Migration

Learn how to assess your current repository content and prepare it for a move to another system

Migration Overview

Data migration for digital collections is a complex process. As with most difficult tasks, it can be helpful to break down what needs to be done into smaller blocks of work. In general, data migrations can be divided into four sub-tasks, each with their own sets of outcomes, tools, and techniques: Migration Planning, Metadata Normalization, Content Migration, and Content Verification.

The following sections describe the importance of each of these steps in more detail and provide resources for effectively planning and executing your next content migration. This page does not assume that you have chosen a target system for your migration or that you are migrating from a specific source platform. This information is intended as a general migration guide for both new and seasoned digital collections professionals.

Migration Planning

Careful planning is arguably the most important step in migration. It requires consideration of both the larger context in which the migration takes place as well as an assessment of the content of the current digital collections and an understanding of the requirements of the system to which you are migrating. While this section aims to outline general guidance for migration planning, it is important to keep in mind that each situation is unique. A thoughtful analysis of these different factors will allow you to be better prepared for migration.

Contextual Considerations

Institutional Characteristics

The broader context in which a migration takes place will shape many of the decisions made. Some points to consider include:

  • Type, size, and budget of institution:
    • These indicate the broader goals of the organization and and overall picture of resources that may be available. Additionally, there may be state or city policies that may impact elements of the migration process.
  • Digital collections staffing:
    • These will likely be key personnel in a migration. Understanding of the people, expertise, and financial resources available will shape the approach you take to a migration. Some factors to consider include:
      • The overall number of staff supporting digital collection management
      • The number of IT professionals supporting digital collection management
      • The departments of staff supporting digital collection management

Digital Collection Management

Other contextual considerations specifically around the management of your digital collections are useful to keep in mind when forming a project team:

  • Primary stakeholders for your digital collections
  • Intended audience(s) for your digital collections
  • Structures, committees, and policies in place related to the administration of digital collections.
    • This may include individuals or groups that create policy, have technical administrative control over the repository, etc.
  • What system interactions and dependencies exist around digital collections?
    • For example, the ILS pulls digital collection data from an API, etc.

Digital Library Analysis

Making a full assessment of your digital library content, including the types of resources included and the condition of your metadata gives you an overall view of the condition of your digital library and reveals the type of pre-work you may need to plan for before migrating.

This analysis is typically most useful when compiled collection by collection, and formatted in such a way to enable visualization of patterns and needs across collections. This exercise also helps identify collections that may be simplest for testing and those that are most challenging.

Overall information that is useful to collect:

  • Number of digital collections to be migrated
  • Number of digital objects to be migrated
  • Total size (TB) of digital objects to be migrated

Content Analysis

Data Types

This is focused on the type and structure of the digital objects in your current system. What type of content is in your digital library? How are your digital objects structured now? Can they be accommodated in the new system? This is one example of some ways you may characterize content:

  • Single sided photograph
  • Double sided photograph
  • Single sided document
  • Multi-page document
  • Single audio
  • Multi-part audio
  • Single video
  • Multi-part video file
  • Hierarchical work
  • Multiple file types (e.g. audio/video file with image or PDF)

File Types

An inventory of file formats currently in use will help you create a full picture of your collections and will underscore your decisions moving forward. What file types will be produced or migrated for access purposes? Does the target system support those file types? Do you require copies or production of other file types for access or preservation?

  • Examples of file types include: JPEG, TIFF, PDF, MP3, MP4 and others.

File Locations

As new standards are implemented and staff come and go, file management practices change. A migration provides an opportunity for an inventory of your files. While migration tools may provide you with the option to migrate existing files or derivatives directly from your current digital library to a new system, you may decide to upload files from your local file system. If you plan for the latter, locating these files, organizing them, and moving them to an accessible or centralized location may be a good strategy and should be a consideration when planning your migration.

Metadata Analysis

To adequately plan and prepare for migration, it is crucial to have a deep understanding of your existing metadata as well as the metadata requirements of the new system. The following areas of focus can provide you with insight to inform migration decisions.

Metadata Profiles

What is/are the metadata profile(s) for your current digital collections?

  • What schema(s) are used?
    • Examples: Dublin Core, MODS, MARC, EAD, Local metadata schema, etc.
  • Is the same schema used across your entire digital library or does it vary by collection?
  • What fields are required?
  • What data type(s) populate those fields?
    • Examples: Strings, Numbers, URIs
  • What controlled vocabularies are used?
    • Examples: Library of Congress Name Authority File (LCNAF), Library of Congress Subject Headings (LCSH), Thesaurus of Geographic Names (TGN), DCMI Type Vocabulary, Local vocabulary, etc.

