Approaches to computational access - Platform


The final approach described in this guide is the provision of computational access to collections through a platform or interface made available by the content-providing organization. This is normally a dedicated online environment where the user is able to manipulate the material within it. The infrastructure behind this approach can be quite similar to the other two, the main difference being that not only does the user have access to the data, but also access to a set of tools to compute over the data. These platforms range from very simple manipulations, such as Google Books, where date ranges can be changed, to more sophisticated setups such as tools provided in the CLARIAH media suite, which give the user the possibility to explore audio-visual material and the Archives Unleashed project which is focused on analyzing web material.

The control around these environments can vary; some make it possible for everyone to log in, sometimes you need to be a registered user. Also, it may be possible to manipulate material in the environment, but not necessarily output any results, due to copyright or other legislative reasons.

The diagram below illustrates the platform model for computational access and shows the organization making data available through a platform or interface within which the user can access and manipulate the data. The dotted line illustrates the fact that some implementations additionally offer the user the option to download the raw or manipulated data.


Users may even get the opportunity to bring in other datasets or software to work with the data on the platform, but again this may be restricted because of constraints around the material. The Archives Unleashed project is completely different in that aspect, as it only offers the tools with public domain material. This makes it possible for users to bring in their own material. However, as the project is not liable for any of the material brought into its platform, users will not be able to save any of the analyses they have carried out. An interesting paper about the Archives Unleashed project is available here: The Archives Unleashed Project: Technology, Process, and Community to Improve Scholarly Access to Web Archives.

The table below gives an overview of a number of these platforms and notes some of the implementation differences, for example:

  • Does the platform offer metadata or data (or both)?

  • Can users supply their own tools and/or data to work with in the platform?

  • Can data be downloaded from the platform after analysis?

  • What constraints or restrictions are associated with the data?

  • Who can access the platform?



Bring your own software/datasets


Constraints on Data

Who can access?

HathiTrust Data Capsule



After being checked by staff

Copyrighted material

Only accessible by members

CLARIAH Media Suite


Not at the moment

No, but derived datasets may be possible in future

Copyrighted material

Accessible to the public, but need to be logged in to access the full functionality

Archives Unleashed

Some public domain material as an example

Yes, only your own material is accepted


Depends on user, but Archives Unleashed is not liable

Currently only accessible to Archive-It account holders and researchers who have worked with Archives Unleashed before



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Providing access to digital content is a core activity for digital preservation practitioners, making up one of the six functional entities of the Reference Model for an Open Archival Information System (OAIS) or ISO 14721:2012. It is widely recognised within the digital preservation community that there is little point in preserving content for the long term if there is not an intention to facilitate access at some point (either now or at a future date), but even providing simpler forms of access to content can be challenge for digital preservation practitioners (as described in Developing an Access Strategy for Born Digital Archival Material), This is particularly the case where large volumes of content and more complex methodologies are employed.

Computational methods of providing access to digital content and metadata are generally considered to be more advanced techniques, certainly a step up from the more standard models of access, for example where a user can browse an online catalogue and view or download one file at a time.

The Levels of Born Digital Access from DLF is a helpful and practical resource which articulates three levels of access under a series of headings, moving from the most simple to the more advanced. Computational access techniques are included at the highest level of the model where the ‘Tools’ section of level 3 states that an organization should “Provide remote access and sophisticated tools for exploring, rendering, and interpretation of data; provide hardware and software to support access to legacy/obscure content, including emulation services.” Examples given within the supporting information include:

  • “Provide open and web-based remote access to materials, including via programming interfaces” and

  • “Provide software for exploring, rendering, and interpreting materials, such as text mining, data visualization, annotation, and natural language processing tools.”

Similarly, the DPC’s Rapid Assessment Model (DPC RAM) puts computational access techniques at the highest level of the model. Level 4 of 'Discovery and access' states that “Advanced resource discovery and access tools are provided, such as faceted searching, data visualization or custom access via APIs”.

There is typically no one-size-fits-all approach to digital preservation and this also extends to access strategies. Organizations are encouraged to weigh up their own priorities, resources and the needs of their users in informing their own approach. So whilst it is acknowledged that not all practitioners will strive for the highest levels of either of these models, many in the community are curious about understanding and exploring these more advanced approaches of access in order to inform their own decision making.

The access strategies of an organization should of course be aligned with the needs of their users. The growing desire for users to be able to carry out their own computational processing on archival metadata for example is mentioned in Born digital archive cataloguing and description. Whilst user needs are not covered in any great detail in our online resource, a helpful introduction can be found in Understanding user needs and it is acknowledged that engaging with users should be a key step in establishing appropriate access strategies.


