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Abstract Although the seed is a key morphological innovation, its origin remains unknown and molecular data outside angiosperms is still limited. Ginkgo biloba, with a unique place in plant evolution, being one of the first extant gymnosperms where seeds evolved, can testify to the evolution and development of the seed. Initially, to better understand the development of the ovules in Ginkgo biloba ovules, we performed spatio-temporal expression analyses in seeds at early developing stages, of six candidate gene homologues known in angiosperms: WUSCHEL, AINTEGUMENTA, BELL1, KANADI, UNICORN, and C3HDZip . Surprisingly, the expression patterns of most these ovule homologues indicate that they are not wholly conserved between angiosperms and Ginkgo biloba . Consistent with previous studies on early diverging seedless plant lineages, ferns, lycophytes, and bryophytes, many of these candidate genes are mainly expressed in mega- and micro-sporangia. Through in-depth comparative transcriptome analyses of Ginkgo biloba developing ovules, pollen cones, and megagametophytes we have been able to identify novel genes, likely involved in ovule development. Finally, our expression analyses support the synangial or neo-synangial hypotheses for the origin of the seed, where the sporangium developmental network was likely co-opted and restricted during integument evolution. 

Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity. This makes them an all-important data source for botanical research. With the increased digitisation of herbaria worldwide and the advances in the fine-grained classification domain that can facilitate automatic identification of herbarium specimens, there are a lot of opportunities for supporting research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution or host institutions. Furthermore, aggregating multiple datasets is difficult as taxa exist under a multitude of different names and the taxonomy requires alignment to a common reference. We present the Herbarium Half-Earth dataset, the largest and most diverse dataset of herbarium specimens to date for automatic taxon recognition. https://doi.org/10.48550/arXiv.2105.13808 

Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.