BotanizeR: A flexible R package with Shiny app to practice plant identification for online teaching and beyond

Weigelt, Patrick; Denelle, Pierre; Brambach, Fabian; Kreft, Holger

doi:10.1002/ppp3.10226

Cited by 6 publications

(7 citation statements)

References 28 publications

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“…The use of open‐source ShinyApps has seen significant growth in stimulating teaching and research activities in the fields of ecology and evolution in the past 5 years (Kass et al., 2023 ; McGuire et al., 2022 ; Segrestin et al., 2021 ; Weigelt et al., 2022 ). However, when it comes to trait‐based ecology, a key challenge lies in the knowledge shortfalls that hinder our ability to select appropriate traits or access information for certain taxonomic groups or geographical regions (Gonçalves‐Souza et al., 2023 ; Maitner et al., 2023 ).…”

Section: Discussionmentioning

confidence: 99%

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

Gonçalves‐Souza,

Milz,

Sanders

et al. 2024

Ecology and Evolution

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Animal trait data are scattered across several datasets, making it challenging to compile and compare trait information across different groups. For plants, the TRY database has been an unwavering success for those ecologists interested in addressing how plant traits influence a wide variety of processes and patterns, but the same is not true for most animal taxonomic groups. Here, we introduce ZooTraits, a Shiny app designed to help users explore and obtain animal trait data for research in ecology and evolution. ZooTraits was developed to tackle the challenge of finding in a single site information of multiple trait datasets and facilitating access to traits by providing an easy‐to‐use, open‐source platform. This app combines datasets centralized in the Open Trait Network, raw data from the AnimalTraits database, and trait information for animals compiled by Gonçalves‐Souza et al. (2023, Ecology and Evolution 13, e10016). Importantly, the ZooTraits app can be accessed freely and provides a user‐friendly interface through three functionalities that will allow users to easily visualize, compare, download, and upload trait data across the animal tree of life—ExploreTrait, FeedTrait, and GetTrait. By using ExploreTrait and GetTrait, users can explore, compare, and extract 3954 trait records from 23,394 species centralized in the Open Traits Network, and trait data for ~2000 species from the AnimalTraits database. The app summarizes trait information for numerous taxonomic groups within the Animal Kingdom, encompassing data from diverse aquatic and terrestrial ecosystems and various geographic regions worldwide. Moreover, ZooTraits enables researchers to upload trait information, serving as a hub for a continually expanding global trait database. By promoting the centralization of trait datasets and offering a platform for data sharing, ZooTraits is facilitating advancements in trait‐based ecological and evolutionary studies. We hope that other trait databases will evolve to mirror the approach we have outlined here.

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Section: Discussionmentioning

confidence: 99%

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

Gonçalves‐Souza,

Milz,

Sanders

et al. 2024

Ecology and Evolution

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“…The web applications can be deployed locally or on a server (e.g., the shiny apps server) and shared with others. Shiny has been used for creating educational materials in various STEM subjects, such as ecology (Moore et al, 2022), statistics (Doi et al, 2016), and biology (Weigelt et al, 2021). The package, learnr was developed to increase efficiency in writing interactive shiny apps for education purposes (Aden-Buie et al, 2022).…”

Section: Using Shiny and Learnr To Create Adaptable Interactive Oersmentioning

confidence: 99%

Teaching beginner-level computational social science: interactive open education resources with learnr and shiny apps

2023

Front. Educ.

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The article presents the use of open, inclusive pedagogical frameworks to develop a series of open education resources (OERs), specifically, interactive shiny web applications for teaching beginner-level computational social science (CSS) in undergraduate social science education. CSS is an emerging discipline that integrates data science methods with social science theories and research designs. However, undergraduate social science students could face a lack of a sense of STEM identity or belonging. Also, compared to STEM majors in natural science or engineering, average undergraduate social science students come in with limited mathematical or statistical knowledge. The OERs developed and tested in this article are designed with pedagogical frameworks that help overcome these barriers faced by students from diverse backgrounds and offer students a jump-start in learning CSS. This article presents the details of the tools, classroom implementation (in the form of a 6-week workshop series), the pedagogy frameworks applied, and the assessment methods and outcomes.

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“…Interestingly, the COVID-19 pandemic facilitated or even accelerated the inclusion of CS approaches and tools in many higher education courses to engage students working from home [28]. For example, free apps were used in some courses to investigate specific research questions [32][33][34], there was a shift toward CS methods and tools for biodiversity monitoring and literacy courses [35,36], and with BotanizeR [37], even an R package was developed to teach botany skills remotely.…”

Section: Introductionmentioning

confidence: 99%

Citizen Science Apps in a Higher Education Botany Course: Data Quality and Learning Effects

Pernat,

Gathof,

Herrmann

et al. 2023

Sustainability

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Although species identification apps are becoming increasingly popular in citizen science, they are hardly used in university courses on biodiversity literacy. In this study, we investigated whether the use of a plant identification app by students provides similar data quality to the use of scientific keys and whether it improves the process of knowledge acquisition. To this end, dry grassland plots were monitored in Berlin to record plant species diversity by two groups, Bachelor’s and Master’s students, with different experience in plant identification. Both groups were asked to survey the plots once in April and once in June, the first time with the app Pl@ntNet, and the second time with scientific keys commonly used in teaching. To evaluate their performance and the respective tools, the results were compared with those of experts from the same plots. The students identified, on average, only half of the plants per plot and misidentified or failed to identify a high proportion of species compared with the experts, regardless of the identification tool. In addition, the number of plants identified that did not occur at all in the region or in the considered habitat was alarmingly high. In view of the worldwide loss of species knowledge, it is becoming clear that apps can trigger the study of a species group, but do not solve the fundamental problem of neglecting biodiversity courses at universities.

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BotanizeR: A flexible R package with Shiny app to practice plant identification for online teaching and beyond

Cited by 6 publications

References 28 publications

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

Teaching beginner-level computational social science: interactive open education resources with learnr and shiny apps

Citizen Science Apps in a Higher Education Botany Course: Data Quality and Learning Effects

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