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ZENODO
Dataset . 2020
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Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
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ZENODO
Dataset . 2020
License: CC BY
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ZENODO
Dataset . 2020
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Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: an annotated dataset of reproductive organs in Strepanthus herbarium specimens

Authors: Goëau, Hervé; Mora-Fallas, Adan; Champ, Julien; Love, Natalie; Mazer, Susan; Mata-Montero, Erick; Joly, Alexis; +1 Authors

Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: an annotated dataset of reproductive organs in Strepanthus herbarium specimens

Abstract

This dataset contains annotations of 31 herbarium specimens of Streptanhus tortuosus Kellogg for which we have we carefully and manually drew and annotated the contours of four reproductive organs: “bud”, “flower”, “immature fruit” and “mature fruit”. The dataset can be used to assess the ability of automated methods to count and detect precisely the shapes of these reproductive organs, with a view to conducting phenological studies. The annotations are formatted in accordance with the COCO data format, a usual format for object detection tasks in the field of Computer Vision. The annotations are divided into two files: train_21_full_masks.json contains the mask coordinates and labels of 21 herbarium sheets that can be used for training models test_10_full_masks.json contains the mask coordinates and labels of 10 other herbarium that can be used as a groundtruth file for evaluating the predictions, typically with the COCO evaluation scripts (https://github.com/cocodataset/cocoapi) Please refer to the following publication for a first assessment of this dataset with a Mask-RCNN approach: H. Goëau, A. Mora-Fallas, J. Champ, N. Love, S. Mazer, E. Mata-Montero, A. Joly, P. Bonnet. 2020. New fine-grained method for automated visual analysis of herbarium specimens: a case study for phenological data extraction. Applications in Plant Sciences

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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