31 March 2025 1700 UTC

[ktoddbrown]

Agenda

These calls are lead by @ktoddbrown and are generally open calls.
Please contact them for call in information.
The purpose of these meetings are to move forward current issues related to the development of Soil-DRaH.
This may include teaching, mutual accountability, code review, paired programming, or other activities.

(10 | 1700) Check ins

• Who is here https://github.com/ktoddbrown/SoilDRaH/wiki/2025-Meeting-Roll-call
• Note taking?
• How are you showing up? https://feelingswheel.com/

(15 | 1710) Formulate: Data Rescue

Given the scope below, what are possible ways we could address this? While we have one approach we are developing we can change or alter course.

Context: Data rescue is the transformation of information from a non-machine readable format to a machine readable one. This can include scanning paper copies of reports or records, transcribing scanned records into flat text documents, or extracting x-y values from a scanned graph. Generally this stage should be as close to the original information as practical for specific purpose, and should be extendable for future purpose. Information here may be primary or meta data. This stage must maintain provenance of the data including links to the original source, how the data was made machine readable, what data was not rescued, and who did this work.

Round: Thinking expansively, how could we address this?

(15 | 1725) Scoping: Data Curation

Context: Data curating is the process of taking data recorded for one purpose and making it fit for a second. This can include: unit conversions, derivative calculations, gap-filling, outlier detection and removal, discarding irrelevant (for purpose) data, and often transforming data from a long intermediate format to a wide set of data tables. This stage should maintain transparency so that it is clear where the data came from and how it was transformed into the final product.

• Solo: Take 3-5 minutes and review the wiki.
• Round: Is the context complete and good enough to build documentation off of? Is there additional scope that is not captured in this context?
• Round: What task is coming out of this item? Who can take this on?

(10 | 1740) Issues checkins and stuck to unstuck

• What are you working on and how can we help?

(5 | 1750) Check outs [appreciations, discoveries, and ToDo]

[vaasukimarupaka]

Agenda

These calls are lead by @ktoddbrown and are generally open calls.
Please contact them for call in information.
The purpose of these meetings are to move forward current issues related to the development of Soil-DRaH.
This may include teaching, mutual accountability, code review, paired programming, or other activities.

(10 | 1700) Check ins

• Who is here https://github.com/ktoddbrown/SoilDRaH/wiki/2025-Meeting-Roll-call
• Note taking?
• How are you showing up? https://feelingswheel.com/

(15 | 1710) Formulate: Data Rescue

Given the scope below, what are possible ways we could address this? While we have one approach we are developing we can change or alter course.

Context: Data rescue is the transformation of information from a non-machine readable format to a machine readable one. This can include scanning paper copies of reports or records, transcribing scanned records into flat text documents, or extracting x-y values from a scanned graph. Generally this stage should be as close to the original information as practical for specific purpose, and should be extendable for future purpose. Information here may be primary or meta data. This stage must maintain provenance of the data including links to the original source, how the data was made machine readable, what data was not rescued, and who did this work.

Round: Thinking expansively, how could we address this?

• Scientific and survey literature
  • Capture the methods section as a markdown
  • capture tables as csv and figures as x-y
  • modify tables as needed to make ‘regular’ tables
• Local knowledge
  • Community language on soils (what they knew about soils)
  • Interviews? Focus groups from soils community
  • How to combine LCA (life cycle assessment) with environmental assessment centered on local practice?
• Data discovery
  • How do we find data? How do we determine license and use rights?
  • Ontology and semantics structures to connect data synthesis efforts
• Data provenance
  • How to trace datum back to orginal source
  • How to develope presentation and documentation of province to end user (Shiney apps)
• What is the larger scope of this workflow?
  • Strategy to support data harmonization
  • Using this workflow as a prototype
    • Rescue, harmonization, and curation
• How to connect data rescue to data harmonization?
  • Documentation of pre to post-processing steps
  • How do we connect across different data tables?

(15 | 1725) Scoping: Data Curation

Context: Data curating is the process of taking data recorded for one purpose and making it fit for a second. This can include: unit conversions, derivative calculations, gap-filling, outlier detection and removal, discarding irrelevant (for purpose) data, and often transforming data from a long intermediate format to a wide set of data tables. This stage should maintain transparency so that it is clear where the data came from and how it was transformed into the final product.

• Solo: Take 3-5 minutes and review the wiki.
• Round: Is the context complete and good enough to build documentation off of? Is there additional scope that is not captured in this context?
• Round: What task is coming out of this item? Who can take this on?

Notes:

• Seperating scope from harmonization and curation
• Building the data set/data product and also the workflow as stand alone pieces.
• How to do and how to use this product? - undergrads, public sector workforce ; developing a user manual
• Keeping up with consistency and maintenance
• Gate keeping the publicly available data
  • It is generally difficult to monitor and track the usage of data that is publicly available (tapping into data governance here!)
  • https://localcontexts.org/
• We do not have a proper data governance rules established with regard to community supported and culturally sensitive data and practices.

(10 | 1740) Issues checkins and stuck to unstuck

• What are you working on and how can we help?

• Going forward updating the wiki page with more details on the three steps; rescue, harmonization, and curation steps
• First round of rescue with Hawaii data is almost close to completion; will pass on that rescue work to the contributors for data annotations, if any.

(5 | 1750) Check outs [appreciations, discoveries, and ToDo]