cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7243478
title: Qualification of 'Bordô' grape clones in Vale do Rio do Peixe, in the state of Santa Catarina, Brazil

description: Abstract: The objective of this work was to characterize the productive and qualitative performances of 11 clones of 'Bordô' grape (Vitis labrusca) destined to wine and juice production, in the region of Vale do Rio do Peixe, in the state of Santa Catarina, Brazil. The experiment was carried out in the 2011, 2012, 2014, and 2015 crop seasons. The analyzed yield components were: number of clusters per vine, yield, cluster weight, accumulated yield, and alternate bearing index. The analyzed morphological characteristics of clusters were: number of berries per cluster, berry diameter, cluster compactness index, and cluster length. In order to assess grape quality, the following parameters were analyzed: soluble solids (SS), pH, titratable acidity (TA), SS/TA ratio, total anthocyanins, and total polyphenols. The 'Bordô' grape clones 13 and 16 ('Paco' and 'Bocaina') are the most suitable ones for cultivation in the wine producing region of Vale do Rio do Peixe, as they show the highest yields, production stability, and grapes with adequate quality for wine and juice elaboration.
DOI: 10.6084/m9.figshare.7243478.v1
title: Qualification of 'Bordô' grape clones in Vale do Rio do Peixe, in the state of Santa Catarina, Brazil

description: Abstract: The objective of this work was to characterize the productive and qualitative performances of 11 clones of 'Bordô' grape (Vitis labrusca) destined to wine and juice production, in the region of Vale do Rio do Peixe, in the state of Santa Catarina, Brazil. The experiment was carried out in the 2011, 2012, 2014, and 2015 crop seasons. The analyzed yield components were: number of clusters per vine, yield, cluster weight, accumulated yield, and alternate bearing index. The analyzed morphological characteristics of clusters were: number of berries per cluster, berry diameter, cluster compactness index, and cluster length. In order to assess grape quality, the following parameters were analyzed: soluble solids (SS), pH, titratable acidity (TA), SS/TA ratio, total anthocyanins, and total polyphenols. The 'Bordô' grape clones 13 and 16 ('Paco' and 'Bocaina') are the most suitable ones for cultivation in the wine producing region of Vale do Rio do Peixe, as they show the highest yields, production stability, and grapes with adequate quality for wine and juice elaboration.
DOI: 10.6084/m9.figshare.7243463
title: Chinese giant salamander gut microbiomes

description: microbial ecology
DOI: 10.6084/m9.figshare.7243475.v1
title: Postharvest longevity of 'BRS Vitória' seedless grapes subjected to cold storage and acibenzolar-S-methyl application

description: Abstract: The objective of this work was to evaluate cold storage periods and postharvest longevity of the seedless table grape 'BRS Vitória' subjected to the application of the disease-resistance inducer acibenzolar-S-methyl (ASM). Bunches were treated or not with 1% ASM, placed individually in plastic clamshell trays and stored in a cold chamber at 2±1ºC, at a high relative humidity, for three periods (30, 45, and 60 days), followed by a 5-day period of shelf life in plastic clamshells, at 22±2ºC. After each storage period, gray mold (Botrytis cinerea) incidence was evaluated, and the physicochemical analysis was performed. A completely randomized design, with four replicates, was used in two 2x3 factorial arrangements: one evaluating ASM application and cold storage periods; and another, ASM and cold storage + shelf-life periods. ASM did not change the physicochemical characteristics of the bunches, and the incidence of gray mold was lower than 1% in all treatments. Water loss from bunches increased with storage periods. Even without ASM application, 'BRS Vitória' grape can preserve its postharvest quality for up to 60 days of cold storage, plus five days of shelf life in plastic clamshells.
DOI: 10.7284/136038
title: Radiometer data collected during research cruise SKQ201921S using a Biospherical QSR-2150 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243460
title: 2013 Pollution Data from Wuxi China

