cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12130980.v114
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.v113
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.16736638.v1
title: Therapeutic potential and ethnopharmacology of dominant mangroves of Bhitarkanika National Park, Odisha, India.

description: <b>Purpose </b>Bhitarkanika National Park is the second largest contiguous mangrove with one of the most diverse mangrove habitations forest of India. Approximately 0.15 million mangrove depending population are found residing in and around the National Park. However, the ethnopharmacological practices are comparatively lower than the other mangrove regions of India and Southeast Asia. The present review is aimed to congregate information on the therapeutic potential and ethnopharmacology of nine dominant mangrove species of the National Park. <b>Materials and methods </b>Information are curated from different scientific electronic databases and also from various published books and literatures from libraries. <b>Results </b>An array of phytocompounds of varied chemical classes has already been characterized from these mangroves throughout the globe targeting a multitude of diseases. But, only a few local mangrove species were evaluated for their biological activities related to their primary or secondary metabolites and any novel phytocompound is yet to be reported from the National park. The therapeutic potential of different plant part extracts and the isolated phytocompounds are summerized alongwith the ethnopharmacological information. <b>Conclusion and future perspectives </b>The present review will generate awareness and promote uses of folklore medicine as a tool to strengthen community health among the mangrove dwellers around Bhitarkanika National Park, which is one of the most under-studied mangrove region of South-east Asia. There are also astounding scopes for following future researches on the mangroves of Bhitarkanika as well as of India, like 1. chemotyping and genotyping of phytocompounds in mangrove plants, 2. characterization of phytocompounds from mangrove endophytes, 3. understanding the epigenetic variation in magroves as an impact of agroclimatic variations, environmental fluctuations with reference to salinity, submergence and ambient oxygen stresses, and 4. possibility of mangrove phytocompounds as epi drugs.
DOI: 10.6084/m9.figshare.15072024.v2
title: Example of Neutrophil Isolation Purity

description: This is an example of a purity check done on isolated neutrophils from 5-month-old female C57Bl/6J mice. Neutrophil markers Ly6G and Cd11b were used. We compared the Stemcell EasySep Neutrophil Enrichment Kit (E1 and E2) to the Miltenyi Biotec Neutrophil Isolation Kit (M1 and M2). Neutrophil samples were analyzed using the MacsQuant10 and gating was performed using FlowLogic version 8.
DOI: 10.6084/m9.figshare.16746583.v2
title: A Multi-view Bidirectional Spatiotemporal Graph Network for Urban Traffic Flow Imputation

description: Data and codes
DOI: 10.6084/m9.figshare.16746583.v1
title: A Multi-view Bidirectional Spatiotemporal Graph Network for Urban Traffic Flow Imputation

description: Data and codes
DOI: 10.6084/m9.figshare.12459056.v17
title: agnostosDB_dbf02445-20200519

description: The agnostosDB (dbf02445-20200519) is a comprehensive dataset of microbial gene clusters (GCs) from genomes and metagenomes. It contains 5,287,759 GCs and more than 280M genes coming from the bacterial and archaeal Genome Taxonomy Database (GTDB) genomes, and from five large-scale metagenomic projects: 583 marine metagenomes from Tara Oceans expedition (TARA), Malaspina expedition, Ocean Sampling Day (OSD), Global Ocean Sampling Expedition (GOS), complemented with 1,246 metagenomes from the Human Microbiome Project (HMP) phase I and II. The dataset is described in Vanni et al. 2020.<br>Additional and more detailed information about the dataset creation and some of its applications, can be found at https://dark.metagenomics.eu/.Related to the agnostosDB is the agnostos-wf, a snakemake workflow stored in the GitHub repository https://github.com/functional-dark-side/agnostos-wf. The agnostos-wf allows to search the agnostosDB gene cluster HMM profiles and/or to integrate new sequence data (genes/contigs) in it.
DOI: 10.6084/m9.figshare.12459056.v16
title: agnostosDB_dbf02445-20200519

description: The agnostosDB (dbf02445-20200519) is a comprehensive dataset of microbial gene clusters (GCs) from genomes and metagenomes. It contains 5,287,759 GCs and more than 280M genes coming from the bacterial and archaeal Genome Taxonomy Database (GTDB) genomes, and from five large-scale metagenomic projects: 583 marine metagenomes from Tara Oceans expedition (TARA), Malaspina expedition, Ocean Sampling Day (OSD), Global Ocean Sampling Expedition (GOS), complemented with 1,246 metagenomes from the Human Microbiome Project (HMP) phase I and II. The dataset is described in Vanni et al. 2020.<br>Additional and more detailed information about the dataset creation and some of its applications, can be found at https://dark.metagenomics.eu/.Related to the agnostosDB is the agnostos-wf, a snakemake workflow stored in the GitHub repository https://github.com/functional-dark-side/agnostos-wf. The agnostos-wf allows to search the agnostosDB gene cluster HMM profiles and/or to integrate new sequence data (genes/contigs) in it.
DOI: 10.6084/m9.figshare.14897697.v3
title: Histology Data

