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We analyse new genomic data (0.05–2.95x) from 14 ancient individuals from Portugal distributed from the Middle Neolithic (4200–3500 BC) to the Middle Bronze Age (1740–1430 BC) and impute genomewide diploid genotypes in these together with published ancient Eurasians. While discontinuity is evident in the transition to agriculture across the region, sensitive haplotype-based analyses suggest a significant degree of local hunter-gatherer contribution to later Iberian Neolithic populations. A more subtle genetic influx is also apparent in the Bronze Age, detectable from analyses including haplotype sharing with both ancient and modern genomes, D-statistics and Y-chromosome lineages. However, the limited nature of this introgression contrasts with the major Steppe migration turnovers within third Millennium northern Europe and echoes the survival of non-Indo-European language in Iberia. Changes in genomic estimates of individual height across Europe are also associated with these major cultural transitions, and ancestral components continue to correlate with modern differences in stature. Index for VCF fileIndex for VCF filepost_imputation_Martiniano_et_al_2017_public.vcf.gz.tbiVCF file containing imputed genotype data belonging to 67 newly sequenced and publicly available ancient samples.VCF file containing imputed genotype data belonging to 67 newly sequenced and publicly available ancient samples which we analysed in Martiniano et al. (2017).post_imputation_Martiniano_et_al_2017_public.vcf.gzREADME_Martiniano_et_al_2017Description of the methods used for genotype imputation.

Monthly gravity fields from Swarm A, B, and C, using the integral equation approach with short arcs. Software: GROOPS; Approach: Short-arc approach (Mayer-Gürr, 2006); Kinematic orbit product: IfG Graz: https://ftp.tugraz.at/outgoing/ITSG/satelliteOrbitProducts/operational/Swarm-1/kinematicOrbit/; Arc length: 45 minutes; Reference GFM: GOCO06s (Kvas et. al, 2021), monthly mean has been added back to the solution; Drag model: NRLMSIS2; SRP and EARP and EIRP models: Vielberg & Kusche (2020); Empirical parameters: + for non-gravitational accelerations (sum of Drag+SRP+EIRP+EARP): Bias per arc and direction; + for Drag: Scale per arc and direction; + for radiation pressure (sum of SRP+EIRP+EARP): Scale per day and direction; Non-tidal model: Atmosphere and Ocean De-aliasing Level 1B RL06 (Dobslaw et al., 2017); Ocean tidal model: 2014 finite element solution FES2014b (Carrere et al., 2015); Atmospheric tidal model: AOD1B RL06 atmospheric tides ; Solid Earth tidal model: IERS2010; Pole tidal model: IERS2010; Ocean pole tidal model: IERS2010 (Desai 2002); Third-body perturbations: Sun, Moon, Mercury, Venus, Mars, Jupiter, and Saturn, following the JPL DE421 Planetary and Lunar Ephemerides (Folkner et al., 2014).

Heterogeneous data collection in the marine environment has led to large gaps in our knowledge of marine species distributions. To fill these gaps, models calibrated on existing data may be used to predict species distributions in unsampled areas, given that available data are sufficiently representative. Our objective was to evaluate the feasibility of mapping cetacean densities across the entire Mediterranean Sea using models calibrated on available survey data and various environmental covariates. We aggregated 302,481 km of line transect survey effort conducted in the Mediterranean Sea within the past 20 years by many organisations. Survey coverage was highly heterogeneous geographically and seasonally: large data gaps were present in the eastern and southern Mediterranean and in non-summer months. We mapped the extent of interpolation versus extrapolation and the proportion of data nearby in environmental space when models calibrated on existing survey data were used for prediction across the entire Mediterranean Sea. Using model predictions to map cetacean densities in the eastern and southern Mediterranean, characterised by warmer, less productive waters, and more intense eddy activity, would lead to potentially unreliable extrapolations. We stress the need for systematic surveys of cetaceans in these environmentally unique Mediterranean waters, particularly in non-summer months. Mediterranean gap analysis in environmental spaceThis .zip folder contains the data and an R script to reproduce the gap analysis documented in Mannocci et al. 2018.Data_Scientific_Reports.zip

This data set contains various data derived from rock and rock analogue testing and analogue models which are presented in Rosenau et al. (2016) to which these data are supplement to..A first group of data contains animations of complementary analogue and numerical models of subduction zone earthquake cycles (A). A second group comprises analogue earthquake data and time series of surface deformation derived from scale models of subduction zone earthquake cycles (B). A third group consist of time series of stick-slip experiments using a ring shear tester (C). Finally, friction data both from rocks and rock analogue materials (D) as well as elasticity data from rock analogues are presented (E).See the Description of data and the List of files in the Data Download section for additional data description.

