# Presentation: 908312

## Metadata and Digital Library Development

- Introduction:
- Background, Goals,
- and Course Outline
- Course design: David Ruddy
- Cornell University Library

- 1

**Notes:**
Suggested activities for getting started, before beginning slide content:
Introduce instructors.
Ask each participant to introduce him/herself, identifying his/her institution and position, and also, why they are there—saying a little about the connection between their job and interest in this course. You should be on the lookout here for any expectations that are not appropriate to this course—such as a survey of metadata schemes, or instructions on choosing the right metadata scheme for a particular project. Make this point when you discuss slide 2 or 3.
Go over with participants the course contents:
Table of contents
Content slides, six sessions
Glossary
Selected bibliography
Exercises 1-6

## Cat21 Series Objectives

- To equip catalogers to deal with new types of resources and to recognize their unique characteristics
- To equip catalogers to evaluate competing approaches to and standards for providing access to resources
- To equip catalogers to think creatively and work collaboratively with others inside and outside their home institutions
- To ensure that catalogers have a broad enough understanding of the current environment to be able to make their local efforts compatible and interoperable with other efforts
- To prepare catalogers to be comfortable with ambiguity and being less than perfect
- To enable practicing catalogers to put themselves into the emerging digital information environment and to continue to play a significant role in shaping library services

- 2

**Notes:**
This helps situate this particular workshop within the broader Cat21 goals. 

Some comments or description of other Cat21 courses (e.g., Course 2 and 5) may be appropriate.

## Goals for this Course

- Understand digital library design and development processes
- Explore the relationship between data/metadata and system functionality
- Understand the motivations and strategies for metadata conversion
- Understand metadata workflow design
- Appreciate the role of metadata and metadata work in digital library development projects
- Recognize the role and contributions of the metadata specialist on a digital library project team

- 3

**Notes:**
This course takes the perspective that digital library design and development is a process, rather than a recipe that can be followed. 

Within that process, this workshop explores the important role of metadata, and the roles and responsibilities of the metadata specialist. Most of the exercises will ask participants to assume the role of the metadata specialist on a larger digital library team project.

Depending on what you heard from participants during introductions, regarding their expectations, you may want to say something about what this course does not explicitly cover. You can do that by referring to other Cat21 courses that cover topics like a survey of metadata schemes (and when to use them), and project management:
Course 2: Metadata Standards and Applications
Course 5: Project Management for the 21st Century

This course will use different metadata schemes in its examples and exercises (MARC, DC, EAD, VRA, etc.), but it is not focused on these schemes. The focus of this workshop is on the points made above—that digital library development is a process and that the metadata specialist has an important role in that process. This workshop tries to give participants a working sense of that process and the role of metadata and the metadata specialist in it.

## Outline of this Course

- Session 1: Digital Library System Objectives,
-      Functionality, and Metadata
- Session 2: Understanding Functional Requirements
- Session 3: Metadata and Functionality
- Session 4: Metadata Conversion: Enhancement and Mapping
- Session 5: Metadata Workflows
- Session 6: Digital Library Development Project Exercise

- 4

**Notes:**
Each session, except the last, has some introductory presentation and discussion, followed by, or intertwined with, exercises. The last session, number 6, is entirely an exercise.
This course takes an exercise-based approach, relying a lot on group exercises and subsequent discussion. The assumption here is that adults learn by participating rather than merely listening.

## Metadata and Digital Library Development

- Session 1:
- Introduction to Digital Library System Objectives, Functionality, and Metadata

- 1

## Goals of Session

- Understand the relationship between system objectives and metadata
- Examine the objectives of the library bibliographic system and how those objectives impact system metadata
- Explore the connection between digital library systems and digital library metadata
- Underscore the importance of system objectives when working with metadata

- 2

**Notes:**
This is an introductory session. It’s meant to make the point that system metadata are intimately connected to system objectives. It starts with something familiar, the library bibliographic system, with the goal of remembering how system objectives have shaped our cataloging rules—we catalog in certain ways in order to support certain objectives. With digital library systems, the same connection applies—the metadata we create and collect, and the form we put it in, will be guided by the objectives the system is meant to achieve. One big difference is that the objectives for digital libraries systems vary enormously, just as the systems themselves vary.

## The Library Catalog

- Why do we describe library materials in the way we do?
- Why do we catalog in the way that we do?
- Why do we assemble certain information (metadata) about library materials, and record this metadata in such a highly defined way?

- 3

**Notes:**
The point here is that there’s a reason for why catalogers do what they do. It’s not to follow rules for rules sake, but because the rules support larger objectives. Sometimes those objectives are general (support known-item discovery), and sometimes they are more specific (present a alphabetized browse list of titles). Library bibliographic cataloging has become codified, and we sometimes forget the connection between objectives and metadata practices.
What follows is an absurdly short intellectual history outlining various proposals regarding the objectives of bibliographic systems. The point is less any one particular proposal and more just to remind people that this kind of articulation of system objectives did go on, and is going on, in the library world and that it is a necessary activity before anyone can build a particular system.
In the following slides, discussion beyond this main point probably isn’t necessary. If you want more discussion, a number of discussion points are listed. It’s important to get to the exercises and have plenty of time for them.

## Cutter (1876)

- Objectives of a bibliographic system
- To enable a user to find a book if the author, title, or subject is known
- To show what the library has by a given author, on a given subject, or of a given kind
- To assist in the choice of a book based on its edition (bibliographically) or its character (literary or topical)

- 4

**Notes:**
Cutter seems to be generally recognized as the first to explicitly articulate the objectives of a bibliographic system.
The objectives or functions of a library bibliographic system are going to impact what data we need to collect about the materials in our library, and the way we record that data.

## IFLA (1998)

- To find entities that correspond to the user’s stated search criteria
- To identify an entity
- To select an entity that is appropriate to the user’s needs
- To acquire or obtain access to an entity described

- 5

**Notes:**
These are all prefaced by “To enable a user to...”
This is quite a leap, in time, from Cutter. You can ask why the object is now an “entity” rather than a “book” (likely because we’re into the world of multiple format resources and electronic databases).
Select is Cutter’s “choose” objective.
IFLA seems to collapse “find” and “collocate” objectives (separated by Svenonius). IFLA’s “find” applies both to a known entity or to a set of entities.
On the other hand, IFLA’s well-known distinction among entities (work, expression, manifestation, and item), would allow for many collocating objectives to be met by “finding” a work, an expression, or a manifestation.

## Svenonius (2000)

- To locate
- Known entity
- Set of entities
- To identify an entity
- To select an appropriate entity
- To acquire or obtain access to an entity
- To navigate a bibliographic database

- 6

**Notes:**
Builds on IFLA. Again, “To enable a user to...”
Alters IFLA’s “find” objective to distinguish between finding a known entity and finding a set of entities that meet some criteria (collocation).
A point that could be made here is the addition of a new objective: navigation. This is perhaps in recognition that many discovery operations are now taking place via electronic databases, which can utilize entity relationship information much more effectively than analog finding tools. In that sense, she is proposing an objective that technology has made feasible.

