cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18141/1909007
title: NEWTS EPA Leachate Dataset

description: Data from the US EPA Leachate Database containing ash landfill leachate data from selected U.S. coal power plants. Original data from: Nguyen, Dan-Tam, Eastern Research Group. Sep 29, 2015. Analytical Database for the Steam Electric Rulemaking - DCN SE05359. https://www.regulations.gov/document/EPA-HQ-OW-2009-0819-5640
DOI: 10.5281/zenodo.7807784
title: A cropland cover dataset for the middle and lower reaches of the Yellow River over the past millennium

description: We developed a new gridding allocation model for croplands with unequal weight factors and reconstructed 58 time-point cropland cover maps at a 10-km resolution for the past millennium in the middle and lower reaches of the Yellow River in China. The cropland dataset can be used to model past climate change, estimate carbon emissions, assess human-activity-induced ecological effects, and improve global historical land use scenarios.
description: This dataset has been supported by the National Natural Science Foundation of China (Grant No. 42201263), the National Key Research and Development Program of China on Global Change (No. 2017YFA0603304), the Major Program of Key Research Base of Humanities and Social Sciences of the Ministry of Education of China (Study on the Evolution of Ecosystem Pattern, Problem Diagnosis, and Governance Model of the Yellow River Basin; Study on Evolution of Man-land Relationship and Its Ecoenvironmental Effects in Floodplain of the Lower Yellow River), the 2021 Henan key R & D and promotion project (Soft Science) (212400410256).
DOI: 10.5281/zenodo.7807783
title: A cropland cover dataset for the middle and lower reaches of the Yellow River over the past millennium

description: We developed a new gridding allocation model for croplands with unequal weight factors and reconstructed 58 time-point cropland cover maps at a 10-km resolution for the past millennium in the middle and lower reaches of the Yellow River in China. The cropland dataset can be used to model past climate change, estimate carbon emissions, assess human-activity-induced ecological effects, and improve global historical land use scenarios.
description: This dataset has been supported by the National Natural Science Foundation of China (Grant No. 42201263), the National Key Research and Development Program of China on Global Change (No. 2017YFA0603304), the Major Program of Key Research Base of Humanities and Social Sciences of the Ministry of Education of China (Study on the Evolution of Ecosystem Pattern, Problem Diagnosis, and Governance Model of the Yellow River Basin; Study on Evolution of Man-land Relationship and Its Ecoenvironmental Effects in Floodplain of the Lower Yellow River), the 2021 Henan key R & D and promotion project (Soft Science) (212400410256).
DOI: 10.18141/1974171
title: NEWTS EPA Gasification Dataset and Case Studies

description: Data from the US EPA Gasification Database containing gasification effluent data from selected U.S. coal power plants. Original data from: Nguyen, Dan-Tam, Eastern Research Group. Sep 29, 2015. Analytical Database for the Steam Electric Rulemaking - DCN SE05359. https://www.regulations.gov/document/EPA-HQ-OW-2009-0819-5640 Files include: Original data tab, processed data and charge balance information in "NEWTS_EPA_Gasification_Effluent_Dataset_all_tabs.xlsx" Individual .csv files for data in format for input into aqueous chemistry modeling software (OLI Systems and Geochemist's Workbench) Data input templates for modeling streams in OLI Systems and Geochemist's Workbench Case studies for selected solved streams modeled in OLI Systems and Geochemist's Workbench
DOI: 10.15485/1987525
title: Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'

description: This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).
DOI: 10.5281/zenodo.8131792
title: The recompiled circuit V from the preparation of the AKLT state

description: This is the json file used for the recompiled optimized circuit \(\hat{V}\)from the paper 'High-fidelity realization of the AKLT state on a NISQ-era quantum processor' (arXiv link: https://arxiv.org/pdf/2210.13840.pdf). See Fig. 3. For the CNOT gate, the first and second index represents the conventions of the CNOT gate where the first index is for the controlled qubit. For the U3 gate, it follows the Qiskit convention where \(U3(\theta, \phi, \lambda) = U(\theta, \phi, \lambda)\): the first three numbers are for the parameters of the U3 gate, and the last one is for the qubit index.
description: {"references": ["Data associated with arXiv: 2210.13840"]}
DOI: 10.5439/1988399
title: noaaaos (b1)

