cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21701417
title: Supplementary Material for: Domains for a Comprehensive Geriatric Assessment of Older Adults with Chronic Kidney Disease: Results from the CRIC Study

description: Introduction: A comprehensive geriatric assessment (CGA) tailored to the chronic kidney disease (CKD) population would yield a more targeted approach to assessment and care. We aimed to identify domains of a CKD-specific CGA (CKD-CGA), characterize patterns of these domains, and evaluate their predictive utility on adverse health outcomes. Methods: We used data from 864 participants in the Chronic Renal Insufficiency Cohort aged ≥55 years and not on dialysis. Constituents of the CKD-CGA were selected a priori. Latent class analysis informed the selection of domains and identified classes of participants based on their domain patterns. Predictive utility of class membership on mortality, dialysis initiation, and hospitalization was examined. Model discrimination was assessed with C-statistic. Results: The CKD-CGA included 16 domains: cardiovascular disease, diabetes, five frailty phenotype components, depressive symptoms, cognition, five kidney disease quality of life components, health literacy, and medication use. A two-class latent class model fit the data best, with 34.7% and 65.3% in the high- and low-burden of geriatric conditions classes, respectively. Relative to the low-burden class, participants in the high-burden class were at increased risk of mortality (aHR=2.09; 95% CI: 1.56, 2.78), dialysis initiation (aHR=1.63; 95% CI: 1.06, 2.52), and hospitalization (aOR=2.00; 95% CI: 1.38, 2.88). Model discrimination was the strongest for dialysis initiation (C-statistics=0.86), and moderate for mortality and hospitalization (C-statistics=0.70 and 0.66, respectively). Conclusion: With further validation in an external cohort, the CKD-CGA has the potential to be used in nephrology practices for assessing and managing geriatric conditions in older adults with CKD.
DOI: 10.5281/zenodo.7509992
title: Turing Data Story Santander Bikes

description: Dataset used in the Turing Data Story on the London Santander Cycle scheme.
DOI: 10.5281/zenodo.7509993
title: Turing Data Story Santander Bikes

description: Dataset used in the Turing Data Story on the London Santander Cycle scheme.
DOI: 10.5281/zenodo.7551619
title: Turing Data Story Santander Bikes

description: Dataset used in the Turing Data Story on the London Santander Cycle scheme.
DOI: 10.5281/zenodo.7546538
title: MAR Antarctica v3.12 albCor=1.05 outputs at daily resolution

description: Outputs of the regional atmospheric model MAR for the Antarctic domain, at 35 km resolution.
DOI: 10.5281/zenodo.7546539
title: MAR Antarctica v3.12 albCor=1.05 outputs at daily resolution

description: Outputs of the regional atmospheric model MAR for the Antarctic domain, at 35 km resolution.
DOI: 10.6084/m9.figshare.21923208
title: Functional diversity and soil nutrients regulate the interannual variability in gross primary productivity

description: <strong>Functional diversity and soil nutrients regulate the interannual variability in gross primary productivity</strong>
DOI: 10.5281/zenodo.7534769
title: Dataset: Evaluation of post-hoc interpretability methods in time-series classification

description: This repository contains the dataset, trained models as well as results for the article <em>Evaluation of post-hoc interpretability methods in time-series classification.</em> The code to reproduce the results presented in the article is available on GitHub. More details on the data and results can be found in the article. <strong>Files:</strong> <strong>datasets.zip: </strong>Include the three datasets used in the article: <strong>ECG: </strong>Processed version of the CPSC dataset from <em>Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020.</em> <strong>fordA: </strong>Dataset from the UCR Time Series Classification Archive <strong>synthetic: </strong>Synthetic dataset developed specifically for the purpose of the article <strong>trained_models.zip: </strong>Include CNN, transformer and bi-lstm trained on the three datasets <strong>results_paper.zip: </strong>Computed relevance and evaluation metrics for the trained models <strong>model_interpretability: </strong>Include the relevance computed using the different interpretability methods as well as the computed metrics for each method <strong>summary_results: </strong>Summary of the evaluation metrics across all interpretability methods for each dataset as well as an excel file summarising the metrics across all datasets.
DOI: 10.6084/m9.figshare.21923289
title: INS and INSR alignment files