## Exercise 1-A

- How does MARC metadata support the objectives of the Library Bibliographic System?
- To find/locate
- To identify
- To select
- To acquire
- What other system objectives can we detect from the system’s metadata?

- 7

**Notes:**
For the exercise, see the trainer notes.
The objectives listed here are just a summary of the previous 3 slides, useful to have in front of us when looking at the MARC records.

## The Library Bibliographic System

- System objectives have led to specific practices in bibliographic description
- Standards such as AACR2
- Uniform record creation is required by global bibliographic databases
- Standard record formats (MARC21)
- Desired functionality requires precise cataloging rules and conventions

- 8

**Notes:**
A summary of what we’ve been discussing. 
System objectives and system data are intimately tied together. In library bibliographic systems, we see how very precise cataloging rules and practices have developed to support specific system objectives. This has taken years to develop. 

## Exercise 1-B

- Exercise 1-B is XML encoded metadata used by some type of digital information system
- What system objectives can we detect by examining this system’s metadata?

- 9

**Notes:**
See the trainers notes to the exercises.

## Digital Library Systems

- No agreed upon definition or objectives
- No agreed upon standards or formats
- Very little interoperability
- A huge number of players, many of whom are not librarians
- What is a “Digital Library,” anyway?
- Digital (Electronic) Information Systems?

- 10

**Notes:**
There is no one definition of a “digital library” or “digital library system,” and definitions proposed have been rapidly evolving since first used in the 1990s. See the Glossary. You can also suggest a Google search. 

Since digital library systems and their objectives vary so greatly, the metadata required to meet those objectives will necessarily vary greatly. There are no agreed upon record formats, record structures, element sets, etc., for digital libraries; and certainly no agreed upon content standards.
There is very little interoperability among systems, as a consequence of so few shared standards. And thus not much possibility of sharing records among systems (such as proposed and made possible by OAI). 
Huge number of players: the business opportunities of the internet have given rise to a lot more commercially driven information systems. Lots of competition for libraries.
Perhaps a broader term, without “library” in it, is more useful and raises fewer questions?  A term such as electronic/digital information system. Are we trying to claim some special status for the library’s information systems by calling them “library” systems? On the other hand, does “library” imply certain values and objectives that are not necessarily part of general electronic information systems, such as bibliographic control and long-term retention of data.

## Digital Library Systems

- A different world from the Library Bibliographic System, but not an alternate universe
- Digital library system development...
- Still requires the articulation of objectives (desired system functionality)
- And those objectives will depend upon certain characteristics of available or generated metadata

- 11

**Notes:**
The connection between functionality and metadata is the same.

The big point of this session, and in some sense the workshop, is this: we can’t really talk about metadata independent of a discussion of system objectives and functional requirements. This point should be made in summing up the second exercise, 1-B (see the trainer notes for exercise 1-B). The point gets emphasized again in Session 2.

## Digital Library System Objectives

- To support...
- Discovery
- Navigation
- Presentation, display
- Access control
- Administration, management
- Preservation
- Others?

- 12

**Notes:**
The objectives listed should sound familiar to librarians.
This is a functional view of metadata. These functions can be associated with types of metadata (discussed in the next session):
Discovery: descriptive metadata
Navigation (of an object and its components): structural metadata
Access control: rights metadata

Other objectives that a digital library might have, besides those listed? Someone will probably come up with some. Here are some possibilities, if no one thinks of any others. 
Interoperability
Certification (of document authenticity)
Status (or perhaps that’s under admin/management metadata, depending on how that’s defined)
Version control

Could think about different contexts: libraries within archives, museums, schools, businesses. And whether different contexts might generate different objectives.

## System Objectives?

- Who decides on the objectives of the digital library system?
- Who decides what functionality to support?
- Who are the players or stakeholders on digital library projects?

- 13

**Notes:**
These are a bit rhetorical. We’ve said that much will depend on objectives, and so it’s logical to ask who comes up with the objectives. The next slide has some answers. If you want interaction, you could ask for ideas before moving to the next slide.

## Digital Library Projects

- Digital library stakeholders:
- Project “owner”
- Project manager
- Subject specialist
- System developer/programmer
- Metadata specialist
- Library administrator/manager
- End-users
- Others?

- 14

**Notes:**
Starts making the point that in libraries, digital projects typically are carried out by a team of people, coming from different areas and bringing different expertise.
Project owner might be faculty member coming to the library with a project, or a librarian that has received grant money for a project.
The project manager might be the “owner”, but might also be assigned by administration, depending on the project.
This workshop will focus a lot on the role of the metadata specialist. 
You can ask about “end users”. How many have been involved in projects where the end user was actually a part of the system design and implementation process? We have typically had end users represented in this process by subject specialists or others, who “speak for” the end user. Evaluation and assessment procedures may actually bring end user feedback into the design process. Those adherents to “user centered design” will claim that the end user must be included earlier in the process.
Someone will think of other stakeholders. Possibilities:
Library digital preservation officer or others charged with ensuring that digital content meets some preservation requirements or standards. Or is the metadata specialist responsible for this? This will depend on the organization.
Interface specialist, or library interface police? Good interface design is essential and the project may be able to draw on those that specialize in this, or an interface specialist may routinely be assigned to web-based projects. The same person or others may also have responsibilities for maintaining a unified web presence to the public—a unified look-and-feel to all the libraries public pages.

## System Objectives?

- How do the stakeholders decide on system objectives?
- How is system functionality developed?
- What’s the process by which decisions are reached?

- 15

**Notes:**
So, we have all the stakeholders. How will they come up with a set of objectives?
That’s what we’ll discuss in Session II.

## Metadata and Digital Library Development

- Session 2:
- Understanding Functional Requirements

- 1

## Goals of Session

- Understand functional requirements and their usefulness
- Recognize how functional requirements inform system metadata decisions
- Understand “use cases” and how they define and record functional requirements
- Learn how a use case should be “read” by a metadata specialist

- 2

**Notes:**
General goal is to demystify “functional requirements,” and give people a flavor of what is meant by that term. In the end, functional requirements are pretty simple. Generating them can be a complex process and may require certain expertise, but the actual requirements, and their expression, should be straightforward and easy to understand. 

## Functional Requirements (1)

- What are functional requirements?
- In this context, functional requirements are those of a software system, not of bibliographic records (FRBR)
- A more specific and detailed description of system objectives
- They describe and define specific, required system behaviors
- Ideally, they are developed through a requirements analysis process
- They guide system implementation and programming work

- 3

## Functional Requirements (2)

- How do project stakeholders develop functional requirements?
- Ideally, system designers use some reasonably formal design process
- Examples of design processes:
- Rational Unified Process (RUP)
- User Centered Design
- Agile Software Development

- 4

**Notes:**
Each of these example design processes are referenced in the bibliography. The point isn’t on any one of these, but just that such design and process technologies exist. Those charged with developing functional requirements will want to investigate. 
The processes listed here are not mutually exclusive. Agile can and does use Unified Process concepts and methods. User Centered is almost a philosophy, but it does tend to have certain methodologies (user observations, interviews, etc.). A Unified Process approach can be carried out employing very User Centered methodologies.

