cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7613025
title: Data from: Direct contribution of invertebrate holobionts to methane release from coastal sediments

description: In this study, we incubated 103 animals specimens equivalent to 19 macrofaunal species to quantify holobiont-associated methane (CH<sub>4</sub>) fluxes and metabolic processes [oxygen (O<sub>2</sub>) - respiration and ammonium (NH<sub>4</sub><sup>+</sup>) - excretion rates]. The specific goals were to quantify holobionts CH<sub>4</sub> production/uptake and to establish correlations between CH<sub>4</sub> fluxes and environmental factors (e.g., salinity). Invertebrates were collected in 4 coastal systems and incubated in 22 mL glass microcosms filled with 0.22 µm twice-filtered <em>in situ</em> water. Individual and Mass-standardized CH<sub>4</sub> Production Rates (IPR and MPR, respectively), O<sub>2</sub> Respiration Rates (IRR and MRR) and NH<sub>4</sub><sup>+ </sup>Excretion Rates (IER and MER) were measured in 103 animals’ incubations. 1. IRR were calculated from linear regression analysis of the solute (O<sub>2</sub>) versus time equation: \(IRR=(Reg.Slope × V)/N\) <em> where IRR (µmol O<sub>2</sub> ind.<sup>−1</sup> day<sup>−1</sup>) is the respiration of the chemical species O<sub>2</sub>; Reg.Slope is the slope of the regression (µmol O<sub>2</sub> L<sup>−1</sup> day<sup>−1</sup>); V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm.</em> 2. IER and IPR were calculated from the difference in concentrations (NH<sub>4</sub><sup>+</sup> and CH<sub>4</sub>) in the water using the equation: \(IER and IPR = ((C_f-C_i )×V)/(N×t)\) <em> where IER and IPR (µmol ind.<sup>−1</sup> day<sup>−1</sup> and nmol ind.<sup>−1</sup> day<sup>−1</sup>) are the excretion or production of the chemical species (NH<sub>4</sub><sup>+</sup> or CH<sub>4</sub>); C<sub>f</sub> and C<sub>i</sub> (µmol or nmol L<sup>−1</sup>) are the final and initial concentrations of the chemical species; V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm; and t (days) is the incubation time. Positive values represent productions while negative values represent uptake.</em> Same equations were used to calculated mass-standardized rates, but instead of <em>N</em> the total animal biomass (g<sub>dw</sub>) was used. Animals’ biomass was determined as dry weight (DW) or as dry weight shell free (DWSF) for bivalves, after the desiccation at 70°C until constant mass. Water temperature and salinity were measured in situ with a multiple probe (556 MPS, YSI). Rates are reported as average ± standard error.
description: This work was supported financially by project INBALANCE funded by the European Social Fund (Grant No. 09.3.3-LMT-K-712-01-0069). Stefano Bonaglia was additionally supported by the Swedish Research Council Formas (Grants No. 2017-01513 and No. 2022-00546).
DOI: 10.5281/zenodo.7613026
title: Data from: Invertebrate holobionts contribute to methane release from coastal ecosystems

