cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/m060-cw82
title: Pulse Amplitudes from electrodermal activity collected from healthy volunteer subjects at rest and under controlled sedation

description: This dataset of electrodermal activity was collected from 11 healthy volunteer
subjects who were awake and at rest in seated position and 11 different
healthy volunteers who were under controlled propofol sedation. For the awake
and at rest subjects, the activity was recorded from each subject's non-
dominant hand for one hour at 256 Hz. For the controlled propofol sedation
subjects, the activity was recorded from each subject's left hand for about
3-4 hours at 500 Hz. From the raw data, EDA pulses were extracted and the
pulse times and amplitudes reported. Electrodermal activity measures changing
electrical conductance of the skin as an indicator of sweat gland activity.
Sweat glands are a primitive part of the fight-or-flight response. These data
were collected as part of a larger study to understand and build computational
models for autonomic nervous system activity (including electrodermal
activity) with approval from the Massachusetts Institute of Technology
Committee on the Use of Humans as Experimental Subjects (COUHES) and the
Massachusetts General Hospital Human Research Committee.
DOI: 10.13026/mmab-c762
title: RadNLI: A natural language inference dataset for the radiology domain

description: The problem of natural language inference (NLI) determines whether a natural
language hypothesis can be justifiably inferred from a natural language
premise. NLI has attracted researchers to benchmark it in a number of settings
including medical ones. While NLI datasets such as the MedNLI dataset exist
for the clinical domain, systems trained with them do not generalize well to
applications that require understanding of radiology reports. We therefore
introduce an NLI dataset in the radiology domain, in which NLI information is
annotated on sentences drawn from radiology reports. Sentence pairs in our
dataset are sampled from MIMIC-CXR and we annotated them with NLI labels by
two experts: one medical expert and one computer science expert. Each pair is
annotated twice, swapping its premise and hypothesis, resulting in 960 pairs.
The set is then split in half, resulting in 480 pair for a validation set and
480 pairs for a test set. We confirmed that a BERT-based NLI model trained
with a distant supervision approach can achieve the accuracy of 77.8% on this
test set.
DOI: 10.13026/r9p1-bk90
title: Pulse Amplitudes from electrodermal activity collected from healthy volunteer subjects at rest and under controlled sedation

description: This dataset of electrodermal activity was collected from 11 healthy volunteer
subjects who were awake and at rest in seated position and 11 different
healthy volunteers who were under controlled propofol sedation. For the awake
and at rest subjects, the activity was recorded from each subject's non-
dominant hand for one hour at 256 Hz. For the controlled propofol sedation
subjects, the activity was recorded from each subject's left hand for about
3-4 hours at 500 Hz. From the raw data, EDA pulses were extracted and the
pulse times and amplitudes reported. Electrodermal activity measures changing
electrical conductance of the skin as an indicator of sweat gland activity.
Sweat glands are a primitive part of the fight-or-flight response. These data
were collected as part of a larger study to understand and build computational
models for autonomic nervous system activity (including electrodermal
activity) with approval from the Massachusetts Institute of Technology
Committee on the Use of Humans as Experimental Subjects (COUHES) and the
Massachusetts General Hospital Human Research Committee.
DOI: 10.13026/x32c-cz47
title: Body Sway When Standing and Listening to Music Modified to Reinforce Virtual Reality Environment Motion

description: The increased likelihood of falls as a consequence of aging or disease is
generally associated with rising levels of body sway during stance.  The use
of virtual reality (VR) for studying the role in vision in regulating body
sway is well established, however the extent to which music incorporated into
a virtual environment can influence sway is not understood. This dataset was
collected as part of a study to explore the hypothesis that music manipulated
to match VR motion provided by an Oculus Rift head mounted display can lead to
increased levels of body sway. Twenty-eight subjects stood for 60 s on a
balance platform that measured anterior posterior (AP) and medial lateral (ML)
center of pressure movement (indicators of body sway).  While standing, the
subjects experienced combinations of 3 visual conditions (VR translation in
the AP direction at 0.1 Hz, no translation, and eyes closed) and 4 music
conditions (Mozart's Jupiter Symphony modified to scale volume at 0.1 Hz and
0.25 Hz, unmodified music, and no music) for a total of 12 trials. Analysis of
the data using frequency domain measures may reveal the extent to which music
influenced body sway.
DOI: 10.13026/zedg-p783
title: Maternal fat ultrasound measurement and nutritional assessment during pregnancy: A dataset centered in gestational outcomes