 What is the purpose of this guide?

Computational access is a term mentioned with increasing frequency by those in the digital preservation community. Many practitioners are aware it might be helpful to them (and indeed to their users), but do not have an understanding of what exactly it entails, how it is best applied and, perhaps most importantly, where to start. To add to the challenge, computational access raises professional and ethical concerns. These well-founded but sometimes partially formed concerns, in combination with a lack of practical experience and know-how, mean computational access has been relatively slow to develop within the digital preservation community despite its potential to help with our ambitions to facilitate greater access to digital archives.

The topic was highlighted as a priority by DPC members at the DPC unconference in June 2021. It was clear from discussions that digital preservation practitioners felt this was an area they would like to explore, but one of the key barriers was simply not knowing where to start. This guide has been created to provide an introduction to this topic, and to help the community move forward in applying computational access techniques.


Who is this guide for?

This guide is primarily aimed at digital preservation practitioners with no prior knowledge of computational access. It is a beginner’s guide, intended to provide an overview of key topics as well as tips on getting started and examples of a range of different implementations. It does not hold all the answers, but instead aims to move practitioners towards an understanding of computational access terms and approaches and give them the necessary information and resources to consider whether these techniques could be used to provide access to the digital archives that they hold.

It is not specifically aimed at researchers or users of collections who might want to use computational access techniques to analyze and understand digital collections. Other resources that will help those users are available – see, for example, the Programming Historian and GLAM Workbench. It is important, however, that digital preservation practitioners keep potential users and use cases in mind whilst reading and using this guide

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Computational access is often linked with terms such as text mining, machine learning and artificial intelligence, so much so that there is understandable confusion around what each of these concepts entails and where the areas of overlap occur. This section provides clear definitions of key terms and the relationships between them.

 Computational Access

The term computational access relates to the ability to enable users to access collections within a digital preservation repository (in a machine-readable manner, e.g., via download or API) in order to analyze, interrogate, or extract new meaning from that material (through, e.g., data or text mining, machine learning) as a means of investigating a particular research question.

This term is first found in the literature in a report by HathiTrust and is closely linked to ‘Collections as Data’ which similarly urges organizations to make their collections available as data, therefore making it possible for users to compute over the material. The term is closely linked to a number of definitions, highlighted in bold above; these and other related terms are discussed below in alphabetical order.


An algorithm is a set of coded instructions  normally followed to solve specific problems. A real-world example would be baking a cake; other examples are the process of doing laundry, or the method used to solve a logistic problem.

 Artificial Intelligence (AI)

Artificial Intelligence (AI) has been defined by the UK Parliament as:

‘Technologies with the ability to perform tasks that would otherwise require human intelligence, such as visual perception, speech recognition, and language translation.’ They also add that ‘AI systems today usually have the capacity to learn or adapt to new experiences or stimuli.’ AI in the UK: ready, willing and able?

Artificial Intelligence typically takes one specific task, which would normally be done by a human, and provides a method of reliably automating it. An example of this is classifying traffic signs, or recognizing the handwriting of a particular scribe. It especially comes in handy when working with large amounts of material which are extremely time consuming to process manually. Recent developments relating to AI and archival thinking and practice are discussed in the article Archives and AI: An Overview of Current Debates and Future Perspectives.

AI can be split into Broad AI and Narrow AI, and these terms are further defined below.

Broad AI represents a system that is sophisticated and adaptive, able to perform any cognitive task based on its sensory perceptions, previous experience, and learned skills. Currently this type of AI is not achievable due to technical limitations. Read more about steps towards a broad AI.

Narrow AI is task focused. This type of AI is very good at doing one single task, for example, classifying documents into different topics. It is also referred to as Weak AI.

Sometimes Narrow AI becomes so good at a specific task that it can give the impression that it is able to think for itself, so falling under the Broad AI marker. An example of this would be the quick improvement of Voice Assistants, such as Alexa. However, current technology is only able to support Narrow AI.

More information on the differences between these two terms can be found here: Distinguishing between Narrow AI, General AI and Super AI.

Algorithms are a large component of AI, but differ slightly, as AI takes the use of these a step further. AI is basically a set of algorithms that can modify and create new algorithms in response to learned inputs and data, as opposed to relying solely on the inputs it was designed to recognize as triggers.

