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Semi‐Automated Workflows for Acquiring Specimen Data from Label
Granzow-de la Cerda & Beach • Acquiring specimen data from herbarium labels TAXON 59 (6) • December 2010: 1830–1842 METHODS AND TECHNIQUES Semi-automated workflows for acquiring specimen data from label images in herbarium collections Íñigo Granzow-de la Cerda1,3 & James H. Beach2 1 University of Michigan Herbarium, 3600 Varsity Dr., Ann Arbor, Michigan 48108, U.S.A. 2 Biodiversity Institute, University of Kansas, 1345 Jayhawk Boulevard, Lawrence, Kansas 66045, U.S.A. 3 Current address: Departament de Biologia Animal, Biologia Vegetal i Ecologia, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain Author for correspondence: Íñigo Granzow-de la Cerda, [email protected] Abstract Computational workflow environments are an active area of computer science and informatics research; they promise to be effective for automating biological information processing for increasing research efficiency and impact. In this project, semi-automated data processing workflows were developed to test the efficiency of computerizing information contained in herbarium plant specimen labels. Our test sample consisted of mexican and Central American plant specimens held in the University of michigan Herbarium (mICH). The initial data acquisition process consisted of two parts: (1) the capture of digital images of specimen labels and of full-specimen herbarium sheets, and (2) creation of a minimal field database, or “pre-catalog”, of records that contain only information necessary to uniquely identify specimens. For entering “pre-catalog” data, two methods were tested: key-stroking the information (a) from the specimen labels directly, or (b) from digital images of specimen labels. In a second step, locality and latitude/longitude data fields were filled in if the values were present on the labels or images. -
Croflora, a Database Application to Handle the Croatian Vascular Flora
Acta Bot. Croat. 60 (1), 31-48,2001 CODEN: ABCRA25 ISSN 0365-0588 UDC 581.9 (497.5): 303.432 CROFlora, a database application to handle the Croatian vascular flora Toni Nikolic1*, Kresimir Fertalj2, Tomo Helman2, Vedran Mornar2, Damir Kalpic2 1 Department of Botany, Faculty of Science, University of Zagreb, Marulicev trg 20/2, HR-10000 Zagreb, Croatia 2 Chair of Computer Science, Department of Applied Mathematics, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia CROFlora is a multi-user database application for species-oriented and specimen-ori ented systematic and taxonomic work on Croatian flora. It is designed for dealing with all kinds of data that are commonly used in systematic botany and floristic work. CROFlora comprises several main modules: (1) taxonomy, (2) herbarium, (3) literature, (4) choro- logy and (5) related data, such as ecology and multimedia. CROFlora was built over a re lational database. The database relies on the normalised data model, which is presented in the paper. Amongst other features, the client application provides the user with extended query by example (QBE) capabilities and with user-customised reports. The reports in clude taxon sheets, taxa checklists, herbarium labels, bibliography labels and other com plex reports. The database can be connected to a geographical information system (GIS), which empowers easy production of distribution maps and other spatial analysis. The Web interface enables Internet searches. Keywords: database, bioinformatics, taxonomy, flora, distribution, geographical data, Croatia, Internet Introduction Increasing concern for the Earth’s biological resources, their inventory, protection and use, has prompted efforts to modernise practices and procedures to manage all types of bo tanical data. -
A Review of Approach and Applications in Ecological Research
