On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

The dataset contains information of 213 cancer patients undergoing clinical or surgical treatment characterized on sociodemographic and clinical data as well as data from the Care Transition Measure (CTM 15-Brazil). Data collection was carried out 7 to 30 days after their discharge from hospital from June to August 2019. Understanding these data can contribute to improving quality of care transitions and avoiding hospital readmissions. To this end, this dataset contains a broad array of variables:

*gender

*age group

*place of residence

*race

*marital status

*schooling

*paid work activity

*type of treatment

*cancer staging

*metastasis

*comorbidities

*main complaint

*continue use medication

*diagnosis

*cancer type

*diagnostic year

*oncology treatment

*first hospitalization

*readmission in the last 30 days

*number of hospitalizations in the last 30 days

*readmission in the last 6 months

*number of hospitalizations in the last 6 months

*readmission in the last year

*number of hospitalizations in the last year

*questions 1-15 from CTM 15-Brazil

The data are presented as a single Excel XLSX file: cancer patient´s care transitions dataset.xlsx.

The analyses of the present dataset have the potential to generate hospital readmission prevention strategies to be implemented by the hospital team. Researchers who are interested in CTs of cancer patients can extensively explore the variables described here.

The project from which these data were extracted was approved by the institution’s research ethics committee (approval n. 3.266.259/2019) at Associação Hospital de Caridade Ijuí, Rio Grande do Sul, Brazil.

UK healthcare expenditure data by financing scheme, function and provider, and additional analyses produced to internationally standardised definitions.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses and certifies more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. This file includes California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification.

To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

Please note that the file contains the data from ELMS as of the 11th business day of the month. See DATA_DATE variable for the specific date of when the data was extracted.

Map of all Health Care Facilities in California: https://go.cdii.ca.gov/cdph-facilities

The dataset contains information from a cohort of 799 patients admitted in the hospital for COVID-19, characterized with sociodemographic and clinical data. Retrospectively, from November 2020 to January 2021, data was collected from the medical records of all hospital admissions that occurred from March 1st, 2020, to December 31st, 2020. The analysis of these data can contribute to the definition of the clinical and sociodemographic profile of patients with COVID-19. Understanding these data can contribute to elucidating the sociodemographic profile, clinical variables and health conditions of patients hospitalized by COVID-19. To this end, this database contains a wide range of variables, such as: Month of hospitalization Sex Age group Ethnicity Marital status Paid work Admission to clinical ward Hospitalization in the Intensive Care Unit (ICU) COVID-19 diagnosis Number of times hospitalized by COVID-19 Hospitalization time in days Risk Classification Protocol Data is presented as a single Excel XLSX file: dataset.xlsx of clinical and sociodemographic characteristics of hospital admissions by COVID-19: retrospective cohort of patients in two hospitals in the Southern of Brazil. Researchers interested in studying the data related to patients affected by COVID-19 can extensively explore the variables described here. Approved by the Research Ethics Committee (No. 4.323.917/2020) of the Federal University of Santa Catarina.

The construction of diabetes dataset was explained. The data were collected from the Iraqi society, as they data were acquired from the laboratory of Medical City Hospital and (the Specializes Center for Endocrinology and Diabetes-Al-Kindy Teaching Hospital). Patients' files were taken and data extracted from them and entered in to the database to construct the diabetes dataset. The data consist of medical information, laboratory analysis. The data attribute are: The data consist of medical information, laboratory analysis… etc. The data that have been entered initially into the system are: No. of Patient, Sugar Level Blood, Age, Gender, Creatinine ratio(Cr), Body Mass Index (BMI), Urea, Cholesterol (Chol), Fasting lipid profile, including total, LDL, VLDL, Triglycerides(TG) and HDL Cholesterol , HBA1C, Class (the patient's diabetes disease class may be Diabetic, Non-Diabetic, or Predict-Diabetic).

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

A free, publicly available Malaysia Covid-19 dataset.

Data Descriptions

28 variables. Include:

New case New case (7 day rolling average) Recovered Active case Local cases Imported case ICU Death Cumulative deaths

People tested Cumulative people tested Positivity rate Positivity rate (7 day rolling average)

Data Sources

Column 1 to 22 are Twitter data, which the Tweets are retrieved from Health DG @DGHisham timeline with Twitter API. A typical covid situation update Tweet is written in a relatively fixed format. Data wrangling are done in Python/Pandas, numerical values extracted with Regular Expression (RegEx). Missing data are added manually from Desk of DG (kpkesihatan).

Column 23 ['remark'] is my own written remark regarding the Tweet status/content.

Column 24 ['Cumulative people tested'] data is transcribed from an image on MOH COVID-19 website. Specifically, the first image under TABURAN KES section in each Situasi Terkini daily webpage of http://covid-19.moh.gov.my/terkini. If missing, the image from CPRC KKM Telegram or KKM Facebook Live video is used. Data in this column, dated from 1 March 2020 to 11 Feb 2021, are from Our World in Data, their data collection method as stated here.

Why does this dataset exist?

MOH does not publish any covid data in csv/excel format as of today, they provide the data as is, along with infographics that are hardly informative. In an undisclosed email, MOH doesn't seem to understand my request for them to release the covid public health data for anyone to download and do their analysis if they do wish.

To be updated periodically

A simple visualization dashboard is now published on Tableau Public. It's is updated daily. Do check it out! More charts to be added in the near future

Inspiration

Create better visualizations to help fellow Malaysians understand the Covid-19 situation. Empower the data science community.

