Learn About the Digital Lab Notebook

Da Vinci Notebook

Learn more about the Digital Lab Notebook. Like a scientist's written account before the digital age, it records the means and circumstances of the capture of digital information from a “real world” subject, tracking all the events associated with the processing of this information into a digital representation. Learn more.


White Paper: “Data Sustainability and Advanced Metadata Management for Scientific Imaging”

Cover of the Data Sustainability white paper

At the conclusion of an NEH Start-up grant awarded to CHI in 2014, the team produced a white paper describing the outcome of the project: a user-friendly software toolkit that greatly simplifies methods of building a Digital Lab Notebook (DLN), an essential component of digital scientific imaging. Read the paper.


Related Publications

See these CHI publications for more information pertaining to the digital lab notebook.


“A Context Metadata Collection and Management Tool for Computational Photography Projects”

Conceptual illustration of the Digital Lab Notebook where user input information about imaging projects produces Linked Open Data

Conceptual illustration of the Digital Lab Notebook where user input information about imaging projects produces Linked Open Data.

About This Publication

Authors Carla Schroer, Mark Mudge, Erich Leisch, Martin Doerr
Published in Proceedings of the Archiving 2017 Conference, published by The Society for Imaging Science and Technology
Publication Date June 2017
PDF file Download the paper (1.5 MB). You can also purchase the entire published proceedings of the conference.

Abstract

This paper presents the first module of an advanced set of metadata and knowledge management tools to record a Digital Lab Notebook (DLN), the equivalent of the traditional scientist’s lab notebook. The DLN:Capture Context (DLN:CC) tool describes the means and context of photographic data capture. The tool is designed for broad use across computational photography technologies. The DLN:CC has already been implemented for Reflectance Transformation Imaging (RTI) and implementation for photogrammetry is underway. The collection and organization of contextual metadata is highly automated, facilitating use during the time the image data is captured and processed, rather than afterward. This project adds ISO-standard compliant metadata, which establishes the provenance of the imaging subject’s digital surrogate. The captured photographic sequences and the DLN metadata contain all the information needed to generate and/or regenerate advanced, image-based 2D and 3D digital surrogates, such as Reflectance Transformation Imaging or photogrammetry’s 3D models with texture. The DLN also provides each digital surrogate a scientific account of their collection and generation.