This is an overview of metadata application profiles and related documentation used within the Cornell University Libraries, generated originally from a MWG Working Session in 2016/2017. The goal is to get a high-level overview of metadata profiles and needs at CUL, which we hope will lead us to see collaboration opportunities. We hope to have another session in the fall of 2017 to see where this overview stands and update/expand it.

What are Metadata Application Profiles (MAPs)?

Wikipedia gives a decent stub on Metadata Application Profiles: https://en.wikipedia.org/wiki/Application_profile Metadata Application Profiles are metadata specifications attached (sometimes loosely, sometimes tightly) to a particular application or metadata service - whether it is a datastore, repository, management system, discovery indexing layer, or other. It helps communicate expectations of the metadata being ingested, processed, managed and exposed by that particular application or service. MAPs are the documentation that connect metadata implementations to shared community models and standards, as well as document where implementers need to diverge from community standards. This makes it easier for outsiders to understand and work with metadata from or headed to your application or system.

Metadata Application Profiles can touch on descriptive, technical, administrative, structural or other (or a mix of all of the above) metadata. They can rely heavily on community standards, but good MAPs don't just copy a community standard over. This is due to the fact that, in implementation, there are points your data needs to diverge or to further specify a datapoint's usage. These MAPs can also be made machine-actionable, with the enabled action being validation of your data against a profile, guiding the creation of data that follows your profile, transforming your data to/from another profile or standard. A popular metadata application profile example is the Digital Public Library of America Profile: https://dp.la/info/wp-content/uploads/2015/03/MAPv4.pdf which has a machine-actionable representation here: https://github.com/dpla/dpla_map. 

What you'll most often see in cultural heritage institutions' metadata management are MAPs represented via spreadsheets or some kind of text documentation with tables. Depending on the data representation (RDF, XML, CSV, JSON, Arvo, (X)HTML, MARC, ...) you are ultimately working with, you might have some technology available for specifying machine-actionable MAPs. A common example is a XSD file which clarifies or validates that metadata is following the MAP specified for your namespace or XML dataset. Another example is the emergence of RDF shapes technologies (ShACL, ShEx, RML) or application-focused RDF-based object management libraries (ActiveTriples / ActiveFedora) for both checking the state of metadata against a specified MAP or converting to/from that MAP. 

CUL-Used or Important Community-Used MAPS

CUL MAPs

Community MAPs

Creating a MAP - Guidelines and a Generic Template

First Steps

A MAP should document and specify the expectations of metadata in an application, service, system, or other. This means the first step to creating a MAP is to understand what it is your metadata is attempting to describe or capture at a conceptually level. Here are some questions to help guide creating your MAP:

1. What are you describing with this metadata? To what level of conceptual difference do you need to go for your MAP to be accurate and complete? An example: eCommons is an Institutional Repository, which means it tends to manage scholarly output objects. Capturing what type of scholarly output is a required metadata field, but doesn't change (at the moment) the system's management of a scholarly output object's metadata record (i.e., a Presentation eCommons metadata record will not diverge in structure or requirements from an Article eCommons record - the same fields are in play, albeit some will make more sense for different objects). Once you decide this diverges enough, you can create forked (but related) MAPs, with a view for system interoperability. This is more determined by needs than any hard and fast rules - i.e., its an art, not a science.
2. What do you intend to do with this metadata? 
   1. Share with or generate from other systems? Then you need to make sure shared concepts/fields are captured in both MAPs. Standards can be very helpful here.
   2. Enable some sort of discovery, lookup, resource management, or other functionality? Clarify this functionality's expectations in the MAPs, particularly the fields that support that work.
   3. Use within a particular system? Make sure your building your MAP to the abilities of that system, and clarifying limitations where they apply.
3. How will this metadata be generated, managed, and exposed? By whom or what processes?
   1. Generation can lead to understanding data expectations and sources, as well as any meta-metadata you might want to capture.
   2. Management helps clarify expectations and understanding of the metadata within the system, as well as guide any enhancement or update work you do to that existing metadata.
   3. Metadata exposure in the format "of record" of that system is vital. This is how you can assess metadata against your profile. This is how you can leverage the MAP in play. This is how you can perform analysis to understand what your MAP covers (or doesn't).

Generic Template

This is a generic MAP template that may be helpful. All the columns aren't needed for all cases, but those with stars beside them are strongly recommended for your MAP. You can make a copy of this template from this Google Spreadsheet.

{number of times expected, number of times can be present}