cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/s309-a395
title: Siena Scalp EEG Database

description: The database consists of EEG recordings of 14 patients acquired at the Unit of
Neurology and Neurophysiology of the University of Siena.  Subjects include 9
males (ages 25-71) and 5 females (ages 20-58). Subjects were monitored with a
Video-EEG with a sampling rate of 512 Hz, with electrodes arranged on the
basis of the international 10-20 System. Most of the recordings also contain 1
or 2 EKG signals. The diagnosis of epilepsy and the classification of seizures
according to the criteria of the International League Against Epilepsy were
performed by an expert clinician after a careful review of the clinical and
electrophysiological data of each patient.
DOI: 10.13026/se6w-f455
title: AMR-UTI: Antimicrobial Resistance in Urinary Tract Infections

description: AMR-UTI is a freely accessible dataset, derived from electronic health record
(EHR) information on over 80,000 patients with urinary tract infections (UTI)
treated at Massachusetts General Hospital and Brigham & Women's Hospital in
Boston, MA, USA between 2007 and 2016.

Each observation in the dataset corresponds to a urine specimen sent to the
clinical microbiology laboratory to assess for antimicrobial resistance (AMR).
Each observation includes (1) the antimicrobial susceptibility profile, (2)
the empiric antibiotic treatment decision, which is made without knowledge of
the susceptibility testing results, and (3) patient and specimen features
useful for prediction of AMR.
DOI: 10.13026/wdhq-rs83
title: Lobachevsky University Electrocardiography Database

description: Lobachevsky University Electrocardiography Database (LUDB) is an ECG signal
database with marked boundaries and peaks of P, T waves and QRS complexes. The
database consists of 200 10-second 12-lead ECG signal records representing
different morphologies of the ECG signal. The ECGs were collected from healthy
volunteers and patients of the Nizhny Novgorod City Hospital No 5 in
2017-2018\. The patients had various cardiovascular diseases while some of
them had pacemakers. The boundaries of P, T waves and QRS complexes were
manually annotated by cardiologists for all 200 records. Also, each record is
annotated with the corresponding diagnosis. The database can be used for
educational purposes as well as for training and testing algorithms for ECG
delineation, i.e. for automatic detection of boundaries and peaks of P, T
waves and QRS complexes.
DOI: 10.13026/yrz5-5909
title: In-hospital physical activity measured with a new Bosch accelerometer sensor system

description: Extended hospital stays and readmissions are associated with high costs and
burden of disease. Effective ways to predict patient outcomes, select those at
risk of adverse outcomes, and to prevent these accordingly, are lacking.
Physical activity measured by accelerometry is a promising surrogate parameter
of health, but it is mainly evaluated in highly selected patient groups and
with few algorithms. We used a new wrist-worn Bosch sensor platform to measure
various characteristics of physical activity in 58 patients of various ages
treated on internal medicine wards over a period of 30 days, with a goal of
developing algorithms to predict duration of hospitalization and 30-day
readmission. New algorithms are required to increase the accuracy of the
predictions and to understand the significance of physical activity in
hospitalized patients. To facilitate research in this area, and to allow the
public to explore these issues, all data generated in the study are published
here as a public dataset.
DOI: 10.13026/zfb2-1g43
title: Mental workload during n-back task captured by TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) monitoring

