Work

Digital Barriers Still Hindering the Retrieval and Analysis of Historical Dark Data in Phenology

Year: 2026

Type: article

Abstract: To deepen our understanding of human–ecosystem interactions, researchers need to be able to retrieve and analyze historical dark data such as plant and animal phenology, but there are often barriers to doing so. Despite the development of online digitization and other tools, including library search engines, digital collections, machine translation, OCR (optical character recognition), HTR (handwritten text recognition), and generative AI technologies, and the establishment of standards and frameworks (e.g., FAIR Principles and the International Image Interoperability Framework), barriers to converting analog records to digital records (“digital barriers”) and to translating local languages to an international common language (“language barriers”) still remain. We present a case study example of the use of historical dark data in phenology in Japan and the digital and language barriers encountered. We then briefly summarize factors and challenges hindering use of this data and describe the benefits of further removal of these barriers.

Source: Data

DOI: 10.3390/data11090212

Authors: Nagai Shin, Taku M. Saitoh, Chifuyu Katsumata

Institutions: Japan Agency for Marine-Earth Science and Technology, Gifu University

Language: English

Topic: Research Data Management Practices

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Japan Society for the Promotion of Science