cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12130980.v78
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.v77
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.3465407.v4
title: Computational fluid dynamics modelling of left valvular heart diseases during atrial fibrillation

description: Computational fluid dynamics modelling of left valvular heart diseases during atrial fibrillation, Scarsoglio, S. and Saglietto, A. and Gaita, F. and Ridolfi, L. and Anselmino, M., 2016, PeerJ 4:e2240 https://doi.org/10.7717/peerj.2240<br><br>Data, main codes and post-processing scripts are available.<br>
DOI: 10.6084/m9.figshare.14049569.v1
title: Data in support of the manuscript "Impacts of climate change on terrestrial hydrological components and crop water requirement in the Chesapeake Bay watershed"

description: This data supports the study to assess the impacts of climate change on terrestrial hydrological components and Crop Water Requirement (CWR) over the Chesapeake Bay watershed using a combination of Global Climate Models (GCMs) and a land surface model. To better understand the impacts of climate change on the hydrological cycle, long-term simulations of multiple earth system models from the Coupled Model Intercomparison Project (CMIP Phase 5) are statistically downscaled and bias-corrected using Multivariate Adaptive Constructed Analogs (MACA) scheme for use as model forcings. Precipitation indices from the twenty MACA-based GCMs are used to identify six best performing models. A mesoscale approach is developed, where CWR is estimated by accounting for the impacts of changing climate conditions and rising carbon dioxide levels. Daily grid-based crop coefficients are derived from evapotranspiration data. The findings indicate an annual increase of 18% in evapotranspiration and an annual decrease of 5% in streamflow for the RCP 8.5 scenario towards the end of the 21st century. A reduction of 13% and 17% in CWR is observed for corn and soybeans, respectively due to wetter antecedent soil moisture conditions resulting from increased total precipitation and rising carbon dioxide levels. This mesoscale approach can be scaled globally to project CWR for use in data-scarce agricultural regions and enabling better management of water resources. <br>
DOI: 10.6084/m9.figshare.13855091.v2
title: Mycoplasma anserisalpingitidis draft genomes

description: The following data are draft whole genome sequences of Mycoplasma anserisalpingitidis strains. The strains can be found in both multi fasta format and .gb annotated format. The annotation was done with the RAST server.
DOI: 10.48370/ofd/z2mu55/crsj9d
title: Betula pendula_liście zainfekowane Phytophthora plurivora - ekstrakt substancji lotnych.pdf

description: :unav
DOI: 10.48370/ofd/z2mu55
title: Betula pendula_liście zainfekowane Phytophthora plurivora - ekstrakt substancji lotnych

description: ekstrakt substancji lotnych
DOI: 10.6084/m9.figshare.14059202.v1
title: Supplementary Data for Comparative Study on the Use of S Curve-Bar Chart and Ms Project-Pdm Methods in Scheduling and Controlling Building Construction Project

description: Building construction projects have very complex activities, so they require precise and accurate methods of scheduling and control. Using the right method, the project executor can carry out the project according to plan and can be controlled if there is schedule deviations. This study aims to compare the effectiveness of using the Bar Chart-curve-S and Ms. Project-PDM methods on scheduling and controlling building construction projects. The method used by contractors in scheduling and controlling a project is the Bar Chart-S curve and Ms. Project-PDM method.
DOI: 10.6084/m9.figshare.12629045.v1
title: Descriptions.xls

description: This dataset is related to the article: "<b>Evolving the structure: climatic and developmental constraints on the evolution of plant architecture. A case study in <i>Euphorbia</i>.</b>" <br><b>Authors: </b>Artémis Anest, Tristan Charles-Dominique, Olivier Maurin, Mathieu Millan, Claude Edelin, Kyle W. Tomlinson<br><i>New Phytologist</i> acceptance date: January 18, 2021
DOI: 10.6084/m9.figshare.11994147.v1
title: baltic_corophiid_data.csv

description: Morphometric data described and analyzed in:<br><br>Borza, P., Arbačiauskas, K., Zettler, M.L. (2021): Multidimensional niche differentiation might buffer invasion impacts: the case of oligohaline corophiids (Crustacea: Amphipoda) in the Baltic Sea. Biological Invasions. doi: 10.1007/s10530-021-02479-7.
DOI: 10.25500/edata.bham.00000780
title: Research data supporting "Probing heavy metal binding to phycobiliproteins"
DOI: 10.6084/m9.figshare.3761562.v1784
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.v1783
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.7571813.v1
title: Dataset: Effects of dopamine receptor antagonism and amphetamine-induced psychomotor sensitization on sign- and goal-tracking after extended training

description: Dataset for a paper describing experiments involving dopamine antagonism using SCH-23390 and eticlopride and amphetamine-induced psychomotor sensitization.<br>
DOI: 10.6084/m9.figshare.14095705.v2
title: LM7c_refMean.txt

description: Reference table of means
DOI: 10.6084/m9.figshare.14095705.v1
title: LM7c_refMean.txt

description: Reference table of means
DOI: 10.6084/m9.figshare.13767502.v1
title: Rat 7M Videos Subject 4 - Day 1

description: All videos for Subject 4, Day 1 in Rat 7M.<br><br>The video naming scheme is s{subject_id}-d{recording_day}-camera{camera_id}-{starting_frame_idx}.mp4.<br><br>Subject ID, recording day, and camera ID match videos to the data in the motion capture and camera calibration parameter .mat files.<br><br>Videos are provided in 3500 frame chunks, with the index of the starting frame in each file denoted by the {starting_frame_idx} portion of the filename.<br><br>Using the 'frames' field in the 'cameras' struct inside 'mocap.mat', the corresponding video file and frame index can be calculated by used frame_idx // 3500 to get the {starting_frame_idx} and frame_idx % 3500 to get the frame in that file. <br>
DOI: 10.6084/m9.figshare.3761562.v1790
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.v1789
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.14129534.v1
title: Dhole mPCR MiSeq Sequences