 Computer Vision

Computer vision is a sub domain of AI that focuses on deriving meaningful information from digital images. In an archives context, computer vision could be used to generate metadata for a set of uncatalogued digital images to enable more effective processing, or search and retrieval. A good example of this can be found here: Libraries Use Computer Vision to Explore Photo Archives. As digital images can be of a complicated nature, machine learning is the methodology typically used to carry out this task. You can find out more about computer vision here: What is computer vision?

 Data Mining

Data mining is the discipline of finding patterns, correlations, and anomalies in data. A broad range of techniques can be used in data mining, including AI. The data mining workflow can be roughly split into data gathering, data preparation, training, and data analysis. AI is most commonly used during the training stage; this is when the algorithm is trained in a specific task. However, as this discipline mainly focuses on using large amounts of data, AI and algorithms can also be used to aid other steps of the workflow. For example, an algorithm could be written to gather certain information from the web. Find out more about data mining here: Data Mining: What it is & why it matters

 Machine Learning

Like computer vision and Natural Language Processing (NLP), machine learning is a sub domain of AI. It differs slightly from computer vision and NLP, as it focuses more on the infrastructure and models than on the techniques and material that are being inputted. Machine learning takes AI to the next level; not only are the algorithms adaptable, but they are also able to perform a task without being explicitly programmed to do so. Find out more about machine learning here: Machine learning. An example of using machine learning on archival collections can be seen on the Archives Hub blog: Machine Learning with Archive Collections.

When talking about AI and machine learning, the terms supervised and unsupervised are sometimes used. These refer to the different approaches that can be taken when applying machine learning. Supervised learning is where a labelled dataset will be used for the algorithm to learn from. A labelled dataset contains items that are tagged (mostly by humans) with an informative label; one example of this is a labelled dataset of images with names of the people who appear in them attached. Unsupervised learning uses a dataset that has not been labelled. This is the biggest difference between these two approaches, but a more nuanced explanation can be found here: Supervised vs. Unsupervised Learning: What’s the Difference?

A term that is also closely linked to machine learning is deep learning. This is a more complex form of machine learning where deep neural networks are used to resemble the complex structure of the human brain. Read more about the differences between deep learning and machine learning here: Deep Learning vs. Machine Learning – What’s The Difference?

 Natural Language Processing (NLP)

NLP, just like computer vision and machine learning, is another sub domain of AI. This sub domain focuses on the ability of a computer program to understand human language as it is spoken and written. You can read more about NLP here: Natural Language Processing (NLP). Most of the time, due to the complexity of human language, machine learning will be used alongside NLP to produce better results. An example of this is text classification, where due to the ambiguous and unstructured nature of human language, this approach has only been able to evolve since the use of machine learning.

 Text Mining

Text mining is very similar to data mining, the biggest difference being that instead of collecting data in general, text mining focuses solely on collecting text. Therefore, while text mining is data mining, data mining is not necessarily text mining. Text mining typically results in a large quantity of unstructured text which is difficult to analyze, so more advanced methods such as machine learning are often used in association with it to help make sense of the resulting dataset. Read more about text mining and its relationship to machine learning and NLP here: What is Text Mining, Text Analytics and Natural Language Processing?


As is apparent from the definitions as described above, there are close relationships between many of the terms used. The diagram below illustrates some of these areas of overlap.

circles full text

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Approaches to computational access

There is more than one way to implement computational access, and the range of approaches can be confusing to those who are new to this topic. This section looks at the pros and cons of the different approaches and provides real-life examples of how they are being applied by different organizations.

Computational access can be approached in several different ways. Four approaches have been identified:



The one an organization selects will depend on its resources and priorities, the needs of its users, and any legal and ethical concerns relating to the collections or material being made available. It is also possible for organizations to apply several approaches, depending on their collections and users. The approaches described in this resource appear in order of complexity, with simpler solutions first, followed by those that require greater commitment of time and resources.


This recording from Leontien Talboom at our launch event in July 2022 provides a helpful introduction to computational access and a clear overview of the four different approaches.

Leontien Talboom, University College London -   An introduction to computational access

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Ethics of computational access

No guide to computational access would be complete without discussion of some of the ethical issues which should be considered when applying these techniques. This section provides a summary of some of the key considerations and signposts further sources of information.


The digital preservation community has inherited and further developed a sophisticated understanding of the ethics surrounding the provision of access to heritage collections, an understanding which is having to become even more sophisticated in respect of the provision of digital access. Some of the challenges around the ethics of access, with a particular focus on approaches and tools that have been applied at the Australian Institute of Aboriginal and Torres Strait Islander Studies (AIATSIS) are summarized in Exploring ethical considerations for providing access to digital heritage collections.

