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Terms of Use

This dataset is shared by the Institutional Data Initiative for research and public-interest use (the “Service”). These terms are intended to support experimentation while encouraging collaboration and feedback as we refine the dataset and work with contributing institutions to define shared, long-term norms for open data reuse. To share questions or feedback, contact us at contact@institutional.org.

By accessing or downloading the dataset or otherwise using the Service, you agree to the following:

  1. Noncommercial Use Only
    You may use the Service solely for noncommercial purposes. Open-source projects and other public-use efforts are welcome, even if they may indirectly support commercial use, so long as they are unaffiliated with commercial actors or intent.

    If you are affiliated with a commercial organization or plan to use the Service for commercial purposes (including AI model training), you will contact us first at contact@institutional.org.

  2. No Redistribution
    You may not share or redistribute the Service or any of the data provided through the Service, in whole or in part, including through public repositories or aggregators. If you want others to access it, please direct them to the attribution link.

  3. Derivative Works
    You may create derivative works for noncommercial use, but you may not make available any such derivative works that substantially reproduce the original dataset. Only outputs that are significantly transformed and cannot substitute for the original—such as evaluations, summary statistics, or visualizations—may be shared, with attribution.

  4. Attribution
    If you use the dataset in public-facing work, you must include attribution substantially similar to:

    Institutional Books provided by the Institutional Data Initiative with source material contributed by Harvard Library, available at https://institutional.org.

    Minor modifications to fit citation style or formatting are permitted, provided the essential elements remain intact.

  5. Provisional Terms
    These terms apply only to this release and may change. We are actively working with contributing institutions to develop a long-term framework for responsible, open data sharing.

  6. DISCLAIMER OF WARRANTY
    TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, ACCESS TO “SERVICE IS PROVIDED “AS IS” WITHOUT WARRANTY OF ANY KIND (EXPRESS, IMPLIED, OR OTHERWISE), INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. WE DO NOT WARRANT THAT THE SERVICE WILL OPERATE IN AN UNINTERRUPTED OR ERROR-FREE MANNER OR THAT THE SERVICE IS FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS. WITHOUT LIMITING THE FOREGOING, WE DO NOT WARRANT THAT (A) THE SERVICE WILL MEET YOUR REQUIREMENTS OR EXPECTATIONS OR ACHIEVE THE INTENDED PURPOSES; (B) THE SERVICE WILL NOT EXPERIENCE OUTAGES OR OTHERWISE WILL BE UNINTERRUPTED, TIMELY, OR SECURE; (C) THE INFORMATION OR SERVICES OBTAINED THROUGH OR FROM THE SERVICE WILL BE ACCURATE, COMPLETE, CURRENT, ERROR-FREE, OR RELIABLE; (D) ANY DEFECTS IN OR ON THE SERVICE WILL BE CORRECTED; OR (E) THAT ANY POLLS OR OTHER SOLICITATIONS OF INFORMATION POSTED THROUGH THE SERVICE BY YOU OR OTHER USERS ARE SAFE OR APPROPRIATE FOR YOUR OR OTHER USERS’ PARTICIPATION. WE MAKE NO REPRESENTATION OR WARRANTY REGARDING YOUR ABILITY TO TRANSMIT AND RECEIVE INFORMATION FROM OR THROUGH THE SERVICE, AND YOU AGREE AND ACKNOWLEDGE THAT YOUR ABILITY TO ACCESS THE SERVICE MAY BE IMPAIRED.

