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Citizen science (CS) is receiving increasing attention as a conduit for Indigenous and local knowledge (ILK) in ecosystem stewardship and conservation. Drawing on field experience and scientific literature, we explore the connection between CS and ILK and demonstrate approaches for how CS can generate useful knowledge while at the same time strengthening ILK systems. CS invites laypersons to contribute observations, perspectives, and interpretations feeding into scientific knowledge systems. In contrast, ILK can be understood as knowledge systems in its own right, with practices and institutions to craft legitimate and useful knowledge. Such fundamental differences in how knowledge is generated, interpreted, and applied need to be acknowledged and understood for successful outcomes. Engaging with complementary knowledge systems using a multiple evidence base approach can improve the legitimacy of CS initiatives, strengthen collaborations through ethical and reciprocal relationships with ILK holders, and contribute to better stewardship of ecosystems.
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http://dx.doi.org/10.1093/biosci/biab023 | DOI Listing |
JMIR Hum Factors
September 2025
Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, United States.
Background: As information and communication technologies and artificial intelligence (AI) become deeply integrated into daily life, the focus on users' digital well-being has grown across academic and industrial fields. However, fragmented perspectives and approaches to digital well-being in AI-powered systems hinder a holistic understanding, leaving researchers and practitioners struggling to design truly human-centered AI systems.
Objective: This paper aims to address the fragmentation by synthesizing diverse perspectives and approaches to digital well-being through a systematic literature review.
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFNephrol Dial Transplant
September 2025
Department of Pediatrics, RWTH Aachen University Hospital, Aachen, Germany.
Adolescents and young adults with chronic kidney disease (CKD), particularly those with genetic kidney diseases, face unique challenges as they transition from pediatric to adult nephrology care. This period is marked not only by changes in healthcare providers but also by significant developmental, psychosocial, and medical complexities. In response, the ERA Working Group on Genes and Kidney and the ESPN Working Group on Inherited Kidney Diseases have collaborated to develop practical advice for healthcare professionals involved in transition care across Europe and beyond.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Songshan Lake Materials Laboratory, Dongguan 523808, PR China.
Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential.
View Article and Find Full Text PDFJ Microbiol Biol Educ
September 2025
School of Marine and Biological Engineering, Yancheng Teachers University, Yancheng, China.
This paper conducts an in-depth investigation and analysis of 25 microbiology course outlines from 23 domestic universities in China, focusing on the structure of prerequisite courses. The study finds that microbiology course outlines typically include basic course information, objectives, content, teaching methods, resources, assessment, and scheduling. Reasonable prerequisite course settings are vital for clarifying logical relationships among courses in talent training programs, organizing key and challenging knowledge systems, and enhancing university course quality.
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