cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0199383.10fu
title: SH0199383.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199384.10fu
title: SH0199384.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199385.10fu
title: SH0199385.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199386.10fu
title: SH0199386.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199387.10fu
title: SH0199387.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199388.10fu
title: SH0199388.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/k7tzmrhr6n.1
title: MeDiaPQA: A Question-Answering Dataset on Persian Medical Dialogues

description: We present the MeDiaPQA: a question-answering dataset on Persian medical dialogues. This dataset has been gathered under the supervision of ComInSys Lab. In this project, we have extracted a novel dataset from dialogues taking place between doctors and patients to contain 15748 dialogues covering more than 70 specialities.
The mentioned dialogues are in the form of questions and doctors’ multiple-choice answers. We have collected these as 7704 question-answer pairs.
DOI: 10.5281/zenodo.13990646
title: Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology

description: The global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology with six cloud types (Solid stratus, Closed MCC, Open MCC, Disorganized MCC, Clustered Cu and Suppressed Cu). The spatial resolution is 1o × 1o  and the temporal resolution is 5 minutes for the years 2018-2022. They were established based on a deep learning model ResNet-50. Trained on daytime radiance data from MODIS (Moderate Resolution Imaging Spectroradiometer) and daytime retrieved COT (Cloud Optical Thickness), this model achieved a high prediction accuracy and can be applied to nighttime cloud classification. For a detailed introduction to the model, please refer to our article:

 

Technical info

Dataset information



day_2018_all.npz: Daytime classification of 2018 global mesoscale low-cloud morphology with a 1°×1° resolution 



date: time of the 1°×1° box, date format is the same as MODIS granule: '2018001.0025'

lon: central longitude, ranging from -180 to 180

lat: central latitude, ranging from -60 to 60

cat: category of the cloud morphology. The numbers 0-5 represent each of the six categories: 0-Solid stratus, 1-Closed MCC, 2-Open MCC, 3-Disorganized MCC, 4-Clustered Cu, 5-Suppressed Cu

cert: model certainty, the probability that this cloud morphology belongs to the category

low_cf: the cloud fraction of low clouds, in the unit of %

COT_CNN: average cloud optical thickness (COT), retrieved using TIR-CNN model from Wang et al

CER_CNN: average cloud effective radius (CER), retrieved using TIR-CNN model from Wang et al, in the unit of μm

LWP_CNN: average cloud liquid path (LWP), calculated from COT_CNN and CER_CNN, in the unit of g/㎡

Sensor_zenith: average sensor zenith angle, in the unit of degree








night_2018_all.npz: Nighttime classification of 2018 global mesoscale low-cloud morphology with a 1°×1° resolution 



same variables as daytime




 

You can extract these variables in each file using the following python code:








import numpy as np

# Load the .npz file
data =  np.load('./night_2022_all.npz')
keys = list(data.keys())
print(keys)
date = data['date']
cat = data['cat']
...
DOI: 10.5281/zenodo.14022258
title: Retrievals of aerosol optical, componential, and radiative properties from joint observations of sun photometer and Lidar using GRASP algorithm

description: The site location is 114°21′E, 30°32′N (Central China). The time period is from 2021.07 to 2022.08.

Data includes AOD (all sequences), SSA, ASY, ASD, CRI (only retrieved from sky irradiance), components (black carbon, brown carbon, dust, iron oxide, water-soluble inorganic salt and water), and vertical profiles of shapes of total extinction, fine-mode extinction, and coarse-mode extinction.
DOI: 10.5281/zenodo.14211250
title: Strain Localization and Seismic Properties in the Heterogeneous  Ailaoshan-Red River Shear Zone, Southeast Asia
DOI: 10.6084/m9.figshare.26948281.v2
title: "Incentives in prescribing, dispensing and pharmaceutical spending: A scientometric mapping" - Supplementary Material

description: Supplementary material to the review article: "Incentives in prescribing, dispensing and pharmaceutical spending: A scientometric mapping". Using the technique of scientometric mapping, our study characterizes the current state of global scientific production related to prescribing, dispensing, and pharmaceutical spending incentives in health systems worldwide in a rigorous and visually practical manner, providing a global and rapid overview of the dynamics of scientific literature production. It also identifies, among other things, the main lines of publication, thematic leaders and representative authors, making it useful for other researchers or public policy makers.
DOI: 10.25549/usctheses-ouc113997l79
title: Evaluating social networks and impact of micro-influencers who promote e-cigarettes on social media
DOI: 10.25934/pr00008428.1
title: Available datapackage for study 'Type 1 Diabetes EXercise Initiative: The Effect of Exercise on Glycemic Control in Type 1 Diabetes Study'