description: In this study, we incubated 103 animals specimens equivalent to 19 macrofaunal species to quantify holobiont-associated methane (CH<sub>4</sub>) fluxes and metabolic processes [oxygen (O<sub>2</sub>) - respiration and ammonium (NH<sub>4</sub><sup>+</sup>) - excretion rates]. The specific goals were to quantify holobionts CH<sub>4</sub> production/uptake and to establish correlations between CH<sub>4</sub> fluxes and environmental factors (e.g., salinity). Invertebrates were collected in 4 coastal systems and incubated in 22 mL glass microcosms filled with 0.22 µm twice-filtered <em>in situ</em> water. Individual and Mass-standardized CH<sub>4</sub> Production Rates (IPR and MPR, respectively), O<sub>2</sub> Respiration Rates (IRR and MRR) and NH<sub>4</sub><sup>+ </sup>Excretion Rates (IER and MER) were measured in 103 animals’ incubations. 1. IRR were calculated from linear regression analysis of the solute (O<sub>2</sub>) versus time equation: \(IRR=(Reg.Slope × V)/N\) <em> where IRR (µmol O<sub>2</sub> ind.<sup>−1</sup> day<sup>−1</sup>) is the respiration of the chemical species O<sub>2</sub>; Reg.Slope is the slope of the regression (µmol O<sub>2</sub> L<sup>−1</sup> day<sup>−1</sup>); V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm.</em> 2. IER and IPR were calculated from the difference in concentrations (NH<sub>4</sub><sup>+</sup> and CH<sub>4</sub>) in the water using the equation: \(IER and IPR =((C_f-C_i )×V)/(N×t)\) <em> where IER and IPR (µmol ind.<sup>−1</sup> day<sup>−1</sup> and nmol ind.<sup>−1</sup> day<sup>−1</sup>) are the excretion or production of the chemical species (NH<sub>4</sub><sup>+</sup> or CH<sub>4</sub>); C<sub>f</sub> and C<sub>i</sub> (µmol or nmol L<sup>−1</sup>) are the final and initial concentrations of the chemical species; V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm; and t (days) is the incubation time. Positive values represent productions while negative values represent uptake.</em> Same equations were used to calculated mass-standardized rates, but instead of <em>N</em> the total animal biomass (g<sub>dw</sub>) was used. Animals’ biomass was determined as dry weight (DW) or as dry weight shell free (DWSF) for bivalves, after the desiccation at 70°C until constant mass. Water temperature and salinity were measured in situ with a multiple probe (556 MPS, YSI). Rates are reported as average ± standard error.
description: This work was supported financially by project INBALANCE funded by the European Social Fund (Grant No. 09.3.3-LMT-K-712-01-0069). Stefano Bonaglia was additionally supported by the Swedish Research Council Formas (Grants No. 2017-01513 and No. 2022-00546).
DOI: 10.5281/zenodo.7627893
title: Data from: Invertebrate holobionts contribute to methane release from coastal ecosystems

description: In this study, we incubated 103 animals specimens equivalent to 19 macrofaunal species to quantify holobiont-associated methane (CH<sub>4</sub>) fluxes and metabolic processes [oxygen (O<sub>2</sub>) - respiration and ammonium (NH<sub>4</sub><sup>+</sup>) - excretion rates]. The specific goals were to quantify holobionts CH<sub>4</sub> production/uptake and to establish correlations between CH<sub>4</sub> fluxes and environmental factors (e.g., salinity). Invertebrates were collected in 4 coastal systems and incubated in 22 mL glass microcosms filled with 0.22 µm twice-filtered <em>in situ</em> water. Individual and Mass-standardized CH<sub>4</sub> Production Rates (IPR and MPR, respectively), O<sub>2</sub> Respiration Rates (IRR and MRR) and NH<sub>4</sub><sup>+ </sup>Excretion Rates (IER and MER) were measured in 103 animals’ incubations. 1. IRR were calculated from linear regression analysis of the solute (O<sub>2</sub>) versus time equation: \(IRR=(Reg.Slope × V)/N\) <em> where IRR (µmol O<sub>2</sub> ind.<sup>−1</sup> day<sup>−1</sup>) is the respiration of the chemical species O<sub>2</sub>; Reg.Slope is the slope of the regression (µmol O<sub>2</sub> L<sup>−1</sup> day<sup>−1</sup>); V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm.</em> 2. IER and IPR were calculated from the difference in concentrations (NH<sub>4</sub><sup>+</sup> and CH<sub>4</sub>) in the water using the equation: \(IER and IPR =((C_f-C_i )×V)/(N×t)\) <em> where IER and IPR (µmol ind.<sup>−1</sup> day<sup>−1</sup> and nmol ind.<sup>−1</sup> day<sup>−1</sup>) are the excretion or production of the chemical species (NH<sub>4</sub><sup>+</sup> or CH<sub>4</sub>); C<sub>f</sub> and C<sub>i</sub> (µmol or nmol L<sup>−1</sup>) are the final and initial concentrations of the chemical species; V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm; and t (days) is the incubation time. Positive values represent productions while negative values represent uptake.</em> Same equations were used to calculated mass-standardized rates, but instead of <em>N</em> the total animal biomass (g<sub>dw</sub>) was used. Animals’ biomass was determined as dry weight (DW) or as dry weight shell free (DWSF) for bivalves, after the desiccation at 70°C until constant mass. Water temperature and salinity were measured in situ with a multiple probe (556 MPS, YSI). Rates are reported as average ± standard error.
description: This work was supported financially by project INBALANCE funded by the European Social Fund (Grant No. 09.3.3-LMT-K-712-01-0069). Stefano Bonaglia was additionally supported by the Swedish Research Council Formas (Grants No. 2017-01513 and No. 2022-00546).
DOI: 10.5281/zenodo.8207393
title: Data from: Direct contribution of invertebrate holobionts to methane release from coastal sediments