Review Article Proceedings of NIE 2020;1(1):9-21 https://doi.org/10.22920/PNIE.2020.1.1.9 pISSN 2733-7243, eISSN 2734-1372 Ecoinformatics: A Review of Approach and Applications in Ecological Research Chau Chin Lin* Society of Subtropical Ecology, Taipei, Taiwan ABSTRACT Ecological communities adapt the concept of informatics in the late 20 century and develop rapidly in the early 21 century to form Ecoinformatics as the new approach of ecological research. The new approach takes into account the data-intensive nature of ecology, the precious information content of ecological data, and the growing capacity of computational technology to leverage complex data as well as the critical need for informing sustainable management of complex ecosystems. It comprehends techniques for data management, data analysis, synthesis, and forecasting on ecological research. The present paper attempts to review the development history, studies and application cases of ecoinformatics in ecological research especially on Long Term Ecological Research (LTER). From the applications show that the ecoinformatics approach and management system have formed a new paradigm in ecological research Keywords: Ecology, EML, Informatics, Information management system, LTER, Metadata Introduction takes into account the data-intensive nature of ecology, the precious information content of ecological data, and Informatics is a distinct scientific discipline, character- the growing capacity of computational technology to ized by its own concepts, methods, body of knowledge, leverage complex data as well as the critical need for in- and open issues. It covers the foundations of computa- forming sustainable management of complex ecosystems. tional structures, processes, artifacts and systems; and It comprehends techniques for data management, data their software designs, their applications, and their im- analysis, synthesis, and forecasting on ecological research pact on society (CECE, 2017). -
January, 1999
FINAL REPORT OF THE OECD MEGASCIENCE FORUM WORKING GROUP ON BIOLOGICAL INFORMATICS January, 1999 Contents Executive Summary.......................................................................................................................................................2 Background....................................................................................................................................................................5 What is Informatics?...............................................................................................................................................5 Why is Biological Informatics Important and Needed?..........................................................................................6 Rationale for Focus on Biodiversity Informatics and Neuroinformatics.................................................................6 Opportunities in Biological Informatics for OECD Countries................................................................................7 Scientific and Infrastructural Challenges and Issues...............................................................................................8 Intellectual Property Rights ..................................................................................................................................11 Support and Funding.............................................................................................................................................12 Report of the Biodiversity Informatics Subgroup ........................................................................................................14 -
A New Era for Specimen Databases and Biodiversity Information Management in South Africa
Biodiversity Informatics, 8, 2012, pp.1-11. A NEW ERA FOR SPECIMEN DATABASES AND BIODIVERSITY INFORMATION MANAGEMENT IN SOUTH AFRICA WILLEM COETZER, OFER GON South African Institute for Aquatic Biodiversity, Somerset Street, Grahamstown, South Africa. Email for correspondence: [email protected]. MICHELLE HAMER South African National Biodiversity Institute, Cussonia Ave, Pretoria, South Africa FATIMA PARKER-ALLIE South African National Biodiversity Institute, Rhodes Drive, Cape Town, South Africa Abstract. ‒ We present observations and a commentary on the inherited legacy and current state of biodiversity information management in South African natural history museums, and make recommendations for the future. We emphasize the importance of using a recognized database application, and training and capacity development to improve the quality and integration of biodiversity information for research. In the last decade, biodiversity information in approximately 26% (Hamer 2011) of vouchered specimen databases of natural history museums specimens in South African zoological collections has seen renewed interest and much innovation could be queried through the SABIF Data Portal and development (Bisby 2000, Soberón and or the GBIF Data Portal. Peterson 2004, Johnson 2007, Peterson et al. The vast majority of information about South 2010). Biodiversity Informatics has been defined African biodiversity, which is relatively well as ‘application of informatics to recorded and yet- sampled (Figure 1), originates from South African to-be discovered -