## Software Design Processes

- Systematic methods for generating and defining functional requirements
- Different design processes emphasize different methodologies, but there are often many similarities among them
- Most processes employ “use cases,” though they may exploit different methods to generate and develop them

- 5

**Notes:**
A design process isn’t purely technical, and it’s not magic. Software design, including requirements analysis, is a social activity, and thus involves a negotiation among stakeholders. This is particularly true when setting priorities. Due to limited time and resources, the system won’t be able to do everything.
This workshop isn’t about software development, so the focus here isn’t software design processes. To give us a more tangible sense of functional requirements, however, we’ll look at “use cases,” which are a fairly typical methodology for recording functional requirements. 

## Use Cases

- Each use case describes a single function of the system
- Each function is an interaction between the system and an external USER
- Each use case describes functionality, but not how that functionality will be accomplished
- The entire system may have dozens or hundreds of use cases
- Taken altogether, the use cases define the system’s functional requirements

- 6

**Notes:**
Each interaction between an external USER and the system will become a use case. USERs are discussed in the next slide. 
One important point about use cases is that they do not explain how the functionality being described will be accomplished. For example, a use case would never stipulate that data should be stored in XML or in a relational database, or that some particular search tool should be used.
Avoiding implementation issues simplifies the requirements phase and the generation of use cases. In precisely describing a functional requirement, it doesn’t matter at this stage how that requirement is met (what programming methods are to be used, what software tools, etc.). Those are later decisions made by software engineers. At this stage, the system is treated as a black box, and the functional requirements only describe the interactions between that box and USERs.

## The USER in a Use Case

- USERs are anything external to the system that will interact with it
- A USER may represent a class of users
- Data entry staff
- System admins
- General public users
- A USER may represent another system
- An OAI harvester

- 7

**Notes:**
Uppercase letters are used for USER to remind us that in use cases, the USER isn’t necessary a person. And if  human at all, the USER is more a class of users, or a role. But the USER can also be another system.
These example USERs all interact with the system (there will be others):
Data entry staff must put data into the system
System admins must install and configure the system.
Public USERs will represent a whole range of possible interactions: searching, browsing, downloading, asking questions, etc. This will depend a lot on what the system is for.
An Open Archives Initiative harvester is a computer somewhere else that can make requests for records from our system. This assumes that one capability of our system is that it is OAI compliant and can respond to OAI requests.
A statistics generation tool, which must interact with the system but is not part of it (it’s external).

## Sample Use Case

- Exercise 2: Sample Use Case
- Typical use case components:
- Priority
- Preconditions
- Flow of Events (scenario)
- Alternative Events (exceptions)
- What in this use case will depend on or impact system metadata?

- 8

**Notes:**
See the trainer notes on the exercises.
Start by looking at the Sample Use Case together, and walk through its typical components.
After we’re comfortable with what the use case is, its components and structure, then we’re going to “read” it as a metadata specialist would need to read it—looking for what will depend upon or impact metadata. On this, see the trainer notes.

## Generating Use Cases

- The design process used will likely guide how use cases are generated
- A typical approach is to enumerate all the possible USERs of the system (everyone and everything that will interact with it), and then list every interaction
- Each of these interactions will become a use case

- 9

**Notes:**
Generating use cases, on one level, is a time-consuming slog. You can invite participants to imagine how they would come up with a use case like the one in Exercise 2. Different design processes will emphasize different methods. For example, a user centered design approach may study actual user search behavior to determine what’s important and what’s not. In any event, the process is never a solo affair—it’s a social negotiation, haggling over what’s important.

You can enumerate USERs by asking:
Who will install the system?
Who will maintain the system?
Who will put data into the system?
What end users will interact with the system?
What other systems will interact with the system? For example, an OAI harvester? Some external search service?

Then you list every way in which these users can interact with the system. Being inclusive at this point is generally encouraged (on this point, see Exercise 2, about Priority). 

## A Complete Set of Use Cases

- Together, they define the functional requirements of the proposed system
- Documented, they form a contract among stakeholders about what the system will do and not do
- Requirements help in the inevitable “panic phase” of a project
- Requirements inform our decisions about metadata, standards, software, vendors...

- 10

**Notes:**
Imagine that you have a document like Exercise 2 for every conceivable feature of the system. Participants should be able to recognize the value of that. 

Functional requirements form a contract among stakeholders about what the system will do, and importantly, what it will not do. This is a critical communication component of a digital library development project. It has a lot to do with managing expectations and getting informed buy-in, sign-off, etc., so that surprises are avoided and successes can be recognized.

Panic phase: the six phases of a project (only half-joking):
Enthusiasm
Disillusionment
Panic
Search for the guilty
Punishment of the innocent
Praise and honors for the non-participants

There are usually always times during a project when things seem out of control, and when original goals have been obscured by current pressing matters. Having documented functional requirements, even if they need some revision, can give everyone some solid ground to return to, to regroup, plan, and move forward. 

## Build or Buy?

- Build or buy decisions are typical in digital library development projects
- Building a digital library system
- Defining one’s own functional requirements
- Hiring programmers to build the system
- Testing, evaluation, maintenance, updates
- Acquiring a pre-built digital library system
- Finding a system with functionality that meets your requirements as nearly as possible

- 11

**Notes:**
“Buying” here may mean “acquiring” Open Source software. Open Source software is software that is typically “free” to download and use, but it may require programming staff to setup, configure, and maintain.

Having well-define requirements is useful not just when developing software but when acquiring existing systems. The more fully your institution has articulated and agreed on a complete set of requirements, the more adequately you can evaluate whether externally developed software is going to meet your needs. If you don’t know what you want, or there’s no agreement on that, then it will be difficult to find a system that ends up being satisfactory.

## Build or Buy

- Both cases require articulating and documenting desired objectives and functionality
- If Build, these will develop into complete use cases
- If Buy, they will be used in the RFP process, and later to evaluate competing systems

- 12

**Notes:**
See notes, last slide.
Also with Buy—many software systems that are acquired can be modified (DSpace, ContentDM, etc.), in order to gain additional functionality or modify existing functionality. So even if you acquire software, your organization may do development work on it.

## Requirements and Metadata

- Certain functional requirements will depend upon or impact system metadata
- The requirements will inform our decisions about system metadata
- What data elements are required
- What content value practices need to be adopted
- Whether metadata standards can or should be used
- If we have existing metadata, requirements will inform our analysis and conversion of it

- 13

**Notes:**
In Exercise 2, we saw the way that requirements will impact and depend upon system metadata. This is a one of the key workshop assumptions. Metadata and metadata decisions do not exist in a vacuum. One of the take-away goals of the workshop is that when participants next hear the question “What metadata standard should we use?”, they will immediately ask “what are the system objectives, what are you trying to achieve?”

