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25-meter elevation lattice grid, Niwot Ridge LTER Project Area, Colorado
25-meter lattice made from the Niwot Ridge LTER TIN model (ltertin). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
Experimental Results for the AERO 5G project (Fed4FIRE+)
<p>The corresponding results refer to the experiments conducted during the life of the Fed4FIRE+ project entitled: "AERO 5G (Augmented Reality Tour Guide Architecture for 5G)". The public availability of the results aim to help future experimenters and researchers to obtain some intuition with regard to the benefits of 5G for content-based, bandwitdh consuming, MAR applications.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Short Description of the Experiments: All values have been rounded to two decimal places. Our team has conducted ten experimental runs for each of the following experimental scenarios.</p> <p>o <strong>4G SDR srsLTE-to-AWS</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node at Amazon Cloud (AWS) that hosts the AR video content.</p> <p>o<strong> 4G SDR srsLTE-to-IMEC</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts the AR video content.</p> <p>o <strong>4G SDR srsLTE-to-Iris-MEC</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a MEC storage node located at the edge of the network infrastructure at the Iris testbed that hosts the AR video content. Although MEC is considered to be a 5G technology, our aim in this scenario is to explore the benefit of deploying edge storage nodes in wireless mobile telecommunication technologies in general. For this purpose, we assume that the video content has been stored at the MEC storage node a priori to the end-user's requests.</p> <p>o <strong>Commercial Three.ie 4G-to-AWS</strong>: A Commercial 4G network deployed by the Three.ie mobile operator in Ireland, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node that hosts the AR video content. Even though a MEC storage node could not be deployed in this scenario, our intention is to estimate the benefit of deploying edge storage nodes empirically by consulting the results concluded for the 4G LTE-to-MEC scenario.</p> <p>o <strong>2.4GHz Wi-Fi-to-IMEC</strong>: A 2.4GHz Wi-Fi (802.11 n) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts 4K AR video. This scenario serves us as a proof-of-concept that a 2.4GHz Wi-Fi network is not capable of supporting the delivery of high quality 4K AR content in areas where there are a lot of 2.4GHz Wi-Fi networks.</p> <p>o <strong>5GHz Wi-Fi-to-AWS</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node at Amazon Cloud (AWS) that hosts the AR video content.</p> <p>o <strong>5GHz Wi-Fi-to-IMEC</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts the AR video content.</p> <p>o<strong> 5GHz Wi-Fi-to-Iris-MEC</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a MEC storage node located at the edge of the network infrastructure that hosts the AR video content. Similar to the 4G LTE-to-MEC scenario, we assume that the video content has been stored at the MEC storage node a priori to the end-user's requests. </p>
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Code for MATSim-NYC project
<p>This file includes the code and parameters for the baseline MATSim-NYC model, network calibration, and other additional features. All the required input files are saved in the input folder. </p> <p>The synthetic population as well as the data dictionary are also incorporated. </p> <p> </p>
EPITOME ship emissions: Projections of shipping emissions towards 2050.
<p>As part of the EPITOME project, we have setup global shipping emission scenarios. They are based on a combination of the global CO<sub>2</sub> ship emission inventory for 2015 produced with the Ship Traffic Emissions Assessment Model (STEAM) (Johansson et al., 2017) and Arctic fuel consumption and emission scenarios calculated with the DCE ship emission model (Winther et al., 2017).</p> <p>The scenarios include a Baseline scenario, a SO<sub>x</sub> Emission Control Area (SECA) and a heavy fuel oil (HFO) ban scenario. The Baseline scenario is calculated in two variants involving Business As Usual (BAU) and High Growth (HiG) traffic growths. The SECA and HFO ban scenarios are given with the BAU traffic development.</p> <p>Additionally a Polar route scenario is included, with new (diversion) ship traffic routes in the future Arctic with less sea ice. The applied traffic growths and the polar routes are Corbett et al. (2010).</p> <p>The emissions are monthly on a spatial resolution of 0.1º×0.1º. </p> <p>Base year is 2015 and the scenarios are for 2050.</p> <p>A scientific paper providing details on the methodology behind these data will be submitted to ACPD (Geels et al, submitted). In this paper we apply the data to assess the contribution from shipping emissions to air pollution in the Nordic and Arctic area and the potential benefits of the mitigation options included in the shipping emission scenarios. This paper should be referenced if the data is used. </p> <p>The data are given as netcdf files for a number of components. The emission related to the diversion routes is given as a separate field and can be added the other field. <br> </p>
Test data for the transverse Mercator projection
