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opencc-by-4.0Sep 2014View details →

Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v2.0)

Agricultural land resources – a global suitability evaluation An inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. Within the context of the GLUES project, researchers at the Ludwig-Maximilians University (LMU) investigated the global agricultural suitability of land under changing climate conditions at high spatial resolution. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes land use/cover change and trade-offs between different uses of land and ecosystem services. In order to ensure food security, agricultural potentials need to be used more efficiently in the future. Therefore, the agricultural suitability of land are important information e.g. in order to identify todays suitable areas and possible future changes. The potential suitability of todays forested and protected areas can be used to identify possible hotspots of land use/cover change. Therefore, LMU is working on improving the knowledge of global agricultural potentials of land and better understanding the interdependencies between ecological and socio-economic systems which are driving land use/cover change. Determining Agricultural Suitability Local climate, soil and topography determine the available energy, water and nutrient supply for agricultural crops and thus their natural suitability. In order to allow for computing the natural agricultural constraints on the globe at 30 arc seconds (1km) spatial resolution, the following high resolution data were applied: Daily data for temperature, precipitation and solar radiation from the global climate model ECHAM5. Soil data comes from the Harmonized World Soil Database (HWSD). Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Topography data was applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the crop’s suitability. It is considered on todays irrigated areas as given by the FAO Aquastat Global Maps of Irrigated Areas (GMIA) dataset. The determinant factors are contrasted with the crop-specific requirements, using a fuzzy-logic approach. The crop requirements are taken from literature. Agricultural Suitability General agricultural suitability at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of the considered 16 plants. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Suitability Change due to Climate until 2100 Change in agricultural suitability and crop suitability due to climate change for SRES A1B scenario conditions for 16 crops between 1981-2010 and 2071-2100 at a spatial resolution of 30 arcsec. Multiple Cropping Potential number of suitable crop cycles for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Growing Cycle Start of the growing cycle for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. In case of multiple cropping, the start of the first growing cycle is shown. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Further information Detailled information are available in the following publication: Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. Online available: <a href="http://dx.plos.org/10.1371/journal.pone.0107522">PLOS ONE</a>. DOI: 10.1371/journal.pone.0107522 Improvements in v2.0 Compared to previous versions, v2.0 uses updated input data for soil and minor improvements of the statistical downscaling and the bias correction of the climate model data. Contact Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department für Geographie, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)

opencc-by-4.0Apr 2020View details →

CoDEC Dataset - Data underlying the paper "A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections "

The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. The CoDEC-ERA5 dataset is the successor of GTSR <a href="https://www.nature.com/articles/ncomms11969">(Muis et al., 2016)</a> and is based on the next generation climate and hydrodynamic models. The main improvements are summarized in Table 2 of the accompanying paper <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/abstract">(Muis et al., 2020)</a>.

opencc-by-4.0Apr 2020View details →

high-resolution data set of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id is 6YP6

high-resolution data set of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id is 6YP6 Data were collected at Diamond I04 on February 8, 2012 using an ADSC detector.

opencc-zeroApr 2020View details →

High-resolution future climate data for species distribution models in Europe

Description This dataset contains a set of 13 climatological variables (<code>Variable</code>, <code>VariableName</code>) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (<code>ClimatePeriod</code>) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. <a href="https://worldclim.org/data/bioclim.html">WorldClim</a>, <a href="http://chelsa-climate.org/bioclim/">CHELSA</a>) that have been specifically developed for species distribution modelling and ecological applications. The climatological data correspond to 35-year (<code>Startyear_Endyear</code> = <code>1971_2005</code>) and 30-year (<code>Startyear_Endyear</code> = <code>2041_2070</code>) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the <a href="https://www.ipcc.ch/">Intergovernmental Panel on Climate Change (IPCC)</a> are used (<code>ClimatePeriod</code> = <code>rcp26</code>, <code>rcp45</code>, <code>rcp85</code>). The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below. The source data for the climate layers were assembled from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a> (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied: <ol> <li>Processing (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (<code>ClimatePeriod</code> = evaluation, historical, rcp) for the climatological variables.</li> <li>Interpolation of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial <a href="http://chelsa-climate.org/">CHELSA</a> (Karger et al., 2017) reference grid (see files <code>lat_1km.csv</code> and <code>lon_1km.csv</code>).</li> <li>Calculate differences between the 1x1km-interpolated variables (<code>Variable</code> = only for var[1-9]) from the evaluation model experiments (or <code>ClimatePeriod</code>) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (<code>ClimatePeriod</code>).</li> </ol> The dimensions of the 1x1km grid (excl. the first row and column): <ul> <li>y-dimension = number of columns = 6071</li> <li>x-dimension = number of rows = 13147</li> </ul> The longitudes and latitudes of respectively the southwest and northeast corner of the grid are: <ul> <li>longitude -44.592; latitude 21.991 (southwest corner)</li> <li>longitude 64.967; latitude 72.583 (northeast corner)</li> </ul> The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the <a href="https://osf.io/7dpgr/">Tracking Invasive Alien Species (TrIAS)</a> project. Variables <ul> <li>Variable (VariableName): Unit</li> <li>var1 (AnnualMeanTemperature): °C</li> <li>var2 (AnnualAmountPrecipitation): mm year-1</li> <li>var3 (AnnualVariationPrecipitation): coefficient of variation</li> <li>var4 (AnnualVariationTemperature): stdev</li> <li>var5 (MaximumTemperatureWarmestMonth): °C</li> <li>var6 (MinimumTemperatureColdestMonth): °C</li> <li>var7 (TemperatureAnnualRange): °C</li> <li>var8 (PrecipitationWettestMonth): mm</li> <li>var9 (PrecipitationDriestMonth): mm</li> <li>var10 (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days</li> <li>var11 (AnnualMeanPotentialEvapotranspiration): mm day-1</li> <li>var12 (AnnualMeanSolarRadiation): W m-2</li> <li>var13 (AnnualVariationSolarRadiation): stdev</li> </ul> Files <ul> <li>varX_VariableName_ClimatePeriod_Startyear_Endyear.csv: climatological data layers for the 13 variables listed above</li> <li>lon_1km.csv: longitudes for the 1x1km grid</li> <li>lat_1km.csv: latitudes for the 1x1km grid</li> </ul>

