cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xtfztj98tw.1
title: Dataset related to Lower limb movement in 10 Meter Walk Test

description: This dataset captures detailed lower limb movement data during a 10-meter walk test, which is useful for gait and mobility analysis. The data is collected using two tilebox sensors, strategically positioned to capture a comprehensive set of motion parameters.

Each sensor records the following types of data:

Accelerometer Data: Measures linear acceleration, providing insights into the speed and direction of leg movements.
Gyroscope Data: Captures angular velocity, aiding in the analysis of limb rotation and stability during walking.
Magnetometer Data: Records magnetic field data, useful for determining the orientation and heading of the sensors relative to the Earth's magnetic field.
Temperature Data: Monitors ambient or sensor temperature, which can impact sensor performance or provide environmental context.

The dataset also includes a YAML file containing demographic and lifestyle information for each participant, which provides critical context for data interpretation. The YAML file details include:

Demographic Information: Gender, age, weight, height.
Physical Activity: Frequency, duration, and type of exercise (e.g., walking).
Diet and Nutrition: Dietary habits, portions of fruits or vegetables consumed, daily water intake.
Health Status: Presence of health pathologies (e.g., motor-related issues), smoking status, and current physical state.
Sleep and Energy Levels: Average hours of sleep and perceived energy levels.
Motivation and Participation: Interest in participating in the study.
Mobility Aids: Use of walking aids, if applicable.
DOI: 10.17909/t94s3x
title: The Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey ("CANDELS")

description: The Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey, CANDELS (Grogin et al. 2011; Koekemoer et al. 2011), PI: S. Faber, Co-PI, H. Ferguson, is designed to document the first third of galactic evolution from z = 8 to 1.5 via deep imaging of more than 250,000 galaxies with WFC3/IR and ACS. It will also discover and characterize Type Ia SNe beyond z &gt; 1.5 and establish their accuracy as standard candles for cosmology.
DOI: 10.18128/d010.v11.0
title: IPUMS USA: Version 11.0

description: IPUMS USA contains harmonized census and American Community Survey (ACS) data from 1790 to the present. For the period 1850 to 1940, IPUMS includes 100% of individuals in the decennial censuses. For the period 1950 to 2010, we have public use samples of decennial censuses. For the period since 2000, IPUMS provides annual ACS public use samples. The available information in the censuses and ACS varies by year, but generally includes basic housing data (type of housing, household appliances, and housing characteristics), demographic data (age, race, sex, marital status, and relationship to the head of household), economic data (occupation, industry, income, work status), and other individual characteristics (migration, disability, veteran status). For the period 1790 to 1840, IPUMS includes household level records for 100% of households. IPUMS USA is available through an online data access system free of charge to registered users at usa.ipums.org.
DOI: 10.18419/darus-3788
title: DuMux 3.8.0

description: Release 3.8.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE.

&lt;!--&lt;p&gt;The individual files correspond to the main module &lt;a href="https://git.iws.uni-stuttgart.de/dumux-repositories/dumux/-/tree/3.8.0"&gt;"dumux"&lt;/a&gt; and two dependent modules &lt;a href="https://git.iws.uni-stuttgart.de/dumux-repositories/dumux-course/-/tree/3.8.0"&gt;"dumux-course"&lt;/a&gt; and &lt;a href="https://git.iws.uni-stuttgart.de/dumux-repositories/dumux-lecture/-/tree/3.8.0"&gt;"dumux-lecture"&lt;/a&gt;.&lt;/p&gt;--&gt;
description: https://git.iws.uni-stuttgart.de/dumux-repositories/dumux/-/blob/3.8.0/CHANGELOG.md
 <br> Use persistent identifiers from Software Heritage ( ) to cite individual files or even lines of DuMux.
DOI: 10.24432/c52p59
title: Breast Cancer Coimbra
DOI: 10.3886/e209030v1
title: Data and code for "Product Differentiation and Oligopoly: a Network Approach"

