A critical analysis of plant science literature reveals ongoing inequities

Amézquita, Erik J.; Percival, Sarah; Rougon‐Cardoso, Alejandra; Chibici-Revneanu, Claudia; Tebele, Shandry M.; Farrant, Jill M.; Chitwood, Daniel H.; VanBuren, Robert

doi:10.1101/2022.10.15.512190

Cited by 3 publications

(3 citation statements)

References 58 publications

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“…We compiled a database of 296,447 plant science papers published between 2000 and 2021 ( https://doi.org/10.5061/dryad.pg4f4qrtb ) ( 32 ). Papers were sourced from a representative set of 127 plant science journals based in 26 different nations across five continents, covering 21 different subspecialties.…”

Section: Resultsmentioning

confidence: 99%

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A critical analysis of plant science literature reveals ongoing inequities

Amézquita

Percival

Rougon‐Cardoso

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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The field of plant science has grown dramatically in the past two decades, but global disparities and systemic inequalities persist. Here, we analyzed ~300,000 papers published over the past two decades to quantify disparities across nations, genders, and taxonomy in the plant science literature. Our analyses reveal striking geographical biases—affluent nations dominate the publishing landscape and vast areas of the globe have virtually no footprint in the literature. Authors in Northern America are cited nearly twice as many times as authors based in Sub-Saharan Africa and Latin America, despite publishing in journals with similar impact factors. Gender imbalances are similarly stark and show remarkably little improvement over time. Some of the most affluent nations have extremely male biased publication records, despite supposed improvements in gender equality. In addition, we find that most studies focus on economically important crop and model species, and a wealth of biodiversity is underrepresented in the literature. Taken together, our analyses reveal a problematic system of publication, with persistent imbalances that poorly capture the global wealth of scientific knowledge and biological diversity. We conclude by highlighting disparities that can be addressed immediately and offer suggestions for long-term solutions to improve equity in the plant sciences.

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Section: Resultsmentioning

confidence: 99%

“…Data associated with this study and a description of data acquisition and curation are deposited in Dryad at https://doi.org/10.5061/dryad.pg4f4qrtb ( 32 ).…”

Section: Data Materials and Software Availabilitymentioning

confidence: 99%

A critical analysis of plant science literature reveals ongoing inequities

Amézquita

Percival

Rougon‐Cardoso

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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“…These global approaches strikingly ignore the actual complexity of human societal and cultural diversity, as exemplified by the myriads of local minorities, minority cultures, vernacular languages and subtly differentiated dialects, which, taken together, constitute a significant part of humankind (Díaz et al, 2019). Such a focus on uniformity may reflect limited knowledge on the extent of human linguistic and cultural diversity (Skirgård et al, 2023), underrepresentation of cultural and linguistic diversity in the science community (Couée, 2022;Graves Jr. et al, 2022a, 2022bKonno et al, 2020; Lynch Marks et al, 2023;Smith et al, 2023), or hidden assumptions that sheer technological, socioeconomical and political efficiency should prevail over the complexities of cultural diversity (Amel et al, 2017;Isbell et al, 2017;Ripple et al, 2022). Alternately, environmental studies that take into account the diversity of human cultures (Copete et al, 2023;Díaz et al, 2019;Mistry & Berardi, 2016;Reyes-García et al, 2019) usually emphasise specific cases of emblematic Indigenous and Tribal Peoples in highly-specialized environments.…”

Section: Introductionmentioning

confidence: 99%

The importance of worldwide linguistic and cultural diversity for climate change resilience

Couée

2024

Ecology Letters

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Local minority languages and dialects, through the local knowledge and expertise associated with them, can play major roles in analysing climate change and biodiversity loss, in facilitating community awareness of environmental crises and in setting up locally‐adapted resilience and sustainability strategies. While the situation and contribution of Indigenous and Tribal Peoples are of emblematic importance, the issue of the relationships between cultural and linguistic diversity and environmental awareness and protection does not solely concern peripheral highly‐specialized communities in specific ecosystems of the Global South, but constitutes a worldwide challenge, throughout all of the countries, whatever their geographical location, their economical development, or their political status. Environmental emergency and climate change resilience should therefore raise international awareness on the need to promote the survival and development of minority languages and dialects and to take into account their creativity and expertise in relation to the dynamics of their local environments.

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Expression-based machine learning models for predicting plant tissue identity

Palande,

Arsenault,

Basurto-Lozada

et al. 2023

Preprint

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The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of modern plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, over twenty years after sequencing the Arabidopsis genome and during which time sequencing data from other plant species has accumulated and computation enabling machine learning has evolved, we critically examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved precision and recall values of up to 0.99 using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.70 to 0.75 and recall from 0.55 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. Our data-driven results suggest that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not as strong as originally assumed, and that in the current era where sequencing data and computation abound, we should decentralize the scientific focus on Arabidopsis and embrace plant diversity.

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A critical analysis of plant science literature reveals ongoing inequities

Cited by 3 publications

References 58 publications

A critical analysis of plant science literature reveals ongoing inequities

A critical analysis of plant science literature reveals ongoing inequities

The importance of worldwide linguistic and cultural diversity for climate change resilience

Expression-based machine learning models for predicting plant tissue identity

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