Hibbett Et Al 2011 Fung Biol
fungal biology reviews 25 (2011) 38e47 journal homepage: www.elsevier.com/locate/fbr Plenary Paper Progress in molecular and morphological taxon discovery in Fungi and options for formal classification of environmental sequences David S. HIBBETTa,*, Anders OHMANa, Dylan GLOTZERa, Mitchell NUHNa, Paul KIRKb, R. Henrik NILSSONc,d aBiology Department, Clark University, Worcester, MA 01610, USA bCABI UK, Bakeham Lane, Egham, Surrey TW20 9TY, UK cDepartment of Plant and Environmental Sciences, University of Gothenburg, Box 461, 405 30 Goteborg,€ Sweden dDepartment of Botany, Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai St., 51005 Tartu, Estonia article info abstract Article history: Fungal taxonomy seeks to discover, describe, and classify all species of Fungi and provide Received 14 December 2010 tools for their identification. About 100,000 fungal species have been described so far, but it Accepted 3 January 2011 has been estimated that there may be from 1.5 to 5.1 million extant fungal species. Over the last decade, about 1200 new species of Fungi have been described in each year. At that Keywords: rate, it may take up to 4000 y to describe all species of Fungi using current specimen-based Biodiversity approaches. At the same time, the number of molecular operational taxonomic units Classification (MOTUs) discovered in ecological surveys has been increasing dramatically. We analyzed Environmental sequences ribosomal RNA internal transcribed spacer (ITS) sequences in the GenBank nucleotide data- Molecular ecology base and classified them as “environmental” or “specimen-based”. We obtained 91,225 MOTU sequences, of which 30,217 (33 %) were of environmental origin. Clustering at an average Taxonomy 93 % identity in extracted ITS1 and ITS2 sequences yielded 16,969 clusters, including 6230 (37 %) clusters with only environmental sequences, and 2223 (13 %) clusters with both envi- ronmental and specimen-based sequences. -
Biological Informatics 2021
Biological Informatics 2021 By Marcus P. Zillman, M.S., A.M.H.A. Executive Director – Virtual Private Library [email protected] Biological Informatics 2021 is a comprehensive listing of biological informatics resources currently available on the Internet. The below list of sources is taken from my Subject Tracer™ Information Blog titled Biological Informatics and is constantly updated with Subject Tracer™ bots at the following URL: http://www.BiologicalInformatics.info/ These resources will help you to discover the many pathways available to you through the Internet to find the latest health informatics, neuroinformatics, biodiversity informatics and biomolecular informatics sources and sites. Figure 1: Biological Informatics 2021 Subject Tracer™ Information Blog 1 [Updated: June 1, 2021] Biological Informatics 2021 White Paper Link Compilation http://www.BiologicalInformatics.info/ [email protected] eVoice: 239-206-3450 © 2008 - 2021 Marcus P. Zillman, M.S., A.M.H.A. Note: This Biological Informatics Subject Tracer Information Blog is divided into the following categories: BIOLOGICAL INFORMATICS HEALTH INFORMATICS (Medical Informatics) NEUROINFORMATICS (NI) BIODIVERSITY INFORMATICS (BDI) BIOMOLECULAR INFORMATICS (BioInformatics) Current Subject Tracer™ Information Blogs Biological Informatics: 1000 Genomes Project http://www.1000genomes.org/ About Bioscience http://www.aboutbioscience.org/ Academic and Scholar Search Engines and Sources 2021 http://www.ScholarSearchEngines.com/ A Genetic Atlas of Human -
The Biodiversity Informatics Landscape: Elements, Connections and Opportunities
