The purpose of the project is to detect unreported Supplemental Security Income (SSI) recipient admissions to Title XIX institutions. A file containing SSN's of SSI recipients (all eligible individuals and members of eligible couples in current pay) will be matched against the Health Care Financing Administration's (HCFA) Minimum Data Set (MDS) database which contains admission, discharge, re-entry and assessment information about persons in Title XIX facilities for all 50 States and Washington, D.C. This database is updated monthly. The match will produce an output file containing MDS data pertinent to SSI eligibility on matched records. This data will be compared back to the SSR data to generate alerts to the Field Offices for their actions.

Variability in mean payment per physician, number of physicians, and aggregated payments for transactions in the Open Payments database, 2014–2018, for each top-category specialty available for allopathic and osteopathic physicians.

Content

Database of HPSA and Low-Income ZIP Codes for Issuers Subject to the Alternate ECP Standard for the purposes of QHP Certification

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

The Physician Compare website was created by the Centers for Medicare & Medicaid Services (CMS) in December 2010 as required by the Affordable Care Act (ACA) of 2010 to help patients assess and find doctors and hospitals. This dataset contains the information supplied to patients via that website, including patient satisfaction surveys and performance scores across over 100 metrics.

Acknowledgements

This dataset was kindly released by the Centers for Medicare & Medicaid Services. You can find the original copy of the dataset here.

This repository contains the input data used in the Jupyter notebook downloadable from Github here.

Such input data consists in two main datasets:

• A raw (global and regional) flood reports from four different databases.
• A global daily rainfall analysis from NOAA (CPC_GLOBAL_PRCP_v1.0).

The Jupyter notebook runs a Python code that post-processes the raw flood reports, using information extracted from other datasets, to select some reports of interest (mainly regarding pluvial and flash floods). At a later stage, such reports are merged into a single database for global pluvial/flash flood reports. The Jupyter notebook also runs a Metview-Python code to visualize partial and final results as map plots.

The four original databases are:

1. FloodList, FL (Global domain): http://floodlist.com/
2. Emergency Events Database, EMDAT (Global domain): https://www.emdat.be/
3. European Severe Weather Database, ESWD (Europe): https://www.essl.org/cms/european-severe-weather-database/
4. Storm Events Database, SED (USA): https://www.ncdc.noaa.gov/stormevents/

NOTE: For more details about these databases (documentation, licenses, etc.), look at the README.md file.

NOTE: The data in this repository is intended for an exclusive NON-COMMERCIAL academic or personal use, and it is released under the Creative Commons Attribution-ShareAlike 4.0 International Public License. For more information, look at the LICENSE.md file.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 12,2025. The AIDSinfo Drug Database provides fact sheets on HIV/AIDS related drugs. The fact sheets describe the drug''s use, pharmacology, side effects, and other information. The database includes: -Approved and investigational HIV/AIDS related drugs -Three versions of each fact sheet: patient, health professional, and Spanish. AIDSinfo is a 100% federally funded U.S. Department of Health and Human Services (DHHS) project that offers the latest federally approved information on HIV/AIDS clinical research, treatment and prevention, and medical practice guidelines for people living with HIV/AIDS, their families and friends, health care providers, scientists, and researchers. Sponsors: -National Institutes of Health (NIH) Office of AIDS Research National Institute of Allergy and Infectious Diseases (NIAID) National Library of Medicine (NLM) -Health Resources and Services Administration (HRSA) -Centers for Disease Control and Prevention (CDC) -Centers for Medicare and Medicaid Services (CMS)

BackgroundDNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.Methodology/Principal FindingsCancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.Conclusions/SignificanceCMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/.

Performance rates on publicly reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for 2024 reporting. Sources: Mathematica analysis of (1) Quality Measure Reporting (QMR) system reports, (2) Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) data; and (3) National Core Indicators (NCI) data submitted by states to the National Association of State Directors of Developmental Disabilities Services (NASDDDS) and the Human Services Research Institute (HSRI) (The NCI National Team) through the Online Data Entry System (ODESA). Agency for Healthcare Research and Quality (AHRQ) and Centers for Medicare & Medicaid Services (CMS) analysis of the AHRQ CAHPS Database. Dataset revised September 2025. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

From http://www.cms.gov/medicare-coverage-database/staticpages/icd-9-code-lookup.aspx?KeyWord=v60&bc=AAAAAAAAAAAQAA%3d%3d&; Accessed November 1, 2014

The Texas Department of Insurance, Division of Workers' Compensation (DWC) maintains a database of institutional medical billing services (SV2). It contains charges, payments, and treatments billed on a CMS-1450 form (UB-92, UB-04) by hospitals and medical facilities that treat injured employees, excluding ambulatory surgical centers, with dates of service more than five years old. For datasets from the past five years, see institutional medical billing services (SV2) header information. The header identifies insurance carriers, injured employees, employers, place of service, and diagnostic information. The bill header information groups individual line items reported in the detail section. The bill selection date and bill ID must be used to group individual line items into a single bill. Find more information in our institutional medical billing services (SV2) header data dictionary. See institutional medical billing services (SV2) detail information- historical for the corresponding detail records related to this dataset. Go to our page on DWC medical state reporting public use data file (PUDF) to learn more about using this information.