<p>This is a set of 287000 geographic points together with their coordinates in the transverse Mercator projection. The WGS84 ellipsoid (equatorial radius <em>a</em> = 6378137 m, flattening <em>f</em> = 1/298.257223563) is used, with central meridian 0°, central scale factor 0.9996 (the UTM value), false easting = false northing = 0 m.</p> <p>Each line of the test set gives 6 space delimited numbers</p> <ul> <li>latitude, φ (degrees, exact)</li> <li>longitude, λ (degrees, exact — see below)</li> <li>easting (meters, accurate to 0.1 pm)</li> <li>northing (meters, accurate to 0.1 pm)</li> <li>meridian convergence (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>scale (accurate to 10<sup>−20</sup>)</li> </ul> <p>These are computed using high-precision calculations using the exact formulas for the projection, see Lee (1976). The latitude and longitude are all multiples of 10<sup>−12</sup> deg and should be regarded as exact, except that λ = 82.63627282416406551° should be interpreted as exactly (1 − <em>e</em>) 90°, where <em>e</em> is the eccentricity given by <em>e</em><sup>2</sup> = <em>f</em> (2 − <em>f</em> ).</p> <p>The contents of the file are as follows:</p> <ul> <li>250000 entries randomly distributed in φ ∈ [0°, 90°], λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 0°</li> <li>1000 entries randomly distributed on φ = 0°, λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 90°</li> <li>1000 entries close to φ = 90° with λ ∈ [0°, 90°]</li> <li>1000 entries close to φ = 0°, λ = 0° with φ ≥ 0°, λ ≥ 0°</li> <li>1000 entries close to φ = 0°, λ = 90° with φ ≥ 0°, λ ≤ 90°</li> <li>2000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° with φ ≥ 0°</li> <li>25000 entries randomly distributed in φ ∈ [−89°, 0°], λ ∈ [(1 − <em>e</em>) 90°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = 90°</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = (1 − <em>e</em>) 90°</li> <li>1000 entries close to φ = 0°, λ = 90° (φ < 0°, λ ≤ 90°)</li> <li>1000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° (φ < 0°, λ ≤ (1 − <em>e</em>) 90°)</li> </ul> <p>The entries for φ < 0° and λ ∈ [(1 − <em>e</em>) 90°, 90°] use the “extended” domain for the transverse Mercator projection explained in Sec. 5 of Karney (2011). The first 258000 entries have φ ≥ 0° and are suitable for testing implementations following the standard convention.</p>
The Open Aurignacian Project. Volume 2: Grotta di Castelcivita in southern Italy
<h2><strong>Overview</strong></h2> <p>The repository contains an extensive dataset (n = 538) comprising 3D meshes representing various classes of lithic artifacts such as cores, blades, bladelets, flakes, and retouched tools. These artifacts originate from the Protoaurignacian (<em>rsa'</em>) and Early Aurignacian (<em>gic</em>, <em>ars</em>) layers of Grotta di Castelcivita (40.49563600N, 015.20922177E) in southern Italy (Gambassini, 1997). The layers date back to approximately 41,000 to 39,800 years ago (Douka<em> et al.</em>, 2014). A new technological assessment of the <em>rsa’</em>–<em>ars </em>sequence has been conducted utilizing the models included in this repository (Falcucci et al., 2024). Grotta di Castelcivita holds significant importance for the study of Early Upper Paleolithic cultural dynamics due to its substantial archaeological content and the presence of the Campanian Ignimbrite geochronological marker, which seals the archaeological sequence of the site (Giaccio<em> et al.</em>, 2008).</p> <p>The 3D scanning of artifacts was performed using the first models of the Artec Space Spider and Artec Micro scanners from Artec Inc., Luxembourg. The scanning process adhered to best practices for lithic digitization (Göldner <em>et al.</em>, 2022), ensuring accurate capture of artifact details. 3D scanning with the Artec Spider follows the third version of the <em>Styrostone </em>protocol outlined by Göldner <em>et al.</em> (2023). For detailed information, please refer to Part 8 (Artec scanning of larger artifacts) of the protocol: <a href="dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3" rel="noopener">dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3</a>. 3D scanning with the Artec Micro follows the <em>Microstone </em>protocol by Falcucci (2022): <a href="dx.doi.org/10.17504/protocols.io.81wgb6781lpk/v1" rel="noopener">dx.doi.org/10.17504/protocols.io.81wgb6781lpk/v1</a>. The use of the Artec Micro was particularly valuable for digitizing extremely small lithics, such as retouched bladelets with lengths around 1 cm.</p> <p>The creation of this open-access repository is intended to encourage archaeologists to participate in collaborative initiatives, thereby contributing to the advancement of research in the field of lithic technology and facilitating broader access to the prehistoric record. This initiative aligns with the promotion of Open Science practices in archaeological sciences, as advocated by Marwick<em> et al.</em> (2017). This dataset is part of the <a href="https://www.armandofalcucci.com/project/open_aurignacian/">Open Aurignacian Project</a>.</p> <h2>Author contact</h2> <p>Dr. Armando Falcucci</p> <p>armando.falcucci@uni-tuebingen.de; falcucciarmando@gmail.com</p> <h2><strong>Description of the dataset</strong></h2> <p>This repository includes the following components:</p> <ol> <li><code>CTC_3D_Meshes.zip</code>:<strong> </strong>Compressed folder containing 3D models in PLY format for the lithic artifacts.