Upcycling Artifacts Dataset
Description
The "Upcycling Artifacts Dataset" is a comprehensive, multidisciplinary collection spanning over a century (1900–2025), encompassing textual and descriptive data from diverse cultural and creative domains. It includes: ▪ Journals: Scholarly articles, periodicals, and essays documenting artistic, social, and cultural trends. ▪ Art Pieces Descriptions: Metadata and textual narratives describing artworks, exhibitions, and creative practices. ▪ Fashion Campaigns: Marketing and editorial content from fashion houses, highlighting design, branding, and cultural influence over time. ▪ Sustainability Reports: Corporate and institutional documents reflecting environmental practices, social responsibility, and resource management in creative industries. This dataset is designed for researchers, data scientists, and creatives interested in analyzing the evolution of art, fashion, and sustainability practices, offering insights into historical and contemporary trends, cultural dialogues, and the intersection of creativity and environmental consciousness.
Files
Steps to reproduce
The Upcycling Artifacts Dataset was analyzed through a multi-step methodology designed to capture the evolution of artistic, fashion, and sustainability practices from 1900 to 2025. The first stage of analysis employed systematic content analysis to examine textual entries, including journals, art piece descriptions, fashion campaigns, and sustainability reports. This step aimed to identify recurring themes and categorize the data into meaningful dimensions, such as artistic movements, campaign motifs, and sustainability practices. Texts were digitized and cleaned to remove inconsistencies, and a coding scheme was applied to classify content. Inter-coder reliability checks were conducted on a subset of the data to ensure consistency in classification. Following content analysis, historical process tracing was used to situate the identified themes within their temporal context. Entries were organized chronologically, enabling the reconstruction of key milestones, innovations, and shifts across creative and sustainability domains. Cross-references among journals, campaigns, and reports helped validate temporal relationships and revealed patterns of influence and adoption over time. Finally, correlation analysis was performed to explore statistical relationships between the coded variables. Through the conversion of qualitative categories into numerical representations, the analysis quantified associations, such as the interplay between eco-conscious fashion campaigns and sustainability report emphasis, and examined how these trends co-evolved across decades. To ensure reproducibility, the dataset is accompanied by a detailed codebook describing variable definitions and coding schemes, along with scripts for content analysis, chronological reconstruction, and correlation computation. Researchers can replicate the workflow by applying the coding scheme to raw data, reconstructing temporal sequences using the provided metadata, and conducting correlation analysis using the included scripts. This approach combines qualitative insight with quantitative rigor, enabling both historical inquiry and data-driven exploration of over a century of cultural and creative practices.
Institutions
- Boston University