cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19929268.v1
title: Optimization of texture profile analysis parameters for commercial guava preserve

description: ABSTRACT Motivated by the lack of studies that standardize and optimize the parameters of texture tests, this study aimed to determine the operating conditions for TPA to maximize the discrimination among samples of fruit preserves. The texture of the commercial guava preserves was evaluated using a texturometer. The Design Central Composite Rotational (DCCR) method was applied with four independent variables: speed test, sample volume, time between compression cycles and compression percentage. Only the compression percentage and test speed were significantly influenced by the texture parameters evaluated. The optimum operating region of TPA to better discriminate differences in texture parameters depended on the variable to be optimized, and for adhesiveness a compression of 75% and a compression speed of 0.23 mm·s are recommended. To detect differences among the samples for the parameters of cohesiveness, gumminess and resilience, the use of 15% compression and 2.59 mm·s speed is suggested. In both cases, one must employ the shortest time between two cycles and use a smaller sample size to save both the time of analysis and of the sample, respectively. For the parameters of hardness, elasticity and chewiness, optimal regions were not identified.
DOI: 10.6084/m9.figshare.19929268
title: Optimization of texture profile analysis parameters for commercial guava preserve

description: ABSTRACT Motivated by the lack of studies that standardize and optimize the parameters of texture tests, this study aimed to determine the operating conditions for TPA to maximize the discrimination among samples of fruit preserves. The texture of the commercial guava preserves was evaluated using a texturometer. The Design Central Composite Rotational (DCCR) method was applied with four independent variables: speed test, sample volume, time between compression cycles and compression percentage. Only the compression percentage and test speed were significantly influenced by the texture parameters evaluated. The optimum operating region of TPA to better discriminate differences in texture parameters depended on the variable to be optimized, and for adhesiveness a compression of 75% and a compression speed of 0.23 mm·s are recommended. To detect differences among the samples for the parameters of cohesiveness, gumminess and resilience, the use of 15% compression and 2.59 mm·s speed is suggested. In both cases, one must employ the shortest time between two cycles and use a smaller sample size to save both the time of analysis and of the sample, respectively. For the parameters of hardness, elasticity and chewiness, optimal regions were not identified.
DOI: 10.5281/zenodo.6591900
title: SuperDARN data in netCDF format (2007-Dec)

description: 2007-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6580065
title: SuperDARN data in netCDF format (2008-Jun)

description: 2008-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6580244
title: SuperDARN data in netCDF format (2008-May)

description: 2008-May SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.6084/m9.figshare.19929269
title: Podocarpo, a plant of ornamental interest: asexual propagation

description: ABSTRACT The objective of this study was to evaluate the potential of rooting of podocarpos cuttings in function of the number of leaves, concentrations of IBA and phytorregulators, application methods and cutting season. Two experiments were carried out under weaning conditions, in a factorial scheme containing 3 replicates of 15 cuttings, the first with 4 concentrations of IBA, 3 leaves at the cuttings and 2 seasons. These were removed in August/2016 and January/2017, prepared with 10 cm in length and immersed in solutions for 10 s. In the second experiment three types of phytoregulators and 2 application forms were tested (slow and quick immersion). Cuttings were prepared in the same manner as in the first experiment, maintaining 4 leaves. The bases of the cuttings were immersed for 10 s and 24 h in the solutions, then taken to root. After 100 days of experimentation, it was concluded that cuttings collected in the summer had a higher percentage of rooting, 4 leaves and IBA concentration should be maintained between 750 and 800 mg L-1. The best form of application of phytoregulator is by rapid immersion.
DOI: 10.6084/m9.figshare.19929269.v1
title: Podocarpo, a plant of ornamental interest: asexual propagation

description: ABSTRACT The objective of this study was to evaluate the potential of rooting of podocarpos cuttings in function of the number of leaves, concentrations of IBA and phytorregulators, application methods and cutting season. Two experiments were carried out under weaning conditions, in a factorial scheme containing 3 replicates of 15 cuttings, the first with 4 concentrations of IBA, 3 leaves at the cuttings and 2 seasons. These were removed in August/2016 and January/2017, prepared with 10 cm in length and immersed in solutions for 10 s. In the second experiment three types of phytoregulators and 2 application forms were tested (slow and quick immersion). Cuttings were prepared in the same manner as in the first experiment, maintaining 4 leaves. The bases of the cuttings were immersed for 10 s and 24 h in the solutions, then taken to root. After 100 days of experimentation, it was concluded that cuttings collected in the summer had a higher percentage of rooting, 4 leaves and IBA concentration should be maintained between 750 and 800 mg L-1. The best form of application of phytoregulator is by rapid immersion.
DOI: 10.5281/zenodo.6580938
title: SuperDARN data in netCDF format (2008-Mar)

description: 2008-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6581066
title: SuperDARN data in netCDF format (2008-Feb)

description: 2008-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6580443
title: SuperDARN data in netCDF format (2008-Apr)

description: 2008-Apr SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6570491
title: SuperDARN data in netCDF format (2010-Dec)

description: 2010-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6581304
title: SuperDARN data in netCDF format (2008-Jan)

description: 2008-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579565
title: SuperDARN data in netCDF format (2008-Jul)

description: 2008-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6575121
title: SuperDARN data in netCDF format (2010-Feb)

description: 2010-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579121
title: SuperDARN data in netCDF format (2008-Dec)

description: 2008-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579448
title: SuperDARN data in netCDF format (2008-Aug)

description: 2008-Aug SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.6084/m9.figshare.19929274.v1
title: Growth and yields parameters of cassava crop (Manihot esculenta Crantz) as affected by plant density, genotype and planting system

description: ABSTRACT In Argentina, cassava (Manihot esculenta) production does not meet the national demand. The aim of this work was to analyze the effects of two planting systems of the stem cuttings (horizontal and vertical), two plant densities (10,000 and 20,000 plant ha-1) and two cassava genotypes (cv. Palomita and Blanca de Santa Catarina) on biometric variables and on root and starch yields by means of a field experiment carried out in Corrientes, Argentina. Eight treatments were distributed following a randomized complete block design. The following variables were evaluated: cutting sprouting percentage, plant height, percentage of intercepted photosynthetically active radiation (PARi); total, shoot and root fresh weight per hectare and/or per plant; starch production per hectare and weed dry biomass. Blanca de Santa Catarina (BSC) demonstrated the best yield potential. Vertical planting determined higher sprouting percentages, plant length and PARi, consequently greater conversion into biomass and starch. Higher plant density was associated with lower weed dry biomass and with lower root yield per plant. In conclusion, in cassava producing areas, such as Corrientes, which has sandy soils, the vertical planting system at a density of 10,000 plant ha-1 with the cv. BSC could be recommended to increase cassava yield without additional costs.