</li> <li><code>Readme_Castelcivita_3D.txt</code>: This README file provides detailed information about the 3D models and metadata associated with this repository. It includes descriptions of the dataset's structure, the scanning and postprocessing protocols, and detailed metadata variables for the lithic artifacts, including scanning technology, resolution, and file formats. The file serves as a comprehensive guide to understanding the dataset and how to properly use and cite the data for research purposes.</li> <li><code>Castelcivita_3D_metadata.csv</code>:<strong> </strong>CSV file containing information, characteristics, and metadata of the lithic artifacts.</li> </ol> <p> </p> <p>The <code>Castelcivita_3D_metadata.csv</code> file includes the following metadata attributes:</p> <ul> <li><strong>ID:</strong> Each artifact has been assigned a unique identifier in the format "CTC" followed by a sequential number, allowing for cross-referencing with techno-typological data presented in related publications.</li> <li><strong>Site:</strong> The archaeological site where the lithic was excavated.</li> <li><strong>Layer: </strong>The stratigraphic origin of the lithic.</li> <li><strong>Raw_material:</strong> Categorization by the type of raw material (e.g., Chert, Radiolarite).</li> <li><strong>Class:</strong> Broad artifact sorting (e.g., Blank, Core, Core-Tool, Tool), following common classifications in lithic analysis. Cores are pieces of any size that lack a dorsal/ventral surface but have two or more blade/bladelet/flake scars. Tools are pieces of any size that exhibit retouch along the margins. Core-tools are pieces that have produced bladelets but can also be classified as tools (e.g., carinated endscrapers and burin cores) following a typological classification. Blanks are flaked pieces with both a dorsal and ventral face.</li> <li><strong>Blank: </strong>Classification of the blank into flake, blade, and bladelet categories. A blade is defined as a flaked blank whose length is at least twice its width, regardless of shape. Bladelets are defined as blades whose maximum width is less than 12 mm.</li> <li><strong>Technology: </strong>Technological classification of the blanks into categories such as initialization, maintenance, optimal, semi-cortical, and others, following Falcucci <em>et al. </em>(2020) and Falcucci <em>et al. </em>(2024).</li> <li><strong>Core_classification: </strong>Technological categories for cores and core-tools (e.g., Carinated, Multi-platform, Narrow-sided, Semicircumferential) following Falcucci & Peresani (2018).</li> <li><strong>Cortex: </strong>Percentage of cortex coverage (0%, 1–33%, 33–66%, 66–99%, 100%), estimated visually.</li> <li><strong>Preservation: </strong>Breakage classification for blanks (e.g., Complete, Distal, Mesial, Proximal, Undetermined). For cores and most core-tools, preservation is marked as "Other".</li> <li><strong>Volume:</strong> The volume of the artifact in cubic millimeters.</li> <li><strong>Surface: </strong>The surface area of the artifact in square millimeters.</li> <li><strong>Length: </strong>Maximum length in millimeters based on technological orientation, recorded with a digital caliper.</li> <li><strong>Width:</strong> Maximum width in millimeters based on technological orientation, recorded with a digital caliper.</li> <li><strong>Thickness:</strong> Maximum thickness in millimeters based on technological orientation, recorded with a digital caliper.</li> <li><strong>File_list: </strong>The list of files in the dataset that correspond to this specific ID.</li> <li><strong>Model_unit:</strong> The unit of measurement used for the 3D model. When viewing the artifact in a 3D viewer that supports real-world units, this is the unit you enter into your program to ensure proper scaling. Note that this is not related to the object's resolution; it's simply the value needed for accurate scaling when importing the model into your 3D program.</li> <li><strong>#_of_polygons:</strong> The number of polygons in the 3D model of the artifact.</li> <li><strong>Avg_edge_length(mm)/Resolution: </strong>The average distance between points on the model, serving as an effective measure of the model's resolution.</li> <li><strong>Resolution_score:</strong> A qualitative value assigned to each model, reflecting its resolution. Based on the entire set of scans from the Open Aurignacian Project, it classifies artifacts into four categories (i.e., ultra-detailed, detailed, moderate detail, low detail) based on their average edge length, providing an assessment of the model's resolution relative to others in the project.</li> <li><strong>Scanner: </strong>The specific model of the scanner used to capture the 3D data of the lithic artifact.</li> <li><strong>Scan_software:</strong> The version of the software used in conjunction with the scanner to capture the 3D data of the artifact.</li> <li><strong>Postprocessing_software:</strong> The version of the software used to execute postprocessing algorithms and generate the final 3D mesh of the artifact.</li> <li><strong>Coating: </strong>Yes/No entry speifying if coating was used for any scan.