description: The aerosol observing system (AOS) is the primary platform for in situ aerosol measurements at Earth’s surface. Each AOS has a different complement of instruments, which are selected to ensure the best measurements at each deployment site. The AOS measures aerosol optical properties to better understand how particles interact with solar radiation and influence the Earth's radiation balance. These measurements are useful for calculating parameters used in radiative forcing calculations, such as the aerosol single scattering albedo, asymmetry parameter, mass scattering efficiency, and hygroscopic growth. Measurements made with the AOS form a long-term record at the ARM fixed sites. Shorter measurement records are available from mobile deployments in a wide variety of geographical regions. These measurements are valuable for: identifying long-term changes in aerosol properties identifying atmospheric processes controlling aerosol life cycle and their influence on clouds providing experimental data for developing and testing atmospheric climate models.
DOI: 10.5439/1988400
title: noaaaosavg (b1)

description: The aerosol observing system (AOS) is the primary platform for in situ aerosol measurements at Earth’s surface. Each AOS has a different complement of instruments, which are selected to ensure the best measurements at each deployment site. The AOS measures aerosol optical properties to better understand how particles interact with solar radiation and influence the Earth's radiation balance. These measurements are useful for calculating parameters used in radiative forcing calculations, such as the aerosol single scattering albedo, asymmetry parameter, mass scattering efficiency, and hygroscopic growth. Measurements made with the AOS form a long-term record at the ARM fixed sites. Shorter measurement records are available from mobile deployments in a wide variety of geographical regions. These measurements are valuable for: identifying long-term changes in aerosol properties identifying atmospheric processes controlling aerosol life cycle and their influence on clouds providing experimental data for developing and testing atmospheric climate models.
DOI: 10.5439/1988401
title: aos (a1)

description: The aerosol observing system (AOS) is the primary platform for in situ aerosol measurements at Earth’s surface. Each AOS has a different complement of instruments, which are selected to ensure the best measurements at each deployment site. The AOS measures aerosol optical properties to better understand how particles interact with solar radiation and influence the Earth's radiation balance. These measurements are useful for calculating parameters used in radiative forcing calculations, such as the aerosol single scattering albedo, asymmetry parameter, mass scattering efficiency, and hygroscopic growth. Measurements made with the AOS form a long-term record at the ARM fixed sites. Shorter measurement records are available from mobile deployments in a wide variety of geographical regions. These measurements are valuable for: identifying long-term changes in aerosol properties identifying atmospheric processes controlling aerosol life cycle and their influence on clouds providing experimental data for developing and testing atmospheric climate models.
DOI: 10.5439/1988403
title: microbasepi (c1)

description: microbasepi
DOI: 10.5439/1988406
title: 10rlprofbe1news (s1)

description: 10rlprofbe1news
DOI: 10.5439/1988407
title: 10rlprofbe1news (c1)

description: 10rlprofbe1news
DOI: 10.5439/1988408
title: 10srlprofmr1turn (c0)

description: 10srlprofmr1turn
DOI: 10.5281/zenodo.7866625
title: Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement

description: MESA (r12115) inlist files used in the preparation of the paper "Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement ". The 'inlist_project_MIST_sun' file evolves the model from pre-main sequence to the TAMS '. This 'inlist_project_MIST_sun' file is an example file for an O-enhanced stellar model, with an input [α/Fe] value of 0.1 and a [O/Fe] value of 0.5. We use a Python code to drive the calculations of the stellar model grid, by modifying the initial y and z values, as well as the initial mass of the 'inlist_project_MIST' file, to obtain the 'inlist_project_MIST_sun' file.
DOI: 10.5281/zenodo.7866478
title: Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement

description: MESA (r12115) inlist files used in the preparation of the paper "Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement ". The 'inlist_project_MIST_sun' file evolves the model from pre-main sequence to the TAMS '. This 'inlist_project_MIST_sun' file is an example file for an O-enhanced stellar model, with an input [α/Fe] value of 0.1 and a [O/Fe] value of 0.5. We use a Python code to drive the calculations of the stellar model grid, by modifying the initial y and z values, as well as the initial mass of the 'inlist_project_MIST' file, to obtain the 'inlist_project_MIST_sun' file.
DOI: 10.6084/m9.figshare.23657841
title: <strong>Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles</strong>

description: Figure S1. Distribution and proportion (%) of TE families in 638 genome assemblies. The bubble size represents the proportion of the TE in the genome. The bar represents the proportion of total TE size to the genome size. Figure S2. Composition and abundance of functional protein groups in 638 genome assemblies. The bubble size represents the number of the protein group. The bar represents the proportion of the secretome size to the total number of proteins per genome. Table S1. A summary table containing genome information of 638 genome assemblies, lineage information, and statistics of TE categories, basic genomic features and functional protein groups. Table S2. Taxonomic and lifestyle coverage of 638 Sordariomycete genomes. Table S3. General statistics of sequencing technologies, assembly completeness and TE sizes of 638 Sordariomycete genomes. Table S4. Pearson Correlations of 25 basic genomic features. Table S5. Comparative analysis results of 25 basic genomic features. Table S6. Pearson Correlations of 24 functional protein features. Table S7. Comparative analysis results of 24 functional protein features. Table S8. Predictive accuracies of six machine learning algorithms. Table S9. Predicted results of 85 undetermined genomes and the observed lifestyles of phylogenetically close groups.
DOI: 10.26179/8a6f-7971
title: Deep Ice Drilling on Law Dome
DOI: 10.5439/1988640
title: Water vapor isotopic composition during TRACER IOP

description: The TRACER-Iso project took place from June 1 to September 30, 2022 as part of the intensive observation period (IOP) of the Tracking Aerosol Convection Interactions (TRACER) project at the main project site at the La Porte, Texas, municipal airport. The project sought to use measurements of the stable isotopic composition of water vapor to better understand cloud-aerosol interactions in deep convection in a polluted urban setting. Water vapor isotopologue and humidity measurements were determined using a Picarro L2130 Water Vapor Isotope Analyzer. The instrument was deployed in the guest van and consists of three main components: a water vapor isotope analyzer, and a Los Gatos Research Water Vapor Isotope Standard Source (WVISS), and a Los Gatos Research Dry Air Source. Ambient air samples were collected through an inlet located about one meter above roof of the shipping container. Samples were then delivered to the analyzer through teflon tubing by use of an external pump to minimize the transport time between the inlet and analyzer. The inlet tubing was surrounded by a Briskheat heat trace and insulating material to ensure ambient air samples did not fall below the dew point and result in condensation. The analyzer uses cavity ringdown spectroscopy to report isotopic ratios of ambient air samples at a frequency of 1 Hz. The calibration unit of the instrument was used in conjunction with the dry air source to periodically measure the δ values of standard waters. It uses a nebulizer to push small water droplets into a hot chamber that vaporizes the water without fractionation. This vapor was then transported to the analyzer using a built-in compressor and the dry air source, which allows each standard with known δ values to be measured at a wide range of humidity values for post-measurement calibration of ambient air samples. There are multiple sources of uncertainty introduced at different stages during the data collection and processing. These include (1) instrument precision, (2) uncertainty in the secondary standards, (3) humidity-correction uncertainty, and (4) VSMOW-SLAP calibration uncertainty. Uncertainty from each step is propagated in quadrature to calculate a total uncertainty of each isotopologue. Uncertainty was determined to be 2.3‰ for δD and 0.4‰ for δ18O.
DOI: 10.5519/qd.mfxh4jgd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (22 records)
DOI: 10.6084/m9.figshare.23657718
title: The original statistics of housing price and M2.xlsx

description: These housing index and M2 data were from 2011 to 2017, obtained from the National Bureau of Statistics of China.
DOI: 10.6084/m9.figshare.23657718.v1
title: The original statistics of housing price and M2.xlsx

