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
Pretty-doc - Composable text objects with python

pretty-doc from __future__ import annotations from dataclasses import dataclass

Taine Zhao 2 Jan 17, 2022
A high-level Python library for Quantum Natural Language Processing

lambeq About lambeq is a toolkit for quantum natural language processing (QNLP). Documentation: https://cqcl.github.io/lambeq/ Getting started Prerequ

Cambridge Quantum 315 Jan 01, 2023
MMDA - multimodal document analysis

MMDA - multimodal document analysis

AI2 75 Jan 04, 2023
Unsupervised Language Modeling at scale for robust sentiment classification

** DEPRECATED ** This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.

NVIDIA Corporation 1k Nov 17, 2022
Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Yoon Kim 43 Dec 23, 2022
A workshop with several modules to help learn Feast, an open-source feature store

Workshop: Learning Feast This workshop aims to teach users about Feast, an open-source feature store. We explain concepts & best practices by example,

Feast 52 Jan 05, 2023
Data and code to support "Applied Natural Language Processing" (INFO 256, Fall 2021, UC Berkeley)

anlp21 Course materials for "Applied Natural Language Processing" (INFO 256, Fall 2021, UC Berkeley) Syllabus: http://people.ischool.berkeley.edu/~dba

David Bamman 48 Dec 06, 2022
Weakly-supervised Text Classification Based on Keyword Graph

Weakly-supervised Text Classification Based on Keyword Graph How to run? Download data Our dataset follows previous works. For long texts, we follow C

Hello_World 20 Dec 29, 2022
Legal text retrieval for python

legal-text-retrieval Overview This system contains 2 steps: generate training data containing negative sample found by mixture score of cosine(tfidf)

Nguyễn Minh Phương 22 Dec 06, 2022
GVT is a generic translation tool for parts of text on the PC screen with Text to Speak functionality.

GVT is a generic translation tool for parts of text on the PC screen with Text to Speech functionality. I wanted to create it because the existing tools that I experimented with did not satisfy me in

Nuked 1 Aug 21, 2022
This simple Python program calculates a love score based on your and your crush's full names in English

This simple Python program calculates a love score based on your and your crush's full names in English. There is no logic or reason in the calculation behind the love score. The calculation could ha

p.katekomol 1 Jan 24, 2022
The following links explain a bit the idea of semantic search and how search mechanisms work by doing retrieve and rerank

Main Idea The following links explain a bit the idea of semantic search and how search mechanisms work by doing retrieve and rerank Semantic Search Re

Sergio Arnaud Gomez 2 Jan 28, 2022
Tracking Progress in Natural Language Processing

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Sebastian Ruder 21.2k Dec 30, 2022
The swas programming language

The Swas programming language This is a language that was made for fun. Installation Step 0: Make sure you have python installed Step 1. Clone this re

Swas.py 19 Jul 18, 2022
A PyTorch-based model pruning toolkit for pre-trained language models

English | 中文说明 TextPruner是一个为预训练语言模型设计的模型裁剪工具包,通过轻量、快速的裁剪方法对模型进行结构化剪枝,从而实现压缩模型体积、提升模型速度。 其他相关资源: 知识蒸馏工具TextBrewer:https://github.com/airaria/TextBrewe

Ziqing Yang 231 Jan 08, 2023
Some embedding layer implementation using ivy library

ivy-manual-embeddings Some embedding layer implementation using ivy library. Just for fun. It is based on NYCTaxiFare dataset from kaggle (cut down to

Ishtiaq Hussain 2 Feb 10, 2022
Build Text Rerankers with Deep Language Models

Reranker is a lightweight, effective and efficient package for training and deploying deep languge model reranker in information retrieval (IR), question answering (QA) and many other natural languag

Luyu Gao 140 Dec 06, 2022
Script to download some free japanese lessons in portuguse from NHK

Nihongo_nhk This is a script to download some free japanese lessons in portuguese from NHK. It can be executed by installing the packages with: pip in

Matheus Alves 2 Jan 06, 2022
AllenNLP integration for Shiba: Japanese CANINE model

Allennlp Integration for Shiba allennlp-shiab-model is a Python library that provides AllenNLP integration for shiba-model. SHIBA is an approximate re

Shunsuke KITADA 12 Feb 16, 2022
⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x using fastT5.

Reduce T5 model size by 3X and increase the inference speed up to 5X. Install Usage Details Functionalities Benchmarks Onnx model Quantized onnx model

Kiran R 399 Jan 05, 2023