DLN Tool Suite

Download the Digital Lab Notebook (DLN) Tools

CHI has released Beta versions of the first two tools in the Digital Lab Notebook software suite to simplify the collection and management of scientifically reliable metadata. Learn more and download the tools.


DLN Instructional Videos

Watch Instructional Videos About the DLN Tools

CHI has posted a video series on our Vimeo website, describing the purpose and application of the DLN tools. Watch the videos.


Project: NEH Preservation and Access Research and Development Grant and Project

The National Endowment for the Humanities (NEH) awarded a grant to CHI in 2017 supporting the development and enhancement of internationalized open source software tools for the collection, management, archiving, and sharing of cultural heritage imaging data and associated metadata. Learn more.


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

Top level interface for the DLN:CaptureContext tool, version 1

This paper, presented by CHI at the Archiving 2017 conference in Riga, Latvia, describes the first module of an advanced set of metadata and knowledge management tools to record a Digital Lab Notebook. Learn more and download the PDF paper.


Related Book Chapter

Copy Culture book

“Keeping Track of How We Scan”

This chapter of the book Copy Culture: Sharing in the Age of Digital Reproduction contains an interview on page 107 with Mark Mudge and Carla Schroer of CHI, where they discuss the Digital Lab Notebook and its software tools designed to ease the process of properly recording how a digital representation is made. More …


2016 NCPTT Grant: “Applying Scientific Rigor to Photogrammetric 3D Documentation for Cultural Heritage and Natural Science Materials”

In collaboration with partner consultants and technical advisors, in 2016 CHI was awarded a grant from the National Center for Preservation Technology and Training (NCPTT) that produces advanced metadata and knowledge management tools to record a “Digital Lab Notebook” (DLN), describing the means and context of 3D photogrammetric data capture. More…

2014 NEH Digital Humanities Start-up Grant

In May 2014, the National Endowment for the Humanities awarded CHI a Digital Humanities Start-up grant to evaluate a new metadata and knowledge management methodology. The project envisioned a “digital lab notebook” in the form of a user-friendly toolkit to document not only the algorithmic transformation of photographic data, but also the context in which the photographs were created. More…

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

At the conclusion of the 2014 NEH Start-up grant, CHI 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. Download and read the paper (PDF).


Digital Lab Notebook

Contents:  What is it?  How does it work?  More Information 

What is it?

The Digital Lab Notebook (DLN) is a software pipeline made up of open source software tools and associated good practices. The DLN provides a greatly simplified, ordinary language-based, nearly automatic method to build the digital equivalent of a scientist’s lab notebook. The goal is to radically simplify the scientific workflow used to digitally capture, build, archive, and reuse the digital representations that document humanity’s cultural heritage.

The transparency provided by the DLN separates “scientific reliability” from “academic authority.” When the data is transparent, the quality of the work speaks for itself. With these tools, both heritage professionals and citizen scholars can know their work will pass on the richness of the human experience to future generations.

The first two software tools, called DLN:CaptureContext (DLNCC) and DLN:Inspector, are at the Beta state of development and are now released to the general public. Additional tools to aid archival submission to data depositories, internationalization of the software for easier translation to local languages, and other associated tools are funded by a National Endowment for the Humanities (NEH) grant award and are under construction. More information about the NEH project and these other new tools can be found in the Archival Submission and Additional Tools and Features sections of the project description.

The DLN is designed for use with computational photography imaging technologies. When a photographer captures a sequence of images for use with computational photography, the rigor of scientific imaging requires a record of the means and circumstances surrounding the photographic capture event and subsequent image processing. Computational photography technologies include photogrammetry, Reflectance Transformation Imaging (RTI), multispectral imaging (MSI), and high dynamic range (HDR) imaging. Currently DLNCC and Inspector support both RTI and photogrammetric technologies. Support for multispectral Imaging is under construction.

How Does It Work?

