cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3734979
title: Benchmark maps of 33 years of secondary forest age for Brazil

description: <strong>Title: </strong>Benchmark maps of 33 years of secondary forest age for Brazil <strong>Contact:</strong> Celso H. L. Silva Junior (celsohlsj@gmail.com) <strong>Data:</strong> Increment (Product 1), Extent (Product 2), Loss (Product 3), and Age (Product 4) of Brazilian Secondary Vegetation <strong>Coverage:</strong> Brazil land <strong>Period:</strong> 1986 to 2018 (1987 to 2018 for secondary forest loss) <strong>Spatial resolution:</strong> 30-meters <strong>Temporal resolution:</strong> Annual <strong>Coordinate reference system:</strong> Geographic Coordinate System with Datum WGS84 <strong>File format:</strong> 131 zip files per year (being 33 for increment, 33 for extent, 33 for age and 32 for loss). Each zip file containing eight tiles in compressed TIFF format. <strong>Code:</strong> https://github.com/celsohlsj/gee_brazil_sv <strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration. <strong>For the use of this dataset, please cite: </strong>Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G., Broggio, I.S., Rosan, T.M., Doblas, J., Anderson, L.O., Rousseau, G.X., Shimabukuro, Y.E., Silva, C.A., House, J.I., Aragão, L.E.O.C. Benchmark maps of 33 years of secondary forest age for Brazil. Scientific Data (2020). https://doi.org/10.1038/s41597-020-00600-4
DOI: 10.5281/zenodo.3734980
title: Benchmark maps of 33 years of secondary forest age for Brazil

description: <strong>We discontinued this version of the dataset.</strong>
DOI: 10.5281/zenodo.6984086
title: The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau

description: The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau
DOI: 10.5281/zenodo.6984085
title: The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau

description: The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau
DOI: 10.5523/bris.2raladk8u6pqu23o1jyc5rueeu
title: Data for SAP-BAM paper

description: This repository contains data pertaining to the article: Saltwater-responsive bubble artificial muscles using superabsorbent polymers, Daniel Gosden, Richard Suphapol Diteesawat, Matthew Studley and Jonathan Rossiter, Frontiers in Robotics and AI, 2022, doi: 10.3389/frobt.2022.960372.
DOI: 10.5281/zenodo.6983170
title: Annex - Unravelling the puzzle of the environmental sustainability of shared mobility: A systematic literature review.

description: Annex from "Unravelling the puzzle of the environmental sustainability of shared mobility: A systematic literature review. ": Annex 1 has the search string used in the literature review. Annex 2 has the dataset of all the extracted information of the article: There are three worksheets, the worksheet "Annex 2" has the whole dataset with all the extracted data, "Annex 2.1" has the classification of the environmental impact categories and "Annex 3" the factor extraction.
DOI: 10.5281/zenodo.6245184
title: Annex - Unravelling the puzzle of the environmental sustainability of shared mobility: A systematic literature review.

description: Annex from "Unravelling the puzzle of the environmental sustainability of shared mobility: A systematic literature review. ": Annex 1 has the search string used in the literature review. Annex 2 has the dataset of all the extracted information of the article: There are three worksheets, the worksheet "Annex 2" has the whole dataset with all the extracted data, "Annex 2.1" has the classification of the environmental impact categories and "Annex 3" the factor extraction.
DOI: 10.5281/zenodo.6927019
title: The impact of tropical cyclone on subaqueous delta front erosion: A numerical investigation with Typhoon Danas (2019) in the Changjiang River Estuary

description: model_result_baroclinic: Used FVCOM model output from 2019-07-01 to 2019-08-01 in Matlab data format. model_verify: Bed elevation data from ADV, wave parameters from buoy, and bottom suspended sediment concentration from OBS in Matlab data format.
DOI: 10.5281/zenodo.6927018
title: The role of tropical cyclone on Changjiang River subaqueous delta geomorphology: a numerical investigation of Tropical Cyclone Danas (2019)

