cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/gwt97q
title: Glyphosate_WTA_LC2_10K_DEF_output.txt

description: :unav
DOI: 10.57745/adaekl
title: data_30062022.tab

description: :unav
DOI: 10.57745/r1qxtw
title: kr_results_pv_Mymy.tab

description: :unav
DOI: 10.17632/jx97ftwnr4.1
title: Time-series records of particulate fluxes reveal temporal variations in hydrothermal activity in the Wocan hydrothermal field, Carlsberg Ridge-Dataset

description: In this study, we report the first detailed mineralogical and geochemical study of settling particles collected using moored time-series sediment traps near the Wocan hydrothermal field on the Carlsberg Ridge. We aimed to characterize the mineral and chemical compositions of the hydrothermal plume particles, quantify the settling flux of the particles dispersed by the hydrothermal plume, and investigate the causes of the vertical and temporal variations in the flux of the hydrothermally derived matter. Our study provides valuable information for understanding plume particle formation, transport, and deposition processes. The dataset includes:
Table 1: Qualitative XRD data for representative samples in traps T5 and T6 at mooring 28-ST02 in the Wocan-1 hydrothermal field;
Table 2: Average elemental concentrations of time-series settling particles in traps T5 and T6 at mooring 28-ST02 in the Wocan-1 hydrothermal field;
Table 3: Settling particle fluxes at the 28-ST02 mooring site in the Wocan-1 hydrothermal field;
Supplementary table 1: Elemental concentrations of time-series settling particles in traps T5 and T6 at mooring 28-ST02 in the Wocan-1 hydrothermal field;
Supplementary table 2: The current measured at 150 m above the bottom from June 2013 to March 2014 at mooring site 28-ST02;
Supplementary table 3: Vertical profiles of the potential temperature and turbidity at (A) 28I-L10 and (B) 28I-L11 in the Wocan-1 hydrothermal field.
DOI: 10.17632/x2h9p8f87t.1
title: Consumption trends, challenges, and future of vegan foods in India: Food service providers' perspective

description: Interview Transcripts
DOI: 10.5258/soton/d2179
title: Dataset supporting the publication "Investigation into the writing dynamics of planar Bragg gratings using pulsed 213 nm radiation".

description: This dataset supports the publication: 'Investigation into the writing dynamics of planar Bragg gratings using pulsed 213 nm radiation' in Optical Materials Express. https://doi.org/10.1364/OME.481901 All data used for plots, calculations and descriptions in the paper as titled above is included. Folders are labelled by powerset_(used average power of the laser) followed by a description of the data contained. There are eight folders, seven folders contains grating's reflectivity data for the waveguides written at seven different writing powers. Each folder (e.g. 'Powerset1_0.6 mW') Contains the raw data files taken from the optical spectrum analyser when measuring the reflectance of the written gratings in the microwave consolidated chip. Related projects: Engineering and Physical Sciences Research Council (EPSRC) (EP/M013294/1, EP/M013243/1, EP/M024539/1, EP/T001062/1, EP/V053213/1, EP/T00097X/1); Innovate UK (50414) Dataset available under a CC BY 4.0 licence
DOI: 10.17632/cxwv6dxdy5.1
title: Pol II CTD droplet assay in different crowding reagent

description: To screen the optimum condition for Pol II CTD droplet formation, the  in different crowding reagent buffers were tested, including Mock, 10% dextran, 16% dextran, 10% glycerol, 1% PEG8000, 2.5 % PEG8000, 5 % PEG8000, and purified mEGFP and RUVBL1/2-mCherry proteins were used as controls. The best buffer for Pol II CTD droplet assay is 16% dextran in 20 mM Tris.HCl (pH7.5) and 150 mM NaCl.
DOI: 10.5281/zenodo.7559544
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2001 (185–361)

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2001 (185–361). <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2001 (001–177)</strong> and <em><strong>click here</strong> to download the MUSES FVC produ</em>ct <strong>in 2002 (001–177)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2001 (185–361) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al.</em> (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7559543
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2001 (185–361)

