cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/qcnm-a155
title: Brno University of Technology ECG Quality Database (BUT QDB)

description: BUT QDB is a database created by the cardiology team at the Department of
Biomedical Engineering, Brno University of Technology, for the purpose of
evaluating ECG quality. The data comprises 18 long-term recordings of single-
lead ECGs and associated 3-axis accelerometer data, collected from 15 subjects
(9 female, 6 male) aged between 21 to 83 years. Recordings were carried out
between August 2018 and October 2019 while the subjects were undertaking
ordinary everyday activities ("free-living conditions"). Data was collected
using a mobile ECG and accelerometer (Bittium Faros 180) with a sampling
frequency of 1,000 Hz for ECG signals and 100 Hz for accelerometer signals.
The minimum length of recording is 24 hours.

Three signals were fully annotated in terms of ECG signal quality. The
remaining 15 signals were annotated in two selected segments, each of 20
minutes in duration. Furthermore, five additional segments of poor signal
quality were also annotated. Signal quality was classified as follows: Class 1
indicates that all ECG significant waveforms (P wave, T wave and QRS complex)
are clearly visible and their onsets and offsets can be detected reliably;
Class 2 indicates that the noise level is increased and ECG significant points
cannot be reliably detected, but the signal enables reliable QRS detection;
Class 3 indicates that QRS complexes cannot be detected reliably and the
signal is unsuitable for any further analysis.
DOI: 10.13026/qfdz-zr67
title: Eye Gaze Data for Chest X-rays

description: We created a rich multimodal dataset for the Chest X-Ray (CXR) domain. The
data was collected using an eye tracking system while a radiologist
interpreted and read 1,083 public CXR images. The dataset contains the
following aligned modalities: image, transcribed report text, dictation audio
and eye gaze data. We hope this dataset can contribute to various fields of
research with applications in machine learning such as deep learning
explainability, multi-modal fusion, disease classification, and automated
radiology report generation to name a few. The images were selected from the
MIMIC-CXR Database and were associated with studies from 1,038 subjects
(female: 495, male: 543) who had age range 20 - 80 years old.
DOI: 10.13026/qgx3-3b28
title: Electrodermal Activity of Healthy Volunteers while Awake and at Rest

description: This dataset of electrodermal activity was collected from 11 healthy volunteer
subjects who were awake and at rest in seated position. The activity was
recorded from each subject's non-dominant hand for one hour at 256 Hz.
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).  The second dataset of electrodermal activity
was collected from 11 healthy volunteer subjects who were under computer-
controlled propofol sedation. The activity was recorded from each subject's
left hand for about 3 hours at 500 Hz. These data were collected with approval
from the Massachusetts General Hospital Human Research Committee.
DOI: 10.13026/sntf-5s35
title: Multilevel Monitoring of Activity and Sleep in Healthy people

description: Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset
provides 24 hours of continuous beat-to-beat heart data, triaxial
accelerometer data, sleep quality, physical activity and psychological
characteristics (i.e., anxiety status, stress events and emotions) for 22
healthy participants. Moreover, saliva bio-markers (i.e.cortisol and
melatonin) and activity log were also provided in this dataset. The MMASH
dataset will enable researchers to test the correlations between physical
activity, sleep quality, and psychological characteristics.
DOI: 10.13026/t92y-x219
title: Cerebromicrovascular Disease in Elderly with Diabetes

description: This prospective observational study evaluated the effects of type 2 diabetes
mellitus (DM) on cerebral vasoregulation, perfusion and functional outcomes,
measured by blood flow responses to hypocapnia and hypercapnia, Valsalva
maneuver, head-up tilt, and sit-to-stand test. This dataset contains data from
older 69 diabetic and control participants  (aged 55 to 75 years) with
continuous measurements of cerebral blood flow using transcranial Doppler and
MRI (magnetic resonance imaging), heart rate, blood pressure, and respiratory
parameters, balance, walking, laboratory and retinopathy measures at baseline,
and 41 subjects who completed the two-years of follow-up. Regional gray, white
matter and cerebrospinal fluid volumes were quantified using a segmentation
method applied on T1- and T2- weighted images and perfusion maps, using a
continuous arterial spin labeling (CASL) images at 3 Tesla MRI. White matter
integrity was determined from fluid attenuated inversion recovery (FLAIR) and
diffusion tensor imaging (DTI) MRI. Dynamics of cerebral vasoregulation to CO2
challenge and orthostatic stress were measured using Transcranial Doppler
ultrasound (TCD).
DOI: 10.13026/v8eq-8v80
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/wrxb-6885
title: Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

description: Clinical question answering (QA) (or reading comprehension) aims to
automatically answer questions from medical professionals based on clinical
texts. We release this dataset, which contains 1287 annotated QA pairs on 36
sampled discharge summaries from MIMIC-III Clinical Notes, to facilitate the
clinical question answering task. Questions in our dataset are either verified
or directly generated by clinical experts.