description: Alignment files for Insulin and its receptor
DOI: 10.6084/m9.figshare.21923289.v1
title: INS and INSR alignment files

description: Alignment files for Insulin and its receptor
DOI: 10.6084/m9.figshare.21923277
title: Metabolomic analysis data from FMT mice (Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis)

description: Metabolomic analysis data from FMT mice.
DOI: 10.6084/m9.figshare.21923277.v1
title: Metabolomic analysis data from FMT mice (Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis)

description: Metabolomic analysis data from FMT mice.
DOI: 10.5281/zenodo.7042930
title: (INITIALIZATION) Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions (Replication Package)

description: This placeholder upload is empty. Use the latest version.
DOI: 10.5281/zenodo.7043102
title: (OUTDATED VERSION) Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions (Replication Package)

description: The replication package has been updated with improved documentation and Docker images of the environment. Please use the latest version.
DOI: 10.5281/zenodo.7547626
title: (OUTDATED VERSION) Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions (Replication Package)

description: The replication package has been cleaned up. Please use the latest version.
DOI: 10.5281/zenodo.7551289
title: (OUTDATED VERSION) Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions (Replication Package)

description: The replication package has been cleaned up and updated with improved documentation. Please use the latest version.
DOI: 10.6084/m9.figshare.21923310
title: RNA sequencing data from FMT pancreas. (Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis)

description: RNA sequencing data from FMT mice.
DOI: 10.6084/m9.figshare.21923310.v1
title: RNA sequencing data from FMT pancreas. (Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis)

description: RNA sequencing data from FMT mice.
DOI: 10.25505/fiz.icsd.cc2d6d78
title: CSD 2210679: Experimental Crystal Structure Determination

description: Related Article: Travis K. Deason, Gregory Morrison, Amir Mofrad, Hunter B. Tisdale, Jake Amoroso, David DiPrete, Gary Was, Kai Sun, Theodore M. Besmann, Hans-Conrad zur Loye|2022|J.Am.Chem.Soc.|145|465|doi:10.1021/jacs.2c10669
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7548605
title: Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy

description: <strong>Introduction</strong>. This database includes the radiomic features used as covariates to train machine learning algorithms in the paper “ Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy”. <strong>Purpose</strong>. Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. <strong>Method</strong>. Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. <strong>Results</strong>. Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. <strong>Conclusion</strong>. This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.
description: Funding: ricerca corrente 2017-2019); ricerca finalizzata RF-2016-02362914) - Ministry of Health (Italy)
DOI: 10.5281/zenodo.7548604
title: Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy

description: <strong>Introduction</strong>. This database includes the radiomic features used as covariates to train machine learning algorithms in the paper “ Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy”. <strong>Purpose</strong>. Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. <strong>Method</strong>. Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. <strong>Results</strong>. Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. <strong>Conclusion</strong>. This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.
description: Funding: ricerca corrente 2017-2019); ricerca finalizzata RF-2016-02362914) - Ministry of Health (Italy)
DOI: 10.26036/cnp0003896
title: Demo data

description: stLFR library sequenced on DNBSEQ-G400 (MGISEQ-2000) platform
DOI: 10.6084/m9.figshare.21923337
title: Targeted amino acid profiling (Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis)

description: We screened the fecal targeted amino acid profiling between FMT-HC and FMT-HTGP mice.
DOI: 10.6084/m9.figshare.21923337.v1
title: Targeted amino acid profiling (Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis)

description: We screened the fecal targeted amino acid profiling between FMT-HC and FMT-HTGP mice.
DOI: 10.6084/m9.figshare.21923322
title: Variables used in the validation section