</li> </ul> <h2><strong>Research and Usage Notes</strong></h2> <p>Users are encouraged to consult the <a href="https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno">GitHub</a> and <a href="https://doi.org/10.5281/zenodo.10639552">Zenodo</a> repositories associated with the main publication on the Aurignacian sequence at Grotta di Castelcivita for further techno-typological data and analytical resources. This dataset is intended to foster open collaboration and reproducibility in lithic analysis, aligning with best practices in archaeological research.</p> <h2><strong>Licensing and Citation</strong></h2> <p>Please cite this repository and related publications when using this dataset in your research. Licensing details and citation formats are provided in the repository documentation.</p> <h2><strong>References</strong></h2> <p>Douka K., Higham T., Wood R.<em> et al.</em> (2014) On the chronology of the Uluzzian. <em>Journal of Human Evolution</em>, 68: 1-13. doi:10.1016/j.jhevol.2013.12.007</p> <p>Falcucci A. (2022) MicroStone: Exploring the capabilities of the Artec Micro in scanning stone tools. <em>protocols.io</em>. doi:<a href="https://dx.doi.org/10.17504/protocols.io.81wgb6781lpk/v1">https://dx.doi.org/10.17504/protocols.io.81wgb6781lpk/v1</a></p> <p>Falcucci A. & Peresani M. (2018) Protoaurignacian Core Reduction Procedures: Blade and Bladelet Technologies at Fumane Cave. Lithic Technology 43: 125-140. doi:10.1080/01977261.2018.1439681</p> <p>Falcucci A., Conard N.J. & Peresani M. (2020) Breaking through the Aquitaine frame: A re-evaluation on the significance of regional variants during the Aurignacian as seen from a key record in southern Europe. Journal of Anthropological Sciences, 98: 99-140. doi:https://doi.org/10.4436/JASS.98021</p> <p>Falcucci A., Arrighi S., Spagnolo V., Rossini M., Higgins O.A., Muttillo B., Martini I., Crezzini J., Boschin F., Ronchitelli A. & Moroni A. (2024) A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy. Scientific Reports, 14: 12783. doi:10.1038/s41598-024-59896-6</p> <p>Gambassini P. (1997) <em>Il Paleolitico di Castelcivita: Culture e Ambiente</em>. Electa, Naples</p> <p>Giaccio B., Isaia R., Fedele F.G.<em> et al.</em> (2008) The Campanian Ignimbrite and Codola tephra layers: Two temporal/stratigraphic markers for the Early Upper Palaeolithic in southern Italy and eastern Europe. <em>Journal of Volcanology and Geothermal Research</em>, 177: 208-226. doi:<a href="https://doi.org/10.1016/j.jvolgeores.2007.10.007">https://doi.org/10.1016/j.jvolgeores.2007.10.007</a></p> <p>Göldner D., Karakostis F.A. & Falcucci A. (2022) Practical and technical aspects for the 3D scanning of lithic artefacts using micro-computed tomography techniques and laser light scanners for subsequent geometric morphometric analysis. Introducing the StyroStone protocol. PLoS One, 17: e0267163. doi:10.1371/journal.pone.0267163</p> <p>Göldner D., Karakostis F.A. & Falcucci A. (2023) <em>StyroStone</em>: A protocol for scanning and extracting three-dimensional meshes of stone artefacts using Micro-CT scanners V.3. protocols.io. <a href="dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3">dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3</a></p> <p>Marwick B., d’Alpoim Guedes J., Barton C.M.<em> et al.</em> (2017) Open science in archaeology. <em>SAA Archaeological Record</em>, 17: 8-14. doi:10.17605/OSF.IO/3D6XX</p>
The Open Aurignacian Project. Volume 1: Grotta di Fumane in northeastern Italy
<h2><strong>Overview</strong></h2> <p>This repository contains a large dataset (n = 948) of 3D meshes of different classes of lithic artifacts (blade and bladelet cores, blades, bladelets, flakes, and retouched tools) from the Aurignacian (A2, A1, D6, D3+D6, D3l, D3d base, D3d, D3b alpha, D3b, and D1c) and Gravettian (D1d, D1e, and D1f) units at Fumane Cave in northeastern Italy (see Bartolomei et al., 1992). The Upper Paleolithic sequence spans from about 41 to 33 ky cal BP (Higham et al., 2009) and several studies have focused on the lithic technology (Bertola et al., 2013; Broglio et al., 2005; Falcucci et al., 2017; Falcucci, 2018; Falcucci & Peresani, 2018; Falcucci et al., 2018; Falcucci et al., 2020). The importance of the site for understanding the earliest phases of the Upper Paleolithic in Mediterranean Europe is well acknowledged (Conard & Bolus, 2015). Recently, all complete blades and bladelets from the best-preserved area of the cave (i.e., the external area of the excavation) were 3D-scanned using a protocol that relies on both Micro-CT and Artec Spider scanners (Göldner et al., 2022). Our main goal was to conduct a geometric morphometric assessment of the laminar products and test hypotheses related to stone tool production and, more broadly, past human behavior (Falcucci et al., 2022; Falcucci & Peresani, 2022). Furthermore, all core types have been scanned throughout the years of research at the site with an Artec Spider (Falcucci<em> et al.</em>, 2024a; Lombao<em> et al.</em>, 2023).</p> <p>The 3D scanning of artifacts was performed using the first model of the Artec Space Spider and a micro-CT scanner. The scanning process adhered to best practices for lithic digitization (Göldner <em>et al.</em>, 2022), ensuring accurate capture of artifact details. 3D scanning and postprocessing for both micro-CT and Artec Spider follow the third version of the <em>Styrostone </em>protocol outlined by Göldner <em>et al.</em> (2023): <a href="dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3" rel="noopener">dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3</a>.