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Q: Why not just use the Dublin Core data model for my image collection?

A: The short answer to the question "why not just use Dublin Core" is "because we don't have to." If you look at the metadata crosswalk at you'll see that many different metadata standards map to Dublin Core. But the reason all these other metadata standards were developed was because Dublin Core did not meet the needs of many communities of practice.  The DC metadata element set was developed to provide basic information elements to improve indexing and retrieval of resources on the Web.   But different communities and different resources found that these basic elements were often not enough to provide the kind of access they needed for their collection materials. So MARCXML, and MODS were developed for library materials, CDWA was developed for museum objects, EAD was developed for archival materials, and the VRA Core was developed for images of all of these materials.  As a very basic set, DC represents the lowest common denominator between all these more specialized standards.  If you look at the crosswalk closely, you'll see that the mapping between a complex schema like CDWA or VRA Core to the DC elements isn't always satisfactory, but it does allow searching across collections if the more complex data is mapped to the DC element set. (There's a good discussion of metadata mapping at  There are better alternatives that meet the more specific needs of specialized collection materials that will talk to Dublin Core without sacrificing the richness and complexity of data that describes cultural works and the images of those works.


Q: Can I use a flat data model (like an Excel spreadsheet) instead of a relational database management system for my image collection metadata?

A: The short answer is “yes you can”, but why would you want to?  The long answer goes like this. For images of cultural objects, there are many arguments for using a relational model (tables linked to other tables via intersection tables) for your cataloging tool and a flat model (rows and columns like a spreadsheet) for your presentation tool, but they all boil down to basically two concepts: complexity and consistency.  Data about cultural objects is often complex and this complexity cannot be captured efficiently in a flat data model because basically you have to leave space in every record to accommodate the most complex object you will ever encounter.  This adds up to a lot of wasted space and wasted space means more money and hardware needed for storage, backup, preservation, etc.  It's much more efficient to catalog in a relational environment, where data can be entered once and then linked to many other records.

Data consistency is the other compelling reason to catalog in a relational environment.  Once there is more than one person doing data entry, the potential for data inconsistency increases exponentially. Differences in opinion, spelling, and transliteration in the source material make it hard enough for one person to keep things consistent.  In a relational model based on the VRA Core categories, one work or collection record is established with appropriate links to one or more titles, dates, artist names, etc. and then individual image records, each representing a "view" of the work, may be linked to that one work record.  This way, all the descriptive data about the work is entered once and every image that shows this work inherits the same information.  This data consistency insures that when you go to search for things, you get consistent results returned.

Management and service of digital assets (the image files), on the other hand, is handled quite well by a flat model because the descriptive fields that apply to each image (file size, pixel dimensions, photographer who captured it, date it was captured, etc.) are fairly straightforward and not generally subject to scholarly debate.  A lot of image metadata can even be harvested automatically by the capture device and the digital asset management tool.  If the relational work data can be denormalized or "flattened" and then imported into a select number of descriptive fields in a digital asset management tool, then you have the best of both worlds: consistent, complex, descriptive metadata about works linked to multiple, individual, views of those works in an efficient discovery and access tool.


Q: Where can I found out more about VRA Core 4?

A: VRA Core 4 schema documentation and example records may be found on the VRA Core page at the Library of Congress website at


Q: What do I tell the bean counters when they ask me if this will save them money?

A: Tell them that the largest cost associated with any archival digitization project is the cost associated with collecting, organizing, entering, and managing the data associated with the images that depict those cultural objects. The time invested up front in the planning process will more than pay for itself over time by resulting in data that is flexible and robust enough to be readable, exportable, and still discoverable across new systems as technology changes.


Q:  I can find any image I need with Google image search, can't I?

A: No.  Many images are hidden in individual silos that are not exposed to the open web and many more are exposed to the web but with inaccurate, irrelevant, or simply no associated metadata that would return that image in a Google image search result set.  All around the world, millions of images of works of art, architecture, literature, and material culture are captured digitally every day and added to the global electronic landscape.   Without effective pre-planning for capturing good metadata to accompany these images, they will remain inaccessible.  A picture may be worth a thousand words, but a few well-chosen words associated with an image are what allow that image to be isolated and retrieved from the blizzard of images that exist in cyberspace.  More on this topic may be found in the VRA White Paper “Advocating for Visual Resources Management in Educational and Cultural Institutions“, at


Q: I want to digitize all my family photos.  Where do I start?

A: A good place to start is the Library of Congress Digital Preservation website at  There is a wealth of information there, including a short video about why digital preservation is important for you.  If you still have more questions than answers, consider hiring an Imageminder!  We have decades of experience we can bring to the project.


Q: What about copyright?  Isn't everything accessible on the web free?

A: No.  Just because you find an image on the internet does not mean it is copyright clear.  How and when it was created, who created it, and its intended use are just a few of the variables that determine how you may or may not use an image you find in cyberspace.  If you are unsure about how you may or may not use an image, check out the Digital Image Rights Computator at  This interactive tool walks you through 5 variables that will help determine how you may or may not use an image.  More on image rights and copyright compliance may be found in the VRA White Paper cited above.  And if you're concerned about rights you your own images, be sure to visit the Plus Coalition website at