description: The authors used this dataset to assess the neurovascular coupling responses
evoked by a cognitive paradigm with the aid of TransCranial Doppler (TCD)
sonography and functional Near-InfraRed Spectroscopy (fNIRS). Fourteen healthy
young individuals were recruited for this study conducted in 2018 at the
University of Oklahoma, Translational Geroscience Laboratory (age: 31±5.94
years, BMI: 24.9±2.95). The following physiological signals were measured non-
invasively during the examination: relative concentration of oxy- and
deoxyhemoglobin (HbO and HbR, respectively), and cerebral Blood Flow Velocity
(BFV) in the middle cerebral arteries for the entire duration of the testing
(~11 minutes).  In this study, fNIRS signals from the frontal cortex were
acquired by a NIRScout platform (NIRx Medical Technologies LLC, NY, USA) at a
sampling frequency of 3.9 Hz,  HbO and HbR time series signals can be obtained
by applying the differential modified Beer Lambert Law on the preprocessed
records of near-infrared intensities. BFV was recorded using TCD sonography
(Digi-Lite, Rimed, Raanana, Israel) by placing 2 Mhz ultrasound probes over
the left and right temporal acoustic windows. The paradigm used for cognitive
testing was an n-back task where the participant was administered a series of
alphabetical letters on the computer screen and had to provide a response
whenever a stimulus is presented that is the same as the one presented _n_
trials previously.  The examination protocol consisted of the following 134
second sessions: 0-back, 1-back, 0-back, and 2-back. This dataset was
collected for the following study: "Increased Cognitive Workload Evokes
Greater Neurovascular Coupling Responses in Healthy Young Adults".
DOI: 10.13026/46y6-6x96
title: VinDr-CXR: An open dataset of chest X-rays with radiologist annotations

description: We describe here a dataset of more than 100,000 chest X-ray scans that were
retrospectively collected from two major hospitals in Vietnam. Out of this raw
data, we release 18,000 images that were manually annotated by a total of 17
experienced radiologists with 22 local labels of rectangles surrounding
abnormalities and 6 global labels of suspected diseases. The released dataset
is divided into a training set of 15,000 and a test set of 3,000. Each scan in
the training set was independently labeled by 3 radiologists, while each scan
in the test set was labeled by the consensus of 5 radiologists. All images are
in DICOM format and the labels from training and test sets are made publicly
available.
DOI: 10.13026/51h0-rs07
title: Tai Chi, Physiological Complexity, and Healthy Aging: Gait and EMG data

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: 10.13026/61rg-q298
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 "Analysis of Clinical Text"
task of the International Workshop on Semantic Evaluation 2014 and 2015
(SemEval 2014 and 2015) [2,3,4,5]. The purpose of the task 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 for entity/acronym/abbreviation recognition and
mapping to Unified Medical Language System (UMLS) Concept Unique Identifiers
(CUIs). It also evaluated systems on the task of template filling [1], which
involves the population of eight attributes of the identified disorders with
their normalized values.
DOI: 10.13026/9ths-ht80
title: Chest ImaGenome Dataset

description: In recent years, with the release of multiple large datasets, automatic
interpretation of chest X-ray (CXR) images with deep learning models have
become feasible for specific abnormalities or for generating preliminary
reports. However, despite reports of performance reaching similar levels to
that of radiologists, a quantitative evaluation of the explainability of these
models is hampered by the lack of locally labeled datasets for different
findings. With the exception of a few human-labeled small-scale datasets for
specific findings, such as pneumonia and pneumothorax, most of the CXR deep
learning models to date are trained on global "weak" labels extracted from
text reports, or trained via a joint image and unstructured text learning
strategy. In our work, a joint rule-based natural language processing (NLP)
and CXR atlas-based bounding box detection pipeline are used to automatically
label 242072 frontal MIMIC CXRs locally. Inspired by the Visual Genome effort
in the computer vision community [20], we constructed the first Chest
ImaGenome dataset with a scene graph data structure to describe the data.
Through a radiologist constructed CXR ontology, the annotations for each CXR
are connected as an anatomy-centered scene graph, useful for image-level
reasoning and multimodal fusion applications. Overall, our dataset contributes
significantly to the research community by providing 1) 1,256 combinations of
relation annotations between 29 CXR anatomical locations (objects with
bounding box coordinates) and their attributes, structured as a scene graph
per image, 2) over 670,000 localized comparison relations (for improved,
worsened, or no change) between the anatomical locations across sequential
exams, as well as 3) a manually annotated gold standard scene graph dataset
from 500 unique patients.
DOI: 10.13026/arty-2540
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 [1]. Sweat glands are a primitive part of
the fight-or-flight response [1]. 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).
DOI: 10.13026/bnj3-7b52
title: Multitaper spectra recorded during GABAergic anesthetic unconsciousness

