cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10503571
title: Global variable-resolution simulations of extreme precipitation over Henan, China in 2021 with MPAS-Atmosphere v7.3

description: This repository encompasses data and software to "Global variable-resolution simulations of the 2021 extreme precipitation event in Henan, China with MPAS-Atmosphere v7.3". The data and model are integral to our study, which focuses on simulating a significant rainstorm event in Henan, China, in July 2021.

Contained within this archive are the following components:

MPAS-Atmosphere v7.3: This is the core model employed in our study. MPAS v7.3 is a versatile global variable-resolution model, adept at simulating extreme weather events at varying scales.

MPAS mesh data: The global meshes generated for the experiments.

CMA Observation Data: Ground-based observational data from the China Meteorological Administration, crucial for the validation of our simulation results.

ERA5 Reanalysis Data: These datasets are used to further validate the outcomes of our simulations, providing a comprehensive set of atmospheric parameters.

GFS Data: The Global Forecast System (GFS) data serve as the input fields for the MPAS model, providing essential initial conditions for our simulations.

This collection is aimed at offering researchers a holistic package for studying, replicating, or extending our findings on extreme weather phenomena. The data and model provided here are not only crucial for reproducing the results presented in our study but also offer a valuable resource for further research in atmospheric sciences and climate modeling.
DOI: 10.15468/dl.u2z7jc
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hemiphylacus novogalicianus L.Hern."
 ]
}

 The dataset includes 20 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0240520-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gcy5ks
title: Occurrence Download

description: A dataset containing 18813 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON((-75.76289 5.61551,-75.65749 5.61551,-75.65749 5.72039,-75.76289 5.72039,-75.76289 5.61551))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 18813 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010662-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xeczy8
title: Occurrence Download

description: A dataset containing 2353 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Echinocactus platyacanthus Link & Otto"
 ]
}

 The dataset includes 2353 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0242838-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndnsyj
title: Occurrence Download

description: A dataset containing 3158 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heuchera rubescens Torr."
 ]
}

 The dataset includes 3158 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0242313-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.45zjgd
title: Occurrence Download

description: A dataset containing 370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Netherlands",
 "DatasetKey is one of (Observations Regelink Ecology and Landscape, iNaturalist Research-grade Observations, Natural History Museum Rotterdam - Specimens, NABU|naturgucker, Naturalis Biodiversity Center (NL) - Mammalia, Mammal records for Europe via the iMammalia app, MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany)",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "TaxonKey is Meles meles (Linnaeus, 1758)",
 "Year 1990-2025"
 ]
}

 The dataset includes 370 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016977-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vpk4rr
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mysidium rickettsi Harrison & Bowman, 1987"
 ]
}

 The dataset includes 18 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016978-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6v44az
title: Occurrence Download

description: A dataset containing 1180 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Estrandia grandaeva (Keyserling, 1886)"
 ]
}

 The dataset includes 1180 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016979-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x555a2
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Saussurea costus (Falc.) Lipsch."
 ]
}

 The dataset's 1 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016972-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.exka3z
title: Occurrence Download

description: A dataset containing 9873 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Staphylea pinnata L."
 ]
}

 The dataset includes 9873 records from 183 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016981-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13026/2ha4-km43
title: Leipzig Heart Center ECG-Database: Arrhythmias in Children and Patients with Congenital Heart Disease

description: Interpretation of Electrocardiograms (ECG) is increasingly complemented by
algorithms. These algorithms are based on large datasets. This ECG database
consists of children and adults with congenital heart defects (CHD) including
many arrhythmia annotations. This dataset, notable for its manual annotations
and inclusion of intracardiac electrograms alongside traditional 12-lead ECGs,
offers 1075.85 minutes of recordings (in total 113924 annotated beats) that
capture various cardiac rhythms and arrhythmias such as supraventricular
tachycardia and ventricular tachycardia. The data were meticulously collected
from patients undergoing electrophysiological studies, with subsequent
annotations by expert reviewers using the LightWAVE® software. The
significance of this database lies in its focus on pediatric arrhythmias and
arrhythmias of patients with congenital heart defect areas currently
underrepresented in existing datasets, which predominantly feature adult
pathologies. This resource aims to enhance algorithmic development for ECG
interpretation, leveraging machine learning to improve diagnosis and treatment
outcomes in these sensitive groups. The dataset not only serves as a critical
tool for developing precision medicine but also sets a precedent for future
expansions to include a broader spectrum of congenital heart defects
conditions, thereby supporting the evolution of cardiac care through advanced
computational techniques.
DOI: 10.13026/7a4j-vn37
title: Leipzig Heart Center ECG-Database: Arrhythmias in Children and Patients with Congenital Heart Disease