  7. LIMITATION OF LIABILITY
    EXCEPT INSOFAR AS THE FOLLOWING LIMITATION MAY BE PROHIBITED BY APPLICABLE LAW, WE SHALL NOT BE LIABLE TO YOU OR TO ANY THIRD PARTY FOR ANY DIRECT, CONSEQUENTIAL, INDIRECT, PUNITIVE, SPECIAL, OR INCIDENTAL DAMAGES, WHETHER FORESEEABLE OR UNFORESEEABLE (INCLUDING, BUT NOT LIMITED TO, LOSS OF PROFITS OR EARNING POWER, LOSS OF DATA, LOSSES DUE TO ERRORS OR INTERRUPTION IN AVAILABILITY OF THE SERVICE, UNAVAILABILITY OF ANY SERVICE, SERVER, OR COMMUNICATIONS FACILITY, OR DAMAGES DUE TO ACTS OR OMISSIONS OF OTHERS USING THE SERVICE), ARISING OUT OF OR RELATING TO THE SERVICE, INCLUDING WITHOUT LIMITATION YOUR AND OTHERS’ USE OF OR INABILITY TO USE THE SERVICE, OR YOUR RELIANCE UPON INFORMATION OBTAINED FROM OR THROUGH THE SERVICE, WHETHER BASED IN CONTRACT, TORT, STATUTORY, OR OTHER LAW. OUR TOTAL CUMULATIVE LIABILITY TO YOU ARISING OUT OF OR RELATED TO THE SERVICE (INCLUDING, WITHOUT LIMITATION, IN THE WAYS DESCRIBED IN THE PRECEDING SENTENCE), WHETHER BASED IN CONTRACT, TORT, STATUTORY, OR OTHER LAW, WILL NOT EXCEED THE AMOUNT, IF ANY, THAT YOU PAID US TO USE THE SERVICE IN THE TWELVE MONTHS PRECEDING THE CLAIM. THE DISCLAIMERS AND LIMITATIONS SET FORTH IN THIS SECTION SHALL APPLY, TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, WHETHER OR NOT WE HAVE BEEN NEGLIGENT OR OTHERWISE AT FAULT.
    YOU ACKNOWLEDGE THAT, FOR PURPOSES OF THE FOREGOING DISCLAIMERS AND LIMITATIONS, AS WELL AS THE INDEMNITY PROVISION IN SECTION 8 BELOW, THE TERMS “WE,” “OUR,” “US,” “INSTITUTIONAL DATA INITIATIVE,” AND “IDI” INCLUDE THE CORPORATE BODY PRESIDENT AND FELLOWS OF HARVARD COLLEGE, ALSO KNOWN AS HARVARD UNIVERSITY, AND ITS VARIOUS SCHOOLS, THE MEMBERS OF ITS GOVERNING BOARDS, AND ITS OFFICERS, FACULTY MEMBERS, EMPLOYEES, FELLOWS, AND TO THE EXTENT WORKING ON IDI, ITS STUDENTS, CONTRACTORS, AND REPRESENTATIVES.

  8. Indemnification
    You agree to indemnify us and hold us harmless from any and all claims, liabilities, damages, losses and expenses, including reasonable attorneys’ fees and costs, relating to or arising out of (a) your use or attempted use of the Service in violation of these Terms of Service; or (b) your violation of any law or rights of any third party in connection with your use of the Service.

  9. Governing Law/ Jurisdiction
    You agree that the Terms of Service and any claim or dispute arising out of or relating to the Service or Terms of Service will be governed by the laws of the Commonwealth of Massachusetts, excluding its conflicts of laws principles. You agree that all such claims and disputes will be heard and resolved exclusively in the federal or state courts located in and serving Middlesex or Suffolk County, Massachusetts, U.S.A. You consent to the personal jurisdiction of those courts over you for this purpose, and you waive and agree not to assert any objection to such proceedings in those courts (including any defense or objection of lack of proper jurisdiction or inconvenience of forum).

  10. Whole Agreement/ Amendment
    These Terms of Service constitute the entire agreement between you and Harvard with respect to your use of the Service. We reserve the right to amend these Terms of Service at any time. The Service will post notice of changes to the terms on this webpage, and by accessing the Service after modifications to these Terms of Service have been posted, you agree to be bound by all the modified terms. Accordingly, you should periodically revisit this page to review the then-current Terms of Service.

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📚 Institutional Books: Harvard Library — Enriched Text

Institutional Books is a growing corpus of public domain books. This release (IB-HL-ET) is a version of the text present in the Institutional Books: Harvard Library (IB-HL) dataset that has been further processed, filtered and optimized for computational access and model training.

