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.

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

Verify the accuracy of SSNs of all individual Medicare providers, owners, managing/directing employees, authorized representatives, ambulance service medical directors, ambulance crew members, technicians, chain organization administrators, Independent Diagnostic Test Facility (IDTF), supervising/directing physicians, and IDTF interpretation service providers. Also included in this Agreement are individual health care providers who apply for a National Provider Identification Number (NPI).

Dataset Summary

This dataset contains CMS information with local and national coverage document data sets (LCD & NCD),as Coverage Articles and [Internet-Only Manuals (IOMs)(https://www.cms.gov/medicare/regulations-guidance/manuals/internet-only-manuals-ioms) A list of Current LCDS, NCDs and Articles is obrained from Medicare Coverage Database. The data itself was obtainted by scrapping the urls and extracting data from the pdf files listed in current articles and current lcds… See the full description on the dataset page: https://huggingface.co/datasets/evekhm/cms_iom_500.

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.

The Revalidation Reassignment List dataset provides information on reassignments of providers who are due for revalidation. Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.This dataset is based on information gathered from the Provider Enrollment, Chain and Ownership System (PECOS).

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 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 Revalidation Due Date List dataset contains revalidation due dates for Medicare providers who are due to revalidate in the following six months. If a provider's due date does not fall within the ensuing six months, the due date is marked 'TBD'. In addition the dataset also includes subfiles with reassignment information for a given provider as well as due date listings for clinics and group practices and their providers. Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.

The Synthetic Patient Data in OMOP Dataset is a synthetic database released by the Centers for Medicare and Medicaid Services (CMS) Medicare Claims Synthetic Public Use Files (SynPUF). It is synthetic data containing 2008-2010 Medicare insurance claims for development and demonstration purposes. It has been converted to the Observational Medical Outcomes Partnership (OMOP) common data model from its original form, CSV, by the open source community as released on GitHub Please refer to the CMS Linkable 2008–2010 Medicare Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) User Manual for details regarding how DE-SynPUF was created." This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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.

The data below 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.

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.

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 is the "Provider of Service" extract from the Quality Improvement Evaluation System (QIES) database maintained by the Centers for Medicare & Medicaid Services (CMS). These data include provider number, name, and address and characterize the participating institutional provider (including hospitals, skilled nursing facilities, home health agencies, and other types of facilities). The data are collected through the CMS Regional Offices. The file contains an individual record for each Medicare-approved provider and is updated quarterly.

http://datawarehouse.hrsa.gov/tools/dataportal.aspx

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.

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)

132 Global import shipment records of Cms Wooden H with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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13 Global import shipment records of Siplus Cms Cable with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.