description: This dataset was collected as part of a prospective study in which abdominal
maternal fat tissue ~~s~~ ultrasound measurements were compared with outcomes
during hospitalization for labor and delivery. Data  was collected in a low-
risk outpatient clinic from October 2016 to December 2017 in Porto Alegre,
Brazil. Of the 272 patients initially included, 61 (22%) were lost to follow-
up, resulting in a final sample of 211 cases.

At baseline, a routine obstetric ultrasound was performed with a maternal
epigastric fat measurement conducted in all included cases. Two assessments
were performed: the epigastric maternal visceral adipose tissue (preperitoneal
m-VAT) and the epigastric maternal subcutaneous adipose tissue (preperitoneal
m-SAT).  Cases included before 20 weeks had an additional periumbilical fat
assessment performed; this included: the periumbilical maternal visceral
adipose tissue (periumbilical m-VAT) and  the periumbilical maternal
subcutaneous adipose tissue (periumbilical m-SAT). Data regarding
anthropometric, nutritional and demographic characteristics were assessed at
baseline. Pregnancy outcomes were determined through hospital charts in order
to establish pregnancy, labor and newborn conditions.

The dataset has 116 variables that include demographic, ultrasound,
nutritional and laboratory characteristics measured at baseline. Likewise, it
presents data related to the birth process and newborn conditions during the
postpartum period. The pregnancy abnormalities data obtained retrospectively
are highlighted. The dataset may be helpful for future studies that seek to
assess the predictive capacity of maternal abdominal fat for abnormal
pregnancy outcomes.
DOI: 10.13026/2f5n-pb92
title: RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

description: RadGraph is a dataset of entities and relations in full-text radiology
reports. We designed a novel information extraction (IE) schema to structure
clinical information in a radiology report with four entities and three
relations. Our train set consists of 500 MIMIC-CXR radiology reports annotated
according to our schema by board-certified radiologists. Our test set consists
of 50 MIMIC-CXR and 50 CheXpert reports, which are independently annotated by
two board-certified radiologists. Additionally, we release annotations
generated by a benchmark deep learning model that achieves a micro F1 of 0.82
(MIMIC-CXR test set) and 0.73 (CheXpert test set) on an evaluation metric for
end-to-end relation extraction, where entity boundaries, entity types, and
relation type must be correct. We use our model to automatically generate
entity and relation labels across 220,763 MIMIC-CXR  reports and 500 CheXpert
reports, where annotations can be mapped to associated chest radiographs in
the MIMIC-CXR and CheXpert datasets respectively. The dataset, which includes
reports, entities, and relations, is de-identified according to the US Health
Insurance Portability Act (HIPAA). This dataset is intended to support the
development of natural language processing (NLP) methods for entity and
relation extraction in radiology as well as enable multi-modal use cases that
can leverage entities, relations, and associated radiographs.
DOI: 10.13026/2qtg-k467
title: MIMIC-III and eICU-CRD: Feature Representation by FIDDLE Preprocessing

description: This is a preprocessed dataset derived from patient records in MIMIC-III and
eICU, two large-scale electronic health record (EHR) databases. It contains
features and labels for 5 prediction tasks involving 3 adverse outcomes
(prediction times listed in parentheses): in-hospital mortality (48h), acute
respiratory failure (4h and 12h), and shock (4h and 12h). We extracted
comprehensive, high-dimensional feature representations (up to ~8,000
features) using FIDDLE ( **F** lex **I** ble  **D** ata- **D** riven pipe
**L** in **E** ), an open-source preprocessing pipeline for structured
clinical data. These 5 prediction tasks were designed in consultation with a
critical care physician for their clinical importance, and were used as part
of the proof-of-concept experiments in the original paper to demonstrate
FIDDLE's utility in aiding the feature engineering step of machine learning
model development. The intent of this release is to share preprocessed MIMIC-
III and eICU datasets used in the experiments to support and enable
reproducible machine learning research on EHR data.
DOI: 10.13026/2tdv-hj07
title: Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management

