cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7962060
title: Supplementary tables master's thesis Carolin Mattausch 2023

description: These table contain information about ChIP-seq files from different sources used in Carolin Mattausch's master's thesis "Integration of transcriptome and epigenome for comparative analysis of various chondrocyte subtypes" conducted November 2022 to June 2023 in the Department of Developmental Biology at the Center of Medical Biotechnology, University Duisburg-Essen, under the supervision of Prof. Dr. Andrea Vortkamp, Dr. Manuela Wuelling, and Christoph Neu. <strong>Supplementary table 4</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from ENCODE. These files were used in in comparison of H3K27me3 level between different murine and human tissues. <strong>Supplementary table 5</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from Roadmap. These files were used in in comparison of H3K27me3 level between different murine and human tissues. <strong>Supplementary table 6</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from Gene Expression Omnibus. These files were used in in comparison of H3K27me3 level between different murine and human tissues.
DOI: 10.5281/zenodo.7962286
title: Supplementary tables master's thesis Carolin Mattausch 2023

description: These table contain information about ChIP-seq files from different sources used in Carolin Mattausch's master's thesis "Integration of transcriptome and epigenome for comparative analysis of various chondrocyte subtypes" conducted November 2022 to June 2023 in the Department of Developmental Biology at the Center of Medical Biotechnology, University Duisburg-Essen, under the supervision of Prof. Dr. Andrea Vortkamp, Dr. Manuela Wuelling, and Christoph Neu. <strong>Supplementary table 4</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from ENCODE. These files were used in in comparison of H3K27me3 level between different murine and human tissues. <strong>Supplementary table 5</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from Roadmap. These files were used in in comparison of H3K27me3 level between different murine and human tissues. <strong>Supplementary table 6</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from Gene Expression Omnibus. These files were used in in comparison of H3K27me3 level between different murine and human tissues.
DOI: 10.6084/m9.figshare.23052329
title: Supplementary data for "Ni-rich mineral nepouite explains the exceptional green color of speleothems"

description: This supplementary data spreadsheet contains the complete list of samples used for the analyses and the relevant results obtained by EPMA analyses.
DOI: 10.6084/m9.figshare.23052329.v2
title: Supplementary data for "Ni-rich mineral nepouite explains the exceptional green color of speleothems"

description: This supplementary data spreadsheet contains the complete list of samples used for the analyses and the relevant results obtained by EPMA analyses.
DOI: 10.6084/m9.figshare.23052329.v1
title: Supplementary data for "Ni-rich mineral nepouite explains the exceptional green color of speleothems"

description: This supplementary data spreadsheet contains the complete list of samples used for the analyses and the relevant results obtained by EPMA analyses.
DOI: 10.5281/zenodo.7962281
title: Supplementary tables master's thesis Carolin Mattausch 2023

description: These table contain information about ChIP-seq files from different sources used in Carolin Mattausch's master's thesis "Integration of transcriptome and epigenome for comparative analysis of various chondrocyte subtypes" conducted November 2022 to June 2023 in the Department of Developmental Biology at the Center of Medical Biotechnology, University Duisburg-Essen, under the supervision of Prof. Dr. Andrea Vortkamp, Dr. Manuela Wuelling, and Christoph Neu. <strong>Supplementary table 4</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from ENCODE. These files were used in in comparison of H3K27me3 level between different murine and human tissues. <strong>Supplementary table 5</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from Roadmap. These files were used in in comparison of H3K27me3 level between different murine and human tissues. <strong>Supplementary table 6</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from Gene Expression Omnibus. These files were used in in comparison of H3K27me3 level between different murine and human tissues.
DOI: 10.5281/zenodo.7854534
title: GAN-based Synthetic VIIRS-like Image Generation over India

