Armando Falcucci, Adriana Moroni, Fabio Negrino, Marco Peresani, Julien Riel-Salvatore
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引用次数: 0
摘要
在这里,我们引入了一个开放访问的石器3D模型数据库(n = 2016),这些石器模型来自阿尔卑斯山脉以南和意大利半岛沿线的四个早期旧石器时代序列,包括Grotta della Cala, Grotta di Castelcivita, Grotta di Fumane和Riparo Bombrini。通过四个独立的Zenodo存储库,这些模型能够深入分析核心还原过程,还原强度和形状可变性。与其他存储库不同,该数据库已被积极用于解决考古问题,提供了在岩石分析中使用3D模型的全面演示。开放奥日尼亚期项目利用各种扫描设备,包括Artec Spider、Artec Micro和微型计算机断层扫描,重点是提高考古数据的可重复性和可访问性。本文介绍了扫描方法、数据集组织和项目的技术验证,同时也讨论了这些数据促进跨大陆研究合作的科学潜力。我们的开放共享计划旨在促进人类行为进化的跨区域研究,为通过FAIR原则解决旧石器时代研究中的问题提供新的机会。
The Open Aurignacian Project: 3D scanning and the digital preservation of the Italian Paleolithic record.
Here, we introduce an open-access database of 3D models of stone tools (n = 2,016) from four Early Upper Paleolithic sequences excavated south of the Alps and along Peninsular Italy, including Grotta della Cala, Grotta di Castelcivita, Grotta di Fumane, and Riparo Bombrini. Available through four self-standing Zenodo repositories, these models enable in-depth analysis of core reduction procedures, reduction intensity, and shape variability. Unlike other repositories, this database has been actively used to address archaeological questions, providing a comprehensive demonstration of the use of 3D models in lithic analysis. The Open Aurignacian Project utilizes various scanning devices, including the Artec Spider, Artec Micro, and micro-computed tomography, with a focus on enhancing the reproducibility and accessibility of archaeological data. This paper presents the scanning methodology, dataset organization, and technical validation of the project, while also discussing the scientific potential of these data to foster cross-continental research collaboration. Our open-sharing initiative is designed to stimulate inter-regional studies of human behavioral evolution, offering new opportunities to address questions in Paleolithic studies through the FAIR principles.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.