cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8385296
title: Database of Short Term Damage Equivalent Loads (DEL) of IWT7.5MW wind turbine depending on wind, TI, yaw and derating

description: Two datasets for short term damage equivalent loads (DEL) and energy production of the IWT7.5 MW research wind turbine (https://doi.org/10.24406/IWES-N-518562) are provided. dataset_Derating is a subset of dataSet_Derating_withYaw. The damage equivalent loads are computed with the rainflow counting algorithm applied to time series loads. They are based on Miner's rule. Further details can be found in: <br> Requate, N., Meyer, T., and Hofmann, R.: From wind conditions to operational strategy: Optimal planning of wind turbine damage progression over its lifetime, Wind Energ. Sci., accepted for publication, 2023. In the paper, the dataset_Derating was used to create surrogate models. A fullfactorial approach to simulate different parameter-combination was applied. <br> Information about the signals and units is provided in the yml files.<br> The range of parameters is given by python code in the yml file. <br> For each set of parameters, 6 different 10 minute-realizations of a turbulent wind field ("seeds") were created. The turbine was then simulated separately for each turbulent wind field.<br> Each folder contains the short term DELs for each 10 minute simulation and the aggregated short term DELs over all 6 wind realizations. Outliers from simulation error were removed.<br> The data was originally intended to create surrogate models of the variation of outputs values throughout the parameter space. Further usage is possible and encouraged.
DOI: 10.6084/m9.figshare.1487693
title: Data from "Increasing aridity reduces soil microbial diversity and abundance in global drylands"

description: Data from "Maestre, F. T., M. Delgado-Baquerizo, M., T. C. Jeffries, V. Ochoa, B. Gozalo, D. J. Eldridge, J. L. Quero, M. García-Gómez, A. Gallardo, W. Ulrich, M. A. Bowker, T. Arredondo, C. Barraza, D. Bran, A. Florentino, J. Gaitán, J. R. Gutiérrez, E. Huber-Sannwald, M. Jankju, R. L. Mau, M. Miriti, K. Naseri, A. Ospina, I. Stavi, D. Wang, N. N. Woods, X. Yuan, E. Zaady & B. K. Singh. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proceedings of the National Academy of Sciences USA, doi: 10.1073/pnas.1516684112" There are two spreadsheets with data. The spreadsheet "Data" contains the data used in the different analyses presented in the article. The spreadsheet "Metadata" contains the associated metadata, where a description of all the variables and units can be found.
DOI: 10.21227/hj46-zs46
title: Third Nurse Care Activity Recognition Challenge

description: Human Activity Recognition (HAR) is the process of handling information from sensors and/or video capture devices under certain circumstances to correctly determine human activities. Nowadays, several simple and automatic HAR methods based on sensors and Artificial Intelligence platforms can be easily implemented.In this challenge, participants are required to determine the nurse care daily activities by utilizing the accelerometer data collected from the smartphone, which is the cheapest and easy-to-implement way in real life.
DOI: 10.21227/jem3-ap07
title: Nurse Care Activities Datasets: In laboratory and in real field

description: Nurse Care Activity RecognitionActivity recognition is specially gaining attention in care facilities for remote monitoring of daily activities for elders living alone and automatic record creation for nurses in hospitals with a goal of providing better care to patients. One challenge for these applications is bridging the gap between models created within the lab and models for real life. In this challenge, participants will create methods for recognizing 12 activities conducted by nurses in both lab and in real-life settings. In real world, there is high chance of getting missing labels during experiments since users are busy at work. The main challenge is leveraging laboratory data to improve models in real-life. This dataset consists of data collected in both settings.
DOI: 10.21227/vchd-s336
title: NURSE CARE ACTIVITY RECOGNITION CHALLENGE DATASETS 2022

description: The accelerometer data has been collected using one smartphone carried by subjects, which are caregivers and nurses, when they were conducting daily works at a healthcare facility. The smartphone was carried in an arbitrary position such as a pocket. There are a total of 27 activities divided into 4 groups.
DOI: 10.25739/5ae3-sw62
title: Genomes to Fields 2021 dataset