description: Interpretation of Electrocardiograms (ECG) is increasingly complemented by
algorithms. These algorithms are based on large datasets. This ECG database
consists of children and adults with congenital heart defects (CHD) including
many arrhythmia annotations. This dataset, notable for its manual annotations
and inclusion of intracardiac electrograms alongside traditional 12-lead ECGs,
offers 1075.85 minutes of recordings (in total 113924 annotated beats) that
capture various cardiac rhythms and arrhythmias such as supraventricular
tachycardia and ventricular tachycardia. The data were meticulously collected
from patients undergoing electrophysiological studies, with subsequent
annotations by expert reviewers using the LightWAVE® software. The
significance of this database lies in its focus on pediatric arrhythmias and
arrhythmias of patients with congenital heart defect areas currently
underrepresented in existing datasets, which predominantly feature adult
pathologies. This resource aims to enhance algorithmic development for ECG
interpretation, leveraging machine learning to improve diagnosis and treatment
outcomes in these sensitive groups. The dataset not only serves as a critical
tool for developing precision medicine but also sets a precedent for future
expansions to include a broader spectrum of congenital heart defects
conditions, thereby supporting the evolution of cardiac care through advanced
computational techniques.
DOI: 10.13026/7ytb-x609
title: ReXErr-v1: Clinically Meaningful Chest X-Ray Report Errors Derived from MIMIC-CXR

description: Interpreting medical images and writing radiology reports is a critical yet
challenging task in healthcare. Despite their importance, both human-written
and AI-generated reports are liable to errors, leaving a need for robust and
representative datasets that capture the diversity of errors present across
different mediums of report generation. Thus, we present Chest X-Ray Report
Errors (ReXErr-v1), a new dataset based on MIMIC-CXR and constructed using
large language models (LLMs) that contains synthetic error reports for the
majority of MIMIC-CXR. Developed with input from board-certified radiologists,
ReXErr-v1 contains plausible errors that closely mimic those found in real-
world scenarios. Furthermore, ReXErr-v1 utilizes a novel sampling methodology
that selects three errors to inject among a set of frequent errors made by
both human and AI models. We include errors both at report and sentence level,
improving the versatility of ReXErr-v1. Our dataset can enhance future AI
reporting tools by aiding the development and evaluation of report-generation
and error-screening algorithms.
DOI: 10.13026/9dns-vd94
title: ReXErr-v1: Clinically Meaningful Chest X-Ray Report Errors Derived from MIMIC-CXR

description: Interpreting medical images and writing radiology reports is a critical yet
challenging task in healthcare. Despite their importance, both human-written
and AI-generated reports are liable to errors, leaving a need for robust and
representative datasets that capture the diversity of errors present across
different mediums of report generation. Thus, we present Chest X-Ray Report
Errors (ReXErr-v1), a new dataset based on MIMIC-CXR and constructed using
large language models (LLMs) that contains synthetic error reports for the
majority of MIMIC-CXR. Developed with input from board-certified radiologists,
ReXErr-v1 contains plausible errors that closely mimic those found in real-
world scenarios. Furthermore, ReXErr-v1 utilizes a novel sampling methodology
that selects three errors to inject among a set of frequent errors made by
both human and AI models. We include errors both at report and sentence level,
improving the versatility of ReXErr-v1. Our dataset can enhance future AI
reporting tools by aiding the development and evaluation of report-generation
and error-screening algorithms.
DOI: 10.13026/2nb5-nf74
title: EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records