description: In this study, we incubated 103 animals specimens equivalent to 19 macrofaunal species to quantify holobiont-associated methane (CH<sub>4</sub>) fluxes and metabolic processes [oxygen (O<sub>2</sub>) - respiration and ammonium (NH<sub>4</sub><sup>+</sup>) - excretion rates]. The specific goals were to quantify holobionts CH<sub>4</sub> production/uptake and to establish correlations between CH<sub>4</sub> fluxes and environmental factors (e.g., salinity). Invertebrates were collected in 4 coastal systems and incubated in 22 mL glass microcosms filled with 0.22 µm twice-filtered <em>in situ</em> water. Individual and Mass-standardized CH<sub>4</sub> Production Rates (IPR and MPR, respectively), O<sub>2</sub> Respiration Rates (IRR and MRR) and NH<sub>4</sub><sup>+ </sup>Excretion Rates (IER and MER) were measured in 103 animals’ incubations. 1. IRR were calculated from linear regression analysis of the solute (O<sub>2</sub>) versus time equation: \(IRR=(Reg.Slope × V)/N\) <em> where IRR (µmol O<sub>2</sub> ind.<sup>−1</sup> day<sup>−1</sup>) is the respiration of the chemical species O<sub>2</sub>; Reg.Slope is the slope of the regression (µmol O<sub>2</sub> L<sup>−1</sup> day<sup>−1</sup>); V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm.</em> 2. IER and IPR were calculated from the difference in concentrations (NH<sub>4</sub><sup>+</sup> and CH<sub>4</sub>) in the water using the equation: \(IER and IPR = ((C_f-C_i )×V)/(N×t)\) <em> where IER and IPR (µmol ind.<sup>−1</sup> day<sup>−1</sup> and nmol ind.<sup>−1</sup> day<sup>−1</sup>) are the excretion or production of the chemical species (NH<sub>4</sub><sup>+</sup> or CH<sub>4</sub>); C<sub>f</sub> and C<sub>i</sub> (µmol or nmol L<sup>−1</sup>) are the final and initial concentrations of the chemical species; V (L) is the water volume in the glass microcosm; N is the number of incubated animals per microcosm; and t (days) is the incubation time. Positive values represent productions while negative values represent uptake.</em> Same equations were used to calculated mass-standardized rates, but instead of <em>N</em> the total animal biomass (g<sub>dw</sub>) was used. Animals’ biomass was determined as dry weight (DW) or as dry weight shell free (DWSF) for bivalves, after the desiccation at 70°C until constant mass. Water temperature and salinity were measured in situ with a multiple probe (556 MPS, YSI). Rates are reported as average ± standard error.
description: This work was supported financially by project INBALANCE funded by the European Social Fund (Grant No. 09.3.3-LMT-K-712-01-0069). Stefano Bonaglia was additionally supported by the Swedish Research Council Formas (Grants No. 2017-01513 and No. 2022-00546).
DOI: 10.6084/m9.figshare.22153955
title: Realizing unipolar and bipolar intrinsic skyrmions in MXenes from high-fidelity first principles calculations

