cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12130980.v72
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.3761562.v1761
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.v1760
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.12497822.v2
title: Data of "An experimental investigation of the effect of grain size on 'dislocation creep' of ice"

description: This data set contains experimental data used in "An experimental investigation of the effect of grain size on 'dislocation creep' of ice". For each experiment PIL#, where # is the experiment number, 5 files are included. PIL#.dat is the raw data file that contains time, pressure, temperature, internal load, external load and displacement. PIL#_options.txt marks the time that are used for peak-stress data and flow-stress data, the sample dimensions (in inches), zero stress at the beginning and the end, and the load-cell calibration. PIL#_output.txt is the processed data that contain time, temperature, pressure, stress, strain rate, strain and displacement. PIL#_ss.txt contains the averaged data over the time given in PIL#_options.txt. PIL#.png are plots of the data.
DOI: 10.6084/m9.figshare.12497822.v1
title: Data of "An experimental investigation on the creep of ice at glaciological pressures and temperatures"

description: This data set contains experimental data used in "An experimental investigation on the creep of ice at glaciological pressures and temperatures". For each experiment PIL#, where # is the experiment number, 5 files are included. PIL#.dat is the raw data file that contains time, pressure, temperature, internal load, external load and displacement. PIL#_options.txt marks the time that are used for peak-stress data and flow-stress data, the sample dimensions (in inches), zero stress at the beginning and the end, and the load-cell calibration. PIL#_output.txt is the processed data that contain time, temperature, pressure, stress, strain rate, strain and displacement. PIL#_ss.txt contains the averaged data over the time given in PIL#_options.txt. PIL#.png are plots of the data.
DOI: 10.6084/m9.figshare.13675201.v1
title: Extended data Table 1.docx

description: <b>Forward and reverse primer sequences used for WES variants verification. </b>
DOI: 10.6084/m9.figshare.13664606.v1
title: CrillyCooper2021.xlsx

description: transcriptomic analysis of innate immune cells after brain haemorrhage in a zebrafish larval model
DOI: 10.6084/m9.figshare.3761562.v1764
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.v1763
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/0onulf/bvsapz
title: Betula pendula_liście zainfekowane Bacillus subtilis i Phytophthora plurivora - ekstrakt substancji lotnych.pdf

description: :unav
DOI: 10.48370/ofd/0onulf
title: Betula pendula_liście zainfekowane Bacillus subtilis i Phytophthora plurivora - ekstrakt substancji lotnych

description: ekstrakt substancji lotnych
DOI: 10.6084/m9.figshare.12102549.v4
title: dataset for "The segmented Campo Felice normal faults: seismic potential appraisal by application of empirical relationships between rupture length and earthquake magnitude in the central Apennines, Ital"

description: This is a dataset used for the preparation of the manuscript "The segmented Campo Felice normal faults: seismic potential appraisal by application of empirical relationships between rupture length and earthquake magnitude in the central Apennines, Italy" by Schirripa Spagnolo, G., Marco, M., Billi, A., Carminati E., Galli, P., submitted to Tectonics.
DOI: 10.6084/m9.figshare.12102549.v3
title: dataset for "The segmented Campo Felice normal faults: seismic potential appraisal by application of empirical relationships between rupture length and earthquake magnitude in the central Apennines, Ital"

description: This is a dataset used for the preparation of the manuscript "The segmented Campo Felice normal faults: seismic potential appraisal by application of empirical relationships between rupture length and earthquake magnitude in the central Apennines, Italy" by Schirripa Spagnolo, G., Marco, M., Billi, A., Carminati E., Galli, P., submitted to Tectonics.
DOI: 10.6084/m9.figshare.13072688.v2
title: [2020-7-21] Downriver Drone Orthomosaic, Digital Surface Model, and Processing Report

description: This is the drone orthomosaic, digital surface model (DSM), and PhotoScan-generated report for the July 21, 2020 sampling date for the Evans et al. Sawyer Mill dam removal study. This data is for the downriver study area on the Bellamy River in Dover, NH, USA. A DJI Phantom 3 Professional small unmanned aerial system was used to collect the aerial imagery. The imagery was processed in Agisoft PhotoScan Professional. A Topcon Hiper Lite+ was mostly used to collect the survey data, with a total station used to collect a handful of ground control point locations where satellite signal was poor. <br>The products' XY is in WGS 84, and the vertical datum corresponds to NAVD88 in meters. Please see Alexandra's dissertation (University of New Hampshire, 2021) for additional information on the drone products.<br><br>These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
DOI: 10.6084/m9.figshare.13072688.v1
title: [2020-7-21] Downriver Drone Orthomosaic, Digital Surface Model, and Metadata

