cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14457585.v1
title: Fig4a Centrin-SYCP3 and PCNT co-staining on adult spermatocyte spread

description: Raw image dataset for Centrin-SYCP3 and PCNT co-staining on Wdr62 genetrap adult spermatocyte spread.<br>Please see Becherel, O. J.<i> et al.</i> Senataxin plays an essential role with DNA damage response proteins in meiotic recombination and gene silencing. <i>PLoS Genet</i> <b>9</b>, e1003435, doi:10.1371/journal.pgen.1003435 (2013) for protocol on making spermatocyte spreads. <br>Briefly, testes were decapsulated, chopped and resuspended in DMEM (Gibco) before passing through 100μm cell strainer. Cells were pelleted by centrifuging at 500rpm at room temperature for 5mins and then resuspended in 0.1M sucrose. 10μL of cell suspension was spread onto glass slides pre-wetted in 100% methanol and fixed for 2 hrs. The slides were subsequently washed with PBS and air-dried in the presence of Kodak Photo-Flo 200 (Kodak, 1 in 250 dilution). Slides were stored at -80°C. For immunostaining, slides were rehydrated in PBS and blocked in blocking buffer (20% FBS, 0.2% Triton X in PBS) for 1 hr before incubating with primary antibody at 4°C overnight. The next day, slides were incubated with secondary antibody and counterstained with DAPI before mounting in Prolong Diamond (Invitrogen). The primary and secondary antibodies used are listed in Supplementary Table 1. <br><br>
DOI: 10.6084/m9.figshare.14411024.v3
title: 722g_pass

description: The raw VCF is download from PRJNA448733 on NCBI.<br>The VCF of 722 canis marked by PASS at autosomes were used for analysis.
DOI: 10.48370/ofd/1fxcfr/rudauk
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-2664.tif

description: :unav
DOI: 10.6084/m9.figshare.14470536.v2
title: London_Museums.csv

description: csv file containing details of 14198 reviews on 8 Museums in London - date, location, rating, review title and review text
DOI: 10.48370/ofd/1fxcfr
title: Golovinomyces circumfusus (Schlltdl.) U. Braun, 2664

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.14470536.v1
title: London_Museums

description: csv file containing details of 14198 reviews on 8 Museums in London - date, location, rating, review title and review text
DOI: 10.6084/m9.figshare.5350804.v1
title: Creating strain-specific genetic markers for beneficial insects: a genomic approach

description: Abstract: <br>Biocontrol is the introduction of one organism to control the population of another organism, and has had a commercial application for over a century. With the latest advances in next generation sequencing, now is the moment to both improve biocontrol as well as counter potential shortcomings, such as non-target effects. For example, when massive numbers of parasitoid biocontrol agents are released into a field situation, the effects on native parasitoid populations can be devastating. This may be especially true if the released species is naturally found in the area of release. In order to differentiate between a commercial <i>Trichogramma brassicae</i> (Bezdenko) population and the local <i>Trichogramma</i> population, strain-specific genetic markers need to be developed. The <i>T. brassicae</i> strain used in biocontrol was isolated from Moldavia over 30 years ago, but the species itself is found throughout Europe. Therefore, newly developed strain-specific markers can only be used if they differentiate between a wide geographic dispersed set of <i>T. brassicae</i> isolates, as well as other <i>Trichogramma</i> species. In order to optimize this marker selection, the genome of a Wolbachia-infected <i>T. brassicae</i> strain is sequenced and constructed via a <i>de novo</i> hybrid approach, allowing for future genomic comparisons between populations.<br><br>This presentation was given at the 16th Congress of the European Society for Evolutionary Biology, which took place in Groningen, The Netherlands. My presentation occurred on August 22nd in session 34. Applications of evolutionary biology in agriculture and industry. All data and methodology is attributed to myself, Sophie Chattington, or my student, Lorraine Latchoumane, within the presentation. I've included both the powerpoint slides and a PDF with annotations that can be toggled on and off.
DOI: 10.48370/ofd/gn2qbc/vgcnt2
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-2556.tif

description: :unav
DOI: 10.6084/m9.figshare.3761562.v1850
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.48370/ofd/gn2qbc
title: Golovinomyces circumfusus (Schlltdl.) U. Braun, 2556

