Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details. The Namoi Leapfrog geological model developed by CDM Smith for the future Santos' Gunnedah Coal Seam Gas Project (NTEC, 2013) using the software package Leapfrog Hydro™. The geological model was available to the Namoi subregion Assessment team and is considered to be fit for purpose as the basis for the Namoi geological model, developed for the Namoi BA programme. Dataset History This Namoi Leapfrog Model was created by CDM Smith and obtained from Santos Ltd for use in the Namoi Bioregional Assessment. There is little documentation available for the geological model, however some is available as part of the Groundwater Impact Assessment for the Gunnedah Coal Seam Gas Project (NTEC, 2013). Each geological layer in the CDM Smith model is represented as a three-dimensional layer that can be continuous or discontinuous within the geological model domain. The thickness of layers and contact between the layers are modelled by the Leapfrog™ software based on interpolation and extrapolation of the input data and the types of stratigraphic relationships assigned in Leapfrog™. The model is discretised into 500 m x 500 m model cells. The thickness of each layer in each cell represents the mean formation thickness at that location. The model domain extends over approximately 53,200 km2 from the Hunter-Mooki Thrust Fault System in the east, to the extent of the Gunnedah Basin units in the south and north, which is outside the boundary of the Namoi subregion. The western boundary of the model domain is marked by the north-westerly groundwater flow direction in the Surat Basin. Sources of data for the model include drilling logs from Santos and the NSW Department of Primary Industries Digital Imaging of Geological System (DIGS®) database, stratigraphic surfaces from the Upper and Lower Namoi groundwater models (McNeilage (2006) and Merrick (2001) respectively), the Gunnedah Bowen Study SEEBASE™ and Santos proprietary mapping of Gunnedah Basin formation tops and outcrop geology from geographic information systems (GIS). The ground surface elevation was determined using the Shuttle Radar Topography Mission (SRTM) 500 m digital elevation model (NTEC, 2013, p. 17, Table 2-1). Dataset Citation Geoscience Australia (2016) Namoi Leapfrog geological model. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/26747362-8e20-49df-ab2c-918fba839aa4.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

Anonymous data on evaluation of library workshops

This is a 3D geological movie of the Utah FORGE area created with Leapfrog Geothermal 3D modelling software. The movie shows the Utah FORGE site, wells, seismic and lithologic cross-sections, and rock unit tops. This illustrates the thick crystalline granitoid EGS reservoir.

This dataset provides information about the number of properties, residents, and average property values for Leapfrog Lane cross streets in Saint Matthews, SC.

Appendix Sup 1: Probability plots of assay data for modelled elements. Plots made in ioGAS software. Power transform applied to y-axes of all elements. Note that Fe, P and S do not follow normal/log-normal distributions. Appendix Sup 2: Summary statistics of assay data for modelled elements. A description of the rock type (lithology) codes used in the geological model are available in Sup 3. The 3D models presented in this paper are available as supplementary data online (Sup 4) and may be viewed in the free Leapfrog Viewer program, which can be downloaded from Leapfrog 3d website

Abstract

These are the rasters that were developed from the source datasets (the Namoi leapfrog model from CDM Smith, the GAB Atlas and GABWRA) to create maps of the extent and thickness of the layers in the Namoi BA geological model. The maps and discussion of the data is presented in Product 2.1.2 for the Namoi subregion.

Dataset History

These are the rasters that were developed from the source datasets (the Namoi leapfrog model from CDM Smith, the GAB Atlas and GABWRA) to create maps of the extent and thickness of the model layers. The layers were created in the following way:

The Rolling Downs Group layer was extracted from the GABATLAS Rolling Downs Aquitard - thickness and extent.

The Pilliga Sandstone layer in the GAB was extracted from the GABATLAS Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent. The Pilliga Sandstone in the Oxley (Gunnedah) Basin was extracted from the Namoi Leapfrog geological model.

The Purlawaugh Formation and Garrawilla Volcanics layer in the GAB was calculated using Layer 10 Great Artesian Basin base of Jurassic-Cretaceous sequence surface (GABWRA) and Layer 05 Great Artesian Basin base of Hooray Sandstone and equivalents surface (GABWRA). The Purlawaugh Formation and Garrawilla Volcanics in the Oxley (Gunnedah) Basin was extracted from the Namoi Leapfrog geological model.

