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LTER-Italy site Bosco Fontana figure

Geographical representation of the LTER-Italy site Bosco Fontana (LTER_EU_IT_037) - DEIMS-ID <a href="https://deims.org/0585b394-faf2-4850-913f-6351aace73e4">https://deims.org/0585b394-faf2-4850-913f-6351aace73e4</a>

LTER-Italy site Laguna di Venezia figure

Geographical representation of the LTER-Italy site Laguna di Venezia (LTER_EU_IT_016) - DEIMS-ID <a href="https://deims.org/f7d94927-17be-4d3d-9810-e3c9bc91829c">https://deims.org/f7d94927-17be-4d3d-9810-e3c9bc91829c</a>

LTER-Italy site Monte Rufeno LAZ1 figure

Geographical representation of the LTER-Italy site Monte Rufeno LAZ1 (LTER_EU_IT_034) - DEIMS-ID <a href="https://deims.org/05e96829-e64a-48d3-a96d-de2aa4cde146">https://deims.org/05e96829-e64a-48d3-a96d-de2aa4cde146</a>

LTER-Italy site Montagna di Torricchio figure

Geographical representation of the LTER-Italy site Montagna di Torricchio (LTER_EU_IT_033) - DEIMS-ID <a href="https://deims.org/6b62feb2-61bf-47e1-b97f-0e909c408db8">https://deims.org/6b62feb2-61bf-47e1-b97f-0e909c408db8</a>

LTER-Italy site Ficuzza SIC1 figure

Geographical representation of the LTER-Italy site Ficuzza SIC1 (LTER_EU_IT_036) - DEIMS-ID <a href="https://deims.org/ec2bba9a-365f-45d8-9e0d-229de0f41332">https://deims.org/ec2bba9a-365f-45d8-9e0d-229de0f41332</a>

LTER-Italy site Appennino centrale Velino-Duchessa figure

Geographical representation of the LTER-Italy site Appennino centrale Velino-Duchessa (LTER_EU_IT_025) - DEIMS-ID <a href="https://deims.org/12c79ecb-7890-4b75-9655-0883dacd8a29">https://deims.org/12c79ecb-7890-4b75-9655-0883dacd8a29</a>

LTER-Italy site Sacca di Goro figure

Geographical representation of the LTER-Italy site Sacca di Goro (LTER_EU_IT_040) - DEIMS-ID <a href="https://deims.org/b7869194-b220-473a-b035-feeadfa21aba">https://deims.org/b7869194-b220-473a-b035-feeadfa21aba</a>

LTER-Italy site Valli di Comacchio figure

Geographical representation of the LTER-Italy site Valli di Comacchio (LTER_EU_IT_041) - DEIMS-ID <a href="https://deims.org/70e1bc05-a03d-40fc-993d-0c61e524b177">https://deims.org/70e1bc05-a03d-40fc-993d-0c61e524b177</a>

LTER-Italy site Lago di Garda figure

Geographical representation of the LTER-Italy site Lago di Garda (LTER_EU_IT_044) - DEIMS-ID <a href="https://deims.org/c713db56-373c-46cc-8828-ce8cadc4f3bb">https://deims.org/c713db56-373c-46cc-8828-ce8cadc4f3bb</a>

LTER-Italy site Lago di Orta figure

Geographical representation of the LTER-Italy site Lago di Orta (LTER_EU_IT_042) - DEIMS-ID <a href="https://deims.org/8bd7d2f8-421a-48bd-b212-04bc1e9f31d5">https://deims.org/8bd7d2f8-421a-48bd-b212-04bc1e9f31d5</a>

LTER-Italy site Val Masino LOM1 figure

Geographical representation of the LTER-Italy site Val Masino LOM1 (LTER_EU_IT_028) - DEIMS-ID <a href="https://deims.org/68a5673c-9172-48cc-88e5-b9408b203309">https://deims.org/68a5673c-9172-48cc-88e5-b9408b203309</a>

opencc-by-4.0Mar 2020View details →

Fine-scale structure of the 2016-2017 Central Italy Seismic Sequence from data recorded at the Italian National Network

