@article {225,
	title = {Five task clusters that enable efficient and effective digitization of biological collections},
	journal = {ZooKeys},
	volume = {209},
	year = {2012},
	month = {07/2012},
	chapter = {19},
	abstract = {This paper describes and illustrates five major clusters of related tasks (herein referred to as task clusters) that are common to efficient and effective practices in the digitization of biological specimen data and media. Examples of these clusters come from the observation of diverse digitization processes. The staff of iDigBio (The U.S. National Science Foundation{\textquoteright}s National Resource for Advancing Digitization of Biodiversity Collections) visited active biological and paleontological collections digitization programs for the purpose of documenting and assessing current digitization practices and tools. These observations identified five task clusters that comprise the digitization process leading up to data publication: (1) predigitization curation and staging, (2) specimen image capture, (3) specimen image processing, (4) electronic data capture, and (5) georeferencing locality descriptions. While not all institutions are completing each of these task clusters for each specimen, these clusters describe a composite picture of digitization of
biological and paleontological specimens across the programs that were observed. We describe these clusters, three workflow patterns that dominate the implemention of these clusters, and offer a set of workflow recommendations for digitization programs.},
	keywords = {ADBC, biodiversity informatics, Biological specimen collections, curation, digitization, EF-1115210, iDigBio, Imaging, paleontological specimen collections, task cluster, workflow},
	doi = {doi: 10.3897/zookeys.209.3135},
	url = {http://www.pensoft.net/journals/zookeys/article/3135/five-task-clusters-that-enable-efficient-and-effective-digitization-of-biological-collections},
	author = {Gil Nelson and Deborah L Paul and Gregory Riccardi and Austin R. Mast}
}
@article {557,
	title = {iConference2013: Linking Data -- Biodiversity Heritage Library -- supporting knowledge discovery from digitized content},
	year = {2013},
	abstract = {The Biodiversity Heritage Library, a global consortium of natural history and botanical libraries, is an ongoing project digitizing the legacy literature in their collections for open access. In its partnership with the Internet Archive and through their portal BHL has made 40 million pages available for open access by the global research community. The rapid growth of the text corpus has led to challenges in identifying and extracting semantic information from it, many of them similar to the challenges faced in OCR workflow and extraction from specimen labels. We will discuss the possible improvements in knowledge extraction that could result from improvements in OCR workflow and accuracy, as well as the implications for more intelligent and integrated data integration for biodiversity informatics. },
	keywords = {Augmenting OCR, BHL, Biodiversity Heritage Library, Data Integration, Data Mining, Semantic Web, Text Extraction, workflow},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/AOCRandBHL.ppt},
	author = {John Mignault}
}
@article {569,
	title = {iConference2013: The Apiary Project: a workflow for text extraction and parsing for herbarium specimens},
	year = {2013},
	abstract = {The Apiary Project: combining OCR technology, OCR output from herbarium specimen or other images containing museum specimen data, well-developed regular expressions for parsing output to Darwin Core fields, and humans-in-the-digitization-loop for an elegant, sophisticated user-interface for a workflow designed to maximize the value of the human interaction, minimize steps, and speed data throughput.},
	keywords = {Augmenting Optical Character Recognition, collaboration, EF-1115210, Hackathon, iConference2013, Machine Learning, Natural Language Processing, parsing algorithms, Research, Transcription, workflow},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/JBest-Apiary-iConference.ppt},
	author = {Jason Best}
}
@article {2477,
	title = {Imaging Workflows for the Digitization of Dry-preserved Vertebrate Specimens},
	year = {2016},
	keywords = {digitization, vertebrate, video, workflow},
	url = {https://vimeo.com/160615629}
}
@article {1324,
	title = {SPNHC 2014: Progress in Digitization: Designing a Workflow to Help with Error Detection in a Paleontology Digitization Project},
	year = {2014},
	abstract = {The Field Museum is implementing a three-year IMLS grant using summer interns to digitize approximately 15,000 Silurian fossil lots. Workflow for this project has each intern working on six drawers of fossils at a time; the fossils are all from the same geologic period and taxonomic group (as they are arranged in the collection). Interns cycle their fossils through three workstations: a label photography station, a fossil photography station, and a KE EMu data entry station. The average time per specimen to complete this cycle is 9.5 minutes with times ranging between 4 to 18 minutes. After cycling their cart through each workstation I edit, upload, and link the images to the catalogue records. In the first year 7,785 image were generated and linked to catalogue records. 
