cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13259732.v1
title: Rhône valley dataset

description: Raster DTM and intermediate results obtained by the glacial valley identification algorithm. The raster was obtained from USGS.<br>
DOI: 10.6084/m9.figshare.13259984.v1
title: Extended data for manuscript "Prevalence of plasmid-mediated AmpC beta-lactamases in Enterobacteria isolated from urban and rural folks in Uganda" by Najjuka FC et. al.

description: Underlying data to enterobacteria isolated from community individuals in urban and rural districts in Uganda. The bacteria were molecularly characterized for carriage of the genes encoding ampC beta-lactamases.
DOI: 10.6084/m9.figshare.3761562.v1685
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.v1684
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.13280882.v2
title: Data set used in "Robust approach for inverse problems based on generalised statistics", da Silva, et al. 2020

description: Data set used in the article "Robust approach for inverse problems based on generalised statistics" submitted to .....<br>
DOI: 10.6084/m9.figshare.13280882.v1
title: Data set used in "Robust approach for inverse problems based on generalised statistics", da Silva, et al. 2020

description: Data set used in the article "Robust approach for inverse problems based on generalised statistics" submitted to .....<br>
DOI: 10.6084/m9.figshare.12601901.v2
title: Mouse Oral Microbial Taxonomy

description: Reference datasets for 16S rRNA gene amplicon sequencing analysis using the mothur and DADA2 pipelines for the murine oral bacterial community
DOI: 10.6084/m9.figshare.12601901.v1
title: Mouse Oral Microbial Taxonomy

description: Reference datasets for 16S rRNA gene amplicon sequencing analysis using the mothur and DADA2 pipelines for the murine oral bacterial community
DOI: 10.6084/m9.figshare.3761562.v1692
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.v1691
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.11626008.v1
title: GitterCom.zip

description: MSR 2020 data paper extension replication package
DOI: 10.6084/m9.figshare.13297655.v1
title: 2bRAD-M species database (default)

description: The default 2bRAD-M unique tag database
DOI: 10.6084/m9.figshare.13298999.v1
title: Small intestinal length

description: Small intestinal length divided on treatment groups.
DOI: 10.6084/m9.figshare.13065986.v5
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v10
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v9
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v13
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v12
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.5286/isis.e.rb2210021
title: Investigation of the Structural Evolution of Small Molecule-Doped Ethomeen 18/12 Self Assembled Structures

description: The precise mechanism by which organic friction modifiers adsorb to a metal surface and reduce friction and wear remains unclear. Conflicting results exist between the currently accepted theory for lubrication and molecular dynamics simulations. In order to utilise these compounds optimally, this mechanism must be precisely understood, informing the next generation of friction modifiers that drastically improve the lifetimes of engines and significantly reduce fuel emissions. This work will exploit the use of SANS and deuteration to understand and characterise the self-assembly and structural evolution of Ethomeen 18/12 with water, acetic acid, and toluene-doping.
DOI: 10.6084/m9.figshare.3761562.v1699
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.v1698
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: 10.5286/isis.e.rb2210021-1
title: Investigation of the Structural Evolution of Small Molecule-Doped Ethomeen 18/12 Self Assembled Structures

description: The precise mechanism by which organic friction modifiers adsorb to a metal surface and reduce friction and wear remains unclear. Conflicting results exist between the currently accepted theory for lubrication and molecular dynamics simulations. In order to utilise these compounds optimally, this mechanism must be precisely understood, informing the next generation of friction modifiers that drastically improve the lifetimes of engines and significantly reduce fuel emissions. This work will exploit the use of SANS and deuteration to understand and characterise the self-assembly and structural evolution of Ethomeen 18/12 with water, acetic acid, and toluene-doping.
DOI: 10.6084/m9.figshare.12987530.v1
title: A new type of dual scale neighborhood based on vectorization for cellular automata models

description: Although the neighborhood of the cellular automata (CA) model has been studied in detail, there is a contradiction in the selection of the neighborhood size, which has not been revealed and addressed. Small neighborhoods can constrain the shape complexity of simulated landscape, but they cannot sufficiently characterize the local interactions, while large neighborhoods do the opposite. This study proposes a new type of dual scale neighborhood (DSN) based on vectorization to avoid this contradiction, which incorporates a small neighborhood of pattern constraining (PCN) and a large neighborhood of influence receiving (IRN). Taking Beijing, Wuhan, and Pearl River Delta as the study areas, two kinds of CA models, namely, CA model using the original neighborhood (ORN-CA) and CA model using the DSN (DSN-CA), were constructed based on serial scalar algorithm and vectorized algorithm, respectively. Comparing their simulation results and time required, the results show that the DSN enable users to choose the appropriate neighborhood configuration to obtain the simulation results with high accuracy and landscape similarity to the actual, and the vectorization can greatly improve the computational efficiency of neighborhood effects. Integrating the DSN with the vectorization can significantly improve the simulation performance and efficiency of CA models.<br>
DOI: 10.6084/m9.figshare.13065986.v17
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v16
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v21
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v20
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.11973693.v9
title: Solar wind in situ data for creating catalogs and statistics of interplanetary coronal mass ejections and high speed streams (Solar Orbiter, Parker Solar Probe, STEREO-A/B, MAVEN, Wind, MESSENGER, VEX, Ulysses) 2007-2020