description: The following is the hourly measured surface measurements of Carbon Monoxide data from all 9 available stations in Wuxi China in the year 2013. Original data provided by Xuemei Wang in July 2016. File Name: wuxi.xlsx
DOI: 10.6084/m9.figshare.7243463.v1
title: Chinese giant salamander gut microbiomes

description: microbial ecology
DOI: 10.6084/m9.figshare.7243460.v1
title: 2013 Pollution Data from Wuxi China

description: The following is the hourly measured surface measurements of Carbon Monoxide data from all 9 available stations in Wuxi China in the year 2013. Original data provided by Xuemei Wang in July 2016. File Name: wuxi.xlsx
DOI: 10.6084/m9.figshare.7243439
title: ALDCNR Project 192 Sea Turtle Inundation Tolerance Data

description: This dataset includes nest productivity and inundation data for sea turtle nests on the Fort Morgan Peninsula (Alabama, USA) from 2016 to 2018. The GPS locations of the nests have been withheld, per the U.S. Fish and Wildlife Service.<br>This dataset was released in accordance with the public access policy specified by the Section 6 agreement between the Alabama Department of Conservation and Natural Resources and the U.S. Fish and Wildlife Service, and accompanies the final report of ALDCNR Project 192 completed by the Florida State University Marine Turtle Research, Ecology, and Conservation Group.
DOI: 10.6084/m9.figshare.7243439.v1
title: ALDCNR Project 192 Sea Turtle Inundation Tolerance Data

description: This dataset includes nest productivity and inundation data for sea turtle nests on the Fort Morgan Peninsula (Alabama, USA) from 2016 to 2018. The GPS locations of the nests have been withheld, per the U.S. Fish and Wildlife Service.<br>This dataset was released in accordance with the public access policy specified by the Section 6 agreement between the Alabama Department of Conservation and Natural Resources and the U.S. Fish and Wildlife Service, and accompanies the final report of ALDCNR Project 192 completed by the Florida State University Marine Turtle Research, Ecology, and Conservation Group.
DOI: 10.7284/136039
title: Singlebeam Echosounder data collected during research cruise SKQ201921S using a L-3 ELAC LAZ 5100 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/136043
title: Water Temperature data collected during research cruise SKQ201921S using a HEITRONICS CT15.10 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/136048
title: Water Temperature data collected during research cruise SKQ201921S using a Sea-Bird SBE-38 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.12886718
title: Emission inventory

description: this data set includes PKUEI and MEIC data
DOI: 10.7284/135196
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise SKQ201922T using a Hawaii UHDAS instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.12886718.v1
title: Emission inventory

description: this data set includes PKUEI and MEIC data
DOI: 10.7284/135193
title: Anemometer data collected during research cruise SKQ201922T using a Gill WindObserver 75 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/135194
title: Anemometer data collected during research cruise SKQ201922T using a Gill WindObserver 75 instrument system onboard the platform RV Sikuliaq

description: Processed data created by vessel technician during a Field_expedition
DOI: 10.7284/135183
title: Expendable Probe data collected during research cruise SKQ201922T using a Sippican MK21 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243184
title: Data for 2018JC014531R

description: Data repository for Journal of Geophysical Research - Oceans (Sensitivity Studies of the Red Sea Eddies Using Adjoint Method, [Paper # 2018JC014531RR])<br>
DOI: 10.6084/m9.figshare.7243181
title: Exotic urban trees conserve similar natural enemy communities to native congeners but have fewer pests

description: Data linked to the publication “Exotic urban trees conserve similar natural enemy communities to native congeners but have fewer pests.” Four data files are provided. CSV file containing scale insect count data collected in 2012 and 2016. We counted scale insects in the lab on branches cut from the study trees. CSV file containing predator count data collected in 2012 and 2016. We collected predators on yellow sticky cards deployed in the study trees. CSV file containing parasitoid wasp count data collected in 2012 and 2016. We collected parasitoid wasps on yellow sticky cards deployed in the study trees.CSV file containing parasitoid wasp data collected in 2016. We collected parasitoid wasps on yellow sticky cards deployed in the study trees.<br>
DOI: 10.7284/135185
title: Flowmeter data collected during research cruise SKQ201922T using a Krohne Optiflux 5000 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243181.v1
title: Exotic urban trees conserve similar natural enemy communities to native congeners but have fewer pests