description: The dataset contains 2021 phenotypic evaluations, field-level weather, and soil data from multi-location field trials of the Maize GxE Project within the Genomes to Fields (G2F) initiative. G2F is an umbrella initiative to support the translation of maize (Zea mays) genomic information for the benefit of growers, consumers, and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments and contribute to the accurate prediction of the phenotypes of corn plants in diverse environments.
DOI: 10.25739/hzzs-a865
title: Genomes to Fields 2020 dataset

description: The dataset contains results from 2020 phenotypic evaluations of multi-location field trials of Maize GxE within the Genomes to Fields (G2F) initiative. Collaborators also collected field-level weather and soil data for the multiple field testing locations. G2F is an umbrella initiative to support the translation of maize (Zea mays) genomic information for the benefit of growers, consumers, and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. The use of large-scale datasets contributes to the accurate prediction of the phenotypes of corn plants in diverse environments.
DOI: 10.5281/zenodo.7881952
title: SONAR: A Nursing Activity Dataset with Inertial Sensors - Machine Learning Version

description: Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities.
DOI: 10.5281/zenodo.7882067
title: SONAR: A Nursing Activity Dataset with Inertial Sensors

description: Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities.
DOI: 10.17188/1663470
title: Materials Data on Fe3B by Materials Project

description: Fe3B crystallizes in the tetragonal I-4 space group. The structure is three-dimensional. there are three inequivalent Fe sites. In the first Fe site, Fe is bonded in a 2-coordinate geometry to two equivalent B atoms. There are one shorter (2.13 Å) and one longer (2.23 Å) Fe–B bond lengths. In the second Fe site, Fe is bonded in a 3-coordinate geometry to three equivalent B atoms. There are a spread of Fe–B bond distances ranging from 2.12–2.31 Å. In the third Fe site, Fe is bonded in a 4-coordinate geometry to four equivalent B atoms. There are a spread of Fe–B bond distances ranging from 2.18–2.21 Å. B is bonded in a 9-coordinate geometry to nine Fe atoms.
DOI: 10.17632/985dh5r9cs.1
title: Quantifying a mass mortality event in freshwater wildlife within the Lower Odra River: Data

description: • Toxins from Golden Algae resulted in an unprecedented mass mortality of fish, bivalves, and water snails. • In the lower Odra, there were 65 million dead Unionidae bivalves, marking an 88% decrease; the native Anodonta anatina declined by 95%; the invasive Sinanodonta woodiana decreased by 15%. • 147 million water snails were found washed ashore in the lower Odra, representing an 85% decline. • The lower Odra witnessed the death of 3.3 million fish (equivalent to 1,025 tons); across the entire 560 km section of the affected river, the toll was 1,650 tons, indicating a 60% reduction in fish population biomass.
DOI: 10.17632/xn5ntwbtjz.1
title: SMA library for metabolites identification and quantification in cheese whey permeate (CWP)

description: Metabolite library built from NMR spectra for metabolomic analysis of cheese whey permeate (CWP) using the Simple Mixture Analysis (SMA) tool implemented in MestreNova 14.3 software.

Metabolites included: 2,3-Butandiol, Acetate, Alanine, Betaine, Choline, Citrate, Creatine, Creatinine, Ethanol, Fumarate, Hippurate, Lactate, Lactose, Orotate, Succinate, α-D-Galactose, β-D-Galactose

Sample CWP in PB 10 mM (10% D2O) pH 7.4, TSP (1mM), Diluition Factor (10%)

File included: SMA library .exp files and optimized .cmp files
DOI: 10.25326/246
title: JOANNE : Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments (v2.0.0)


description: As part of the EUREC4A field campaign which took place over the tropical North Atlantic during January-February 2020, 1215 dropsondes from the HALO and WP-3D aircraft were deployed through 26 flights to improve the current understanding of the atmospheric environment in the trade-wind regions, complementing simultaneous measurements from a multitude of platforms. We present JOANNE, the dataset that contains these dropsonde measurements of temperature, pressure, relative humidity and horizontal winds, along with the products derived from them to characterize the dynamic and thermodynamic environment in the trade-wind regions. Together with the raw measurement profiles and basic post-processing, the dataset also includes a homogenized and gridded data set with 10 m vertical spacing (Level 3 product). The gridded data are used as a basis for deriving diagnostics of the area-averaged meso-scale circulation properties such as divergence, vorticity, vertical velocity and gradient terms (Level 4 product), making use of sondes dropped at regular intervals along a circular flight path. 85 such circles, 222 km in diameter, were flown during EUREC4A. The sampling strategy for dropsonde measurements during EUREC4A, the quality control for the data, the methods of estimation of additional products from the measurements and the different post-processed levels of the dataset are explained in the JOANNE publication on ESSD.
DOI: 10.5281/zenodo.6390869
title: Imaging topological defects in a non-collinear antiferromagnet