description: ecs_mesh: Model domain used for numerical simulation in Matlab data format. Variable description of ecs_mesh.mat: nodex/nodey: Longitude/Latitude of the vertices of the triangles cellx/celly: Longitude/Latitude of the faces of the triangles nodeh: Bathymetry of the vertices nv: Vertices composition of the triangles model_result_baroclinic: Used FVCOM model output from 2019-07-01 to 2019-08-01 in Matlab data format. Variable description of model_result_baroclinic.mat: SSC: Total suspended sediment concentration of all sediment classes, units: g/L deposition_flux: Deposition flux of suspended sediment, units: kg/m^2/h divergence_sed_flux: depth-integrated divergence of sediment flux, units: kg/m^2/h erosion_flux: Erosion flux of sea-bed surface sediment, units: kg/m^2/h salinity: sea water salinity, units: psu taub: Total bed stress, units: Pa time_model: Time of the results, time zone: LST u: Eastward water velocity, units: m/s v: Northward water velocity, units: m/s zeta: Water surface elevation model_verify: Bed elevation data from ADV, wave parameters from buoy, and bottom suspended sediment concentration from OBS in Matlab data format. Variable description of model_verify.mat: ADV_BEC_xx: Bed Elevation measurement from ADV at Station xx, units: mm ADV_time_xx: Measurement time of bed elevation of ADV at Station xx, time zone: LST Buoy_hs_S1: Significant wave height measurement from Buoy at Station S1, units: m Buoy_time_S1: Measurement time of Buoy at Station S1, time zone: LST Buoy_tpeak_S1: Peak wave period measurement from Buoy at Station S1, units: s OBS_SSC_S1: Suspended sediment concentration measurement from OBS at Station S1, units: g/L OBS_time_S1: Measurement time of OBS at Station S1, time zone: LST tauc_mtke_S1: In-situ bottom shear stress calculated from turbulence kinetic energy method, units: Pa time_mtke_S1: Time of calculated bottom shear stress, time zone: LST time_wind: Time of surface wind at S1 from CFSv2 model from 2019-06-01 to 2019-08-01 time zone: LST u_wind: Eastward velocity of surface wind at S1 from CFSv2 model, units: m/s v_wind: Northward velocity of surface wind at S1 from CFSv2 model, units: m/s
DOI: 10.5281/zenodo.6934117
title: The role of tropical cyclone on Changjiang River subaqueous delta geomorphology: a numerical investigation of Tropical Cyclone Danas (2019)

description: model_result_baroclinic: Used FVCOM model output from 2019-07-01 to 2019-08-01 in Matlab data format. Variable Description of model_result_baroclinic.mat: SSC: Total suspended sediment concentration of all sediment classes, units: g/L deposition_flux: Deposition flux of suspended sediment, units: kg/m^2/h divergence_sed_flux: depth-integrated divergence of sediment flux, units: kg/m^2/h erosion_flux: Erosion flux of sea-bed surface sediment, units: kg/m^2/h salinity: sea water salinity, units: psu taub: Total bed stress, units: Pa time_model: Time of the results, time zone: LST u: Eastward water velocity, units: m/s v: Northward water velocity, units: m/s zeta: Water surface elevation model_verify: Bed elevation data from ADV, wave parameters from buoy, and bottom suspended sediment concentration from OBS in Matlab data format. Variable Description of model_verify.mat: ADV_BEC_xx: Bed Elevation measurement from ADV at Station xx, units: mm ADV_time_xx: Measurement time of bed elevation of ADV at Station xx, time zone: LST Buoy_hs_S1: Significant wave height measurement from Buoy at Station S1, units: m Buoy_time_S1: Measurement time of Buoy at Station S1, time zone: LST Buoy_tpeak_S1: Peak wave period measurement from Buoy at Station S1, units: s OBS_SSC_S1: Suspended sediment concentration measurement from OBS at Station S1, units: g/L OBS_time_S1: Measurement time of OBS at Station S1, time zone: LST tauc_mtke_S1: In-situ bottom shear stress calculated from turbulence kinetic energy method, units: Pa time_mtke_S1: Time of calculated bottom shear stress, time zone: LST time_wind: Time of surface wind at S1 from CFSv2 model from 2019-06-01 to 2019-08-01 time zone: LST u_wind: Eastward velocity of surface wind at S1 from CFSv2 model, units: m/s v_wind: Northward velocity of surface wind at S1 from CFSv2 model, units: m/s
DOI: 10.6084/m9.figshare.12130980.v151
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.v150
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: 10.5281/zenodo.6323227
title: Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions

