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Black opal is a rare variety of opal-CT, which is pigmented by organic matter (OM) and can therefore be considered as an example of geo-bio interaction (Gouzy et al., 2025). The locality of Volyn, Ukraine with its famous chamber pegmatites is well-known for interaction between OM and igneous rocks (Franz et al., 2017). The locality was recently renamed Khoroshiv, but because in the geological-mineralogical literature the name Volyn was introduced, we use this name here. The intrusion of the pegmatites is closely connected to the intrusion of the host rocks, granites of the southwestern part of the Korosten Pluton, and the intrusion age was determined as 1.76 Ga (Shumlyanskyy et al., 2021). OM was identified as kerite (fossilized remains of organisms; Franz et al., 2023, and references therein), and in fluid inclusions in beryl and topaz (Vozniak et al., 2012; Vozniak and Pavlyshin, 2008). Furthermore, formation of NH4-bearing feldspar (buddingtonite) and muscovite (tobelite) in breccia (identified together with the pegmatites) point to the interaction between decayed OM and the igneous minerals (Franz et al., 2017). The timing of the interaction between OM and igneous (and other) fluids is an important question (Franz et al., 2024), and therefore we also give age constraints on the formation of the black opal, which from textural arguments seems to be one of the latest mineral formations. We give detailed information about the sample sites, the macroscopic features of the samples of different types of opal, and the analytical procedures. The description of the black opal samples is presented in images from secondary electron microscopy (SEM), back-scattered electron images (BSE) obtained with by electron microprobe (EMPA), element distribution maps obtained by µXRF (X-ray fluorescence), Fourier-transformed infrared spectroscopy (IR), and X-ray powder (XRD) characterization. Chemical analyses were obtained by wave-length dispersive (WDS) analyses with the EMPA as well as by energy-dispersive (EDX) analyses with both the SEM and the EMPA instruments, to identify and characterize inclusions in the black opal. Trioctahedral Li-mica (polylithionite) is included by opal in one sample, and because this type of mica has not been described in detail from the Volyn peg-matites, we present the EMPA analytical data here in detail. The presence of OM, which is known to absorb U in sufficient amounts, allows dating by the U-Pb decay system. The results of the isotopic dating with the laser-ablation sector-field inductively-coupled mass spectroscopy system (LA-SF-ICP-MS) is presented for the selected individual do-mains in three samples. The operating conditions are summarized in a separated pdf document.
Emeralds from Colombia are among the most highly-prized and valuable gemstones. Their growth phenomena including information about the geology of the area were described in detail by Pig-natelli et al. (2015, 2022) and Schmetzer and Martayan (2023). Here, we present data obtained by scanning electron microscopy (SEM), electron microprobe analysis (EMPA), and infrared absorption spectroscopy (IR) for five emerald crystals. The surface of the crystals shows both, growth phe-nomena as well as dissolution phenomena, the latter as etch pits (EP). Such EP have been used previously for other beryl types, mainly from pegmatitic environments (Kurumathoor and Franz, 2018); the Colombian emeralds come from low-grade metamorphosed black shales and thus offer the possibility to extend the use of EP as a provenance indicator to other types of beryl deposits. Internal structures are manifested in chemical zoning, investigated by EMPA and micro-X-ray fluo-rescence (µXRF). The crystals are characterized chemically by EMPA, polarized IR spectroscopy of oriented crystals showed the presence of fluids in the channels of the beryl structure.
The periodic volcanic activity of Stromboli Volcano, Aeolian Islands, Italy, is interrupted by paroxysmal eruptions on a decadal interval. In 2019, two strong paroxysms on July 3rd and August 28th, ended a more than a decade long period of regular strombolian activity. During normal strombolian activity the volcano erupts highly porphyritic scoria and lava (HP) with a shoshonitic basalt composition. In paroxysmal eruptions the HP material is mingled with low porphyritic (LP) pumices. This dataset includes the first radiogenic isotope data on the bulk compositions of the LP and HP components erupted on July 3rd 2019, and a HP sample from the lava flow that followed the paroxysm, and a LP sample from the paroxysm on August 28th 2019. The analyzed radiogenic isotope ratios include Sr, Nd, Hf, and Pb. This dataset further includes in-situ EPMA and LA-ICP_MS measurements of major and trace elements in the glass, olivine, plagioclase and clinopyroxene phases.
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.
The datasets in this collection include input and output components of the seismic exposure model developed within the framework of the Earthquake Model Central Asia and used for seismic risk assessment. In particular the collection includes: - A dataset of around 7’000 individual building observations in Kyrgyzstan and Tajikistan collected using the Remote Rapid Visual Survey (RRVS) methodology developed at GFZ, along with the class schema used to map the individual taxonomic observations into vulnerability-related building classes. These are used to develop suitable prior distribution and to constrain locally the resulting exposure models - The seismic exposure models for the following central Asian countries: Kazakhstan , Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan, aggregated over a set of heterogeneous tessellations (geo-cells) The methodology employed for the development of the exposure models is described in Pittore, M., Haas, M., and Silva, V. (2020) “Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications”, Earthquake Spectra. Two versions of the models obtained with two different parameter settings are included. The models are provided in .csv and in .xml (nrml 0.5) format, for compatiliby with the OpenQuake hazard and risk assessment engine.
Version History 11 Sep 2019: Release of Version 1.1 with the following changes: (1) new licence: CC BY SA 4.0, modification of the title: removal of file name and version); (2) addition of ORIDs when available. The metadata of the first version 1.0 is available in the download folder.. Data and file names remain unchanged. The EMCA (Earthquake Model Central Asia) catalogue (Mikhailova et al., 2015) includes information for 33620 earthquakes that occurred in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan). The catalogue provides for each event the estimated magnitude in terms of MLH (surface wave magnitude) scale, widely used in former USSR countries.MLH magnitudes range from 1.5 to 8.3. Although the catalogue spans the period from 2000 BC to 2009 AD, most of the entries (i.e. 33378) describe earthquakes that occurred after 1900. The catalogue includes the standard parametric information required for seismic hazard studies (i.e., time, location and magnitude values). The catalogue has been composed by integrating different sources (using different magnitude scales) and harmonised in terms of MLH scale. The MLH magnitude is determined from the horizontal component of surface waves (Rautian and Khalturin, 1994) and is reported in most of the seismic bulletins issued by seismological observatories in Central Asia. For the instrumental period MLH magnitude was estimated, when not directly measured, either from body wave magnitude (Mb), the energy class (K) or Mpva (regional magnitude by body waves determined by P-wave recorded by short-period instruments) using empirical regression analyses. The following relationships were used to estimate MLH (see Mikhailova, internal EMCA report, 2014): (1) MLH=0.47 K-1.15 (2) MLH=1.34 Mb-1.89 (3) MLH=1.14 Mpva-1.45 When multiple scales were available for the same earthquake, priority was given to the conversion from K class. For the historical period, the MLH values were obtained from macroseismic information (Kondorskaya and Ulomov, 1996).
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 (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for 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
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.
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 (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for 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
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.
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