description: I present a new theory of oligopoly and markups in general equilibrium, based on an innovative, scalable hedonic demand system, which I take to the data for the universe of US public firms. In my model, firms compete in a network of product market rivalries that emerge endogenously out of the characteristics of the products they supply. I estimate that consumer surplus is at least three times as large as profits, I decompose firm-level markups into metrics of quality-adjusted productivity and market centrality, and analyze the extent, evolution and drivers of monopoly power in the USA between 1995 and 2021.
DOI: 10.5281/zenodo.10650853
title: Supporting Data for "Air Quality Estimation and Forecasting via Data Fusion with Uncertainty Quantification: Theoretical Framework and Preliminary Results"

description: Output data needed to generate reuslts and figures presented in the paper "Air Quality Estimation and Forecasting via Data Fusion with Uncertainty Quantification: Theoretical Framework and Preliminary Results".

Figures.py is a python code which generates the figures presented in the manuscript. This code takes input from data saved in NetCDF files with the naming format "Crossvalidation_Results_AA_MMMYYYY.nc", where "AA" is the application area (SF for San Francisco; NY for New York City) and "MMMYYY" is the month and year of the application period.

All data and codes should be downloaded to a local directory. This directory needs to be specified within the python code as the s_folder variable.
DOI: 10.5281/zenodo.12668622
title: Incidence  and Predictive Factors of Hyperventilation Syndrome in Patients after COVID 19 pneumonia: A Prospective Cohort Study
DOI: 10.5281/zenodo.13863745
title: Supplementary Dataset for Perez-Consuegra et al. Limits to Relief in the Northern Andes

description: Supplementary tables

Supplementary Table 1: Contains the 10Be cosmogenic nuclide data and erosion rates

Supplementary Table 2: Contains the erosion rates and geomorphic metrics for the same basins

Supplementary Table 3: Contains the published single grain AHe data compiled for this study.

Supplementary Tables 4–6: Contains the input parameters for the Age2exhume exhumation rates calculations, the input thermochronology data and the output exhumation rates.

Supplementary Table 7: Contains the geomorphic metrics of drainage basins along the eastern flank of the study area.
DOI: 10.5281/zenodo.13895620
title: Aberrant gene expression prediction benchmark based on GTEx v8

description: This repository contains the aberrant gene expression prediction benchmark data as well as the necessary expected gene expression across tissues and tissue-specific isoform contribution scores for AbExp prediction. 

The aberrant gene expression prediction benchmark data (aberrant_expression_prediction_benchmark.parquet) contains the following columns:



individual: GTEx individual

gene: Ensembl gene identifier

tissue: GTEx tissue

tissue_type: GTEx tissue type

mu: OUTRIDER-estimated expected gene expression

theta: OUTRIDER-estimated gene dispersion

counts: Raw gene expression count

normalized_counts: OUTRIDER-normalized gene expression count

l2fc: log2 fold change between observed and expected gene expression count

zscore: z-score of gene expression, obtained by quantile-mapping the OUTRIDER-estimated distribution to the standard normal distribution

nominal_pvalue: OUTRIDER-estimated p-value of being an expression outlier

FDR: FDR-adjusted p-value of being an expression outlier

is_in_benchmark: Whether this observation is part of the aberrant gene expression prediction benchmark

is_underexpressed_outlier: Whether this observation is an underexpression outlier at FDR < 5%. This is the benchmark prediction label.


The isoform proportions table (gtex_v8_isoform_proportions.tsv) contains the following columns:



gene: Ensembl gene identifier

tissue_type: GTEx tissue type

tissue: GTEx tissue

transcript: Ensembl transcript identifier

mean_transcript_proportions: mean transcript proportions across individuals in GTEx v8

median_transcript_proportions: median transcript proportions across individuals in GTEx v8

sd_transcript_proportions: standard deviation of transcript proportions across individuals in GTEx v8


