Understanding the benefits of using computational access is key to helping practitioners decide whether it is something they should explore. Awareness of the hurdles that might be encountered along the way is equally important in aiding those decisions and in helping understand the potential pitfalls once implementation begins. This section summarizes some of the key points to consider.


Computational access opens up potential benefits both for digital preservation practitioners and for users of their digital collections. Some of the key benefits are listed below:

  • Handling bulk requests will no longer need manual intervention by staff (though note that staff time may well be spent supporting users in other ways).

  • It is empowering for the digital preservation community, as the importance of documentation and contextualization can be emphasized. See Nothing About Us Without Us for a discussion on this theme.

  • Computational access techniques enable and encourage the possibility of collaborative working with other disciplines, such as digital humanities/cultural analytics/computational social science, especially when considering access through an API, and this may bring wider professional benefits.

  • It allows digital preservation practitioners to more effectively meet the needs of users, empowering them to explore collections in novel and innovative ways and opening up possibilities for new types of research. Platforms such as GLAM Workbench are helpful in demonstrating what is possible.

  • It provides opportunities for developing new digital skills, both for digital preservation practitioners and for users.


Alongside the benefits described above, there are several potential drawbacks to using computational access techniques. These are listed below:

  • Unintended bias or privacy concerns in the collections may not be revealed until the material is made available at scale.

  • Loss of control. It can be unclear who is using the material, therefore there could be unanticipated and unpredicted outcomes. This could be partly controlled with the implementation of terms of use or a policy on the use of derivative datasets, such as that published by HathiTrust.

  • Providing computational access is a long-term commitment with associated costs. It needs to be planned and resourced correctly for it to be successful.

  • Users may become reliant on computational access services that are being trialled by an organization. Managing user expectations when prototyping new types of access is important to factor in.

  • Data comes with an erroneous aura of objectivity, which can lead to the idea that it is unbiased; this is far from the truth for a lot of organizations. For more detail see Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning.


There are also several constraints or barriers that are stopping the community moving forward with computational access. Some ideas to help overcome these can be found in the ‘Practical Steps’ section of this guide.

  • Lack of technical skills and resources within an organization to implement this approach.

  • Sustainability of the implemented architecture. Digital infrastructures and tools need to be appropriately resourced in order to ensure they are maintained over time.

  • Depending on the type of collection you want to enable access to, licensing and other copyright restrictions could make computational access difficult. An example of this is UK copyright laws around data and text mining.

  • Lack of use cases or demand for computational access can make it difficult to know what approach to implement to meet user requirements.

  • The available tools that can enable and help with this type of access are not necessarily tailored towards the requirements of digital preservation practitioners, which can make it time consuming to implement and maintain.

Scroll to top