Overview

10x Genomics maintains a repository of publicly available datasets generated through three main platforms: Chromium, Visium, and Xenium. The repository includes Chromium single-cell molecular profiles, Visium spatial transcriptomics (ST) data, and Xenium single-cell spatial gene expression measurements. Datasets span a wide range of tissue types, species, and disease states, with each platform offering multiple dataset formats and analysis outputs depending on the assay chosen. As an example, the Human Glioblastoma: Whole Transcriptome Analysis dataset is a Visium dataset containing ST data on fresh frozen human glioblastoma multiforme tissue, and will be referenced throughout this overview to illustrate typical dataset structure and content.

Methodology and Generation

Datasets in the 10x repository are generated using standardized wet-lab and computational protocols specific to each platform. For Visium datasets, tissue is typically obtained from a specialized tissue bank (e.g., BioIVT Asterand, the source for the glioblastoma dataset), encased in a supportive medium, rapidly frozen, and sliced into 10 µm-thick layers. A slice of tissue is placed onto a Visium Gene Expression slide, which is embedded with thousands of microscopic capture spots acting as biological barcodes. The tissue is fixed (typically with methanol), stained with Hematoxylin and Eosin (H&E), and imaged using a microscope such as the Nikon Eclipse Ti2-E. To capture genetic data, a sequencing instrument such as the Illumina NovaSeq 6000 is used to read the RNA in the tissue and build a genomic library accordingly. The resulting data are run through the software Space Ranger to match the genetic code back to the tissue image. In the glioblastoma dataset specifically, this process yielded 3,468 distinct spots occupied by tissue on the slide, with a median of 4,326 genes and 11,596 unique molecular identifier (UMI) counts detected per spot. Figures like these vary by dataset, tissue type, and sequencing depth.

Access and Analysis

To access a dataset, create a free account through 10x Genomics. Navigate to the Datasets page and use the sidebar to filter by platform, software, species, sample/tissue type, and more. Choose a dataset and look at the "Dataset overview" tab for metadata such as the source, preparation, imaging, sequencing, and key metrics. Look at the "Output and supplemental files" tab for the files to download. For most ST analyses, a minimum of four files are needed: the gene expression matrix, available in either HDF5 (.h5) or Matrix Market (.mtx.gz) format, with .h5 preferred because it is a single file and typically smaller; the spatial coordinates, which map each spot to its location on the slide, available in .csv format; the histology image, required for overlaying expression onto the tissue, available in .png format; and the scale factors, required for aligning spot coordinates with the image, available in .json format. As an example, in the glioblastoma dataset, the display text for the .h5 file is "Feature / barcode matrix HDF5 (filtered)," which downloads a file named "filtered_feature_bc_matrix.h5." For the other three files, the display text "Spatial imaging data" downloads a folder named "spatial" that contains "tissue_positions_list.csv," "tissue_hires_image.png," and "scalefactors_json.json."

In terms of analysis, the software toolkit Seurat is designed to help beginners analyze ST data. Seurat contains user-friendly workflows that streamline complex computational steps to visualize where specific genes are active and identify distinct cell types. Other built-in functions of Seurat include clustering, quality control, and data normalization. While Seurat provides a focused framework for ST analysis, there is also a rapidly expanding ecosystem of ST software. To help navigate these options, Gillespie et al. conducted a meta-review synthesizing benchmarking studies across tissue architecture identification, spatially variable gene detection, cell-cell communication analysis, and deconvolution.This paper can be referenced to help with software selection for ST analysis.

In 10x Genomics gene expression matrices, genes are typically identified using HUGO Gene Nomenclature Committee (HGNC) gene symbols, which are unique abbreviations assigned to each human gene. For example, in the glioblastoma .h5 file, the first three genes are MIR1302-2HG, FAM138A, and OR4F5.

1. Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R. Nat Biotechnol. 2024 Feb;42(2):293-304.

