cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12130980.v8
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published 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><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>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.12130158
title: The effect of ligand design on the structural and photophysical properties of Nd(III) complexes with Schiff bases of the [(phenylimino)methyl]phenol-type

description: Three mononuclear Nd(III) complexes [Nd(HL<sub>1</sub>)<sub>2</sub>(NO<sub>3</sub>)<sub>3</sub>] (<b>1</b>, HL<sub>1</sub>=2-methoxy-6-[(<i>E</i>)-(phenylimino)methyl]phenol), [Nd(HL<sub>2</sub>)<sub>3</sub>(NO<sub>3</sub>)<sub>3</sub>] (<b>2</b>, HL<sub>2</sub>=5-methoxy-2-[(<i>E</i>)-(phenylimino)methyl]phenol) and [Nd(HL<sub>3</sub>)<sub>2</sub>(NO<sub>3</sub>)<sub>3</sub>]·CH<sub>3</sub>OH (<b>3</b>, HL<sub>3</sub>=2-methoxy-6-{(<i>E</i>)-[(2-methoxyphenyl)imino]methyl}phenol) were prepared in a 1:3 ratio (M:L) in methanol. The effect of the ligand substituents and their positions (<i>meta versus para</i>) on the electronic and photophysical properties of the complexes is demonstrated. Single-crystal X-ray diffraction studies show that HL<sub>1</sub> and HL<sub>3</sub> coordinate bidentate <i>via</i> the phenolate and methoxy oxygens, while HL<sub>2</sub> is bound monodentately through the phenolate oxygen, with the coordination environment completed by three <i>η</i><sup>2</sup>-chelated nitrates. The significant differences observed in the coordination chemistry of the three Nd(III) complexes provides the opportunity for biological application investigations.
DOI: 10.6084/m9.figshare.12130161
title: Experimental Dataset for Oculographic User Interface Research

description: <br> The experimental dataset supports UI research based on video-oculography method, or eye tracking. <br> <br> Oculography is a non-contact optical method for detecting eye movement, based on the registration (using an optical sensor) of infrared illumination reflected by the eye's pupil. This method finds wide application in usability to assess the attractiveness of fragments of the interface, search efficiency, ease of transition between sections and other aspects of the interface design of a software product.<br> <br> We used an Eye Tracker device from Eye Tribe and OGAMA software ver. 5.0.5754 (http://www.ogama.net/). <br> <br> During the experiment, five users independently solved the problem of finding and obtaining information about the selected object on a virtual map. This task implied the following steps: - find and press the "Map" button (Slide0);<br> - find on the map and select a natural object called “Big Round Estuary” (Slide2);<br> - read all the information about this object and go to the main menu (Slide3).<br> <br> Procedure for preparing and conducting the experiment: launch the OGAMA application; calibrate the device taking into account the individual characteristics of the user; record eye tracking while the user completes the task of finding information on the screen; stop recording.<br> <br> The work was carried out in 2019 by Valery Grigoryev, graduate student of the Southern Federal University as part of the dissertation “User interface mobile guide application railway traveler”, supervised by Assoc. Prof. Vitaly Kompaniets, Master's program "Ergodesign user interface".<br> <br> List of variables in the dataset:<br> <br> SubjectName<br> SubjectCategory<br> Age<br> Sex<br> Handedness<br> Comments<br> TrialID<br> TrialName<br> TrialSequence<br> TrialCategory<br> TrialStartTime<br> Duration<br> EliminateData<br> EventEventID<br> EventSequence<br> EventTime<br> EventType<br> EventTask<br> EventParam<br> Time<br> PupilDiaX<br> PupilDiaY<br> GazePosX<br> GazePosY<br> MousePosX<br> MousePosY<br> EventID
DOI: 10.6084/m9.figshare.12130158.v1
title: The effect of ligand design on the structural and photophysical properties of Nd(III) complexes with Schiff bases of the [(phenylimino)methyl]phenol-type

description: Three mononuclear Nd(III) complexes [Nd(HL<sub>1</sub>)<sub>2</sub>(NO<sub>3</sub>)<sub>3</sub>] (<b>1</b>, HL<sub>1</sub>=2-methoxy-6-[(<i>E</i>)-(phenylimino)methyl]phenol), [Nd(HL<sub>2</sub>)<sub>3</sub>(NO<sub>3</sub>)<sub>3</sub>] (<b>2</b>, HL<sub>2</sub>=5-methoxy-2-[(<i>E</i>)-(phenylimino)methyl]phenol) and [Nd(HL<sub>3</sub>)<sub>2</sub>(NO<sub>3</sub>)<sub>3</sub>]·CH<sub>3</sub>OH (<b>3</b>, HL<sub>3</sub>=2-methoxy-6-{(<i>E</i>)-[(2-methoxyphenyl)imino]methyl}phenol) were prepared in a 1:3 ratio (M:L) in methanol. The effect of the ligand substituents and their positions (<i>meta versus para</i>) on the electronic and photophysical properties of the complexes is demonstrated. Single-crystal X-ray diffraction studies show that HL<sub>1</sub> and HL<sub>3</sub> coordinate bidentate <i>via</i> the phenolate and methoxy oxygens, while HL<sub>2</sub> is bound monodentately through the phenolate oxygen, with the coordination environment completed by three <i>η</i><sup>2</sup>-chelated nitrates. The significant differences observed in the coordination chemistry of the three Nd(III) complexes provides the opportunity for biological application investigations.
DOI: 10.6084/m9.figshare.12130257.v1
title: results_FINAL.xlsx