description: 
Brief Summary: The Type 1 Diabetes Exercise Initiave (T1-DEXI) was an observational study of adults living with type 1 diabetes in the U.S., conducted remotely outside of clinics, designed to develop a better understanding of the effects of different levels of exercise intensity and duration on glycemic control during and after exercise across a wide range of patient characteristics.  This dataset incorporates aggregated data around exercise events including pertinent diabetes management information (insulin and continuous glucose monitoring data), self-reported and objectively measured physical activity levels (Polar H10 sensor and Verily Study Watch), self-reported stress levels and life-event data such as the timing and composition of meals (Remote Food Photography Method). Genotyping, completed for a subset of participants, may help researchers understand how variations in DNA may be associated with exercise, diabetes, and glycemic response to exercise.
DOI: 10.5066/f7dr2tn4
title: Global rare earth element occurrence database

description: This spatial database "usgs_Global_REE.gdb" was created for use in a geographic information system (GIS) to support research on global rare earth deposits and occurrences by the U.S. Geological Survey. This inventory documents the geologic occurrence of rare earths, including mineralogy, type of deposit or occurrence, host rocks and alteration, and any quantitative data related to size and grade from publicly available data. Rare earths, as used in this report, includes the chemically similar lanthanide group of elements, as well as yttrium. Databases that summarize the distribution of known occurrences and their geologic setting are an integral part of a geologically-based evaluation of undiscovered mineral resources. The distribution of known occurrences allows us to understand the factors that control their distributions, the degree of variation within deposit types, and, through the use of analogy, to forecast areas where similar deposits and occurrences may occur. The geodatabase contains more than 3100 records with latitudes and longitudes and more than 800 records without plottable locations. Spatial and descriptive data for 3100 rare earth deposits and occurrences around the world are stored in the rare earth geodatabase feature class "Global_REE" for use in a geographic information system (GIS). In addition, 820 deposits and occurrences for which no location was found or determined are stored in a geodatabase table "Global_REE_nonspatial_table" and the over 1590 references are stored in a geodatabase table "All_Global_REE_references" which were used to compile these data. The databases of rare earth deposits and occurrences provides descriptive information where available on mineralogy, host and associated rocks and ages, alteration, sizes and grades of resources and production, and references; the data in the spatial database include location.
DOI: 10.5067/p7o0hgjlyuqu
title: Northern Hemisphere EASE-Grid 2.0 Weekly Snow Cover and Sea Ice Extent, Version 4
DOI: 10.5285/53ded3f2-a4e9-4f49-8084-2c8b3b485268
title: UKGEOS Glasgow Test Pumping Groundwater Chemistry Data Release

description: The data release includes groundwater chemistry data from 15 samples collected during the borehole test pumping phase at the UK Geoenergy Observatories (UKGEOS) Glasgow facility. This release from the British Geological Survey (BGS) covers groundwater samples collected between 14 January 2020 and 21 February 2020 and then analysed for the concentrations of selected parameters at BGS and other laboratories. It contains a report and 2 data sheets. Further details can be found in the accompanying report http://nora.nerc.ac.uk/id/eprint/531098/.
DOI: 10.5517/cc135q99
title: CCDC 1018390: Experimental Crystal Structure Determination

description: Related Article: Satyanarayana Reddy Jaggavarapu|2014|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5523/bris.2fahpksont1zi26xosyamqo8rr
title: MRC IEU UK Biobank GWAS pipeline version 1

description: This dataset is superseded by https://doi.org/10.5523/bris.pnoat8cxo0u52p6ynfaekeigi 

This deposit provides full details of the genome wide association stuy (GWAS) pipeline developed by the MRC-IEU for the full UK Biobank (July 2017) genetic data.
For any issues with use of this documentation please contact: mrc-ieu@bristol.ac.uk.
DOI: 10.6084/m9.figshare.26047105
title: Dataset overweight and discapacity

description: Background: Musculoskeletal disorders comprise injuries that limit the ability of the human body to move and include elements such as muscles, tendons, and ligaments. They are considered significant problems within the practice of clinical dentistry, arising mainly because of physical overload resulting from dynamic and static activities. The aim of the research was to evaluate the relationship between shoulder, arm, and hand disability and body mass index in dentists. Methods: The study was classified as applied research with a non-experimental and correlational design. The sample consisted of 222 private practice´s dentists. A simple random probabilistic sampling was chosen. A questionnaire was considered to collect demographic, occupational, anthropometric, and exercise data. To assess the presence of symptoms of functional disability of the shoulder, arm, and hand, the shortened Disability of the Arm, Shoulder, and Hand scale (QuickDASH) was used. Results: There are differences in shoulder, arm, and hand disability scores among the body mass index classification groups (PR=12.74, 95% CI [9.08; 16.4], p&lt;0.001), even after adjusting for occupational and physical activity variables (PR=10544.08, 95% CI [302.84; 367111.40], p&lt;0.001). Conclusion: This study highlights the significant relationship between BMI and functional disability of the shoulder, arm, and hand in dental surgeons, emphasizing the need for preventive strategies focused on weight management and the promotion of physical activity.
DOI: 10.6084/m9.figshare.27914580
title: Top-Down Estimates of Global Fire Emissions of Reactive Nitrogen (2015–2023)

