Difference between revisions of "Digitization"

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(Six task clusters of digitization)
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*video recordings
 
*video recordings
 
4. Image processing
 
4. Image processing
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5. Georeferencing
 
5. Georeferencing
 
*Specialized Georeferencing Tools: https://www.idigbio.org/wiki/index.php/Georeferencing#Specialized_Georeferencing_Tools
 
*Specialized Georeferencing Tools: https://www.idigbio.org/wiki/index.php/Georeferencing#Specialized_Georeferencing_Tools

Revision as of 18:50, 5 December 2016

Statement of Purpose

These links and documents contain information about digitization of natural history collections and their associated data.

Introduction

Contributors

Content generated during The American Society of Ichthyologists and Herpetologists (ASIH) Annual Joint Meeting - 2016, during an iDigBio sponsored workshop by the following individuals participating in the "Digitization" working group of the aforementioned workshop:

  • Gil Nelson - Florida State University, Courtesy Facaulty
  • Larry Page - The Florida Museum of Natural History, Ichthyology Curator
  • Cristina Cox-Fernandes - UMass Amherst Biology, Adjunct Research Associate Professor
  • Mark Sabaj - ANSP, Ichthyology Collection Manager
  • Adam Summers - University of Washington, Professor - Friday Harbor Labs
  • Kevin Love - iDigBio, IT Expert
  • Ken Thompson - Lock Haven University, Professor (Retired)
  • Randy Singer - Florida Museum of Natural History
  • Gregory Watkins-Colwell - Yale Peabody Museum - Herps and Fishes, Collection Manager

Digitization Overview

Introduction to Digitization

So you want to digitize your collection. In the context of this wiki ‘digitize’ means converting ALL analog data to digital data according to standard vocabularies such as DarwinCore and AudubonCore. That is, we start with the concept of a specimen that has been accessioned in a collection. We envision these digital data eventually to include the entirety of analog data that are associated with a particular specimen. This may include but is not limited to: textual data from labels and ledgers associated with specimens, images of specimens, DNA and other ‘omics, field notes, tomographic imaging data, specimen history (including preservation), and specimen-associated literature. Digitizing might be accomplished by collections managers, technicians, contractors, and other entities, the results of which are included within the institution’s collection management system. We envision that in many instances these data may be generated off site by investigators.

iDigBio’s workflows for digitization

iDigBio, the U.S. National Science Foundation’s national resource for facilitating and enabling collections digitization throughout the United States has developed several sets of digitization workflows (including transcription and imaging) for various disciplines and preparation types, including fluid-preserved specimens. These workflows are the product of several working groups and workshops and are publically available at: https://www.idigbio.org/content/workflow-modules-and-task-lists#spirits

Drafts of revisions of workflows for making digital photographs of specimens:

https://drive.google.com/drive/folders/0B9zqKK80tdzhUFhmUW9KeUxkRWc

https://docs.google.com/document/d/1mk2Hu6agItvhfNq9dkt2cAYbhIO_qdgyfAXOelVrQ6w/edit

Six task clusters of digitization

Foundation for above workflows

1. Predigitization Curation

  • locating specimens
  • checking determinations (nomenclature) and other information in database
  • Insert institutionally and/or globally unique identifiers for specimens.
  • transferring specimens to work station

2. Data Entry

  • Transcribing label data into an electronic database.

3. Imaging and other media

  • Recording digital representations
  • 2D photographs
  • 3D tomographic images such as CT, MRI and PET scanning
  • audio recordings
  • video recordings

4. Image processing

5. Georeferencing

6. Data mobilization By data mobilization we mean contributing your data and media to a designated aggregator (e.g. iDigBio, GBIF, VertNet) via a protocol similar to GBIF’s Integrated Publishing Toolkit. These data are then integrated with data from many institutions to ensure research and other access to more complete datasets. Examples include:

Adam Summers workflow for CT scanning

Bullet list of how I get many specimens scanned at the same time: https://docs.google.com/document/d/1mMvCIee4uIVqaDEgc6vSsljRdx60wTwNECAIvwWdHFs/edit

Adam’s example of a lot of data associated with specimens: https://osf.io/ecmz4/wiki/Fishes/

Links

Consensus Documents

Community Standards

Review Documents

References