Mother’s Significant Feature (MSF) Dataset has been designed to provide data to researchers working towards woman and child health betterment. MSF dataset records are collected from the Mumbai metropolitan region in Maharashtra, India. Women were interviewed just after childbirth between February 2018 to March 2021. MSF comprise of 450 records with a total of 130 attributes consisting of mother’s features, father’s features and health outcomes. A detailed dataset is created to understand the mother’s features spread across three phases of her reproductive age i.e.
We have provided the copy of forms used to collect data for datset and a read me guide to undertand the features provided in dataset along with the content of all the 6 dataset submitted in excel sheet format.
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This repository contains:
- age-stratified Covid-19 case and fatality data for different countries and at different points in time, and
- an interactive Jupyter notebook for mediation analysis of age-related causal effects on case fatality rates,
published as part of the following paper:
"Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects". J von Kügelgen*, L Gresele*, B Schölkopf. (*equal contribution). https://arxiv.org/abs/2005.07180
We provide the following three separate datasets:
- a dataset containing only the most recent numbers from: Argentina, China, Colombia, Italy, Netherlands, Portugal, South Africa, Spain, Sweden, Switzerland, South Korea and the Diamond Princess cruise ship (last checked: end of May 2020)
- a longitudinal dataset containing several reports from Italy (9 March - 26 May 2020)
- a longitudinal dataset containing several reports from Spain (22 March - 29 May 2020)
All numbers of confirmed cases and fatalities are stratified by age into groups of 10 years (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+), and contain the date and country of reporting, as well as links to the corresponding sources (generally health agenices/ministries, or scientific publications).
Please consult the paper and notebook for further details.
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The Cross-sectional Diabetes Risk survey aims to assess the prevalence of diabetes and its risk factors at the same point in time and also provide a "snapshot" of diseases and risk factors simultaneously for individuals belonging to the western region of the Kingdom of Saudi Arabia (KSA).
The survey is available at the following URL:
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https://www.munnamotorgarage.com/fcitrabet/saudi_diabetise_survey_2019-2....
The instructions for the use of the dataset and analysis tools are available in our submitted manuscript
(Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-sectional Study) on IEEE access for review.
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Datasets contain survey data of 873 rural poor households in the states of Maharashtra, Odisha, Madhya Pradesh, and Rajasthan.
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Spectroreflectometric techniques for dactiloscopy and optical nail diagnostics are proposed and aproved on "DATACOLOR 3890"-based measuring system.
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