description: Monthly nighttime lights (NTL) can clearly depict an area's prevailing intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992 onwards). In the current study, an attempt has been made to generate synthetic monthly VIIRS-like products of 1992-2012, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992-2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done using low and high thresholds. Improved DMSP and corrected VIIRS images from April 2012 - December 2013 are used in a conditional generative adversarial network (cGAN) along with Land Use Land Cover, as auxiliary input, to generate VIIRS-like imagery from 1992-2012. The modelled imagery was aggregated annually and showed an <em>R</em><sup>2</sup> of 0.94 with the results of other annual-scale VIIRS-like imagery products of India, <em>R</em><sup>2</sup> of 0.85 w.r.t GDP and <em>R</em><sup>2</sup> of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an <em>R</em><sup>2</sup> of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an <em>R</em><sup>2 </sup>of 0.56 and 0.53 respectively. Qualitative analysis for also performed on both national and state scales. Visual analysis over 1992-2013 confirms a gradual increase in the brightness of the lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, a synthetically generated monthly VIIRS-like product is delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.
DOI: 10.5281/zenodo.7854533
title: GAN-based Synthetic VIIRS-like Image Generation over India

description: Monthly nighttime lights (NTL) can clearly depict an area's prevailing intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992 onwards). In the current study, an attempt has been made to generate synthetic monthly VIIRS-like products of 1992-2012, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992-2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done using low and high thresholds. Improved DMSP and corrected VIIRS images from April 2012 - December 2013 are used in a conditional generative adversarial network (cGAN) along with Land Use Land Cover, as auxiliary input, to generate VIIRS-like imagery from 1992-2012. The modelled imagery was aggregated annually and showed an <em>R</em><sup>2</sup> of 0.94 with the results of other annual-scale VIIRS-like imagery products of India, <em>R</em><sup>2</sup> of 0.85 w.r.t GDP and <em>R</em><sup>2</sup> of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an <em>R</em><sup>2</sup> of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an <em>R</em><sup>2 </sup>of 0.56 and 0.53 respectively. Qualitative analysis for also performed on both national and state scales. Visual analysis over 1992-2013 confirms a gradual increase in the brightness of the lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, a synthetically generated monthly VIIRS-like product is delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.
DOI: 10.25446/oxford.22240486
title: COVID19 Pandemic and Learning Loss

description: Background: The pandemic has disrupted schooling across the world, with potentially devastating long-term consequences for children’s learning and life outcomes. School close ures have restricted learning activities to each individual homes, and limited digital infrastructure and parent’s inexperience in supporting children’s learning is almost certain to result in learning loss. However, the effect is unlikely to have affected everyone equally. Children from disadvantaged background are likely to have suffered more from the closures. Given the financial strain that COVID19 has afflicted to the government and the public, it is important to identify policies that could effectively mitigate the long-term consequences of the impact, particularly for those who are most affected, who are also most likely to not receive sufficient support after school have reopened. To achieve this, we need to further our understanding beyond the scale of the impact of COVID19 on learning. Identifying the factors that have influenced differences in learning outcomes during the pandemic will strengthen policymaking using research-based evidence. <br> Objective: This research has a twofold aim. The first is to measure the scale of learning loss that is caused by the disruption during the COVID19 Pandemic. Second is to identify the factors that have influenced the difference in the scale of learning loss between different groups, including ones who were exposed to different policies. <br> Methodology: This research is conducted using primary data, both that have been collected before this research began from past RISE studies, and one that is collected during. There are two main information types that is going to be collected during the study: 1. Student learning assessment results to measure learning outcomes between students, collected periodically and 2. Principal and parent’s survey to obtain information on the different behaviours and policies each child is exposed to, collected once during school closures, and once after school have reopened. The two data will be analysed together to potentially identify the effect of each factors and policies on learning outcomes during the pandemic.
DOI: 10.25446/oxford.22240486.v1
title: COVID19 Pandemic and Learning Loss