This includes:

  • 983K books, published largely in the 19th and 20th centuries
  • 217B o200k_base tokens
  • 7B sentences in 250 languages, grouped into 1.39B subtopic paragraphs and 297M subtopic sections.
  • HTML-based annotated data at paragraph level, allowing easy filtering

Each record in this dataset can be connected to its metadata via the barcode_src field, also present in the IB-HL, IB-HL Metadata and IB-HL-VE datasets.

Use of this data is governed by the IDI Terms of Use.

The Institutional Data Initiative at the Harvard Law School Library works with knowledge institutions—from libraries and museums to cultural groups and government agencies—to refine and publish their collections as data. Reach out to collaborate on your collections.

More information:

See also:

Quickstart

Here are a few ways to jump into using this dataset.

Zero Configuration

Although a version of the dataset is available with HTML tag annotations, we've provided an opinionated plaintext version in the processed_middlematter_gen column.

from itertools import islice
from datasets import load_dataset

ds = load_dataset(
    "institutional/institutional-books-hl-enriched-text",
    split="train",
    streaming=True
)

for row in islice(ds, 10):     # <-- for demonstration, take 10 books
    print(row['barcode_src'], row['primary_language_gen'])
    processed_text = row['processed_middlematter_gen']
    print(len(processed_text))
    print(processed_text[1000:1200])  # random middle portion of text
    # do_something_with(processed_text)

The processed_middlematter_gen text is deduplicated at the paragraph level across the collection (i.e. each paragraph will appear exactly once) and filtered by bits-per-byte (BPB) across each book (i.e. paragraphs with BPB in the bottom or top 10 percentiles in each book are omitted).

With our parser library

For maximal convenience, we wrote a small parser library to interact with this dataset. This is available at github.com/institutional/institutional-books-enriched-text-parser and has no library dependencies.

With this parser, basic interaction looks like

from itertools import islice
from datasets import load_dataset
from ibet_parser import BookDataset

ds = load_dataset(
    "institutional/institutional-books-hl-enriched-text",
    split="train",
    streaming=True
)
books = BookDataset(ds)

for book in islice(books, 10):  # <-- for demonstration, take 10 books
    print(book.barcode, book.primary_language, book.token_count)
    for paragraph in book.paragraphs:
        print(paragraph.text[:100])

Or slightly more involved:

# same setup as above

for book in islice(books.filter(language="eng"), 10): # books in English
    for p in book.paragraphs.filter(
        language="eng",        # paragraphs in English
        deduplicated=True,     # exclude duplicate paragraphs
        bpb_min=book.bpb.p10,  # omit least perplexing paras
        bpb_max=book.bpb.p90,  # omit most perplexing paras
    ):
        print(p.text)
    ...

Without our parser library

The ibet_parser library is mostly a small wrapper around an html parser. All text annotations for middlematter are in HTML tag attributes. This leads to a 3-step approach to interacting with the data:

  1. Filter by HTML tag
  2. Remove remaining tags
  3. Use the text

For example, to ignore all annotations and directly access the underlying text (using python's built-in HTMLParser):

from html.parser import HTMLParser
from datasets import load_dataset

class StripTags(HTMLParser):
    def __init__(self):
        super().__init__()
        self.text = []

    def handle_data(self, data):
        self.text.append(data)

    def get_text(self):
        return "".join(self.text)


dataset = load_dataset("institutional/institutional-books-hl-enriched-text", split="train", streaming=True)


for row in dataset:
    print(row["barcode_src"])
    parser = StripTags()
    parser.feed(row["middlematter_gen"])
    plain_text = parser.get_text()
    # first 500 chars
    print(plain_text[:500])