description: These housing index and M2 data were from 2011 to 2017, obtained from the National Bureau of Statistics of China.
DOI: 10.5281/zenodo.8133273
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Daniel Solander, http://www.wikidata.org/entity/Q39789. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q39789
DOI: 10.6084/m9.figshare.23657925
title: Raw data of fecal metabolites

description: <strong>(UHPLC-MS/MS) analysis (</strong> Positive and negative ion scanning <strong>)</strong> The instrument platform for this LC-MS analysis is UHPLC-Q Exactive HF-X system of Thermo Fisher Scientific. Chromatographic conditions： 2μL of sample was separated by HSS T3 column (100 mm×2.1 mm i.d., 1.8μm) and then entered into mass spectrometry detection.The mobile phases consisted of 0.1% formic acid in water:acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile:isopropanol:water (47.5:47.5:5, v/v)(solvent B). The solvent gradient changed according to the following conditions: from 0 to 3.5 min, 0% B to 24.5% B (0.4 mL/min); from 3.5 to 5 min, 24.5% B to 65% B (0.4 mL/min); from 5 to 5.5 min, 65% B to 100% B (0.4 mL/min); from 5.5to 7.4 min, 100% B to 100% B (0.4 mL/min to 0.6 mL/min); from 7.4 to 7.6 min, 100% B to 51.5% B (0.6 mL/min); from 7.6 to 7.8 min, 51.5% B to 0% B (0.6 mL/min to 0.5 mL/min); from 7.8 to 9 min, 0% B to 0% B (0.5 mL/min to 0.4 mL/min);from 9 to 10 min, 0% B to 0% B (0.4 mL/min) for equilibrating the systems. The sample injection volume was 2 µL and the flow rate was set to 0.4 mL/min. The column temperature was maintained at 40℃. During the period of analysis, all these samples were stored at 4℃. MS conditions: The mass spectrometric data was collected using a Thermo UHPLC -Q Exactive HF-X Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in either positive or negative ion mode. The optimal conditions were set as followed: heater temperature, 425℃ ; Capillary temperature, 325℃; sheath gas flow rate, 50 arb; Aux gas flow rate, 13 arb; ion-spray voltage floating (ISVF), -3500V in negative mode and 3500V in positive mode, respectively; Normalized collision energy , 20-40-60V rolling for MS/MS. Full MS resolution was 60000, and MS/MS resolution was 7500. Data acquisition was performed with the Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70-1050 m/z.
DOI: 10.6084/m9.figshare.23657925.v1
title: Raw data of fecal metabolites

description: <strong>(UHPLC-MS/MS) analysis (</strong> Positive and negative ion scanning <strong>)</strong> The instrument platform for this LC-MS analysis is UHPLC-Q Exactive HF-X system of Thermo Fisher Scientific. Chromatographic conditions： 2μL of sample was separated by HSS T3 column (100 mm×2.1 mm i.d., 1.8μm) and then entered into mass spectrometry detection.The mobile phases consisted of 0.1% formic acid in water:acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile:isopropanol:water (47.5:47.5:5, v/v)(solvent B). The solvent gradient changed according to the following conditions: from 0 to 3.5 min, 0% B to 24.5% B (0.4 mL/min); from 3.5 to 5 min, 24.5% B to 65% B (0.4 mL/min); from 5 to 5.5 min, 65% B to 100% B (0.4 mL/min); from 5.5to 7.4 min, 100% B to 100% B (0.4 mL/min to 0.6 mL/min); from 7.4 to 7.6 min, 100% B to 51.5% B (0.6 mL/min); from 7.6 to 7.8 min, 51.5% B to 0% B (0.6 mL/min to 0.5 mL/min); from 7.8 to 9 min, 0% B to 0% B (0.5 mL/min to 0.4 mL/min);from 9 to 10 min, 0% B to 0% B (0.4 mL/min) for equilibrating the systems. The sample injection volume was 2 µL and the flow rate was set to 0.4 mL/min. The column temperature was maintained at 40℃. During the period of analysis, all these samples were stored at 4℃. MS conditions: The mass spectrometric data was collected using a Thermo UHPLC -Q Exactive HF-X Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in either positive or negative ion mode. The optimal conditions were set as followed: heater temperature, 425℃ ; Capillary temperature, 325℃; sheath gas flow rate, 50 arb; Aux gas flow rate, 13 arb; ion-spray voltage floating (ISVF), -3500V in negative mode and 3500V in positive mode, respectively; Normalized collision energy , 20-40-60V rolling for MS/MS. Full MS resolution was 60000, and MS/MS resolution was 7500. Data acquisition was performed with the Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70-1050 m/z.
DOI: 10.6084/m9.figshare.23657901
title: Raw Data for Congenital Anomaly in Early Neonatal Mortality in an Indonesian Neonatal Intensive Care Unit.xlsx