The Interburden 1 layer was extracted from the Namoi Leapfrog geological model and combined the source layers of the Deriah and Napperby Formation, the Digby Formation and the Black Jack group above the Hoskissons Coal layer (layers 7, 8 and 9 in Namoi Leapfrog geological model).

The Hoskissons Coal layer was extracted directly from the Namoi Leapfrog geological model (layer 10 in the Namoi Leapfrog geological model).

The Interburden 2 layer was extracted from the Namoi Leapfrog geological model and combined the source layers of the Black Jack Group below the Hoskissons coal and the Millie Group (Watermark and Porcupine formations) (layers 11 and 12 in Namoi Leapfrog geological model).

The Maules Creek Formation layer was extracted directly from the Namoi Leapfrog geological model (layer 13 in the Namoi Leapfrog geological model).

Dataset Citation

Bioregional Assessment Programme (2017) Namoi BA geological model. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/e7e08a78-d57e-4ad0-8ce2-ce9876ff899f.

Dataset Ancestors

• Derived From GABATLAS - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent

• Derived From GABATLAS - Rolling Downs Aquitard - Thickness and Extent

• Derived From Namoi Leapfrog geological model

• Derived From Layer 05 Great Artesian Basin base of Hooray Sandstone and equivalents surface (GABWRA)

• Derived From Layer 10 Great Artesian Basin base of Jurassic-Cretaceous sequence surface (Great Artesian Basin Water Resource Assessment)

Abstract: This code has been used for the numerical experiments in the thesis "On leapfrog-Chebyshev schemes for second-order differential equations" by Constantin Carle; see https://www.doi.org/10.5445/IR/1000147725. TechnicalRemarks: The computations are done with Python3. Requirements The code is tested on Ubuntu 20.04.4 LTS with Python 3.8.10 and the following versions of its modules

The datasets used in the experimental evaluation of the paper CompactLTJ: Space & Time Efficient Leapfrog Triejoin are included in this tar.gz.

This file consists of the following components:

• wikidata-enum.dat – The complete Wikidata graph represented as triples, where each component is an integer.

• wikidata-str.nt – The full Wikidata graph with the original triples in string format.

• 80id – A folder containing 80% of wikidata-enum.dat along with the updates used in the experimental evaluation.

• 80str – A folder containing 80% of wikidata-str.nt along with the updates used in the experimental evaluation.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Abstract: This code was used for the numerical experiments in the preprint (CRC Preprint 2021/26; URL: https://www.waves.kit.edu/downloads/CRC1173_Preprint_2021-26.pdf) and in the paper "Error analysis of multirate leapfrog-type methods for second-order semilinear odes" by C. Carle and M. Hochbruck. TechnicalRemarks: The scripts inside the subfolders are intended to reproduce the figures from the preprint Error analysis of multirate leapfrog-type methods for second-order semilinear ODEs by Constantin carle and Marlis Hochbruck Requirements The codes are tested with Ubuntu 20.04.2 LTS and Python 3.8.5 and the following version of its modules: numpy - 1.17.4

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Viruses and their hosts can undergo coevolutionary arms races where hosts evolve increased resistance and viruses evolve counter-resistance. Given these arms race dynamics (ARD), both players are predicted to evolve along a single trajectory as more recently evolved genotypes replace their predecessors. By coupling phenotypic and genomic analyses of coevolving populations of bacteriophage lambda and Escherichia coli, we find conflicting evidence for ARD. Virus-host infection phenotypes fit the ARD model, yet genomic analyses revealed fluctuating selection dynamics (FSD). Rather than coevolution unfolding along a single trajectory, cryptic genetic variation emerges and is maintained at low frequency for generations until it eventually supplants dominant lineages. These observations suggest a hybrid ‘leapfrog’ dynamic, revealing weaknesses in the predictive power of standard coevolutionary models. The findings shed light on the mechanisms that structure coevolving ecological net...