description: We introduce Q-Pain, a dataset for assessing bias in medical QA in the context
of pain management. We developed 55 medical question-answer pairs across five
different types of pain management: each question includes a detailed patient-
specific medical scenario ("vignette") designed to enable the substitution of
multiple different racial and gender "profiles" and to evaluate whether bias
is present when answering whether or not to prescribe medication.

Along with the dataset, we propose a new framework, including a sample
experimental design, to measure the potential bias present during medical
decision-making for patients with particular demographic profiles. We
demonstrate its use in assessing two reference QA systems, GPT-2 and GPT-3,
selected for their documented ability to answer questions given only a few
examples. We hope that our dataset and framework will be used to assess bias
across a wide variety of pain management QA systems.
DOI: 10.13026/3pqw-8411
title: Flatten: COVID-19 Survey Data on Symptoms, Demographics and Mental Health in Canada

description: The expedited collection and analysis of pre-clinical syndromic data is
delivering novel insights into public health conditions during the ongoing
COVID-19 pandemic. We provide Canada's first publicly available pre-clinical
COVID-19 dataset, based on survey responses collected from 294,106 Canadians
from March 23rd until July 30th 2020, using a platform developed by Flatten, a
Canadian non-profit organization. We provide this open-source data for
academic and industry research.
DOI: 10.13026/46zt-f692
title: Curated Data for Describing Blood Glucose Management in the Intensive Care Unit

description: Analysis of real-world glucose and insulin clinical data recorded in
electronic health records can provide insights into tailored approaches to
clinical care, but still present many analytic challenges. The present data
subsets are the result of a detailed curation process that extracts and pairs
glucose readings to insulin therapy on a per-patient basis during an admission
to the Intensive Care Unit (ICU) in the Medical Information Mart for Intensive
Care (MIMIC-III) database. Curated data include over 500,000 glucose readings
and more than 140,000 insulin entries for nearly 9,600 patients distributed in
more than 11,000 ICU stays. Also, the proposed curation process involved the
creation of glucose - insulin pairing rules according to clinical expert-
defined physiologic and pharmacologic parameters. With the proposed rules, it
was possible to pair nearly 76% of insulin administration events to a
preceding blood glucose reading. The two shared data subsets have the
potential to reveal insights regarding real-world practice for the glycemic
control in the ICU. Moreover, the shared material serves as a framework for
future studies of glucose management and insulin therapy replacement in the
ICU, which may allow researchers to tailor queries and data processing to
their own study objectives.
DOI: 10.13026/517s-2q57
title: Curated Data for Describing Blood Glucose Management in the Intensive Care Unit

description: Analysis of real-world glucose and insulin clinical data recorded in
electronic health records can provide insights into tailored approaches to
clinical care, but still present many analytic challenges. The present data
subsets are the result of a detailed curation process that extracts and pairs
glucose readings to insulin therapy on a per-patient basis during an admission
to the Intensive Care Unit (ICU) in the Medical Information Mart for Intensive
Care (MIMIC-III) database. Curated data include over 500,000 glucose readings
and more than 140,000 insulin entries for nearly 9,600 patients distributed in
more than 11,000 ICU stays. Also, the proposed curation process involved the
creation of glucose - insulin pairing rules according to clinical expert-
defined physiologic and pharmacologic parameters. With the proposed rules, it
was possible to pair nearly 76% of insulin administration events to a
preceding blood glucose reading. The two shared data subsets have the
potential to reveal insights regarding real-world practice for the glycemic
control in the ICU. Moreover, the shared material serves as a framework for
future studies of glucose management and insulin therapy replacement in the
ICU, which may allow researchers to tailor queries and data processing to
their own study objectives.
DOI: 10.13026/61xq-mj56
title: Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management

description: We introduce Q-Pain, a dataset for assessing bias in medical QA in the context
of pain management. We developed 55 medical question-answer pairs across five
different types of pain management: each question includes a detailed patient-
specific medical scenario ("vignette") designed to enable the substitution of
multiple different racial and gender "profiles" and to evaluate whether bias
is present when answering whether or not to prescribe medication.