2. Gillespie J, Pietrzak M, Song MA, Chung D. Cells. 2025 Jul 10;14(14):1060.

• Jaume G, Doucet P, Song AH, Lu MY, Almagro-Pérez C, Wagner SJ, Vaidya AJ, Chen RJ, Williamson DF, Kim A, Mahmood F. Adv Neural Inf Process Syst. 2024 Dec 10;37:53798-833.

• Zhang L, Sagan A, Qin B, Wang H, Kim E, Hu B, Osmanbeyoglu HU. Nucleic Acids Res. 2026 Jan 5;54(1):gkaf1473.

• Visium-Generated Spatial Transcriptomics Dataset

10x Genomics maintains a repository of publicly available datasets generated through three main platforms: Chromium, Visium, and Xenium. The repository includes Chromium single-cell molecular profiles, Visium spatial transcriptomics (ST) data, and Xenium single-cell spatial gene expression measurements.

The All of Us dataset is a large, longitudinal U.S. health research resource that integrates clinical, behavioral, genomic, and wearable-device data from a diverse participant population.

The Behavioral Risk Factor Surveillance System (BRFSS) was established in 1984 by the U.S. Centers for Disease Control and Prevention (CDC) to collect prevalence data regarding risk behaviors and preventive health practices among adult residents across all 50 states, the District of Columbia, and participating U.S. territories.

The cBioPortal for Cancer Genomics is an open-access, open-source platform for the interactive exploration of multidimensional cancer genomics datasets with an emphasis on multimodal cancer visualization.

The DailyMed Full SPL Dataset refers to the downloadable full release of all drug labels in the United States.

The Healthcare Cost and Utilization Project (HCUP) is a family of databases, software tools, and related products developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ).

The Medical Information Mart for Intensive Care (MIMIC) is a publicly available repository of deidentified electronic health record (EHR) data from patients admitted to the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts.

The National Plan and Provider Enumeration System (NPPES) is the national registry, operated by the Centers for Medicare & Medicaid Services (CMS), that assigns and maintains the National Provider Identifier (NPI) — the standard unique identifier for health care providers in the United States.

The Pediatric Psychiatry Emergency Services (PediPES) dataset consists of electronic health record (EHR) data for patients younger than 18 years old evaluated in the emergency department at Hasbro Children's Emergency Department in Providence, Rhode Island for psychiatric concerns.

The Surveillance, Epidemiology, and End Results (SEER) Program provides information on cancer statistics in an effort to reduce the cancer burden among the U.S. population.

SyntheticMass is a Synthea-generated data set that contains realistic but fictional residents of the state of Massachusetts.

The SyntheticRI 2026 datasets consist of patient-centric synthetic health records representing the population of Rhode Island.

The Youth Risk Behavior Surveillance System (YRBSS) was established in 1990 by the U.S. Centers for Disease Control and Prevention (CDC) to monitor priority health risk behaviors and experiences that contribute to death, disability, and social problems among youth and young adults in the United States.

Stronghold

A core component of the URSA Initiative is Brown's Stronghold. Stronghold is a secure computing and storage environment that enables Brown researchers and associates to analyze sensitive data while complying with regulatory or contractual requirements.

Stronghold is maintained by the Brown University's Center for Computation and Visualization (CCV) in the Office of Information Technology (OIT). To learn more about Stronghold, you may refer to CCV's Stronghold Documentation.

URSA Stronghold

URSA Stronghold is one of the research "tenants" within Stronghold. It is collaboratively managed by CCV, the Brown Center for Biomedical Informatics (BCBI), and the Biomedical Informatics, Bioinformatics, and Cyberinfrastructure Enhancement (BIBCE) Core of Advance RI-CTR. URSA Stronghold offers both Linux and Windows computing platforms; database management systems such as Microsoft SQL, PostgreSQL, and MySQL; and, a broad range of data analysis tools including Julia, Python, R, SAS, and Stata.

Researchers may work within the URSA tenant or request their own dedicated Stronghold tenant. Refer to Brown CCV's documentation for more information about available features and how to request a Stronghold tenant.

Oscar

Oscar (Ocean State Center for Advanced Resources) is Brown University's high performance computing cluster. Oscar is maintained and supported by Brown's Center for Computation and Visualization ().

