SlowGuess/NeurIPS_2023_OCR huggingface dataset:
- Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.  
N/A.
- Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.  
N/A.
- Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description. N/A.
- Any other comments? No.
G.3 Collection Process
As with the questions in the previous section, dataset creators should read through these questions prior to any data collection to flag potential issues and then provide answers once collection is complete. In addition to the goals outlined in the previous section, the questions in this section are designed to elicit information that may help researchers and practitioners to create alternative datasets with similar characteristics. Again, questions that apply only to datasets that relate to people are grouped together at the end of the section.
- 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 the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.
The dataset is linked from other 6 datasets. Please find the contribution of original daatsets in Appendix E.1)
- What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated?
We used both algorithm and human efforts to collect the data. Algorithms were used to choose hard samples from various datasets. We then used two human groups and their agreement to make sure the high quality of the process. Details for the human validation in Sec. 4.4. Finally, we removed samples that have debatable labels (e.g. sunglass vs. sunglasses).
- If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?
No, the dataset was not a subset of a larger set.
- 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)?
In the data collection process, we involved students who voluntarily participated.
- 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.
Feedback data was collected from April 20 2023 - May 4 2023.
- 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.
N/A. In this study, humans are not the subjects. Their voluntary feedback, however, is used to filter out incorrectly labelled samples from the original 6 datasets.
- Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?
We only involved individuals in the label verification step (i.e. 3133 samples). The answers from individuals directly affect if one of those 3133 samples will be kept or not.
- 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. N/A. In this study, humans are not the subjects. Their voluntary feedback, however, is used to filter out incorrectly labelled samples from the original 6 datasets.
- 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.
G.4 Preprocessing/cleaning/labeling
Dataset creators should read through these questions prior to any preprocessing, cleaning, or labeling and then provide answers once these tasks are complete. The questions in this section are intended to provide dataset consumers with the information they need to determine whether the "raw" data has been processed in ways that are compatible with their chosen tasks. For example, text that has been converted into a "bag-of-words" is not suitable for tasks involving word order.
- 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 remaining questions in this section.
Yes, we did cleaning up the data. We removed 370 images associated with the labels sunglass, sunglasses, tub, bathtub, cradle, bassinet, projectile, and missile, i.e., the classes that often contain similar images that belong to more than one class. Also, the agreement human setup in Sec. 4.4 helps us remove bad samples.
- Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the "raw" data.
No, we did not save. However, the "raw" data (i.e. six image datasets) can be found on the Internet.
- Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.
Yes, we provided the source code for the algorithms that can be found at here.
G.5 Uses
The questions in this section are intended to encourage dataset creators to reflect on the tasks for which the dataset should and should not be used. By explicitly highlighting these tasks, dataset creators can help dataset consumers to make informed decisions, thereby avoiding potential risks or harms.
- Has the dataset been used for any tasks already? If so, please provide a description.
The dataset was used for image classification in this paper.
- 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.
The papers that use the dataset could be found at this paperwithcode repository.
- What (other) tasks could the dataset be used for?
Studying the effects of upscaling on image classification.
- 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 dataset consumer 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 risks or harms (e.g., legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms?
- Are there tasks for which the dataset should not be used? If so, please provide a description.
G.6 Distribution
Dataset creators should provide answers to these questions prior to distributing the dataset either internally within the entity on behalf of which the dataset was created or externally to third parties.
- 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.
The dataset will be shared with the common public.
- How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?
It has a GitHub repository and Hugging Face page. It has no DOI.
- When will the dataset be distributed?
Until June 5 2023, the dataset had been already distributed.
- 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.
- 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 such restrictions.
- 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.
G.7 Maintenance
As with the questions in the previous section, dataset creators should provide answers to these questions prior to distributing the dataset. The questions in this section are intended to encourage dataset creators to plan for dataset maintenance and communicate this plan to dataset consumers.
- Who will be supporting/hosting/maintaining the dataset?
The authors of the paper will be supporting/hosting/maintaining the dataset.
- How can the owner/curator/manager of the dataset be contacted (e.g., email address)?
Please reach out to mtaesiri@gmail.com and anh.ng8@gmail.com.
- Is there an erratum? If so, please provide a link or other access point.
No, 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 dataset consumers (e.g., mailing list, GitHub)?
Yes, we anticipate either correcting the labels or removing some images entirely to reduce the noise in the dataset. We manage the versioning through the Hugging Face Dataset platform.
- If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the 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.
- 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 dataset consumers.
No, the older versions will not be updated. Users are encouraged to use the latest version.
- 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 dataset consumers? If so, please provide a description.
Please reach out to mtaesiri@gmail.com or anhng8@gmail.com.