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  • 1
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    A suite of global, cross-scale topographic variables for environmental and biodiversity modeling (2018)
    Springer Nature
    Details
    Publication Date: 2018-03-20
    Electronic ISSN: 2052-4463
    Topics: Nature of Science, Research, Systems of Higher Education, Museum Science
    Published by Springer Nature
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    PAPER CURRENT
  • 2
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    Global data products help assess changes to ocean carbon sink (2012)
    American Geophysical Union
    Details
    Publication Date: 2012-03-20
    Print ISSN: 0096-3941
    Electronic ISSN: 2324-9250
    Topics: Geosciences
    Published by American Geophysical Union
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    PAPER CURRENT
  • 3
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    A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, digital elevation model source: global 250 m GMTED, links to files in GeoTIFF format
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Keywords: File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 1960 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 4
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    A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, digital elevation model source: near-global 90 m SRTM, links to files in GeoTIFF format
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Keywords: File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 1000 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 5
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    Surface Ocean CO2 Atlas (SOCAT) V1.4 (2013)
    PANGAEA
    In:  Supplement to: Pfeil, Benjamin; Olsen, Are; Bakker, Dorothee C E; Hankin, Steven; Koyuk, Heather; Kozyr, Alexander; Malczyk, Jeremy; Manke, Ansley; Metzl, Nicolas; Sabine, Christopher L; Akl, John; Alin, Simone R; Bellerby, Richard G J; Borges, Alberto Vieira; Boutin, Jacqueline; Brown, Peter J; Cai, Wei-Jun; Chavez, Francisco P; Chen, Arthur; Cosca, Catherine E; Fassbender, Andrea J; Feely, Richard A; González-Dávila, Melchor; Goyet, Catherine; Hardman-Mountford, Nicolas J; Heinze, Christoph; Hood, E Maria; Hoppema, Mario; Hunt, Christopher W; Hydes, David; Ishii, Masao; Johannessen, Truls; Jones, Steve D; Key, Robert M; Körtzinger, Arne; Landschützer, Peter; Lauvset, Siv K; Lefèvre, Nathalie; Lenton, Andrew; Lourantou, Anna; Merlivat, Liliane; Midorikawa, Takashi; Mintrop, Ludger J; Miyazaki, Chihiro; Murata, Akihiko; Nakadate, Akira; Nakano, Yoshiyuki; Nakaoka, Shin-Ichiro; Nojiri, Yukihiro; Omar, Abdirahman M; Padín, Xose Antonio; Park, Geun-Ha; Paterson, Kristina; Pérez, Fiz F; Pierrot, Denis; Poisson, Alain; Ríos, Aida F; Santana-Casiano, Juana Magdalena; Salisbury, Joe; Sarma, Vedula V S S; Schlitzer, Reiner; Schneider, Bernd; Schuster, Ute; Sieger, Rainer; Skjelvan, Ingunn; Steinhoff, Tobias; Suzuki, Toru; Takahashi, Taro; Tedesco, Kathy; Telszewski, Maciej; Thomas, Helmuth; Tilbrook, Bronte; Tjiputra, Jerry; Vandemark, Doug; Veness, Tony; Wanninkhof, Rik; Watson, Andrew J; Weiss, Ray F; Wong, Chi Shing; Yoshikawa-Inoue, Hisayuki (2013): A uniform, quality controlled Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 5(1), 125-143, https://doi.org/10.5194/essd-5-125-2013
    Details
    Publication Date: 2024-02-02
    Description: A well-documented, publicly available, global data set of surface ocean carbon dioxide (CO2) parameters has been called for by international groups for nearly two decades. The Surface Ocean CO2 Atlas (SOCAT) project was initiated by the international marine carbon science community in 2007 with the aim of providing a comprehensive, publicly available, regularly updated, global data set of marine surface CO2, which had been subject to quality control (QC). Many additional CO2 data, not yet made public via the Carbon Dioxide Information Analysis Center (CDIAC), were retrieved from data originators, public websites and other data centres. All data were put in a uniform format following a strict protocol. Quality control was carried out according to clearly defined criteria. Regional specialists performed the quality control, using state-of-the-art web-based tools, specially developed for accomplishing this global team effort. SOCAT version 1.5 was made public in September 2011 and holds 6.3 million quality controlled surface CO2 data points from the global oceans and coastal seas, spanning four decades (1968-2007). Three types of data products are available: individual cruise files, a merged complete data set and gridded products. With the rapid expansion of marine CO2 data collection and the importance of quantifying net global oceanic CO2 uptake and its changes, sustained data synthesis and data access are priorities.
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    Type: Dataset
    Format: application/zip, 1851 datasets
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  • 6
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    A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format (2018)
    PANGAEA
    In:  Supplement to: Amatulli, Giuseppe; Domisch, Sami; Tuanmu, Mao-Ning; Parmentier, Benoit; Ranipeta, Ajay; Malczyk, Jeremy; Jetz, Walter (2018): A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5, 180040, https://doi.org/10.1038/sdata.2018.40
    Details
    Publication Date: 2024-04-20
    Description: Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale basic research and analytical applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains.
    Type: Dataset
    Format: application/zip, 2 datasets
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