description: Background: The pandemic has disrupted schooling across the world, with potentially devastating long-term consequences for children’s learning and life outcomes. School close ures have restricted learning activities to each individual homes, and limited digital infrastructure and parent’s inexperience in supporting children’s learning is almost certain to result in learning loss. However, the effect is unlikely to have affected everyone equally. Children from disadvantaged background are likely to have suffered more from the closures. Given the financial strain that COVID19 has afflicted to the government and the public, it is important to identify policies that could effectively mitigate the long-term consequences of the impact, particularly for those who are most affected, who are also most likely to not receive sufficient support after school have reopened. To achieve this, we need to further our understanding beyond the scale of the impact of COVID19 on learning. Identifying the factors that have influenced differences in learning outcomes during the pandemic will strengthen policymaking using research-based evidence. <br> Objective: This research has a twofold aim. The first is to measure the scale of learning loss that is caused by the disruption during the COVID19 Pandemic. Second is to identify the factors that have influenced the difference in the scale of learning loss between different groups, including ones who were exposed to different policies. <br> Methodology: This research is conducted using primary data, both that have been collected before this research began from past RISE studies, and one that is collected during. There are two main information types that is going to be collected during the study: 1. Student learning assessment results to measure learning outcomes between students, collected periodically and 2. Principal and parent’s survey to obtain information on the different behaviours and policies each child is exposed to, collected once during school closures, and once after school have reopened. The two data will be analysed together to potentially identify the effect of each factors and policies on learning outcomes during the pandemic.
DOI: 10.6084/m9.figshare.22761986
title: Genotype/Haplotype datasets M. Merluccius

description: Three different files containing: 1. Genotypes of 5 microsatellites of M. merluccius (Genepop format) 2. Genotypes of 5 ESTs of M. merluccius (Genepop format) 3. Cytochrome b haplotypes of M. merluccius (Arlequin format)
DOI: 10.5281/zenodo.7962304
title: SciExpeM - 10.5281/zenodo.7962304

description: Sun et al., Combustion and Flame 244 (2022) 112247
DOI: 10.5281/zenodo.7962303
title: SciExpeM - 10.5281/zenodo.7962304

description: Sun et al., Combustion and Flame 244 (2022) 112247
DOI: 10.25446/oxford.22240465
title: COVID19 Pandemic and Learning Loss

description: Background: The pandemic has disrupted schooling across the world, with potentially devastating long-term consequences for children’s learning and life outcomes. School close ures have restricted learning activities to each individual homes, and limited digital infrastructure and parent’s inexperience in supporting children’s learning is almost certain to result in learning loss. However, the effect is unlikely to have affected everyone equally. Children from disadvantaged background are likely to have suffered more from the closures. Given the financial strain that COVID19 has afflicted to the government and the public, it is important to identify policies that could effectively mitigate the long-term consequences of the impact, particularly for those who are most affected, who are also most likely to not receive sufficient support after school have reopened. To achieve this, we need to further our understanding beyond the scale of the impact of COVID19 on learning. Identifying the factors that have influenced differences in learning outcomes during the pandemic will strengthen policymaking using research-based evidence. <br> Objective: This research has a twofold aim. The first is to measure the scale of learning loss that is caused by the disruption during the COVID19 Pandemic. Second is to identify the factors that have influenced the difference in the scale of learning loss between different groups, including ones who were exposed to different policies. <br> Methodology: This research is conducted using primary data, both that have been collected before this research began from past RISE studies, and one that is collected during. There are two main information types that is going to be collected during the study: 1. Student learning assessment results to measure learning outcomes between students, collected periodically and 2. Principal and parent’s survey to obtain information on the different behaviours and policies each child is exposed to, collected once during school closures, and once after school have reopened. The two data will be analysed together to potentially identify the effect of each factors and policies on learning outcomes during the pandemic.
DOI: 10.25446/oxford.22240465.v1
title: COVID19 Pandemic and Learning Loss