Along with the dataset, we propose a new framework, including a sample
experimental design, to measure the potential bias present during medical
decision-making for patients with particular demographic profiles. We
demonstrate its use in assessing two reference QA systems, GPT-2 and GPT-3,
selected for their documented ability to answer questions given only a few
examples. We hope that our dataset and framework will be used to assess bias
across a wide variety of pain management QA systems.
DOI: 10.13026/669h-cx11
title: Effect of 24-hour sleep deprivation on cerebral hemodynamics and cognitive performance

description: Sleep deprivation (SD) is associated with decreased cognitive performance, but
the underlying mechanisms are poorly understood. To assess the impact of SD on
cerebral hemodynamics in the frontal lobe, functional near-infrared
spectroscopy (fNIRS) measurements were carried out on young subjects. Ten
young healthy adults (males, age of 27.6±3.7 years, one left-handed
participant) were recruited for this study conducted in 2018 at University of
Oklahoma, Translational Geroscience Laboratory. The examination protocol began
with assessment of cognitive performance with the aid of Cambridge
Neuropsychological Test Automated Battery (CANTAB). The following tests were
used: motor screening task, rapid visual processing, reaction time, spatial
working memory, paired associated learning, and delayed matching to visual
sample. Subsequently fNIRS measurements were taken from participants while
carrying out a finger-tapping exercise. Each subject was presented with an
auditory command to perform a finger-tapping task over three sets of 10
seconds using the left index finger and three sets of 10 seconds using the
right index finger, with 10 seconds inter-stimulus periods between each task.
Functional NIRS recordings were acquired using a NIRScout platform (NIRx
Medical Technologies LLC, NY, USA). Relative oxy- and deoxyhemoglobin time
series can be obtained from measured intensities by applying differential
modified Beer Lamber Law. All measurements were performed before and after 24
hours of SD. The dataset forms the basis for two studies that explore: (1) the
relationship between sleep deprivation, cognitive performance, and
neurovascular coupling response in young healthy adults; and (2) sleep
deprivation and functional connectivity of the frontal cortex.
DOI: 10.13026/d9gh-4m08
title: Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19

description: The prospective upper body thermal images SARS-CoV2 association study was
designed to test the hypothesis that thermal videos may aid in the early
diagnosis of COVID-19. The study recorded a set of measurements from 252
participants regarding PCR results, demographics, vital signs, participant
activities, medications, respiratory symptoms, and a thermal video session
where the volunteers performed simple breath-hold in four different positions.
The acquired data may be used to test clinical association questions regarding
temperature patterns, demographics, and vital signs. Furthermore, it could be
valuable to develop new computer algorithms for extracting useful scientific
information from thermal videos.
DOI: 10.13026/de8x-qg95
title: Treadmill Maximal Exercise Tests from the Exercise Physiology and Human Performance Lab of the University of Malaga

description: The present database is an ensemble of the cardiorespiratory measurements
acquired during 992 treadmill maximal graded exercise tests (GET) performed in
the Exercise Physiology and Human Performance Lab of the University of Malaga.
Heart rate, oxygen consumption, carbon dioxide generation, and pulmonary
ventilation are measured on a breath-to-breath basis along with the treadmill
speed during maximal effort tests. Participants are amateur and professional
athletes are of ages ranging from 10 to 63 years old. The age, height, and
weight of the participants are provided, as well as the temperature and
humidity during the test.
DOI: 10.13026/e0dj-8498
title: REFLACX: Reports and eye-tracking data for localization of abnormalities in chest x-rays