    # remove to process all books
    break

More involved example: duplicates and bits-per-byte

For a more involved example — to filter out paragraphs that are duplicated elsewhere in the dataset or having very low or very high bits-per-byte (BPB):

from html.parser import HTMLParser
from datasets import load_dataset

class FilteredExtractor(HTMLParser):
    def __init__(self, p10, p90):
        super().__init__()
        self.p10 = p10
        self.p90 = p90
        self.paragraphs = []
        self._current_text = []
        self._in_duplicate = False
        self._in_p = False
        self._current_bpb = None

    def handle_starttag(self, tag, attrs):
        attrs = dict(attrs)
        if tag == "aside":
            self._in_duplicate = True
        elif tag == "p":
            self._in_p = True
            self._current_text = []
            bpb = attrs.get("data-bpb")
            self._current_bpb = float(bpb) if bpb else None
  
    def handle_endtag(self, tag):
        if tag == "aside":
            self._in_duplicate = False
        elif tag == "p":
            if (
                not self._in_duplicate
                and self._current_bpb is not None
                and self.p10 <= self._current_bpb <= self.p90
            ):
                self.paragraphs.append("".join(self._current_text))
            self._in_p = False
            self._current_bpb = None
  
    def handle_data(self, data):
        if self._in_p:
            self._current_text.append(data)

dataset = load_dataset("institutional/institutional-books-hl-enriched-text", split="train", streaming=True)

for row in dataset:
    print(row["barcode_src"])
    p10 = row["bpb_p10_gen"]
    p90 = row["bpb_p90_gen"]

    parser = FilteredExtractor(p10, p90)
    parser.feed(row["middlematter_gen"])

    for paragraph in parser.paragraphs[:20]:
        # print first 20 non-duplicate, middle-bpb paragraphs.
        print(paragraph)

    # remove to process all books
    break

Fields

Suffixes

Suffix Description
_src "From source". This field's data comes from information we gathered from the collection itself.
_gen "Generated". This field's data was generated as part of our analysis / post-processing.

Row-level fields

Field name Type Description Ref. in technical report
barcode_src String The volume's barcode. Serves as a primary key/identifier and join key to IB-HL. –
primary_language_gen String ISO 639-3 code for the main language of this book, taken from IB-HL for convenience. –
language_distribution_gen List[Dict] Distribution of languages detected by our per-paragraph text-level analysis, as a list of {language, proportion} entries ordered most- to least-common. Only languages with at least 5 paragraphs are kept. §4.12
token_count_gen Int Total tokens in middlematter, as measured with o200k_base. §4.12
char_count_gen Int Total characters in middlematter. §4.12
word_count_gen Int Total words in middlematter. §4.12
sentence_count_gen Int Total sentences in middlematter. §4.8
paragraph_count_gen Int Total subtopic paragraphs in middlematter. §4.9
section_count_gen Int Total subtopic sections in middlematter. §4.9
bigram_count_gen Int Total bigrams in middlematter. §4.12
bigram_count_unique_gen Int Total unique bigrams in middlematter. §4.12
trigram_count_gen Int Total trigrams in middlematter. §4.12
trigram_count_unique_gen Int Total unique trigrams in middlematter. §4.12
tokenizability_ratio_gen Float Measure of how close to 1.25 o200k_base tokens per word this text is. §4.12
bpb_min_gen Float Minimal bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_max_gen Float Maximal bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_median_gen Float Median bits-per-byte of paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_avg_gen Float Average bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_p10_gen Float 10th percentile of bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_p30_gen Float 30th percentile of bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_p70_gen Float 70th percentile of bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
bpb_p90_gen Float 90th percentile of bits-per-byte across paragraphs in volume according to Qwen/Qwen3-0.6B-Base. §4.11
processed_middlematter_gen String Plaintext middlematter. Duplicates are omitted. Paragraphs with BPB below p10 or above p90 are also omitted. §4.12
frontmatter_gen String Combined HTML-annotated frontmatter. §4.4 and §4.12
middlematter_gen String Combined HTML-annotated middlematter. §4.12
backmatter_gen String Combined HTML-annotated backmatter. §4.4 and §4.12

Terms of Use

This dataset is shared by the Institutional Data Initiative for research and public-interest use (the “Service”). These terms are intended to support experimentation while encouraging collaboration and feedback as we refine the dataset and work with contributing institutions to define shared, long-term norms for open data reuse. To share questions or feedback, contact us at contact@institutional.org.

