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
Open source annotation tool for machine learning practitioners.

doccano doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequ

7.1k Jan 01, 2023
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Microsoft 105 Jan 08, 2022
Code and data accompanying Natural Language Processing with PyTorch

Natural Language Processing with PyTorch Build Intelligent Language Applications Using Deep Learning By Delip Rao and Brian McMahan Welcome. This is a

Joostware 1.8k Jan 01, 2023
Stand-alone language identification system

langid.py readme Introduction langid.py is a standalone Language Identification (LangID) tool. The design principles are as follows: Fast Pre-trained

2k Jan 04, 2023
Open-source offline translation library written in Python. Uses OpenNMT for translations

Open source neural machine translation in Python. Designed to be used either as a Python library or desktop application. Uses OpenNMT for translations and PyQt for GUI.

Argos Open Tech 1.6k Jan 01, 2023
Flaxformer: transformer architectures in JAX/Flax

Flaxformer: transformer architectures in JAX/Flax Flaxformer is a transformer library for primarily NLP and multimodal research at Google. It is used

Google 114 Dec 29, 2022
A benchmark for evaluation and comparison of various NLP tasks in Persian language.

Persian NLP Benchmark The repository aims to track existing natural language processing models and evaluate their performance on well-known datasets.

Mofid AI 68 Dec 19, 2022
Code associated with the Don't Stop Pretraining ACL 2020 paper

dont-stop-pretraining Code associated with the Don't Stop Pretraining ACL 2020 paper Citation @inproceedings{dontstoppretraining2020, author = {Suchi

AI2 449 Jan 04, 2023
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
The RWKV Language Model

RWKV-LM We propose the RWKV language model, with alternating time-mix and channel-mix layers: The R, K, V are generated by linear transforms of input,

PENG Bo 877 Jan 05, 2023
Enterprise Scale NLP with Hugging Face & SageMaker Workshop series

Workshop: Enterprise-Scale NLP with Hugging Face & Amazon SageMaker Earlier this year we announced a strategic collaboration with Amazon to make it ea

Philipp Schmid 161 Dec 16, 2022
Converts text into a PDF of handwritten notes

Text To Handwritten Notes Converts text into a PDF of handwritten notes Explore the docs » · Report Bug · Request Feature · Steps: $ git clone https:/

UVSinghK 63 Oct 09, 2022
A fast, efficient universal vector embedding utility package.

Magnitude: a fast, simple vector embedding utility library A feature-packed Python package and vector storage file format for utilizing vector embeddi

Plasticity 1.5k Jan 02, 2023
Code repository for "It's About Time: Analog clock Reading in the Wild"

it's about time Code repository for "It's About Time: Analog clock Reading in the Wild" Packages required: pytorch (used 1.9, any reasonable version s

52 Nov 10, 2022
:P Some basic stuff I'm gonna use for my upcoming Agile Software Development and Devops

reverse-image-search-py bash script.sh img_name.jpg Requirements pip install requests pip install pyshorteners Dry run [ Sudhanva M 3 Dec 18, 2021

Contact Extraction with Question Answering.

contactsQA Extraction of contact entities from address blocks and imprints with Extractive Question Answering. Goal Input: Dr. Max Mustermann Hauptstr

Jan 2 Apr 20, 2022
ConvBERT: Improving BERT with Span-based Dynamic Convolution

ConvBERT Introduction In this repo, we introduce a new architecture ConvBERT for pre-training based language model. The code is tested on a V100 GPU.

YITUTech 237 Dec 10, 2022
ASCEND Chinese-English code-switching dataset

ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong.

CAiRE 11 Dec 09, 2022
华为商城抢购手机的Python脚本 Python script of Huawei Store snapping up mobile phones

HUAWEI STORE GO 2021 说明 基于Python3+Selenium的华为商城抢购爬虫脚本,修改自近两年没更新的项目BUY-HW,为女神抢Nova 8(什么时候华为开始学小米玩饥饿营销了?) 原项目的登陆以及抢购部分已经不可用,本项目对原项目进行了改正以适应新华为商城,并增加一些功能

ZhangLiang 111 Dec 22, 2022
Fine-tune GPT-3 with a Google Chat conversation history

Google Chat GPT-3 This repo will help you fine-tune GPT-3 with a Google Chat conversation history. The trained model will be able to converse as one o

Nate Baer 7 Dec 10, 2022