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.3761562.v1849
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.48370/ofd/woxy6f/se95ug
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-1222.tif

description: :unav
DOI: 10.48370/ofd/woxy6f
title: Golovinomyces circumfusus (Schlltdl.) U. Braun, 1222

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.5568229.v1
title: SNP_variant_calls_using_V14167_as_genome_reference_for_alignments

description: Prior to calling SNPs between parents, the raw transcriptome sequence reads of each parent were aligned against reference sequences. Initial alignment of raw sequences from KSSc38901 and GKP10017 for identification of SNPs. Raw sequences of GKP10017 and KSSc38901 were aligned against the KSSc38901 transcriptome (Chopra et al. 2014) and A. duranensis V14617 genome sequences (Bertioli et al. 2016) using the bwa.0.7.5a (Li and Durbin, 2009) aligner with the backtrack approach. After alignment of the raw reads of KSSc38901 and GKP10017, the GATK version 2.6 variant calling tool (DePristo et al. 2011) was used to identify SNPs and InDels between these genotypes using the default parameters for UnifiedGenotyper and ploidy level of 2. Parameters of “Quality Depth (QD) &lt; 10.0 || Mapping Quality &lt; 40.0 || Allele Frequency &lt; 0.25 || Read Depth (DP) &lt; 40” were used to filter the SNP calls using vcftools version 1.1 (Danecek et al. 2011). Variant statistics were calculated using the vcf-stats script from vcftools version 1.1 (Danecek et al. 2011).
DOI: 10.48370/ofd/cockaf/vaeaqb
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-0436.tif

description: :unav
DOI: 10.48370/ofd/cockaf
title: Golovinomyces circumfusus (Schlltdl.) U. Braun, 436

description: Fungarium Mikromycetes
DOI: 10.25405/data.ncl.19087478
title: Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network

description: Data Supporting the Publication of the same title.<br>
DOI: 10.25405/data.ncl.19087478.v1
title: Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network

description: Data Supporting the Publication of the same title.<br>
DOI: 10.6084/m9.figshare.12130980.v91
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as 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>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <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>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<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, c/o Dept Zoology, University of 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.12130980.v90
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as 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>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <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>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<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, c/o Dept Zoology, University of 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.14499822.v2
title: Next Generation Sequencing Raw Data Bredeck et al., 2021, Nanotoxicology

description: In this repository you find the raw data of the next generation sequencing described in: <br><br> Bredeck et al., 2021. "Effects of dietary exposure to the engineered nanomaterials CeO<sub>2</sub>, SiO<sub>2</sub>, Ag, and TiO<sub>2</sub> on the murine gut microbiome." Nanotoxicology. PMID: 34380002. doi:10.1080/17435390.2021.1940339.<br>
DOI: 10.6084/m9.figshare.14499822.v1
title: Next Generation Sequencing Raw Data Bredeck et al., 2021, Nanotoxicology

description: In this repository you find the raw data of the next generation sequencing described in: <br><br> Bredeck et al., 2021. Effects of the foodborne engineered Nanomaterials CeO2, SiO2, Ag, and TiO2 on the murine Gut Microbiome. Submitted to Nanotoxicology.
DOI: 10.6084/m9.figshare.12966707.v2
title: Figures

description: figures for article
DOI: 10.6084/m9.figshare.12966707.v1
title: Figures

description: figures for article
DOI: 10.48370/ofd/cq2gak/rpkelk
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-4365 (5).tif

description: :unav
DOI: 10.48370/ofd/cq2gak/eozgur
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-4365.tif

description: :unav
DOI: 10.6084/m9.figshare.3761562.v1855
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.48370/ofd/cq2gak/7yr7to
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-4365 (4).tif

description: :unav
DOI: 10.48370/ofd/cq2gak/lm0l88
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-4365 (2).tif

description: :unav
DOI: 10.6084/m9.figshare.3761562.v1854
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.48370/ofd/cq2gak/m0jqwl
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-4365 (3).tif

description: :unav
DOI: 10.48370/ofd/cq2gak
title: Golovinomyces circumfusus (Schlltdl.) U. Braun, 4365