By accessing or downloading the dataset or otherwise using the Service, you agree to the following:

  1. Noncommercial Use Only
    You may use the Service solely for noncommercial purposes. Open-source projects and other public-use efforts are welcome, even if they may indirectly support commercial use, so long as they are unaffiliated with commercial actors or intent.

    If you are affiliated with a commercial organization or plan to use the Service for commercial purposes (including AI model training), you will contact us first at contact@institutional.org.

  2. No Redistribution
    You may not share or redistribute the Service or any of the data provided through the Service, in whole or in part, including through public repositories or aggregators. If you want others to access it, please direct them to the attribution link.

  3. Derivative Works
    You may create derivative works for noncommercial use, but you may not make available any such derivative works that substantially reproduce the original dataset. Only outputs that are significantly transformed and cannot substitute for the original—such as evaluations, summary statistics, or visualizations—may be shared, with attribution.

  4. Attribution
    If you use the dataset in public-facing work, you must include attribution substantially similar to:

    Institutional Books provided by the Institutional Data Initiative with source material contributed by Harvard Library, available at https://institutional.org.

    Minor modifications to fit citation style or formatting are permitted, provided the essential elements remain intact.

  5. Provisional Terms
    These terms apply only to this release and may change. We are actively working with contributing institutions to develop a long-term framework for responsible, open data sharing.

  6. DISCLAIMER OF WARRANTY
    TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, ACCESS TO “SERVICE IS PROVIDED “AS IS” WITHOUT WARRANTY OF ANY KIND (EXPRESS, IMPLIED, OR OTHERWISE), INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. WE DO NOT WARRANT THAT THE SERVICE WILL OPERATE IN AN UNINTERRUPTED OR ERROR-FREE MANNER OR THAT THE SERVICE IS FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS. WITHOUT LIMITING THE FOREGOING, WE DO NOT WARRANT THAT (A) THE SERVICE WILL MEET YOUR REQUIREMENTS OR EXPECTATIONS OR ACHIEVE THE INTENDED PURPOSES; (B) THE SERVICE WILL NOT EXPERIENCE OUTAGES OR OTHERWISE WILL BE UNINTERRUPTED, TIMELY, OR SECURE; (C) THE INFORMATION OR SERVICES OBTAINED THROUGH OR FROM THE SERVICE WILL BE ACCURATE, COMPLETE, CURRENT, ERROR-FREE, OR RELIABLE; (D) ANY DEFECTS IN OR ON THE SERVICE WILL BE CORRECTED; OR (E) THAT ANY POLLS OR OTHER SOLICITATIONS OF INFORMATION POSTED THROUGH THE SERVICE BY YOU OR OTHER USERS ARE SAFE OR APPROPRIATE FOR YOUR OR OTHER USERS’ PARTICIPATION. WE MAKE NO REPRESENTATION OR WARRANTY REGARDING YOUR ABILITY TO TRANSMIT AND RECEIVE INFORMATION FROM OR THROUGH THE SERVICE, AND YOU AGREE AND ACKNOWLEDGE THAT YOUR ABILITY TO ACCESS THE SERVICE MAY BE IMPAIRED.