description: This repository contains the supplementary data, images, and codes from the research work "<b>Realizing unipolar and bipolar intrinsic skyrmions in MXenes from high-fidelity first-principles calculations.</b>"<b>Paper DOI: https://doi.org/10.1038/s41524-023-01129-x</b>
DOI: 10.6084/m9.figshare.22153955.v1
title: Realizing unipolar and bipolar intrinsic skyrmions in MXenes from high-fidelity first principles calculations

description: This repository contains the supplementary data, images, and codes from the research work "<b>Realizing unipolar and bipolar intrinsic skyrmions in MXenes from high-fidelity first-principles calculations.</b>"Paper DOI: 10.1038/s41524-023-01129-x
DOI: 10.22008/fk2/44tkzr/ifjdfm
title: Havnsø_2020_publ_Petrel.zip

description: :unav
DOI: 10.5281/zenodo.8369902
title: Dataset for publication: Supporting the assignment of NMR spectra with variable-temperature experiments

description: 1. The raw one- and two-dimensional NMR data for Products (1-7) listed in the article (Agilent 700 MHz). 2. The raw 1D variable-temperature NMR data for Products 1-7 (Agilent 700 MHz). 3. The 1D variable-temperature NMR data converted into a Bruker format to open in the Radon peak-picker, Radon peak-picker Python script (picker.py), and trained neural network model (model) folder. To open the Radon peak-picker see the Manual from: https://doi.org/10.1016/j.jmro.2022.100083
DOI: 10.5281/zenodo.8369901
title: Dataset for publication: Supporting the assignment of NMR spectra with variable-temperature experiments

description: 1. The raw one- and two-dimensional NMR data for Products (1-7) listed in the article (Agilent 700 MHz). 2. The raw 1D variable-temperature NMR data for Products 1-7 (Agilent 700 MHz). 3. The 1D variable-temperature NMR data converted into a Bruker format to open in the Radon peak-picker, Radon peak-picker Python script (picker.py), and trained neural network model (model) folder. To open the Radon peak-picker see the Manual from: https://doi.org/10.1016/j.jmro.2022.100083
DOI: 10.23729/7d9b2db6-0c87-4e40-9c04-86004e9ed2e2
title: A binder-free Nickel-Rich cathode composite utilizing low-bundled single-walled carbon nanotubes

description: Original measurement data files related to the publication.
DOI: 10.6084/m9.figshare.24183696
title: Annotation results of <i>Monochamus</i> <i>alternatus</i><i> </i>genome

description: Annotation results of <i>Monochamus</i> <i>alternatus</i><i> </i>genome using eggNOG
DOI: 10.5281/zenodo.8370558
title: Inovação ou progresso social? Uma análise dos fatores preditores para o avanço mundial da igualdade de gênero

description: Complete Dataset for the article "Inovação ou progresso social? Uma análise dos fatores preditores para o avanço mundial da igualdade de gênero" / "Innovation or social progress? An analysis of the predictors for worldwide advancement of gender equality" / "¿Innovación o progreso social? Un análisis de los factores que predicen el avance global de la igualdad de género" It includes all stages of data cleaning up to the final one used in the analysis, under the name of "Compiled Merged Dataset".
DOI: 10.5281/zenodo.8370557
title: Inovação ou progresso social? Uma análise dos fatores preditores para o avanço mundial da igualdade de gênero

description: Complete Dataset for the article "Inovação ou progresso social? Uma análise dos fatores preditores para o avanço mundial da igualdade de gênero" / "Innovation or social progress? An analysis of the predictors for worldwide advancement of gender equality" / "¿Innovación o progreso social? Un análisis de los factores que predicen el avance global de la igualdad de género" It includes all stages of data cleaning up to the final one used in the analysis, under the name of "Compiled Merged Dataset".
DOI: 10.5281/zenodo.8370337
title: Data for "Validating Ionospheric Models Against Technologically Relevant Metrics"

description: Data for "Validating Ionospheric Models Against Technologically Relevant Metrics"
DOI: 10.5517/ccdc.csd.cc1yvmw9
title: CCDC 1813062: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1yvmyc
title: CCDC 1813064: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1yvn0g
title: CCDC 1813066: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1yvmzd
title: CCDC 1813065: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1yvmxb
title: CCDC 1813063: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc223pjf
title: CCDC 1910174: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc223phd
title: CCDC 1910173: Experimental Crystal Structure Determination