</p> <p>The creation of this open-access repository is intended to encourage archaeologists to participate in collaborative initiatives, thereby contributing to the advancement of research in the field of lithic technology and facilitating broader access to the prehistoric record. This initiative aligns with the promotion of Open Science practices in archaeological sciences, as advocated by Marwick<em> et al.</em> (2017). This dataset is part of the <a href="https://www.armandofalcucci.com/project/open_aurignacian/">Open Aurignacian Project</a>.</p> <h2>Author contact</h2> <p>Dr. Armando Falcucci</p> <p>armando.falcucci@uni-tuebingen.de; falcucciarmando@gmail.com</p> <h2><strong>Description of the dataset</strong></h2> <p>This repository includes the following components:</p> <ol> <li><code>RF_3D_Meshes.zip</code>:<strong> </strong>Compressed folder containing 3D models in PLY format for the lithic artifacts.</li> <li><code>Readme_Fumane_3D.txt</code>: This README file provides detailed information about the 3D models and metadata associated with this repository. It includes descriptions of the dataset's structure, the scanning and postprocessing protocols, and detailed metadata variables for the lithic artifacts, including scanning technology, resolution, and file formats. The file serves as a comprehensive guide to understanding the dataset and how to properly use and cite the data for research purposes.</li> <li><code>Fumane_3D_metadata.csv</code>:<strong> </strong>CSV file containing information, characteristics, and metadata of the lithic artifacts.</li> </ol> <p>Each artifact has been assigned a unique identifier in the format "RF.b" (for blanks and tools) and "RF.c" (for cores) followed by a sequential number, allowing for cross-referencing with the techno-typological data presented in related publications.</p> <p>The <code>Fumane_3D_metadata.csv</code> file includes the following metadata attributes:</p> <ul> <li><strong>ID:</strong> Each artifact has been assigned a unique identifier in the format "RF.b" (for blanks and tools) and "RF.c" (for cores) followed by a sequential number, allowing for cross-referencing with the techno-typological data presented in related publications.</li> <li><strong>Site:</strong> The archaeological site where the lithic was excavated.</li> <li><strong>Layer: </strong>The stratigraphic origin of the lithic.</li> <li><strong>Raw_material:</strong> Categorization by the type of raw material (e.g., Maiolica, Scaglia Variegata, Scaglia Rossa).</li> <li><strong>Class:</strong> Broad artifact sorting (e.g., Blank, Core, Core-Tool, Tool), following common classifications in lithic analysis. Cores are pieces of any size that lack a dorsal/ventral surface but have two or more blade/bladelet/flake scars. Tools are pieces of any size that exhibit retouch along the margins. Core-tools are pieces that have produced bladelets but can also be classified as tools (e.g., carinated endscrapers and burin cores) following a typological classification. Blanks are flaked pieces with both a dorsal and ventral face.</li> <li><strong>Blank: </strong>Classification of the blank into flake, blade, and bladelet categories. A blade is defined as a flaked blank whose length is at least twice its width, regardless of shape. Bladelets are defined as blades whose maximum width is less than 12 mm.</li> <li><strong>Technology: </strong>Technological classification of the blanks into categories such as initialization, maintenance, optimal, semi-cortical, and others, following Falcucci <em>et al. </em>(2020) and Falcucci <em>et al. </em>(2024b).</li> <li><strong>Core_classification: </strong>Technological categories for cores and core-tools (e.g., Carinated, Multi-platform, Narrow-sided, Semicircumferential) following Falcucci & Peresani (2018).</li> <li><strong>Cortex: </strong>Percentage of cortex coverage (0%, 1–33%, 33–66%, 66–99%, 100%), estimated visually.</li> <li><strong>Preservation: </strong>Breakage classification for blanks (e.g., Complete, Distal, Mesial, Proximal, Undetermined). For cores and most core-tools, preservation is marked as "Other".</li> <li><strong>Volume:</strong> The volume of the artifact in cubic millimeters.</li> <li><strong>Surface: </strong>The surface area of the artifact in square millimeters.</li> <li><strong>Length: </strong>Maximum length in millimeters based on technological orientation, recorded with a digital caliper.</li> <li><strong>Width:</strong> Maximum width in millimeters based on technological orientation, recorded with a digital caliper.</li> <li><strong>Thickness:</strong> Maximum thickness in millimeters based on technological orientation, recorded with a digital caliper.</li> <li><strong>File_list: </strong>The list of files in the dataset that correspond to this specific ID.</li> <li><strong>Model_unit:</strong> The unit of measurement used for the 3D model. When viewing the artifact in a 3D viewer that supports real-world units, this is the unit you enter into your program to ensure proper scaling. Note that this is not related to the object's resolution; it's simply the value needed for accurate scaling when importing the model into your 3D program.</li> <li><strong>#_of_polygons:</strong> The number of polygons in the 3D model of the artifact.</li> <li><strong>Avg_edge_length(mm)/Resolution: </strong>The average distance between points on the model, serving as an effective measure of the model's resolution.