cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4458185
title: Inventory of glacial lakes in the Jhelum basin, Kashmir Himalayas, India

description: Globally, the dynamic evolution of glacial lakes in the high-mountainous regions triggered due to climate change-induced glacier recession has resulted into the formation, growth and disappearance of various types of glacial lakes. Himalayas have received a serious attention from the earth science researchers to study climate change and glaciers. However, no study has been attempted to prepare an inventory of glacial lakes using remote sensing-based approach in the Jhelum Basin of Kashmir Himalayas. We identified, mapped and analysed glacial lakes spatial distribution in the study region by using Indian Remote Sensing (IRS) high resolution LISS IV multi-spectral data. The lakes in the inventory with size greater than and equal to 1000 m<sup>2</sup> (0.001 km²) are situated in the elevation range of 2940 - 4411 m a.s.l. We prepared our glacial lake inventory in the shape file format, which includes attributes, such as, ID (code), coordinates (latitude and longitude), District name, Watershed name, Local name, Nearest settlement/place, Lake type, Area (Km<sup>2 </sup>and m<sup>2</sup>), Perimeter (m), Length (m), Width (m) and elevation (m). We added certain parameters (e.g. depth, volume and potential peak discharge) by using empirical formulas in the excel format. Our study will provide a reference database to quantify impact of climate change on glacial lakes and facilitate in disaster risk evaluation studies of evolving potentially dangerous lakes in the region.
DOI: 10.6084/m9.figshare.12702140
title: Data for "Trends in Digital Newspaper Discovery: A Natural Experiment"

description: 6 files; two require SPSS, 1 PDF, remaining 3 are Excel documents containing multiple sheets per file. Data in the files support a natural experiment method applied to an implementation of an additional feature in academic library discovery layers for the querying of newspaper content. <br>Pre-analysis equivalence between control and intervention groups was determined via quantitative checks on Carnegie Classification Data contained in the CarnegieStatisticalCheck.spv and CarnegieData.sav files.The Excel files each contain 12 sheets, a Calculations sheet, a Notes sheet, and one sheet per campus in the study. <br>
DOI: 10.6084/m9.figshare.12702140.v1
title: Data for "Trends in Digital Newspaper Discovery: A Natural Experiment"

description: 6 files; two require SPSS, 1 PDF, remaining 3 are Excel documents containing multiple sheets per file. Data in the files support a natural experiment method applied to an implementation of an additional feature in academic library discovery layers for the querying of newspaper content. <br>Pre-analysis equivalence between control and intervention groups was determined via quantitative checks on Carnegie Classification Data contained in the CarnegieStatisticalCheck.spv and CarnegieData.sav files.The Excel files each contain 12 sheets, a Calculations sheet, a Notes sheet, and one sheet per campus in the study. <br>
DOI: 10.6084/m9.figshare.13626491
title: Data Set Gesture feedback and Fluency

description: The dataset contains data for 20 variables of interest for the paper on gesture enhancing feedback and fluency
DOI: 10.6084/m9.figshare.13626491.v1
title: Data Set Gesture feedback and Fluency

description: The dataset contains data for 20 variables of interest for the paper on gesture enhancing feedback and fluency
DOI: 10.5281/zenodo.4458181
title: A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets

description: Includes data relevant for publication with DOI 10.1038/s42004-020-00422-w plus a table with information about how the data were obtained and processed.
DOI: 10.5281/zenodo.4458180
title: A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets

description: Includes data relevant for publication with DOI 10.1038/s42004-020-00422-w plus a table with information about how the data were obtained and processed.
DOI: 10.6084/m9.figshare.13625138
title: Qian et al, ACS Energy Lett. 2021 - EXAFS Data

description: <strong>Understanding the mesoscale degradation in nickel-rich cathode materials through machine-learning-revealed strain-redox decoupling</strong><strong></strong>The degradation of nickel-rich cathode materials for lithium-ion batteries upon prolonged electrochemical cycling features a complicated interplay among electronic structure, lattice configuration, and micro morphology. The underlying mechanism for such an entanglement of different material properties at nano- to meso- scales is fundamental to the battery performance but not well-understood yet. Here we investigate the correlation between the local redox reaction and lattice mismatch through a nano-resolution synchrotron spectro-microscopy study of LiNi0.8Co0.1Mn0.1O2 (NCM 811) cathode particles. With assistance from a machine-learning-based data classification method, we identify local regions that demonstrate a strain-redox decoupling effect, which can be attributed to different side reactions. Our result highlights the mesoscale reaction heterogeneity in the battery cathode and suggests that the particle structure engineering could be a viable approach to mitigate the chemomechanical degradation of cathode materials.
DOI: 10.6084/m9.figshare.13625138.v1
title: Qian et al, ACS Energy Lett. 2021 - EXAFS Data

description: <strong>Understanding the mesoscale degradation in nickel-rich cathode materials through machine-learning-revealed strain-redox decoupling</strong><strong></strong>The degradation of nickel-rich cathode materials for lithium-ion batteries upon prolonged electrochemical cycling features a complicated interplay among electronic structure, lattice configuration, and micro morphology. The underlying mechanism for such an entanglement of different material properties at nano- to meso- scales is fundamental to the battery performance but not well-understood yet. Here we investigate the correlation between the local redox reaction and lattice mismatch through a nano-resolution synchrotron spectro-microscopy study of LiNi0.8Co0.1Mn0.1O2 (NCM 811) cathode particles. With assistance from a machine-learning-based data classification method, we identify local regions that demonstrate a strain-redox decoupling effect, which can be attributed to different side reactions. Our result highlights the mesoscale reaction heterogeneity in the battery cathode and suggests that the particle structure engineering could be a viable approach to mitigate the chemomechanical degradation of cathode materials.
DOI: 10.15468/jx2b3t
title: A new genus of Pharaxonothinae from Australia (Coleoptera: Erotylidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Yoshida, Takahiro, Leschen, Richard A. B. (2020): A new genus of Pharaxonothinae from Australia (Coleoptera: Erotylidae). Zootaxa 4838 (2): 273-282, DOI: https://doi.org/10.11646/zootaxa.4838.2.7
DOI: 10.5281/zenodo.4458086
title: Mass spectrometry data Spo11 and Msh4-1