description: Related Article: Yosuan Ávila, Hernández Osiry, Yosdel Plasencia, Ana E. Torres, Marlene González, Ana A. Lemus-Santana, Edilso Reguera|2019|Chem.-Eur.J.|25|11327|doi:10.1002/chem.201902168
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17894/ucph.32dcdf98-bd2c-4a1c-b606-5dfeb3f6b0e0
title: CALVADOS simulations of designed swap variants of hnRNPA1-LCD
DOI: 10.34740/kaggle/dsv/6519993
title: SSN-UNAM-Earthquakes-1925-2023
DOI: 10.6084/m9.figshare.24114390
title: <b>Medical education in Bangladesh from student and teacher’s perspective: Impact and challenges of the COVID-19 pandemic</b>

description: In low- and middle-income countries like Bangladesh, where medical education faces a range of challenges-such as lack of infrastructure, well-trained educators, and advanced technologies-the abrupt changes in methodologies without adequate preparation are more challenging than in higher-income countries The situation became more intense during the COVID-19 pandemic. These challenges have resulted in a significant change in medical education methodology. This study assesses the medical education procedure, impacts and adaptation strategies and challenges of the COVID-19 pandemic in the medical education system of Bangladesh from learners' as well as educators' perspectives
DOI: 10.6084/m9.figshare.24114390.v2
title: <b>Medical education in Bangladesh from student and teacher’s perspective: Impact and challenges of the COVID-19 pandemic</b>

description: In low- and middle-income countries like Bangladesh, where medical education faces a range of challenges-such as lack of infrastructure, well-trained educators, and advanced technologies-the abrupt changes in methodologies without adequate preparation are more challenging than in higher-income countries The situation became more intense during the COVID-19 pandemic. These challenges have resulted in a significant change in medical education methodology. This study assesses the medical education procedure, impacts and adaptation strategies and challenges of the COVID-19 pandemic in the medical education system of Bangladesh from learners' as well as educators' perspectives
DOI: 10.5281/zenodo.8132380
title: OCR model for lexical lists in Chinese-IPA Glossing, Ground Truth

description: The ground truth dataset for the OCR model consisted of jpg, pdf, and xml files. The training process was conducted using Transkribus, employing a PyLaia model constructed using ground truth data derived from lexical lists encompassing ten literary works that document Burmish languages. These languages include Achang, Bola, Chashan, Langsu, Leqi, and Zaiwa. The lexical lists utilized for the training phase were predominantly composed in both Chinese characters and International Phonetic Alphabet (IPA) symbols. The training was done on 311 pages and validation on 34 pages of ten lexical lists of Burmish languages on Transkribus with the default PyLaia model: (1) Achang<br> • adapted by Hill & Cooper (2020) from Dai & Cui (1985)<br> (2) Bola<br> • adapted by Hill & Cooper (2020) from He & Chen (2004)<br> (3) Bola<br> • adapted by Hill & Cooper (2020) from Dai et al. (2007)<br> (4) Chashan<br> • adapted by Hill & Cooper (2020) from Dai et al. (2010)<br> (5) Langsu<br> • adapted by Hill & Cooper (2020) from He & Chen (2004)<br> (6) Langsu<br> • adapted by Hill & Cooper (2020) from Dai (2005)<br> (7) Leqi<br> • adapted by Hill & Cooper (2020) from He & Chen (2004)<br> (8) Leqi<br> • adapted by Hill & Cooper (2020) from Dai & Li (2006)<br> (9) Leqi<br> • adapted by Hill & Cooper (2020) from Dai & Jie (2007)<br> (10) Zaiwa<br> • adapted by Hill & Cooper (2020) from Xu & Xu (1984) The material utilized for assessing the performance of the trained models is enclosed within the dataset, comprising a lexical list of the Tujia language as documented by Tian in 1986. The primary objective of the trained model was the recognition of printed lexical lists employing Chinese-IPA glossing.