results or for new investigations. While these principles offer good guidance for data, they are silent on how to achieve these goals in systems that use data. Further, not all data categories are covered omitting in particular HPC data sets and non-data digital assets in general.

FAIR Data Principles seek to address Findability, Accessibility, Interoperability, and Reuse of digital assets. While this seems straightforward on the surface, delving a little deeper reveals challenges. For example, is the metadata provided meaningful for anyone beyond the original data providers? How do we tell if a particular copy of the data is authentic or if it has been tampered with? Is the data format readable by standard tools and in chunk sizes reasonable for science yet workable for tools on some platform? How do I describe data in a new domain without agreed upon metadata? For derived data sets from massive sources too large to access generally, how do we describe it such that others could generate the same subsets?

These kind of questions offer insights into the practical coding/software engineering aspects of adopting FAIR. This workshops seeks new contributions working to answer these and other similar questions as well as offering experiences about adopting FAIR within an application to help others see potential hurdles and a way it was crossed successfully.

The application domains targeted by this workshop runs the gamut from small, embedded systems all the way up to the largest scale out and scale up data and workflows. Each application has lessons that may offer help for other applications at wildly different scales.

Agenda

9:00-9:05 Welcome and Introduction

9:05-10:05 Keynote: "FAIR enabling re-use of data-intensive workflows and scientific reproducibility", Dr. Line Pouchard, Brookhaven National Laboratory, US Department of Energy

10:05-10:30 Fadoua Rafii, Horacio Gonzalez-Velez and Adriana E. Chis, "Automatic FAIR Provenance Collection and Visualization for Time Series" 

10:30-11:00 Break

11:00-11:30 Invited talk: Daniel Pedro de Jesus Faria, "FAIR data and the biological sciences"

11:30-12:30 Open discussion