description: Electronic Health Records (EHRs) are integral for storing comprehensive
patient medical records, combining structured data (e.g., medications) with
detailed clinical notes (e.g., physician notes). These elements are essential
for straightforward data retrieval and provide deep, contextual insights into
patient care. However, they often suffer from discrepancies due to unintuitive
EHR system designs and human errors, posing serious risks to patient safety.
To address this, we developed EHRCon, a new dataset and task specifically
designed to ensure data consistency between structured tables and unstructured
notes in EHRs. EHRCon was crafted in collaboration with healthcare
professionals using the MIMIC-III EHR dataset, and includes manual annotations
of 3,943 entities across 105 clinical notes checked against database entries
for consistency. EHRCon has two versions, one using the original MIMIC-III
schema, and another using the OMOP CDM schema, in order to increase its
applicability and generalizability.
DOI: 10.13026/m4vd-y789
title: EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records

description: Electronic Health Records (EHRs) are integral for storing comprehensive
patient medical records, combining structured data (e.g., medications) with
detailed clinical notes (e.g., physician notes). These elements are essential
for straightforward data retrieval and provide deep, contextual insights into
patient care. However, they often suffer from discrepancies due to unintuitive
EHR system designs and human errors, posing serious risks to patient safety.
To address this, we developed EHRCon, a new dataset and task specifically
designed to ensure data consistency between structured tables and unstructured
notes in EHRs. EHRCon was crafted in collaboration with healthcare
professionals using the MIMIC-III EHR dataset, and includes manual annotations
of 4,101 entities across 105 clinical notes checked against database entries
for consistency. EHRCon has two versions, one using the original MIMIC-III
schema, and another using the OMOP CDM schema, in order to increase its
applicability and generalizability.
DOI: 10.13026/x1ea-np24
title: EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records

description: Electronic Health Records (EHRs) are integral for storing comprehensive
patient medical records, combining structured data (e.g., medications) with
detailed clinical notes (e.g., physician notes). These elements are essential
for straightforward data retrieval and provide deep, contextual insights into
patient care. However, they often suffer from discrepancies due to unintuitive
EHR system designs and human errors, posing serious risks to patient safety.
To address this, we developed EHRCon, a new dataset and task specifically
designed to ensure data consistency between structured tables and unstructured
notes in EHRs. EHRCon was crafted in collaboration with healthcare
professionals using the MIMIC-III EHR dataset, and includes manual annotations
of 4,101 entities across 105 clinical notes checked against database entries
for consistency. EHRCon has two versions, one using the original MIMIC-III
schema, and another using the OMOP CDM schema, in order to increase its
applicability and generalizability.
DOI: 10.13026/5mbq-9k80
title: CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays

description: Chest radiography presents a "long-tailed" distribution of findings, where a
few diseases are common, but most are rare. Diagnosis is further complicated
by its multi-label nature, as patients often exhibit multiple co-occurring
findings. While recent research has attempted to address the long-tailed
medical image classification problem, the interplay between class imbalance
and label co-occurrence remains underexplored. The **CXR-LT 2024 challenge**
builds on the success of CXR-LT 2023, expanding the dataset of 377,110 chest
X-rays (CXRs) to 45 disease labels, including 19 new rare disease findings.
This year 's challenge introduces three tasks: (i) long-tailed classification
on a large, noisy test set, (ii) long-tailed classification on a manually
annotated "gold standard" subset, and (iii) zero-shot generalization to five
previously unseen disease findings. CXR-LT 2024 addresses critical challenges
in long-tailed, multi-label, and zero-shot learning for medical imaging by
synthesizing state-of-the-art solutions from the international research
community. Further, our dataset contributions -- expanding disease coverage to
better reflect real-world clinical settings -- offer a valuable resource for
future research. This project contains labels from the CXR-LT 2024 and CXR-LT
2023 challenges, as well as a related subset used in the MICCAI 2023 paper,
"How Does Pruning Impact Multi-Label Long-Tailed Learning?"
DOI: 10.13026/721s-vs37
title: CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays

