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README.md
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# Dataset Card for Dataset Name
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United States governmental agencies often make proposed regulations open to the public for comment.
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Proposed regulations are organized into "dockets". This dataset will use Regulation.gov public API
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to aggregate and clean public comments for dockets that mention opioid use.
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Each example will consist of one docket, and include metadata such as docket id, docket title, etc.
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Each docket entry will also include information about the top 10 comments, including comment metadata
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and comment text.
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In this version, the data is called directly from the API, which can result in slow load times.
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If the user wants to simply load a pre-downloaded dataset,
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reference https://huggingface.co/datasets/ro-h/regulatory_comments.
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## Dataset Details
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### Dataset Description and Structure
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This dataset will contain approximately 70 dockets. The number of dockets included are rate-limited by the
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government API. If a larger set of dockets are required, consider requesting a rate-unlimited API key and
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directly calling from the API using [https://huggingface.co/datasets/ro-h/regulatory_comments_api].
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Each docket will be associated with at least one comment. The maximum number of comments per docket is 10.
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Comments will be retrieved in relevance order according to Regulation.gov.
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The following information is included in this dataset:
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**Docket Metadata**
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id (int): A unique numerical identifier assigned to each regulatory docket.
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title (str): The official title or name of the regulatory docket. This title typically summarizes the main issue or area of regulation covered by the docket.
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context (str): The date when the docket was last modified on Regulations.gov.
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purpose (str): Whether the docket was rulemaking, non-rulemaking, or other.
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keywords (list): A string of keywords, as determined by Regulations.gov.
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**Comment Metadata**
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comment_id (int): A unique numerical identifier for each public comment submitted on the docket.
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comment_title (str): The title or subject line of the individual public comment.
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comment_url (str): A URL or web link to the specific comment or docket on Regulations.gov. This allows direct access to the original document or page for replicability purposes.
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comment_date (str): The date when the comment was posted on Regulations.gov. This is important for understanding the timeline of public engagement.
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commenter_fname (str): The first name of the individual or entity that submitted the comment. This could be a person, organization, business, or government entity.
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commenter_lname (str): The last name of the individual or entity that submitted the comment.
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comment_length (int): The length of the comment in terms of the number of characters (spaces included)
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**Comment Content**
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text (str): The actual text of the comment submitted. This is the primary content for analysis, containing the commenter's views, arguments, and feedback on the regulatory matter.
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- **Curated by:** Ro Huang
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### Dataset Sources
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- **Repository:** [https://huggingface.co/datasets/ro-h/regulatory_comments_api]
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- **Original Website:** [https://www.regulations.gov/]
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- **API Website:** [https://open.gsa.gov/api/regulationsgov/]
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## Uses
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This dataset may be used by researchers or policy-stakeholders curious about the influence of
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public comments on regulation development. For example, sentiment analysis may be run on
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comment text; alternatively, simple descriptive analysis on the comment length and agency regulation
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may prove interesting.
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## Dataset Creation
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### Curation Rationale
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After a law is passed, it may require specific details or guidelines to be practically enforceable or operable.
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Federal agencies and the Executive branch engage in rulemaking, which specify the practical ways that legislation
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can get turned into reality. Then, they will open a Public Comment period in which they will receive comments,
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suggestions, and questions on the regulations they proposed. After taking in the feedback, the agency will modify their
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regulation and post a final rule.
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As an example, imagine that the legislative branch of the government passes a bill to increase the number of hospitals
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nationwide. While the Congressman drafting the bill may have provided some general guidelines (e.g., there should be at
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least one hospital in a zip code), there is oftentimes ambiguity on how the bill’s goals should be achieved.
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The Department of Health and Human Services is tasked with implementing this new law, given its relevance to national
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healthcare infrastructure. The agency would draft and publish a set of proposed rules, which might include criteria for
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where new hospitals can be built, standards for hospital facilities, and the process for applying for federal funding.
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During the Public Comment period, healthcare providers, local governments, and the public can provide feedback or express
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concerns about the proposed rules. The agency will then read through these public comments, and modify their regulation
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accordingly.
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While this is a vital part of the United States regulatory process, there is little understanding of how agencies approach
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public comments and modify their proposed regulations. Further, the data extracted from the API is often unclean and difficult
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to navigate. This dataset seeks to offer some clarity through aggregating comments related to Opioid Use Disorders,
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an issue that a diversity of stakeholders have investment in.
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#### Data Collection and Processing
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**Filtering Methods:**
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For each docket, we retrieve relevant metadata such as docket ID,
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title, context, purpose, and keywords. Additionally, the top 10 comments
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for each docket are collected, including their metadata (comment ID, URL, date,
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title, commenter's first and last name) and the comment text itself. The process
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focuses on the first page of 25 comments for each docket, and the top 10 comments
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are selected based on their order of appearance in the API response.
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**Data Normalization:**
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The collected data is normalized into a structured format. Each docket and
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its associated comments are organized into a nested dictionary structure.
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This structure includes key information about the docket and a list of comments,
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each with its detailed metadata.
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**Tools and Libraries Used:**
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Requests Library: Used for making API calls to the Regulations.gov API to fetch dockets and comments data.
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Datasets Library from HuggingFace: Employed for defining and managing the dataset's structure and generation process.
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Python: The entire data collection and processing script is written in Python.
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**Error Handling:**
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In the event of a failed API request (indicated by a non-200 HTTP response status),
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the data collection process for the current docket is halted, and the process moves to the next docket.
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