</li> <li><strong>Resolution_score:</strong> A qualitative value assigned to each model, reflecting its resolution. Based on the entire set of scans from the Open Aurignacian Project, it classifies artifacts into four categories (i.e., ultra-detailed, detailed, moderate detail, low detail) based on their average edge length, providing an assessment of the model's resolution relative to others in the project.</li> <li><strong>Scanner: </strong>The specific model of the scanner used to capture the 3D data of the lithic artifact.</li> <li><strong>Scan_software:</strong> The version of the software used in conjunction with the scanner to capture the 3D data of the artifact.</li> <li><strong>Postprocessing_software:</strong> The version of the software used to execute postprocessing algorithms and generate the final 3D mesh of the artifact.</li> <li><strong>Coating: </strong>Yes/No entry speifying if coating was used for any scan.</li> </ul> <h2><strong>What's new in this release (Version 3.0.1)</strong></h2> <p>In this new version, we have reworked all 3D models of cores and core-tools to enhance their overall quality and improve analysis. This was accomplished using Artec Studio Professional software by adjusting the settings for Global Registration and, in particular, Sharp Fusion (i.e., using 0.1 instead of 0.3 in 3D Resolution, mm) . These changes mainly affect models with IDs starting with "RF.c". This change was applied only to the PLY files, while the WRL files were not included in this release. The WRL files can be downloaded from previous versions of this repository.</p> <h2><strong>Research and Usage Notes</strong></h2> <p>Users are encouraged to consult the <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">GitHub</a> and <a href="https://zenodo.org/doi/10.5281/zenodo.10965413">Zenodo</a> repositories associated with the main publication on the Aurignacian sequence at Grotta di Fumane for further techno-typological data and analytical resources. This dataset is intended to foster open collaboration and reproducibility in lithic analysis, aligning with best practices in archaeological research.</p> <h2><strong>Licensing and Citation</strong></h2> <p>Please ensure that this dataset is properly cited in any research or publication that utilizes it. Detailed licensing and citation information is provided within the dataset documentation.</p> <h2><strong>References</strong></h2> <p>Bartolomei G., Broglio A., Cassoli P. et al. (1992) La Grotte de Fumane. Un site aurignacien au pied des Alpes. Preistoria Alpina, 28: 131-179</p> <p>Bertola S., Broglio A., Cristiani E. et al. (2013) La diffusione del primo Aurignaziano a sud dell'arco alpino. Preistoria Alpina, 47: 17-30</p> <p>Broglio A., Bertola S., De Stefani M. et al. (2005) La production lamellaire et les armatures lamellaires de l’Aurignacien ancien de la grotte de Fumane (Monts Lessini, Vénétie). In F. Le Brun-Ricalens (ed.): Productions lamellaires attribuées à l’Aurignacien, pp. 415-436. MNHA, Luxembourg.</p> <p>Conard N.J. & Bolus M. (2015) Chronicling modern human’s arrival in Europe. Science. doi:10.1126/science.aab0234</p> <p>Falcucci A., Conard N.J. & Peresani M. (2017) A critical assessment of the Protoaurignacian lithic technology at Fumane Cave and its implications for the definition of the earliest Aurignacian. PLoS One, 12: e0189241. doi:10.1371/journal.pone.0189241</p> <p>Falcucci A. & Peresani M. (2018) Protoaurignacian Core Reduction Procedures: Blade and Bladelet Technologies at Fumane Cave. Lithic Technology 43: 125-140. doi:10.1080/01977261.2018.1439681</p> <p>Falcucci A. (2018) Towards a renewed definition of the Protoaurignacian. Mitteilungen der Gesellschaft für Urgeschichte, 27: 87-130</p> <p>Falcucci A., Peresani M., Roussel M. et al. (2018) What’s the point? Retouched bladelet variability in the Protoaurignacian. Results from Fumane, Isturitz, and Les Cottés. Archaeol. Anthropol. Sci., 10: 539-554. doi:10.1007/s12520-016-0365-5</p> <p>Falcucci A., Conard N.J. & Peresani M. (2020) Breaking through the Aquitaine frame: A re-evaluation on the significance of regional variants during the Aurignacian as seen from a key record in southern Europe. J. Anthropol. Sci., 98: 99-140. doi:10.4436/JASS.98021</p> <p>Falcucci A., Karakostis F.A., Göldner D. et al. (2022) Bringing shape into focus: Assessing differences between blades and bladelets and their technological significance in 3D form. Journal of Archaeological Science: Reports, 43: 103490. doi:https://doi.org/10.1016/j.jasrep.2022.103490</p> <p>Falcucci A. & Peresani M. (2022) The contribution of integrated 3D model analysis to Protoaurignacian stone tool design. PLoS One, 17: e0268539. doi:10.1371/journal.pone.0268539</p> <p>Falcucci A., Giusti D., Zangrossi F., De Lorenzi M., Ceregatti L. & Peresani M. (2024a) Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology. Journal of Paleolithic Archaeology, 8: 2. doi:10.1007/s41982-024-00203-0</p> <p>Falcucci A., Arrighi S., Spagnolo V., Rossini M., Higgins O.A., Muttillo B., Martini I., Crezzini J., Boschin F., Ronchitelli A. & Moroni A. (2024b) A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy. Scientific Reports, 14: 12783. doi:10.1038/s41598-024-59896-6</p> <p>Göldner D., Karakostis F.A. & Falcucci A. (2022) Practical and technical aspects for the 3D scanning of lithic artefacts using micro-computed