Data Set  Catalog of 33,983 earthquakes located during the 2016-2017 Central Italy seismic sequence. The velocity model used is the 1D gradient P- and S-wave velocity models (after Carannante et al., 2013). We used the highest quality P- and S-wave arrival times manually picked by analysts of the National Institute of Geophysics and Volcanology (INGV) seismic monitoring room, having an uncertainty lower than 0.6 s.   Events were located by means of a 2-step procedure: the INGV routine absolute locations computation for all events with ML ≥ 1.5 that occurred in the study area between August 2016 and January 2018, using the method described in Chiaraluce et al. (2017); the determination of relative locations by applying the HypoDD code (Waldhauser, 2001) to the catalog picks and phase delay times measured from waveform cross correlation. The time domain cross-correlation method (Schaff et al., 2004; Schaff and Waldhauser, 2005) was applied to seismograms of all pairs of events separated by 3 km or less and recorded at common stations.  Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. The correlations measurements were performed on 0.7 s long window for P-waves and 1 s windows for S-waves. Only measurements with correlation coefficients greater than 0.7 were kept, resulting in a total of ~4.4 million P and ~1.1 million S wave delay times.  We sub-divided the entire dataset in 18 rectangular boxes, containing a maximum of 6000 earthquakes, orthogonal to and centered on the mean strike of the seismic sequence. The overlap between neighboring boxes is 50% with respect to the NW-SE extension. HypoDD is run separately on each box. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008). The final double-difference catalog includes 33,982 events occurring between 24th of August 2016 and 18th of January 2018. The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following: <ul> <li>Id-ingv: ingv eventid, useful to link to the QuakeML phase file through the INGV fdsnws/event webservice (<a href="https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2Fwebservices.ingv.it%2Fswagger-ui%2Fdist%2F%3Furl%3Dhttps%3A%2F%2Fingv.github.io%2Fopenapi%2Ffdsnws%2Fevent%2F0.0.1%2Fevent.yaml">http://webservices.ingv.it/swagger-ui/dist/?url=https://ingv.github.io/openapi/fdsnws/event/0.0.1/event.yaml</a>) and to the reported magnitude;</li> <li>Latitude(°) expressed in decimal degrees;</li> <li>Longitude(°) expressed in decimal degrees;</li> <li>Depth(km) hypocentral depth expressed in kilometers;</li> <li>Year of origin time in the format yyyy;</li> <li>Month of origin time in the format mm;</li> <li>Day of origin time in the format dd; </li> <li>Hour of origin time in the format hh;</li> <li>Minute of origin time in the format min;</li> <li>Second of origin time in the format ??.?????? s;</li> <li>Magnitude: the value available at the phases downloading time (see Id-ingv fdsnws/event)</li> </ul>

opencc-by-4.0Mar 2020View details →

Dataset of Soil hydraulic properties of Valle Telesina (Italy)