Download Free Sample
This statistic denotes the global market size across several regions including North America, Europe, APAC, South America, and MEA. The medical transcription market size was estimated to be at USD 16.64 bn in 2020-2024.

The size of the global medical transcription market has been derived by triangulating data from multiple sources and approaches. While arriving at the market size, we have considered data points, such as the size of the parent market and the revenues of key market participants, such as Acusis LLC, Excel Transcriptions Inc., Global Medical Transcription LLC, iMedX Inc., Lingual Consultancy Services Pvt. Ltd., MModal IP LLC, MTBC Inc., nThrive Inc., Nuance Communications Inc., and World Wide Dictation Service of New York Inc.

Abstract

The National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.

The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.

The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.

The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.

For further details on sample design, see Section 1.2 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).

Cleaning operations

Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.

Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.

Response rate

A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.

In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.

[2021-03-23] Updated: Enjoy!

Consent

By downloading this complete Mini-DDSM Data Set, you agree to the following:

1. This page on Kaggle remains the main source of this public data set (i.e., no redistribution of this data set)
2. In any resultant publications of research that uses the paper / data set, due credits (to recognize the efforts of my team) must be provided to: [Ref paper/Mini-DDSM] C.D. Lekamlage, F. Afzal, E. Westerberg and A. Cheddad, “Mini-DDSM: Mammography-based Automatic Age Estimation,” in the 3rd International Conference on Digital Medicine and Image Processing (DMIP 2020), ACM, Kyoto, Japan, November 06-09, 2020, pp: 1-6. And [Ref DDSM] Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer, in Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001. ISBN 1-930524-00-5.

Context & Data Set Characteristics

You can read the Paper that describes the initial attempt to collect this free data set and the experiments we conducted. It required a tremendous time, coding and machine processing power to get it in shape to make it as much as possible accessible for the research community. Below, are some of the merits of this new Mini-DDSM version:

1. There is a scarcity in the availability of large public and fully annotated healthcare data sets
2. The intention here is to make an easy access to the DDSM (half resolution though)
3. The data set comes along with the age/density attributes, patient folders (condition: benign, cancer, healthy), original filename identification, and suspicious/tumor contour binary mask.
4. The lesion binary mask is constructed based on the original freeman chain-coding, so this data set prevents you that inconvenience.
5. The data set can act as a validation platform for machine learning developed/under development algorithms **(see an example (imputation of missing data using DL) of such interesting ML topics in the "Tasks" tab above)- Tasks tab has been removed by Kaggle- **
6. There are still open research questions that this data set along with deep learning may need to address
7. No complication of extracting/loading images from tfrecords. You want images, you get images! So, whether you are using Python, MATLAB, JAVA, C++, you have the images stored as images.
8. Free of charge and open access, no lengthy protocols and no forms to fill/sign
9. This data set comes with an excel sheet that gives you a direct access to all image attributes and metadata (see Fig. 1) ==> Get it here**
10. Due to several requests from people having machine/internet bandwidth limitations that do not allow them to download the 47 GB data set, Folder: -MINI-DDSM-Complete-PNG-16-, we also provide this dataset in JPEG format (~4 GB), Folder: -MINI-DDSM-Complete-JPEG-8-.**

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1822946%2F3905483ba6e03b7142a9121a03824558%2FRaws.png?generation=1609421580586145&alt=media" alt="">

                  Figure 1. The first few rows of the accompanying excel sheet.

Content

This is the light-weight version of the popular DDSM (Digital Database for Screening Mammography) [Ref] data set which currently is obsolete. To answer the nagging question why Mini-DDSM, it is important to know that the DDSM database has a website maintained at the University of South Florida for purposes of keeping it accessible on the web. However, image files are compressed with lossless JPEG (i.e., “.LJPEG”) encoding that are generated using a broken software (or at least an outdated tool as described on the DDSM website). CBIS-DDSM provides an alternative host of the original DDSM, but unfortunately, images are stripped from their original identification filename and from the age attribute. Figure 2 illustrates the age distribution in this complete Mini-DDSM and Fig.3 exhibits the density (amount of Fibroglandular tissue) distribution using Bi-Rads scoring.

https://raw.githubusercontent.com/ARDISDataset/MiniDDSM/master/AgeDistributionW.png" alt="Age Distr"> Figure 2. Age distribution in this complete version of the Mini-DDSM data set.

https://raw.githubusercontent.com/ARDISDataset/MiniDDSM/master/BIRADS.png" alt="Density"> Figure 3. Density distribution in this complete version of the Mini-DDSM data set.

Inspiration

Please give us feedback/suggestions to improve the data set to: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1822946%2F756766cadde4657770f39cc63613908f%2FContact.png?generation=1605701971315133&alt=media" alt="">

Mental Health reports the prevalence of the mental illness in the past year by age range.

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

The NHS Safety Thermometer is a local improvement tool for measuring, monitoring and analysing patient harms and 'harm free' care. Download it to your desktop and run the Excel application to browse data entered from April to December 2012.

Dataset

This dataset was created by Bunty Shah

Contents

This dataset provides restaurant inspections, violations, grades and adjudication information

The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (November 2015 to December 2018) concerning non-life motor insurance portfolio. This dataset comprises 105,555 rows and 30 columns. Each row signifies a policy transaction, while each column represents a distinct variable.

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources. https://medium.com/@openaq/where-does-openaq-data-come-from-a5cf9f3a5c85 Note: this dataset is temporary not updated. We're currently working to update it as soon as possible.Disclaimers:- Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.- Some dates are set in the future: those issues also come from the original data and were not corrected.