HCPCS Level II codes are alphanumeric medical procedure codes, primarily for non-physician services such as ambulance services, durable medical equipment, prosthetics, orthotics, and supplies when used outside a physician's office.

This Dataset was derived from CMS's database. This is PUBLIC information and I do not OWN this data at all. This dataset was added to Kaggle due to discrepancies in downloading the original .csv provided on the website. Please refer to CMS's primary database for more information regarding this data. Thank you.

Unplanned Hospital Visits: provider data. This data set includes provider data for the hospital return days (or excess days in acute care [EDAC]) measures, the unplanned readmissions measures, and measures of unplanned hospital visits after outpatient procedures. NOTICE: Data from the 1st and 2nd quarters of 2020 are not being reported due to the impact of the COVID-19 pandemic. For more information, please reference https://qualitynet.cms.gov/files/5fb838aef61c410025a64709?filename=2020-111-IP.pdf.

The OPP began using the Prosecution Records Information Systems Management (PRISM) database in 1996, as a replacement and improvement on the Case Management System (CMS). The system allocated numbers to cases, defendants, appeals, legal advice (on, for example, whether actions witnessed constituted a crime, or whether evidence gathered was sufficient to begin a prosecution), briefings, FOI requests and trials.

PRISM also drew on information from both the Magistrates' Court's CourtLink database and the Criminal Trial Listings database (CLTD). This allowed PRISM users access to an overview of the status of a case, its hearings, victims, offences alleged, appeals, witnesses, sentence, presiding Magistrate, prosecutor, counsel for the defence and other similar information. Its other main function was to produce reports broadly aggregating the activities of the DPP, detailing, for example, the number of cases handled over a time period or which were outstanding, expenditure on appeals, or any other combination of fields filtered by the user.

Officers of the DPP, the Magistrates' Court or even prosecutors might all at some stage have entered data residing in PRISM.

CMS differed from PRISM in that only trials already committed, miscellaneous files, some large advice files and direct presentments were allocated a control number.

The number functioning as the DPP's Primary Key, common to (for example) the several phases of a trial, its co-defendants, witnesses and their contact details etc. was the Magistrate Court Ref[erence]. This number was generated by the Magistrates' Court in CourtLink as an ID controlling all aspects of their management of a case.

The Keywords field was used by the DPP to link any records numbered by the CMS (in the form ZA 1234 or ZB 4321) to records in PRISM.

PRISM was not used by DPP to control routine correspondence, minutes, personnel files, payroll or other administrative records. It was, however, a large and complex database, of which only the most salient elements have been described.