The expected gene expression table (gtex_v8_expected_expression.tsv) contains the following columns:



gene: Ensembl gene identifier

tissue_type: GTEx tissue type

tissue: GTEx tissue

gene_is_expressed: Whether the gene is expressed in the tissue

median_expression: median OUTRIDER-estimated expected gene expression (mu) across individuals

expression_dispersion: OUTRIDER-estimated gene dispersion (theta)
DOI: 10.5281/zenodo.13971253
title: Lithic raw data for Sheppard's (1987) thesis on the Capsian

description: This is a collection of six lithic raw data spreadsheets created in the course of Peter Sheppard’s doctoral project on the prehistoric tradition of Northwest Africa known as the Capsian. Sheppard’s project investigated technological and stylistic variation in Capsian lithics. The spreadsheets provided here contain about 507,000 observations collected on some 20,100 artefacts (blanks, cores, burins, backed bladelets, and geometric microliths) from 16 sites. We recovered these data uncorrupted, byte-for-byte from a 40-year-old tape. In the provided PDF document, we restate with additional clarifications the data definitions from Sheppard (1987). We also detail the steps we took to transform the files found on the tape into usable data spreadsheets.
DOI: 10.5281/zenodo.14004907
title: ChannelLeveeModel: Decoupled Channel-levee Evolution Model for MATLAB

description: ChannelLeveeModel v1.0.1

This is the archive of a numerical model that decoupled channel bed and levee evolution with associated simulations, dataset, and figures using MATLAB

Features



Generate random weekly hydrographs and solve the divided channel method to compute the flooded water surface elevation (Lotter, 1933)

Identify two flood styles: Front loading and Back loading

Run advection-settling model (Han and Kim, 2022) for Front loading events and Ponded water model (Nicholas and Walling, 1996) for Back loading events

Visualizes results with MATLAB plotting functions


File lists



Main file: DecoupledCLM.m

Function files: HydraulicGeometry.m, Hydrograph.m, OverflowLevel.m, LeveeBimodal.m, Backloading_wellmix.m

Plotting files: plot_BE.m, plot_ConfinedRelease.m, plot_figures.m

Output folder: simulation_code/output/ contains example model simulations in .m format. and figures in .pdf format.

README.md and LICENSE
DOI: 10.5281/zenodo.7589045
title: Geophysical data collected at Obsidian Pool Thermal Area, Yellowstone National Park, in 2015-2016

description: Geophysical data (EMI, GTEM, Seismic, Resistivity) collected over Obsidian Pool Thermal Area, Yellowstone National Park, in 2015 and 2016.
DOI: 10.5285/76ebdc0b-0eed-4f70-b89e-55e606bcd568
title: VolcanEESM: Global volcanic sulphur dioxide (SO2) emissions database from 1850 to present - Version 1.0

description: The database is a combination of all available information possible from the wider literature with as many observations of the amount and location of SO2 emitted by each eruption. The database includes no information about the size, mass, distribution or optical depth of resulting aerosol. As such the database is model agnostic and it is up to each modeling group to make decisions about how to implement the emission file in their prognostic stratospheric aerosol scheme. 

The dataset is divided into two parts based on the availability of satellite data. For the pre-satellite era, the necessary information about the emissions was gathered from the latest ice core records of sulphate deposition in combination historical accounts available in the wider literature (see references included in the database for specific citation for each record). In the satellite era, volcanic emissions were primarily derived from remotely sensed observations. 

For the period 1850 CE to 1979 the project combined the most recent volcanic sulfate deposition datasets from ice cores with volcanological and, where applicable, petrological estimates of the SO2 mass emitted as well as historical records of large-magnitude volcanic eruptions. In detail, for the majority of eruptions between 1850 CE to 1979 , there are few direct measurement of SO2 emissions or quantitative observations of the plume height and very few measurements of the aerosol optical depth (AOD). 

The project therefore focused on large-magnitude (VEI&gt;4) eruptions, which was used to identify using the Smithsonian Global Volcanism Program database and ice-core data. The total amount of SO2 emitted by an eruption, including the spatial distribution of the volcanic injection, was determined by using parameters that based on analogous eruptions that occurred during the satellite era following the methodology of Stoffel et al. [2015] and Gao et al. [2008]. 

