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      Toward a service-based workflow for automated information extraction from herbarium specimens

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          Abstract

          Over the past years, herbarium collections worldwide have started to digitize millions of specimens on an industrial scale. Although the imaging costs are steadily falling, capturing the accompanying label information is still predominantly done manually and develops into the principal cost factor. In order to streamline the process of capturing herbarium specimen metadata, we specified a formal extensible workflow integrating a wide range of automated specimen image analysis services. We implemented the workflow on the basis of OpenRefine together with a plugin for handling service calls and responses. The evolving system presently covers the generation of optical character recognition (OCR) from specimen images, the identification of regions of interest in images and the extraction of meaningful information items from OCR. These implementations were developed as part of the Deutsche Forschungsgemeinschaft-funded a standardised and optimised process for data acquisition from digital images of herbarium specimens (StanDAP-Herb) Project.

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            Taverna: lessons in creating a workflow environment for the life sciences

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              Herbarium specimens reveal the footprint of climate change on flowering trends across north-central North America

              Shifting flowering phenology with rising temperatures is occurring worldwide, but the rarity of co-occurring long-term observational and temperature records has hindered the evaluation of phenological responsiveness in many species and across large spatial scales. We used herbarium specimens combined with historic temperature data to examine the impact of climate change on flowering trends in 141 species collected across 116,000 km2 in north-central North America. On average, date of maximum flowering advanced 2.4 days °C−1, although species-specific responses varied from − 13.5 to + 7.3 days °C−1. Plant functional types exhibited distinct patterns of phenological responsiveness with significant differences between native and introduced species, among flowering seasons, and between wind- and biotically pollinated species. This study is the first to assess large-scale patterns of phenological responsiveness with broad species representation and is an important step towards understanding current and future impacts of climate change on species performance and biodiversity.
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                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2018
                08 October 2018
                08 October 2018
                : 2018
                Affiliations
                [1 ]Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Königin-Luise-Str. Berlin, Germany
                [2 ]Fraunhofer-Institute of Optronics, System Technologies and Image Exploitation, Fraunhofer Str. Karlsruhe, Germany
                [3 ]Faculty I—Electrical Engineering and Information Technology, Hannover University of Applied Sciences and Arts, Ricklinger Stadtweg, Hannover, Germany
                Author notes
                *Corresponding author: Email: a.kirchhoff@ 123456bgbm.org
                Article
                bay103
                10.1093/database/bay103
                6174549
                30295725
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 11
                Product
                Funding
                Funded by: German Research Foundation 10.13039/501100001659
                Award ID: BE 2283/12-1
                Award ID: STE 1635/1-1
                Award ID: US 118/1-1
                Funded by: Freie Universität Berlin 10.13039/501100007537
                Categories
                Original Article

                Bioinformatics & Computational biology

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