The dataset contain a .xls file with the hydraulic properties georeferenced of 47 soil profiles of the "Valle Telesina (Italy) site, according to the parametrization of the van Genuthen-Mualem model (van Genuchten, 1980). Moreover a zipped folder with the shape files for the same area is provided. Following there is the description of the methods applied for the soil hydraulic characterization: Undisturbed soil samples were collected from the horizons using cylindrical steel samplers (8.5 cm diameter and 12.0 cm high). In the laboratory, the samples were saturated by slowly wetting from the bottom in order to remove all the air entrapped in the soil. The maximum water content,θ0, was gravimetrically determined and the saturated hydraulic conductivity, ks, was measured by a falling-head permeameter. Then, the Wind method was applied to simultaneously determine the water retention and hydraulic conductivity functions by subjecting the soil samples to an evaporation process. After sealing the bottom surface to prevent drainage, during the evaporation process - at appropriate pre-set time intervals - the weight of the whole sample and the pressure head at three different depths were measured. An iterative procedure was applied for estimating the water retention curve from these measurements. Then, the instantaneous profile method was applied to determine the unsaturated hydraulic conductivity. θr, θs, α and n parameters were derived by fitting the soil water retention data; under the restriction m=l−l/n, τ and k0 parameters were derived by fitting the hydraulic conductivity data. Details of the tests and overall calculation procedures are described in Basile et al. (2012). The parameters obtained in the laboratory were then scaled to better reproduce the field behaviour by following the procedure suggested by Basile et al. (2003; 2006). Finally, for the few soils having considerable stone content, a correction of θs and k0, to take into account the stoniness, was applied (Coppola et al., 2013). References: Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898. Basile, A., Buttafuoco, G., Mele, G., & Tedeschi, A. (2012). Complementary techniques to assess physical properties of a fine soil irrigated with saline water. Environmental Earth Sciences,66(7), 1797–1807. Basile, A., Ciollaro, G., & Coppola, A.(2003). Hysteresis in soil water characteristics as a key to interpreting comparisons of laboratory and field measuredhydraulic properties.Water Resources Research, 39(12). Basile, A., Coppola, A., De Mascellis, R., & Randazzo, L. (2006). Scaling approach to deduce field unsaturated hydraulic properties and behavior from laboratory measurements on small cores. Vadose Zone Journal,5(3), 1005–1016. Coppola, A., Dragonetti, G., Comegna, A., Lamaddalena, N., Caushi, B., Haikal, M., & Basile, A. (2013). Measuring and modeling water content in stony soils. Soil and Tillage Research,128, 9–22.

opencc-by-4.0Jan 2021View details →

Infrasound array data recorded in July-August, 2019, at Mt. Etna (Italy) during the VOSSIA field experiment

We present infrasound data recorded by two infrasound arrays installed at Mt. Etna (Italy) within the framework of the VOSSIA (Volcanic emissions analysis through Seismic and Infrasound Advanced monitoring) project. VOSSIA was supported by the Trans-National Access component of the EUROVOLC project (European Network of Observatories and Research Infrastructures for Volcanology, EU Horizon 2020 Research Infrastructure Project grant No 731070). This data repository includes continuous raw waveforms recorded by two 6-element, small-aperture, infrasound arrays during July-August, 2019. The arrays, ENEA and ENCR, were installed on Mt. Etna in proximity of the summit Nord East and South East craters, respectively. ENEA was equipped with Chaparral M60 sensors (<a href="http://chaparralphysics.com/specs/specs_model60UHP.pdf">http://chaparralphysics.com/specs/specs_model60UHP.pdf</a>), while IST2018 microphones (<a href="https://doi.org/10.1016/j.jvolgeores.2019.106668">https://doi.org/10.1016/j.jvolgeores.2019.106668</a>) were installed at ENCR. Data at both arrays were recorded with a sampling frequency of 100 Hz and 24-bit resolution using DiGOS Datacube3 digitizers (<a href="https://digos.eu/seismology-and-cubes/">https://digos.eu/seismology-and-cubes</a>). Waveform data are provided as day-long files in MSEED format (<a href="https://ds.iris.edu/ds/nodes/dmc/data/formats/">https://ds.iris.edu/ds/nodes/dmc/data/formats/</a>). We also provide metadata including: 1) Station coordinates (.csv file station_coords.csv); 2) Instrument_response.rar: Instrument response information in different formats (individual RESP files, SEED DATALESS, xlm DATALESS)

