Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

Skills training in activities of daily living for persons living with dementia: a scoping review protocol

INTRODUCTION

The need for rehabilitation interventions for persons living with dementia is increasing. Improvements in the activities of daily living (ADLs) are often required in this process. On the other hand, reduced learning ability among these people complicates decision-making regarding interventions, so research is needed to support this. Structured skills training is commonly used to teach ADLs to persons living with dementia, and studies have reported it to be useful. However, a synthesis of the evidence on this topic is lacking. Therefore, this scoping review aims to systematically integrate the results of randomised controlled trials (RCTs) on skills training for persons living with dementia, identify gaps in existing knowledge and suggest future research and practice.

METHODS AND ANALYSIS

This scoping review was planned based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. We comprehensively searched target articles using five electronic databases (PubMed, Cochrane Central Register of Controlled Trials, Scopus, Physiotherapy Evidence Database and Cumulative Index to Nursing and Allied Health Literature) on 20 March 2024 and are currently screening their titles and abstracts. Eligible studies are RCTs published in English peer-reviewed journals that evaluated the results of skills training for ADLs in participants diagnosed with dementia. We are excluding studies that included mild cognitive impairment, used an intervention that is non-specific to ADLs and lacks teaching strategies, applied group settings, or addressed only smooth movement or stability. The search process and extracted data will be presented using structured figures and tables. By integrating and interpreting the findings, we will identify the gaps in the existing knowledge.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study because the data will be collected from existing research articles. This scoping review will be disseminated through conference presentations and peer-reviewed journals.

TRIAL REGISTRATION NUMBER

UMIN000054539.

Pytaxon: A Python software for resolving and correcting taxonomic names in biodiversity data

Background

The standardisation and correction of taxonomic names in large biodiversity databases remain persistent challenges for researchers, as errors in species names can compromise ecological analyses, land-use planning and conservation efforts, particularly when inaccurate data are shared on global biodiversity portals.

New information

We present pytaxon, a Python software designed to resolve and correct taxonomic names in biodiversity data by leveraging the Global Names Verifier (GNV) API and employing fuzzy matching techniques to suggest corrections for discrepancies and nomenclatural inconsistencies. The pytaxon offers both a Command Line Interface (CLI) and a Graphical User Interface (GUI), ensuring accessibility to users with different levels of computing expertise. Tests on spreadsheets derived from datasets published in the Global Biodiversity Information Facility (GBIF) demonstrated its effectiveness in identifying and resolving taxonomic errors. By mitigating the propagation of inaccuracies from researchers' datasets to global biodiversity databases, pytaxon supports more reliable conservation decisions and robust scientific investigations. Its contributions enhance data integrity and promote informed biodiversity management in a rapidly evolving global environment.

Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology

Numerous conceptual frameworks exist for best practices in research data and analysis (e.g., Open Science and FAIR principles). In practice, there is a need for further progress to improve transparency, reproducibility, and confidence in ecology. Here, we propose a practical and operational framework for researchers and experts in ecology to achieve best practices for building analytical procedures from individual research projects to production-level analytical pipelines. We introduce the concept of atomization to identify analytical steps that support generalization by allowing us to go beyond single analyses. The term atomization is employed to convey the idea of single analytical steps as "atoms" composing an analytical procedure. When generalized, "atoms" can be used in more than a single case analysis. These guidelines were established during the development of the Galaxy-Ecology initiative, a web platform dedicated to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way to reach higher levels of reproducibility in ecological sciences by increasing the accessibility and reusability of analytical workflows once atomized and generalized.

Harnessing open science practices to teach ecology and evolutionary biology using interactive tutorials

Open science skills are increasingly important for a career in ecology and evolutionary biology (EEB) as efforts to make data and analyses publicly available continue to become more commonplace. While learning core concepts in EEB, students are also expected to gain skills in conducting open science to prepare for future careers. Core open science skills like programming, data sharing, and practices that promote reproducibility can be taught to undergraduate students alongside core concepts in EEB. Yet, these skills are not always taught in biology undergraduate programs, and a major challenge in developing open science skills and learning EEB concepts simultaneously is the high cognitive load associated with learning multiple disparate concepts at the same time. One solution is to provide students with easily digestible, scaffolded, pre-formatted code in the form of vignettes and interactive tutorials. Here, we present six open source teaching tutorials for undergraduate students in EEB. These tutorials teach fundamental ecological concepts, data literacy, programming (using R software), and analysis skills using publicly available datasets while introducing students to open science concepts and tools. Spanning a variety of EEB topics and skill levels, these tutorials serve as examples and resources for educators to integrate open science tools, programming, and data literacy into teaching EEB at the undergraduate level.

Welcoming More Participation in Open Data Science for the Oceans

Open science is a global movement happening across all research fields. Enabled by technology and the open web, it builds on years of efforts by individuals, grassroots organizations, institutions, and agencies. The goal is to share knowledge and broaden participation in science, from early ideation to making research outputs openly accessible to all (open access). With an emphasis on transparency and collaboration, the open science movement dovetails with efforts to increase diversity, equity, inclusion, and belonging in science and society. The US Biden-Harris Administration and many other US government agencies have declared 2023 the Year of Open Science, providing a great opportunity to boost participation in open science for the oceans. For researchers day-to-day, open science is a critical piece of modern analytical workflows with increasing amounts of data. Therefore, we focus this article on open data science-the tooling and people enabling reproducible, transparent, inclusive practices for data-intensive research-and its intersection with the marine sciences. We discuss the state of various dimensions of open science and argue that technical advancements have outpaced our field's culture change to incorporate them. Increasing inclusivity and technical skill building are interlinked and must be prioritized within the marine science community to find collaborative solutions for responding to climate change and other threats to marine biodiversity and society.

Assessing opportunities and inequities in undergraduate ecological forecasting education

Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21st century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.

Cloud-controlled microscopy enables remote project-based biology education in underserved Latinx communities

Project-based learning (PBL) has long been recognized as an effective way to teach complex biology concepts. However, not all institutions have the resources to facilitate effective project-based coursework for students. We have developed a framework for facilitating PBL using remote-controlled internet-connected microscopes. Through this approach, one lab facility can host an experiment for many students around the world simultaneously. Experiments on this platform can be run on long timescales and with materials that are typically unavailable to high school classrooms. This allows students to perform novel research projects rather than just repeating standard classroom experiments. To investigate the impact of this program, we designed and ran six user studies with students worldwide. All experiments were hosted in Santa Cruz and San Francisco, California, with observations and decisions made remotely by the students using their personal computers and cellphones. In surveys gathered after the experiments, students reported increased excitement for science and a greater desire to pursue a career in STEM. This framework represents a novel, scalable, and effective PBL approach that has the potential to democratize biology and STEM education around the world.