2019 ESIP Summer Meeting

This workshop is structured to provide Earth scientists and practitioners with an authentic experience in making use of current cloud computing resources and related tools and machine learning services available. Participants should bring their own computers and plan on working through a use case and complete some data analysis on the cloud.

10:15 Introduction

• Why and when should we use the cloud? 
• Who is / are AWS? 
• How do we use the cloud?

10:35  Storing data in the cloud

• What are the three primary ways of talking to the cloud? 
• What are the main activities supported by cloud consoles?

11:05  Doing computations in the cloud

• What can we use a cloud machine for?

View Session Recording on YouTube.

Session Take-Aways

1. Clouding computing is very powerful, yet complicated to set up even with people who are familiar with the tools. Provides insight into the world of cloud computing - lots of good concepts and vocab.
2. We need to consider security issues when setting up VMs, particularly when dealing with controlled networks like at government institutions.
3. Amazon Machine Images (AMI) can be a useful tool for reproducibility of instances in the AMS structure. This produces a snapshot of an instance in time so that parameters can be replicated.
   
   
   

In the fourth installment of the Metadata Improvement Lab, participants will utilize Python, XSL, and Jupyter Notebooks to determine if metadata collections contain the concepts needed to be FAIR. Participants will be able to utilize their own metadata, regardless of standard or choose from many sample collections from ESIP member organizations. Participants can load as many metadata collections as they would like to compare.

No coding experience will be needed, though a basic understanding of XML will be helpful. A step by step set up for using Google Collaboratory, a Jupyter based web accessible computational environment, will be given. Participants will only need a Google account and a connected web browser to access and run the repository which will allow them to create a shape visualization that describes the fitness of their metadata’s FAIRness. No changes will be made to the device or account used. Participants may also import the workshop repository into their own Jupyter environment.

Since there are many ideas of what it means to be FAIR, this workshop will allow participants to work together or on their own to create a recommendation using Google Docs to facilitate collaboration. During the workshop we will discuss a draft of what FAIR means for EML producing membernodes that was compiled during a workshop this March at DataONE. The Documentation Cluster has built many wiki pages containing recommendations and the XPaths needed in many popular metadata standards, which will aid in the creation of a FAIR recommendation that works for the many standards used throughout ESIP’s member organizations.

The recommendation will then be applied to the collections that participants have chosen to analyze. The workshop framework is highly portable and reusable, even including the generation of the raw data needed to evaluate the content of the metadata, though only the structure of documents will be utilized in this workshop. A report on the outcomes of the analysis will be created as a sharable Google Sheet. The report generated allows for comparison of collections, so that improvement can be measured, documented and visualized.

Presenter: Sean Gordon
Talk Title: Metadata Improvement Lab at ESIP 4: Visualizing FAIRness
Slides: https://doi.org/10.6084/m9.figshare.9273179

View the Recording on YouTube