The DLNCC tool captures information about the imaging subject, the people involved in the imaging session, locations, the project’s stakeholders, related documents, and the imaging acquisition equipment configuration. For example, to generate a Digital Lab Notebook account of the imaging equipment used during a capture session, the capture team first must enter a one-time description of all the equipment they will use for image capture into the DLN:CaptureContext software. This tool enables the capture team to organize the listed equipment into sub-assemblies. For example, cameras and tripods can be grouped into templates, and lighting equipment can be grouped into other frequently used configurations. These sub-assemblies can then be easily used to describe the equipment configuration for a specific capture session.

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

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


The Inspector tool ensures that each acquired image set meets the requirements for high-quality computational photography imaging. The tool automatically checks the metadata of the collected images and compares it against documented rules and recommendations for camera settings, archival workflow, and preparation of images for archiving and future processing. For example, DLN:Inspector can check that aperture does not change across a given RTI image set. The tool will also check for image-processing errors, such as sharpening, that should not be applied to photogrammetry or RTI image data.

This validation step will ensure users are alerted to many types of potential issues in their image sets. Note that the tool cannot automatically check for out-of-focus or improperly exposed images, so additional quality control may be required beyond these automatic checks. The user has the ability to correct indicated errors and rerun the DLN:Inspector to generate a satisfactory report.

More Information about the Digital Lab Notebook (DLN)

Overview

The Digital Lab Notebook (DLN) records how a scientific digital representation is made. The DLN describes the means and circumstances of digital information capture from a “real world” subject and tracks all the events that happen during the processing of this information into a completed digital representation. When archived or shared, the digital representation carries along with it the information in the DLN. That way anyone can evaluate the quality of the digital representation by looking in the notebook.

The DLN tools greatly simplify scientific imaging, keeping the complexity almost entirely “under the hood.” This functional design nearly or completely automates many difficult tasks for the user, from capture to archiving. Just as a cell phone user needs no understanding of the phone’s electronic architecture, the DLN user can concentrate on the information capture and processing. Underneath the level of the user experience, the tools use advanced, ISO standard-based knowledge management methods to retain important information relationships and make this information widely accessible across archives.

DLN:CaptureContext (DLNCC) and DLN:Inspector are designed to be an integral part of, and used during, the capture process and subsequent initial image processing. DLNCC collects and enables the organization of user entered metadata before the image capture work. During image capture, the user needs only a small number of interactions with the software. This workflow avoids the problem of “after the fact” metadata entry, which can result in a high percentage of user failure to record, or to record correctly, the information necessary for assessment and reuse by others.

The Democratization of Technology

From nation states to local caretakers, work is underway to save our knowledge of humanity’s cultural legacy. In many areas of the world, the people who do this work know they are in danger. Cultural Heritage Imaging (CHI) and our worldwide collaborators want to offer these courageous people a working understanding of the robust digital ways and means to save their history. We want to offer them the knowledge that they have the tools and methods to do world-class work and the means to prove it to everyone. We want to offer them the assurance that the treasures they have saved will be enjoyed and studied by people around the world, over and over again, and preserved for future generations. The DLN tools will help to make this democratization of technology happen, and these assurances, in due time, come true.

The central innovation of the DLN lies in the understanding that a digital representation’s data transparency separates “scientific reliability” from “academic or institutional authority.” This transparency permits a digital representation made by a local caretaker, who learns a scientific imaging workflow and does it properly, to stand toe-to-toe with the work from the most respected and authoritative sources. When the data is transparent, the work’s quality and reliability speaks for itself.

The way we document our past is important. Democratization of this scientific imaging technology to a worldwide, local user base is key. The process must scale quickly by being straightforward, inexpensive, and compatible with the existing practices of local cultural heritage experts. The digital data must be acquired in a way to ensure transparency, enabling its qualitative and quantitative evaluation. The DLN organizes the information in a way that makes archiving simple, makes the information easily and broadly sharable, and structures the information in a way conducive to its long-term preservation and reuse.

