SlowGuess/NeurIPS_2023_OCR huggingface dataset:
How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.
The pipeline used to create layouts and article transcriptions from page images is described in detail within the paper. The dataset described here is the output of that pipeline.
What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated?
The data extraction pipeline is described and evaluated in the main paper text.
If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?
Release 1.0 will include everything in the Chronicling America scan collection.
Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?
A large group of professors, research assistants, and students collaborated on all aspects of the data collection process, including labeling training data, training and validating models, data engineering, and conceptual design. All were compensated for their work, according to the regulations of Harvard University and New York University.
Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news
articles)? If not, please describe the timeframe in which the data associated with the instances was created.
Scans from Chronicling America were processed between 6/1/23 and 6/7/23. The data associated with the instances were created between 1780 and 1963, when they were published in local newspapers.
Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.
The data are entirely in the public domain and hence do not fall under the jurisdiction of university institutional review boards.
Yes, the articles in the dataset talk about people.
Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?
We collected this data from a third party, the Library of Congress, which has verified that all data are in the public domain.
Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.
The data are in the public domain and cover many millions of individuals, most of whom are deceased.
Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.
If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).
Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.
No such analysis has been conducted.
Preprocessing/cleaning/labeling
Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of
instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section.
No preprocessing was conducted.
Has the dataset been used for any tasks already? If so, please provide a description.
Example uses are detailed in the main text.
Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point.
No such repository currently exists.
What (other) tasks could the dataset be used for?
There are a large number of potential uses in the social sciences, digital humanities, and deep learning research, discussed in more detail in the main text.
Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?
This dataset contains unfiltered content composed by newspaper editors, columnists, and other sources. It reflects their biases and any factual errors that they made.
Are there tasks for which the dataset should not be used? If so, please provide a description.
We would urge caution in using the data to train generative language models - without additional filtering - as it contains content that many would consider toxic.
Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description.
Yes. The dataset is available for public use.
How will the dataset will be distributed (e.g., tarball on website, API, GitHub) Does the dataset have a digital object identifier (DOI)?
The dataset is available via HuggingFace. Download and instructions are at https://huggingface.co/datasets/dell-research-harvard/AmericanStories. The dataset's DOI is: https://doi.org/10.57967/hf/0757
When will the dataset be distributed?
The dataset is currently available.
Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license
and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.
The dataset is distributed under a Creative Commons CC-BY license. The terms of this license can be viewed at https://creativecommons.org/licenses/by/2.0/
Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.
There are no third party IP-based or other restrictions on the data.
Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.
No export controls or other regulatory restrictions apply to the dataset or to individual instances.
Who will be supporting/hosting/maintaining the dataset?
HuggingFace will continue to host the dataset. The authors will provide support, updates, and maintenance.
How can the owner/curator/manager of the dataset be contacted (e.g., email address)?
Melissa Dell can be contacted via email at melissadell@fas.harvard.edu
Is there an erratum? If so, please provide a link or other access point.
There is no erratum.
Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?
The dataset will continue to be updated as new scans are processed. New versions will be added to HuggingFace. Anyone can subscribe to notifications about the dataset via HuggingFace.
If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced.
All data are in the public domain.
Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users.
Older versions of the dataset will still be visible via the HuggingFace repo.
If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process
for communicating/distributing these contributions to other users? If so, please provide a description.
The dataset is privately created and maintained. There is no current plan to allow open source contributions.
Table S-2: Reproducibility checklist
Item	Response	Comment
For all models and algorithms presented, check if you include:
A clear description of the mathematical setting, algorithm, and/or model.	Yes
A clear explanation of any assumptions.	Yes
An analysis of the complexity (time, space, sample size) of any algorithm.	NA	Complexity can depend upon application-specific architecture and methods. We are using transformer-based models within our framework and we have reported the time profile and other related details
For any theoretical claim, check if you include:
A clear statement of the claim.	Yes
A complete proof of the claim.	Yes
For all datasets used, check if you include:
The relevant statistics, such as number of examples	Yes
The details of train / validation / test splits	Yes
An explanation of any data that were excluded, and all pre-processing step.	Yes
A link to a downloadable version of the dataset or simulation environment	Yes	Link to Hugging Face Hub repo provided
For new data collected, a complete description of the data collection process, such as instructions to annotators and methods for quality control.	Yes
For all shared code related to this work, check if you include:
Specification of dependencies.	Yes
Training code.	Yes
Evaluation codes	Yes
(Pre-)trained model(s).	Yes	Available on HuggingFace
README file includes table of results accompanied by precise command to run to produce those results	Yes
For all reported experimental results, check if you include:
The range of hyper-parameters considered, method to select the best hyper-parameter configuration, and specification of all hyper-parameters used to generate results	Yes
The exact number of training and evaluation runs.	Yes
A clear definition of the specific measure or statistics used to report results.	Yes
Table S-2: Reproducibility checklist (Continued)
A description of results with central tendency (e.g. mean) variation (e.g. error bars).	NA
The average runtime for each result, or estimated energy cost.	Yes	Both training and inference times have been reported
A description of the computing infrastructure used	Yes