Metadata Training for Staff and Librarians for the New Data Environment

Today’s Task
Part 1: Audiences, current training strategies, cost-effectiveness
Part 2: A taste of the training
“ From Metadata to a Web of Data”
Part 3: Structured feedback session
Can you help us make this better?
DLF Forum, Nov. 2, 2011

Why Are We Doing This?
Increasing frustration with webinars
Not particularly good for anything but introductions
Very few opportunities for interaction or follow-up
One day seminars at various institutions and conferences also seems limited in terms of participation
‘ Older’ model of repeatable workshops (with a group of trainers) is still useful if tweaked
Better opportunities for participation and learning

Goals
Offer direct training for libraries in a format that encourages participatory learning
Building on the successful library workshop model is one option
Encourage other library organizations and conference planners to include training options in their regular meetings
Generally requires members to lobby for workshops, pre-conferences, etc.

Part I: Intro to Metadata
Questions:
Do we have a shared understanding of metadata
What are some of the practical definitions and modes of thinking that you can use in practice?
What is the basis for understanding the technology context of today’s data?

Intro to Metadata
What is metadata?
not: data about data
Instead: Data with a purpose
constructed (human-made, artificial)
constructive (designed for a purpose, not theoretical)
computable (all metadata today will be used by computer applications as well as managed and understood by humans)

Exercise 1: Data With a Purpose
Each group has a book on the table. What metadata is needed for:
A warehouse that will ship books to bookstores
A brick-and-mortar bookstore that orders books, displays and sells them
An online bookstore that will take orders and ship books to customers
Look over your lists—it will cost you $1 for every metadata field you create. If you use this field in your operation, you get back the $1
Have you changed your mind?

Part II: Understanding DATA
Goals:
Understand the difference between data and text by thinking about computability
Learn some basic data types
Recognize data types in library data

Standard Data Types
Text – ‘text’ (we know this one!)
Defined data types:
Date (& time)
Currency
Numbers (integers, etc.)
Controlled lists: finite sets of values to use
Languages (ISO)
Countries (ISO)

Why Data?
Enables machine processing of amounts of data too large for humans to grasp (which is just about all of our information)
processing across patron files, or bibliographic database
processing on retrieved sets (e.g. extracting facets)
Enables libraries to move beyond ‘artisanal metadata’ towards more efficient and cost-effective assignment of tasks to humans and machines
Comes with new sources of data and new collaborations

Data Use Examples
Making decisions
If user for more than 5 years, then …
If book height greater than x, then …
Making connections
These books have the same author
These books have the same (or similar topic)
These CDs have the same orchestra
This place of publication has lat/long info and can be located on a map

Things: What Your Metadata Talks About
Book
Author
Place
Person (in subject)
Historical period
All of these exist outside your metadata, and are independent of it
You can talk about these ‘things’ in many different contexts
If you assign them identifiers that can be shared with others, then you have a ‘thing’ or entity
Things become points of connection between metadata descriptions (e.g., all books by the same author)

Strings: Limited Connections
Metadata statements using strings don’t represent (to machines) something outside the metadata
They aren’t linkable to other things or strings
They often can’t be effectively parsed by machines
Transcribed data in traditional library metadata is often ‘strings’
Titles are good examples
Some strings are intended to identify something else (controlled author names, for instance) but may be used for display as well

Exercise 2: Things & Strings
Start with a simple file
Each group has a ‘record’ (BBC, etc.—not MARC)
A general description is provided of the purpose of the data
Tasks:
Pick out the strings and things in your example
Bonus points: any data types?
Reporting by groups and discussion

Identifiers
Uniquely identify a variety of resources
On the web they use http and domain names
Advantages
Language independent
Display independent
Unambiguous
Usage should be oriented towards machines, hidden from humans
Humans have different requirements

Identifiers: What They Identify
Easier to attach an identifier than understand what it actually identifies
ISBN – identifies publisher’s product
LCCN – identifies LC-created metadata; = ISBN even though may have very similar metadata to publisher’s
DOI – identifies item in DOI system, but may link to a general sales page

Identifiers must …
Be unique within a domain (private db; web)
Be consistent (identifier must always ID the same thing; DO NOT RE-USE!)
Be persistent (must live as long as thing it identifies)
Be in a standard format