Experiments such as CMS (Compact Muon Solenoid, at CERN) have enormous computing requirements for both simulation and subsequent analysis of the recorded data.Within CMS, BOSS [1,2] was developed as a job monitoring framework within the context of local batch farms. Deployment of BOSS on to the Grid would be problematic as it requires direct access to the DBMS from running jobs, raising concerns regarding network access, firewalls, and the distribution of DBMS access credentials to remote sites. We therefore investigated using R-GMA [3] to transport BOSS' monitoring messages from jobs running across LCG testbeds back to a database local to a user.We have written bossmin (C++), a BOSS "emulator" which publishes into R-GMA simple monitoring messages corresponding to a single test job, and bossminj (Java) which comprises both a CMS job simulator and message publisher, and a corresponding archiver to log messages received via R-GMA into a local database. Each bossminj "simulation" task can masquerade as a large number of individual CMS production jobs ("simjobs"), allowing us to stress the R-GMA framework without using significant CPU resources at the remote sites.By comparing the messages submitted to R-GMA by the remote bossminj instances (logged within text files returned via the usual Grid job sandbox mechanism) with those received from R-GMA and stored in the local BOSS database we were able to assess the performance and scalability of the R-GMA framework. Tests on a dedicated testbed in 2003 initially struggled to monitor 400 jobs [4,5] but after improvements to both the code and the infrastructure, the framework was able to monitor 6000 virtual jobs [6]. In October 2005 we tracked 1000 simultaneously-running virtual jobs [7,8] across the LCG 2.6.0 Grid for 6 hours. Of 23000 simjobs submitted, 14000 (61%) ran at a remote site, of which 13683 (98%) transferred all of their messages into our local database. Every single one of the 1017052 individual messages logged as published into R-GMA was also transferred successfully.CounterDemo is a simplified demonstration of message publishing with R-GMA.Materialbossmin_v2.1.zip (3/10/2003): bossmin (v2.1) - BOSS emulator (for R-GMA 3.2.22, for testing basic R-GMA functionality).bossmin_v2.3.zip (7/10/2004): bossmin (v2.3) - BOSS emulator (for LCG 2.2.0 testbed, for testing basic R-GMA functionality).bossminj-NSS05.zip (11/11/2005): R-GMA/BOSS tests for IEEE papers (NSS '05 version, for LCG 2.6.0).CounterDemo_v1.0.zip (28/08/2003): CounterDemo (v1.0) - demo/test.BOSSRGMAtestResults03.zip (3/03/2004): Output files from Grid submissions.ee_results.tar.gz (27/09/2005): Output files from Grid submissions.Young-rgma_res.zip (10/11/2006): Output files from Grid submissions.young.HistTable.sql.gz (23/01/2008): SQL dump of R-GMA HistoryProducer DB table.young.LPTable.sql.gz (23/01/2008): SQL dump of R-GMA LatestProducer DB table.AcknowledgementsHenry Nebrensky wrote bossmin (emulating BOSS' job wrapper) and CounterDemo.Paul Kyberd and Henry Nebrensky wrote bossminj.Henry Nebrensky submitted the jobs to the Grid, monitored their progress and tabulated the results.bossmin and CounterDemo are distibuted as Open Source under the terms of the EU DataGrid Software License. bossmin, bossminj and CounterDemo were first made publicly available on the WWW in 2003.Jobs were submitted to the CMS/LCG0, LCG 2.2.0 and LCG 2.6.0 Grid testbeds. The R-GMA project, as well as this work itself, were supported by GridPP [9] in the UK. Many individuals helped by supporting the underlying Grid and R-GMA frameworks [4-8].DisclaimerThis data is provided in the form of log files and database dumps as saved to disk over a decade ago - timestamps listed above. Supporting information is mostly from memory.References1. C. Grandi and A. Renzi: "Object Based System for Batch Job Submission and Monitoring (BOSS)" CMS Note 2003/005 (2003)2. C. Grandi: "BOSS: a tool for batch job monitoring and book-keeping" in CHEP03 - Computing in High Energy and Nuclear Physics, La Jolla, California USA; Conference record THET001 (2003)3. A. Cooke et al.: "R-GMA: First results after deployment" in CHEP03 - Computing in High Energy and Nuclear Physics, La Jolla, California USA; Conference record MOET004 (2003)4. D. Bonacorsi et al.: “Scalability tests of R-GMA based grid job monitoring system for CMS Monte Carlo data production” in IEEE Nuclear Science Symposium/Medical Imaging Conference, Portland, Oregon USA; Conference Record 3 pp.1630-1632. DOI: 10.1109/NSSMIC.2003.1352190 (2003)5. D. Bonacorsi et al.: “Scalability tests of R-GMA-based grid job monitoring system for CMS Monte Carlo data production” IEEE Transactions on Nuclear Science, 51(6) pp.3026-3029. DOI: 10.1109/TNS.2004.839094 (2004)6. R. Byrom et al.: “Performance of R-GMA based grid job monitoring system for CMS data production” in IEEE Nuclear Science Symposium/Medical Imaging Conference, Rome, Italy; Conference Record 4 pp.2033-2037. DOI: 10.1109/NSSMIC.2004.1462663 (2004)7. R. Byrom et al.: “Performance of R-GMA for monitoring grid jobs for CMS data production” in IEEE Nuclear Science Symposium/Medical Imaging Conference, Fajardo, Puerto Rico; Conference Record pp.860-864 DOI: 10.1109/NSSMIC.2005.1596391 (2005)8. R. Byrom et al.: “Performance of R-GMA for monitoring grid jobs for CMS data production” poster shown at IEEE Nuclear Science Symposium/Medical Imaging Conference, Fajardo, Puerto Rico, 23rd – 29th October 2005. [ BURA ]9. The GridPP Collaboration: “GridPP: development of the UK computing Grid for particle physics” Journal of Physics G: Nuclear and Particle Physics, 32(1) pp. N1-N20. DOI: 10.1088/0954-3899/32/1/N01 (2006)

The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments (CAAs) are part of this process, and provide the foundation upon which a resident's individual care plan is formulated. MDS assessments are completed for all residents in certified nursing homes, regardless of source of payment for the individual resident. MDS assessments are required for residents on admission to the nursing facility, periodically, and on discharge. All assessments are completed within specific guidelines and time frames. In most cases, participants in the assessment process are licensed health care professionals employed by the nursing home. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. When reviewing the MDS 3.0 Frequency files, some common software programs e.g., ‘Microsoft Excel’ might inaccurately strip leading zeros from designated code values (i.e., "01" becomes "1") or misinterpret code ranges as dates (i.e., O0600 ranges such as 02-04 are misread as 04-Feb). As each piece of software is unique, if you encounter an issue when reading the CSV file of Frequency data, please open the file in a plain text editor such as ‘Notepad’ or ‘TextPad’ to review the underlying data, before reaching out to CMS for assistance.