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2001 (185–361). <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2001 (001–177)</strong> and <em><strong>click here</strong> to download the MUSES FVC produ</em>ct <strong>in 2002 (001–177)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2001 (185–361) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al.</em> (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.17632/g7ryvnrkmg.1
title: Swimming data of Sparicotyle chrysophrii

description: Swimming data of Sparicotyle chrysophrii by water temperature. Data on swimming periods estimated from oncomiracidia in shallow wells (1.1 cm diameter x 0.8 cm height, with 400 µl). Data on swimming speed estimated from oncomiracidia in Petri dish (3.5 cm diameter with 0.5 ml water layer). Data on vertical swimming speed from  <4h-old and 24h-old oncomiracidia in test tubes (10 cm length x 0.8 cm diameter, with 5 ml of seawater).
DOI: 10.17632/hkp48gh4t8.1
title: 2023-01-08 - Black Metal Grup Verisi-Metallum

description: 8 Ocak 2023'te Encyclopaedia Metallum'dan çekilen black metal grupları verisi (kısa ve uzun formda).
DOI: 10.5281/zenodo.7589057
title: CMIP6 data for the analysis in the article "Storylines of Maritime Continent dry period precipitation changes under global warming"

description: The files include the data required to generate the figures based on CMIP6 model simulation outputs
DOI: 10.5281/zenodo.7589056
title: CMIP6 data for the analysis in the article "Storylines of Maritime Continent dry period precipitation changes under global warming"

description: The files include the data required to generate the figures based on CMIP6 model simulation outputs
DOI: 10.5519/qd.kbghibe1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5379762 records)
DOI: 10.5519/qd.h278d7n0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.7589044
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: 10.6084/m9.figshare.21981719
title: Literature Review Dataset

description: The data available here are a .xlsx spreadsheet with four sheets, and those individual sheets as .csv files. The dataset consists of the following information: Raw Query Output: Raw output from the Publish or Perish software. Selection Criteria: An overview of papers from the query output and which selection criteria they fit. Snowballed Papers: An overview of the snowballing process. All Included Papers: A summary of all papers included in the literature review.
DOI: 10.25625/9vekun
title: BSc data: Activity of echolocating bats in Jambi, Sumatra, Indonesia

description: Data and R-Scripts used by Viviane Riemer for her BSc thesis entitled: The effect of rainforest transformation on the activity of echlocating bats in lowland Sumatra. The thesis was conducted within the framework of the EFForTS-Landscape Assessement. The data sets contain information on bat activity and bat calls within 60 plots, recorded through the use of ultrasonic sound recordings. Four different land cover types are covered (forest, shrubland, oil palm plantations, rubber plantations, 20 plots each). Next to a variety of plot metadata (locations, ownership status, management intensity), the data also contains local and landscape predictors, such as LIDAr vegetation indicies and land cover proportions in different buffer radii around plots.
DOI: 10.25403/upresearchdata.21951167
title: Predictive fourier transform infrared (FT-IR) models for disease resistance in Eucalyptus clones.

description: The datasets entails 3 Fourier Transform Infrared (FT-IR) models for <em>Eucalyptus</em> hybrid clones resistance and susceptibility as well as the sample sets for the study (disseration). The samples were collected from an inoculation trial and then analyzed for FT-IR. To develop the FT-IR models, spectra converted to spc format using terminal and Github.The spectral data was then analyzed on R-Studio using the Eucalyptus script. <br> The dataset comparing two methods (VSURF & SVM) has also been included. As well as the 3 models classification perfomance using Support Vector Machine (SVM) and the Partial Least Square-Discriminant Analysis (PLS-DA) performance analysis.
DOI: 10.5281/zenodo.7582668
title: Data and scripts from "Unsupervised learning for structure detection in plastically deformed crystals"