Brown’s are used to determine data access and storage options. Stronghold is the institutionally-designated environment for Risk Level 3, identified datasets that include protected health information () or personally identifiable information (PII). With approval from the data provider, researchers may leverage Brown's high-performance computing environment or other Brown-managed computers for the storage and analysis of (Brown Risk Level 2) datasets. In addition, Brown’s can be used for storage of de-identified data and other research files. Data analysis is initiated "locally" on a researcher's computer while the data remain secure.

Overview

SyntheticMass is a Synthea-generated data set that contains realistic but fictional residents of the state of Massachusetts. The synthetic population aims to statistically mirror the state population in terms of demographics, disease burden, vaccinations, medical visits, and social determinants. [1] Refer to the SyntheticMass website for more information.

There are several data sets available on the SyntheticMass downloads page.

• Complete SytheticMass data sets: "SyntheticMass Data Version 2 (24 May, 2017)". This ZIP file is quite large (21GB), so make sure you move the file to a location with enough storage before attempting to unzip.

• Sample data sets (<100MB) containing 100 or 1,000 patient records

• Specialized data sets that have been generated using Synthea by other study teams. These include COVID-19 data sets, a Childhood Obesity data set and more.

The following versions are available for each data set.

• CSV ( describing all CSV tables)

• C-CDA (xml files)

• FHIR (json files)

1. MITRE Corporation. (n.d.). . Retrieved May 1, 2024.

• Walonoski J, Klaus S, Granger E, Hall D, Gregorowicz A, Neyarapally G, Watson A, Eastman J. . Intell Based Med. 2020 Nov;1:100007. doi: 10.1016/j.ibmed.2020.100007. Epub 2020 Oct 2. PMID: 33043312; PMCID: PMC7531559.

• Walonoski J, Kramer M, Nichols J, Quina A, Moesel C, Hall D, Duffett C, Dube K, Gallagher T, McLachlan S. . J Am Med Inform Assoc. 2018 Mar 1;25(3):230-238. doi: 10.1093/jamia/ocx079. Erratum in: J Am Med Inform Assoc. 2018 Jul 1;25(7):921. PMID: 29025144; PMCID: PMC7651916.

Source: npidata_pfile (full file, 2005-05-23 to 2026-04-12). Total records: 9,494,438.

1. Entity Type

Overview

The National Plan and Provider Enumeration System (NPPES) is the national registry, operated by the Centers for Medicare & Medicaid Services (CMS), that assigns and maintains the National Provider Identifier (NPI) — the standard unique identifier for health care providers in the United States. Established under the Administrative Simplification provisions of HIPAA, NPPES enumerates both individual clinicians and provider organizations, each of which receives a single, permanent 10-digit NPI used to identify them in administrative and financial transactions such as claims, eligibility checks, and remittances. [1,5] The system holds millions of active and deactivated records and functions as core infrastructure for the U.S. health care system, where the NPI serves as a common key for linking providers across enrollment, claims, and quality datasets.

Methodology and Generation

The data are generated primarily through self-reported provider enumeration. A provider becomes part of NPPES by applying for an NPI and submitting the information that forms the core of its record. [2]

Upon a successful application, NPPES generates and assigns a single 10-position, intelligence-free NPI. The identifier is assigned once, is permanent, and is not affected by later changes to name, address, or other fields; nor is it reused or reassigned to a different provider.⁵ Because the record is built from provider-supplied information, the database depends on providers to maintain their own data and to submit updates when their circumstances change. To keep provider enrollment and enumeration information aligned, NPPES is integrated with CMS's Provider Enrollment, Chain, and Ownership System (PECOS) through an API. Updates made in PECOS can automatically populate the corresponding NPPES record, helping ensure provider information remains accurate and consistent across both systems.