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.14504937.v1
title: fig1B1500bp.dat

description: The data is related to the manuscript 'Two-Phase Dynamics of DNA Supercoiling based on DNA Polymer Physics'.
DOI: 10.48370/ofd/6ydbwv/yxjxqz
title: Golovinomyces circumfusus (Schltdl.) U. Braun F-0439.tif

description: :unav
DOI: 10.48370/ofd/6ydbwv
title: Golovinomyces circumfusus (Schlltdl.) U. Braun, 439

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.14507823.v1
title: Microbial Composition and Co-Occurrence Patterns in the Gut Microbial Community of Normal and Obese Mice in Response to Astaxanthin

description: The microbial genomic DNA from the colon contents of NO, NA, MO and MA groups were extracted with a QIAamp DNA stool Mini kit (Qiagen, cat. No 51604). The 16S rRNA sequencing and analysis was used for obtaining the gut microbiota profiling. The V3-V4 regions of the 16S rRNA gene were selected for amplification. The total DNA were amplified with specific primers (forward primer, 341/357F: NNNNCCTACGGGNGGCWGCAG; reverse primer, 805/785R: GACTACHVGGGTATCTAATCC) tagged with barcodes. The PCR reactions were carried out with Phusion high-fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, USA). The concentration of the library pool was quantified by a BioAnalyzer DNA chip kit (Agilent) and the library was sequenced by an Illumina sequencer.
DOI: 10.6084/m9.figshare.14516955.v1
title: Five edge images of an octagonal aperture using for sample drift detection

description: When the sample plane drifts from the focal plane, we realize the feedback-lock of the sample plane by analyzing the varied blurriness of aperture incoherent images.These are five edge images of an octagonal aperture, corresponding to five sample drift position spaced 0.3 microns apart. Drift distances are the name of the figure film.
DOI: 10.6084/m9.figshare.12747104.v1
title: recalculo 150819 VCL to PAR

description: FRET PAR VCL
DOI: 10.6084/m9.figshare.12747083.v1
title: recalculo 150819 PAR to VCL

description: FRET PAR VCL
DOI: 10.6084/m9.figshare.12747065.v1
title: aux FRET_070815_serie B procesados 3

description: FRET PAR VCL
DOI: 10.6084/m9.figshare.13299593.v1
title: Lung metabolomics in AKI supplementary data and figures

description: Supplementary data and figures for manuscript resubmission titled 'Lung metabolomics after ischemic acute kidney injury reveals increased oxidative stress, altered energy production, and ATP depletion'
DOI: 10.6084/m9.figshare.14552337.v1
title: Environmental Data NC

description: See the codebook.
DOI: 10.6084/m9.figshare.13169189.v1
title: Supporting data for "chiA and gbpA genes are not uniformly distributed amongst diverse Vibrio cholerae"

description: Members of the bacterial genus <i>Vibrio </i>utilize chitin both as a metabolic substrate and a signal to activate natural competence. <i>Vibrio cholerae </i>is a bacterial enteric pathogen, sub-lineages of which can cause pandemic cholera. However, the chitin metabolic pathway in <i>V. cholerae</i> has been dissected using only a limited number of laboratory strains of this species. Here, we survey the complement of key chitin metabolism genes amongst 195 diverse <i>V. cholerae</i>. We show that the gene encoding GbpA, known to be an important colonization and virulence factor in pandemic isolates, is not ubiquitous amongst <i>V. cholerae</i>. We also identify a putatively novel chitinase, and present experimental evidence in support of its functionality. Our data indicate that the chitin metabolic pathway within <i>V. cholerae</i> is more complex than previously thought, and emphasize the importance of considering genes and functions in the context of a species in its entirety, rather than simply relying on traditional reference strains.
DOI: 10.6084/m9.figshare.12130980.v93
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as 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>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <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>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<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, c/o Dept Zoology, University of 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.12130980.v92
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as 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>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <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>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<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, c/o Dept Zoology, University of 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.14480316.v2
title: Whole-exome sequencing in a cohort of high myopia patients in northwest China

description: A total of 201 potential variants were identified in 27 families: 139 ones were well cosegregated with the disease in 15 families. Sixty-two variants were not cosegregated
DOI: 10.6084/m9.figshare.14480316.v1
title: Whole-exome sequencing in a cohort of high myopia patients in northwest China

description: A total of 201 potential variants were identified in 27 families: 139 ones were well cosegregated with the disease in 15 families. Sixty-two variants were not cosegregated