description: Background: The pandemic has disrupted schooling across the world, with potentially devastating long-term consequences for children’s learning and life outcomes. School close ures have restricted learning activities to each individual homes, and limited digital infrastructure and parent’s inexperience in supporting children’s learning is almost certain to result in learning loss. However, the effect is unlikely to have affected everyone equally. Children from disadvantaged background are likely to have suffered more from the closures. Given the financial strain that COVID19 has afflicted to the government and the public, it is important to identify policies that could effectively mitigate the long-term consequences of the impact, particularly for those who are most affected, who are also most likely to not receive sufficient support after school have reopened. To achieve this, we need to further our understanding beyond the scale of the impact of COVID19 on learning. Identifying the factors that have influenced differences in learning outcomes during the pandemic will strengthen policymaking using research-based evidence. <br> Objective: This research has a twofold aim. The first is to measure the scale of learning loss that is caused by the disruption during the COVID19 Pandemic. Second is to identify the factors that have influenced the difference in the scale of learning loss between different groups, including ones who were exposed to different policies. <br> Methodology: This research is conducted using primary data, both that have been collected before this research began from past RISE studies, and one that is collected during. There are two main information types that is going to be collected during the study: 1. Student learning assessment results to measure learning outcomes between students, collected periodically and 2. Principal and parent’s survey to obtain information on the different behaviours and policies each child is exposed to, collected once during school closures, and once after school have reopened. The two data will be analysed together to potentially identify the effect of each factors and policies on learning outcomes during the pandemic.
DOI: 10.14272/zetlvsoyhdfnpz-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2-(4-methoxyphenyl)isoindole-1,3-dione)
title: InChI=1S/C15H11NO3/c1-19-11-8-6-10(7-9-11)16-14(17)12-4-2-3-5-13(12)15(16)18/h2-9H,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3 [7.26 ppm], ppm): δ = 7.97–7.92 (m, 2H), 7.83–7.73 (m, 2H), 7.36–7.31 (m, 2H), 7.07–6.98 (m, 2H), 3.85 (s, 3H)).
DOI: 10.25446/oxford.22240450
title: COVID19 Pandemic and Learning Loss

description: Background: The pandemic has disrupted schooling across the world, with potentially devastating long-term consequences for children’s learning and life outcomes. School close ures have restricted learning activities to each individual homes, and limited digital infrastructure and parent’s inexperience in supporting children’s learning is almost certain to result in learning loss. However, the effect is unlikely to have affected everyone equally. Children from disadvantaged background are likely to have suffered more from the closures. Given the financial strain that COVID19 has afflicted to the government and the public, it is important to identify policies that could effectively mitigate the long-term consequences of the impact, particularly for those who are most affected, who are also most likely to not receive sufficient support after school have reopened. To achieve this, we need to further our understanding beyond the scale of the impact of COVID19 on learning. Identifying the factors that have influenced differences in learning outcomes during the pandemic will strengthen policymaking using research-based evidence. <br> Objective: This research has a twofold aim. The first is to measure the scale of learning loss that is caused by the disruption during the COVID19 Pandemic. Second is to identify the factors that have influenced the difference in the scale of learning loss between different groups, including ones who were exposed to different policies. <br> Methodology: This research is conducted using primary data, both that have been collected before this research began from past RISE studies, and one that is collected during. There are two main information types that is going to be collected during the study: 1. Student learning assessment results to measure learning outcomes between students, collected periodically and 2. Principal and parent’s survey to obtain information on the different behaviours and policies each child is exposed to, collected once during school closures, and once after school have reopened. The two data will be analysed together to potentially identify the effect of each factors and policies on learning outcomes during the pandemic.
DOI: 10.25446/oxford.22240450.v1
title: COVID19 Pandemic and Learning Loss

description: Background: The pandemic has disrupted schooling across the world, with potentially devastating long-term consequences for children’s learning and life outcomes. School close ures have restricted learning activities to each individual homes, and limited digital infrastructure and parent’s inexperience in supporting children’s learning is almost certain to result in learning loss. However, the effect is unlikely to have affected everyone equally. Children from disadvantaged background are likely to have suffered more from the closures. Given the financial strain that COVID19 has afflicted to the government and the public, it is important to identify policies that could effectively mitigate the long-term consequences of the impact, particularly for those who are most affected, who are also most likely to not receive sufficient support after school have reopened. To achieve this, we need to further our understanding beyond the scale of the impact of COVID19 on learning. Identifying the factors that have influenced differences in learning outcomes during the pandemic will strengthen policymaking using research-based evidence. <br> Objective: This research has a twofold aim. The first is to measure the scale of learning loss that is caused by the disruption during the COVID19 Pandemic. Second is to identify the factors that have influenced the difference in the scale of learning loss between different groups, including ones who were exposed to different policies. <br> Methodology: This research is conducted using primary data, both that have been collected before this research began from past RISE studies, and one that is collected during. There are two main information types that is going to be collected during the study: 1. Student learning assessment results to measure learning outcomes between students, collected periodically and 2. Principal and parent’s survey to obtain information on the different behaviours and policies each child is exposed to, collected once during school closures, and once after school have reopened. The two data will be analysed together to potentially identify the effect of each factors and policies on learning outcomes during the pandemic.