description: Histology Data<br><br>
DOI: 10.6084/m9.figshare.16752067.v2
title: Modern floral occurrence data

description: Filtered floral occurrence data derived from RGBIF using the occ_search() function in R. The flora included are suggested to be the nearest living relatives of palynotaxa described from the pre-PETM and PETM.
DOI: 10.6084/m9.figshare.16752067.v1
title: Vera's Abies data.csv

description: Filtered floral occurrence data derived from RGBIF using the occ_search() function in R. The flora included are suggested to be the nearest living relatives of palynotaxa described from the pre-PETM and PETM.
DOI: 10.6084/m9.figshare.16602314.v3
title: Source Data

description: Source data underlying all figures
DOI: 10.6084/m9.figshare.3761562.v2005
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: 10.6084/m9.figshare.3761562.v2004
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: 10.6084/m9.figshare.15138570.v5
title: 14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons

description: Data sets for manuscript, "14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons"
DOI: 10.6084/m9.figshare.15138570.v4
title: 14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons

description: Data sets for manuscript, "14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons"
DOI: 10.6084/m9.figshare.16750508.v1
title: Multisensory responses in the mouse superior colliculus: spikes, processed results, and code

description: Support data files and code for "Nonlinear visuoauditory integration in the mouse superior colliculus" accepted by PLOS Computational Biology.
DOI: 10.6084/m9.figshare.14938056.v1
title: More is not always better - the double-headed role of fibronectin in Staphylococcus aureus host cell invasion

description: This data file contains additional immunofluorescence microscopy images, images from Western blots and raw data used for the manuscript "More is not always better - the double-headed role of fibronectin in Staphylococcus aureus host cell invasion"<br>
DOI: 10.6084/m9.figshare.12459056.v20
title: agnostosDB_dbf02445-20200519

description: The agnostosDB (dbf02445-20200519) is a comprehensive dataset of microbial gene clusters (GCs) from genomes and metagenomes. It contains 5,287,759 GCs and more than 280M genes coming from the bacterial and archaeal Genome Taxonomy Database (GTDB) genomes, and from five large-scale metagenomic projects: 583 marine metagenomes from Tara Oceans expedition (TARA), Malaspina expedition, Ocean Sampling Day (OSD), Global Ocean Sampling Expedition (GOS), complemented with 1,246 metagenomes from the Human Microbiome Project (HMP) phase I and II. The dataset is described in Vanni et al. 2020.<br>Additional and more detailed information about the dataset creation and some of its applications, can be found at https://dark.metagenomics.eu/.Related to the agnostosDB is the agnostos-wf, a snakemake workflow stored in the GitHub repository https://github.com/functional-dark-side/agnostos-wf. The agnostos-wf allows to search the agnostosDB gene cluster HMM profiles and/or to integrate new sequence data (genes/contigs) in it.
DOI: 10.6084/m9.figshare.12459056.v19
title: agnostosDB_dbf02445-20200519

description: The agnostosDB (dbf02445-20200519) is a comprehensive dataset of microbial gene clusters (GCs) from genomes and metagenomes. It contains 5,287,759 GCs and more than 280M genes coming from the bacterial and archaeal Genome Taxonomy Database (GTDB) genomes, and from five large-scale metagenomic projects: 583 marine metagenomes from Tara Oceans expedition (TARA), Malaspina expedition, Ocean Sampling Day (OSD), Global Ocean Sampling Expedition (GOS), complemented with 1,246 metagenomes from the Human Microbiome Project (HMP) phase I and II. The dataset is described in Vanni et al. 2020.<br>Additional and more detailed information about the dataset creation and some of its applications, can be found at https://dark.metagenomics.eu/.Related to the agnostosDB is the agnostos-wf, a snakemake workflow stored in the GitHub repository https://github.com/functional-dark-side/agnostos-wf. The agnostos-wf allows to search the agnostosDB gene cluster HMM profiles and/or to integrate new sequence data (genes/contigs) in it.
DOI: 10.6084/m9.figshare.16712935.v1
title: Structures, IR specra and wavefunctions of acetylene, ethane and their dimers, optimized at the MP2/cc-pVDZ level.