description: Data linked to the publication “Exotic urban trees conserve similar natural enemy communities to native congeners but have fewer pests.” Four data files are provided. CSV file containing scale insect count data collected in 2012 and 2016. We counted scale insects in the lab on branches cut from the study trees. CSV file containing predator count data collected in 2012 and 2016. We collected predators on yellow sticky cards deployed in the study trees. CSV file containing parasitoid wasp count data collected in 2012 and 2016. We collected parasitoid wasps on yellow sticky cards deployed in the study trees.CSV file containing parasitoid wasp data collected in 2016. We collected parasitoid wasps on yellow sticky cards deployed in the study trees.<br>
DOI: 10.6084/m9.figshare.7243184.v1
title: Data for 2018JC014531R

description: Data repository for Journal of Geophysical Research - Oceans (Sensitivity Studies of the Red Sea Eddies Using Adjoint Method, [Paper # 2018JC014531RR])<br>
DOI: 10.7284/135186
title: GNSS (Global Navigation Satellite System) data collected during research cruise SKQ201922T using a C&C C-Nav 3050 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243178
title: The Signature of a Windy Radio Supernova Progenitor in a Binary System. The curious case of SN1979C in which ripples observed in radio can be explained by the interaction of the wind of the supernova progenitor with a companion star in relatively wide orbit.

description: Simulation of the wind of a 18MSun (ZAMS) star shortly before it explodes in a supernova, in orbit around a 15MSun companion star. The simulation is for the last 14000 years before the supernova explosion. simulations are performed using AMUSE, with Fi as the hydro code and Huayno for integrating the planetary orbit.Mass per SPH particle is 10e-5MSun, orbital period is 2000yrs, circular. Binary plan is in the X-Y plane.<br>datafiles containing particle data from the simulations.snapshot_000xxx_gas.amuse (35 in total)snapshot_000xxx_stars.amuse (35 in total)<br>datafiles containinig observations of SN1979C:SN1979C_488GHz_fit.csv (fitted data) <br>SN1979C_488GHz.csv (raw data)<br>plotting routines:compare_lightcurve.pyplot_pd.py
DOI: 10.7284/135184
title: Gravimeter data collected during research cruise SKQ201922T using a Bell BGM-3 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/135187
title: Gyrocompass data collected during research cruise SKQ201922T using a Sperry NAVIGAT 2100 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/135188
title: Gyrocompass data collected during research cruise SKQ201922T using a Sperry NAVIGAT 2100 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243070
title: Data McLester et al 4C.xlsx

description: Body composition predicted by four compartment models in normal weight and overweight individuals with varying waist circumferences<br>
DOI: 10.7284/135197
title: INS (Inertial Navigation System) data collected during research cruise SKQ201922T using a Kongsberg Seapath 320+ instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243070.v1
title: Data McLester et al 4C.xlsx

description: Body composition predicted by four compartment models in normal weight and overweight individuals with varying waist circumferences<br>
DOI: 10.7284/135189
title: Meteorological Station data collected during research cruise SKQ201922T using a Vaisala WXT520 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243046
title: The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification

description: Compound identification is the main hurdle in LC-HRMS-based metabolomics, given the high number of ‘unknown’ metabolites. In recent years, numerous <i>in silico</i> fragmentation simulators have been developed to simplify and improve mass spectral interpretation and compound annotation. Nevertheless, expert mass spectrometry users and chemists are still needed to select the correct entry from the numerous candidates proposed by automatic tools, especially in the plant kingdom due to the huge structural diversity of natural compounds occurring in plants. In this work, we propose the use of a supervised machine learning approach to predict molecular substructures from isotopic patterns, training the model on a large database of grape metabolites. This approach, called ‘Compounds Characteristics Comparison’ (CCC) emulates the experience of a plant chemist who ‘gains experience’ from a (proof-of-principle) dataset of grape compounds. The results show that the CCC approach is able to predict with good accuracy most of the sub-structures proposed. In addition, after querying MS/MS spectra in Metfrag 2.2 and applying CCC predictions as scoring terms with real data, the CCC approach helped to give a better ranking to the correct candidates, improving users’ confidence in candidate selection. Our results demonstrated that the proposed approach can complement current identification strategies based on fragmentation simulators and formula calculators, assisting compound identification. The CCC algorithm is freely available as R package (https://github.com/lucanard/CCC) which includes a seamless integration with Metfrag. The CCC package also permits uploading additional training data, which can be used to extend the proposed approach to other systems biological matrices. <b>List of abbreviations:</b> Acidic: acidic moiety; aliph: aliphatic chain; AUC: area under the ROC curve; bs: best glycosidic structure; CCC: Compounds’ Characteristics Comparison; Cees: Carbons estimation errors; CO: Carbon to Oxygen ratio; Het: Heterocyclic moiety; IMD: Isotopic Mass Defect (and Pattern); LC-HRMS: Liquid Chromatography – High Resolution Mass Spectrometry; md: mass defect; MM: Monoisotopic Mass; MS: Mass Spectrometry; MSE: Mean Squared Error; nC: number of Carbons; NN: Nitrogen; pC: percentage of Carbon mass on the total mass; Pho: Phosphate; PLSr: Partial Least Square regression; ppm: parts per million; QSRR: Quantitative structure-retention relationship; RMD: Relative Mass Defect; ROC: Receiver Operating Characteristics; rRMD: residual Relative Mass Defect; RT: retention time; Sul: Sulphur; UPLC-ESI-Q-TOF-MS: Ultra Performance Liquid Chromatography – ElectroSpray Ionization –Quadropole – Time of Flight – Mass Spectrometry; VAT: <i>Vitis arizonica</i> Texas
DOI: 10.7284/135198
title: Multibeam Echosounder data collected during research cruise SKQ201922T using a Kongsberg EM302 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7243046.v1
title: The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification

description: Compound identification is the main hurdle in LC-HRMS-based metabolomics, given the high number of ‘unknown’ metabolites. In recent years, numerous <i>in silico</i> fragmentation simulators have been developed to simplify and improve mass spectral interpretation and compound annotation. Nevertheless, expert mass spectrometry users and chemists are still needed to select the correct entry from the numerous candidates proposed by automatic tools, especially in the plant kingdom due to the huge structural diversity of natural compounds occurring in plants. In this work, we propose the use of a supervised machine learning approach to predict molecular substructures from isotopic patterns, training the model on a large database of grape metabolites. This approach, called ‘Compounds Characteristics Comparison’ (CCC) emulates the experience of a plant chemist who ‘gains experience’ from a (proof-of-principle) dataset of grape compounds. The results show that the CCC approach is able to predict with good accuracy most of the sub-structures proposed. In addition, after querying MS/MS spectra in Metfrag 2.2 and applying CCC predictions as scoring terms with real data, the CCC approach helped to give a better ranking to the correct candidates, improving users’ confidence in candidate selection. Our results demonstrated that the proposed approach can complement current identification strategies based on fragmentation simulators and formula calculators, assisting compound identification. The CCC algorithm is freely available as R package (https://github.com/lucanard/CCC) which includes a seamless integration with Metfrag. The CCC package also permits uploading additional training data, which can be used to extend the proposed approach to other systems biological matrices. <b>List of abbreviations:</b> Acidic: acidic moiety; aliph: aliphatic chain; AUC: area under the ROC curve; bs: best glycosidic structure; CCC: Compounds’ Characteristics Comparison; Cees: Carbons estimation errors; CO: Carbon to Oxygen ratio; Het: Heterocyclic moiety; IMD: Isotopic Mass Defect (and Pattern); LC-HRMS: Liquid Chromatography – High Resolution Mass Spectrometry; md: mass defect; MM: Monoisotopic Mass; MS: Mass Spectrometry; MSE: Mean Squared Error; nC: number of Carbons; NN: Nitrogen; pC: percentage of Carbon mass on the total mass; Pho: Phosphate; PLSr: Partial Least Square regression; ppm: parts per million; QSRR: Quantitative structure-retention relationship; RMD: Relative Mass Defect; ROC: Receiver Operating Characteristics; rRMD: residual Relative Mass Defect; RT: retention time; Sul: Sulphur; UPLC-ESI-Q-TOF-MS: Ultra Performance Liquid Chromatography – ElectroSpray Ionization –Quadropole – Time of Flight – Mass Spectrometry; VAT: <i>Vitis arizonica</i> Texas
DOI: 10.7284/135199
title: Multibeam Echosounder data collected during research cruise SKQ201922T using a Kongsberg EM302 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7242998
title: Visual Perceptual Narrowing