Note that the primary purpose of this dataset is to test the generalizability
of a QA model, i.e., whether a QA model that is trained on other datasets can
answer questions on this dataset (which may have a different distribution
compared with the training data), rather than to train a QA model. Hence the
scale of our annotations is relatively small compared to some existing QA
datasets.
DOI: 10.13026/xb00-c765
title: Analysis of Clinical Text: Task 14 of SemEval 2015

description: SemEval (Semantic Evaluation) is an ongoing series of evaluations of
computational semantic analysis systems, organized under the umbrella of
SIGLEX, the Special Interest Group on the Lexicon of the Association for
Computational Linguistics. This project describes Task 14 ("Analysis of
Clinical Text") of the International Workshop on Semantic Evaluation 2015
(SemEval 2015). The purpose of Task 14 is to enhance current research in
natural language processing (NLP) methods used in the clinical domain, and to
introduce clinical text processing to the broader NLP community. The task aims
to combine supervised methods for text analysis with unsupervised approaches.
More specifically, the task aims to combine supervised methods for
entity/acronym/abbreviation recognition and mapping to Unified Medical
Language System (UMLS) Concept Unique Identifiers (CUIs) with access to larger
clinical corpus for utilizing unsupervised techniques. Additionally, it will
also evaluate systems on the task of template filling, which involves the
population of eight attributes of the identified disorders with their
normalized values.
DOI: 10.13026/z82k-3w96
title: BigIdeasLab_STEP: Heart rate measurements captured by smartwatches for differing skin tones

description: This dataset was collected as part of a study on investigating the sources of
inaccuracy in wearable optical heart rate sensors across a variety of
activities (sitting, deep breathing, walking, and typing). The original study
examined covariates including skin tone, signal lag, and device type. The
dataset includes the heart rate given by the ECG and corresponding heart rate
given by each smartwatch used in the study. Activity, skin tone, and subject
ID are also included. This dataset was recorded in July-August 2019 and
includes 53 participants. The data is completely de-identified and
observations are time-synced.
DOI: 10.13026/393a-ky74
title: NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research

description: The development of algorithms for the extraction of the foetal ECG (fECG) from
non-invasive recordings is hampered by the lack of publicly-available
reference datasets, which could be used to benchmark different algorithms
while providing a ground truth on the foetal heart activity when an invasive
scalp lead is unavailable. By enriching the electrophysiological recordings
with simultaneous multimodal signals, these datasets could also help the
investigation of the foetal cardiac physiology, providing ground truth for the
analysis in early pregnancy, when the fECG is not directly accessible.

The Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal
Cardiology Research (NInFEA) is the first open-access dataset featuring
simultaneous non-invasive electrophysiological recordings, fetal pulsed-wave
Doppler (PWD) and maternal respiration signals. The dataset includes 60
entries from 39 voluntary pregnant women, between the 21st and the 27th week
of gestation. Every entry is composed of 27 electrophysiological channels
(2048 Hz, 22 bits, acquired by means of the TMSi Porti7 system), maternal
respiration signal (through a resistive thoracic belt), synchronised foetal
trans-abdominal PWD and clinical annotations provided by expert clinicians at
the time of the signal collection.
DOI: 10.13026/42eg-8e59
title: Shiraz University Fetal Heart Sounds Database

description: The Shiraz University (SU) fetal heart sounds database (SUFHSDB) contains
fetal and maternal phonocardiogram (PCG) recordings from 109 pregnant women in
single and twin pregnancies. The recordings were made at Hafez Hospital of
Shiraz University of Medical Sciences, Shiraz, Iran, from mothers aged from 16
to 47 years with a digital stethoscope placed on the lower maternal abdomen.
In total, 99 subjects had one signal recorded, three subjects had two and
seven cases of twins were recorded individually, resulting in 119 recordings.
The average duration of each record is about 90 seconds. The sampling rate was
generally 16,000 Hz with 16-bit quantization and a few recordings at 44,100
Hz. The data was recorded in wide-band mode of the digital stethoscope, with a
frequency response of 20 Hz to 1 kHz.
DOI: 10.13026/6kfs-9v69
title: CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

description: We created a dataset of clinical action items annotated over MIMIC-III. This
dataset, which we call CLIP, is annotated by physicians and covers 718
discharge summaries, representing 107,494 sentences. Annotations were
collected as character-level spans to discharge summaries after applying
surrogate generation to fill in the anonymized templates from MIMIC-III text
with faked data. We release these spans, their aggregation into sentence-level
labels, and the sentence tokenizer used to aggregate the spans and label
sentences. We also release the surrogate data generator, and the document IDs
used for training, validation, and test splits, to enable reproduction. The
spans are annotated with 0 or more labels of 7 different types, representing
the different actions that may need to be taken: Appointment, Lab, Procedure,
Medication, Imaging, Patient Instructions, and Other. We encourage the
community to use this dataset to develop methods for automatically extracting
clinical action items from discharge summaries.
DOI: 10.13026/7fxy-3653
title: Influence of the MHD effect on 12-lead and 3-lead ECGs recorded in 1T to 7T MRI scanners