opencc-by-4.0Feb 2024View details →

The Open Aurignacian Project. Volume 2: Grotta di Castelcivita in southern Italy

<h2>Overview</h2> 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 (rsa') and Early Aurignacian (gic, ars) 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 et al., 2014). A new technological assessment of the rsa’–ars 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 et al., 2008). 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 et al., 2022), ensuring accurate capture of artifact details. 3D scanning with the Artec Spider follows the third version of the Styrostone protocol outlined by Göldner et al. (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 Microstone 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. 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 et al. (2017). This dataset is part of the <a href="https://www.armandofalcucci.com/project/open_aurignacian/">Open Aurignacian Project</a>. <h2>Author contact</h2> Dr. Armando Falcucci armando.falcucci@uni-tuebingen.de; falcucciarmando@gmail.com <h2>Description of the dataset</h2> This repository includes the following components: <ol> <li><code>CTC_3D_Meshes.zip</code>: 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>: CSV file containing information, characteristics, and metadata of the lithic artifacts.</li> </ol>   The <code>Castelcivita_3D_metadata.csv</code> file includes the following metadata attributes: <ul> <li>ID: 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>Site: The archaeological site where the lithic was excavated.</li> <li>Layer: The stratigraphic origin of the lithic.</li> <li>Raw_material: Categorization by the type of raw material (e.g., Chert, Radiolarite).</li> <li>Class: 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>Blank: 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>Technology: Technological classification of the blanks into categories such as initialization, maintenance, optimal, semi-cortical, and others, following Falcucci et al. (2020) and Falcucci et al. (2024).</li> <li>Core_classification: Technological categories for cores and core-tools (e.g., Carinated, Multi-platform, Narrow-sided, Semicircumferential) following Falcucci & Peresani (2018).</li> <li>Cortex: Percentage of cortex coverage (0%, 1–33%, 33–66%, 66–99%, 100%), estimated visually.</li> <li>Preservation: Breakage classification for blanks (e.g., Complete, Distal, Mesial, Proximal, Undetermined). For cores and most core-tools, preservation is marked as "Other".</li> <li>Volume: The volume of the artifact in cubic millimeters.</li> <li>Surface: The surface area of the artifact in square millimeters.</li> <li>Length: Maximum length in millimeters based on technological orientation, recorded with a digital caliper.</li> <li>Width: Maximum width in millimeters based on technological orientation, recorded with a digital caliper.</li> <li>Thickness: Maximum thickness in millimeters based on technological orientation, recorded with a digital caliper.</li> <li>File_list: The list of files in the dataset that correspond to this specific ID.</li> <li>Model_unit: 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>#_of_polygons: The number of polygons in the 3D model of the artifact.</li> <li>Avg_edge_length(mm)/Resolution: The average distance between points on the model, serving as an effective measure of the model's resolution.</li> <li>Resolution_score: 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>Scanner: The specific model of the scanner used to capture the 3D data of the lithic artifact.</li> <li>Scan_software: The version of the software used in conjunction with the scanner to capture the 3D data of the artifact.</li> <li>Postprocessing_software: The version of the software used to execute postprocessing algorithms and generate the final 3D mesh of the artifact.</li> <li>Coating: Yes/No entry speifying if coating was used for any scan.</li> </ul> <h2>Research and Usage Notes</h2> 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. <h2>Licensing and Citation</h2> 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. <h2>References</h2> Douka K., Higham T., Wood R. et al. (2014) On the chronology of the Uluzzian. Journal of Human Evolution, 68: 1-13. doi:10.1016/j.jhevol.2013.12.007 Falcucci A. (2022) MicroStone: Exploring the capabilities of the Artec Micro in scanning stone tools. protocols.io. doi:<a href="https://dx.doi.org/10.17504/protocols.io.81wgb6781lpk/v1">https://dx.doi.org/10.17504/protocols.io.81wgb6781lpk/v1</a> 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 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 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 Gambassini P. (1997) Il Paleolitico di Castelcivita: Culture e Ambiente. Electa, Naples Giaccio B., Isaia R., Fedele F.G. et al. (2008) The Campanian Ignimbrite and Codola tephra layers: Two temporal/stratigraphic markers for the Early Upper Palaeolithic in southern Italy and eastern Europe. Journal of Volcanology and Geothermal Research, 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> 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 Göldner D., Karakostis F.A. & Falcucci A. (2023) StyroStone: 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> Marwick B., d’Alpoim Guedes J., Barton C.M. et al. (2017) Open science in archaeology. SAA Archaeological Record, 17: 8-14. doi:10.17605/OSF.IO/3D6XX