What is the metadata profile for your new repository? (See examples above)

  • What schema(s) are used?
  • Is the same schema used across the entire entire digital library or does it vary in some way?
  • What fields are required?
  • What data type(s) populate those fields?
  • What controlled vocabularies are used/suggested?

Data Quality

Assessing your current metadata quality will help you make decisions around metadata remediation needs, timing, and strategies. Consulting resources on metadata quality and remediation strategies can help frame your analysis and path forward, but the following areas of focus will give you a broad view of metadata quality in your collections.

  • Do you have local metadata input guidelines?
  • Have metadata values been entered consistently across your digital collections?
  • Does you metadata - elements and/or values - align with any other standards or best practices?
    • Examples: Describing Archives: A Content Standard (DACS), The Digital Public Library of American Application Profile (DPLA-MAP), etc.
  • How do you indicate copyright in your digital collections?
    • What metadata field(s) are used?
    • What values appear in this field? Varying local statements? Standard local statements? Rightsstatements.org values or URIs?

Normalize Metadata

After analyzing your metadata and possibly establishing a new Metadata Application Profile (see “Map Metadata” section below), you may learn that you need to do some metadata normalization. For the purposes of this discussion, metadata normalization includes both:

  1. Standardization: standardizing inputs and aligning values with your Metadata Application Profile
  2. Enrichment: utilizing/reconciling with new controlled vocabularies to standardize values; adding URIs to metadata

There are a variety of reasons that metadata may not be standardized within and across your collections, but in many cases it is desirable to bring metadata values into alignment. You may still have some outliers, but you can institute some normalization across collections.

To help you plan and work through normalization, consider the following:

Why

It is important to both understand and communicate to stakeholders the reasons why metadata normalization is necessary. Among migration practitioners, metadata normalization consistently surfaces as a critical component to the migration process. Putting this work into context and identifying its importance to the end goal can help manage expectations and keep folks motivated.

What

The first step in normalization is to identify and prioritize metadata issues and enhancements.

Identification

Through metadata assessment and analysis, you can identify areas for improvement. Examples of normalization may include:

Prioritization

Once issues have been identified, they will need to be prioritized. Prioritization should be done in concert with other stakeholders using the criteria works for your situation. For example, you may focus on normalizations that are the easiest or those that are most impactful. If your migration includes multiple collections, it is also good to prioritize the collections as well.

How

Next, you must determine how you plan to address the issues you have identified and prioritized. The type of issues, the resources - time, personnel, skills - available, and the scale of your collection(s) will help determine what strategies will work best. Broadly, these approaches are manual, programmatic, or a mix of both.

Manual normalization

This strategy consists manually correctly metadata field by field, collection by collection, possibly taking advantage of batch export, import, and/or edit functions provided by your digital library system. Manual normalization may not require the advanced technical skills that programmatic work may require, but it is not particularly scalable.

Programmatic normalization

This approach utilizes scripts and other automated tools to normalize data. In practice, programmatic normalization is rarely entirely automated. Often, there is a need for manual process initiation and data review. Programmatic normalization requires additional skills and training, but is far more scalable than a manual approach.

When

You must also consider when in your migration you should you normalize your data: before the migration; in-transit, or after the migration is complete. There are benefits and drawbacks to each approach.

ApproachBenefitsDrawbacks
Before Migration

Normalizing data in the current system

  • The work is done before migration to a new system
  • Data going into your system is more normalized
  • Time intensive
  • Could extend migration timeline
  • Dependent on your current system’s editing/batch process capabilities
In transit

Normalize data after it is exported from the current system, but before it is imported into the new system

  • Data going into your system is more normalized
  • May have improved flexibility for normalization than before export
  • Time intensive
  • Could extend migration timeline
After migration

Normalize the data in the new system

  • Less time intensive up-front
  • Can be done after new system functionality is understood
  • Data going into the new system may be less normalized
  • May not be as likely to go back and normalize data. There may be more motivation to do this before migration

Migrate Content

Once you’ve normalized your metadata, you have to move it into your new system. If you are moving onto a hosted platform, this might require another round of metadata changes to accommodate the platform’s framework. The steps here include: identify the platform’s requirements, crosswalk your schema to the new one, export your content, and finally import your content.

Model Work Types

Your new digital asset management system should have some basic way of housing metadata. In some systems, like CONTENTdm, the metadata profile is based on the collection. In Hyku/Hyrax, metadata is structured around a work type. Hyku currently uses a “generic work”, which is a Dublin Core-based schema that is standard for Hyrax. The two most important pieces of information to gather from your new platform are the schema preference (Dublin Core, MODS, MARC, etc.) and the fields available.

