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The goal of the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) project is to establish a collection of nonhuman ovary histology images for multiple species as a resource for researchers and educators. An important component of sharing scientific data is the inclusion of the contextual metadata that describes the data. MOTHER extends the Ecological Metadata Language (EML) for documenting research data, leveraging its data provenance and usage license with the inclusion of metadata for ovary histology images. The design of the MOTHER metadata includes information on the donor animal, including reproductive cycle status, the slide and its preparation. MOTHER also extends the ezEML tool, called ezEML+MOTHER, for the specification of the metadata. The design of the MOTHER database (MOTHER-DB) captures the metadata about the histology images, providing a searchable resource for discovering relevant images. MOTHER also defines a curation process for the ingestion of a collection of images and its metadata, verifying the validity of the metadata before its inclusion in the MOTHER collection. A Web search provides the ability to identify relevant images based on various characteristics in the metadata itself, such as genus and species, using filters. 

Abstract New graduate students in biology programs may lack the quantitative skills necessary for their research and professional careers. The acquisition of these skills may be impeded by teaching and mentoring experiences that decrease rather than increase students’ beliefs in their ability to learn and apply quantitative approaches. In this opinion piece, we argue that revising instructional experiences to ensure that both student confidence and quantitative skills are enhanced may improve both educational outcomes and professional success. A few studies suggest that explicitly addressing productive failure in an instructional setting and ensuring effective mentoring may be the most effective routes to simultaneously increasing both quantitative self-efficacy and quantitative skills. However, there is little work that specifically addresses graduate student needs, and more research is required to reach evidence-backed conclusions.