The final bullet point works off this key assumption and leads toward the next two sessions. The metadata context we’ve been discussing in this session is the system and its functional requirements. What’s required of the metadata? Can it meet the requirements of the system? To answer those questions and make that connection between requirements and metadata, we’ll need to analyze metadata, to determine what functionality it can support. Then, if it requires alteration, we’ll need to figure out whether and how it can be converted.

## Metadata and Digital Library Development

- Session 3:
- Metadata and Functionality

- 1

**Notes:**
Trainer should have plans for Exercise 3—read the exercise notes. If you want to squeeze in Part II of Exercise 3, you will need to move quickly through the slides, not asking questions as you go but just making the points you want to about each. 

In the typical two-day format for this workshop, this session falls after lunch on the first day. It’s a tough time, no matter what the content.

## Goals of Session

- Review or familiarize ourselves with concepts and vocabulary of metadata assessment and analysis
- Explore the connection between metadata and functionality

- 2

## Metadata Specialist Scenario

- The typical digital library development situation facing the metadata specialist:
- We have some functional requirements to meet, AND we have some metadata
- BUT the metadata must be altered in some way (cleaned-up, augmented, enhanced, mapped...) so that it will meet our requirements

- 3

**Notes:**
This is the typical situation facing the metadata specialist in digital library development. There is usually some metadata, but it requires work to make it effectively support our system objectives—the desired functionality.

It’s rare that your existing metadata perfectly fits what an existing digital library system requires, or that a digital library system developed in-house is tailored precisely for an existing set of metadata. 

## Metadata and Functionality

- In order to match metadata with functionality, we need first to assess, or analyze, our existing metadata
- Then we can begin to evaluate whether our metadata will or won't support particular functionality and how it will need to be converted

- 4

**Notes:**
This just presents the logic for why we’re going to discuss and practice analyzing metadata. The ability to assess, or analyze, metadata is fundamental to making use of metadata. We can’t evaluate whether some set of metadata will meet certain functional requirements, or determine how certain metadata will need to be converted, unless we can effectively analyze the metadata before us.

## Metadata Assessment

- If we look at existing metadata, how do we describe what we observe?
- File Format
- Type of metadata
- Semantics
- Content values
- Structure
- Use
- Status

- 5

**Notes:**
These are some analytic categories for assessing metadata, and we’ll walk through them in subsequent slides.

Important point: The vocabulary here is somewhat arbitrary—that is, there’s no standardized vocabulary for talking about these things. Trainers and participants may have encountered other terms for the same or similar concepts. The emphasis here should be on these underlying concepts and on the process of metadata analysis, not on any particular terminology. If trainers are more comfortable with other terms, they should mention that, by way of making the above point.

## Metadata Analysis: File Format

- File, or data exchange, formats:
- SGML / HTML
- XML / XHTML
- MARC
- “Delimited” plain-text file
- Binary (not plain-text) formats, either open or proprietary

- 6

**Notes:**
The concept here is: How is the metadata encoded, or communicated/conveyed?
This is not the same question as asking, is this a METS or MODS file. Data exchange format is not about the data elements or data values per se, but rather about how they are expressed (encoded). Sort of like asking, is this French or is this English?
There are different ways of expressing, or encoding, the same metadata:
Dublin Core terms and values in a delimited plain-text file (term/value pairs) vs. an XML encoding vs. a proprietary spreadsheet format (MS Excel).
 MARC (binary file) vs. MARCXML (XML file)
A delimited text file means that there’s some structure to the file (see the notes to slide 19 ahead, about structure)—some way to take it apart programmatically.
Technically, SGML and XML files are plain text files—you can open and view them in any common text editor.
On binary files, see notes at slide 9. MARC is a binary file format.

## 7

**Notes:**
XML: We know this is XML because of the XML Declaration, on the first line, which is true of all XML documents.
It’s also a plain-text file; you can open it in any text editor and see all the data. You need special XML capable software to do certain things with an XML file (parsing it, validating it, etc.), but there’s a low-barrier to accessing the information in it.
Both SGML and HTML are similar: text files with angle bracket delimited data.
Advantages of XML: it’s common and thus there’s a wide range of tools available, free and commercial, for working with XML (parsing it, validating it, picking out specific pieces of it, etc.). 

## 8

**Notes:**
Plain-text file (you can read it, no apparent garbage characters). You don’t need any specialized software merely to read the file—any text editor can open the file. We can also observe that it appears to be delimited with “pipe” character (|). The fact that it’s delimited gives it some structure (another analytic category that we’ll get to), and structure will allow some degree of machine processing. You may want to ignore discussion of structure, until you get to that section, up ahead. Then come back to this and some other slides as examples of what we mean by structure.
It’s hard to say what the data really is without knowing. The second column contains page numbers, some encoded in ways to capture non-numbered pages or those with roman numerals. The last two column hold sequencing data.
Main point: data appears to be regularly structured and a programmer could write some software to “process” it. But that software created would likely not be reusable, since the data structure is probably idiosyncratic. 

## 9

**Notes:**
If you look at a PDF file in a text editor, this is what you see. The fact that there are all these “garbage” characters is an indication that it’s a binary file and not plain-text. The garbage characters are instructions to specialized software for how to present the data in the file. 
The file above will not look nice unless you view it with software designed to understand the PDF markup. There is such software, other than that produced by Adobe, because there is a public PDF format specification, making PDF more or less and open format.
But for Microsoft Office files, for example (which would also look like this in a text editor), the format specification is controlled by Microsoft. They may license it or sell it to other software vendors, but they don’t make it publicly available.
Image files, even image files in an open standard, are binary files. Some image files may have “headers” or other sections with some metadata, and it may be possible to extract that metadata.
Point? If your metadata is in binary files, it may be tricky to extract it. There may be software tools that can help, but some customized solution may have to be developed.

## Metadata Analysis: Type

- Types of metadata
- Descriptive
- Structural
- Administrative
- Technical
- Preservation
- Access/Rights

- 10

**Notes:**
“Type” in this sense has to do with the question “what’s the metadata about,” “what is it concerned with.”

These categories aren’t fixed. Some people might see Preservation metadata as a kind of Technical metadata. Administrative metadata could be construed very broadly to include Technical, Preservation, and Rights metadata.

## 11

**Notes:**
Primarily descriptive metadata, some sort of monograph record. The bottom half has what look like page numbers, and thus that would be considered structural metadata (metadata that tells you something about the structure of the object). 
(File format here is plain-text, delimited somewhat by codes which you can sort of figure out; so it’s got some structure, but a peculiar/unique structure—to process would require someone to write a unique parsing tool for. You may want to come back to the structure discussion when you treat structure up ahead.)

## 12

**Notes:**
Example of structural metadata, the structMap section of a METS document. This is different than asking whether the metadata itself has structure! (Which it does, since it’s nested XML.) Again, metadata “type” is about the information convey by the metadata, not how the metadata is conveyed.
The structMap uses <div>s to nest all the various components, pointing to the appropriate files for each component.
There is some descriptive metadata here, but there doesn’t really need to be. They are just labels, intended to make it easier to create a browse list of contents.
(File format here is XML.)