description: Data related to the publication, arXiv 2202.02243.
description: Other funding: ANR project TATOO (ANR-21-CE09-0033-01), DARPA TEE program, LABEX NanoSaclay, ref. ANR-10-LABX-0035, SPiCY, Sesame Ile de France IMAGeSPIN project (No. EX039175)
DOI: 10.5281/zenodo.7622683
title: Dataset for paper Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda "Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model" submitted to JAMES.

description: The dataset consists of the directory tree of .zarr archives. See Github repository for the description of the dataset. The directory tree is: <pre><code>├── eddy │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires ├── jet │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires</code></pre> Every individual dataset is a <code>.zarr</code> archive <code>eddy/jet</code> - configuration of the pyqg; eddy is default; See Ross2022 for description <code>hires.zarr</code> - high-resolution simulation at 256x256 grid <code>48/64/96</code> - resolution of the coarse models <code>lores.zarr</code> - low-resolution simulation <code>gauss.zarr</code>, <code>sharp.zarr</code> - training datasets for prediction of subgrid forcing obtained with Gaussian or Sharp filters <code>hires-gauss.zarr</code>, <code>hires-sharp.zarr</code> - high-resolution simulation projected onto coarse grid with Gaussian or Sharp filters The directory tree is split into small tar.gz files each representing a separate .zarr archive. Download any required parts of the dataset and unpack with: <strong>tar -xf *.tar.gz </strong> <strong>The directory tree will be restored automatically!</strong>
DOI: 10.5281/zenodo.8277231
title: Dataset for paper Pavel Perezhogin, Andrey Glazunov "Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition" submitted to JAMES.

description: The data is updated due to the response to review in the third round. The directory structure is : <pre><code>├── barotropic │ ├── data │ ├── figures │ └── code ├── pyqg │ ├── data │ ├── figures │ └── code ├── NEMO │ ├── data │ ├── figures │ └── code </code></pre> Where <strong>barotropic</strong> - experiments in barotropic fluid, <strong>pyqg</strong> - experiments in QG model, <strong>NEMO</strong> - experiments in primitive equation model. Subfolder <strong>data</strong> contains numerical simulations, <strong>figures</strong> contains plotters of Figures for Publication, and <strong>code</strong> is source code with parameterized barotropic/QG/primitive equation ocean models.
DOI: 10.5281/zenodo.8299866
title: Data for "Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity"

description: The trained 600-member ensemble convolutional neural networks (CNNs) for seasonal tropical cyclone (TC) activity to allow future studies. Please refer Fu et al. (2023; <em>Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity</em>) for more details.
DOI: 10.57745/o2ixmp
title: README.md

description: :unav
DOI: 10.57745/tbmdkp
title: 20230926_EcoStab_GC-MS_data.tab

description: :unav
DOI: 10.5281/zenodo.10184307
title: Quantitative evaluation of mantle flow traction on overlying tectonic plate: Linear versus power-law mantle rheology

description: Dataset for Quantitative evaluation of mantle flow traction on overlying tectonic plate: Linear versus power-law mantle rheology
DOI: 10.5281/zenodo.10184308
title: Quantitative evaluation of mantle flow traction on overlying tectonic plate: Linear versus power-law mantle rheology

description: Dataset for Quantitative evaluation of mantle flow traction on overlying tectonic plate: Linear versus power-law mantle rheology
DOI: 10.18712/nsd-nsd2342-v11
title: Norwegian Citizen Panel Round 4, 2015