description: This excel sheet contains the data gathered during my master's thesis project. In this project I examined refused bequest and inappropriate intimacy code smells in Java and Python source code. <br>The excel sheet contains the metadata for each of the datasets and how many times each code smell occurred.
DOI: 10.6084/m9.figshare.12130980.v9
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published 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><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>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.12130035
title: Table S2. Spatial and temporal influence of Pacific subduction on South China: Geochemical migration of Cretaceous mafic-intermediate rocks

description: Table S2: Sr–Nd isotope compositions for the Cretaceous mafic-intermediate igneous rocks in the southeastern SCB
DOI: 10.6084/m9.figshare.12130035.v1
title: Table S2. Spatial and temporal influence of Pacific subduction on South China: Geochemical migration of Cretaceous mafic-intermediate rocks

description: Table S2: Sr–Nd isotope compositions for the Cretaceous mafic-intermediate igneous rocks in the southeastern SCB
DOI: 10.6084/m9.figshare.12130032
title: Table S3. Spatial and temporal influence of Pacific subduction on South China: Geochemical migration of Cretaceous mafic-intermediate rocks

description: Table S3: Zircon Hf–O isotope 850 compositions for the Cretaceous mafic-intermediate igneous rocks in the southeastern SCB
DOI: 10.6084/m9.figshare.12130980.v10
title: Maps of human mobility change during the COVID-19 outbreak

description: Google has published 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><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>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.12130020
title: Table S1. Spatial and temporal influence of Pacific subduction on South China: Geochemical migration of Cretaceous mafic-intermediate rocks

description: Table S1: Major oxide (wt.%) and trace element (ppm) compositions for the Cretaceous mafic-intermediate igneous rocks in the southeastern SCB
DOI: 10.6084/m9.figshare.12130020.v1
title: Table S1. Spatial and temporal influence of Pacific subduction on South China: Geochemical migration of Cretaceous mafic-intermediate rocks

description: Table S1: Major oxide (wt.%) and trace element (ppm) compositions for the Cretaceous mafic-intermediate igneous rocks in the southeastern SCB
DOI: 10.6084/m9.figshare.12130032.v1
title: Table S3. Spatial and temporal influence of Pacific subduction on South China: Geochemical migration of Cretaceous mafic-intermediate rocks

description: Table S3: Zircon Hf–O isotope 850 compositions for the Cretaceous mafic-intermediate igneous rocks in the southeastern SCB
DOI: 10.6084/m9.figshare.12130980.v11
title: MOOD Maps of human 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><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>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.12129984
title: Spatial orientation based on multiple visual cues in non-migratory monarch butterflies

description: raw data (.zip) and analyzed data (.xls) for paper by Franzke et al. (2020), Journal of Experimental Biology.
DOI: 10.6084/m9.figshare.12130008
title: Metadata record for: Genome sequence analysis of multidrug-resistant Mycobacterium tuberculosis from Malaysia

description: This dataset contains key characteristics about the data described in the Data Descriptor Genome sequence analysis of multidrug-resistant Mycobacterium tuberculosis from Malaysia. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br>
DOI: 10.6084/m9.figshare.12129984.v1
title: Spatial orientation based on multiple visual cues in non-migratory monarch butterflies

description: raw data (.zip) and analyzed data (.xls) for paper by Franzke et al. (2020), Journal of Experimental Biology.
DOI: 10.6084/m9.figshare.12130980.v12
title: MOOD Maps of human 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><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>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.12130008.v1
title: Metadata record for: Genome sequence analysis of multidrug-resistant Mycobacterium tuberculosis from Malaysia

description: This dataset contains key characteristics about the data described in the Data Descriptor Genome sequence analysis of multidrug-resistant Mycobacterium tuberculosis from Malaysia. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br>
DOI: 10.6084/m9.figshare.12130980.v13
title: MOOD Maps of human 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><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>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.12129876
title: Teleost gut microbiome literature summary from The role of the gut microbiome in sustainable teleost aquaculture

description: Data obtained from the systematic review, detailed in the supplementary material.