description: This documents contains the scripts and dataset used for the paper "Unsupervised learning for structure detection in plastically deformed crystals". More precisely it contains 4 folders : <br> DumpForFigures : subfolder containing the atomic positions in .dump format (see lammps documentation) used for the article figures. DumpForTraining : subfolder containing the atomic position in .dump format (see lammps documentation) used for training the autoencoder. ScriptsToDetectStructuresFromDump : subfolder containing the script sused to detect the substructures of the system by combining autoencoder and clustering methods. This folder contains a readme with the details of the contents. ScriptToGenerateDump : subfolder containing the scripts used to generate the atomic data with molecular dynamics. These data are then used to train the autoencoder. This folder contains a readme with the details of the contents. REQUIREMENTS : Lammps Python3 with packages : -numpy -matplotlib -pyscal -sci-kit learn -pytorch -glob
DOI: 10.5281/zenodo.7582667
title: Data and scripts from "Unsupervised learning for structure detection in plastically deformed crystals"

description: This documents contains the scripts and dataset used for the paper "Unsupervised learning for structure detection in plastically deformed crystals". More precisely it contains 4 folders : <br> DumpForFigures : subfolder containing the atomic positions in .dump format (see lammps documentation) used for the article figures. DumpForTraining : subfolder containing the atomic position in .dump format (see lammps documentation) used for training the autoencoder. ScriptsToDetectStructuresFromDump : subfolder containing the script sused to detect the substructures of the system by combining autoencoder and clustering methods. This folder contains a readme with the details of the contents. ScriptToGenerateDump : subfolder containing the scripts used to generate the atomic data with molecular dynamics. These data are then used to train the autoencoder. This folder contains a readme with the details of the contents. REQUIREMENTS : Lammps Python3 with packages : -numpy -matplotlib -pyscal -sci-kit learn -pytorch -glob
DOI: 10.6084/m9.figshare.21971690
title: MATEO HPTML model data

description: In this Figsahre repository we release the dataset used to train/validate the MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization Web Server. Supporting Info File: <strong>SI01_HPTML model.xlsx</strong> Content: Data used to train and validate the model. <br> Summary: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products (products) or chiral catalysts (tools). The enantioselectivity is sensitive to many factors, from the nature of the nucleophile and the catalyst to the experimental conditions (solvent, temperature, etc.). Although computational chemistry has been used to rationalize experimental results, it is still difficult to understand the influence of different parameters (solvent, temperature, etc.) on the quantitative reaction outcome (as yield or regio- and stereoselectivities).Both experimental and computational (Quantum Chemistry) study of a large number of reactions may become costly in terms of resources and time. Thus, the development of fast-track public computational tools to predict the enantioselectivity [enantiomeric excess <em>ee</em>(%)obs] would be very useful. Furthermore, making the new tool available online could save time and experimental resources in many labs worldwide. We used an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm to seek a predictive model with R2 = 0.91 in training and validation series has been developed. It involves a Monte Carlo sampling of&gt;100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived <em>N</em>-trifylphosphoramides as chiral catalysts is reported as a case of study. After validation of the model, it was implementedin a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization. This tool is available online at:https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo.This new user-friendly online computational tool may become useful to explore a large number of combinations of reactants, catalysts, and experimental conditions. This public tool would enable sustainable optimization of reaction conditions that could lead to the design of new catalysts, substrates, nucleophiles, and/or products.
DOI: 10.6084/m9.figshare.21971690.v2
title: MATEO HPTML model data