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Heteropatric differentiation is a mode of speciation with gene flow in which divergence occurs between lineages that are in sympatry and allopatry at different times during cyclic spatial movements. Empirical evidence suggests that heteropatric differentiation may prove to be common among seasonally migratory organisms. We examined genetic differentiation between the sedentary Aleutian Islands population of green-winged teal (Anas crecca nimia) and its close migratory relative, the Eurasian, or Old World (OW), Anas c. crecca population, a portion of which passes through the range of nimia during its seasonal migrations. We also examined its relationship with the parapatric North American, New World (NW), A. c. carolinensis population. Sequence data from eight nuclear introns and the mtDNA control region showed that the nimia-crecca divergence occurred much more recently than the deeper crecca-carolinensis split (~83,000 y vs. ~1.1 My). Despite considerable spatial overlap between crecca...

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

3D geoscience models in Leapfrog Geothermal relevant to geothermal projects, built from public and/or proprietary data. Models are multidisciplinary and often include: topographic data, GIS datasets, drilling data, geology, geophysics and geochemistry information. Include 3D models built for Clients and Research programmes.

DOI: https://doi.org/10.21420/5KZ5-Q382?x=y

Cite model as: GNS Science. (2009). Geoscience 3D model (Geothermal) [Data set]. GNS Science. https://doi.org/10.21420/5KZ5-Q382?x=y

PREMISE: Events of accelerated species diversification represent one of Earth’s most celebrated evolutionary outcomes. Northern Andean high-elevation ecosystems, or páramos, host some plant lineages that have experienced the fastest diversification rates, likely triggered by ecological opportunities created by mountain uplifts, local climate shifts and key trait innovations. However, the mechanisms behind rapid speciation into the new adaptive zone provided by these opportunities have long remained unclear.Â

METHODS: We address this issue by studying the Venezuelan clade of Espeletia, a species-rich group of páramo-endemics showing a dazzling ecological and morphological diversity. We performed a number of comparative analyses to study both lineage and trait diversification, using an updated molecular phylogeny of this plant group.

KEY RESULTS: We showed that sets of either vegetative or reproductive traits have conjointly diversified in Espeletiaalong different vegetation belts, ...

The global geological modeling software market is experiencing robust growth, driven by the increasing demand for efficient and accurate resource exploration and extraction. The market's expansion is fueled by several key factors, including the rising complexity of geological projects, the growing adoption of 3D modeling techniques for improved visualization and analysis, and the increasing need for sustainable resource management. Technological advancements, such as the integration of artificial intelligence and machine learning algorithms into geological modeling software, are further enhancing the market's potential. The market is segmented by software type (e.g., 2D, 3D, integrated platforms), deployment model (cloud-based, on-premise), and industry vertical (mining, oil & gas, environmental). Major players, including Leapfrog Geo, GOCAD, and Petrel, are continuously investing in research and development to enhance their software capabilities and expand their market share. Competition is primarily driven by innovation, user-friendliness, and the ability to integrate with other geological data management systems. The market is projected to maintain a healthy Compound Annual Growth Rate (CAGR) over the forecast period. The competitive landscape is characterized by a mix of established players and emerging companies. Established players like Schlumberger (SLB) leverage their extensive industry experience and customer base to maintain a strong market presence. Meanwhile, smaller, specialized companies are focusing on niche applications and innovative features to gain market share. The increasing availability of cloud-based solutions is lowering the barrier to entry for smaller companies, and promoting greater accessibility to sophisticated geological modeling capabilities. However, the market faces some constraints such as the high cost of software licenses and the need for specialized expertise to effectively utilize these tools. Furthermore, data security and integration challenges can also hinder wider adoption. Nevertheless, the overall market outlook remains positive, with consistent growth anticipated throughout the forecast period, driven by technological innovation and the growing importance of accurate geological modeling in various industries.

Aim: Every year birds prepare for their migration journey, and understanding how this phenomenon is carried out allows us to infer whether human activities have influenced and modified the way in which the birds migrate. Many bird species perform long-distance movements to find sites with better conditions and optimize the use of resources during the annual cycle. These migration movements in birds have been studied using different methods, from mist nets, mark and recapture techniques, geolocators to document movements, and stable hydrogen isotopes. In this study, we used a genetic tool to create a graphic representation of the genetic variation across the species breeding range or genoscape as a reference to assess the connectivity of a migratory bird across its annual cycle. The advantage of the genoscape approach is that it allows us to identify distinct genetic units in the breeding range and then detect the migration pattern of those units through time and space. Methods: We studi...