description: XYZ files of optimized structures, JCAMP-DX containing IR spectra of optimized structures and MOLDEN format of wavefunctions for the optimized structures have been deposited at figshare.org under DOI:10.6084/m9.figshare.16712935 and are publicly available as of the date of publication.<br>The files in XYZ format contain optimum structures, energies and zero point energies at the MP2/cc-pVDZ level. Files in MOLDEN format can be analyzed by Multiwfn. Text files contain the values of properties at the critical points found by topology analysis of dimers 7 and 8. In both cases the critical point of interest is #16.
DOI: 10.6084/m9.figshare.16574834.v4
title: A Collection of Building Footprint Data in US Counties_Part 6

description: This figshare repository is a collection of building footprint in US Counties. See the references below for more details.<br>References:Heris, M.P., Foks, N.L., Bagstad, K.J. et al. A rasterized building footprint dataset for the United States. Sci Data 7, 207 (2020). https://doi.org/10.1038/s41597-020-0542-3
DOI: 10.6084/m9.figshare.16574834.v3
title: A Collection of Building Footprint Data in US Counties_Part 6

description: This figshare repository is a collection of building footprint in US Counties. See the references below for more details.<br>References:Heris, M.P., Foks, N.L., Bagstad, K.J. et al. A rasterized building footprint dataset for the United States. Sci Data 7, 207 (2020). https://doi.org/10.1038/s41597-020-0542-3
DOI: 10.6084/m9.figshare.16727506.v1
title: Supplementary Materials: High-frequency turbulent fluctuations promote elastin fatigue degradation and aortic lumen dilatation in swine

description: Supplementary Materials: High-frequency turbulent fluctuations promote elastin fatigue degradation and aortic lumen dilatation in swine
DOI: 10.6084/m9.figshare.16551375.v1
title: Phasmatodea GENE 2021

description: The phylogenic position of Aschiphasmatidae in Euphasmatodea based on mitochondrial genomic evidence <br>
DOI: 10.6084/m9.figshare.14724684.v1
title: Genome-wide reconstruction of rediploidization following autopolyploidization across one hundred million years of salmonid evolution

description: AORe_concatenated_alignments - all AORe alignments used for MCMCtree<br>Supplementary_data_description.txt - description of supplementary datasets 1-16<br>Supplementary Data 1-16: Excel datasheets<br><br>dnds_alignments_trees.zip - all alignments and trees used in dn/ds analysis<br><br>aore_alignments_trees.zip - Supplementary data 7 : alignments and trees from AORe region<br><br>SSR_alignments_trees.zip - Supplementary data 7 : alignments and trees from SSR region<br><br>SSR_concatenated_alignments.zip - all SSR alignments used for MCMCtree<br><br>
DOI: 10.6084/m9.figshare.14376749.v1
title: Contemporary Global Syphilis Lineages 2021

description: Source files and Rnotebook for analysis of global Treponema pallidum subspecies pallidum genomic diversity, distribution and lineage dynamics "Contemporary syphilis is characterised by rapid global spread of pandemic <i>Treponema pallidum</i> lineages" by M.A Beale, 2021 @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536859905 -1073697537 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin-top:6.0pt; margin-right:0cm; margin-bottom:6.0pt; margin-left:0cm; line-height:150%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}div.WordSection1 {page:WordSection1;}
DOI: 10.6084/m9.figshare.3761562.v2007
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: 10.6084/m9.figshare.3761562.v2006
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: 10.6084/m9.figshare.14390768.v7
title: sleep-diary-sleepmeter

description: Annotated daily sleep diary over years of a subject (author and scientific researcher) with the non-24 circadian rhythm disorder.<br>The sleep diary was manually curated using the Sleepmeter app on Android (pro version). It is duly annotated with all the controlled factors such as use of light therapy, light exposure (morning? evening?), disturbances such as noise or appointments, diet composition and timing, melatonin use, etc.<br>This should be considered the baseline biomarker of the subject's sleep-wake patterns, it is the ground truth for actigraphy and other biomarkers. The sleep diary was duly curated to minimize errors and was updated continuously everyday, once at bedtime by tapping a widget to mark the fall asleep time (with a delay from bedtime, the exact fall asleep time may not be 100% accurate but it was corrected the morning after to try to match as much as possible the subjectively estimated sleep onset delay), and on wake up by tapping again the widget as soon as the subject woke up, and then noting all the relevant comments and tags.<br>Most tags and comments pertain to the day before, although some tags and comments pertain to what happens in the day after (in the latter case, this is usually denoted in the tag name).<br>Use the latest sleep diary, it contains the whole history. Other sleep diaries are past versions, which may have some differences such as renamed tags or missing comments that were added aposteriori (sometimes months later when the culprit of a major sleep disturbance was discovered, such as secondary pernio due to long covid).<br>The sleep diary currently covers almost 2 years. Keep in mind that during the period of 2020-2022, the data was acquired while in lockdown or pseudo-lockdown most of the time due to the SaRS-nCOV-2 (COVID-19) pandemic. More data will be added in the future.<br>The files contain the whole history up to the timestamp in the filename. Hence, to study the longest period, use only the latest file, do not concatenate with previous ones.