cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12132762.v2
title: Figure 4 Data Table

description: Data used to create figure 4 comparing FAA based domain detection to genome based detection.
DOI: 10.6084/m9.figshare.12132834.v1
title: Tables S1 & S2 from Offspring reverse transcriptome responses to maternal deprivation when reared with pathogens in an insect with facultative family life

description: Table S1. Comparisons of different transcriptome assemblies. The table on the sheet "Transrate output" shows data obtained using Transrate v.1.03 (Smith-unna et al., 2016). We compared assemblies created using Trinity and CLC Assembly Cell, as well as a merged "hybrid" obtained using CAP3 (Huang and Madan, 1999). The assembly comparison was used to determine the best assembly for continued analyses. Table S2. Full list of all DEGs revealed by the LRTs testing for main and interaction effects, as well as the associated annotations and read counts per sample. Note that no DEGs depended on maternal presence alone. Table headers are default DEseq2 headers, while the Blast annotation was added by the authors. Note that the adjusted p-value was used for determining significance, and the Log2-Fold-Change values in the interaction sheet only represent a single pairwise comparison and are thus of limited usefulness (due to the nature of the LRT test).
DOI: 10.6084/m9.figshare.12132762.v1
title: Figure 4 Data Table

description: Data used to create figure 4 comparing FAA based domain detection to genome based detection.
DOI: 10.6084/m9.figshare.12132642
title: CHEST APP Data

description: Data obtained from the CHEST App during home visits. Fidelity Check.
DOI: 10.6084/m9.figshare.12132642.v1
title: CHEST APP Data

description: Data obtained from the CHEST App during home visits. Fidelity Check.
DOI: 10.23645/epacomptox.12132621
title: Supplemental information for Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling

description: Supplementary information for the manuscript “<i>Bioactivity screening of environmental chemicals using imaging-based high-throughput </i><i>phenotypic profiling</i>” <br>(https://doi.org/10.1016/j.taap.2019.114876) by Nyffeler et al. 2020. <br>The supplementary information contains supplementary data objects with all the experimental data used in the manuscript, as well as code used to analyze the data.
DOI: 10.23645/epacomptox.12132621.v2
title: Supplemental information for Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling

description: Supplementary information for the manuscript “<i>Bioactivity screening of environmental chemicals using imaging-based high-throughput </i><i>phenotypic profiling</i>” <br>(https://doi.org/10.1016/j.taap.2019.114876) by Nyffeler et al. 2020. <br>The supplementary information contains supplementary data objects with all the experimental data used in the manuscript, as well as code used to analyze the data.
DOI: 10.23645/epacomptox.12132621.v1
title: Supplemental info for Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling

description: Supplementary information for the manuscript “<i>Bioactivity screening of environmental chemicals using imaging-based high-throughput </i><i>phenotypic profiling</i>” <br>(https://doi.org/10.1016/j.taap.2019.114876) by Nyffeler et al. 2020. <br>The supplementary information contains supplementary data objects with all the experimental data used in the manuscript, as well as code used to analyze the data.
DOI: 10.6084/m9.figshare.12130980.v1
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504) and separate worksheets calculating the changes between specific dates.<br>These data have also been mapped. Examples for the changes of values for Parks and Residential locations at 0504 from the January baseline and for the changes between 2903 and 0504 are provided as 600 dpi jpegs.<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130878
title: Data evidence for top 10-ranked SNPs at each locus

description: Extracted data from the final evidence dataset for top 10-ranked variants at each locus. Each filename represents a combination of index SNP and SNP under consideration.
DOI: 10.6084/m9.figshare.12130980.v2
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504) and separate worksheets calculating the changes between specific dates.<br>These data have also been mapped. Examples for the changes of values for Parks and Residential locations at 0504 from the January baseline and for the changes between 2903 and 0504 are provided as 600 dpi jpegs.<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130857
title: SNP ranking at each locus

description: Candidate causal SNP ranking for each locus from Paternoster et al. (2015) atopic dermatitis GWAS and represented by index SNP
DOI: 10.6084/m9.figshare.12130863
title: Data evidence for top 10-ranked genes at each locus

description: Extracted data from the final evidence dataset for top 10-ranked genes at each locus. Each filename represents a combination of index SNP and gene under consideration.
DOI: 10.6084/m9.figshare.12130854
title: sim_timesteps.zip

description: simulation data skyrmion nucleation<br>
DOI: 10.6084/m9.figshare.12130863.v1
title: Data evidence for top 10-ranked genes at each locus

description: Extracted data from the final evidence dataset for top 10-ranked genes at each locus.
DOI: 10.6084/m9.figshare.12130854.v1
title: sim_timesteps.zip

description: simulation data skyrmion nucleation<br>
DOI: 10.6084/m9.figshare.12130857.v1
title: SNP ranking at each locus

description: Candidate causal SNP ranking for each locus from Paternoster et al. (2015) atopic dermatitis GWAS and represented by index SNP
DOI: 10.6084/m9.figshare.12130824
title: Gene ranking at each locus

description: Candidate causal gene ranking for each locus from Paternoster et al. (2015) atopic dermatitis GWAS and represented by index SNP
DOI: 10.6084/m9.figshare.12130980.v3
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504) and separate worksheets calculating the changes between specific dates.<br>These data have also been mapped. Examples for the changes of values for Parks and Residential locations at 0504 from the January baseline and for the changes between 2903 and 0504 are provided as 600 dpi jpegs.<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130701
title: Full data table for atopic dermatitis GWAS prioritisation