description: This is the drone orthomosaic, digital surface model (DSM), and PhotoScan-generated report for the July 21, 2020 sampling date for the Evans et al. Sawyer Mill dam removal study. This data is for the downriver study area on the Bellamy River in Dover, NH, USA. A DJI Phantom 3 Professional small unmanned aerial system was used to collect the aerial imagery. The flight path was created in DJIFlightPlanner. The imagery was processed in Agisoft PhotoScan Professional. A Topcon Hiper Lite+ was used to collect the survey data. Please see Alexandra's dissertation (University of New Hampshire) for additional information on the drone products. <br>These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
DOI: 10.6084/m9.figshare.13653269.v1
title: VLSM of motor-cognitive function in pediatric posterior fossa tumor survivors

description: Dataset of motor and cognitive tasks, normalized cerebellar lesions, lesion symptom maps.
DOI: 10.6084/m9.figshare.12130980.v75
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.v74
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.3761562.v1766
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.v1765
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.12798296.v13
title: Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"

description: Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”.<br>The “SupportingCode.html” file provides an in-depth description and discussion of our R workflow to estimate home range size of snowshoe hare and investigate its relationship with the ecological stoichiometry of preferred plant forage species. <br><br>If you would like to reproduce our analyses, you can recompile the R notebook “SupportingCode.Rmd”. This is a modular document that collects the output of 8 other R notebooks. For correct compilation of the SupportingCode.Rmd file, please organize the files in this repository on your machine as follows:<br>- all .Rmd and .R files in a "../Code/“ folder- all .csv files in a "../Data/" folder- all .tiff files in a “../Data/StDMs_Rasters/Ratios/" folder- unzip the SamplePoints.zip file in a "../Data/GridPoints/" folder- all .rds files in a "../Results/" folder<br>Where the Code, Data, and Results folder all share the same root.<br>Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador and the Federal Government of Canada. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding author (M. Rizzuto) for any question. <br> See README.md for details on the repository’s contents.
DOI: 10.6084/m9.figshare.12798296.v12
title: Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"

description: Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”.<br>The “SupportingCode.html” file provides an in-depth description and discussion of our R workflow to estimate home range size of snowshoe hare and investigate its relationship with the ecological stoichiometry of preferred plant forage species. <br><br>If you would like to reproduce our analyses, you can recompile the R notebook “SupportingCode.Rmd”. This is a modular document that collects the output of 8 other R notebooks. For correct compilation of the SupportingCode.Rmd file, please organize the files in this repository on your machine as follows:<br>- all .Rmd and .R files in a "../Code/“ folder- all .csv files in a "../Data/" folder- all .tiff files in a “../Data/StDMs_Rasters/Ratios/" folder- unzip the SamplePoints.zip file in a "../Data/GridPoints/" folder- all .rds files in a "../Results/" folder<br>Where the Code, Data, and Results folder all share the same root.<br>Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador and the Federal Government of Canada. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding author (M. Rizzuto) for any question. <br> See README.md for details on the repository’s contents.
DOI: 10.6084/m9.figshare.13721800.v1
title: Hexafluorobenzene nonadiabatic dynamics trajectories - S2

description: Output energies and geometries for 400 fewest-switches surface hopping trajectories initialized in the S2 electronic state. Initial conditions for each trajectory were sampled from the Wigner distribution. Trajectories were propagated for 1 picosecond with a 0.5 femtosecond timestep, using the surface hopping including arbitrary coupling (SHARC) method. Electronic structure at each time step was computed at the SA-5-CAS(6,6)/ANO-S-VDZP level.
DOI: 10.6084/m9.figshare.13560281.v3
title: An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

description: The brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenges to the practical application. This study presents an open dataset collected with a wearable SSVEP-based BCI system that compared wet and dry electrodes comprehensively with continuous recording of multiple sessions. The dataset consists of 8-channel SSVEP data from 102 healthy subjects while they performed a cue-guided target selecting task with a 12-target SSVEP-based BCI. For each subject, wet and dry electrodes were used to record 10 consecutive blocks respectively in an overall duration of around two hours. The dataset can be used to evaluate the performance of wet and dry electrodes in SSVEP-based BCIs. The dataset also provide sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.<br>
DOI: 10.6084/m9.figshare.13710109.v2
title: Ips hauseri.xlsx

description: Raw data of monoterpenoid signals production of Ips hauseri<br>
DOI: 10.6084/m9.figshare.13710109.v1
title: Ips hauseri.xlsx

description: Raw data of monoterpenoid signals production of Ips hauseri<br>
DOI: 10.6084/m9.figshare.3761562.v1771
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.v1770
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.13781179.v1
title: case_stduy.xls

description: 基于六节点热网络的新测试台<br>
DOI: 10.6084/m9.figshare.3761562.v1774
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.v1773
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.12909878.v5
title: Annual, Warm Season, and Monthly TROPOMI NO2 averages over CONUS

description: Level 3 monthly averaged TROPOMI NO2 tropospheric vertical column contents re-gridded to 0.01 x 0.01 degree spatial resolution. All data with a quality assurance value (qa_value) below 0.75 have been removed when computing these averages.<br>Latitudes and Longitudes are the center points. Add or subtract 0.005 degrees to get the edges.