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# 1
Blanchet, Cécile L.
Abstract: The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (see supplemental references). The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores.csv"). The dataset will be updated bi-annually and might be extended to reach a global geographical extent and/or add other type of samples. This dataset contains two csv tables: "Dataset_Nd_Sr_isotopes.csv" and "Age_determination_Sediment_Cores.csv". "Dataset_Nd_Sr_isotopes.csv" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores.csv" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A human readable data description is provided in portable document format, as well. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (see supplemental references).
# 2
Blanchet, Cécile L.
Abstract: The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (e.g. Blanchet, 2018b, 2018a). The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments”, “rocks” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores_V2.txt"). A first version of the database was published in September 2018 and presented data for the African sector. A second version was published in April 2019, in which the dataset has been extended to reach a global extent. The dataset will be further updated bi-annually to increase the geographical resolution and/or add other type of samples. This dataset consists of two tab separated tables: "Dataset_Nd_Sr_isotopes_V2.txt" and "Age_determination_Sediment_Cores_V2.txt". "Dataset_Nd_Sr_isotopes_V2.txt" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores_V2.txt" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A full reference list is provided in the file “References_Database_Nd_Sr_isotopes_V2.rtf”. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (Blanchet, 2018b, 2018a).
# 3
Broerse, Taco • Norder, Ben • Picken, Stephen • Govers, Rob • Willingshofer, Ernst • (et. al.)
Abstract: This dataset provides strain and strain rate data on mixtures of plasticine, silicone oils and iron powder that has been used in slab break-of analogue experiments in the Tectonic Laboratory (TecLab) at Utrecht University (NL) as an analogue for viscously deforming lithosphere. The materials have been analyzed in a creep and recovery test, applying a parallel plate setup using an AR-G2 rheometer (by TA Instruments). The materials can in general be described as viscoelastic materials with a power-law rheology (see previous work on plasticine-silicone polymer mixtures Weijermars [1986], Sokoutis [1987], Boutelier et al. [2008]). For a couple of the tested materials we find a complementary Newtonian behavior at the low end of the tested stress levels, with a transition to power-law behavior at increasing stress. Furthermore, the materials exhibit elastic and anelastic (recoverable) deformation. The corresponding paper (Broerse et al., 2018) describes the rheology, while this supplement describes the raw data and important details of the measurement setup. The raw data concerns mostly (uncorrected) strain and strain rate data. The rheometry has been performed at the Advanced Soft Matter group at the Department of Chemical Engineering, Delft University of Technology, The Netherlands.
# 4
Korte, Monika • Brown, Maxwell
Abstract: Global spherical harmonic paleomagnetic field model LSMOD.2 describes the magnetic field evolution from 50 to 30 ka BP based on published paleomagnetic sediment records and volcanic data. It is an update of LSMOD.1, with the only difference being a correction to the geographic locations of one of the underlying datasets. The time interval includes the Laschamp (~41 ka BP) and Mono Lake (~34 ka BP) excursions. The model is given with Fortran source code to obtain spherical harmonic magnetic field coefficients for individual epochs and to obtain time series of magnetic declination, inclination and field intensity from 49.95 to 30 ka BP for any location on Earth. For details see M. Korte, M. Brown, S. Panovska and I. Wardinski (2019): Robust characteristics of the Laschamp and Mono lake geomagnetic excursions: results from global field models. Submitted to Frontiers in Earth Sciences
File overview: LSMOD.2 -- ASCII file containing the time-dependent model by a list of spline basis knot points and spherical harmonic coefficients for these knot points.LSfield.f -- Fortran source code to obtain time series predictions of declination, inclination and intensity from the model file.LScoefs.f -- Fortran source code to obtain the spherical harmonic coefficients for an individual age from the time-dependent model file. The data are licenced under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0) and the Fortran Codes under the Apache License, Version 2.0. The Fortran source code should work with any standard Fortran 77 or higher compiler. Each of the two program files can be compiled separately, all required subroutines are included in the files. The model file, LSMOD.1 or LSMOD.2, is read in by the executable program and has to be in the same directory. The programs work with interactive input, which will be requested when running the program.