description: 1. Annual_total_fire_NH3_emissions.xlsx, Annual total fire NH<sub>3</sub> emission intensities in each gird (2.5° longitude ×2° latitude, units: kg N ha<sup>-1</sup> yr<sup>-1</sup>) from 2015 to 2023.2. Annual_total_fire_NOx_emissions.xlsx, Annual total fire NO<sub>x</sub> emission intensities in each gird (2.5° longitude ×2° latitude, units: kg N ha<sup>-1</sup> yr<sup>-1</sup>) from 2015 to 2023.3. Fire_emissions.xlsx, Monthly emissions of NH<sub>3</sub> and NO<sub>x</sub> from boreal (50°–70°N) and tropical (40°S–20°N) fires, units: Tg N month<sup>-1</sup>.
DOI: 10.15468/dl.3mz2ur
title: Occurrence Download

description: A dataset containing 3681 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ognorhynchus icterotis (Massena & Souance, 1854)"
 ]
}

 The dataset includes 3681 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0286471-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/s1n5-ym39
title: SN Zwicky Template Observations

description: Template HST observations of SN Zwicky used to measure updated time delays and magnifications
DOI: 10.18462/iif.nitec38.06.2019
title: 38th Note on Refrigeration Technologies: The Role of Refrigeration in the Global Economy (2019)
DOI: 10.24432/c51s4n
title: Cardiotocography
DOI: 10.3334/cdiac/atg.031
title: Atmospheric CH4 Concentrations from the Commonwealth Scientific and Industrial Research Organization (CSIRO) GASLAB Flask Sampling Network (1984 - 2001)

description: The listed data were obtained from flask air samples returned to the CSIRO GASLAB for analysis. Typical sample storage times ranged from days to weeks for some sites (e.g., Cape Grim) to as much as one year for Macquarie Island and the Antarctic sites. Experiments carried out to test for any change in sample CH4 mixing ratio during storage have shown no drift to within detection limits over test periods of several months to years (Cooper et al., 1999).
DOI: 10.3334/obspack/1002
title: obspack_co2_1_GLOBALVIEW-CO2_2013_v1.0.4_2013-12-23

description: Compiled by NOAA Global Monitoring Division: Boulder, Colorado, U.S.A.
Data product accessed at http://dx.doi.org/10.3334/OBSPACK/1002
DOI: 10.34740/kaggle/dsv/2762649
title: Hypersensitive Narcissism Dirty Dozen
DOI: 10.5281/zenodo.14221768
title: Dataset for "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al.

description: The dataset used in the paper "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al. The doi of the preprint of the paper is https://doi.org/10.5194/egusphere-2024-654. The dataset contains the diurnal cycles of delta_atm(18O), delta(O2/N2), y(CO2), and delta(Ar/N2) at Tsukuba, Japan averaged for the period 2013-2022 ("data_for_Fig4.csv"), the rolling average values of delta_atm(18O) and delta(O2/N2) anomalies for 300 data at TKB during August and February, 2017 ("data_for_Fig6.csv"), and the monthly mean values of delta_atm(18O) and the delta(O2/N2) at TKB for the period 2013-2022("data_for_Fig7.csv"). The raw data before averaging are also presented ("raw_data_2013.csv" to "raw_data_2022.csv"). See text of Ishidoya et al. in more details.
DOI: 10.5281/zenodo.14605501
title: The data behind the ApJ article "LensWatch: II. Improved Photometry and Time Delay Constraints on the Strongly-Lensed Type Ia Supernova 2022qmx ("SN Zwicky") with HST Template Observations"

description: Data from "LensWatch: II. Improved Photometry and Time Delay Constraints on the Strongly-Lensed Type IaSupernova 2022qmx (“SN Zwicky”) with HST Template Observations", NASA ADS

To unpack the file, run tar -xvf lenswatch_ii.tar on the command line. The data for each figure is separated into an individual, labeled directory. For reference, the figures represent the following analyses and use the following data:

Figure 1: The template observations as well as the template subtracted data and PSF photometry results. The data include the individual HST frames that went into the figure.

Figure 2: The linear fit showing how bright the SN has to be for the contaminating flux from the host + lens galaxies to not greatly affect the photometric measurements. The data behind this figure include the drizzled HST template images and the new and old SN photometry.

Figure 3: The corner plot showing our time delay results. The data behind this are the samples from the SNTD package.

Figure 4: The magnification posteriors of the four images of SN Zwicky. The data are the posteriors from our SALT2 fits after applying a fiducial SN standardization.

Figure 5: SALT2 fits from optical photometry shown in the F160W band, with our F160W photometry overplotted. The data include the F160W photometry.

Figure 6: The PDFs of the expected F160W flux with the measured F160W fluxes shown. The data are the DOLPHOT output file, which contains the F160W fluxes, as well as the raw files used to measure the total F160W flux of all four images combined.

Figure 7: Millilensing analysis results from pyHalo with the LS1 lensing parameters used. The data are the posteriors from the individual pyHalo runs, with varying parameters.