The project estimated lower and upper bounds of the SO2 emitted and constrained the season by an eruption [similar to Stoffel et al., 2015] and expect the uncertainties on our inventory to be of comparable magnitude to those prescribing the forcing. Before running long model integrations, they assessed the representativeness of the emission inventory and quantified uncertainties by comparing simulated sulphate deposition patterns for selected eruptions to ice-core records and by comparing simulated AOD to Crowley et al. [2008] and Sato et al. [1993]. Where applicable they also compared simulated optical properties to astronomical estimates of atmospheric transmission [Stothers  1996; 2001].
DOI: 10.6073/pasta/0219f79d9e6d9485a892a669a8ce23b8
title: SBC LTER: Reef: Long-term experiment: Taxon-specific seasonal net primary production (NPP) for macroalgae

description: This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots of the SBC LTER's long-term kelp removal experiment sites. The experiment was initiated in 2008 at 4 sites; a fifth site as added in 2011. Data collection is ongoing. NPP of understory taxa was calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, was calculated using linear relationships between frond density in a given season and average NPP for that season.
DOI: 10.6073/pasta/0e4d59bfe757781ed51661267865af5c
title: SBC LTER: Reef: Long-term experiment: biomass of kelp forest species, ongoing since 2008

description: These data represent values of biomass density for more than 200 species of macroalgae, invertebrates and fish measured in fixed plots at five reefs as part of a long-term experiment designed to evaluate the effects of disturbance to giant kelp on the structure and productivity of the benthic community. Taxon-specific relationships between size and mass were applied to field measurements of species abundance to estimate biomass density of each species. The five reefs (Arroyo Quemado 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region. A common (but not always persistent) feature on these reefs was the presence of the giant kelp, which forms a dense canopy at the sea surface that suppresses recruitment and growth of understory algae below it. See Methods for more information.
DOI: 10.6073/pasta/0fe9233dabe35df5d61fb3b07f8fb51e
title: SBC LTER: Reef: Coefficients for estimating biomass from body size or percent cover for kelp forest species

description: These data provide coefficients to estimate the biomass of macroalgae, invertebrates and fish from field measurements of body size or percent cover. We developed quantitative relationships between mass and length or mass and percent cover, and conversion factors for transforming wet mass into dry mass, shell free and decalcified dry mass, and ash-free dry mass for taxa of benthic macroalgae and macroinvertebrates common to giant kelp forests in southern California. We also compiled literature-based relationships between mass and total length for reef fish common to giant kelp forests in southern California.
DOI: 10.6073/pasta/48686fef076a23310a0e48f69d76260c
title: SBC LTER: Reef: Macrocystis pyrifera CHN content (carbon, hydrogen, nitrogen), ongoing since 2002

description: The purpose of monthly kelp CHN sampling is to track the carbon, hydrogen, and nitrogen concentrations in Macrocystis pyrifera tissue at SBC LTER sites over time. A total of fifteen M. pyrifera blades are collected at each of three reef sites once a month. Blades are selected approxiamtely two meters from the growing end of several different plants. Core samples of the blades are processed and run through an Organic elemental analyzer with a combustion method. See Methods for more information.
DOI: 10.6073/pasta/a7ad060a4dbc4e7dfcb763a794506524
title: OLIGOTREND, a global database of multi-decadal timeseries of chlorophyll-a and nutrient concentrations in inland and transitional waters, 1986-2023

description: The Oligotrend database is a collection of multi-decadal chlorophyll-a and nutrient timeseries in inland and transitional waters. The objective of this Data Package was to explore how inland and transitional aquatic ecosystems respond to oligotrophication trends.
 Overall, the Oligotrend L1 database is made of 4.3 million valid observations originating from 1,894 stations. There are 238, 687 and 969 stations located in estuaries, lakes and rivers, respectively. The top 3 largest sources of data are the French national water quality monitoring (775 stations), the global database of lake datasets from Naderian et al. 2024 (378 stations), and the Chesapeake Bay Program (199 stations).
 The data was harmonized through a reproducible data processing pathway. In this Data Package, quality-checked level L1 data is provided, together with data sources, geographical coordinates of the stations, and the output of a trend analysis of all timeseries (level L2).
DOI: 10.6073/pasta/cbcaa708134af385ce164e28a22c6bd3
title: SBC LTER : REEF: Ammonium excretion rates of macroinvertebrates