description: This database contains electroencephalography (EEG) multitaper spectra and
associated conscious/unconscious labels for a cohort of 10 healthy volunteers
undergoing stereotyped anesthetic administration and direct monitoring of
subject responsiveness, and 44 patients receiving anesthesia care in an
operating room (OR) context. Cases recorded in an OR context are further
divided into those administered propofol alone under a total intravenous
anesthesia (TIVA) approach, and those who received either sevoflurane alone or
sevoflurane following a propofol induction. These data were used to train and
validate classification models for tracking unconsciousness. Corresponding
code for building and testing classification models is publicly available.
DOI: 10.13026/c61a-z467
title: RR interval time series from healthy subjects

description: Heart rate variability (HRV) is mainly controlled by the autonomic nervous
system (ANS) interacting with receptors on the sinoatrial node (SAN). HRV is
influenced by many several factors such as chemical, hormonal and neural
modulations, circadian changes, exercise, emotions, posture, and preload. The
heart rate adaptation to changing factors is carried out by the activity of a
variety of regulatory subsystems and results in a complex linear and non-
linear time behaviour, which changes with age, gender and pathological
conditions. Many studies explore an age range of 1-99 years. This study
focused on the development of a database of long-term time series of healthy
volunteers aged between 1 month and 55 years.
DOI: 10.13026/cdb3-8925
title: Kinematic dataset of actors expressing emotions

description: We produced a kinematic dataset to assist in recognizing cues from all parts
of the body that indicate human emotions (happy, sad, angry, fearful, ,
disgust, surprise) and neutral. The present dataset was created using a
portable wireless motion capture system. Twenty-two semi-professional actors
(50% female) completed performances. A total of 1402 recordings at 125 Hz were
collected, consisting of the position and rotation data of 72 anatomical
nodes. We hope this dataset will contribute to multiple fields of research and
practice, including social neuroscience, psychiatry, computer vision, and
biometric and information forensics.
DOI: 10.13026/ckz9-ph15
title: CPAP Pressure and Flow Data from a Local Trial of 30 Adults at the University of Canterbury

description: A pressure and flow dataset for CPAP (continuous positive airway pressure)
breathing obtained from 30 subjects for model-based identification of patient-
specific lung mechanics using a specially designed sensor system comprising an
array of differential pressure sensors (Sensirion AG SDP816-125PA) and gauge
pressure sensors (NXP MPVZ4006GW7U). Relevant medical information was
collected using a questionnaire, including: sex; age; weight; height; smoking
history; and history of asthma. Subjects were tasked with breathing at five
different rates (including passive) from very slow to very fast breathing,
matched to an online pacing sound and video, at two different levels of CPAP
(4 and 7 cmH2O) for between 50 and 180 seconds depending on rate. Each data
set for a breathing rate comprises ~17 breaths, including rest periods between
breathing rates and CPAP levels. Data with significant observed sensor error
was removed during processing. A better-fitted mask would be expected to
reduce error in fully capturing expiration by reducing leaks around the masks
seal with the face. The sensors measure inspiration and expiration using
separate unidirectional differential flow sensors, which are combined in post-
processing to for complete breathing data.
DOI: 10.13026/g6qh-pf06
title: In-hospital physical activity measured with a new Bosch accelerometer sensor system

description: Extended hospital stays and readmissions are associated with high costs and
burden of disease. Effective ways to predict patient outcomes, select those at
risk of adverse outcomes, and to prevent these accordingly, are lacking.
Physical activity measured by accelerometry is a promising surrogate parameter
of health, but it is mainly evaluated in highly selected patient groups and
with few algorithms. We used a new wrist-worn Bosch sensor platform to measure
various characteristics of physical activity in 58 patients of various ages
treated on internal medicine wards over a period of 30 days, with a goal of
developing algorithms to predict duration of hospitalization and 30-day
readmission. New algorithms are required to increase the accuracy of the
predictions and to understand the significance of physical activity in
hospitalized patients. To facilitate research in this area, and to allow the
public to explore these issues, all data generated in the study are published
here as a public dataset.
DOI: 10.13026/gz5h-e561
title: Critical care database comprising patients with infection at Zigong Fourth People's Hospital

