Heritage Data Analytics: Sustainable strategies for large and complex stratigraphic and chronometric data.

遗产数据分析：大型复杂地层和计时数据的可持续策略。

• 批准号: 2114672
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2114672
• 关键词: Heritage; Data; Analytics; Sustainable; strategies

Our chronological understanding of archaeological sites develops gradually during field and post excavation work. First, we understand the relative chronological and contextual information, as represented by the stratigraphy and cultural finds respectively. Later, scientific (eg radiocarbon) dates are obtained and, finally, statistical modelling is used to draw everything together. At present this final stage is extremely laborious and requires considerable expertise because no tools exist to automate anything except the final statistical calculations. The proposed PhD student will develop numerical, analytical and graphical tools to improve this situation, with the goal of semi-automating chronology construction.The student will begin by characterizing the most common types of data that heritage practitioners and researchers create and work with, and particularly those created by fieldwork investigations. They will consider how the re-use of such datasets could be made easier and more effective by enhancing or developing better standards for digital data archives of excavation records. Working with Historic England, the student will then focus on protocols and algorithms for interfacing with current and legacy site databases, while Buck and others will work on improvements to the modelling software. Working in this extended team of archaeologists, modellers and programmers, the student will learn techniques and protocols from field and laboratory archaeology, software engineering, graphical and statistical modelling and Bayesian inference.The project is important because it addresses growing problems caused by a lack of standardized approaches to the archiving of excavation data, especially key stratigraphic and phasing data, often held in hard-copy matrix diagrams or unstructured database tables. The PhD will help inform decisions on digital archiving standards and best practice for stratigraphic data deposition and re-use, while developing our understanding of how statistical and technical approaches can best address the problems involved. It will provide a better understanding of the user requirements for interface tools to make analysis of large sites (especially legacy sites) practical.Dye and Buck (2015) have recently shown that at least semi-automation of the link between archaeological data and Bayesian modelling is practical. This PhD will extend the tools they have begun to develop, to accommodate a wider range of archaeological site database protocols and complement the the developments in Bayesian chronological modelling being undertaken by other members of Buck's team.Key research questions to be addressed include: What are the most common types of data that heritage practitioners create and work with? How can those various types of data be best characterised? How might adoption of better standards for digital archives of excavation records make re-use of such data sets easier and more useful? How might approaches to excavation data recording and archiving be improved to better enable the use of Big Data technologies?The proposed PhD research is highly interdisciplinary, and so the student will spend the first year reviewing literature and data (with Historic England support) while learning the basics of several specialist computer protocols and languages (with the support of Buck). In years 2 and 3, the student will work closely with Buck, Ayala and Dye, extending or rewriting the inputs to the prototype software written by Dye (to accompany Dye and Buck, 2015), interfacing it to key file formats and protocols identified in year one. The software developed by Dye is only a prototype and far from fully functional, so thestudent will have considerable scope to decide exactly how and in what way the PhD should progress. Dye T.S. & Buck C.E. (2015). Archaeological sequence diagrams and Bayesian chronological models. Journal of Archaeological Science , 83, 84-93.

我们对考古遗址的时间顺序的理解是在实地和发掘后的工作中逐渐发展起来的。首先，我们了解相关的年代和背景信息，分别由地层和文化发现。之后，科学（如放射性碳）日期被获得，最后，统计模型被用来把一切都联系在一起。目前，这最后一个阶段非常费力，需要相当多的专业知识，因为除了最后的统计计算之外，没有任何工具可以自动化。该博士生将开发数字、分析和图形工具来改善这种情况，目标是半自动化年表构建。该学生将开始描述遗产从业者和研究人员创建和使用的最常见类型的数据，特别是那些通过实地调查创建的数据。他们将考虑如何通过增强或开发更好的挖掘记录数字数据档案标准来使此类数据集的重复使用变得更容易、更有效。与历史英格兰工作，学生将专注于与当前和遗留网站数据库接口的协议和算法，而巴克和其他人将致力于改进建模软件。在这个由考古学家，建模师和程序员组成的扩展团队中工作，学生将学习现场和实验室考古学，软件工程，图形和统计建模以及贝叶斯推理的技术和协议。该项目很重要，因为它解决了由于缺乏标准化的方法来归档挖掘数据，特别是关键的地层和相位数据，通常保存在硬拷贝矩阵图或非结构化数据库表中。该博士将有助于为数字存档标准和地层数据沉积和再利用的最佳实践的决策提供信息，同时发展我们对统计和技术方法如何最好地解决所涉及问题的理解。它将更好地理解用户对界面工具的需求，使大型遗址（特别是遗产遗址）的分析切实可行。Dye和Buck（2015）最近表明，考古数据和贝叶斯建模之间的联系至少是半自动化的。这个博士学位将扩展他们已经开始开发的工具，以适应更广泛的考古遗址数据库协议，并补充Buck团队其他成员正在进行的贝叶斯年代建模的发展。要解决的关键研究问题包括：遗产从业者创建和使用的最常见的数据类型是什么？如何最好地描述这些不同类型的数据？采用更好的挖掘记录数字档案标准如何使这些数据集的再利用更容易和更有用？如何改进挖掘数据记录和存档的方法，以更好地使用大数据技术？拟议的博士研究是高度跨学科的，所以学生将花第一年审查文献和数据（与历史英格兰的支持），同时学习几个专业的计算机协议和语言的基础知识（与巴克的支持）。在第2年和第3年，学生将与Buck，Ayala和Dye密切合作，扩展或重写由Dye编写的原型软件的输入（伴随Dye和Buck，2015），将其与第一年确定的关键文件格式和协议进行接口。Dye开发的软件只是一个原型，远没有完全功能化，因此学生将有相当大的空间来决定博士学位应该如何以及以何种方式取得进展。染料T.S. & Buck C.E.（2015年）。考古序列图和贝叶斯年代模型。考古学杂志，83，84-93。