A Hyku generic work has 16 fields

FieldRequiredPredicateDefinition
Titledct:titleA name to aid in identifying a resource.
Creatordce:creatorThe person or group responsible for the resource. Usually this is the author of the content. Personal names should be entered with the last name first, e.g. “Smith, John”.
Keyworddce:relationWords or phrases you select to describe what the resource is about. These are used to search for content.
Rights statementedm:rightsIndicates the copyright and reuse status of the resource. While licenses cannot always be asserted, a rights statement can be. See RightsStatements.org for more information.
Contributordce:contributorA person or group you want to recognize for playing a role in the creation of the resource, but not the primary role.
Descriptiondce:descriptionFree-text notes about the resource. On Hyku's dashboard it is called “Abstract or Summary"
Licensedct:rightsLicensing and distribution information governing access to the work.
Publisherdce:publisherThe person or group making the resource available.
Date createddct:createdThe date on which the resource was created. Strongly recommended to select a particular date encoding (such as EDTF) to guide date formats.
Subjectdce:subjectHeadings or index terms describing what the resource is about; these need to conform to an existing vocabulary (Keywords should be used for uncontrolled values).
Languagedce:languageThe language of the resource’s content. Best practice is to select a language representation to follow, such as ISO 639-1 or full names taken from a controlled vocabulary.
Identifierdct:identifierA unique handle identifying the resource. This does not affect the identifier minted for managing your resource in Hyku.
Locationfoaf:basedNearA place name related to the resource, such as its site of publication, or the city, state, or country the work contents are about. Best practice is to select, if possible, one definition (such as ‘place of origin of the work’) for this field across objects in a collection or collections in your repository.
Related URLrdfs:seeAlsoA link to a website or other specific content (audio, video, PDF document) related to the resource.
Sourcedct:sourceAn identifier for a related resource from which the described resource is derived, in whole or in part.
Resource Typedct:typePre-defined categories in Hyku to describe the type of content being uploaded. More than one type may be selected.

For more information regarding these Fields, including expected values and examples, please visit the Hyku Metadata Documentation (DRAFT) and Hyrax Metadata Technical Documentation.

Map Metadata

Once you know your target system’s metadata schema, you will need to map your current schema to it. The Getty Institute “refer[s] to mapping as the intellectual activity of comparing and analyzing two metadata schemas, and to crosswalks as the visual product of mapping.”

Many of these crosswalks have become standardized and are available from the Library of Congress. Here are some examples:

Crosswalking is not always as simple as changing the field name, as laid out in the white paper “Issues in Crosswalking Content Metadata Standards” (1998) by St. Pierre and LaPlant. Some common issues are:

  • One-to-Many: When an element in your current schema has separate elements in your target system. Example: if your current system only uses "date", it’s possible to enter a schema that has multiple date fields.
  • No clear binary: When an existing element has no clear equivalent in the new system. This occurs often when the granularity of your system is higher than that of the target. Typically this will result in a broader list of entries within the keywords or description fields. When confronting this challenge, you will often lose specificity or may choose to omit fields from your existing metadata altogether.
  • Structural differences: Some schemas (EAD, etc) allow for hierarchical metadata, while others (MARC, etc) are flat.

There are no easy ways around the issues above, and your institution’s decisions on these should come from internal knowledge and context. CONTENTdm and Hyku both use Dublin Core, so the crosswalking required is at the field-level and will require mapping decisions that could be unique to your CONTENTdm instance or even your individual collections. The Bridge2Hyku toolkit includes CDM-Bridge which comes preloaded with Hyku’s standard target metadata to get you a head start on determining your mapping and easily exporting your metadata through it.

Source Repository Export

If you’ve already pulled your metadata out of your current system, you’ve likely stumbled upon the way to get your files out too. Many repositories have some file export functionality within their admin interface, explore what your current repository is capable before doing your wholesale export. If your repository does not have any export functionality, this is a portion of your migration that will likely require IT or consortial support. The Bridge2Hyku project team has created CDM Bridge, a tool to help with exporting out of CONTENTdm, one of the most widely used repository platforms.

Target Repository Import

add content here…

Content Verification

Once you have migrated your content to a new system, it is a good idea to make sure that it has transferred over intact. There are many approaches to this ranging from no review (Bad idea!) to reviewing each individual item. Possible approaches between these two extremes include:

Approaches

Spot check

A spot check entails the review of a limited number of random items. This can be done on different scales. You could systematically perform a spot check of several items from each collection migrated or you could spot check random items across the entire repository.

Metadata review

If your new system supports metadata export functionality, you could use this to assess the content in the new repository. This review could be done in conjunction with a spot check of items in the new repository.

Measures

What should you be checking for?

  • Data Integrity:
    • Did the metadata transfer over properly? Are the values in the appropriate fields? Does the metadata match the particular item?
  • Data Presentation:
    • Is the file present? Does it load properly? Were thumbnails supposed to be created? Is faceted browsing working correctly?

Migration Resources

Deciding to Make The Leap

Metadata Migration Resources

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