Research Ideas and Outcomes 3: e14059 doi: 10.3897/rio.3.e14059 Research Article The Biodiversity Informatics Landscape: Elements, Connections and Opportunities Heather C Bingham‡‡, Michel Doudin , Lauren V Weatherdon‡, Katherine Despot-Belmonte‡, Florian Tobias Wetzel§, Quentin Groom |, Edward Lewis‡¶, Eugenie Regan , Ward Appeltans#, Anton Güntsch ¤, Patricia Mergen|,«, Donat Agosti », Lyubomir Penev˄, Anke Hoffmann ˅, Hannu Saarenmaa¦, Gary Gellerˀ, Kidong Kim ˁ, HyeJin Kimˁ, Anne-Sophie Archambeau₵, Christoph Häuserℓ, Dirk S Schmeller₰, Ilse Geijzendorffer₱, Antonio García Camacho₳, Carlos Guerra ₴, Tim Robertson₣, Veljo Runnel ₮, Nils Valland₦, Corinne S Martin‡ ‡ UN Environment World Conservation Monitoring Centre, Cambridge, United Kingdom § Museum fuer Naturkunde - Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany | Botanic Garden Meise, Meise, Belgium ¶ The Biodiversity Consultancy, Cambridge, United Kingdom # Ocean Biogeographic Information System (OBIS), Intergovernmental Oceanographic Commission of UNESCO, Oostende, Belgium ¤ Freie Universität Berlin, Berlin, Germany « Royal Museum for Central Africa, Tervuren, Belgium » Plazi, Bern, Switzerland ˄ Pensoft Publishers & Bulgarian Academy of Sciences, Sofia, Bulgaria ˅ Leibniz Institute for Research on Evolution and Biodiversity, Berlin, Germany ¦ University of Eastern Finland, Joensuu, Finland ˀ Group on Earth Observations, Geneva, Switzerland ˁ National Institute of Ecology, Seocheon, Korea, South ₵ Global Biodiversity Information Facility France, Paris, -
Synthesis of Phylogeny and Taxonomy Into a Comprehensive Tree of Life
Synthesis of phylogeny and taxonomy into a comprehensive tree of life Cody E. Hinchliffa,1, Stephen A. Smitha,1,2, James F. Allmanb, J. Gordon Burleighc, Ruchi Chaudharyc, Lyndon M. Coghilld, Keith A. Crandalle, Jiabin Dengc, Bryan T. Drewf, Romina Gazisg, Karl Gudeh, David S. Hibbettg, Laura A. Katzi, H. Dail Laughinghouse IVi, Emily Jane McTavishj, Peter E. Midfordd, Christopher L. Owenc, Richard H. Reed, Jonathan A. Reesk, Douglas E. Soltisc,l, Tiffani Williamsm, and Karen A. Cranstonk,2 aEcology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109; bInterrobang Corporation, Wake Forest, NC 27587; cDepartment of Biology, University of Florida, Gainesville, FL 32611; dField Museum of Natural History, Chicago, IL 60605; eComputational Biology Institute, George Washington University, Ashburn, VA 20147; fDepartment of Biology, University of Nebraska-Kearney, Kearney, NE 68849; gDepartment of Biology, Clark University, Worcester, MA 01610; hSchool of Journalism, Michigan State University, East Lansing, MI 48824; iBiological Science, Clark Science Center, Smith College, Northampton, MA 01063; jDepartment of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS 66045; kNational Evolutionary Synthesis Center, Duke University, Durham, NC 27705; lFlorida Museum of Natural History, University of Florida, Gainesville, FL 32611; and mComputer Science and Engineering, Texas A&M University, College Station, TX 77843 Edited by David M. Hillis, The University of Texas at Austin, Austin, TX, and approved July 28, 2015 (received for review December 3, 2014) Reconstructing the phylogenetic relationships that unite all line- published phylogenies are available only as journal figures, rather ages (the tree of life) is a grand challenge. The paucity of homologous than in electronic formats that can be integrated into databases and character data across disparately related lineages currently renders synthesis methods (7–9). -
A Taxonomic Information Model for Botanical Databases: the IOPI Model
TAXON 46 ϑ MAY 1997 283 A taxonomic information model for botanical databases: the IOPI Model Walter G. Berendsohn1 Summary Berendsohn, W. G.: A taxonomic information model for botanical databases: the IOPI Model. ϑ Taxon 46: 283-309. 1997. ϑ ISSN 0040-0262. A comprehensive information model for the recording of taxonomic data from literature and other sources is presented, which was devised for the Global Plant Checklist database project of the International Organisation of Plant Information (IOPI). The model is based on an approach using hierarchical decomposition of data areas into atomic data elements and ϑ in parallel ϑ abstraction into an entity relationship model. It encompasses taxa of all ranks, nothotaxa and hybrid formulae, "unnamed taxa", cultivars, full synonymy, misapplied names, basionyms, nomenclatural data, and differing taxonomic concepts (potential taxa) as well as alternative taxonomies to any extent desired. The model was developed together with related models using a CASE (Computer Aided Software Engineering) tool. It can help designers of biological information systems to avoid the widely made error of over-simplification of taxonomic data and the resulting loss in data accuracy and quality. Introduction Data models. ϑ A model is "a representation of something" (Homby, 1974). In the technical sense, a model is the medium to record the structure of an object in a more or less abstract way, following pre-defined and documented rules. The objecti- ve of applying modelling techniques is either to describe and document the structure of an existing object, or to prescribe the structure of one to be created. In both cases, the model can be used to test (physically, or, in most cases, intellectually) the functi- on of the object and to document it, for example for future maintenance. -