description: Consumers may be important mediators of nutrient cycling in giant kelp forests. To aid in this assessment, we measured the ammonium excretion rates of 17 species of macroinvertebrates which comprise 85-97% of the biomass among reefs surveyed by the LTER in the Santa Barbara Channel. We paired these species-specific data with time series of their standing biomass to assess their contributions to kelp forest nitrogen recycling and to examine patterns in their nutrient dynamics over annual and decadal scales. This is the data package supporting the article “Peters, JR, Reed, DC, Burkepile, DE. Climate and fishing drive regime shifts in consumer‐mediated nutrient cycling in kelp forests. Glob Change Biol. 2019; 25: 3179– 3192. https://doi.org/10.1111/gcb.14706”.
DOI: 10.6084/m9.figshare.24716832
title: Voxelwise difference

description: <b>Voxelwise differences in GMV and ReHo between OC and non-OC measured by Cohen’s </b><b><i>d </i></b>
DOI: 10.6084/m9.figshare.27958434.v4
title: Chromosomal-level genome dataset of <i>Mythimna separata</i>

description: This high-quality chromosome-level genome assembly was generated using a combination of Illumina, PacBio HiFi, and Hi-C technologies, resulting in a 714.5 Mb genome with a scaffold N50 of 22 Mb and GC content of 38.78%.
DOI: 10.6084/m9.figshare.28558211
title: Supplementary Material for: Machine learning-based model for predicting severe exacerbations in adult-onset type 2 inflammatory asthma

description: Introduction: Currently, scholars have applied machine learning to the clinical prediction of acute asthma exacerbations. However, given the heterogeneity of inflammatory phenotypes in asthma, it is imperative to develop machine learning models tailored to specific asthma inflammatory phenotypes.
Objective: To develop predictive models to identify risk factors for the severe exacerbations in adult-onset type 2 inflammatory asthma, which could help facilitate early diagnosis and intervention, potentially reducing healthcare costs.
Methods: Retrospective analysis of patients with acute exacerbations of type 2 inflammatory asthma at Shenzhen People's Hospital from May 2017 to September 2022. Patients were categorized into mild-to-moderate exacerbation (n=300) and severe exacerbation groups (n=209). We collected clinical data from all participants, including demographic characteristics, laboratory results, pulmonary function test results, comorbidities, and asthma medication use. We tested four models: decision trees, logistic regression, random forests, and LightGBM. For each model, 80% of the dataset was used for training and 20% was used to validate the models. The area under (AUC) the receiver operator characteristic (ROC) curve was calculated for each model.
Results: Multivariate Logistic regression revealed that low ACT scores, low FEV1/FVC ratio, a history of diabetes, high absolute neutrophil count, and a family history of asthma were independent risk factors for severe exacerbations of type 2 inflammatory asthma. LightGBM outperformed all other models, achieving the highest AUC of 0.9344, with sensitivity = 0.8293, specificity =0.9180, PPV = 0.8718, and NPV = 0.8889. The accuracy stood at 0.8824, with an F1 score of 0.8500. The top ten clinical variables impacting the prediction outcome in the LightGBM model were ACT score, FEV1/FVC ratio, age, lactate dehydrogenase, FEV1 % predicted, fasting blood glucose, angiotensin-converting enzyme, duration of disease, Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio. Finally, through DCA, the clinical decision-support value of the LightGBM model was confirmed, demonstrating its maximum net benefit for type 2 asthma patients across a threshold probability range of 20% to 80%.
Conclusions: We have developed and established a prediction model for severe exacerbations of adult-onset type 2 inflammatory asthma using the LightGBM machine learning approach, which exhibits good predictive performance. This model can aid in the early prediction and prevention of severe exacerbations of adult-onset type 2 inflammatory asthma.
DOI: 10.71795/21dv-7d74
title: Periods, Organized
title: PeriodO