During the process of connecting images to the catalogue there are three opportunities to check for errors. First, KE EMu generates an error log of images that fail to link during the batch input.  The error rate is less than 2.2\%. Common errors include simple format errors in the batch upload, specimens not entered into the catalogue, and typos in the specimen{\textquoteright}s catalogue number.  Second, because specimens in each batch are from the same geologic period and taxonomic group it is easy to spot any inconsistencies or missing data when reviewing the records in a table format. Finally each record should have two images linked to it. Any missing images are easy to add.
},
	keywords = {digitization, error checking, Field Museum, fossils, interns, Silurian, SPNHC 2014, workflow},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/spnhc2014/07_1130_MayerPS.pdf},
	author = {Paul S. Mayer and L. Connolly and N. Karpus and A. P. Layng}
}
@article {1329,
	title = {SPNHC 2014: Progress in Digitization: Incorporating OCR into a digitisation and curation workflow},
	year = {2014},
	abstract = {The digitisation of natural history collections is a priority for many institutes for reasons including opening access to users around the world, incorporation of specimen data in research, disaster planning, etc. However, digitisation of natural history specimens is expensive and labour intensive. For this reason, digitisation of specimens has moved towards minimal data capture and imaging as part of large scale processes. The workflow can be summarised in the following steps: 1) minimal curation; 2) attach barcode as a unique identifier; 3) minimal data entry; 4) image specimen; 5) additional data entry. There has been some investigation by institutes into the use of Optical Character Recognition (OCR) within the digitsation and curation workflow. The Royal Botanic Garden Edinburgh (RBGE) now routinely processes all specimen images through OCR software. The OCR process is integrated into the overall digitisation and curation workflow and has been used to speed up the process of adding data to over 100,000 specimens. The following additional steps have been incorporated into the workflow at RBGE: 4a) assess condition of specimen; 4b) process image through OCR software; 5a) additional curation. The incorporation of OCR into digitisation workflows is being explored by the Synthesys project funded by the European Union within Framework 7, and by iDigBio funded by the United States Government within the National Science Foundation programme. The work being carried out at the Royal Botanic Garden Edinburgh to integrate OCR into the digitisation and curation workflow is discussed as part of the work of Synthesys and iDigBio.},
	keywords = {digitization, optical character recognition, SPNHC 2014, SPNHC 2014: Progress in Digitization, workflow},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/spnhc2014/12_Haston_et_al_Incorporating_OCR.pdf},
	author = {Elspeth Haston and Robyn Drinkwater and Robert Cubey}
}
@article {1323,
	title = {SPNHC 2014: Progress in Digitization: Workflows in the cooperative IMLS Silurian Digitization project},
	year = {2014},
	abstract = {Milwaukee Public Museum (MPM) is implementing a three-year IMLS grant in conjunction with The Field Museum to digitize our Midwest Silurian collections and create a shared online database.  The first workflow was between the institutions.  The joint nature of the project required information exchange between museums.  To coordinate a shared online database an effort was made to use shared vocabulary and Internal Record Numbers (IRN) for lithostratigraphy, chronostratigraphy and taxonomy data. Internal Records were generated at The Field Museum and shared with MPM.  After the first large data export smaller updates have been made.  MPM maintains a shared Google Drive spreadsheet with corrections and new data to be updated at The Field.  Sharing data creates an error check point.  
The second workflow for the digitization project is within MPM. The collection is organized stratigraphically.  The specimens all have a locality number but most do not have a unique specimen number.  Workflow starts with data entry into KE EMu using specimen labels.   Specimens without numbers are assigned the KE EMu Internal Record Number as specimen number.  The photography station is divided into two parts, fossil photography and photo processing. The fossils are processed by drawers and include a photo of the drawer and photos of each fossil in the drawer. A batch process is used to convert the raw image to .dng, .tif and .jpeg formats. The .jpeg images are then associated with the KE EMu catalogue data and the .dng and .tif images are saved in MPM{\textquoteright}s image repository.
},
	keywords = {digitization, Field Museum, fossils, Milwaukee Public Museum, Silurian, SPNHC 2014, SPNHC 2014: Progress in Digitization, taxonomy, workflow},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/spnhc2014/06_Workflows\%20in\%20the\%20cooperative\%20IMLS\%20Silurian\%20Digitization\%20project_B.pdf},
	author = {Patricia Coorough Burke and A. Caywood and M. James and E. Malueg and D. Miller}
}