Biodiversity Informatics: Managing Knowledge Beyond Humans and Model Organisms
Pacific Symposium on Biocomputing 12:340-342(2007) BIODIVERSITY INFORMATICS: MANAGING KNOWLEDGE BEYOND HUMANS AND MODEL ORGANISMS INDRA NEIL SARKAR† Marine Biological Laboratory, 7 MBL Street Woods Hole, MA 02543, USA E-mail: [email protected] In the biomedical domain, researchers strive to organize and describe organisms within the context of health and epidemiology. In the biodiversity domain, researchers seek to understand how organisms relate to one another, either historically through evolution or spatially and geographically in the environment. Currently, there is limited cross-communication between these domains. As a result, valuable knowledge that could inform studies in either domain often goes unnoticed. Biodiversity knowledge has long been a valuable source for many biomedical advances [1]. Before the creation of synthetic compounds, medicinal compounds originated from solely natural plant and animal extracts. Although there are upward estimates of between 10-100 million organisms on Earth [2], biomedical research primarily focuses on only a fraction of these as “model” organisms [3]. Furthermore, much knowledge may be lost from the biomedical community because many organisms are only described within biodiversity resources. These studies can form the basis for research on evolution, speciation, and distribution, and also provide an important baseline for studies of not only conservation but also the study of emerging diseases. The integration of biodiversity knowledge from museum collections, for example, has provided significant insights into the etiology and distribution of diseases such as hantavirus [4]. Understanding the etiology of diseases and their host epidemiology may also further the development of vaccinations and treatments that can help prevent epidemics or pandemics, such as the looming threat of the avian flu [5]. -
Development of a Biodiversity Database Kangaroo Island
DEVELOPMENT OF A BIODIVERSITY DATABASE FOR ASSESSING CONSERVATION VALUES KANGAROO ISLAND CASE STUDY By A.C.Robinson, P.K.Gullan, K.D.Casperson and S.J.Pillman Natural Resources Group DEPARTMENT OF ENVIRONMENT AND NATURAL RESOURCES DEVELOPMENT OF A BIODIVERSITY DATABASE FOR ASSESSING CONSERVATION VALUES KANGAROO ISLAND CASE STUDY by A.C.Robinson, P.K. Gullan*, K.D.Casperson and S.J.Pillman Biological Survey and Research Natural Resources Group Department of Environment and Natural Resources, South Australia *Director Viridans Pty Ltd 1995 The views and opinions expressed in this report are those of the authors and do not reflect those of theCommonwealth Government, the Minister for the Environment, Sport and Territories, or the Director of the Australian Nature Conservation Agency AUTHORS A. C. Robinson, K.D.Casperson and S.J.Pillman, Biological Survey and Research, Natural Resources Group, Department of Environment and Natural Resources, South Australia GPO Box 1047 ADELAIDE 5001 P.K.Gullan, Viridans Pty Ltd, Suite 4 614 Hawthorn Road, Brighton East, Victoria 3187 CARTOGRAPHY AND DESIGN Biological Survey and Research Group, Resource Management Branch ©Director, Australian Nature Conservation Agency 1995 Cover Design Compiled from the title screen graphics of the South Australian Biodiversity Database using COREL DRAW. The upper image is the underside of a leaf of the pale groundsel (Senecio hypoleucus) and the lower image is a tail feather of the Glossy Black Cockatoo (Calyptorhynchus lathami) HI Development of a Biodiversity Database Abstract In South Australia, databases which contain records of species of vascular plants and terrestrial vertebrate animals where the records are associated with an accurate geocode, are dispersed between a number of Government agencies including the Department of Environment and Natural Resources and the Department of Housing and Urban Development as well as the long term custodians of such data at the South Australian Museum and the State Herbarium.