Note on “Consistent”
The same thing may have more than one identifier – this happens naturally in the creation of metadata. It ’s not a huge problem as long as you have a way of saying that:
A = B
… so that you can bring together the identifiers for the same thing. (cf. VIAF; also xISBN)
This is the basis for mapping between vocabularies so that metadata can be more easily re-used

Identifier Readability
Opaque: no meaning to the identifier, ex.: LCCN example (just a number)
Readable: makes sense to a human, ex.: Wikipedia page IDs (include page name or partial page name)
Can be both: system can add readable bit to opaque identifier, ex.: Open Library thing IDs
Choices here are controversial, and have a big impact on multilingual efforts

The Open World Assumption
“ The open world assumption (OWA) is used in knowledge representation to codify the informal notion that in general no single agent or observer has complete knowledge, and therefore cannot make the closed world assumption.”
--Wikipedia

Things with relationships to other things Thing Thing Relationship DLF Forum, Nov. 2, 2011

Things with relationships to other things Thing Thing Relationship Subject Predicate (verb) Object DLF Forum, Nov. 2, 2011

object can be URI or "string" URI is a thing some examples: book -- has author – [lcname#] book -- has author -- "John Doe" Subject and Predicate Must be URIs DLF Forum, Nov. 2, 2011

[diagram that shows this -- i have a slide]

Triples or Graphs?
Machines work with triples
Statements about the same thing have the same subject
Graphs are easier for humans to understand
In libraries we’re not used to visualizing data as graphs
More used to databases, files, hierarches
Making this new world work for us is as much about changing how we think as it is changing what we do

DLF Forum, Nov. 2, 2011 http://milicicvuk.com/blog/2011/10/04/the-web-is-just-a-bunch-of-trees-plus-shorcuts/ “ Graph Thinking” Graph relationships are different than tree relationships …

Exercise 3: Statements
Present a set of triples and ask participants to turn them into sentences
Ex.: Book has title ‘Moby Dick’
Ex.: Book has author [lcna] or ‘Herman Melville’
Ex.: Author has death date XXXX
Suggest participants try drawing graphs to represent statements with the same subject
Suggest that participants represent how ‘strings’ create dead ends and ‘things’ can be linked

Exercise 4: Statements
Give each group a web page with a description
Ask them to organize the data as statements
See if the site you are using has data for persons, subjects or places
Discussion
How hard was it to find the ‘things’?
Did you always have the predicates you needed?
How different is this from today’s metadata?

Properties and Classes
Record-based metadata is often in the form of ‘records’, using elements from only one schema
Statement-based metadata is often more flexible
Proper declaration, definition and management of the elements is very important
Mix and match is part of the value
Some current schemas might find the transition to from records to statements more challenging
Especially where the definition of the property depends on its place in a hierarchy (MODS and ONIX for example)

Hierarchy (top-down organization) A  Military Assets  Dogs ≠ B  Pets  Dogs DLF Forum, Nov. 2, 2011 A B Military assets Pets Guns Dogs Cats Dogs

Caveats
Unless … there is a definition of dog and it can be used in either hierarchy
But if the meaning is defined by the hierarchy, the hierarchy is part of its meaning

Bottom-up organization “ Dogs” has meaning on its own, and can be used in multiple contexts. DLF Forum, Nov. 2, 2011 Dogs Military assets Pets

Exercise 6: Mix & Match
Each group is assigned an entity to describe in metadata
Around the room are poster-sized depictions of various vocabularies and their definitions
Groups are instructed to study their task, determine what elements they need, then get up and look at the posters
Getting up and contemplating the posters encourages conversation!
Discussion: How do you decide what’s fit for purpose?

Overview of Training Plan DLF Forum, Nov. 2, 2011

Feedback
Important questions as we continue to build this program
Does the program plan seem useful? If not, what’s missing?
Does the content of the session seem at an appropriate level? What could be improved?
What advice can you give about bringing this program to libraries?
Is there a place for F2F training in your budgets?
Would you pay for personalized online training for staff or local trainers?

Slide Credits:
Karen Coyle
Diane Hillmann
Contact info: [email_address]
Metadata Matters: http://managemetadata.com/blog

Metadata Training for Staff and Librarians for the New Data Environment

by Diane I. Hillmann

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Metadata Training for Staff and Librarians for the New Data Environment Presentation Transcript

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