description: Detailed notes are provided in the file titled "README.txt".<br>
DOI: 10.6084/m9.figshare.3407932.v2
title: Comparison of 14 classifiers

description: Data, programs, results, and analysis software for the paper "Comparison of 14 different families of classification algorithms on 115 binary data sets" https://arxiv.org/abs/1606.00930<br>
DOI: 10.6084/m9.figshare.3407932.v1
title: Comparison of 14 classifiers

description: Data, programs, results, and analysis software for the paper "Comparison of 14 different families of classification algorithms on 115 binary data sets" https://arxiv.org/abs/1606.00930<br>
DOI: 10.6084/m9.figshare.12130980.v81
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.v80
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.4256624.v2
title: Critical current characterisation of SuperPower M3 Advanced Pinning 2G HTS superconducting wire

description: Detailed measurements of the critical current of a commercial 2G HTS YBCO wire as a function of temperature, magnetic field, and field angle, performed at the Robinson Research Institute of Victoria University of Wellington.This data is referenced in N. M. Strickland and S. C. Wimbush, <i>The magnetic-field dependence of critical current: what we really need to know</i>, IEEE Trans. Appl. Supercond. 27 (2016) 8000505.
DOI: 10.6084/m9.figshare.14137916.v2
title: Taxon_data_Caribbean_InvadersPlantInvadersCaribbean

description: TaxonList
DOI: 10.6084/m9.figshare.14137916.v1
title: Taxon_data_Caribbean_Invaders

description: TaxonList
DOI: 10.6084/m9.figshare.14150069.v1
title: Geometry Files AJP Submission

description: Geometry Datasets for Animal Models in AJP submission 2021
DOI: 10.6084/m9.figshare.3761562.v1794
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.v1793
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.14151998.v2
title: Series elasticity facilitates safe plantar flexor muscle-tendon shock absorption during perturbed human hopping.

description: In our everyday lives, we negotiate complex and unpredictable environments. Yet much of our knowledge regarding locomotion has come from studies conducted under steady-state conditions. We have previously shown that humans rely on the ankle joint to absorb energy and recover from perturbations, however, the muscle-tendon (MTU) behaviour and motor control strategies that accompany these joint-level responses are not yet understood. In this study, we determined how neuromuscular control and plantar flexor MTU dynamics are modulated to maintain stability during unexpected vertical perturbations. Participants performed steady-state hopping and, at an unknown time, we elicited an unexpected perturbation via rapid removal of a platform. In addition to kinematics and kinetics, we measured gastrocnemius and soleus muscle activations using electromyography and <i>in vivo</i>fascicle dynamics using B-mode ultrasound. Here, we show that an unexpecteddrop in ground height introduces an automatic phase shift in the timing of plantar flexor muscle activity relative to MTU length changes. This altered timing initiates a cascade of responses including increased MTU and fascicle length changes and increased muscle forces which, when taken together, enables the plantar flexors to effectively dissipate energy. Our results also show another mechanism, whereby increased co-activation of the plantar- and dorsiflexors enables shortening of the plantar flexor fascicles prior to ground contact. This co-activation improves the capacity of the plantar flexors to rapidly absorb energy upon ground contact, and may also aid in the avoidance of potentially damaging muscle strains. Our study provides novel insight into how humans alter their neural control to modulate <i>in vivo </i>muscle-tendon interaction dynamics in response to unexpected perturbations. These data provide essential insight to help guide design of lower-limb assistive devices that can perform within varied and unpredictable environments.
DOI: 10.6084/m9.figshare.14151998.v1
title: series elasticity

description: bbbbb
DOI: 10.6084/m9.figshare.14160377.v1
title: Supplementary file_Data.xlsx

description: A dataset was constructed to conduct a meta-analysis on feed energy conversion efficiency in cattle
DOI: 10.6084/m9.figshare.5525263.v1
title: Predicting tropical maize inbred lines with external panels from public dataset

description: Throughout this dataset we proposed a hypothetical and realistic scenario in order to apply Genomic Selection (GS) - use of a limited budget and the need to increase genetic variability. To reach that we tested the feasibility of using genomic and phenotypic information from public databases for prediction of tropical maize inbred lines, seeking to identify how the population structure, the use of optimized training sets (OTS), and the amount of information coming from public databases affect the predictive ability. The results were encouraging, opening up possibilities to others replicate the same approach with diferent crops or dataset.<br>
DOI: 10.6084/m9.figshare.14161568.v1
title: An Event Related Brain Potential (ERP) study of Complex Anaphora in Spanish

description: This study examines the event- related brain potential (ERP) of 25 Mexican monolingual Spanish-speakers when reading Spanish sentences with single entity anaphora or complex anaphora. Complex anaphora is an expression that refer to propositions, states, facts or events while, a single entity anaphora is an expression that refers back to a concrete object. Here we compare the cognitive cost in processing a single entity anaphora (<i>ésta</i> <sub>feminine</sub>; La renuncia [resignation) from a complex anaphora (<i>esto</i> <sub>neuter</sub>; La renuncia fue aceptada [The resignation was accepted]). <br>
DOI: 10.6084/m9.figshare.3761562.v1796
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.v1795
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.14167391.v1
title: Playpen agonism focal sampling

description: Focal sampling of agonistic interactions in a temporary playpen environment for standard-housed lab mice. Number of mice was either 2 (C57 strain mouse absent) or 3 (C57 strain mouse present).