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Mineral chemistry of chromite, orthopyroxene and plagioclase of chromitite layers in the Lower and Middle Group of the northwestern Bushveld Complex

This data set contains mineral chemical analyses of chromite, orthopyroxene and plagioclase of five chromitite layers and their immediate host rocks from drill core ZK135 of the northwestern Bushveld Complex. The sampled interval of ZK135 covers the transition of the Lower and Middle Group chromitites (LG6, LG6a, MG1, MG2, MG2 II). Detailed geochemical profiles are presented by data sheets and graphically to reveal small-scale variations in mineral chemistry.Mineral chemical analyses were conducted on drill core material from borehole ZK135 from the Thaba mine, operated by Cronimet Chrome Mining SA (Pty) Ltd. Briefly, each layer is hosted in pyroxenite and comprises a main chromitite layer and in some chromitites additional stringers above or below the main layer. Chromitites are composed of chromite, which are cemented by intercumulus plagioclase and orthopyroxene oikocrysts. A detailed study of the orthopyroxene oikocrysts in MG1 was published by Kaufmann et al. (2018). Additionally, euhedral to subhedral cumulus orthopyroxene is present in the LG6a, MG1 and MG2 layers. Pyroxenitic partings, small layers of pyroxenite within the chromitite, occur throughout the LG6 to MG2 layers. The main mineral phases chromite, orthopyroxene and plagioclase were analyzed by electron microprobe in each layer and the adjacent pyroxenitic host rocks.Major-element compositions of chromite, orthopyroxene and plagioclase were analyzed by electron microprobe at the Museum für Naturkunde Berlin with a JEOL JXA-8500F EMP equipped with a field emission cathode and five wavelength-dispersive spectrometers. For more information please consult the data description file and Kaufmann et al. (2019) to which these data are supplementary material.

Mineral reflectance spectra and chemistry of 29 rare earth-bearing minerals and rare earth oxide powders including niobium- and tantalum-oxide powder

The data set contains mineral chemical analyses of 32 rare earth element (REE) -bearing minerals (REMin) and rare-earth oxides (REO) and their corresponding hyperspectral spectra. The hyperspectral data was acquired with the HySpex system in a range of 400 – 2500 nm and is presented in a spectral library. The resulting reflectance data are scaled from 0 - 10000. The two Rare Earth Element (REE) libraries consist of the spectra of 16 rare earth oxides powders (REO) and 14 REE-bearing minerals (REMin). In addition, it contains the spectra of niobium- and tantalum oxide, two elements technically not part of the REEs. The spectral library presented here is part of a bigger collection of spectral libraries including copper-bearing surface samples from Apliki copper-gold-pyrite mine (Koerting et al., 2019a, http://doi.org/10.5880/GFZ.1.4.2019.005) and copper-bearing minerals (Koellner et al., 2019, http://doi.org/10.5880/GFZ.1.4.2019.003). These libraries aim to give a spectral overview of important resources and ore mineralization.

Mineral spectra and chemistry of 20 copper bearing minerals

The data set contains mineral chemical analyses of 20 different copper bearing minerals and their corresponding hyperspectral spectra. The hyperspectral data were acquired with the HySpex system in a range of 400 – 2500 nm and are presented in a spectral library. Detailed information about the mineral specimen, sample area and geochemistry is presented in the data sheets and associated data description. The spectral library presented here is part of a bigger collection of spectral libraries including samples from rare-earth minerals, rare-earth-oxides (Koerting et al., 2019a, http://doi.org/10.5880/GFZ.1.4.2019.004) and field samples from a copper-gold-pyrite mine in the Republic of Cyprus (Koerting et al., 2019b, http://doi.org/10.5880/GFZ.1.4.2019.005).

Environmental Footprinting - Der ökologische Fußabdruck von Produkten und Dienstleistungen

Die Ermittlung und Bewertung der Umweltwirkungen von Produkten und Dienstleistungen und ihre Kommunikation im Rahmen von Nachhaltigkeitsstrategien von Unternehmen oder als Teil des Marktgeschehens gegenüber privaten und öffentlichen Verbrauchern erfolgt mit verschiedenen Instrumenten. Als Weiterentwicklung von Ökobilanzen hat sich in den letzten Jahren der Carbon Footprint (CO2-Bilanz) oder der Water Footprint (Wassernutzung und deren Bewertung) etabliert. Neuere Indikatoren versuchen noch stärker die nachhaltige Nutzung der natürlichen Ressourcen zu adressieren. Hierfür wurden beispielsweise der Ecological Footprint, der die Nutzung von biologisch produktiven Land- und Wasserflächen misst oder der umweltgewichtete Materialverbrauch (Environmental Weighted Material Consumption) entwickelt. Diese und weitere Indikatoren sollen mit dem Vorhaben auf ihre Anwendbarkeit im Hinblick auf Datenverfügbarkeit, Aufwand und Aussagekraft für bestehende Instrumente zur Produktbewertung, wie Umweltzeichen, untersucht werden.

Buildings data from Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan

The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code description lon longitude in fraction of degrees lat latitude in fraction of degrees object_id unique id of the building surveyed MAT_TYPE Material Type MAT_TECH Material Technology MAT_PROP Material Property LLRS Type of Lateral Load-Resisting System LLRS_DUCT System Ductility HEIGHT Height YR_BUILT Date of Construction or Retrofit OCCUPY Building Occupancy Class - General OCCUPY_DT Building Occupancy Class - Detail POSITION Building Position within a Block PLAN_SHAPE Shape of the Building Plan STR_IRREG Regular or Irregular STR_IRREG_DT Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE Type of Irregularity NONSTRCEXW Exterior walls ROOF_SHAPE Roof Shape ROOFCOVMAT Roof Covering ROOFSYSMAT Roof System Material ROOFSYSTYP Roof System Type ROOF_CONN Roof Connections FLOOR_MAT Floor Material FLOOR_TYPE Floor System Type FLOOR_CONN Floor Connections. For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.

EMCA Seismic exposure model for Turkmenistan

Multi-resolution exposure model for seismic risk assessment in Turkmenistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Turkmenistan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process) For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Radiogenic isotope compositions of eruption products from the 2019 paroxysmal eruptions at Stromboli Volcano

Other

EMCA Seismic exposure model for the Kyrgyz Republic

Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

EMCA Seismic exposure model for Kazakhstan

Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan

The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code, description: lon, longitude in fraction of degrees lat, latitude in fraction of degrees object_id, unique id of the building surveyed MAT_TYPE,Material Type MAT_TECH,Material Technology MAT_PROP,Material Property LLRS,Type of Lateral Load-Resisting System LLRS_DUCT,System Ductility HEIGHT,Height YR_BUILT,Date of Construction or Retrofit OCCUPY,Building Occupancy Class - General OCCUPY_DT,Building Occupancy Class - Detail POSITION,Building Position within a Block PLAN_SHAPE,Shape of the Building Plan STR_IRREG,Regular or Irregular STR_IRREG_DT,Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE,Type of Irregularity NONSTRCEXW,Exterior walls ROOF_SHAPE,Roof Shape ROOFCOVMAT,Roof Covering ROOFSYSMAT,Roof System Material ROOFSYSTYP,Roof System Type ROOF_CONN,Roof Connections FLOOR_MAT,Floor Material FLOOR_TYPE,Floor System Type FLOOR_CONN,Floor Connections For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.

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