The Alps are one of the best studied mountain ranges in the world, yet significant unknowns remain regarding their crustal structure and density distribution at depth. Previous published interpretations of crustal features within the orogen have been primarily based upon 2D seismic sections, and those that do integrate multiple geo-scientific datasets in 3D, have either focused on smaller sub-sections of the Alps or included the Alps, in low resolution, as part of a much larger study area. Therefore the generation of a 3D, crustal scale, gravity constrained, structural model of the Alps and their forelands at an appropriate resolution has been created here to more accurately describe crustal heterogeneity in the region. The study area of this work focuses on a region of 660 km x 620 km covering the vast majority of the Alps and their forelands are included, with the Central and Eastern Alps and the northern foreland being the best covered regions. Surface Generation All referenced data was integrated to constrain sub-surface lithospheric features including: previous regional scale gravitationally and seismically constrained models of the TRANSALP study area, the Upper Rhine Graben, the Mollasse Basin and the Po Basin; continental scale integrative best fit models (EuCRUST-07 and EPcrust); and seismic reflection depths from numerous published deep seismic surveys (e.g. ALP’75, EGT’86, ALP 2002 and EASI). The software package Petrel was used for the creation and visualisation of the modelled surfaces in 3D, representing the key structural and density contrasts within the region. All surfaces were generated with a grid resolution of 20 km x 20 km using Petrel’s convergent interpolation algorithm. During interpolation, a hierarchy of data source types was used in the case of contradiction between the different data sources and was as follows: 1. regional scale, gravitationally and seismically constrained models; 2. regional scale, seismically constrained models; 3. individual seismic reflection surfaces and interpreted sections; 4. continental scale, seismically constrained, integrative best fit models. No subduction interfaces were modelled. Topography and bathymetry were taken from ETOPO1 and the LAB from Geissler (2010). Gravity Modelling The generated surfaces and the calculated free-air anomaly from the global gravity model EIGEN-6C4, at a fixed height of 6 km above the datum were used in the 3D gravity modelling software IGMAS+ for the constrained of lithospheric density distribution. The layers of the generated model were split laterally into domains of different density, to reflect the heterogeneous nature of the crust within the region. Densities used in the initial structural model were derived from empirical P wave velocity to density conversions (Brocher, 2005) from the input seismic data sources. The densities were then modified, through multiple iterations, until the resulting model produced a gravity field within ± 25 mGal of the observed one. Surfaces generated as part of the integration work were not modified. Files The surface depths, thicknesses and densities of the model can be found as tab separated text files for each individual layer of the model (Unconsolidated Sediments, Consolidated Sediments, Upper Crust, Lower Crust and Lithospheric Mantle). The columns in each file are identical: the Easting is given in the “X COORD (UTM Zone 32N)”, Northing in the “Y COORD (UTM Zone 32N)”, the top surface depth of each layer is given as TOP (m.a.s.l), the thickness of each layer is given as THICKNESS (m), and the bulk density of that layer is given as DENSITY (Kg/m^3).

Core samples have been taken for complementary laboratory seismic measurements and mineralogical analyses on whole rock core from the COSC-1 borehole, Sweden (UTM 63.3124, 13.5259). These samples were used to provide and characterize the seismic properties (i.e., seismic velocities and anisotropy) of the drilled rocks from the highly metamorphosed and deformed Seve Nappe Complex, an orogenic thrust zone in the Scandinavian Caledonides, in central Sweden. The laboratory seismic and mineralogical analysis in general comprises three distinct measurements (i.e., data sets), which will be described in detail in the following subsections: (1) P- and S-wave laboratory seismic measurements on three perpendicular core plugs, under different confining (hydrostatic) pressure conditions (10 + 6 samples), (2) Bulk mineralogy of core plugs using X-ray powder diffraction (XRD) and mineral chemical composition measurements using an electron probe micro-analyzer (EPMA, here microprobe), on 10 thin sections and (3) Microstructural investigations based on electron-backscatter diffraction analyses on 5 thin sections. The laboratory seismic measurements were initially conducted on 6 samples by Wenning et al. (2016) and extended by another 10 samples by Kästner et al. (2020). Despite these authors were using the same sensor setup, the provided data files may differ due to individual acquisition parameters. Where different acquisition, processing, or calibration parameters are used this is indicated in the text using the abbreviations FK and QW referring to each examiner and their related sample measurements. International Geo Sample Numbers (IGSN) are provided for each core sample in the complete sample data table.

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. Fil: Aguirre, Marina Laura. Universidad Nacional de La Plata. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata; Argentina. Fil: Richiano, Sebastián Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Geología y Paleontología; Argentina Facultad de Ciencias Naturales y Museo

We are providing the geophysical data used to develop a gravity validated 3D lithospheric configuration of the Caribbean and north South American plates. The sources of these data are described in Section 4 of this README. Republication of subsets of these data are with permission of the authors or allowed by the licences of the input data. This data repository contains the lithospheric layers of the gravity validated 3D structural and density model of the Caribbean and north South American plates. In this model, the integration of different publicly available geophysical datasets was made, after an interpolation to a homogeneous spatial resolution of 25 km was performed. The data repository also contains the average density of the crystalline crust obtained after forward modelling the gravity anomalies. Additionally, the rotation files of the GPlates reconstructions of the Caribbean Large Igneous Plateau (CLIP) back to 90 Ma are included. This kinematic analysis was based on different reconstructions previously published by other authors. Further information and citations are given on the README file associated to this data repository.