opencc-by-4.0Mar 2022View details →

The Open Aurignacian Project. Volume 1: Grotta di Fumane in northeastern Italy

<h2>Overview</h2> 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 et al., 2024a; Lombao et al., 2023). 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 et al., 2022), ensuring accurate capture of artifact details. 3D scanning and postprocessing for both micro-CT and Artec Spider follow the third version of the Styrostone protocol outlined by Göldner et al. (2023): <a href="dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3" rel="noopener">dx.doi.org/10.17504/protocols.io.4r3l24d9qg1y/v3</a>. 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 et al. (2017). This dataset is part of the <a href="https://www.armandofalcucci.com/project/open_aurignacian/">Open Aurignacian Project</a>. <h2>Author contact</h2> Dr. Armando Falcucci armando.falcucci@uni-tuebingen.de; falcucciarmando@gmail.com <h2>Description of the dataset</h2> This repository includes the following components: <ol> <li><code>RF_3D_Meshes.zip</code>: 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>: CSV file containing information, characteristics, and metadata of the lithic artifacts.</li> </ol> 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. The <code>Fumane_3D_metadata.csv</code> file includes the following metadata attributes: <ul> <li>ID: 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>Site: The archaeological site where the lithic was excavated.</li> <li>Layer: The stratigraphic origin of the lithic.</li> <li>Raw_material: Categorization by the type of raw material (e.g., Maiolica, Scaglia Variegata, Scaglia Rossa).</li> <li>Class: 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>Blank: 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>Technology: Technological classification of the blanks into categories such as initialization, maintenance, optimal, semi-cortical, and others, following Falcucci et al. (2020) and Falcucci et al. (2024b).</li> <li>Core_classification: Technological categories for cores and core-tools (e.g., Carinated, Multi-platform, Narrow-sided, Semicircumferential) following Falcucci & Peresani (2018).</li> <li>Cortex: Percentage of cortex coverage (0%, 1–33%, 33–66%, 66–99%, 100%), estimated visually.</li> <li>Preservation: Breakage classification for blanks (e.g., Complete, Distal, Mesial, Proximal, Undetermined). For cores and most core-tools, preservation is marked as "Other".</li> <li>Volume: The volume of the artifact in cubic millimeters.</li> <li>Surface: The surface area of the artifact in square millimeters.</li> <li>Length: Maximum length in millimeters based on technological orientation, recorded with a digital caliper.</li> <li>Width: Maximum width in millimeters based on technological orientation, recorded with a digital caliper.</li> <li>Thickness: Maximum thickness in millimeters based on technological orientation, recorded with a digital caliper.</li> <li>File_list: The list of files in the dataset that correspond to this specific ID.</li> <li>Model_unit: 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>#_of_polygons: The number of polygons in the 3D model of the artifact.</li> <li>Avg_edge_length(mm)/Resolution: The average distance between points on the model, serving as an effective measure of the model's resolution.</li> <li>Resolution_score: 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>Scanner: The specific model of the scanner used to capture the 3D data of the lithic artifact.</li> <li>Scan_software: The version of the software used in conjunction with the scanner to capture the 3D data of the artifact.</li> <li>Postprocessing_software: The version of the software used to execute postprocessing algorithms and generate the final 3D mesh of the artifact.</li> <li>Coating: Yes/No entry speifying if coating was used for any scan.</li> </ul> <h2>What's new in this release (Version 3.0.1)</h2> 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. <h2>Research and Usage Notes</h2> 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. <h2>Licensing and Citation</h2> 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. <h2>References</h2> 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 Bertola S., Broglio A., Cristiani E. et al. (2013) La diffusione del primo Aurignaziano a sud dell'arco alpino. Preistoria Alpina, 47: 17-30 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. Conard N.J. & Bolus M. (2015) Chronicling modern human’s arrival in Europe. Science. doi:10.1126/science.aab0234 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 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 Falcucci A. (2018) Towards a renewed definition of the Protoaurignacian. Mitteilungen der Gesellschaft für Urgeschichte, 27: 87-130 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 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 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 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 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 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 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 Göldner D., Karakostis F.A. & Falcucci A. (2023) StyroStone: 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> 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

opencc-by-4.0Jan 2023View details →

Net Ecosystem Exchange, Ecosystem Respiration and meteoclimatic data of Alpine grasslands at Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023