The provision of computational access will require a similar evolution in understanding for several reasons. Firstly, because computational access, as defined in this guidance, generally involves the use of algorithms or other computational methods, some of which are not in themselves commonly understood or easily explainable. This leads to issues with maintaining transparency and accountability with respect to, and hence trust in, their use and the conclusions and outcomes that arise from that. Arguably, we should for this reason be circumspect in adopting such techniques for our own (digital preservation) purposes.

Secondly, a common factor in the use of algorithms and other computational methods is the desire to be able to work at scale, processing larger amounts of data at one time than was previously possible using more traditional methods, and being able to combine disparate datasets to create a clearer picture. The bigger the scale, the bigger the potential for harm and unexpected outcomes, particularly when the data being worked on are about people. This potential for harm is heightened still further by the fact that the regulatory environment for the use of algorithms and computational methods (in all contexts, not just for the provision of computational access to digital heritage collections) is itself in an unsophisticated state of development, with discussion ongoing and the absence of any form of widely held consensus on the topic. There is a good discussion of some of these questions here: Algorithmic accountability for the public sector.

In large part then, there is an element of ‘watch this space’. However, a first step could be to enhance our documentation of any ‘sets’ of material or data to which computational access is anticipated or offered. Within the AI and data science communities increased attention is being paid to dataset documentation; see, for example, Datasheets for Datasets – Microsoft Research or the work by Eun Seo Jo and Timnit Gebru from the machine learning community. While some of the questions asked as prompts to documentation may seem odd to those used to describing digital assets for non-computational use, this highlights the information needed in order to ensure that they can use it safely. Similarly, projects such as The Data Nutrition Project and Data Hazards flag up some of the already known dangers for those wishing to analyze data using computational means and a blog from the Archives Hub describes some of the challenges of understanding the effectiveness of, and bias within the tools.

When providing access to collections, improving the documentation we offer to support users to use it more safely would seem to be the least we, as practitioners, can do. It would provide users with a context for the material and help them to make an informed and ethically responsible choice as to how to use it. Although what users ultimately do with this material is their responsibility, there is perhaps still a debate to be had about whether, in light of the increased potential for harm and the nascent regulatory environment, we feel it is our responsibility to police the uses to which our collections are put even more stringently than in the past. A good way to get started is to provide terms of use, discussed in the approaches section of this guide. Should we prohibit certain types of use? And if so, how do we design systems and procedures to prevent these?

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Benefits and drawbacks

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.

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Practical steps for moving forward with computational access

Taking the first steps to implement or work with a new technique or technology can be a challenge, particularly when you do not know how, or even where, to start. This section offers practical tips to help digital preservation practitioners move forward, as well as case studies demonstrating how others have tackled the challenge.


Circumstances will vary widely in terms of the resources and technical skills available to an organization as well as in the type of material they are hoping to provide access to. However, the following ideas should provide a useful starting point for further exploration, whatever your circumstances. These '23 things' focus on actions aimed at both individual practitioners and at organizations, though of course there will be some overlap and you are encouraged to consider all points.

Steps for an individual practitioner
  1. Do something! It is easy to be overwhelmed by the possibilities available, but the best way to learn is to experiment. For example, if you can safely do so, consider making a small dataset available and seeing how people use it.

  2. There are great free resources available to learn more about computational techniques – use these to build your capacity. Good places to start are the Programming Historian or Library Carpentry. There are links to more resources at the end of this guide.

  3. Talk to people you do not normally talk to. A computational access project will benefit from a multi-disciplinary approach, and the more people with different perspectives you can talk to about it, the better. This could include people within your organization or in the wider community.

  4. Scope out your project meaningfully. While it might be tempting to just say ‘I want all the data’, that would not work in an 'analogue' project and will not work in a digital one either.

  5. Check the licences and terms of access that apply to the material you are working on, collate these in a document so you know, and can share, what users can legally do with your content.

  6. Look for existing datasets you can use to enrich the data you will be working with. For example, you might work in an organization that has catalogue entries you could incorporate into your project as metadata.

  7. Familiarize yourself with the various approaches to computational access discussed in this guide. They might all be useful to different types of user, but there are implications for the skills and resources you need for your project.