description: The procedure for Spo11 data is based on the peptide intensities reported in the evidence file provided by MaxQuant: 1) peptide intensities are normalized using the variance stabilization transformation (bioconductor package vsn) 2) Doing Top3, I chose not to impute any further the missing peptide intensities 3) Taking the protein groups identified by MaxQuant and reported in the proteinGroup file, the protein intensities are calculated as the mean of the three most intense peptide of the leading razor protein. 4) If no protein is found in a sample, the reported intensity is zero 5) Differential analysis using the empirical Bayes statistics from the bioconductor limma packed is performed with the false discovery rate set at 0.01. Most proteins showing significant difference are only in SPO11. MaxQuant proteinGroups and evidence file are joined as sheets in the Mass Spec Spo11_raw.xlsx. The first sheet, "Differential Analysis", marks the proteins significantly different in SPO versus CTR (first column), under the test condition described above. The next 6 columns are the Top3 intensities in each sample. You may also want to ignore proteins with Q&gt;0 (column BL in the file).
DOI: 10.5281/zenodo.4458085
title: Mass spectrometry data Spo11 and Msh4-1

description: The procedure for Spo11 data is based on the peptide intensities reported in the evidence file provided by MaxQuant: 1) peptide intensities are normalized using the variance stabilization transformation (bioconductor package vsn) 2) Doing Top3, I chose not to impute any further the missing peptide intensities 3) Taking the protein groups identified by MaxQuant and reported in the proteinGroup file, the protein intensities are calculated as the mean of the three most intense peptide of the leading razor protein. 4) If no protein is found in a sample, the reported intensity is zero 5) Differential analysis using the empirical Bayes statistics from the bioconductor limma packed is performed with the false discovery rate set at 0.01. Most proteins showing significant difference are only in SPO11. MaxQuant proteinGroups and evidence file are joined as sheets in the Mass Spec Spo11_raw.xlsx. The first sheet, "Differential Analysis", marks the proteins significantly different in SPO versus CTR (first column), under the test condition described above. The next 6 columns are the Top3 intensities in each sample. You may also want to ignore proteins with Q&gt;0 (column BL in the file).
DOI: 10.5281/zenodo.4458246
title: qdgc Chad

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 22nd of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.5281/zenodo.4458245
title: qdgc Chad

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 22nd of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.5281/zenodo.4458250
title: qdgc Comoros

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 21th of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.5281/zenodo.4458249
title: qdgc Comoros

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 21th of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.6084/m9.figshare.9250862.v1
title: Database: Optimized cropping rotations increase biomass production without changing soil carbon stock

description: To meet the twin key challenges from food security and climate change, optimized cropping rotations (CRs) should aim to maximize biomass production while minimizing carbon (C) footprints. However, the effects of CRs on biomass production and soil carbon stock (SCS) remain poorly understood, because they are not frequently studied within the same agroecosystem. In this study, we simultaneously investigated biomass production and SCS from three conventional agricultures and from four CRs in central Denmark. Averaged across the five-year rotations, CRs significantly increased biomass production by 32%. Meanwhile, CRs had no effect on the SCS, even when examined by equivalent soil mass method (by considering changes in soil bulk density). Changes in biomass production were not significantly correlated with the corresponding changes in SCS, whereas relatively increases in SCS were closely associated with lower biomass production variability. Our results highlight that future optimized CRs should aim to enhance biomass production and SCS simultaneously, not only for the climate mitigation benefits but also for increasing biomass production stability.
DOI: 10.6084/m9.figshare.9250862
title: Database: Optimized cropping rotations increase biomass production without changing soil carbon stock

description: To meet the twin key challenges from food security and climate change, optimized cropping rotations (CRs) should aim to maximize biomass production while minimizing carbon (C) footprints. However, the effects of CRs on biomass production and soil carbon stock (SCS) remain poorly understood, because they are not frequently studied within the same agroecosystem. In this study, we simultaneously investigated biomass production and SCS from three conventional agricultures and from four CRs in central Denmark. Averaged across the five-year rotations, CRs significantly increased biomass production by 32%. Meanwhile, CRs had no effect on the SCS, even when examined by equivalent soil mass method (by considering changes in soil bulk density). Changes in biomass production were not significantly correlated with the corresponding changes in SCS, whereas relatively increases in SCS were closely associated with lower biomass production variability. Our results highlight that future optimized CRs should aim to enhance biomass production and SCS simultaneously, not only for the climate mitigation benefits but also for increasing biomass production stability.
DOI: 10.5066/f7jm28kg
title: Hakalau Forest National Wildlife Refuge Vegetation Map 2016

description: This data layer identifies the boundaries of the mapped plant communities for the vegetation map of the Hakalau Forest National Wildlife Refuge Hakalau Forest Unit and adjacent lands, island of Hawaii.
DOI: 10.6084/m9.figshare.13625870
title: Differentially Methylated CpGs from each pairwise comparison

description: Differentially methylated cytosines between groups were identified using the Chi-squared test in methylKit and annotated using the genomation package (Akalin et al., 2012; Akalin et al., 2015 [77, 78]). Results were filtered for ‘in gene/promoter’ to remove intergenic regions, and for a minimum difference of &gt;20% methylation (Du et al., 2010; Slieker et al., 2015) [79, 80].