tomography techniques and laser light scanners for subsequent geometric morphometric analysis. Introducing the StyroStone protocol. PLoS One, 17: e0267163. doi:10.1371/journal.pone.0267163</p> <p>Göldner D., Karakostis F.A. & Falcucci A. (2023) <em>StyroStone</em>: A protocol for scanning and extracting three-dimensional meshes of stone artefacts using Micro-CT scanners V.3. protocols.io. <a href="dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3">dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3</a></p> <p>Lombao D., Falcucci A., Moos E. & Peresani M. (2023) Unravelling technological behaviors through core reduction intensity. The case of the early Protoaurignacian assemblage from Fumane Cave. Journal of Archaeological Science, 160: 105889. doi:https://doi.org/10.1016/j.jas.2023.105889</p>
INFORMATE Project - CHORUS Report Summaries - 20231106
<p>These data provide a summary of the All, Author Affiliation, and Dataset Reports generated by the <a href="https://dashboard.chorusaccess.org/">CHORUS Dashboard</a> for three agencies: the U.S. National Science Foundation, U.S. Geological Survey, and the U.S. Agency for International Development. The reports summarized here was collected on November 6-7, 2023 as part of the INFORMATE Project funded by NSF.</p><p>The columns are:</p><p>Column Definition</p><p>agency The funding agency [NSF, USGS, or USAID]</p><p>date. The date of data retrieval (YYYYMMDD)</p><p>report. The report [all, authors, datasets]</p><p>Property Name of the column in the input file</p><p>count Number of values (rows) of the property</p><p>unique Number of unique values of the property</p><p>top Most common value of the property</p><p>freq Number of occurrences (frequency) of the most common value</p><p>Count % The percentage of rows that include the property</p>
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
Maxillofacial bone dataset for the MARGO project
<h2>Bone atlas</h2><p>for the <a href="https://sites.google.com/view/margoflagera/">[MAxillofacial bone Regeneration by 3D-printed laser-activated Graphene Oxide Scaffolds] (MARGO) FLAG–ERA JTC 2019 project.</a></p><p>Dataset includes:</p><ul><li>Info.xlsx: Datasheet with info for each subject.</li><li>Thumbnails.zip: Thumbnails of the CT and CBCT images.<br>240 image files in .png format, 826x736 pixels, of each dataset, rendered using a volume rendering transfer function.</li><li>M_mandible.zip: Segmented mesh surfaces of the mandible (.ply files). Currently 115 files are included (see Info.xlsx).</li><li>MargoTemplate.zip: Template of landmarks. This file is compatible with the <a href="https://www.dhal.com">Viewbox software (www.dhal.com)</a>.</li><li>Margo100_GM_slide.xml: The landmark coordinates of 100 mesh surfaces. This file is compatible with the <a href="https://www.dhal.com">Viewbox software (www.dhal.com)</a>.</li></ul><p>Please note: previous versions contained some duplicates; these have been removed.</p><p>Please contact <a href="mailto:dhal@dhal.com">dhal@dhal.com</a> for information about the Template and landmark coordinates files.</p>
Accord-Project/CODE-ACCORD: v1.0.0
<p>The CODE-ACCORD corpus contains annotated sentences from the building regulations of England and Finland and has been developed as part of the Horizon European project for Automated Compliance Checks for Construction, Renovation or Demolition Works (<a href="https://accordproject.eu/" rel="nofollow">ACCORD</a>). The corpus is in English, and it consists of both the English Building Regulations and the English translation of the Finnish National Building Code.</p> <h3>1.0.0</h3> <p>CODE-ACCORD first release</p> <ul> <li>Regulatory sentence corpus</li> <li>Entity-annotated sentences</li> <li>Relation-annotated sentences</li> </ul>
GEroNIMO project EP database related to KERs
<p>European Patents dataset performed using <a href="http://www.lens.org">www.lens.org</a> free database for the 7 Key Exploitable Results (KER) identified on the Grant Agreement and selected keywords for GEroNIMO projects. Set up parameters included.</p>
Zero-degree isotherm latitude (ZIL) position over Antarctica: Historical and Projections
<p>This is the dataset associated to the research 'Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula: extent and implications' published in <i>Science of the Total Environment</i>.</p><p>This repository contains:</p><ul><li><strong>ZIL_ERA5_1957-2020_position.zip:</strong> Historical position of the ZIL for every longitude point in ERA5 from 1957 to 2020 for different <i>seasons</i>. Files named:<ul><li>ZIL_ERA5_1957-2020<i>[season]</i>position.csv<ul><li>Dimensions: [lons, years]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_timeseries.csv:</strong> Historical spatially averaged position of the ZIL for Antartica (Ant) and the Antarctic Peninsula (AP) in ERA5 from 1957 to 2020 for different <i>seasons</i>. File named:<ul><li>ZIL_ERA5_1957-2020_timeseries.csv<ul><li>Dimensions: [years, season_area]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_meanposition.csv:</strong> Historical temporally averaged position of the ZIL in ERA5 from 1957 to 2020 for different <i>seasons </i>and <i>months</i>. File named:<ul><li>ZIL_ERA5_1957-2020_meanposition.csv<ul><li>Dimensions: [lons, season/month]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_Historical_position.zip:</strong> Mean position of the ZIL for every longitude point in Historical simulations of CEMIP6 from 1957 to 2014 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_Hist_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP2-45.zip:</strong> Mean position of the ZIL for every longitude point under the SSP2-4.