## 13

## Metadata Analysis: Semantics

- Metadata element sets (“schemes”)
- MARC21
- Dublin Core (DC)
- EAD
- MODS
- VRA Core
- METS

- 14

**Notes:**
Standard element sets form a kind of alphabet soup, typical in the library.
These are covered in Course 2.
If data is in XML, it’s usually easy to detect the metadata scheme: look at the “root” element, the element at the top of the file that holds all other element inside of it.
Some of these schemes are bound very closely with a particular data exchange format, to the point where they seem inseparable. For example, METS is expressed in XML; EAD is expressed in SGML or XML; But what we are looking at right here are the data element definitions, which at least in theory can be separated from an exchange syntax. This distinction is much easier to make with DC and VRA, which can be expressed, or encoded, in different file formats.

## 15

**Notes:**
A MODS record; we can tell because the root element identifies it as such.
The file format is XML.

## 16

**Notes:**
A Dublin Core record of same object as the last slide.
(File format is XML.)

## 17

**Notes:**
This is a “source” view of an HTML page. It shows what would not normally be seen within a browser—the HTML meta tags.
The metadata here is Dublin Core, expressed in HTML meta tags, as name/content pairs.
(File format is XML. The format is XHTML, which is HTML that adheres to some basic XML restrictions.)

## Metadata Analysis: Content

- Does the metadata...
- Adhere to any published content standards or best practices?
- AACR2/RDA, EAD Best Practice (RLG), CCO
- Use any known and shared vocabularies?
- LCSH, AAT, TGM
- Adhere to any application profiles?
- Degree of conformance to any employed standards, practices, or vocabularies?

- 18

## Metadata Analysis: Structure

- Structure
- What is the record structure?
- Flat or hierarchical (nesting)
- What relationships are possible? How complex can they be?
- Is element qualification allowed?
- Degree of ambiguity within data?
- General character and complexity
- Simple unstructured
- Simple structured
- Richly structured

- 19

**Notes:**
Structure is a somewhat fuzzy or impressionistic concept, with no fixed distinction between “simple” and “rich” structure.

But it is useful to take note of what structure any particular metadata may have, especially when there isn’t much. Structure allows for machine processing. If the data has no structure, then it will require hand processing to convert it to some other format or scheme.

You can illustrate structure by looking back at these slides (or perhaps you developed the concept of structure as you worked through the slides):
8: plain-text with delimiters. Two structural elements in the file. Line breaks, dividing on line from another. And | delimiters separating parts of a line. Together these allow for a table with rows and columns. That’s useful structure, and can certainly be taken apart and converted to something else fairly easily. Simple structured.
11: also simple structured. This could be machine parsed with simple but customized software.
17: the meta tags make for simple structured data. Simple Dublin Core, however it’s expressed (even as an XML file), is simple structured data; it’s flat, non-hierarchical, few relationships conveyed by the structure.
Examples of richly structured: slides 7, 12, probably 13 and 15.
Simple unstructured: no example, but imagine slide 11 without the codes on the left. It would require a human to understand what the different data elements were.

## Metadata Analysis: Use

- What is, or was, the intended or potential use of this metadata?
- Understanding why metadata was created and how it was used can help tell you what you can expect from it, in terms of consistency, reliability, interoperability...

- 20

**Notes:**
This may require some speculation, or sort of detective analysis.

## Metadata Analysis: Status

- Static vs. dynamic
- Static metadata will not be updated, augmented, etc.—it is essentially “dead”
- Dynamic metadata is “living,” maintained by someone, updated when needed, perhaps regularly supplemented
- This distinction will have an impact on conversion strategies and workflows

- 21

**Notes:**
This distinction (static vs. dynamic) will be picked up in Session 5, on workflows.

## Metadata Analysis Exercise

- Exercise 3: Metadata Analysis
- Metadata assessment
- Functional evaluation of metadata
- Will this metadata support the required functionality?

- 22

## Metadata and Digital Library Development

- Session 4:
- Metadata Conversion: Enhancement and Mapping

- 1

**Notes:**
Trainers could begin by asking whether any participants are involved in any “database enrichment” or “database maintenance” activities within their library. Basically, any efforts to fix, improve, clean-up MARC records in their catalogs.

What this session is about is something very similar, but in the digital library context, where we have less history and fewer defined practices for what we do. In any event, doing this work with the catalog is very useful experience when working with non-MARC and non-MARC systems.

Alternatively, you could wait and make this point at slide 3.

## Goals of Session

- Explore the reasons for converting metadata
- Discuss measures for assessing and ensuring the quality of metadata
- Examine metadata mapping and its purposes
- Learn how to create a metadata map

- 2

## Metadata Conversion

- Two broad categories or types of metadata conversion work:
- Enhancement: cleaning up, adding, expanding, disambiguating, updating metadata
- Mapping: moving metadata from one format to another

- 3

**Notes:**
Enhancement is any kind of record improvement. The point about this being a common tech services activity (see slide 1) could be made here. 
This Session, and the workshop, adopt a fairly simple vocabulary when it comes to mapping:
Mapping is an activity, the one of moving metadata from one format to another
A map is a thing, an instrument of some sort that makes the mapping activity possible. 
The term “crosswalk” is avoided here only to avoid confusion. Many (including some later slides here) would likely equate crosswalk to map, and crosswalking to mapping, but some people will make subtle distinctions among all these terms, none of which seem particularly relevant to the general ideas discussed in this workshop. See the bibliography for more in-depth discussions.

## Why Enhance Metadata?

- To correct inaccuracies
- To achieve consistency
- To improve “quality”
- To fill gaps
- To provide greater or different functionality
- To foster interoperability

- 4

**Notes:**
The first three and the last are covered in the following slides.

## Metadata Accuracy

- <DC_record>
-    <creator>Mitchell, William J.</creator>
-    <creator>Stevenson, Daniel C.</creator>
-    <creator>Schoonover, Regina</creator>
-    <title>Urbanowski, Frank</title>
-    <subject>City of Bits: Space, Place, and the Infobahn</subject>
-    <subject>Electronically mediated environments</subject>
-    <subject>Cyberspace</subject>
-    <type>Urbanism</type>
-    <format>Text</format>
-    <date>text/html</date>
-    <identifier>1995</identifier>
-    <language>http://press.opt.edu/CityOfBits.html</language>
- </DC_record>

- 5

**Notes:**
What’s wrong here? Clearly this record is messed up. It’s inaccurate, as opposed to inconsistent.

We can speculate on what happened. Probably it was produced by a mechanical process, and perhaps that process didn’t expect more than three creators. But we don’t really know.