description: This is the raw data for research article titled 'Congenital Anomalies in Early Neonatal Mortality in an Indonesian Neonatal Intensive Care Unit' submitted to Pediatric Research.
DOI: 10.6084/m9.figshare.23657901.v1
title: Raw Data for Congenital Anomaly in Early Neonatal Mortality in an Indonesian Neonatal Intensive Care Unit.xlsx

description: This is the raw data for research article titled 'Congenital Anomalies in Early Neonatal Mortality in an Indonesian Neonatal Intensive Care Unit' submitted to Pediatric Research.
DOI: 10.5281/zenodo.7025462
title: structural variants data

description: The origin data are used to replicate our work. The topic is "<strong>Accuracy assessment of structural variation calling methods for breast cancer gene panel in the absence of gold standard</strong>".
DOI: 10.5281/zenodo.7025463
title: structural variants data

description: The origin data are used to replicate our work. The topic is "Assessment of accuracy in identifying structural variants using third-generation sequencing for breast cancer in the absence of gold standard".
DOI: 10.5281/zenodo.8115442
title: structural variants data

description: The origin data are used to replicate our work. The topic is "Assessment of accuracy in identifying structural variants using third-generation sequencing for breast cancer in the absence of gold standard".
DOI: 10.5281/zenodo.8133263
title: structural variants data

description: The origin data are used to replicate our work. The topic is "<strong>Accuracy assessment of structural variation calling methods for breast cancer gene panel in the absence of gold standard</strong>".
DOI: 10.6084/m9.figshare.23657856
title: <strong>Codebook -</strong> Software Architectural Practices: Influences on the Open Source Ecosystem Health

description: Codebook - Software Architectural Practices: Influences on the Open Source Ecosystem Health <br> Paper: <strong>Software Architectural Practices: Influences on the Open Source Ecosystem Health </strong> Simone da Silva Amorim [ Federal Institute of Bahia | <em>simone.amorim@ifba.edu.br </em>] John D. McGregor [ Clemson University | <em>johnmc@clemson.edu </em>] Eduardo Santana de Almeida [ Federal University of Bahia | <em>esa@rise.com.br </em>] Christina von Flach Garcia Chavez [ Federal University of Bahia | <em>flach@ufba.br </em>] <br> Published at <em>Journal of Software Engineering Research and Development</em> (JSERD 2023), 11:8, 2023, Brazil. https://doi: 10.5753/jserd.2023.967
DOI: 10.6084/m9.figshare.23657856.v1
title: <strong>Codebook -</strong> Software Architectural Practices: Influences on the Open Source Ecosystem Health

description: Codebook - Software Architectural Practices: Influences on the Open Source Ecosystem Health <br> Paper: <strong>Software Architectural Practices: Influences on the Open Source Ecosystem Health </strong> Simone da Silva Amorim [ Federal Institute of Bahia | <em>simone.amorim@ifba.edu.br </em>] John D. McGregor [ Clemson University | <em>johnmc@clemson.edu </em>] Eduardo Santana de Almeida [ Federal University of Bahia | <em>esa@rise.com.br </em>] Christina von Flach Garcia Chavez [ Federal University of Bahia | <em>flach@ufba.br </em>] <br> Published at <em>Journal of Software Engineering Research and Development</em> (JSERD 2023), 11:8, 2023, Brazil. https://doi: 10.5753/jserd.2023.967