  7. LIMITATION OF LIABILITY
    EXCEPT INSOFAR AS THE FOLLOWING LIMITATION MAY BE PROHIBITED BY APPLICABLE LAW, WE SHALL NOT BE LIABLE TO YOU OR TO ANY THIRD PARTY FOR ANY DIRECT, CONSEQUENTIAL, INDIRECT, PUNITIVE, SPECIAL, OR INCIDENTAL DAMAGES, WHETHER FORESEEABLE OR UNFORESEEABLE (INCLUDING, BUT NOT LIMITED TO, LOSS OF PROFITS OR EARNING POWER, LOSS OF DATA, LOSSES DUE TO ERRORS OR INTERRUPTION IN AVAILABILITY OF THE SERVICE, UNAVAILABILITY OF ANY SERVICE, SERVER, OR COMMUNICATIONS FACILITY, OR DAMAGES DUE TO ACTS OR OMISSIONS OF OTHERS USING THE SERVICE), ARISING OUT OF OR RELATING TO THE SERVICE, INCLUDING WITHOUT LIMITATION YOUR AND OTHERS’ USE OF OR INABILITY TO USE THE SERVICE, OR YOUR RELIANCE UPON INFORMATION OBTAINED FROM OR THROUGH THE SERVICE, WHETHER BASED IN CONTRACT, TORT, STATUTORY, OR OTHER LAW. OUR TOTAL CUMULATIVE LIABILITY TO YOU ARISING OUT OF OR RELATED TO THE SERVICE (INCLUDING, WITHOUT LIMITATION, IN THE WAYS DESCRIBED IN THE PRECEDING SENTENCE), WHETHER BASED IN CONTRACT, TORT, STATUTORY, OR OTHER LAW, WILL NOT EXCEED THE AMOUNT, IF ANY, THAT YOU PAID US TO USE THE SERVICE IN THE TWELVE MONTHS PRECEDING THE CLAIM. THE DISCLAIMERS AND LIMITATIONS SET FORTH IN THIS SECTION SHALL APPLY, TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, WHETHER OR NOT WE HAVE BEEN NEGLIGENT OR OTHERWISE AT FAULT.
    YOU ACKNOWLEDGE THAT, FOR PURPOSES OF THE FOREGOING DISCLAIMERS AND LIMITATIONS, AS WELL AS THE INDEMNITY PROVISION IN SECTION 8 BELOW, THE TERMS “WE,” “OUR,” “US,” “INSTITUTIONAL DATA INITIATIVE,” AND “IDI” INCLUDE THE CORPORATE BODY PRESIDENT AND FELLOWS OF HARVARD COLLEGE, ALSO KNOWN AS HARVARD UNIVERSITY, AND ITS VARIOUS SCHOOLS, THE MEMBERS OF ITS GOVERNING BOARDS, AND ITS OFFICERS, FACULTY MEMBERS, EMPLOYEES, FELLOWS, AND TO THE EXTENT WORKING ON IDI, ITS STUDENTS, CONTRACTORS, AND REPRESENTATIVES.

  8. Indemnification
    You agree to indemnify us and hold us harmless from any and all claims, liabilities, damages, losses and expenses, including reasonable attorneys’ fees and costs, relating to or arising out of (a) your use or attempted use of the Service in violation of these Terms of Service; or (b) your violation of any law or rights of any third party in connection with your use of the Service.

  9. Governing Law/ Jurisdiction
    You agree that the Terms of Service and any claim or dispute arising out of or relating to the Service or Terms of Service will be governed by the laws of the Commonwealth of Massachusetts, excluding its conflicts of laws principles. You agree that all such claims and disputes will be heard and resolved exclusively in the federal or state courts located in and serving Middlesex or Suffolk County, Massachusetts, U.S.A. You consent to the personal jurisdiction of those courts over you for this purpose, and you waive and agree not to assert any objection to such proceedings in those courts (including any defense or objection of lack of proper jurisdiction or inconvenience of forum).

  10. Whole Agreement/ Amendment
    These Terms of Service constitute the entire agreement between you and Harvard with respect to your use of the Service. We reserve the right to amend these Terms of Service at any time. The Service will post notice of changes to the terms on this webpage, and by accessing the Service after modifications to these Terms of Service have been posted, you agree to be bound by all the modified terms. Accordingly, you should periodically revisit this page to review the then-current Terms of Service.

