Browse by Author "Heidorn, P. Bryan"
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Cragin, Melissa H.; Heidorn, P. Bryan; Palmer, Carole L.; Smith, Linda C. (2007-06-25)Several models of service are emerging in academic and research libraries for the collection and management of scientific data – an important segment of an institution’s total scholarly production. As libraries work to ...
Paul, Deborah L.; Heidorn, P. Bryan; Best, Jason; Gilbert, Edward; Neill, Amanda; Nelson, Gil; Ulate, William (iSchools, 2013-02)Integrated Digitized Biodiversity Collections, iDigBio, is funded under the National Science Foundation's (NSF) Advancing Digitization of Biological Collections (ADBC) program to help hundreds of natural history museums ...
Paul, Deborah L.; Heidorn, P. Bryan; Best, Jason; Gilbert, Edward; Neill, Amanda; Ulate, William (iSchools, 2013-02)Integrated Digitized Biocollections (iDigBio) is a nation-wide effort funded by the National Science Foundation (NSF) to digitize data from hundreds of millions of natural history museum specimens. In a concerted five-part ...
Heidorn, P. Bryan (Graduate School of Library and Information Science. University of Illinois at Urbana-Champaign, 1999)
Improving the Character of Optical Character Recognition (OCR): iDigBio Augmenting OCR Working Group Seeks Collaborators and Strategies to Improve OCR Output and Parsing of OCR Output ... Anglin, Robert; Best, Jason; Figueiredo, Renato; Gilbert, Edward; Gnanasambandam, Nathan; Gottschalk, Stephen; Haston, Elspeth; Heidorn, P. Bryan; Lafferty, Daryl; Lang, Peter; Nelson, Gil; Paul, Deborah L.; Ulate, William; Watson, Kimberly; Zhang, Qianjin (iSchools, 2013-02)There are an estimated 2 – 3 billion museum specimens world – wide (OECD 1999, Ariño 2010). In an effort to increase the research value of their collections, institutions across the U. S. have been seeking new ways to ...
Bruce, Bertram C.; Bishop, Ann Peterson; Heidorn, P. Bryan; Lunsford, Karen J.; Poulakos, Steven; Won, Mihye (American Library Association, 2003-01)Some may think of the unveiling a digital library (DL) as an end point. Yet, even though lesson plans may be provided with it, a DL only potentially affords meaningful learning opportunities. Unfortunately, the better a ...
Interactive Machine Learning (IML) Markup of OCR Generated Text by Exploiting Domain Knowledge: A Biodiversity Case Study Wei, Qin; Heidorn, P. Bryan (2008-02-28)
Heidorn, P. Bryan; Zhang, Qianjin (iSchools, 2013-02)The LABELX (Label Annotation through Biodiversity Enhanced Learning) is an extension of the HERBIS NLP system reported previously (Heidorn & Wei, 2008). The objective of the system is to formaly structure output from Optical ...
Wei, Qin; Heidorn, P. Bryan; Freeland, Chris (2010-02-03)Taxonomic Name Recognition is prerequisite for more advanced processing and mining of full-text taxonomic literatures. This paper investigates three issues of current TNR tools in detail: (1) The difficulties and methods ...
Heidorn, P. Bryan (The Johns Hopkins University Press, 2008-10)One of the primary outputs of the scientific enterprise is data, but many institutions such as libraries that are charged with preserving and disseminating scholarly output have largely ignored this form of documentation ...
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Heidorn, P. Bryan (Johns Hopkins University Press and the Graduate School of Library and Information Science. University of Illinois at Urbana-Champaign, 2008)One of the primary outputs of the scientific enterprise is data, but many institutions such as libraries that are charged with preserving and disseminating scholarly output have largely ignored this form of documentation ...
Supporting Biological Information Work: Research and Education for Digital Resources and Long-lived Data Palmer, Carole L.; Cragin, Melissa H.; Heidorn, P. Bryan; Wright, Daniel T. (IEEE, 2006-06-12)New practices are emerging in all stages of biological research, from data collection through dissemination of results. In addition, libraries and museums are increasingly being called upon to become the long-term curators ...
Wei, Qin; Freeland, Chris; Heidorn, P. Bryan (2008-10-17)Taxonomic Name Recognition (TNR) algorithm – identifying a text string as a taxonomic name or not and recognizing the boundaries of the name – is very important in BHL digitization project in determining whether the ...
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