  8. Computational approaches and their outputs can lend work an aura of ‘objectivity’ that should be treated with caution. Keep in mind that your outputs might look more authoritative than perhaps they should, so think about how you can explain and qualify them. Make your assumptions and processes transparent to the user if you can.

 Steps for an organization
  1. Consider how computational access may align with, or support, organizational strategy. Being able to demonstrate a link may help you to make the case to explore new methods of access and gain the support of colleagues.

  2. Apply an active outreach approach. Speak to your users, set up user groups, conduct interviews. Try to help them articulate what they want (or might want if they knew about it) from computational access.

  3. Consider your own colleagues as a user community for computational access. While you might aspire to attract a new group of external users, computational access can also be of great benefit internally, by, for example, increasing your own understanding of your collections.

  4. A good way to generate ideas is to create a single dataset in a familiar format such as CSV, and host an internal hackathon, giving staff time to play with the dataset and see what they come up with.

  5. Think carefully about where the expertise to guide service provision for computational access lies in your organization, and how you can engage the right people. Bear in mind that expertise and responsibility might not always go together.

  6. Consider sustainability from the start. Remember that users may come to depend on the services you are planning to deliver, and you have a responsibility to think about how you will sustain them. Consider the resource commitment you will need and the environmental impact of your project. Though not specifically focusing on computational access, the article Towards Environmentally Sustainable Digital Preservation discusses the need for a paradigm shift in archival practice and touches on the environmental impact of mass digitization and instant access to digital content.

  7. Make documentation an integral part of your project from the very beginning. Part of the appeal of computational access is that others can build on your work in new and unexpected ways. A well-documented service, ideally with worked examples, will help facilitate this.

  8. Consider funding or empowering other users to approach your collections in new computational ways. For example, you could run a content hacking competition or workshop for postgraduate students and/or community practitioners to imagine potential uses of your content.

  9. Find a public domain or CC0 ‘No Rights Reserved’ collection, dataset or set of metadata and publicly release it. This can serve as a trial run to work through your processes, and as a way of engaging with users and working out what to do next.

  10. Search for existing standards you can use. Do not reinvent the wheel if other organizations have done already done similar work. For example, if you adopt similar data output forms to other organizations, it will make it easier for users who are already familiar with the standard.

  11. Ask lots of awkward questions during the procurement of any system. This might feel uncomfortable but will save problems in the long run. Engage expert colleagues to help you with this if needed.

  12. When testing your system, and when you are in production, be sure to question your results – are you able to identify and describe bias in the results? Do not take the computer's answer as right! The following article is an interesting reflection on selection and technological bias of the Living with Machines project: Living with Machines: Exploring bias in the British Newspaper Archive.

  13. It is important that users can contact you with questions or to request further data from your collection, so provide a primary contact or a contact form as part of your project.

  14. Make sure you have a defined plan to transition discussions of ethics to concrete actions in systems, processes, and collaborations.

  15. Going forward, embed computational access in your day-to-day collection accessioning and processing. New methods of access may require adjustments to organizational policy, accessioning and appraisal workflows and conversations or agreements with donors and depositors. The Reconfiguration of the Archive as Data to Be Mined is a helpful article which discusses changing practices brought about by the move to online digital records.


Further practical tips for getting started can be found in '50 things' which has been published as part of the Collections as Data framework and inspired this section of the guide. Though 50 things is not aimed specifically at digital preservation practitioners many of the tips and ideas are helpful and their key message "start simple and engage others in the process" is great advice!

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How this guide was created

This guide to computational access was created collaboratively by Leontien Talboom (who received support from the Software Sustainability Institute to carry out this work, the Digital Preservation Coalition, and invited experts from across the community.

The development of this guide was informed by an initial expert workshop held online in February 2022. Invited experts were encouraged to share their thoughts on the definitions of key terms related to computational access and answer questions such as ‘what are the strengths, risks, opportunities, and barriers of computational access for digital archives?’, ‘what practical steps can practitioners take to move forward?’ and ‘what are the key resources people should access in order to find out more?’. The discussion was conducted across time zones and fuelled by cookies and cake. This workshop helped firm up the key elements that would be needed within the guide, and engagement with this group of experts continued as the resource was developed.

Also key to the evolution of this guide was an online launch event held on 6 July 2022. Lowering the Barriers to Computational Access for Digital Archivists : a launch event  was intended not only to share this work with the community for the first time, but also to gather a range of helpful case studies that could be made available to further illustrate the online guide.

zoom workshop

Attendees at the expert workshop on computational access held online in February 2022.


This guide was written by Leontien Talboom with contributions from Jacob Bickford, Jenny Bunn and Jenny Mitcham.