opencc-by-4.0Jul 2020View details →

High-resolution neutron imaging: a new approach to characterize water in anodic aluminum oxides_data

In this document, we share the raw data related to the Al oxides and hydroxide reference samples collected for the manuscript entitled "High-resolution neutron imaging: a new approach to characterize water in anodic aluminum oxides". More specifically, the following data can be found: <ul> <li>high-resolution neutron imaging raw data for Al oxides (C-sapphire, Al2O3 sintered, Al2O3 plasma sprayed and Anodisc 13 membrane) and hydroxide (Al(OH)3 gibbsite)</li> <li>the corresponding XRD diffractograms.</li> </ul>

opencc-zeroJul 2020View details →

Data from: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

Taphonomists have long struggled with identifying carnivore agency in bone accumulation and modification. Now that several taphonomic techniques allow identifying carnivore modification of bones, a next step involves determining carnivore type. This is of utmost importance to determine which carnivores were preying on and competing with hominins and what types of interaction existed among them during prehistory. Computer vision techniques using deep architectures of convolutional neural networks (CNN) have enabled significantly higher resolution in the identification of bone surface modifications (BSM) than previous methods. Here, we apply these techniques to test the hypothesis that different carnivores create specific BSM that can enable their identification. To make differentiation more challenging, we selected two types of carnivores (lions and jaguars) that belong to the same mammal family and have similar dental morphology. We hypothesize that if two similar carnivores can be identified by the BSM they imprint on bones, then two more distinctive carnivores (e.g. hyenids and felids) should be more easily distinguished. The CNN method used here shows that tooth scores from both types of felids can be successfully classified with an accuracy greater than 82%. The first hypothesis was successfully tested. The next step will be to differentiate diverse carnivore types involving a wider range of carnivore-made BSM. The present study demonstrates that resolution increases when combining two different disciplines (taphonomy and artificial intelligence computing) in order to test new hypotheses that could not be addressed with traditional taphonomic methods.

opencc-zeroAug 2020View details →

Data from: Inner ear morphology of diadectomorphs and seymouriamorphs (Tetrapoda) uncovered by high-resolution x-ray microcomputed tomography, and the origin of the amniote crown group

The origin of amniotes was a key event in vertebrate evolution, enabling tetrapods to break their ties with water and invade terrestrial environments. Two pivotal clades of early tetrapods, the diadectomorphs and the seymouriamorphs, have played an unsurpassed role in debates about the ancestry of amniotes for over a century, but their skeletal morphology has provided conflicting evidence for their affinities. Using high-resolution X-ray microcomputed tomography, we reveal the three-dimensional architecture of the well preserved endosseous labyrinth of the inner ear in representative species belonging to both groups. Data from the inner ear are coded in a new cladistic matrix of stem and primitive crown amniotes. Both maximum parsimony and Bayesian inference analyses retrieve seymouriamorphs as derived non-crown amniotes and diadectomorphs as sister group to synapsids. If confirmed, this sister group relationship invites re-examination of character polarity near the roots of the crown amniote radiation. Major changes in the endosseous labyrinth and adjacent braincase regions are mapped across the transition from non-amniote to amniote tetrapods, and include: a ventral shift of the cochlear recess relative to the vestibule and the semicircular canals; cochlear recess (primitively housed exclusively within the opisthotic) accommodated within both the prootic and the opisthotic; development of a distinct fossa subarcuata. The inner ear of seymouriamorphs foreshadows conditions of more derived groups, whereas that of diadectomorphs shows a mosaic of plesiomorphic and apomorphic traits, some of which are unambiguously amniote-like, including a distinct and pyramid-like cochlear recess.