Records can be deactivated as well as created. NPPES carries a deactivation reason code for each deactivated record — death, disbandment, fraud, or other — together with a deactivation date, and these records are retained (and, since 2018, included in the full monthly file rather than dropped). A deactivated NPI is never reissued to another provider and may be reactivated if the original provider returns to practice. CMS does not independently verify most of the self-reported content of a record, which is the methodological reason the data are comprehensive in coverage but variable in currency and accuracy. The publicly available dataset is the FOIA-disclosable subset of these records, extracted and released on the cycles described in Data Access and Dissemination.

CMS releases only the subset of each NPPES record that is disclosable under the Freedom of Information Act; identifying elements such as Social Security Number, ITIN, and date of birth are withheld. Public dissemination of this FOIA-disclosable data began in September 2007 and is offered through three channels:

1. NPI Registry — a free, real-time web lookup for individual provider records, suited to verifying or retrieving a single NPI.

2. NPI Registry API — a public programming interface (npiregistry.cms.hhs.gov) that returns individual records as JSON in response to per-record queries. It is built for record-level lookups and does not provide bulk export.

3. Downloadable files — the route for working with the dataset in bulk. CMS publishes a full replacement monthly NPI file, weekly incremental files (covering NPIs newly assigned, updated, or deactivated during that week), and a monthly deactivation file in Excel format for users who need only deactivated NPIs and their deactivation dates.

The downloadable package is a single ZIP containing a Read Me, the Code Values reference, a Header (file-layout) document, and the FOIA-disclosable data file itself — a comma-separated file carrying both the header row and the data. Since June 18, 2018, the package has also bundled three reference files that externalize repeating attributes so the main file stays manageable: an Other Name reference file (additional names for Type 2 organizations), a Practice Location reference file (non-primary practice locations for Types 1 and 2), and an Endpoint reference file (health information exchange endpoints for Types 1 and 2). Each links back to the core record by NPI.

The weekly file is supplemental and must be used alongside the most recent monthly full-replacement file, which should be re-downloaded each month to stay current. CMS does not retain historical copies of the downloadable file — each release reflects the present state of NPPES — and the full data file contains several million records (individual and organizational, active and deactivated), making it too large to open directly in standard spreadsheet software.

1. Centers for Medicare & Medicaid Services (US). [Internet]. Baltimore (MD): CMS; 2006 Mar [cited 2026 Jun 12].

2. Centers for Medicare & Medicaid Services (US). [Internet]. Baltimore (MD): CMS; [cited 2026 Jun 12].

3. Centers for Medicare & Medicaid Services (US). [Internet]. Baltimore (MD): CMS; [cited 2026 Jun 12].

• Bindman AB. Medicare Medicaid Res Rev. 2013;3(3):mmrr.003.03.b03. Published 2013 Jul 30. doi:10.5600/mmrr.003.03.b03. PMID: 24753977; PMCID: PMC3983736.

1. Entity Type Code

2. Sole Proprietor

Overview

The Healthcare Cost and Utilization Project (HCUP) is a family of databases, software tools, and related products developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP databases are derived from administrative data and contain encounter-level, clinical, and nonclinical information including all-listed diagnoses and procedures, discharge status, patient demographics, and charges for all patients, regardless of payer, beginning in 1988. [1]

HCUP offers the following databases:

• National (Nationwide) Inpatient Sample (NIS): largest publicly available all-payer hospital inpatient care database in the United States

• Kids' Inpatient Database (KID): hospital inpatient stays for children and is specifically designed to allow researchers to study a broad range of conditions and procedures related to children's health

• Nationwide Emergency Department Sample (NEDS): emergency department (ED) visits that do not result in an admission as well as ED visits that result in an admission to the same hospital

• Nationwide Readmissions Database: designed to support various types of analyses of national readmission rates for all payers and uninsured individuals

• State Inpatient Databases (SID): inpatient discharge abstracts from participating States, translated into a uniform format to facilitate multi-State comparisons and analyses

• State Ambulatory Surgery and Services Databases (SASD): encounter-level data for ambulatory surgery and other outpatient services from hospital-owned facilities

• State Emergency Department Databases (SEDD): discharge information on all emergency department visits that do not result in an admission

Please note: Access to HCUP databases is not free. Database releases must be purchased through the .