description: Labels localizing anomalies are rare in current chest x-rays datasets. We
collected a dataset, using a method that can potentially be scaled up, to be a
proof-of-concept for collecting implicit localization data through an eye-
tracker. This dataset, named REFLACX, provides eye-tracking data collected
while radiologists dictated reports for frontal chest x-rays from the MIMIC-
CXR dataset, paired with the timestamped transcription of the dictation. The
dataset contains 3,032 cases of synchronized eye-tracking and transcription
pairs labeled by five radiologists. For each case, we also provide other
labels for validating algorithms derived from this dataset. These labels
include image-level labels of anomalies found during the reading, ellipses
localizing these anomalies, and bounding boxes around lungs and heart. A
subset of 109 chest x-rays has readings from all radiologists to estimate the
variability of the provided labels.
DOI: 10.13026/ehkk-8y70
title: Two-tiered response of cardiorespiratory-cerebrovascular networks to orthostatic challenge

description: The authors used this dataset to evaluate the responsiveness of the
cardiorespiratory-cerebrovascular networks by capturing linear and nonlinear
interdependencies to postural changes. Ten young healthy adults participated
in our study between December 2009 and February 2010 at Semmelweis University,
Faculty of Medicine. Non-invasive measurements of arterial blood pressure
(ABP), cardiac cycle duration (derived from ABP signals), breath-to-breath
interval (capnography), cerebral blood flow velocity (BFV, recorded by
transcranial Doppler sonography), and cerebral hemodynamics (hemoglobin
signals monitored by near-infrared spectroscopy) were performed for 30-minutes
in resting state, followed by a one-minute stand-up and a one-minute sit-down
period. BFV and ABP signals were calibrated, and the dataset contains values
in cm/s and mmHg, respectively. NIRS measurement yielded intensity values (raw
signals) from which change of HbO (oxyhemoglobin), HbR (deoxyhemoglobin) and
HbT (total hemoglobin) concentration in tissue can be calculated (in
micromol/liter). Raw NIRS signals were synchronized to BFV, ABP and
capnography time series yielding 33 minutes of resampled data (sampling
frequency: ~ 3 Hz).
DOI: 10.13026/gp48-ea60
title: Haaglanden Medisch Centrum sleep staging database

description: A collection of 154 whole-night polysomnographic (PSG) sleep recordings (88
Male, 66 Female, mean Age of 53.8 ± 15.4) collected during 2018 at the
Haaglanden Medisch Centrum (HMC, The Netherlands) sleep center. Patient
recordings were randomly selected and include a heterogeneous population which
was referred for PSG examination on the context of different sleep disorders.
The dataset contains electroencephalographic (EEG), electrooculographic (EOG),
chin electromyographic (EMG), and electrocardiographic (ECG) activity, as well
as event annotations corresponding to scoring of sleep patterns (hypnogram)
performed by sleep technicians at HMC. The dataset was collected as part of a
study evaluating the generalization performance of an automatic sleep scoring
algorithm across multiple heterogeneous datasets.
DOI: 10.13026/h37m-r024
title: Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021

description: Atrial fibrillation (AF) is the most frequent arrhythmia, but paroxysmal
atrial fibrillation (PAF) often remains unrecognized. Early detection of PAF
is of great value for AF surgery options, drug intervention and diagnosis and
treatment of various clinical complications. Although accurate detection of
paroxysmal AF is very important, there is currently no algorithm that can
efficiently measure the onset and end of AF episode in dynamic or wearable
ECGs. Previous AF detection algorithms usually focus on the classification of
AF rhythm instead of locating the onsets and ends of AF episodes. Thus, the
clinical significance for the personalized treatment and management of AF
patients is limited. The identification of AF rhythm is also influenced by
other abnormal rhythms in clinical applications.

The 4th China Physiological Signal Challenge 2021 (CPSC 2021) aims to
encourage the development of algorithms for searching the AF episodes in
dynamic ECG records. A new dynamic ECG database was constructed to encourage
the development of more efficient and robust algorithms for PAF detection. We
also develop a new scoring metric to evaluate detection methods for PAF
events.