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP2-45_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP2-45_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP5-85.zip:</strong> Mean position of the ZIL for every longitude point under the SSP5-8.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 and trends for the period 2015-99 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP5-85_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP5-85_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li></ul><p><i><strong>seasons</strong></i> are:</p><ul><li>ANN: Annual mean</li><li>DJF: December-January-February (Summer)</li><li>MAM: March-April-May (Autumn)</li><li>JJA: June-July-August (Winter)</li><li>SON: September-October-November (Spring)</li></ul><p><i><strong>areas</strong></i> are:</p><ul><li>Ant: All Antarctica</li><li>AP: Antarctic Peninsula</li></ul><p><strong>Note:</strong> CEMIPT6 models include a column with CEMIP6 model average</p><p><strong>Version control</strong></p><p>v1.0 - Initial version<br>v1.1 - Change ERA5 dataset calculations from preliminary version of ERA5 to final version of ERA5</p><p> </p><p><strong>How to cite</strong></p><p>If you use this dataset, please cite the accompanying paper as:</p><p> </p><p><strong>Complementary code</strong></p><p>You can find the jupyter notebooks to complement the research in: <a href="https://doi.org/10.5281/zenodo.10063849">https://doi.org/10.5281/zenodo.10063849</a></p><p> </p><p><strong>Contact</strong></p><p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
List of capacity building resources for combating climate mis/disinformation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on combating climate change misinformation and disinformation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
List of capacity building resources for climate change adaptation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.1. Location of hoards mentioned in the text: white dots represent locations of hoards examined in the Biography of Hoards project; black dots represent locations of hoards examined in other multi-faceted projects
<p>The set contains a figure, with data, on the location of the hoards included (described in the related paper).<br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.3. Workflow in the Biography of Hoards project
<p>The set contains a figure and editable files associated with the figure.</p> <p>Figure presenting workflow of the project described in the related paper.</p> <p>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)
<p>Contacts for further details:</p> <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> <p>This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods <strong>over December-Febrary months only</strong> (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources. </p> <h1>Data sources</h1> <ul> <li><strong>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</strong>. A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>)<em>,</em> with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li><strong>Measured wind gusts from automatic weather stations (AWS)</strong>. Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li><strong>The ERA5 reanalysis </strong>from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li><strong>The ERA-Interim reanalysis</strong> from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> <p>This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison.</p> <p>This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below.</p> <h1>/daily_points</h1> <p>This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows:</p> <ul> <li> <p><code>barpac_m_aws_dmax_obs.csv</code><br>Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km.csv</code><br>Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R.</p> </li> <li> <p><code>barpac_m_aws_dmax_12km.csv</code><br>Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions.</p> </li> <li> <p><code>barpac_m_aws_dmax_erai.csv</code><br>Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code><br>As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum.</p> </li> </ul> <h1>/monthly_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)):</p> <ul> <li> <p><code>barpac_scws_<experiment>_monthly.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpar_<experiment>_monthly.nc</code><br>Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_scws_bdsd_<experiment>.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_max_<experiment>_monthly.nc</code><br>Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> </ul> <h1>/daily_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date):</p> <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> <p>Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS.</p> <h3>Table 1</h3> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Name</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from BARPA-R</p> </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from ERA5</p> </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table> <p> </p>
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