Disclaimers

Harmful Language and Content in this Dataset

This dataset is a collection of historical works that reflect the language, imagery, culture, and perspectives of their time. Users should be aware that some materials may contain language or portrayals that are outdated, offensive, or harmful today, such as racism, sexism, colonial attitudes, and other forms of discrimination. Some content may include inaccurate information, providing insight into historical contexts that existed at the time of writing. The materials are maintained in their original form to retain contextual understanding and facilitate research efforts, but we encourage critical awareness and cultural sensitivity for the creators and/or subjects of the collection. These materials are offered as part of a historical perspective, but should not be considered a stand-alone research collection constructed to give a balanced perspective on any topic.

Harmful Language in Bibliographic Description

Metadata for this collection may contain language that is overtly or implicitly harmful, outdated, or biased, or may by omission fail to represent important perspectives. Metadata may contain language created decades ago. It is common practice within the field of library science to reuse descriptions provided from the creator of the materials. While in some instances this allows communities and individuals to represent their materials in their own words, unexamined use of this practice may mean that racist or other offensive terminologies appear in our description. We also use national standardized terms in our work that can be outdated and harmful. Note that terminology in historical materials and in library descriptions does not always match the language we currently understand to be preferred by members of the communities depicted.

Furthermore, we acknowledge that the act of collecting materials is not always neutral, and the work of describing and classifying library materials is influenced by inherent personal, institutional, and societal biases. Outdated or offensive terminologies may be present in metadata such as subject headings, and harmful language or bias may be introduced by catalogers supplying titles and descriptions. In other cases, books themselves present racist, offensive or otherwise harmful viewpoints in titles or descriptions that are routinely transcribed by catalogers.

Note: Some language in this statement was adopted from Harvard Library’s statement on Harmful Language in Library collections.

Generated and Experimental Content

This dataset contains generated and/or experimental content. While reasonable care was taken to ensure its quality, it is provided "as is," without warranties of any kind. It may contain errors or inaccuracies; users should verify the data independently and apply their own judgment.

Rights Determination Statement

We respect the intellectual property rights of authors, publishers, and other rights holders. While we have taken deliberate steps to include only those volumes for which there is no known copyright restriction, specifically those identified by the HathiTrust Digital Library with a status of “public domain,” “public domain in the United States,” or “CC-Zero,” copyright determinations are complex and context-dependent, and occasionally subject to error.

While this is relatively low risk, some volumes in this dataset may be in the public domain in the United States but still subject to copyright or other rights protections in other jurisdictions. Additionally, the absence of an explicit copyright claim or rights status does not guarantee that a work is in the public domain, either in the U.S. or abroad. Information about the copyright status of individual volumes is provided on a good-faith basis and reflects available data at the time of determination, but we cannot guarantee its completeness or accuracy.

Users of this dataset will be solely responsible for making independent legal assessments about how and where they use the materials. Some uses of materials may also be restricted by trademark, privacy, publicity rights, or other such rights or restrictions. It is the user's sole responsibility to consider the possibility that such rights or restrictions may be involved and to secure any needed permissions. If any rights holder believes that a work included in this release is misidentified or improperly included, we welcome contact and will promptly review any concerns. Our goal is to provide broad public access while maintaining respect for intellectual property rights and ensuring responsible data stewardship.

Cite 

@misc{lowryduda2026institutionalbooksenriched,
      title={Institutional Books - Enriched Text: A customizable multilingual open-source pipeline for denoising, deduplicating, and annotating OCR text at scale}, 
      author={David Lowry-Duda and Matteo Cargnelutti and Catherine Brobston and Salwa Ismail and Greg Leppert and Amanda Watson and Jonathan Zittrain},
      year={2026},
      eprint={2608.19026},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.19026}, 
}

@misc{cargnelutti2025institutionalbooks10242b,
      title={Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability}, 
      author={Matteo Cargnelutti and Catherine Brobston and John Hess and Jack Cushman and Kristi Mukk and Aristana Scourtas and Kyle Courtney and Greg Leppert and Amanda Watson and Martha Whitehead and Jonathan Zittrain},
      year={2025},
      eprint={2506.08300},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.08300}, 
}
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