description: The data represent human adults looking at human and monkey faces obtained via remote infrared eye-tracking. Files are separated by overall looking at the primate faces and the time course of looking at the primate faces. Data are further separated to include looking at human and rhesus monkey faces in files containing "Exp1" and human and barbary monkey faces in files containing "Exp2."
DOI: 10.6084/m9.figshare.7242998.v1
title: Visual Perceptual Narrowing

description: The data represent human adults looking at human and monkey faces obtained via remote infrared eye-tracking. Files are separated by overall looking at the primate faces and the time course of looking at the primate faces. Data are further separated to include looking at human and rhesus monkey faces in files containing "Exp1" and human and barbary monkey faces in files containing "Exp2."
DOI: 10.6084/m9.figshare.7242884
title: SOM 3 - data for experiment 2 from The unreliability of egocentric bias across self-other and memory-belief distinctions in the Sandbox Task

description: Dataset for experiment 2 analyses
DOI: 10.7284/135190
title: Radiometer data collected during research cruise SKQ201922T using a RMR Co. PRP instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7242881
title: SOM 2 - data for experiment 1 from The unreliability of egocentric bias across self-other and memory-belief distinctions in the Sandbox Task

description: Dataset for experiment 1 analyses
DOI: 10.6084/m9.figshare.7242881.v1
title: SOM 2 - data for experiment 1 from The unreliability of egocentric bias across self-other and memory-belief distinctions in the Sandbox Task

description: Dataset for experiment 1 analyses
DOI: 10.6084/m9.figshare.7242884.v1
title: SOM 3 - data for experiment 2 from The unreliability of egocentric bias across self-other and memory-belief distinctions in the Sandbox Task

description: Dataset for experiment 2 analyses
DOI: 10.7284/135191
title: Radiometer data collected during research cruise SKQ201922T using a Biospherical QSR-2150 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7241678
title: A statistical forecasting approach to metapopulation viability analysis

description: R code for the custom Metropolis-within-Gibbs sampler and data needed to fit spatially-explicit metapopulation models and forecast metapopulation viability.<br>
DOI: 10.7284/135195
title: Water Temperature data collected during research cruise SKQ201922T using a HEITRONICS CT15.10 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7241678.v1
title: A statistical forecasting approach to metapopulation viability analysis

description: R code for the custom Metropolis-within-Gibbs sampler and data needed to fit spatially-explicit metapopulation models and forecast metapopulation viability.<br>
DOI: 10.6084/m9.figshare.7241651
title: Presented Data from Supersaturation with water explains the unusual adhesion of aggregate glue in the webs of the moth-specialist spider, <i>Cyrtarachne akirai</i>

description: Raw Data used to create figures 2,3,4,6,7, and comments on presentage salt by mass
DOI: 10.7284/135200
title: Water Temperature data collected during research cruise SKQ201922T using a Sea-Bird SBE-38 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7241651.v1
title: Presented Data from Supersaturation with water explains the unusual adhesion of aggregate glue in the webs of the moth-specialist spider, <i>Cyrtarachne akirai</i>

description: Raw Data used to create figures 2,3,4,6,7, and comments on presentage salt by mass