CHI hopes that the availability of this workflow and the Digital Lab Notebook toolset will start a revolutionary democratization of digital data acquisition, preservation, and use throughout the world.

Scientific Imaging

The Digital Lab Notebook serves the same function as a written scientist’s lab notebook before the digital age. For centuries scientists wrote down their experiences and subsequent analysis that described the evidence and results of their inquiries. The lab notebook was an integral element of their published results. Scientific information cannot be understood without the meaning of the data and the history of its generation.

image from Leonardo da Vinci's notebooks

Drawing by Leonardo da Vinci, ca. 1510-1515. Left: Reflection of light in spherical concave mirror.
Right: Paths of light rays in parabolic mirror.

Empirical observations, the experience of the senses, and lab notebook provenance accounts that describe their acquisition and processing are the core components of scientific activity. Several CHI publications explore these ideas, including this 2008 tutorial: Image-Based Empirical Information Acquisition, Scientific Reliability, and Long-Term Digital Preservation for the Natural Sciences and Cultural Heritage.

How the Knowledge Management Works in the Digital Lab Notebook

The DLN records the scientific information (metadata) about the “who, what, when, where, why, and how” a set of computational photography photos were taken and processed. The DLNCC and DLN:Inspector software tools greatly simplify the collection of this data. The DLN retains knowledge of key relationships within this metadata by automatically organizing this information according to the international ISO standard (ISO 21127:2014), the Conceptual Reference Model (CRM). The International Federation of Library Associations and Institutions and the International Council of Museums jointly adopted the CRM standard in 2016.

First, the DLNCC collects and stores ordinary-language, user-provided information in a Postgres database, installed with the software. This information is then used to create a new XML format DLN file. This XML file may be used on its own or mapped to the CRM. In the latter case, a separate CRM-based mapping file is applied to this XML file. The CRM mapping file is included with the software. It is separated from DLNCC’s main software code to enable revision of the CRM mapping file without modifying the core software code. The DLNCC software also contains the open source X3ML tool, which reads the XML file, applies the CRM mapping, and exports this CRM-organized metadata into the machine-readable Resource Description Framework (RDF) format.

RDF statements are simple subject-predicate-object statements, such as “the dog is named Fido,” sometimes referred to as triplets. RDF is emerging as a key standard for encoding metadata and other knowledge on the Internet. The RDF statements are represented as Linked Open Data (LOD) containing Universal Resource Locators (URLs) and/or Universal Resource Identifiers (URIs). When stored together, “in the same bucket,” the RDF Linked Open Data triplets link together to form an integrated representation of the information and its interrelationships.

These interrelationships can be represented graphically and explored with software tools. The graphical organization is a new way of representing and exploring knowledge. A researcher can follow the graph’s connection points, like stepping stones, and see where the connections lead. This path can result in the discovery of new, sometimes serendipitous, information relationships, building new knowledge. Likewise, this Linked Open Data is an accessible and efficient way to examine the DLN’s “who, what, when, where, why, and how” a scientific digital representation is captured and built. Anyone who wants to reuse digital representations archived with a DLN can find this information.

While some leading institutions have already adopted CRM-mapped, RDF Linked Open Data, many are still using legacy knowledge and metadata architectures. Functions for translating this DLN information output to other commonly used archival structures are under development.

The Digital Lab Notebook and the Long-term Preservation of Digital Information

For digital scientific imaging to have widespread usefulness, it must be archived and accessible. For investments in digital scientific imaging to grow in value over time, the imaging information must be designed to enhance its chances of long-term preservation. The Digital Lab Notebook tools will manage the digital archive submission and contain crucial information for digital curators to improve the chance for the information’s long-term survival. As discussed above, this archival package will contain the digital representations and their original empirical data in open file formats, along with all the metadata information needed to recreate them. Once archived, the metadata knowledge needed to recreate the digital representations, found within the archival package, will be available over time to the consecutive digital conservators who will care for the digital representation’s long-term digital preservation.