opencc-by-4.0Sep 2020View details →

A digital mapping application for quantifying and displaying air temperatures at high spatiotemporal resolutions in near real-time across Australia

This repository hosts the raw data and source code for the paper entitled " A digital mapping application for quantifying and displaying air temperatures at high spatiotemporal resolutions in near real-time across Australia" [DOI: 10.7717/peerj.10106]. Two zip archives are available: Raw data and methods evaluation.zip - Retains empirical data and method evaluation code (R scripts) that relate to data presented in the results and discussion sections of the article, specifically, Figures 3 -11 and Tables 1 and 2. RTmap_source_code_and_data_structure.zip - This repository hosts the source code for <a href="http://austemperature.live/">http://austemperature.live/</a>, as described in the methods section of the article. A README.docx presents the contents and subsequent procedures for deploying the mapping code and web map application. For further information about this files please email: mweb7041@uni.sydney.edu.au

opencc-by-4.0Sep 2020View details →

DC2 High resolution future hydrological data for Sweden

Hourly river flow and total runoff were computed for the southern part of Sweden using the hourly version of a high resolution hydrological model S-HYPE, which is operationally used by SMHI. The model was calibrated and validated using radar based hourly precipitation and an operationally used hourly reanalysis temperature data. Projection of the impact of climate change was performed by running the model with hourly forcing data from an ensemble of EURO-COREX climate model simulations over 1971 - 2100. Four GCM-RCM combinations were used under two emission scenarios, RCP4.5 and RCP8.5. The results can be used to assess the risk of riverine flooding in areas located along a small to meso-scale river basin. The results can, in particular, be used to assess the risk of flash flooding that can result from heavy precipitation of short duration.

opencc-zeroOct 2020View details →

Data for: High-resolution land value maps reveal underestimation of conservation costs in the United States

The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I develop the first high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimated conservation costs, especially at the expensive tail of the distribution. This might have led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges, and can assist with the targeting of public ecosystem service investments.

opencc-zeroOct 2020View details →

Data from: Corrigendum to: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

Corrigendum to "Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars". In a previous paper, we presented some convolutional neural network (CNN) models to classify images of tooth scores made by lions and jaguars through deep learning computer vision. In that work, we reached an accuracy of 82% of the testing set correctly classified. However, such an accuracy is biased, since the original sample was highly unbalanced. Therefor, now we present the results which correct the problems of the previously published models by producing more balanced classifications and also by achieving higher accuracy.

opencc-by-4.0Apr 2020View details →

Temperature control on high-resolution SIMS oxygen isotopic compositions in Porites coral skeletons

The dataset includes all data for "Temperature control on high-resolution SIMS oxygen isotopic compositions in Porites coral skeletons" by Zou et al. from Guangzhou Institute of Geochemistry.

opencc-zeroJan 2021View details →

High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)

On the Caribbean island of St. Eustatius, Coralita (Antigonon leptopus) is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest and urban areas, but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans.

FIGURE 95 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 95. FMNH PR2081, Tyrannosaurus rex. Right femur in posterior (A), lateral (B), anterior (C), and medial (D) views. Left femur in posterior (E), lateral (F), anterior (G), and medial (H) views. I, right femur, proximal view. J, Right femur, distal view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.

FIGURE 39 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 39. FMNH PR2081, Tyrannosaurus rex. Horizontal CT slice through braincase, 1 cm ventral to floor of endocranial cavity. Note hollow nature of parasphenoid rostrum and complex of recesses within basioccipital. Abbreviations in Appendix 1.

FIGURE 15 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 15. FMNH PR2081, Tyrannosaurus rex. Sutural relationships between nasal, lacrymal, and maxilla above the antorbital fenestra. Left lateral view. See Appendix 1 for abbreviations. Photographs by J. Weinstein.

FIGURE 96 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 96. FMNH PR2081, Tyrannosaurus rex. Right tibia, calcaneum, and astragalus in medial (A), posterior (B), lateral (C), and anterior (D) views. Left tibia, calcaneum, and astragalus in anterior (E), lateral (F), posterior (G), and medial (H) views. I, left tibia, proximal view. J, left astragalus and calcaneum, distal view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.

FIGURE 10 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 10. FMNH PR2081, Tyrannosaurus rex. Closeup of right premaxilla, showing extensive premaxillary flooring of external naris. Anterior extent of nasal under the naris cannot be determined. See Appendix 1 for abbreviations.

FIGURE 43 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 43. FMNH PR2081, Tyrannosaurus rex. Medial view of left mandible focusing on dorsal tip of intramandibular joint, where the splenial, prearticular, coronoid, and supradentary converge. Abbreviations in Appendix 1.

FIGURE 92 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull

FIGURE 92. FMNH PR2081, Tyrannosaurus rex. A, Left ilium, lateral view. B, Left ilium, medial view. C, Right ilium (with sacrum attached), lateral view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.