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2,859 results for “volume”
CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes
Open the record for dataset details and reuse information.
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 8-bit Sub-Volumes
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of each dataset is 500x1000x1000. Below is a summary of the pixel-sizes and associated datasets on Zenodo.</p> <blockquote> <p>Key:</p> <ul> <li>160695 = 0.3125 Micron = https://zenodo.org/records/13327692</li> <li>169066 = 0.8125 Micron = https://zenodo.org/records/13327682</li> <li>169067 = 1.625 Micron = https://zenodo.org/records/13327651</li> <li>169068 = 2.6 Micron = https://zenodo.org/records/12206815</li> </ul> </blockquote> <p>The purpose of this dataset is to provide an easy to download sub-volumes of the larger (>50GB) datasets in the above Zenodo entries.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention: raw dataset
<p>Raw dataset for the publication:</p> <p><strong>Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention</strong></p>
Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis
<p>Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression.</p> <p>The data was saved with numpy 1.21 with <a href="https://numpy.org/doc/1.21/reference/generated/numpy.lib.format.html#format-version-1-0">NumPy format version 1.0</a> as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data.</p> <p>In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with <a href="https://imagej.nih.gov/ij/">ImageJ</a>.</p> <p> </p> <p>A <a href="https://github.com/Kitware/MetaIO">METAImage</a> header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,</p>
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</p>
Ionic composition of particulate matter (PM10) from high-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Aerosol particles originate from a variety of sources (Tomasi and Lupi, 2017). Information on particle chemical composition can be utilized to access particle origin. During the Antarctic Circumnavigation Expedition (ACE) cruise around the Southern Ocean, off-line filter sampling of ambient air was performed. Filters were stored on the ship (at -20 degrees C) and after the cruise concluded analysed at Leibniz-Institute for Tropospheric Research (TROPOS) concerning ionic composition of sampled material. Here, we give mass concentrations for inorganic ions (chloride, sodium, potassium, magnesium, calcium, ammonium, nitrate, sulphate, and bromide), organic constituents (methane-sulfonic acid and oxalate), and total filter load of particles with a mobility diameter smaller 10 micrometers (PM10) for each 24 hour-sampled filter.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_particulate_matter_pm10_ionic_composition_highvolume.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This ionic composition of particulate matter (PM10) from high-volume sampling dataset during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Ice Nucleating Particle number concentration from low-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract </strong></p> <p>Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>data_file_header_frozen_fraction.txt, metadata, text format</li> <li>data_file_header_number_concentration.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to reflect low-volume sampling method</li> <li>addition of INP number concentration data from different temperatures</li> <li>addition of fraction of frozen droplets data</li> <li>addition of field blank filter data</li> <li>create separate data_file_header files</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p> <p> </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>
Data for: Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis
<p>The data is supplementary to the publication "Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis", DOI: <a href="https://doi.org/10.1016/j.polymertesting.2024.108340" target="_blank" rel="noopener">10.1016/j.polymertesting.2024.108340</a></p> <p>Key words: Thermal volume expansion, Temperature-modulated optical refractometry, Thermo-mechanical analysis, Dilatometry, Epoxy thermoset</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a model epoxy polymer in the viscoelastic temperature range.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> <li>n-tetradecane, C14H30</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>(Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Chemitecture”, project-no.: 21647048)</li> </ul>
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
Finite amplitude sound propagation effects in volume backscattering measurements for fish abundance estimation