description: Full dataset with evidence used to rank and prioritise genes and variants at loci from Paternoster et al. (2015) atopic dermatitis GWAS. .Each entry represents one locus signal and is identified by its index SNP.
DOI: 10.6084/m9.figshare.12130824.v1
title: Gene ranking at each locus

description: Candidate gene ranking for each locus from Paternoster et al. (2015) atopic dermatitis GWAS and represented by index SNP
DOI: 10.6084/m9.figshare.12130980.v4
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504) and separate worksheets calculating the changes between specific dates.<br>These data have also been mapped. Examples for the changes of values for Parks and Residential locations at 0504 from the January baseline and for the changes between 2903 and 0504 are provided as 600 dpi jpegs.<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130701.v1
title: Full data table for atopic dermatitis GWAS prioritisation

description: Full dataset with evidence used to rank and prioritise genes and variants at loci from Paternoster et al. (2015) atopic dermatitis GWAS.
DOI: 10.6084/m9.figshare.12130539
title: Data for: Analyzing inequalities in China’s 5A ecological attraction access using social media data

description: The data file users_per_year.csv recordes the information online user that have been to the Chinese 5A ecological attractions between 2011 to 2016, including user identification at the SIna Weibo, total AR for each user, total OD for each user, AR at off season for each user, OD at off-season for each user, AR at high-season for each user, OD at high-season for each user.The data file city.csv contains five parts of statistics information for each city in China, which are city basic information, AR&OD information, demographic characteristic, social-spatial structure, attractions, socioeconomic status. City_cd represents the code for the administrative divisions of China; Region is the name for each city; AR_city represents Average access frequency in city j each year; AR_city_off_season represents average access frequency in city j in off season each year; AR_city_high_season represents average access frequency in city j in high season each year; AR_capita represents average access frequency per capita in city j each year; AR_capita_off_season represents average access frequency per capita in city j in off season each year; AR_capita_high_season represents average access frequency per capita in city j in high season each year; OD_city represents average OD distance in city j each year (km); OD_city_off_season represents average OD distance in city j in off season each year (km); OD_city_high_season represents average OD distance in city j in high season each year (km); OD_capita represents average OD distance per capita in city j each year (km); OD_capita_off_season represents average OD distance per capita in city j in off season each year (km); OD_capita_high_season represents average OD distance per capita in city j in off season each year (km); % local hukou rate represents percentage of people with local city hukou in city j; % under age 14 represents percentage of people younger than age 14 in city j; % between ages 15 and 64 represents percentage of people between 15 and 64 years old in city j; % above age 65 represents percentage of people older than age 65 in city j; Resident population density represents density of resident population in city j; Migrant population density represents density of migrant population in city j; Attraction number represents number of attractions in city j.
DOI: 10.6084/m9.figshare.12130980.v5
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130404
title: mc_001.tar.gz

description: 1/10 CAPTCHA data multicolor .
DOI: 10.6084/m9.figshare.12130980.v6
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130386
title: The Antiproliferation Activity of <i>Ganoderma formosanum</i> Extracts on Prostate Cancer Cells

description: Androgen-independent prostate cancer accounts for mortality in the world. In this study, various extracts of a medical fungus dubbed Ganoderma <i>formosanum</i> were screened for inhibition of DU145 cells, an androgen-independent prostate cancer cell line. Results demonstrated that both hexane (GF-EH) and butanol (GF-EB) fraction of <i>G. formosanum</i> ethanol extract inhibited DU145 cell viability in a dose-dependent manner. GF-EH induced cell-cycle arrest in G1 phase of DU145 cells <i>via</i> downregulation of cyclin E2 protein expression. In addition, GF-EB triggered extrinsic apoptosis of DU145 cells by activating <i>caspase 3</i> gene expression resulting in programed cell death. Above all, both GF-EH and GF-EB show lower toxicity to normal human fibroblast cell line compared to DU145 cell, implying that they possess specific drug action on cancer cells. This study provides a molecular basis of <i>G. formosanum</i> extract as a potential ingredient for treatment of androgen-independent prostate cancer.
DOI: 10.6084/m9.figshare.12130404.v1
title: mc_001.tar.gz

description: 1/10 CAPTCHA data multicolor .
DOI: 10.6084/m9.figshare.12130386.v1
title: The Antiproliferation Activity of <i>Ganoderma formosanum</i> Extracts on Prostate Cancer Cells

description: Androgen-independent prostate cancer accounts for mortality in the world. In this study, various extracts of a medical fungus dubbed Ganoderma <i>formosanum</i> were screened for inhibition of DU145 cells, an androgen-independent prostate cancer cell line. Results demonstrated that both hexane (GF-EH) and butanol (GF-EB) fraction of <i>G. formosanum</i> ethanol extract inhibited DU145 cell viability in a dose-dependent manner. GF-EH induced cell-cycle arrest in G1 phase of DU145 cells <i>via</i> downregulation of cyclin E2 protein expression. In addition, GF-EB triggered extrinsic apoptosis of DU145 cells by activating <i>caspase 3</i> gene expression resulting in programed cell death. Above all, both GF-EH and GF-EB show lower toxicity to normal human fibroblast cell line compared to DU145 cell, implying that they possess specific drug action on cancer cells. This study provides a molecular basis of <i>G. formosanum</i> extract as a potential ingredient for treatment of androgen-independent prostate cancer.
DOI: 10.6084/m9.figshare.12130980.v7
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors. More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19
DOI: 10.6084/m9.figshare.12130257
title: Results of Code Smell Detection

description: This excel sheet contains the data gathered during my master's thesis project. In this project I examined refused bequest and inappropriate intimacy code smells in Java and Python source code. <br>The excel sheet contains the metadata for each of the datasets and how many times each code smell occurred.