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Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland. https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html Data set contains 3D model in dxf file, additional images, selected handheld spectra. Publication summary: We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain. {"references": ["Jackisch, R. et al. Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland, Solid Earth Discuss. [preprint], https://doi.org/10.5194/se-2021-133, in review, 2021."]}

This dataset contains MALDI-TOF spectral data in .mzML format for zooarcheology by mass spectrometry (ZooMS) samples referenced in Bori�� et al. (2021). Folder names correspond to the ZooMS sample names referenced in the article. Files in the same folder are technical replicates.

This dataset contains MALDI-TOF spectral data in .mzML format for zooarcheology by mass spectrometry (ZooMS) samples referenced in Borić et al. (2021). Folder names correspond to the ZooMS sample names referenced in the article. Files in the same folder are technical replicates.

3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:10.1016/j.marpetgeo.2021.105344. Models are built using surface-based modelling approach (doi:10.1007/s11004-018-9764-8). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance. For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results: - Only clinoforms and facies boundaries - 1 dolomite body per clinothem (~20% dolomite) - 2 dolomite bodies per clinothem (~40% dolomite) - 3 dolomite bodies per clinothem (~60% dolomite) - 4 dolomite bodies per clinothem (~80% dolomite) Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats. Flow simulation settings are provided for IC-FERST in .mpml files (multifluids.github.io) Flow simulation results are provided as: 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month. In- and outflow rates and volumetric proportions per phase in .csv Naming of files and folders: Sxxxxxx_yyyyz where: 'xxxxxx' is the stochastic seed number used to sample the input statistics and create the geological model 'yyyy' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies 'z' corresponds to the number of dolomite bodies per clinothem {"references": ["https://doi.org/10.1016/j.marpetgeo.2021.105344", "https://doi.org/10.1007/s11004-018-9764-8"]}

3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:10.1016/j.marpetgeo.2021.105344. Models are built using surface-based modelling approach (doi:10.1007/s11004-018-9764-8). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance. For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results: - Only clinoforms and facies boundaries - 1 dolomite body per clinothem (~20% dolomite) - 2 dolomite bodies per clinothem (~40% dolomite) - 3 dolomite bodies per clinothem (~60% dolomite) - 4 dolomite bodies per clinothem (~80% dolomite) Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats. Flow simulation settings are provided for IC-FERST in .mpml files (multifluids.github.io) Flow simulation results are provided as: 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month. In- and outflow rates and volumetric proportions per phase in .csv Naming of files and folders: Sxxxxxx_yyyyz where: 'xxxxxx' is the stochastic seed number used to sample the input statistics and create the geological model 'yyyy' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies 'z' corresponds to the number of dolomite bodies per clinothem {"references": ["https://doi.org/10.1016/j.marpetgeo.2021.105344", "https://doi.org/10.1007/s11004-018-9764-8"]}

These are ice-penetrating radar data connecting the newly chosen Beyond EPICA Little Dome C core site to the EPICA Dome C core site, collected in late 2019. These data are presented in a paper in The Cryosphere (https://doi.org/10.5194/tc-2020-345), where full processing and collection methods are described. Data collection and processing Data were collected using a new very high frequency (VHF) radar, built by the Remote Sensing Center at the University of Alabama (Yan et al., 2020). The system transmitted 8 us chirps, with peak transmit power of 125--250 W per channel, at 200 MHz center frequency and 60 MHz bandwidth. There were 5--8 operational channels at various points. The antennas were pulled behind a tracked vehicle, with controlling electronics in the rear of the vehicle. Data were collected at travel speeds of 2--3.5 m/s. Data processing consisted of coherent integration (i.e. unfocused SAR), pulse compression, motion compensation (by tracking internal horizons), coherent channel combination, and de-speckling using a median filter. Two-way travel time was converted to depth assuming a correction of 10 m of firn-air and a constant radar wave speed of 168.5 m/us (e.g., Winter et al., 2017). After other processing was complete, different radargrams were spliced together to create a continuous profile extending from EPICA Dome C to the Beyond EPICA Little Dome C core site, and then the data were interpolated to have constant, 10-m horizontal spacing. The re-interpolated data were used for horizon tracing, which was done semi-automatically to follow amplitude peaks between user-defined clicks. For the bed reflection, we always picked the first notable return in the region of the bed. File description The file format is hdf5, which can be read with many programming languages. There are three groups in the file: processed_data, picks, and geographic_information. The processed_data gives the return power matrix (dB), and the depth (m) and two-way travel time (us) for the fast-time dimension. The picks give the depths (m) of different reflecting horizons traced in the corresponding paper. Ages and age uncertainties (kyr), interpolated from the AICC2012 timescale, are included as attributes on each pick. Bed and basal unit picks are included (ageless). The geographic_information gives latitude and longitude (decimal degrees), and the distance along-profile (km). References Bazin, L., Landais, A., Lemieux-Dudon, B., Toyé Mahamadou Kele, H., Veres, D., Parrenin, F., Martinerie, P., Ritz, C., Capron, E., Lipenkov, V., Loutre, M. F., Raynaud, D., Vinther, B., Svensson, A., Rasmussen, S. O., Severi, M., Blunier, T., Leuenberger, M., Fischer, H., Masson-Delmotte, V., Chappellaz, J., and Wolff, E.: An optimized multi-proxy, multi-site Antarctic ice and gas orbital chronology (AICC2012): 120-800 ka, 9, 1715–1731, https://doi.org/10.5194/cp-9-1715-2013, 2013. Winter, A., Steinhage, D., Arnold, E. J., Blankenship, D. D., Cavitte, M. G. P., Corr, H. F. J., Paden, J. D., Urbini, S., Young, D. A., and Eisen, O.: Comparison of measurements from different radio-echo sounding systems and synchronization with the ice core at Dome C, Antarctica, 11, 653–668, https://doi.org/10.5194/tc-11-653-2017, 2017. Yan, J.-B., Li, L., Nunn, J. A., Dahl-Jensen, D., O’Neill, C., Taylor, R. A., Simpson, C. D., Wattal, S., Steinhage, D., Gogineni, P., Miller, H., and Eisen, O.: Multiangle, Frequency, and Polarization Radar Measurement of Ice Sheets, 13, 2070–2080, https://doi.org/10.1109/JSTARS.2020.2991682, 2020. These data were generated in the frame of Beyond EPICA. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 815384 (Oldest Ice Core). It is supported by national partners and funding agencies in Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland, The Netherlands and the United Kingdom. Logistic support is mainly provided by PNRA and IPEV through the Concordia Station system. The radar shipment and personnel transportation to Antarctica were provided by U.S. NSF under grant 1921418, which also partly supported the development of the VHF radar. Radar development was further supported by internal funding from the University of Alabama. DL and DDJ were partially supported by the Villum Foundation (grant number 16572). Any opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency or other national funding bodies.