description: This ECG dataset was acquired in different magnetic resonance imaging (MRI)
scanners to study the magnetohydrodynamic (MHD) effect. The MHD effect, which
is caused an interaction of the MRI's strong static magnetic field and the
patient's blood flow, superimposes the ECG signal during MRI exams. As a
consequence, a detailed morphological analysis of the ECG (e.g. of the P wave,
ST segment or the T wave) is prevented. The MHD effect might be a useful
signal for extracting further physiological information, e.g. about blood flow
or stroke volume. The 12-lead and 3-lead ECG signals were acquired from
different subjects in various MRI scanners with magnetic field strengths
ranging from 1T up to 7T. All signals were manually annotated.
DOI: 10.13026/9v35-xa84
title: Tai Chi, Physiological Complexity, and Healthy Aging - Gait

description: This dataset contains gait and electromyography (EMG) data collected during a
hybrid study that included a two-arm randomized clinical trial (RCT) along
with an additional observational comparison group. The RCT arm investigated
the short-term effects of a Tai Chi intervention on 60 Tai Chi healthy naive
subjects, aged 50-79 years, living within the Greater Boston area, and
reporting no regular Tai Chi practice within the past 5 years. Subjects were
either randomized to 6 months of Tai Chi or usual care. Subjects came in for a
baseline visit along with 3 month and 6 month follow-ups. The observational
comparison group consisted of 27 healthy Tai Chi experts, aged 50-79 years,
currently engaged in an active Tai Chi training regimen, each with at least 5
years of Tai Chi experience. The expert group was tested only at their
baseline visit. The gait and EMG data were simultaneously recorded during
walking at the subjects preferred speed for 10 minutes under the single-task
condition and for 90 seconds under the dual-task condition. Our aim for this
study was to use gait and EMG data to gain a better understanding of the
mobility changes associated with Tai Chi training.
DOI: 10.13026/d18t-tf45
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/dfv0-sb95
title: MICRO Motion capture data from groups of participants standing still to auditory stimuli (2015)

description: This project contains head movement data recorded from groups of participants
asked to stand as still as possible and presented with a series of auditory
stimuli. Data was collected in units of mm with a Qualisys motion capture
system at 100Hz. Data was collected at the University of Oslo on March 12th
2015 from a total of 108 participants. Code to read and process these files
has been made publicly available.
DOI: 10.13026/e4hr-yh29
title: Clinical BERT Models Trained on Pseudo Re-identified MIMIC-III Notes

description: This project contains the weights of 7 different BERT models trained over a
surrogate re-identified set of MIMIC-III (v1.4) notes. Each model is suitable
for direct compatibility with the HuggingFace framework; users can easily load
and apply the model. We release these model weights with the intent of
facilitating research into the dangers of revealing patient information via
model sharing, specifically for models pretrained on non-deidentified
electronic health record (EHR) data. We additionally release our post-
processed data, which most importantly contains a mapping of patient subject
id to condition(s) that the given patient has. The purpose of these "subject
id to condition" files is to measure how much of a correlation can be
extracted from a surrogate patient name and the medical conditions that the
given patient has. This will allow users to quantify the amount of Protected
Health Information (PHI) "leakage" these large pretrained language models may
exhibit.
DOI: 10.13026/fqq5-r521
title: Labeled raw accelerometry data captured during walking, stair climbing and driving

description: The database contains raw accelerometry data collected during outdoor walking,
stair climbing, and driving for 32 healthy adults. Accelerometry data were
collected simultaneously at four body locations: left wrist, left hip, left
ankle, and right ankle, at a sampling frequency of 100 Hz. The 3-axial
ActiGraph GT3X+ devices were used to collect the data. The data include labels
of activity type performed (walking, descending stairs, ascending stairs,
driving, clapping) for each time point of data collection. Basic demographic
information of participants is also provided. All data are anonymized.
DOI: 10.13026/hfks-3d71
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/jv2f-az31
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/kw00-z903
title: CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

description: We created a dataset of clinical action items annotated over MIMIC-III. This
dataset, which we call CLIP, is annotated by physicians and covers 718
discharge summaries, representing 107,494 sentences. Annotations were
collected as character-level spans to discharge summaries after applying
surrogate generation to fill in the anonymized templates from MIMIC-III text
with faked data. We release these spans, their aggregation into sentence-level
labels, and the sentence tokenizer used to aggregate the spans and label
sentences. We also release the surrogate data generator, and the document IDs
used for training, validation, and test splits, to enable reproduction. The
spans are annotated with 0 or more labels of 7 different types, representing
the different actions that may need to be taken: Appointment, Lab, Procedure,
Medication, Imaging, Patient Instructions, and Other. We encourage the
community to use this dataset to develop methods for automatically extracting
clinical action items from discharge summaries.