description: Patients treated in the intensive care unit (ICU) are closely monitored and
receive intensive treatment. Such aggressive monitoring and treatment will
generate high-granularity data from both electronic healthcare records and
nursing chart. These data not only provide infrastructure for daily clinical
practice, but also can help to inform clinical researches. It is technically
challenging to integrate and cleanse medical data from a variety of sources.
Although there are several open access critical care databases from western
countries, there is a lack of this kind of database for Chinese adult
patients. We established a critical care database involving patients with
infection. A large proportion of these patients have sepsis and/or septic
shock. High-granularity data comprising laboratory findings, baseline
characteristics, medications, ICD code, nursing charts and follow-up results
were integrated to generate a comprehensive database. The database can be
utilized for a variety of clinical studies.
DOI: 10.13026/hm87-5p47
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/j69z-2802
title: MICRO Motion capture data from groups of participants standing still to auditory stimuli (2012)

description: This dataset comprises head motion observations collected as part of an
experiment in which a group of people were asked to stand still for 6 minutes,
with the first 3 minutes in silence, followed by 3 minutes with music. Head
motion was captured in units of mm at 100Hz using a Qualisys infra-red optical
system. The experiment was carried out at the University of Oslo on March 8th
2012 from a total of 113 participants. Code to read and process these files is
available. The paper corresponding to the work is Jensenius et al., "The
Musical Influence on People's Micromotion when Standing Still in Groups",
Proceedings of the 14th Sound and Music Computing Conference (2017).
DOI: 10.13026/jzqa-j937
title: Wide-field calcium imaging sleep state database

description: A collection of wide-field calcium imaging (WFCI) sleep and wake recordings
collected from transgenic mice expressing GCaMP6f in excitatory neurons. Each
mouse underwent a three-hour undisturbed WFCI recording session where wake,
NREM and REM sleep was recorded. Each WFCI recording is manually scored by
sleep scoring experts in 10-second epochs as wake, NREM or REM by use of
adjunct EEG/EMG. The dataset contains annotated WFCI recordings, brain mask
and the Paxinos atlas used for defining the brain regions. The dataset was
collected as part of a study evaluating a deep learning-based automated sleep
state classification method.
DOI: 10.13026/n2v5-5b32
title: MIMIC-III - SequenceExamples for TensorFlow modeling

description: This dataset contains TensorFlow SequenceExamples derived from patient records
in MIMIC-III, a freely available set of deidentified medical records from
critical care patients at Beth Israel Deaconess Medical Center. Each
SequenceExample converts data from an individual patient encounter and any
previous encounters into a set of timestamped "feature lists" describing the
patient history up to a certain time, beyond which predictions can be made.
These data are suitable for direct input into TensorFlow modeling pipelines,
and include labels for inpatient mortality and discharge diagnosis codes for
each encounter. The intent of this release is to provide a preprocessed,
ready-to-use version of MIMIC-III to support and enable reproducible machine
learning research for electronic health records.
DOI: 10.13026/nrbg-cs59
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/q712-yv80
title: Hospitalized patients with heart failure: integrating electronic healthcare records and external outcome data

description: Heart failure is a common reason for hospitalization in the elderly and it is
associated with significant mortality and morbidity. To facilitate
epidemiological studies of heart failure, there is a need for high quality
datasets to be made available to researchers. While several heart failure
datasets have been established in Western countries, there is a paucity of
data available from China. Understanding differences in patient populations
and healthcare systems between China and other countries is important in
providing optimal care. To help address this issue, we created a retrospective
heart failure dataset using electronic health data collected from patients who
were admitted to a hospital in Sichuan, China between 2016 and 2019\. The
dataset includes 168 variables for 2,008 patients with heart failure.