description: Many real-world problems, including diagnostic medical imaging exams, are
"long-tailed" - there are a few common findings followed by more relatively
rare conditions. In chest radiography, diagnosis is both a **long-tailed** and
**multi-label** problem, as patients often present with multiple disease
findings simultaneously. This is distinct from most large-scale image
classification benchmarks, where each image only belongs to one label and the
distribution of labels is relatively balanced. While researchers have begun to
study the problem of long-tailed learning in medical image recognition, few
have studied its interplay with label co-occurrence. This competition will
provide a challenging large-scale multi-label long-tailed learning task on
chest X-rays (CXRs), encouraging community engagement with this emerging
interdisciplinary topic.
DOI: 10.13026/c4tr-kr83
title: CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays

description: Many real-world problems, including diagnostic medical imaging exams, are
"long-tailed" - there are a few common findings followed by more relatively
rare conditions. In chest radiography, diagnosis is both a **long-tailed** and
**multi-label** problem, as patients often present with multiple disease
findings simultaneously. This is distinct from most large-scale image
classification benchmarks, where each image only belongs to one label and the
distribution of labels is relatively balanced. While researchers have begun to
study the problem of long-tailed learning in medical image recognition, few
have studied its interplay with label co-occurrence. This competition will
provide a challenging large-scale multi-label long-tailed learning task on
chest X-rays (CXRs), encouraging community engagement with this emerging
interdisciplinary topic. This project contains labels for the CXR-LT 2023
competition dataset, containing 377,110 CXRs from 26 classes, and a related
subset used in the MICCAI 2023 paper,  "How Does Pruning Impact Multi-Label
Long-Tailed Learning?" containing 257,018 frontal CXRs from 19 classes.
DOI: 10.13026/ryj9-x506
title: CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays

description: Chest radiography presents a "long-tailed" distribution of findings, where a
few diseases are common, but most are rare. Diagnosis is further complicated
by its multi-label nature, as patients often exhibit multiple co-occurring
findings. While recent research has attempted to address the long-tailed
medical image classification problem, the interplay between class imbalance
and label co-occurrence remains underexplored. The **CXR-LT 2024 challenge**
builds on the success of CXR-LT 2023, expanding the dataset of 377,110 chest
X-rays (CXRs) to 45 disease labels, including 19 new rare disease findings.
This year 's challenge introduces three tasks: (i) long-tailed classification
on a large, noisy test set, (ii) long-tailed classification on a manually
annotated "gold standard" subset, and (iii) zero-shot generalization to five
previously unseen disease findings. CXR-LT 2024 addresses critical challenges
in long-tailed, multi-label, and zero-shot learning for medical imaging by
synthesizing state-of-the-art solutions from the international research
community. Further, our dataset contributions -- expanding disease coverage to
better reflect real-world clinical settings -- offer a valuable resource for
future research. This project contains labels from the CXR-LT 2024 and CXR-LT
2023 challenges, as well as a related subset used in the MICCAI 2023 paper,
"How Does Pruning Impact Multi-Label Long-Tailed Learning?"
DOI: 10.15468/dl.vgezvr
title: Occurrence Download

description: A dataset containing 161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "DatasetKey is iNaturalist Research-grade Observations",
 "GadmGid is FRA.11.1_1",
 "IucnRedListCategory is one of (VU, NT, EN)",
 "TaxonKey is Animalia",
 "Year 2020-2025"
 ]
}

 The dataset includes 161 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016984-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bfbu6v
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mysidium rickettsi Harrison & Bowman, 1987"
 ]
}

 The dataset's 1 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016976-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y8qt53
title: Occurrence Download

description: A dataset containing 17913 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Living Specimen)",
 "TaxonKey is Erebia alberganus von Prunner, 1798"
 ]
}

 The dataset includes 17913 records from 182 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016985-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vzfmx9
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-66.94427 -25.3191,-67.08361 -34.85458,-58.34442 -34.77495,-57.34907 -24.50292,-65.71003 -23.62701,-66.94427 -25.3191))",
 "TaxonKey is Camponotus punctulatus Mayr, 1868"
 ]
}

 The dataset includes 56 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016986-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.akxn4b
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Incilius cristatus (Wiegmann, 1833)",
 "Year 1950-2010"
 ]
}

 The dataset includes 90 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016987-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.