## Metadata Consistency

- DC records with a <dc:date> element
- Most formatted in full W3C-DTF format (e.g., <dc:date>YYYY-MM-DD</dc:date>),
- except for:
- <dc:date>2000-08</dc:date>
- <dc:date>1996</dc:date>
- <dc:date>July 5, 2001</dc:date>
- <dc:date>2000 Revision</dc:date>
- <dc:date>July 19, 1996</dc:date>
- <dc:date>2001.06.04</dc:date>

- 6

**Notes:**
A simple example of metadata enhancement.
These are not inaccuracies, but illustrate potential inconsistencies, which could create problems in handling and using this metadata. If the metadata is shared, these may become barriers to interoperability.
The first two are not necessarily data inconsistencies. The W3C-DTF format allows for truncated forms: YYYY or YYYY-MM. So this has more to do with being sure the system can handle such truncated forms properly.

## “Quality” Metadata

- “Objects, metadata, and collections must now be viewed not only within the context of the projects that created them but as building blocks that others can reuse, repackage, and build services upon.”
*A Framework of Guidance for Building Good Digital Collections*. 2nd ed.NISO, 2004. _[http://www.niso.org/framework/framework2.html](http://www.niso.org/framework/framework2.html)_

- 7

**Notes:**
In today's digital environment, the context of content is a vast international network of digital materials and services. Objects, metadata and collections should be viewed not only within the context of the projects that created them but as building blocks that others can reuse, repackage, and build services upon. Indicators of goodness correspondingly must now also emphasize factors contributing to interoperability, reusability, persistence, verification and documentation. 
Example: if metadata is too narrowly defined for a particular project, it may not be useable, or easily useable, for other projects. The loss of contextual information, with regard to metadata, is sometimes called the “on a horse” problem. Say you have many photographs, all of Teddy Roosevelt. One of them is of Roosevelt on a horse, and the metadata title that photo is given is “On a horse”. The title has meaning in its immediate context, but only there. If the record of that photo is taken out of that context, the title is of little value. On this, see: Robin Wendler, “The eye of the beholder: Challenges of image description and access at Harvard,” In Diane I. Hillmann and Elaine L. Westbrooks, eds. Metadata in Practice. Chicago: American Library Association, 2004. (Hillmann & Westbrooks is in the Bibliography.)
Metadata “quality” is a topic of Cat21 Course 2. 

## Indicators of Metadata Quality

- Appropriate to the collection, its users, and the use of the objects in it
- Supports interoperability
- Uses standard controlled vocabularies
- States conditions and terms of use
- Possesses the qualities of good objects
- Supports long-term management

- 8

**Notes:**
These points are from the Framework document of the previous slide.

We're not creating metadata in a vacuum. This should be a collaborative process. Digital library development increasingly takes a team approach to implementation.

## Approaches to Interoperability

- Convert to a single metadata scheme
- Allow diverse metadata schemes and map to a common scheme for particular purposes
- For example: access, or sharing metadata
- Use a hybrid approach that involves some uniformity and some mapping

- 9

**Notes:**
Assuming we wish to foster interoperability....

Again, this is largely about thinking outside the immediate context. See “Best Practices for OAI Data Provider Implementations and Shareable Metadata” in the Bibliography.

## Tools for Interoperability

- Metadata standards
- Application profiles
- Community developed best practices
- Community accepted metadata maps (crosswalks)

- 10

## Metadata Mapping

- A formal, repeatable conversion of metadata
- A potentially ongoing or regularly repeated conversion process
- Assumes consistent incoming metadata
- Requires a specification (called a “map” or “crosswalk”) that describes how to convert one metadata scheme format  to another

- 11

## http://www.loc.gov/marc/marc2dc.html

**http://www.loc.gov/marc/marc2dc.html**

- 12

**Notes:**
A good example of a metadata map.

Note, this maps from MARC to simple DC, not the other way around. That is typical of simple DC mapping, moving from a richer data format to the simpler (DC).

You can also note that this is only a start. To actually implement such a mapping at the local level would require discussion of strategies and many detailed decisions. For example, in mapping the 245 field into Title, do we include subfield c? There are many such decisions that can only be answered at the local level.

## Why Map Metadata?

- To accommodate a change or upgrade in an existing system
- To “ingest” metadata into another system, but maintain original metadata format
- To share metadata with a wider community, improving interoperability
- Metadata is diverse—we will never all use the same metadata formats

- 13

## Metadata Mapping Caveats

- Requires good knowledge of both source and target metadata formats
- Often not a one-to-one correspondence between elements
- Typically involves some conversion operations
- Data types and values may differ
- Structure, hierarchy may differ
- Element optionality/repeatability may differ

- 14

## Mapping Exercise

- Exercise 4: Metadata Mapping
- Creating “shareable” metadata
- Designing a detailed metadata map
- Converting from relatively rich metadata to simple Dublin Core records

- 15

## Metadata and Digital Library Development

- Session 5:
- Metadata Workflows

- 1

## Goals of Session

- Understand the components of workflow design
- Understand the management aspects of metadata workflows (tasks, costs, constraints)
- Examine practical aspects of metadata conversion workflows
- Design a metadata workflow

- 2

**Notes:**
Trainer can begin by asking how many have created or been involved in workflows in their current jobs. Probably close to 100%, and that’s the point. Data workflows are fundamental to library technical services. This session therefore deals with what the participants probably have a fair bit of experience with. The slides here may take a more analytic approach to an activity that participants are engaged with by common sense and experience. Some of this analytic approach may therefore be useful to their appreciation for what they are already doing.

The first half the slides take a more analytic approach to workflows. The second half cover some practical questions about metadata conversion workflows.

## Workflow Fundamentals

- The movement of data through a work process
- 	Input  Transformations  Output
- A work process will typically involve multiple components or individual steps (tasks and subtasks)
- Each task also has its own data movement:
- Input  Transformations  Output

- 3

**Notes:**
This is the 2,000 mile high view of workflow, a theoretical perspective.
The idea is that there is, typically, one main (perhaps very large) work process, with generally defined input and output.
Then you start breaking that large task into subtasks, each with their own inputs and outputs.
And then breaking each of those sub-tasks into sub-tasks, and so on and so forth until you are working at a level where a task can actually be implemented.
The Output of one task will be the Input of the following task, within a workflow.

## Components of Workflow Design

- Workflow definition and goals
- Identifying constraints
- Defining the metadata workflow tasks and subtasks
- Designing the workflow
- Maintaining the workflow
- Cost considerations and opportunities

- 4

**Notes:**
This is an outline of what will be considered in the first half of this sessions slides (#5-12). This is presented at a somewhat abstract and theoretical level. The last half of the session will look at more concrete and mechanical considerations.

## Workflow Definition and Goals

- Defining the workflow objectives
- Analysis of overall work process input and output
- Understand the characteristics of the workflow input (e.g., source metadata)
- Understand the characteristics of the workflow output (e.g., target metadata)
- Specifying the required transformation

- 5

**Notes:**
First step is definition of the overall objectives. For metadata conversion projects, this will involve analysis of the source and target metadata (the workflow’s input and output). With that knowledge, we can develop a specification for transforming the input to output.

