Sample data associated with the Aurora-BP study

Overview

The Aurora-BP Study and Dataset

This repository contains sample code, sample data, and explanatory information for working with the Aurora-BP dataset released alongside the publication of the Aurora-BP study, i.e., Mieloszyk, Rebecca, et al. "A Comparison of Wearable Tonometry, Photoplethysmography, and Electrocardiography for Cuffless Measurement of Blood Pressure in an Ambulatory Setting." IEEE Journal of Biomedical and Health Informatics (2022). The dataset includes de-identified participant information, raw sensor data aligned with each measurement, and a wide variety of features derived from sensor data. The publishing of this dataset as well as the characterization of multiple feature groups across a broad population and multiple settings are intended to aid future cardiovascular research.

Note that the data contained in this repository represent a very small sample of the full dataset, meant only to illustrate the structure of the files and allow testing with the sample code. For access to the full dataset, see the Data Use Application section below.

Navigation:

  • docs:
    • Data file descriptions, a detailed overview of the Aurora-BP Study protocol, and supplemental results not included in the Aurora-BP Study publication
  • notebooks:
    • Sample Jupyter notebooks and environment files for basic analyses using Aurora-BP Study data
  • sample:
    • Example data files, to run sample Jupyter notebooks and provide researchers a direct look at the data format before application for full data access.

Citation

If you use this repository, part or all of the full dataset, and/or our paper as part of your research, please refer to the dataset as the Aurora-BP dataset and cite the publication as below:


Data Access

Data Access Committee

Requests for data access are reviewed by the Data Access Committee. During review, the submitting investigator and primary investigator may be contacted for verification. The information you will need to gather to submit a Data Use Application as well as a link to the form are listed below. For additional questions regarding data access, contact: [email protected]


Data Use Application

Full data files are stored separately from this repo within an Azure data lake. To gain access to these data files, a data use application (detailed below and on the data lake landing page) must be submitted. Any researcher may submit a data use application, which includes:

  • Principal investigator information
    • Academic credentials, affiliation, contact information, curriculum vitae, signature attesting accuracy of data use application
  • Additional investigator information
    • Academic credentials, affiliation, contact information
  • Research proposal
  • Acknowledgement to comply with data use agreement. Key points are listed below:
    • No sharing of data with anyone outside of approved PI and other specified investigators. New investigators must be reviewed.
    • No data use outside of stated proposal scope
    • No joining of data with other data sources
    • No attempt to identify participants, contact participants, or reconstruct PII
    • Storage with appropriate access control and best practices
    • You may publish (or present papers or articles) on your results from using the data provided that no confidential information of Microsoft and no Personal Information are included in any such publication or presentation
    • Any publication or presentation resulting from use of the data should include reference to the Aurora-BP Study, with full reference to the source publication when appropriate
    • Aurora-BP Study authors and Microsoft are under no obligation to provide any support or additional materials related to the use of these data
    • Aurora-BP Study authors and Microsoft are not liable for any losses, damages, or harms of any kind in connection to the use of these data
    • Aurora-BP Study authors and Microsoft are not responsible or liable for the accuracy, usefulness or availability of these data
    • Primary Investigator will provide a signature of attestation that they have read, understood, and accept the data use agreement
Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Linear programming solver for paper-reviewer matching and mind-matching

Paper-Reviewer Matcher A python package for paper-reviewer matching algorithm based on topic modeling and linear programming. The algorithm is impleme

Titipat Achakulvisut 66 Jul 05, 2022
Score-Based Point Cloud Denoising (ICCV'21)

Score-Based Point Cloud Denoising (ICCV'21) [Paper] https://arxiv.org/abs/2107.10981 Installation Recommended Environment The code has been tested in

Shitong Luo 79 Dec 26, 2022
A simple chatbot based on chatterbot that you can use for anything has basic features

Chatbotium A simple chatbot based on chatterbot that you can use for anything has basic features. I have some errors Read the paragraph below: Known b

Herman 1 Feb 16, 2022
News-Articles-and-Essays - NLP (Topic Modeling and Clustering)

NLP T5 Project proposal Topic Modeling and Clustering of News-Articles-and-Essays Students: Nasser Alshehri Abdullah Bushnag Abdulrhman Alqurashi OVER

2 Jan 18, 2022
NLP tool to extract emotional phrase from tweets 🤩

Emotional phrase extractor Extract phrase in the given text that is used to express the sentiment. Capturing sentiment in language is important in the

Shahul ES 38 Oct 17, 2022
Text-Based zombie apocalyptic decision-making game in Python

Inspiration We shared university first year game coursework.[to gauge previous experience and start brainstorming] Adapted a particular nuclear fallou

Amin Sabbagh 2 Feb 17, 2022
Application to help find best train itinerary, uses speech to text, has a spam filter to segregate invalid inputs, NLP and Pathfinding algos.

T-IAI-901-MSC2022 - GROUP 18 Gestion de projet Notre travail a été organisé et réparti dans un Trello. https://trello.com/b/X3s2fpPJ/ia-projet Install

1 Feb 05, 2022
GPT-3: Language Models are Few-Shot Learners

GPT-3: Language Models are Few-Shot Learners arXiv link Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-trainin

OpenAI 12.5k Jan 05, 2023
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

Steven Loria 8.4k Dec 26, 2022
A very simple framework for state-of-the-art Natural Language Processing (NLP)

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. IMPORTANT: (30.08.2020) We moved our models

flair 12.3k Dec 31, 2022
Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech

epub2audiobook Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech Input examples qual a pasta do seu

7 Aug 25, 2022
Code for the paper "Flexible Generation of Natural Language Deductions"

Code for the paper "Flexible Generation of Natural Language Deductions"

Kaj Bostrom 12 Nov 11, 2022
OpenChat: Opensource chatting framework for generative models

OpenChat is opensource chatting framework for generative models.

Hyunwoong Ko 427 Jan 06, 2023
OCR을 이용하여 인원수를 인식 후 줌을 Kill 해줍니다

How To Use killtheZoom-2.0 Windows 0. https://joyhong.tistory.com/79 이 글을 보면서 tesseract를 C:\Program Files\Tesseract-OCR 경로로 설치해주세요(한국어 언어 추가 필요) 상단의 초

김정인 9 Sep 13, 2021
Deduplication is the task to combine different representations of the same real world entity.

Deduplication is the task to combine different representations of the same real world entity. This package implements deduplication using active learning. Active learning allows for rapid training wi

63 Nov 17, 2022
GCRC: A Gaokao Chinese Reading Comprehension dataset for interpretable Evaluation

GCRC GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Eva

Yunxiao Zhao 5 Nov 04, 2022
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN

artificial intelligence cosmic love and attention fire in the sky a pyramid made of ice a lonely house in the woods marriage in the mountains lantern

Phil Wang 2.3k Jan 01, 2023
Toward a Visual Concept Vocabulary for GAN Latent Space, ICCV 2021

Toward a Visual Concept Vocabulary for GAN Latent Space Code and data from the ICCV 2021 paper Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Kl

Sarah Schwettmann 13 Dec 23, 2022
Chinese Named Entity Recognization (BiLSTM with PyTorch)

BiLSTM-CRF for Name Entity Recognition PyTorch version A PyTorch implemention of Bi-LSTM-CRF model for Chinese Named Entity Recognition. 使用 PyTorch 实现

5 Jun 01, 2022