Visit for a more information about the databases. Learn more about HCUP on the .

1. Agency for Healthcare Research and Quality. (n.d.). . Retrieved May 1, 2024.

Below is a list of current Health Data Partners (HDPs) engaged with the URSA Initiative.

To learn more about requesting data from any of these HDPs, please submit the Advance RI-CTR Service Request Form.

Brown University Health

Since March 2015, Brown University Health has used LifeChart built on the Epic EHR platform. There are a variety of reporting and analytic tools, such as SlicerDicer and Reporting Workbench, which can be used within Brown University Health. Reports or data extracts can also be requested.

Care New England

Care New England uses the Epic and Cerner electronic health record (EHR) systems for outpatient and inpatient respectively. Both systems have a suite of reporting and analytic tools for use within the health systems. Reports and data extracts can also be requested.

Rhode Island Department of Health

The Rhode Island Department of Health supports and maintains a range of health datasets and systems that can be used for research.

(RI APCD or HealthFacts RI) is a large-scale database that systematically collects healthcare claims data from a variety of payer sources, including Medicare, Medicaid, and RI’s nine largest commercial payers.

Founded in 2001, the Rhode Island Quality Institute (RIQI) is the state-designated Regional Health Information Organization (RHIO). RIQI manages , Rhode Island’s state-designated Health Information Exchange (HIE). CurrentCare contains EHR and health data from all acute care hospital systems in Rhode Island and from many ambulatory and laboratory facilities across the state. RIQI has provided public health and research data to external partners such as the Rhode Island Department of Health and Brown University. More information about the data in CurrentCare can be found in the .

The SyntheticRI 2026 datasets consist of patient-centric synthetic health records representing the population of Rhode Island. The data was generated using Synthea™, an open-source simulation framework that models the clinical journey of synthetic patients from birth to death. The primary objective of these datasets is to provide a realistic environment for healthcare research, software development, and clinical informatics training.

These datasets contains no real-world patient data. Because all individuals and clinical events are computationally generated from the ground up, the records are entirely free of personally identifiable information (PII). This allows for unrestricted sharing and analysis in compliance with HIPAA and GDPR standards.

To use these datasets for educational or reseach purposes, please contact bcbi@brown.edu

The patient population and their respective health trajectories were simulated using the Synthea™ generator (Walonoski et al., 2018). The simulation utilizes a modular, logic-based approach where clinical outcomes are driven by transition-based state machines.

Demographics: Patient demographics, including age, gender, race, and socio-economic status, are statistically aligned with the United States Census Bureau data for the state of Rhode Island to ensure regional representativeness.

Overview

The Medical Information Mart for Intensive Care (MIMIC) is a publicly available repository of deidentified electronic health record (EHR) data from patients admitted to the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts. This resource was created in collaboration with Massachusetts Institute of Technology to lower barriers to reproducible clinical research by making real world hospital data accessible in a deidentified form.

MIMIC is organized into four datasets, which altogether contain five modules. The following info pertain to their latest versions:

MIMIC-IV (version 3.1): This dataset contains info derived from 2008-2022.

• The Hosp module contains hospital-level tables related to 546,028 hospitalizations for 223,452 unique individuals.

• The ICU module is for for intensive care unit admissions and contains 94,458 ICU stays for 65,366 unique individuals

MIMIC-IV-ED (version 2.2):

• The ED module contains tables related to emergency department (ED) visits from 2011 to 2019. There are 425,087 ED visits of which 203,016 led to hospital admissions.

MIMIC-IV-Note (version 2.2):

• The Note module contains tables related to 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department.

MIMIC-CXR (latest version 2.0) :

• The CXR module contains tables related to 377,110 images corresponding to 227,835 radiographic studies in Digital Imaging and Communications in Medicine (DICOM) format with free text radiology reports made in the emergency department from 2011 to 2016

Other notable modules include the following:

• The ECG module contains approximately 800,000 diagnostic electrocardiograms from nearly 160,000 unique patients. These diagnostic ECGs use 12 leads, are 10 seconds in length and are sampled at 500 Hz.