## Identifying Constraints

- Resources
- Money
- Staff
- Time
- Environmental constraints
- Knowledge and expertise

- 6

**Notes:**
We need a complete picture of what the constraints are before we can begin to design a workflow, since the constraints, by definition, are going to constrain what we can do and how we do it. Resources and time are the usual suspects, but there are other considerations.

A fundamental problem is whether we have the resources to carry out a workflow. Do you have the staff to perform the required work? If not, do you have the funds to hire additional staff? Is there potential revenue in the work being carried out to pay for additional staff or current staff time (see slide on Opportunities). 
Often, we have the staff to do the work, but they are not currently available. It may not be feasible to hire additional staff. Then it is a question of time and the scheduling of work.
Environmental constraints are typically those imposed by the containing institution—like other demands on staff time, the need to share material resources (such as computer processing), or issues arising from the need to interface with other departments/units that may be part of this workflow (perhaps you depend upon them for deliveries of material, and they only deliver on Tuesdays).
A particular metadata workflow may require more knowledge and expertise than we currently have. We will have to allot time to gain this knowledge, or perhaps funds for consultants or for travel to interview experts, etc.

## Defining the Tasks

- Breaking overall goal down into tasks and subtasks, small enough to be implemented
- At that level, determine each task’s...
- Requirements
- Specifying task input and output
- Complexity of transformation (input to output)
- Dependencies
- Duration
- Resource requirements

- 7

**Notes:**
From the project description, and an appreciation of the constraints you face, you begin to break apart what needs to get done into large areas of work, then break these down into smaller (sub)tasks, and eventually you get to tasks that are at a level where they can be implemented by someone or some process. At that level (implementable tasks), you can analyze and define each task by asking about its requirements....etc.

These different aspects of a task are interrelated:
Greater task complexity adds to duration, and/or makes it less predicable.
Complexity also adds to resource requirements (such as manual review of output).
Task dependencies: there is always at least one dependency...the task input. But the task may have many others, including computing or human factors. Workflow constraints and task dependencies are related. Perhaps only certain software can perform certain tasks, and it can only be put on one computer. Perhaps only one staff member has the expertise to apply specialized subject terms. Human dependencies add to greater uncertainty in predicting duration. People can be sick, quit, go crazy.

You can likely get examples from participants to illustrate any of these aspects of a task.

## Designing the Workflow

- Given the constraints, how do we put all the pieces of the workflow puzzle together in the most optimal way?
- How should tasks be structured in workflow?
- Sequencing and scheduling of tasks
- Who or what will perform each task?
- What are the communication needs of the workflow?

- 8

**Notes:**
Workflow design is like a puzzle. Many different tasks and players. What is the optimal, most cost-effective way to put all these pieces together.

Communication needs: how does one task know that its inputs are ready? (Remember the input-to-output analysis can be applied to every little sub-task. So the outputs of one task will be the inputs of the next.) How do tasks (or the people performing them) communicate with each other? 

These are presented as considerations in the design of a workflow. Alternatively, you can think of them as considerations in the analysis of an existing workflow, perhaps leading to redesign.

## Maintaining the Workflow

- How will the workflow and its tasks be tracked and evaluated?
- Who is responsible for the workflow?
- How will improvements or other changes to the workflow be made?
- Once operational, what are the workflow’s ongoing management requirements?
- How much human oversight is needed?
- How much tracking can be automated?

- 9

**Notes:**
We can think of the workflow as a living thing, requiring some degree of “care and feeding”.

Tracking is to determine whether transformations are being done as expected. Are the outputs what we wanted?
Evaluation is similar, but asks also whether any task transformation can be optimized to improve productivity. Evaluation may lead to redesigning, or tweaking, the workflow puzzle.
How much management oversight is necessary to carry out routine tracking? Are there a lot of human factors involved in the workflow, and thus more task complexity? Or is it only a computer process running nightly? 
Can management be automated by a programmatic approach to tracking? For example, if we have an automated process running nightly, can we define potential problems systematically enough such that software analysis will be able to detect problems and send alerts? Or does the data need to be manually inspected? Each day? Every so often? Do we inspect all output or can we sample?

## Workflow Cost Considerations

- Workflow setup
- What is the current and required level of staff expertise with source and target metadata schemes?
- What staff skills are required to implement workflow transformations?
- What can be automated?
- Are there existing, re-usable tools available?
- What must be done manually?
- Any prior experience with this type of processing?

- 10

**Notes:**
Staff expertise with the relevant metadata schemes is valuable, not just in carrying out the work and/or serving as a resource for other metadata staff, but for the initial evaluation and assessment necessary to define accurate time/cost estimates. The ability to quickly spot deficiencies is extremely valuable.
Bringing staff up to speed, or giving them the time to investigate on their own, is a cost.
What staff resources and skills will you have for effecting the actual transformations required? Programmers? Those familiar with bibliographic control? Those that are good with detailed manual work?
Can you identify what you don’t know? If you are not familiar with a metadata transformation, for instance, you will tend to be overly optimistic about the time required. Same for familiarity with metadata types. If you are unfamiliar, you will probably underestimate the time required. 

## Workflow Cost Considerations

- Workflow maintenance
- We need to quantify the type and extent of ongoing support and maintenance the workflow will require
- Cost differences in maintaining manual vs. automated workflows
- How much management oversight does the workflow require?

- 11

**Notes:**
Maintaining a workflow requires resources. Those responsible for department budgets need to know the workflow’s costs over the period which it will be maintained. This in fact may become a decisive factor in whether to take on this work or not.
Managers need to be aware of how much of their time will be devoted to oversight.

## Opportunities and Benefits

- Increased knowledge and expertise
- Revenue potential
- Greater use of collections and resources
- Greater visibility of institution

- 12

**Notes:**
Putting together and executing a workflow has its costs, but also its benefits.

The first two items here are opportunities/benefits that may accrue to the metadata specialist, or the metadata specialist’s immediate unit/department.

Obviously, when cost-effective and successful workflows are in place, benefits accrue also to the entire organization and the communities/users served by the library (additional services, better meeting the needs of users, etc.).
Increasing use of collections, resources
Increasing visibility of institution

So much metadata expertise is really just experience, including making mistakes. Some of the most experienced metadata practitioners are those that have made the most “mistakes,” (or, if not mistakes, just things they would now do differently).

## Practical Aspects of Workflows

- Types of workflows
- Characteristics of source and target metadata, and the impact on workflow design
- When to convert metadata
- How to convert metadata

- 13

**Notes:**
This slide is an outline of the remainder of the slides in this session.

We’re moving to a more practical level, talking about more mechanical aspects of metadata workflows, and specifically workflows that deal with the conversion of metadata.

