about
Institutional Books: A 242B token dataset from Harvard Library's collections (arxiv.org)
79 points by strangecasts on Jun 11, 2025 | hide | past | pdf | 22 comments on HN

In plain words: Harvard Library's scanned public-domain books were cleaned up—fixing errors in the scanned text and adding details like author, date, and source—into a free dataset for training AI. The release covers about 242 billion tokens across many languages, with clear records of each book's origin.

Abstract · Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability

Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of the datasets used to train these models, or to support their work at inference time, have a direct impact on their quality. The rapid development and adoption of LLMs of varying quality has brought into focus the scarcity of publicly available, high-quality training data and revealed an urgent need to ground the stewardship of these datasets in sustainable practices with clear provenance chains. To that end, this technical report introduces Institutional Books 1.0, a large collection of public domain books originally digitized through Harvard Library's participation in the Google Books project, beginning in 2006. Working with Harvard Library, we extracted, analyzed, and processed these volumes into an extensively-documented dataset of historic texts. This analysis covers the entirety of Harvard Library's collection scanned as part of that project, originally spanning 1,075,899 volumes written in over 250 different languages for a total of approximately 250 billion tokens. As part of this initial release, the OCR-extracted text (original and post-processed) as well as the metadata (bibliographic, source, and generated) of the 983,004 volumes, or 242B tokens, identified as being in the public domain have been made available. This report describes this project's goals and methods as well as the results of the analyses we performed, all in service of making this historical collection more accessible and easier for humans and machines alike to filter, read and use.

Matteo Cargnelutti, Catherine Brobston, John Hess, Jack Cushman, Kristi Mukk, Aristana Scourtas, Kyle Courtney, Greg Leppert, Amanda Watson, Martha Whitehead, Jonathan Zittrain
arXiv:2506.08300 · cs.CL, cs.DL · submitted Jun 10, 2025
abstract · pdf

add comment on HN

Although this is characterized as 1.0, it is governed by the Terms of Use for Early-Access, which are quite limiting, including: "You may use the Service solely for noncommercial purposes."
It really is rather peculiar to me. They frame it like this (emphasis mine):

"With the preliminary publication of this dataset, we further seek to establish a community-led process to grow, improve, and use institutional data in ways that strengthen the knowledge ecosystem and assert the importance of ongoing stewardship of training data from the originating knowledge institutions themselves. To this end, we are experimenting to find the best way to release this data in a manner that facilitates collaboration. We encourage input on this process to guide the full publication of this and future dataset dataset releases, beginning with the following decisions:

* At preliminary launch, we have published the metadata, including experimental metadata, in full for anyone to access and use.

* At preliminary launch, we have published the dataset including OCR-extracted text under a noncommercial license, and with a 'click-through' that requires users to accept this license, additional terms of use, and to share basic contact information with us so that we can engage the community in its early use.

* At preliminary launch, we have chosen to postpone the release of the raw scan images, though we will share them liberally with researchers and libraries who wish to review them. While we know AI developers and researchers are eager for more raw materials, we believe this minor friction can help build the relationships and norms necessary to grow a collaborative community."

It is the fruit of their labour (well, the digitalisation is), so it is up to them to license it as they see fit. But it feels odd to me that they seem to want to be in control to this degree. In open source and my own research field, the pattern we tend to follow is to release freely, observe, and then build relationships rather than holding a "license gun" towards the head of potential collaborators.

Lastly, I have only skimmed the pre-print, but I noted no commitment to a final license either. Not even a direction for it. Thus, as a natural language processing researcher I will stay clear from this dataset for the time being and hope the licensing situation improves.

“noncommericial” seems pernicious to me lately. I can see why people reach for it, but it really seems hard to define (there are many ways to profit off of something without simply selling it directly).
This has always been my problem with CC-NC, its just not clear to me what counts as "commercial" or not.

Can't sell the item itself? Okay, makes sense.

What about a downstream manufactured item? Such as a CC-NC STL, where you have since 3d printed it? You can't sell the STL, but what about the printed object? If not for profit, must you necessarily take a loss, or could you sell the items at-cost?

Or offering a CC-NC item for free, in the same place you are selling other products for profit? Where the CC-NC item may be acting as a "loss-leader" to get customers to purchase your commercial offerings?

Or giving everything away, the CC-NC item and all other items, but while representing a commercial entity who is doing such for marketing purposes with the end-goal of generating more revenue for the business?

I much prefer GPL/CC-SA licenses, they're much clearer where the line sits in regards to usage.

Don't most of these licenses also include "derived works". The trivial case, you get an STL, you print the object, it's clearly derived, you get some code, you edit parts of it for a new application, it's clearly derived.

Personally I feel it's also fairly trivial that an AI model is a derived work, but...there is so much money, people risk it (eg early Spotify and sourcing music) and hope it becomes a non-issue.

HOWEVER, as China and co are going to wholesale ignore any IP/copyright to train AI models, the choice we have...may not be much of a choice at all.

>>Can't sell the item itself? Okay, makes sense.

IANAL, but I think it goes even one step beyond that, which is that the item and derived works can't even be used to support a commercial enterprise, even if the (derived) work isn't being sold or seen by the outside public.

Interesting; If true, that effectively means the answer to all my questions would be "no" then
I'm sure Harvard doesn't consider its use as commercial, even though some people there get big salaries. Claudine Gay, for instance, makes more than $1m/year even after losing the job of President in the scandal. There are only a few "commercial" businesses that pay that well.
That's exactly why it's problematic in licenses unless explicitly defined.
These are all public domain books, which they don't have the rights to relicense like this.
AIs lizard brain will be 60% 1800s apparently, it might act like a villainous steampunk anglosaxon twirling a mustache in moments of survival, or at least some blend of those values while playing 5d chess. Read it H G Wells "World brain" to calm it down like a fond childhood memory
This would be the funniest possible future, and a very distinct possibility depending on how the NYT lawsuit turns out in regards to IP holder rights versus AI "copyright laundering".