<p>The upload contains measurement and simulation data for finite-amplitude sound propagation effects in volume backscattering measurements. The experimental data are from a trawl survey conducted in the North Sea with R/V "G. O. Sars", 6-7 November 2004, passing several times over a group of Atlantic mackerel schools. The measurements are of the relative area backscattering coefficient, relative to 38 kHz, 2000 W power setting, at</p> <p>(1) 120 kHz with 250 W transmit power setting, 200 kHz with 120 W transmit power setting<br> (2) 120 kHz with 1000 W power setting, 200 kHz with 1000 W power setting.</p> <p>A Simrad EK60 echosounder system was used, alternating between the low (1) and high (2) power settings through the measurement series.</p> <p>The corresponding simulation data are calculated using the Bergen Code numerical solver of the KZK Equation. The medium parameters input to the simulations are based on CTD data from the field survey . The transducer and amplitude data were found by laboratory measurements on echo sounders of the same type as used in the survey.</p> <p>.m files are included for both .mat data files, with details on how to read the data.</p> <p>An article describing the data has been submitted by the authors to Acta Acustica, 2022.</p>
Data for: Vitrimer transition phenomena from the perspective of thermal volume expansion and shape (in)stability
<p>The data is supplementary to the publication "Vitrimer transition phenomena from the perspective of thermal volume expansion and shape (in)stability", DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.macromol.4c00207" target="_blank" rel="noopener">10.1021/acs.macromol.4c00207</a></p> <p>Key words: Vitrimer transition temperature, Thermo-mechanical analyses, Temperature-modulated optical refractometry, Thermal volume expansion, Dynamic polymer networks, Shape instabilities</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a epoxy-based vitrimer and a reference material.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) + 1,5,7-triazabicyclo[4.4.0]dec-5-en (TBD, 10 mol-% relative to carboxylic acid functions)</li> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>Sample preparation: (Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Repairtecture”, project-no.: 904927)</li> </ul>
3D Reconstruction of Shoulder Muscles in Hominoid Primates: Correlating Scapular Attachment Areas with Muscle Volume
<h2><strong>How To Cite:</strong></h2> <p>If you use this data or code in your research, please cite the associated open-access <strong>manuscript, </strong>which you can find here: <a href="https://doi.org/10.1111/joa.14199">https://doi.org/10.1111/joa.14199</a><br>and this <strong>zenodo repository</strong>.</p> <h2><strong>Online Visualization:</strong></h2> <p>You can access an interactive, web-based view of the notebooks and analyses <a title="Shoulder Muscle Reconstruction Code" href="https://juliavanbeesel.github.io/ShoulderMuscleReconstructions/intro.html" target="_blank" rel="noopener">here</a>.</p> <h2><strong>Repository Description:</strong></h2> <p>This repository contains two zip files related to the analysis and visualization of 3D reconstructed muscle volumes and lengths from various hominoid specimens.</p> <ol> <li> <p><strong>MeshFiles.zip:</strong></p> <ul> <li><strong>Contents:</strong> This zip file includes all <code>.obj</code> files for 3D reconstructed muscle volumes and associated anatomical structures. Specifically, it contains: <ul> <li><strong>Muscles:</strong> Supraspinatus, Infraspinatus, Subscapularis, Teres Major, Teres Minor</li> <li><strong>Bones:</strong> Scapula and Humerus</li> <li><strong>Attachment Sites</strong></li> </ul> </li> <li><strong>Organization:</strong> The files are organized into folders by specimen. There are 9 hominoid specimens from the following species: <ul> <li><em>Hylobates lar</em></li> <li><em>Symphalangus syndactylus</em></li> <li><em>Pongo pygmaeus</em></li> <li><em>Pongo abelii</em></li> <li><em>Gorilla gorilla</em></li> <li><em>Pan troglodytes</em></li> <li><em>Homo sapiens</em></li> </ul> </li> <li><strong>Surface Scans of Muscle Geometry: </strong>The specimens <em>Pongo</em> (ID 3) and <em>Symphalangus </em>(ID 122) also contain surface scans that depict the muscle geometry of the listed muscles. These surface scans can be used for training with the iterative polygonal modelling approach. The scans are stored as <code>.obj</code>, <code>.mtl</code> and <code>.png</code> files. To view textures on these meshes, keep all three files together in the same folder.</li> <li><strong>Additional Details:</strong> Muscle reconstructions were performed for different arm positions. Each folder contains multiple humerus files, with each file representing a humerus in a specific position aligned with the corresponding muscles. The humerus file names indicate the muscles the humerus is aligned with.