Related Experiment Video
Updated: Nov 6, 2025
13:03
Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
Published on: March 8, 2018
10.9K
Estimating herbarium specimen digitization rates: Accounting for human experience.
Caleb Powell1, Alaina Krakowiak1, Rachel Fuller1
1Department of Biology, Geology, and Environmental Science University of Tennessee at Chattanooga 615 McCallie Avenue Chattanooga Tennessee 37403 USA.
Applications in Plant Sciences
|May 10, 2021
Summary
Digitizing herbaria collections requires labor estimates that account for experience. Productivity improves with time, potentially reducing labor needs by 20% and highlighting the importance of the learning curve in project planning.
Area of Science:
- Botany and Biodiversity Science
- Natural History Collections Management
Background:
- Herbaria are crucial for natural world research.
- Digitizing these collections is an ongoing global effort.
- Accurate labor estimation is needed for efficient digitization projects.
Purpose of the Study:
- To develop a method for estimating labor needs in herbarium digitization.
- To analyze digitization productivity data from multiple institutions.
- To derive generalized labor estimates considering human experience.
Main Methods:
- Analysis of digitization productivity reports from 105 participants across eight herbaria.
- Grouping individual digitization rates by cumulative task experience.
- Development of generalized labor projection models based on observed productivity trends.
Main Results:
- Digitization productivity generally increases with experience.
- Longer technician retention can lead to an estimated 20% reduction in labor requirements.
- Learning curve effects are significant in initial digitization phases.
Conclusions:
- Student labor is common in digitization, offering valuable training opportunities.
- The learning curve associated with new personnel must be factored into labor estimations.
- Effective planning requires accounting for experience-based productivity gains in natural history collection digitization.