description: These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.<br><br>See AAREADME_insitu.txt for more explanation.<br>If you use these data for peer reviewed scientific publications, please get in touch by christian.moestl@oeaw.ac.at or twitter @chrisoutofspace concerning usage and possible co-authorship.<br>Made with https://github.com/cmoestl/heliocats
DOI: 10.6084/m9.figshare.11973693.v8
title: Solar wind in situ data for creating catalogs and statistics of interplanetary coronal mass ejections (MESSENGER, VEX, Wind, Ulysses, STEREO-A/B, MAVEN, Parker Solar Probe) 2007-2019

description: These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.<br><br>See AAREADME_insitu.txt for more explanation.<br>If you use these data for peer reviewed scientific publications, please get in touch by christian.moestl@oeaw.ac.at or twitter @chrisoutofspace concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin, R. M. Winslow, C. L. Simon Wedlund, D. Stansby).<br>Made with https://github.com/cmoestl/heliocats
DOI: 10.6084/m9.figshare.13065986.v24
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v23
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v26
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13065986.v25
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13336877.v1
title: ForageQuality.Dataset.xls

description: <b>Article title</b>: Floristic composition mediates change in forage nutritive quality of Atlantic mountain grasslands after experimental grazing exclusion <br> <b>Authors</b>: Gonzalo García-Baquero, Iñaki Odriozola & Arantza Aldezabal <br> <b>Location</b>: Aralar massif, northern Iberian Peninsula. <br> <b>Experiment design</b>: four grassland sites (blocks) were selected in the Aralar grassland system; then three "treatments" (three levels of the fixed factor ) were applied in each of these four blocks. Eight forage quality variables as well as floristic composition were measured in each Site x Treatment combination 5-6 times per year during 6 years. This corresponds to a repeated-measures Randomized Block Design, where is the random factor, is the fixed factor and is the within-block (fixed) factor. <br> <b>Measured variables</b>: The first three columns (Site, Treatment, Time) specify the experiment design.The levels of the random factor Site are Alotza, Igaratza, Oidui, and Uzkuiti. <br>The levels of the fixed factor Treatment are A.Grazing, B. Cage, and C. Exclosure. Time is specified as a quantitative (discrete) variable (1, 2, 3…), where Time = 1 corresponds to measurements taken in June 2005
DOI: 10.6084/m9.figshare.6741422.v9
title: COCI CSV dataset of all the citation data

description: This dataset contains all the citation data (in CSV format) included in COCI, released on 7 December 2020. In particular, each line of the CSV file defines a citation, and includes the following information:<b>[field "oci"]</b> the Open Citation Identifier (OCI) for the citation;<b>[field "citing"]</b> the DOI of the citing entity;<b>[field "cited"]</b> the DOI of the cited entity;<b>[field "creation"]</b> the creation date of the citation (i.e. the publication date of the citing entity);<b>[field "timespan"]</b> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity);<b>[field "journal_sc"]</b> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal);<b>[field "author_sc"]</b> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).<br>This version of the dataset contains:<br>759,516,507 citations;60,778,357 bibliographic resources.The size of the zipped archive is 18.4 GB, while the size of the unzipped CSV file is 122.6 GB.<br>Additional information about COCI can be retrieved in the official webpage.<br>
DOI: 10.6084/m9.figshare.6741422.v8
title: COCI CSV dataset of all the citation data

description: This dataset contains all the citation data (in CSV format) included in COCI, released on 6 September 2020. In particular, each line of the CSV file defines a citation, and includes the following information:<b>[field "oci"]</b> the Open Citation Identifier (OCI) for the citation;<b>[field "citing"]</b> the DOI of the citing entity;<b>[field "cited"]</b> the DOI of the cited entity;<b>[field "creation"]</b> the creation date of the citation (i.e. the publication date of the citing entity);<b>[field "timespan"]</b> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity);<b>[field "journal_sc"]</b> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal);<b>[field "author_sc"]</b> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).<br>This version of the dataset contains:<br>733,367,140 citations;59,455,917 bibliographic resources.The size of the zipped archive is 17.7 GB, while the size of the unzipped CSV file is 118.4 GB.<br>Additional information about COCI can be retrieved in the official webpage.<br>
DOI: 10.6084/m9.figshare.12130980.v61
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) 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.v60
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) 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.13382744.v2
title: 2020 01 30 Field Notes

description: Field notes including image and/or video records.
DOI: 10.6084/m9.figshare.13382744.v1
title: 2020 01 30 Field Notes

description: Field notes including image and/or video records.
DOI: 10.6084/m9.figshare.12798062.v1
title: Version 2 Quadripolar MS Suppl AJP.docx

description: Murninkas et al. Supplementary Figures and Tables
DOI: 10.6084/m9.figshare.13065986.v33
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.6084/m9.figshare.13032740.v1
title: Data from: Novel trophic interactions under climate change promote alpine plant coexistence

description: --- Page in construction --- <br>All data and codes will be made available <br><br><br>============================================================<br>============================================================<br>============================================================<br><b><br></b><b>When using these datasets, please also cite the following reference: </b><br><br>Descombes, P., et al. (X).Novel trophic interactions under climate change promote alpine plant coexistence. <i>XXX</i>, X(X), X-X.<br>============================================================<br>============================================================<br>============================================================<br><b><br></b><b>Datasets descriptions:<br></b><b><br></b>Details on the data sampling are fully described in the aforementioned reference.<br>
DOI: 10.6084/m9.figshare.13414910.v1
title: Articles and Selected Posts and Outputs Published in 2020

description: I have shared a basic sheet listing a selection of my articles, blog posts and outputs I published openly during 2020. V1 is just a dump that would need sorting in chronological order or by type, but at least there's some kind of record. <br>