# 5
Caricchi, Chiara • Lucchi, Renata Giulia • Sagnotti, Leonardo • Macrì, Patrizia • Di Roberto, Alessio • (et. al.)
Abstract: This data publication includes the paleomagnetic and rock magnetic dataset from two Calypso giant piston cores collected at the crest of the Bellsund (GS191-01PC) and Isfjorden (GS191-02PC) sediment drifts during the Eurofleets-2 PREPARED cruise, on board the R/V G.O. Sars (Lucchi et al., 2014). These sediments drift are located on the eastern side of the Fram Strait (western Spitsbergen margin).The dataset gave the opportunity to define the behavior of past geomagnetic field at high latitude and to constrain the palaeoclimatic events that occurred in a time framework spanning Marine Isotope Stage (MIS) 3 to Holocene (Caricchi et al., in press). The data are provided as raw data in .dat format and interpreted data in .xlx and tab-delimited text formats. The raw data files can be opened using a text-editor, MS Excel or equivalent software. The interpreted data are presented as a metadata table with definitions of the column heads and 5 individual tables with the content: - Metadata: definition of columns heads - Rock Magnetic-Paleomag Data 01: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); ARM/k (A/m); MDF (mT); ΔGRM/ΔNRM; NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core GS191-01PC - Rock Magnetic-Paleomag Data 02: down core variation of rock magnetic and paleomagnetic data [k (10E-05 SI); ARM (A/m); ARM/k (A/m); MDF (mT); ΔGRM/ΔNRM; NRM (A/m); MAD (°); Incl PCA (°) Decl PCA (°)] for Core GS191-02PC - Cores Correlation: Depth of Core GS191-02PC and depth of Core GS191-02PC correlated to Core GS191-01PC, NRM (A/m); ARM(A/m) and RPI down-core variations for core GS191-02PC; Depth of Core GS191-01PC NRM (A/m); ARM(A/m) and RPI down-core variations for core GS191-01PC; tie points values. - Age Model 01: age model for Core GS191-01PC - Age Model 02: age model for Core GS191-01PC
Raw data were measured at the paleomagnetic laboratory of INGV and have been analysed by DAIE software (Sagnotti, 2013). The obtained along-core variation of rock magnetic and paleomagnetic trends have been integrated with the distribution of characteristic lithofacies and the 14C ages in order to define high-resolution correlation between the cores. Core to core correlation has been computed by means of StratFit software (Sagnotti and Caricchi, 2018). The correlation process is based on the Excel forecast function and linear regression between subsequent couples of selected tie-points. This process results in the estimate of the equivalent depth of the correlated curve (core GS191-02 PC) into the depth scale of the “master” curve (GS191-01PC). Using the same method and taking into account the constraints provided by the calibrated radiocarbon ages and the litostratigraphic information, PREPARED cores have been compared to RPI and inclination variations expected at the core sites according to global geomagnetic field models (SHA.DIF.14k of Pavón-Carrasco et al., 2014; GGF.110k of Panovska et al., 2018).
# 6
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables: We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference): 1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
# 7
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
# 8
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1:improved calculation and mapping, for details see README.PDF
# 9
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables: We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference): 1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1improved calculation and mapping, for details see README.PDF
This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
# 10
Lussem, Ulrike • Herbrecht, Marina
Abstract: This data set contains the land use classification of 2018 for the study area of the CRC/Transregio 32: "Patterns in Soil-Vegetation-Atmosphere Systems: monitoring, modelling and data assimilation", which corresponds to the catchment of the river Rur. The study area is mainly situated in the western part of North Rhine-Westphalia (Germany) and parts of the Netherlands and Belgium. The classification is provided in GeoTIFF and in ASCII format. Spatial resolution: 15 m; projection: WGS84, UTM Zone 32N.
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