This dataset presents georeferenced measurements collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps. The dataset includes the Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables, i.e. air and soil temperature, air relative humidity, soil volumetric water content, atmospheric pressure and solar irradiance. The measurements were conducted between 2017 and 2023 at five different sites at an elevation of approximately 2550-2750 meters a.s.l. To estimate NEE and ER, we employed the flux chamber method, measuring the temporal variation of carbon dioxide (CO2) concentration inside the chamber over a period of about 90 seconds. We used a customized portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyzer (IRGA) and a portable weather station. Measurements were taken at around 20 points per site during the snow-free season, spanning from June to October. The dataset is provided in a comma-separated text file (.csv) format. Each record corresponds to a single measurement point, with semicolons used as separators. The "NA" notation indicates values that are not available or have been excluded during quality control processes (e.g., due to battery failure). We use point as decimal separator. The sign convention for the fluxes is: a negative value indicates a CO2 flux from the atmosphere to the ecosystem, while a positive value represents a CO2 flux from the soil/ecosystem to the atmosphere. Consequently, ER values are positive, while NEE values can be positive or negative. The units for NEE and ER fluxes are molCO2 m-2 day-1 and μmolCO2 m-2 second-1. The first values in each record of the dataset indicate the observation details (sampling date, site, etc.), followed by the corresponding measured or calculated variables. NEE and ER values were estimated from the slope of the linear regression of CO2 concentration over time (ppm s-1) using a laboratory calibration curve. The calibration curve was created by relating known and pre-set CO2 fluxes (within the range expected in the field) with the corresponding measured slopes. The flux values were then scaled up based on the area of the chamber base (0.036 m2) and adjusted using the ratio of atmospheric pressure and air temperature during the measurement to those recorded during the calibration in the laboratory.

opencc-by-4.0Nov 2021View details →

A comparative dataset on public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden

These datasets are the result of two nation-wide surveys conducted in Italy and Sweden in August 2020 and in november 2020. The surveys (which are identical in the two rounds) explore the respondents' risk perception, preparedness, knowledge, and experience regarding a set of hazards, namely: epidemics, floods, droughts, earthquakes, wildfires, terror attacks, domestic violence, economic crises, and climate change.   The data files include the questionnaire survey (the Italian and Swedish versions as well as the English translation) and the two datasets of all the answers to the two surveys. Each column in the dataset refers to an item in the survey (e.g. a question or a sub-question), and each row represents a single respondent.  For additional information on the August 2020 dataset, see <a href="https://www.nature.com/articles/s41597-020-00778-7">Mondino et al. (2020)</a>.

opencc-by-4.0Jun 2022View details →

A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification Across EU (Italy)

In order to shortlist possible options from a pool of 2700 crops, a crop-climate-soil matching ex-ercise was performed across Italian territory and crops with more than 70% suitability where chosen for further analysis. In the second phase, a multicriteria ranking index was employed to assign ranks to chosen crops of 4 main types; (i) cereals and pseudocereals, (ii) legumes, (iii) starchy roots/ tubers and (iv) vegetables. In order to provide a comprehensive analysis, major crops that are grown in the region where also included in the analysis. The results of evaluation of 4 major criteria (a) calorie and nutrition demand b) functions and uses c) availability and acces-sibility to their genomic material d) possession of adaptive traits, and e) physiological traits) re-vealed the potential for teff, faba bean, cowpea, green arrow arum, Jerusalem artichoke, Fig-leaved Gourd and Watercress.