## Types of Metadata Workflows

- Enhancement and mapping
- Source data  Transformations  Target data
- Other workflows:
- Augmentation of records
- Analysis or evaluation
- Quality control/assurance

- 14

**Notes:**
Metadata enhancement activities included cleaning up, adding, expanding, disambiguating, updating metadata. Mapping is moving metadata from one format to another. Both of these have the basic notion of transforming one set of data to another.
There are other metadata workflows/tasks that could be considered not to involve conversion or transformation, at least strictly:
Augmentation would be some sort of regular process for adding additional records to a pile of existing records.
Metadata analysis or evaluation may be carried out regularly as part of larger processes.
QA may also be a regular set of tasks applied to particular sets of metadata.
All of these activities require a set of defined tasks and thus workflows. Both analysis and QA may be an end in themselves (perhaps for hire, consulting work), may be in preparation for some specific conversion activity, or may be regular insurance activities built into larger data maintenance processes.
What about data creation? Many digital projects will require the generation of metadata. Is metadata creation it’s own category of workflow, or can it be considered a type of conversion workflow?
In the rest of the session, we’ll talk mostly about workflows and tasks related to metadata conversion.

## Metadata Conversion Workflows

- Many aspects of the workflow will depend on the characteristics of the source and target metadata
- Static vs. dynamic source metadata
- Other source metadata considerations
- Target metadata

- 15

**Notes:**
This sets up the next few slides, which discuss the bulleted points.

Static vs. dynamic: we discussed this in Session 3, under Metadata Analysis. Now we’ll look at the impact this has on designing metadata workflows.

## Source Metadata

- Static source metadata suggests...
- A one time transfer of metadata from the creator or supplier
- The creator or supplier is, or will eventually be, out of the picture
- Dynamic source metadata implies...
- An ongoing, periodic transfer of the same, updated, or augmented metadata

- 16

**Notes:**
static vs. dynamic: a refresher from Session 3.
Static implies that that your conversion workflow will be a one time process. It may be a huge conversion project, but the conversion won’t have to be repeated.
Dynamic implies that your workflow is, in a sense, in the midst of some ongoing data pipeline. You are doing some data conversion that will need to be repeated every time refreshed data comes to you.

## The Impact on Workflow of...

- Static source metadata
- Manual processing is at least feasible
- No disincentive to apply manual work, except for cost
- A more extensive and subtle range of data enhancement is possible
- Workflow may not be *directly* reusable

- 17

**Notes:**
With static source metadata, there is more potential for refined “value add” work, since it could be done by people.

## The Impact on Workflow of...

- Dynamic source metadata
- Much depends upon the nature and rate of change of the source metadata
- There is a disincentive to use manual processing
- Correcting errors
- Manual “value-add” features
- There is an incentive to apply programmable transformations
- Workflow processes must be re-usable to be cost-effective

- 18

**Notes:**
The incentive to apply programmatic transformation is a cost incentive.
There is no incentive to do manual work. For example, you don’t want to correct data values that will be continually refreshed, clobbering your correction. The incentive in this case is to get the correction made in the source metadata, which is upstream from, and not part of, your workflow.
You can refer to the DC mapping exercise in Session 4 as an example. The journal article data is presumably dynamic—corrections could be made, articles might acquire a DOI identifier in the future, etc. So our mapping would not want a step in it where a human was required to evaluate something and convert the value (say, the publication dates were not in a standard form). There would be no incentive to that since that work may be overwritten. 
Now, if that journal source metadata were for some reason static, and we were sure it would never change, then the disincentives against manual enhancement of the metadata go away (except cost, of course). Perhaps we would then chose to add special subject terms or convert data in some way that required more human intervention, knowing that this investment would not be lost.

## Source Metadata:Other Considerations

- What or who created or supplied the metadata?
- Is there a clear and single owner?
- Multiple suppliers?
- Is the source metadata complete?
- Why was this metadata created?
- Was it created to meet specific functional needs?

- 19

**Notes:**
Some other questions to consider about source metadata, which will impact how we design workflows to convert this metadata. These are related to the analysis of metadata in Session 3.
Single supplier or producer: the metadata will tend to be more uniform. There is one person or organization to go to with questions. The workflow may require less normalization work.
Multiple suppliers: This is the situation of many places are contributing metadata to a common project (perhaps a union catalog of Dublin Core records). Data will be less uniform, unless contributors have agreed to some standards and/or practices. And even then, there will be differences. Workflow will likely require normalization steps.
Is the metadata complete or was something not finished, or some parts of it not delivered? This may impact scheduling of work. You don’t want to run processes on data and then find that you don’t have half of it.
Why was the metadata created: the intended use factor in the metadata analysis session. Knowing what and how the metadata supported functional objectives will tell you about its reliability, and whether uniformity was important or not to creators.

## Target Metadata

- What purposes are the metadata serving?
- Is this a locally defined element set or larger community standard?
- Is the metadata format supported, and by whom?
- Is there documentation?
- Is the format maintained and evolved over time?

- 20

**Notes:**
In the mapping exercise of Session 4, Dublin Core was the target metadata.
Questions to ask with regard to target metadata, to help us determine the nature or characteristics of that data and what will be involved in working with it.
By supported we mean, is it documented? Are there instructions for applying it and examples of its use?
Knowledge about the target metadata scheme is important. Is it possible to get a DTD or XML Schema that defines the element set? Is there documentation and/or help available? For DC, there is an entire community devoted to its use. 

## When/How to Convert Metadata

- Will depend on the type of metadata conversion required
- Two broad categories or types of metadata conversion work:
- Enhancement: cleaning up, adding, expanding, disambiguating, updating metadata
- Mapping: moving metadata from one format to another

- 21

**Notes:**
The two broad types of metadata conversion were discussed in the last session (Session 4).

## When to Convert Metadata?

- Once and only once
- Abandon source metadata in favor of improved set
- Continuously
- On-the-fly, when requested
- To feed some downstream processes
- Only when you have to
- Fixing problems when they are pointed out

- 22

**Notes:**
This is very broadly speaking. Different approaches to converting metadata, each with there own type of workflow.

Once and for all applies to a one time data conversion project. Source data is probably static.
Continuous conversion suggests that your conversion workflow is just one part of a data pipeline, and the same data will be coming again. This will probably be some sort of mapping solution. 
“Only when you have to” may be a cost effective way to detecting and making corrections in data—wait until someone notices the problem and draws your attention to it. This is probably useful where data problems are not systematic or regular and thus defy batch cleanup processes.

## How to Convert Metadata?

- Manually, record-by-record
- In batch, with automated processes
- Planning, testing, evaluation, more planning...
- Conversion
- Final, or ongoing, evaluation
- A hybrid approach, with some manual and some automated processing

- 23

**Notes:**
When and how to convert metadata will, again, depend enormously on the type of conversion you’re doing. Record cleanup may require manual record-by-record work, although some systematic problems can be corrected in batch. Mapping conversion will be done by some automated process.
The hybrid approach is very common in digital library projects.

## Workflow Exercise

- Exercise 5: Metadata Workflow
- Library Publishing—designing a workflow for a metadata conversion project

- 24

## Metadata and Digital Library Development

- Session 6:
- Digital Library Development Project Exercise

- 1

**Notes:**
This session is entirely an exercise. These two slides are intended for the participants materials, but probably do not require projecting.

## The Slide Library Project

- Exercise 6: Digital Library Development Project—The Slide Library

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