description: In this Figsahre repository we release the dataset used to train/validate the MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization Web Server. Supporting Info File: <strong>SI01_HPTML model.xlsx</strong> Content: Data used to train and validate the model. <br> Summary: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products (products) or chiral catalysts (tools). The enantioselectivity is sensitive to many factors, from the nature of the nucleophile and the catalyst to the experimental conditions (solvent, temperature, etc.). Although computational chemistry has been used to rationalize experimental results, it is still difficult to understand the influence of different parameters (solvent, temperature, etc.) on the quantitative reaction outcome (as yield or regio- and stereoselectivities).Both experimental and computational (Quantum Chemistry) study of a large number of reactions may become costly in terms of resources and time. Thus, the development of fast-track public computational tools to predict the enantioselectivity [enantiomeric excess <em>ee</em>(%)obs] would be very useful. Furthermore, making the new tool available online could save time and experimental resources in many labs worldwide. We used an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm to seek a predictive model with R2 = 0.91 in training and validation series has been developed. It involves a Monte Carlo sampling of&gt;100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived <em>N</em>-trifylphosphoramides as chiral catalysts is reported as a case of study. After validation of the model, it was implementedin a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization. This tool is available online at:https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo.This new user-friendly online computational tool may become useful to explore a large number of combinations of reactants, catalysts, and experimental conditions. This public tool would enable sustainable optimization of reaction conditions that could lead to the design of new catalysts, substrates, nucleophiles, and/or products.
DOI: 10.6084/m9.figshare.21971696
title: SI02_MATEO Ref Recctions.xlsx

description: In this Figsahre repository we release the dataset used to train/validate the MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization Web Server. SI File: <strong>SI02_MATEO Ref Recctions.xlsx</strong> Content: Data of reactions of reference uploaded to the internal database of the web server. <br> Summary: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products (products) or chiral catalysts (tools). The enantioselectivity is sensitive to many factors, from the nature of the nucleophile and the catalyst to the experimental conditions (solvent, temperature, etc.). Although computational chemistry has been used to rationalize experimental results, it is still difficult to understand the influence of different parameters (solvent, temperature, etc.) on the quantitative reaction outcome (as yield or regio- and stereoselectivities).Both experimental and computational (Quantum Chemistry) study of a large number of reactions may become costly in terms of resources and time. Thus, the development of fast-track public computational tools to predict the enantioselectivity [enantiomeric excess <em>ee</em>(%)obs] would be very useful. Furthermore, making the new tool available online could save time and experimental resources in many labs worldwide. We used an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm to seek a predictive model with R2 = 0.91 in training and validation series has been developed. It involves a Monte Carlo sampling of&gt;100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived <em>N</em>-trifylphosphoramides as chiral catalysts is reported as a case of study. After validation of the model, it was implementedin a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization. This tool is available online at:https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo.This new user-friendly online computational tool may become useful to explore a large number of combinations of reactants, catalysts, and experimental conditions. This public tool would enable sustainable optimization of reaction conditions that could lead to the design of new catalysts, substrates, nucleophiles, and/or products.
DOI: 10.6084/m9.figshare.21971696.v1
title: SI02_MATEO Ref Recctions.xlsx

description: In this Figsahre repository we release the dataset used to train/validate the MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization Web Server. SI File: <strong>SI02_MATEO Ref Recctions.xlsx</strong> Content: Data of reactions of reference uploaded to the internal database of the web server. <br> Summary: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products (products) or chiral catalysts (tools). The enantioselectivity is sensitive to many factors, from the nature of the nucleophile and the catalyst to the experimental conditions (solvent, temperature, etc.). Although computational chemistry has been used to rationalize experimental results, it is still difficult to understand the influence of different parameters (solvent, temperature, etc.) on the quantitative reaction outcome (as yield or regio- and stereoselectivities).Both experimental and computational (Quantum Chemistry) study of a large number of reactions may become costly in terms of resources and time. Thus, the development of fast-track public computational tools to predict the enantioselectivity [enantiomeric excess <em>ee</em>(%)obs] would be very useful. Furthermore, making the new tool available online could save time and experimental resources in many labs worldwide. We used an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm to seek a predictive model with R2 = 0.91 in training and validation series has been developed. It involves a Monte Carlo sampling of&gt;100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived <em>N</em>-trifylphosphoramides as chiral catalysts is reported as a case of study. After validation of the model, it was implementedin a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization. This tool is available online at:https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo.This new user-friendly online computational tool may become useful to explore a large number of combinations of reactants, catalysts, and experimental conditions. This public tool would enable sustainable optimization of reaction conditions that could lead to the design of new catalysts, substrates, nucleophiles, and/or products.