<br><br></li> </ul> </li> <li> <p><strong>DataAndCode.zip:</strong></p> <ul> <li><strong>Contents:</strong> <ul> <li><strong>Excel File:</strong> The original data used for analysis, presented in Table 2 of the manuscript.</li> <li><strong>Jupyter Notebook Files: </strong>These notebooks provide the analyses and figures as described in the manuscript: <ul> <li><em>Accuracy_Muscle_Length_Reconstruction:</em> Analysis of muscle length measurement comparisons, detailed in Supplementary Information Section 3: <em>Accuracy of estimating Muscle Length from 3D reconstructions</em>.</li> <li><em>Accuracy_Muscle_Volume_Reconstruction:</em> Analysis of muscle volume measurement comparisons, detailed in Results Section 3.2: <em>Accuracy of Muscle Volume and Length Reconstruction</em>.</li> <li><em>Correlation_Analysis_SIS:</em> Correlation analysis of muscle origin area to volume for the supraspinatus, infraspinatus, and subscapularis muscles, detailed in Results Section 3.3:<em> Correlation Analysis</em>.</li> <li><em>Correlation_Analysis_TT:</em> Correlation analysis of muscle origin area to volume for the teres major and minor muscles, detailed in Supplementary Information Section 1: <em>Correlation results of teres major and minor</em>.</li> </ul> </li> <li><strong>Requirements.txt:</strong> A file listing the necessary packages required to run the Jupyter notebooks.</li> </ul> </li> <li><strong>Purpose:</strong> The Python files include code for performing statistical analyses and generating figures as described in the manuscript.</li> </ul> </li> </ol> <h2><strong>Usage Instructions:</strong></h2> <ul> <li>For analyzing muscle volumes and lengths, refer to the Jupyter notebooks included in the <code>DataAndCode.zip</code>. Ensure all dependencies listed in the <code>requirements.txt</code> file are installed.</li> <li>The <code>MeshFiles.zip</code> contains the 3D models necessary for visualizing muscle and bone reconstructions, organized by specimen and arm position.</li> </ul>
FAB2_sapling_volume_2021-2022 in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie-grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes. This data package examines mortality in the first six years of the experiment.
ANA01 Weekly, seasonal and annual measurement of precipitation volume and chemistry collected as part of the National Atmospheric Deposition Program at Konza Prairie
Data set contains results of chemical analysis of wetfall samples collected on Konza Prairie. Analysis is done by the Central Analytical Lab (CAL), Champaign, IL as part of the National Atmospheric Deposition Program (NADP). NADP data products available on the NADP/NTN web site (nadp.slh.wisc.edu/data/NTN/) include: Annual Data Summaries, Semiannual Data Reports, Annual and Seasonal Averages, Monthly Averages, and Weekly data. Konza Prairie LTER archives and provides the weekly data in electronic form before May 2019.
NTF01 Volume and chemistry of throughfall in tallgrass prairie
Amounts and nitrogen content of water passing through the canopy of tallgrass prairie are compared to similar measurements of bulk precipitation. Measurements include nitrate, ammonia, phosphate and organic nitrogen and phosphorus content of bulk precipitation and throughfall. Variables of interest include vegetation type and amounts, time of year, and time since burning.
ASA³P Software & Database volume
<p>ASA³P is an automatic and highly scalable assembly, annotation and higher-level analyses pipeline for closely related bacterial isolates. <a href="https://github.com/oschwengers/asap">https://github.com/oschwengers/asap</a></p> <p>ASA³P is a fully automatic, locally executable and scalable assembly, annotation and higher-level analysis pipeline creating results in standard bioinformatics file formats as well as sophisticated HTML5 documents. Its main purpose is the automatic processing of NGS WGS data of multiple closely related isolates, thus transforming raw reads into assembled and annotated genomes and finally gathering as much information on every single bacterial genome as possible. Per-isolate analyses are complemented by comparative insights. Therefore, the pipeline incorporates many best-in-class open source bioinformatics tools and thus minimizes the burden of ever-repeating tasks. Envisaged as a preprocessing tool it provides comprehensive insights as well as a general overview and comparison of analysed genomes along with all necessary result files for subsequent deeper analyses. All results are presented via modern HTML5 documents comprising interactive visualizations.</p> <p>Schwengers et al, 2020 PLOS Comp Bio DOI:10.1371/journal.pcbi.1007134</p>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.