description: The variable data used in the validation section mainly contains corporate economic performance indicators, corporate environmental performance indicators, corporate social performance indicators, comprehensive verification index and control variables.
DOI: 10.6084/m9.figshare.21923322.v1
title: Variables used in the validation section.xlsx

description: The variable data used in the validation section mainly contains corporate economic performance indicators, corporate environmental performance indicators, corporate social performance indicators, comprehensive verification index and control variables.
DOI: 10.7284/139771
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR43 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139096
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR44 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139669
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR46 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139112
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR47 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141120
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR48 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141081
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR49 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142323
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR52-A using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142347
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR52-B using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142369
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR53 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142390
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR54 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144745
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR55 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/143768
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR56 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144570
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR57 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/143793
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR58 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144021
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR59 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/151347
title: Magnetometer data collected during research cruise AT37-05 using a iXSea MAGIS instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150101
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise SP2216 using a Hawaii UHDAS instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150105
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise SP2216 using a SeaBird SBE-911+ instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150106
title: GNSS (Global Navigation Satellite System) data collected during research cruise SP2216 using a Furuno GP-150 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150107
title: GNSS (Global Navigation Satellite System) data collected during research cruise SP2216 using a Furuno GP-170 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150108
title: GNSS (Global Navigation Satellite System) data collected during research cruise SP2216 using a Trimble ABX-Two instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150104
title: Gyrocompass data collected during research cruise SP2216 using a Furuno Furuno SC-30 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150103
title: Meteorological Station data collected during research cruise SP2216 using a SIO MET-System instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150102
title: Winch data collected during research cruise SP2216 using a Markey DEYSH-3 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150109
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise SP2217 using a Hawaii UHDAS instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150113
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise SP2217 using a SeaBird SBE-911+ instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150114
title: GNSS (Global Navigation Satellite System) data collected during research cruise SP2217 using a Furuno GP-150 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150115
title: GNSS (Global Navigation Satellite System) data collected during research cruise SP2217 using a Furuno GP-170 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150117
title: GNSS (Global Navigation Satellite System) data collected during research cruise SP2217 using a Trimble ABX-Two instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150112
title: Gyrocompass data collected during research cruise SP2217 using a Furuno Furuno SC-30 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150111
title: Meteorological Station data collected during research cruise SP2217 using a SIO MET-System instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150116
title: Singlebeam Echosounder data collected during research cruise SP2217 using a Knudsen 3260 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/150110
title: Winch data collected during research cruise SP2217 using a Markey DEYSH-3 instrument system onboard the platform RV Robert Gordon Sproul

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.34740/kaggle/dsv/4873251
title: Egypt Gold Prices | Daily Updated
DOI: 10.13026/901c-yv54
title: Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center

description: The medical specialty of critical care, or intensive care, provides emergency
medical care to patients suffering from life-threatening complications and
injuries. The medical specialty is featured by the generation of a huge amount
of high-granularity data in routine practice. The data comprise hourly vital
signs, ventilator waveforms, medical orders, and laboratory results.
Currently, these data are well archived in the hospital information system for
the primary purpose of routine clinical practice. However, data scientists
have noticed that in-depth mining of such big data may provide insights into
the pathophysiology of underlying diseases and healthcare practices. Clinical
questions related to risk factors, predictive analytics, cost-effectiveness,
and causal inference can be addressed with the critical care database. There
have been several openly accessible critical care databases being established,
which have generated hundreds of scientific outputs published scientific
journals. However, such work is still in its infancy in China. China has a
large patient population, which contributes to the generation of a large
healthcare database in hospitals. The establishment and sharing of such a
database can help to promote the open data science and discover novel
scientific knowledge in a collaborative way. In this data descriptor article,
we report the establishment of an openly accessible critical care database
generated from hospital information system. The database comprises 8180 unique
hospital admissions for 7638 individual patients from January 2012 to May
2022. The database contains 11 plain text relational tables that can be linked
by _hospital_ID_.