The Rfam database is a collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models (CMs). The families in Rfam break down into three broad functional classes: non-coding RNA genes, structured cis-regulatory elements and self-splicing RNAs. Typically these functional RNAs often have a conserved secondary structure which may be better preserved than the RNA sequence. The CMs used to describe each family are a slightly more complicated relative of the profile hidden Markov models (HMMs) used by Pfam. CMs can simultaneously model RNA sequence and the structure in an elegant and accurate fashion.

Northern elephant seals (Mirounga angustirostris) have been integral to the development and progress of biologging technology and movement data analysis. Adult female elephant seals at Año Nuevo State Park and other colonies along the west coast of North America were tracked annually from 2004 to 2020 for a total of 653 instrument deployments and 561 recoveries. These high-resolution diving and location data have been compiled, curated, and processed. This repository has netCDF files containing the raw tracking and diving data. The processed data are available in a second repository (https://doi.org/10.7291/D18D7W). Methods These data were collected from biotelemetry devices attached to adult female northern elephant seals (Mirounga angustirostris) from 2004 to 2020. The instruments collected locations (Argos and/or GPS) and continuously recorded depth throughout the animals' trips. Data were processed in MATLAB and R using custom code, the IKNOS package for dive data processing, and the aniMotum package for track processing. The details of data collection and processing are documented in the data descriptor paper associated with this dataset. In addition, all code used to process the data are available on GitHub and Zenodo.

The data presented here are freely available for use under the CC0 (Creative Commons Zero), and attribution is encouraged to be given to the data descriptor (DOI: 10.1038/s41597-024-04084-4) and this Dryad repository. We encourage users to reach out to the data owner for richer insight into the dataset. Subsets of this dataset have been made available through other projects and data portals and we caution users that these are not independent northern elephant seal datasets. This includes the AniBOS/MEOP data portal (https://www.meop.net/database/meop-databases/), the Animal Tracking Network (ATN) (https://portal.atn.ioos.us/), Movebank (https://www.movebank.org/cms/movebank-main), and MegaMove (https://megamove.org/data-portal/).

Additional data about the instrumented animals, such as morphometrics, demographics, and other biologging data (e.g., acceleration, jaw motion, temperature), are available for many of these animals but are beyond the scope of this dataset. For more information, contact the author at rholser@ucsc.edu.

Sampling Biases

Generally, we have been careful to select healthy animals for sedation and instrumentation. For animals deployed at Año Nuevo (most of the tracks), typically individuals with known site fidelity to the colony were selected and if age was known it was usually restricted to 4- to 12-year-olds. Furthermore, the data reported here span two decades of work. During this time, different studies prompted additional non-random population sampling. Examples include focusing on one age for a year, repeat tracking the same individuals two trips in a row, and intentionally selecting previously tracked females who had used a coastal foraging strategy. Many individuals in the dataset have been tracked multiple times. We strongly encourage researchers to evaluate the metadata provided carefully and contact the author with inquiries at rholser@ucsc.edu.

Code Availability

All the code written for data processing and NetCDF data import code for MATLAB, R, and Python are available at GitHub (https://github.com/rholser/NES_TrackDive_DataProcessing) and Zenodo (https://doi.org/10.5281/zenodo.12511548). Extensive documentation of functions and scripts is also provided there. In addition, the authors have provided code in Python, R, and MATLAB for basic access to the netCDF files (https://github.com/rholser/NES-Read-netCDF). They should serve as a model to enable users unfamiliar with the format to access the data.

Scenario illustration (Venn diagrams), group description, and allocation method used for each scenario are also presented.

The Table below, updated weekly, contains newly reported, active covered outpatient drugs which were reported by participating drug manufacturers since the last quarterly update of the Drug Products in the Medicaid Drug Rebate Program (MDRP) database. Each file on this table represents a snapshot of data in the system and is not updated by subsequent changes. Once the covered outpatient drugs in each of these files appear in the quarterly MDRP database, the file will be removed from this table. States can utilize these files to identify newly reported covered outpatient drugs.

Description: This consists of the re-implementation, in the MadAnalysis 5 framework, of a CMS search for electroweakinos when they are pair-produced in association with soft leptons. 35.9/fb of LHC proton-proton collisions at 13 TeV has been analyzed.

Information on how to use this code and a detailed validation summary are available on the Public Analysis Database of MadAnalysis. The CMS analysis is documented on the collaboration wiki.