Liang Abu is an archaeological site in East Kalimantan, Indonesia. This dataset describes the relationships between pottery fragments found during excavations (2009-2012). Two types of relationships are defined. A connection relationship refers to a physical connection between two fragments that were part of the same object. A similarity relationship between fragments is defined if there is an acceptable likelihood that those fragments were part of the same object. The dataset is composed of three tables: relations-connection.csv (56x2): "connection" relationships between fragments. matrix. Each line describes a connection relationship between two fragments. There respective unique identifiers are given in column "frg_id1" and in column "frg_id2". relations-similarity.csv (47x2): "similarity" relationships between fragments. matrix. Column "frg_id" gives a fragment unique identifier, column "su_id" gives a unique identifier for the group of similar fragments it belongs to (similarity unit). fragments.csv (177x8): contextual information concerning each fragment, with the following columns: frg_id: unique fragment identifier layer: stratigraphic layer zmin: minimal depth in centimetres where the fragment was found zmax: maximal depth in centimetres where the fragment was found square: square where the fragment was found sherd.type: type of pottery sherd thickness: thickness of the fragments in millimetres length: length of the fragments in millimetres {"references": ["Plutniak, Sebastien, Astolfo Araujo, Simon Puaud, Jean-Georges Ferrie, Adhi Agus Oktaviana, Bambang Sugiyanto, Jean-Michel Chazine et Francois-Xavier Ricaut. 2015. \"Borneo as a half empty pot: Pottery assemblage from Liang Abu, East Kalimantan, Quaternary International, doi: 10.1016/j.quaint.2015.11.080."]}

This data set contains time series of two-metre air temperature (tas), surface temperature (ts), total precipitation (pr), oxygen-18 isotopic composition in precipitation (oxy), and deuterium isotopic composition in precipitation (dtr) from the past-millennium (800-1999 CE) simulation of the fully coupled ECHAM5/MPI-OM-wiso atmosphere-ocean general circulation model equipped with stable isotope diagnostics (Sjolte et al., 2018, Werner et al., 2016) used in the publication of Münch et al. (2021). The data here are provided for the Antarctic region, i.e., all model grid cells south of 60° S. The model's atmospheric component was run with a T31 spectral resolution (3.75° x 3.75°) and with 19 vertical levels, resulting in a total of N = 768 model grid cells covered by this data set. Note, however, that all time series off the continent of Antarctica have been set to NA values, so that the effectively available number of model grid cells is Neff = 442. Time series are provided at the original monthly resolution of the model output and on annual resolution obtained from the monthly resolution data. At annual resolution, the temperature and isotopic composition data are available as normal time averages and as precipitation-weighted time averages. In addition to the time series, the spatial field of time-invariant means is supplied, also as normal and precipitation-weighted time averages. Data are available as netcdf files and as R data files. In addition, processing code (bash and R scripts) are provided to reproduce the processing from monthly to annnual and time-invariant resolution and to read the data into the R data format. To process the R data, you will need the CRAN packages "ncdf4" and "lubridate", and the package "pfields" available on GitHub (see References). {"references": ["pfields: An R package to analyse gridded field data. https://github.com/EarthSystemDiagnostics/pfields"]}

The dataset consists of a spreadsheet containing data on GPS surveys, dynamic topography extracted from published models (gplates.org), Shell preservation scoring, Strontium Isotopic Stratigraphy ages, and Global mean Sea Level calculations. Facultad de Ciencias Naturales y Museo

Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The GBDB data associated with this article can be found at https://www.geobiodiversity.com/