description: Data from: Wolf, C., Belopolsky, A.V., & Lappe, M. (2022). Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions. iScience. The zip folder "Data" contains one .dat file with the data of every individual. "Data" contains all trials of all individuals. Each file has three columns.<br> 1st column: condition index, range: 1-4,<br> 1: no preview / noise inspection;<br> 2: no preview / face inspection;<br> 3: preview / noise inspection;<br> 4: preview / face inspection<br> 2nd column: decision outcome, range 0-1<br> 0: noise image selected<br> 1: face image selected<br> 3rd column: fixation duration in seconds The zip folder "FixData" contains 3 .dat files. Every row in SacIndex.dat corresponds to one trial, every column to one millisecond after primary saccade offset (1000 columns in total) and thus depicts the fixation duration on the inspection target.<br> 0: participant was fixating inspection target<br> 1: participant was making a smaller saccade that started and landed on the inspection target<br> NaN: Fixation period for that trial has ended.<br> The two additional files "ParticipantIndex.dat" and "ConditionIndex.dat" are column vectors that contain one entry for every trial (the participant number or the condition index respectively). The condition index is the same as in the other zip folder (see above). Please note that the trial order in FixData does not reflect the actual trial order of the experiment. For questions please contact chr.wolf[at]wwu.de
DOI: 10.5281/zenodo.6323226
title: Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions

description: Data from: Wolf, C., Belopolsky, A.V., & Lappe, M. (2022). Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions. iScience. The zip folder "Data" contains one .dat file with the data of every individual. "Data" contains all trials of all individuals. Each file has three columns.<br> 1st column: condition index, range: 1-4,<br> 1: no preview / noise inspection;<br> 2: no preview / face inspection;<br> 3: preview / noise inspection;<br> 4: preview / face inspection<br> 2nd column: decision outcome, range 0-1<br> 0: noise image selected<br> 1: face image selected<br> 3rd column: fixation duration in seconds The zip folder "FixData" contains 3 .dat files. Every row in SacIndex.dat corresponds to one trial, every column to one millisecond after primary saccade offset (1000 columns in total) and thus depicts the fixation duration on the inspection target.<br> 0: participant was fixating inspection target<br> 1: participant was making a smaller saccade that started and landed on the inspection target<br> NaN: Fixation period for that trial has ended.<br> The two additional files "ParticipantIndex.dat" and "ConditionIndex.dat" are column vectors that contain one entry for every trial (the participant number or the condition index respectively). The condition index is the same as in the other zip folder (see above). Please note that the trial order in FixData does not reflect the actual trial order of the experiment. For questions please contact chr.wolf[at]wwu.de
DOI: 10.5517/ccdc.csd.cc12k7vq
title: CCDC 999683: Experimental Crystal Structure Determination

description: Related Article: V.Kh. Sabirov|2015|Zh.Strukt.Khim.|56|747|doi:10.1134/S0022476615040137
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc12k7wr
title: CCDC 999684: Experimental Crystal Structure Determination

description: Related Article: V.Kh. Sabirov|2015|Zh.Strukt.Khim.|56|747|doi:10.1134/S0022476615040137
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc12k7xs
title: CCDC 999685: Experimental Crystal Structure Determination

description: Related Article: V.Kh. Sabirov|2015|Zh.Strukt.Khim.|56|747|doi:10.1134/S0022476615040137
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc12k7yt
title: CCDC 999686: Experimental Crystal Structure Determination

description: Related Article: V.Kh. Sabirov|2015|Zh.Strukt.Khim.|56|747|doi:10.1134/S0022476615040137
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c356w
title: CCDC 2177787: Experimental Crystal Structure Determination

description: Related Article: Peter W. R. Corfield|2022|IUCrData|7|x220607|doi:10.1107/S2414314622006071
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.20407648
title: Hounslow_SummaryStats_1 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to calculate summary statistics found in main article results section.
DOI: 10.6084/m9.figshare.20407648.v2
title: Hounslow_SummaryStats_1 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to calculate summary statistics found in main article results section.
DOI: 10.6084/m9.figshare.20407651
title: Hounslow_GAMM_Plots_6 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to create Fig. 5 in main article results.
DOI: 10.6084/m9.figshare.20407651.v2
title: Hounslow_GAMM_Plots_6 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to create Fig. 5 in main article results.
DOI: 10.6084/m9.figshare.20407648.v1
title: Hounslow_SummaryStats_1.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.