Crowdsourcing Platforms for Collaborative Analysis of Archaeological Big Data

EasyChair Preprint 14252

Abstract

Crowdsourcing platforms have emerged as transformative tools for managing and analyzing archaeological big data, leveraging the collective intelligence of diverse contributors to enhance research capabilities and insights. This paper explores how crowdsourcing platforms facilitate the collaborative analysis of extensive archaeological datasets, highlighting their advantages, challenges, and the potential for innovative research methodologies. We discuss various platforms and their applications in archaeology, such as data classification, artifact identification, and spatial analysis. By harnessing the power of large, distributed networks of contributors, these platforms enable researchers to process vast amounts of data more efficiently than traditional methods. However, issues related to data quality, contributor engagement, and the integration of crowd-sourced results with expert analysis remain significant challenges. This study aims to provide a comprehensive overview of current crowdsourcing platforms in archaeology, evaluating their effectiveness and suggesting best practices for future applications. Through case studies and practical examples, we illustrate the impact of crowdsourcing on archaeological research, offering insights into how these platforms can be optimized to advance our understanding of past human societies.

Keyphrases: Technology, computing, crowdsourcing platforms

@booklet{EasyChair:14252,
  author    = {Favour Olaoye and Chris Bell and Peter Broklyn},
  title     = {Crowdsourcing Platforms for Collaborative Analysis of Archaeological Big Data},
  howpublished = {EasyChair Preprint 14252},
  year      = {EasyChair, 2024}}