Our thanks go to the experts who contributed to the workshop and provided helpful comments on earlier drafts of this text:

  • Jefferson Bailey, Internet Archive

  • Jacob Bickford, The National Archives (UK)

  • Jenny Bunn, The National Archives (UK)

  • Catherine Jones, STFC

  • William Kilbride, Digital Preservation Coalition

  • Ian Milligan, University of Waterloo

  • Thomas Padilla, Center for Research Libraries

  • Alexander Roberts, University of Swansea

  • Tom J. Smyth, Library and Archives Canada

  • Jane Winters, University of London

Thanks also go to those who provided case studies on this topic at our online launch event:

  • Sarah Ames, National Library of Scotland

  • Jefferson Bailey, Internet Archive

  • Jacob Bickford, The National Archives (UK)

  • Ryan Dubnicek, HathiTrust Research Center, University of Illinois Urbana-Champaign

  • Glen Layne-Worthey, HathiTrust Research Center, University of Illinois Urbana-Champaign

  • Ian Milligan, University of Waterloo

  • Tim Sherratt, University of Canberra



Elements of this work were funded by the Software Sustainability Institute.

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Approaches to computational access - Terms of use

 Terms of use

All openly available data and collections should come with terms of use, as it is possible for open data to be harvested and computed over by users, even if it was not intentionally made available for computational access. The terms of use are a guide for users who wish to collect and compute over the material made available. As Krotov and Silva argue, if no terms of use are available, the scraping of sensitive or copyrighted material is left to the discretion of the user.

Here are some examples of terms of use:


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Approaches to computational access - Bulk dataset/Downloads

Bulk dataset/Downloads

This type of computational access is where an organization makes its material available through a data download. The organization creates a dataset from its material (this may be all of its collections, or a sub section of them), processes it and then makes it possible for users to download it through an online interface or portal. These datasets are normally available in CSV (Comma Separated Values) and JSON formats, as they are ubiquitous and easy to read by both humans and computers. Read more about the CSV and JSON formats here: Our Friends CSV and JSON.

This type of access gives an organization a lot of control over the material, as it sets the parameters of what is being made available. However, this approach also requires a lot of maintenance, as the dataset will need to be updated and uploaded manually. Versioning is also something to take into consideration. For single files or downloads this may not be as problematic, but when working with large amounts of data this can be important, as results may differ from version to version, depending on what has changed and why. Even if providers cannot retain all versions of the data, users should be encouraged to correctly cite the version they have used to aid in potential reproducibility.

Once the users download the file, they will have to set up their own environment and decide what they want to do with this material. Archivists may see this as the easiest way to make data computationally accessible as it fits well with existing concepts of access and use. They are used to packaging and storing information for users to request and access and the bulk datasets approach could be seen as a very similar process.

The diagram below illustrates the simplicity of the bulk datasets approach. An organization makes a dataset available through an interface or portal. A user can then download this dataset to work with.

bulk datasets

There are different ways of providing access through bulk datasets. The type of material made available as datasets may differ; some organizations will only make their metadata available in bulk, whereas others include both the data and metadata. The hosting of the datasets does not necessarily have to be done by the organization itself. It may decide to upload this material to a third-party provider. For example, large datasets from the Museum of Modern Art (MoMA) in New York are hosted through GitHub; these files are automatically updated monthly and include a time-stamp for each dataset.

A similar approach is taken by Pittsburgh’s Carnegie Museum of Art (CMoA) which also has a GitHub repository; however, this one is updated less regularly.

OPenn is another repository for datasets, specifically archival images. It is managed by a cultural heritage institution which provides access to its own material as well as material from contributing institutions.

A slightly different approach was taken by the International Institute of Social History (IISH) in the Netherlands, which uses open-source software to make its datasets accessible. A slightly modified version of Dataverse is hosted on their website.

The table below showcases a variety of ways in which the bulk dataset model has been applied at different organizations including the following:

  • Are data or metadata (or both) are made available?

  • Is data updated and versioned?

  • Which file formats are available?

  • Where is it hosted?





Terms of use

Downloadable format

Type of Data

Hosted on

HathiTrust – extracted feature datasets





Available through rsync

Unstructured book files

Own website

MoMA Collection





Several formats through GitHub

Metadata of collection


IISH Data Collection





Depends on the dataset

Structured research datasets

Own website with use of Dataverse

Carnegie Museum of Art






Object from museum








High Resolution archival images

Own website

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