description: The Norwegian Citizen Panel is a platform for internet surveys of public opinion in important areas of society and politics in Norway. Participants are randomly recruited from the Norwegian population register, and they are encouraged to participate over time. The panel was fielded for the first time the fall of 2013 and as of 2017 the survey is carried out three times a year. The data set for The Norwegian Citizen Panel Wave 4, 2015 also contains variables from previous waves, the wave number is indicated by the number after "r" in the variable name. The University of Bergen owns and is responsible for the Citizen panel. The company ideas2evidence recruits respondents, produces the survey, and provides documentation of the data. Data is stored and shared by Sikt - Norwegian Agency for Shared Services in Education and Research. The different datasets of the Norwegian Citizen Panel have different ID numbers to prevent the identification of individual participants across rounds. If access to data that follows participants over time (time-series/panel data) is desired, then it must be applied for by contacting DIGSSCORE (uib.no/digsscore / digsscore@uib.no). The same applies to text data and other data that is withheld in these datasets for privacy reasons. The Norwegian Citizen Panel welcomes research proposals for survey content. More information about calls and other updates are available at www.uib.no/en/citizen

The full text for the answer options can be found in the codebook, which is located further down the page.
DOI: 10.5281/zenodo.8263038
title: BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020

description: Belgian National Burden of Disease Study


Estimates of the burden of disease


Causes of death


Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.


Years of Life Lost


In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.


Prevalence


Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.


Years Lived with Disability


In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.


Disability-Adjusted Life Years


Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.
description: For more information on the Parquet format, please see https://arrow.apache.org/docs/r/reference/read_parquet.html Explore the estimates via https://burden.sciensano.be/shiny/daly
DOI: 10.18712/nsd-nsd2343-v10
title: Norwegian Citizen Panel Round 5, 2015

description: The Norwegian Citizen Panel is a platform for internet surveys of public opinion in important areas of society and politics in Norway. Participants are randomly recruited from the Norwegian population register, and they are encouraged to participate over time. The panel was fielded for the first time the fall of 2013 and as of 2017 the survey is carried out three times a year. The data set for The Norwegian Citizen Panel Wave 5, 2015 also contains variables from previous waves, the wave number is indicated by the number after "r" in the variable name. The University of Bergen owns and is responsible for the Citizen panel. The company ideas2evidence recruits respondents, produces the survey, and provides documentation of the data. Data is stored and shared by Sikt - Norwegian Agency for Shared Services in Education and Research. The different datasets of the Norwegian Citizen Panel have different ID numbers to prevent the identification of individual participants across rounds. If access to data that follows participants over time (time-series/panel data) is desired, then it must be applied for by contacting DIGSSCORE (uib.no/digsscore / digsscore@uib.no). The same applies to text data and other data that is withheld in these datasets for privacy reasons. The Norwegian Citizen Panel welcomes research proposals for survey content. More information about calls and other updates are available at www.uib.no/en/citizen

The full text for the answer options can be found in the codebook, which is located further down the page.
DOI: 10.57745/7quorw
title: README.md

description: :unav
DOI: 10.57745/uleu1p
title: 20230927_EcoStab_Volatile_fatty_acids.tab

description: :unav
DOI: 10.18712/nsd-nsd2344-v10
title: Norwegian Citizen Panel Round 6, 2016

description: The Norwegian Citizen Panel is a platform for internet surveys of public opinion in important areas of society and politics in Norway. Participants are randomly recruited from the Norwegian population register, and they are encouraged to participate over time. The panel was fielded for the first time the fall of 2013 and as of 2017 the survey is carried out three times a year. The data set for The Norwegian Citizen Panel Wave 6, 2016 also contains variables from previous waves, the wave number is indicated by the number after "r" in the variable name. The University of Bergen owns and is responsible for the Citizen panel. The company ideas2evidence recruits respondents, produces the survey, and provides documentation of the data. Data is stored and shared by Sikt - Norwegian Agency for Shared Services in Education and Research. The different datasets of the Norwegian Citizen Panel have different ID numbers to prevent the identification of individual participants across rounds. If access to data that follows participants over time (time-series/panel data) is desired, then it must be applied for by contacting DIGSSCORE (uib.no/digsscore / digsscore@uib.no). The same applies to text data and other data that is withheld in these datasets for privacy reasons. The Norwegian Citizen Panel welcomes research proposals for survey content. More information about calls and other updates are available at www.uib.no/en/citizen

The full text for the answer options can be found in the codebook, which is located further down the page.