Code4Lib Montreal meeting notes 20181023 - Revision history

Dbs: Correct a name

2018-10-25T13:02:40Z

Correct a name

Dbs: Formatting for Tim's presentation

2018-10-25T00:33:14Z

Formatting for Tim's presentation

Dbs: First stab at notes

2018-10-25T00:30:49Z

First stab at notes

New page

= Code4Lib Montreal 2018-10-23 =

== Attendees ==

* Chris Trudeau - recent McGill SIS graduate
* Martin ?? - Health Sciences liaison, McGill
* Stephana Bretweiser - CCA
* Tim Walsh - Digital Preservation librarian, Concordia
* John ?? - Digital Archivist, Concordia
* Clara Turp - Metadata Analyst Librarian, McGill
* Jessica Reeve - Senior Electronic Resources
* Tomasz Langenbauer - Digital Projects, Concordia
* Rebecca Nicholson - Web group, McGill
* Eka Grigorik - Web Librarian, McGill
* Dan Scott - Systems librarian, Laurentian / McGill student

== Mandat du groupe et description / Group's description and mandate ==

Brief discussion about what the mandate of the group should be:

* Learning about technology and coding through doing; workshops
* Building a community - across universities, colleges, public institutions in Montreal
* Informal

We like https://code4lib.org/about

* '''Action''' Clara will customize the Code4Lib statement, ensuring it reflects a Montreal & bilingual context

== Presentations ==

Sarah Severson: sick, will present conference report from DLF next time

=== Chris Trudeau: citations to reserves ===

Idea: instead of faculty emailing the library with their individual requests for items that need to be placed on reserve, why not extract the citations from the course outline / syllabus (in PDF or Word format) and automatically generate reserve requests?

==== Feedback ====

* McGill used to have faculty upload syllabi, but eventually stopped because of resistance ("private information")
* McGill accepts reserve requests in any format: email, in person, paper
* Tomasz built something like this for Concordia in 2009 and is willing to share it; but faculty wanted the ability to submit the entire syllabus; or paste in a full citation; or fill out the parts field-by-field

=== Tim Walsh, Bulk Reviewer ===
Identifies, reviews, and removes sensitive files in disk images and directories, regardless of file format
Sensitive info - SSN, credit card numbers, phone numbers, email addresses, internet history, EXIF metadata, GPS data, custom search terms, Windows registry (program install history)
Built using Django, Vue.js, bulk_extractor, DFXML, and Docker
bulk_extractor generates text files or a SQLite database that normally gets processed into a histogram; this processes the data to instead support a Web browser front end and identify the individual files that may be problematic
Problems:
Many false positives (e.g. all 9 digit numbers are identified as SSNs); Tim isn't sure any of these tools have a high level of confidence
Tooling is all American-based, so adding something like a SIN requires C++ (Tomasz is willing to help!)

== Next meeting ==

* November - Sarah and John to present
* Mid-December - social

@@ Line 12: / Line 12: @@
 * Tomasz Langenbauer - Digital Projects, Concordia
 * Rebecca Nicholson - Web group, McGill
-* Eka Grigorik - Web Librarian, McGill
+* Eka Grguric - Web Librarian, McGill
 * Dan Scott - Systems librarian, Laurentian / McGill student

@@ Line 42: / Line 42: @@
 === Tim Walsh, Bulk Reviewer ===
 Identifies, reviews, and removes sensitive files in disk images and directories, regardless of file format
-Sensitive info - SSN, credit card numbers, phone numbers, email addresses, internet history, EXIF metadata, GPS data, custom search terms, Windows registry (program install history)
+This is a project Tim started working on while a Harvard Fellow over the summer; the idea is to use forensics tools for the power of archives. Requires identifying individual files accurately rather than the broader-based "yeah it looks like there are credit card numbers on this hard drive" approach that forensics are interested in.
 Built using Django, Vue.js, bulk_extractor, DFXML, and Docker
-bulk_extractor generates text files or a SQLite database that normally gets processed into a histogram; this processes the data to instead support a Web browser front end and identify the individual files that may be problematic
+* Identifies, reviews, and removes sensitive files in disk images and directories, regardless of file format
-Problems:
+* Sensitive info - SSN, credit card numbers, phone numbers, email addresses, internet history, EXIF metadata, GPS data, custom search terms, Windows registry (program install history)
-Many false positives (e.g. all 9 digit numbers are identified as SSNs); Tim isn't sure any of these tools have a high level of confidence
+* Built using Django, Vue.js, bulk_extractor, DFXML, and Docker
-Tooling is all American-based, so adding something like a SIN requires C++ (Tomasz is willing to help!)
+* bulk_extractor generates text files or a SQLite database that normally gets processed into a histogram; this processes the data to instead support a Web browser front end and identify the individual files that may be problematic
 == Next meeting ==