• The ECHO module contains 206,488 echocardiogram studies (including 179,928 transthoracic, 16,389 stress, and 10,171 transesophageal echocardiograms) from 91,372 unique patients between 2008 and 2022.

• The MIMIC-IV-ECHO module contains structured echocardiographic measurements and DICOM files from echocardiography exams.

Data across the MIMIC-IV family were sourced from Beth Israel Deaconess Medical Center's (BIDMC) systems, with each component drawn from their respective sources. For instance, MIMIC-IV Core drew from the hospital-wide EHR and ICU bedside systems, excluding patients under 18 at first visit or on an enhanced-protection list; MIMIC-IV-Note included only notes within one year of a hospital or ED encounter; MIMIC-IV-ED drew emergency department records in XML format; and MIMIC-CXR separately sourced chest radiograph studies via the local EHR and Radiology Information System (RIS), alongside their associated free-text reports.

Raw data were converted into structured, analysis-ready formats while preserving the integrity of the original clinical record. MIMIC-IV was transformed via custom SQL into a schema organized into the Hosp and ICU modules, designed for backward compatibility with MIMIC-III, with tables exported as CSV files and no data cleaning applied so the dataset reflects real-world clinical data as recorded. Out-of-hospital deaths were linked to Massachusetts state vital records using a custom workflow combining exact and fuzzy matching (name, date of birth, social security number), prioritizing sensitivity over specificity. MIMIC-IV-ED's XML extracts were converted into a denormalized relational database to support analysis. MIMIC-CXR integrated three separately processed streams into a single unified dataset: EHR data, DICOM-format imaging, and extracted radiology reports (stripped of administrative/clinical metadata).

All datasets meet HIPAA Safe Harbor requirements, though each applies methods suited to its data type. Structured data across MIMIC-IV, MIMIC-IV-ED, and MIMIC-CXR had direct identifiers removed and replaced with randomly assigned IDs (subject_id, hadm_id, stay_id) that remain consistent across datasets to enable cross-linkage. Dates were shifted using patient-specific offsets to preserve within-patient chronological relationships. Free-text and unstructured data required additional handling: MIMIC-IV-Note and MIMIC-CXR's radiology reports were deidentified using a combination of rule-based methods, a purpose-built neural network, and manual review; MIMIC-IV-ED's free-text fields were scrubbed and replaced with placeholders; and MIMIC-CXR's DICOM imaging had PHI removed from both metadata and pixel data via a custom algorithm with manual validation. Finally, released datasets were exported in a character-based, comma-delimited format for distribution.

The MIMIC team provides and maintains code in the public mimic-code repository () to generate tables and common variables such as severity scores to make analysis easier.

Access to MIMIC is provided via Physionet and restricted. For Brown University staff and students, access can be provided on OSCAR after meeting the following requirements. Researchers generally need to be credentialed, complete human subjects training via CITI courses and sign a data use agreement before obtaining the full dataset. For more detailed instructions, please refer to your Primary Investigator or Brown faculty.

• ICD-9-CM diagnoses

• ICD-9-PCS procedures

• ICD-10-CM diagnoses

• ICD-10-PCS procedures

• National Drug Code (NDC)

• Logical Observation Identifiers, Names and Codes(LOINC)

• DICOM

1. , Pub. L. No. 104-191.

• Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, Lehman LH, Celi LA, Mark RG. Sci Data. 2023 Jan 3;10(1):1. doi: 10.1038/s41597-022-01899-x. Erratum in: Sci Data. 2023 Jan 16;10(1):31. doi: 10.1038/s41597-023-01945-2. Erratum in: Sci Data. 2023 Apr 18;10(1):219. doi: 10.1038/s41597-023-02136-9. PMID: 36596836; PMCID: PMC9810617.

• Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng CY, Mark RG, Horng S. Sci Data. 2019 Dec 12;6(1):317. doi: 10.1038/s41597-019-0322-0. PMID: 31831740; PMCID: PMC6908718.

• Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. Circulation. 2000 Jun 13;101(23):E215-20. doi: 10.1161/01.cir.101.23.e215. PMID: 10851218.

The cBioPortal for Cancer Genomics is an open-access, open-source platform for the interactive exploration of multidimensional cancer genomics datasets with an emphasis on multimodal cancer visualization. This resource aims to significantly lower the barriers between complex genomic data and cancer researchers by providing rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects, empowering researchers, clinicians, and translational scientists to extract biological insights and create clinical applications from these rich datasets. cBioPortal provides visualization, analysis, and download from a wide range of datasets predominantly sourced from The Cancer Genome Atlas, with a growing number of institutional and consortium cohorts added over time. Data types span somatic mutations, structural variants and gene fusions, copy number alterations, mRNA and microRNA expression, DNA methylation, and protein/phosphoprotein levels (RPPA), with samples also linked to clinical and treatment attributes. The platform was originally developed at Memorial Sloan Kettering Cancer Center and is now maintained by a multi-institutional team consisting of MSK, the Dana-Farber Cancer Institute, Princess Margaret Cancer Centre, Children's Hospital of Philadelphia, Caris Life Sciences, The Hyve, SE4BIO, and Bilkent University.

cBioPortal was developed to address the data-integration challenges posed by large-scale cancer genomics projects, making raw data from these efforts more easily and directly available to the cancer research community, supporting both basic and translational research. Each study loaded into the portal is built from a standardized set of source files (clinical data tables, mutation files, copy-number segment files, expression matrices, etc.) that are validated and harmonized before import. cBioPortal generally does not generate primary experimental data; it hosts, curates, annotates, harmonizes, and visualizes data generated by external projects, consortia, publications, or institutional pipelines.

The Behavioral Risk Factor Surveillance System (BRFSS) was established in 1984 by the U.S. Centers for Disease Control and Prevention (CDC) to collect prevalence data regarding risk behaviors and preventive health practices among adult residents across all 50 states, the District of Columbia, and participating U.S. territories. The state-based, cross-sectional telephone survey was conducted monthly by state health departments. The survey has consisted of standard core questions on emerging health issues, rotating core questions asked every other year, optional modules, and state-added questions on health priorities specific to the respective state. Collected data was processed through editing, weighting, and analysis to provide critical information on health status. The BRFSS aims to inform the management of health-related policies and priorities at both the state and national levels. Published data, reports, questionnaires, and analysis tools are publicly accessible through the CDC BRFSS website. Data files are provided in ASCII (fixed record length) and SAS Transport (.XTP) formats.

The BRFSS dataset was created using a standardized questionnaire developed in collaboration between the CDC and U.S. state health departments. The survey consisted of a standard core, rotating core, and optional modules. Data was collected by states or private contractors following CDC protocols, which included multiple call attempts and specific schedules to ensure representative participation on weeknights and weekends. The sampling design utilized disproportionate stratified sampling (DSS) for landlines to increase the efficiency of finding residential numbers and a randomly generated frame for cellular phones. The collected data underwent design weighting–which adjusts for the number of adults and telephones in a household–followed by iterative proportional fitting–which aligns the sample with population demographics like age, sex, race, education, and phone ownership to ensure the final results are representative of the population.

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).

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.

1. AMIA Informatics Summit. (2013 Mar 18.). . Retrieved June 25, 2026.

• Malec SA, Boyce RD. AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:403-412. PMID: 32477661; PMCID: PMC7233092.

The Unified Research data Sharing and Access (URSA) Initiative was launched in 2015 with the overall goal of making electronic health record (EHR) and other health data accessible and usable for research purposes across Rhode Island. This initiative is supported by Advance RI-CTR and coordinated by the Advance RI-CTR Biomedical Informatics, Bioinformatics, and Cyberinfrastructure Enhancement () Core with leadership and expertise provided by the Brown Center for Biomedical Informatics ().

