> For the complete documentation index, see [llms.txt](https://docs.bcbi.brown.edu/codiac-for-health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bcbi.brown.edu/codiac-for-health/ursa-ri/datasets/dailymed-spl-dataset.md). # DailyMed SPL Dataset ## Overview The DailyMed Full SPL Dataset refers to the downloadable full release of all drug labels in the United States. The dataset is hosted by the National Library of Medicine of the National Institute of Health (NIH). There are five subsets of drug labels available as full SPL releases: human prescription labels, human over-the-counter (OTC) labels, homeopathic labels, animal labels, and remaining labels. ## Methodology and Generation Pharmaceutical companies are required to submit a standard product label (SPL) to the Food and Drug Administration (FDA). SPLs are typically encoded in XML format, standardized by the Health Level Seven International (HL7) schema to ensure the labels are organized and machine readable. This includes the tagging of various sections within the label using Logical Observation Identifiers Names and Codes (LOINC®). After submission to the FDA, labels are stored by the National Library of Medicine, which makes all SPLs available for download via DailyMed. ## Access and Analysis Accessing the Full SPL release through DailyMed is completely free and does not require an account. Downloading the largest of the datasets requires at least 16.38GB of space on the user’s device. To download, navigate to the DailyMed website, then click “Download Data” under the header NLM SPL Resources. The page will present the option to download full drug labels, indexing and risk evaluation and mitigation strategy (REMS) files, or mapping files that link an SPL’s set id to other relevant information and metadata. To view the full SPL release with labels, click on the first. The full releases will be at the bottom of this page in five parts. A more common way of leveraging the dataset is through DailyMed’s API client, RESTful. Querying via the RESTful API requires no account, key, or authentication, and returns data in either XML or JSON format. Related endpoints allow lookups by NDC code (/ndcs), drug name (/drugnames), RxNorm concept (/rxcuis), active ingredient (/uniis), and pharmacologic class (/drugclasses). ## Standardized Healthcare Vocabularies The main way by which sections of an SPL are standardized is through Logical Observation Identifiers Names and Codes (LOINC®) code tagging. This allows for streamlined indexing into areas such as indications and usage, adverse reactions, dosage and administration, and other common sections. ## References 1. AMIA Informatics Summit. (2013 Mar 18.). [Extending the “Web of Drug Identity” with Knowledge Extracted from United States Product Labels](https://pmc.ncbi.nlm.nih.gov/articles/PMC3814463/). Retrieved June 25, 2026. ## Resources ### Articles * Malec SA, Boyce RD. [Exploring Novel Computable Knowledge in Structured Drug Product Labels.](https://pmc.ncbi.nlm.nih.gov/articles/PMC7233092/) AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:403-412. PMID: 32477661; PMCID: PMC7233092. ### Links * [DailyMed Full SPL Release download link](https://dailymed.nlm.nih.gov/dailymed/spl-resources-all-drug-labels.cfm) * [DailyMed website](https://dailymed.nlm.nih.gov/dailymed/index.cfm) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.bcbi.brown.edu/codiac-for-health/ursa-ri/datasets/dailymed-spl-dataset.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.