The Advance RI-CTR BIBCE Core collaborates with Brown University's Office of Information Technology and the Division of Research, including Research Integrity and Research Agreements & Contracting, and health data partners across Rhode Island to:

• Coordinate processes for health data sharing and secure access within and across institutions;

1. Main Provider File · npidata_pfile_*.csv

One row per NPI. Repeating groups use a _N suffix; positions reflect the CSV column order (330 columns total).

Non-primary practice locations for Type 1 & Type 2 NPIs. Join back to the main file on NPI.

Additional 'other names' for Type 2 (organization) NPIs beyond the one stored in the main file.

Health IT endpoints (Direct messaging, FHIR servers, HIE, etc.) associated with NPIs.

SyntheticRI 1K

Overview

The Youth Risk Behavior Surveillance System (YRBSS) was established in 1990 by the U.S. Centers for Disease Control and Prevention (CDC) to monitor priority health risk behaviors and experiences that contribute to death, disability, and social problems among youth and young adults in the United States. The system monitors critical variables established during childhood and adolescence, including demographics, substance use, sexual behaviors, violence, as well as other student health behaviors and experiences. Data has been collected from over 5 million high school students through more than 2,300 separate surveys at national, state, territorial, tribal, and local levels. The YRBSS aims to determine the prevalence of health risk behaviors and assess trends over time. Published data, reports, questionnaires, and analysis tools are publicly accessible through the CDC YRBSS website. Data files are provided in Access and ASCII formats. All other data must be requested through the YRBSS Data Request Form.

Methodology and Generation

The YRBSS was created using a three-stage cluster sample design to produce a representative sample of 9th through 12th grade students in both public and private high schools. The selection process involved identifying schools systematically with probability proportional to enrollment, followed by systematic equal probability sampling of classes within a required subject or specific period. The survey has been distributed every two years. Once data was collected, responses underwent automated data edits to ensure range validity, logical consistency, and biologically reasonable values for height, weight, and body mass index (BMI). Records that were overly incomplete or with suspicious response patterns were excluded from the final dataset. A weighting factor was applied to each usable record to adjust for nonresponse and oversampling, rendering generalizable data that matches nationwide population projections.

Standardized Healthcare Vocabularies

Internal Question Codes (Questions 1-107)

The core alphanumeric identifiers used to categorize each survey question and its corresponding clinical or behavioral concept.

A standardized system of calculated variables where "1" represents a specific "Response of Interest" (ROI) and "2" represents all other valid responses.

The specific reference data and sex-specific percentile limits used to define clinical classifications for BMI, Overweight, and Obesity.

A nominal categorical classification system (values 1-8) that standardizes the reporting of American Indian/Alaskan Native, Asian, Black, Native Hawaiian/Pacific Islander, White, and Hispanic/Latino identities.

Standardized response options used to identify and categorize sexual minority youth (ie. gay, lesbian, or bisexual).

Standardized variables required for statistical software to account for the three-stage cluster sample design, ensuring that prevalence estimates accurately represent the national youth population.

Standardized, shortened versions of response options (under 40 characters) used specifically as value labels in datasets and programs like SAS and SPSS to maintain technical clarity.

1. U.S. Centers for Disease Control and Prevention. [Internet]. 2024. Site Index.

2. U.S. Centers for Disease Control and Prevention. [Internet]. 2023 Sep.

• Brener ND, Mpofu JJ, Krause KH, Everett Jones S, Thornton JE, Myles Z, et al. . MMWR Suppl. 2024 Oct 10;73(4):1–12. doi:10.15585/mmwr.su7304a1 PubMed PMID: 39378301; PubMed Central PMCID: PMC11559678.

• Jorge V. Verlenden P, Ari Fodeman P, Natalie Wilkins P, Sherry Everett Jones P, Shamia Moore MPH, Kelly Cornett MS, et al. . MMWR Suppl. 2024;73. doi:10.15585/mmwr.su7304a9

• Swedo EA, Niolon PH, Anderson KN, Li J, Brener N, Mpofu J, et al. . Pediatrics. 2024 Nov 1;154(5):e2024066633. doi:10.1542/peds.2024-066633 PubMed PMID: 39463258; PubMed Central PMCID: PMC11756604.