An official website of the United States government
Abstract Technological advances in three imaging techniques have opened the door to advanced morphological analyses and habitat mapping for biologists and ecologists.At the same time, the challenge of translating complex 3D data into meaningful metrics that can be used in conjunction with biological data currently hinders progress and accessibility.We introducehabtools, an R package that provides R functions to efficiently calculate complexity and shape metrics from DEMs, 3D meshes and 2D shapes as well as some helper functions to facilitate workflow.We expect the functionality ofhabtoolsto continue to expand as new metrics and faster methods become available, and we welcome new contributions and ideas.
more »
« less
A FAIR and modular image‐based workflow for knowledge discovery in the emerging field of imageomics
Abstract Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi‐step processes on data. These processes often include machine learning techniques, often built by others, that are difficult to reuse by other members in a collaboration.We present a conceptual workflow model for machine learning applications using image data to extract biological knowledge in the emerging field of imageomics. We derive an implementation of this conceptual workflow for a specific imageomics application that adheres to FAIR principles as a formal workflow definition that allows fully automated and reproducible execution, and consists of reusable workflow components.We outline technologies and best practices for creating an automated, reusable and modular workflow, and we show how they promote the reuse of machine learning models and their adaptation for new research questions. This conceptual workflow can be adapted: it can be semi‐automated, contain different components than those presented here, or have parallel components for comparative studies.We encourage researchers—both computer scientists and biologists—to build upon this conceptual workflow that combines machine learning tools on image data to answer novel scientific questions in their respective fields.
more »
« less
- PAR ID:
- 10523737
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 15
- Issue:
- 6
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 1129 to 1145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
AbstractRecent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross adifficulty threshold,and (2) thepoint of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback. Practitioner notesWhat is already known about this topicCollaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI.What this paper addsWe extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration.We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom.From our discussions with the teacher, we define two types ofinflection pointsto address students' STEM+C difficulties—thedifficulty thresholdand theintervention point—and discuss how thefeedback latency intervalseparating them can inform educator interventions.We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline.Implications for practice and/or policyOur case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback.Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment.Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula.more » « less
-
Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood‐based approaches (e.g. packageslme4) and machine learning methods.These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journalNature Ecology & Evolution, and we found that the majority of published papers didnotreport any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods.Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journalMethods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement.Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.more » « less
-
Abstract This paper provides an experience report on a co‐design approach with teachers to co‐create learning analytics‐based technology to support problem‐based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal roles of the participants in the design process. Our results provide precedent knowledge on co‐designing with experienced and novice teachers and co‐constructing actionable insight that can help teachers engage more effectively with their students' learning and problem‐solving processes during classroom PBL implementations. Practitioner notesWhat is already known about this topicSuccess of educational technology depends in large part on the technology's alignment with teachers' goals for their students, teaching strategies and classroom context.Teacher and researcher co‐design of educational technology and supporting curricula has proven to be an effective way for integrating teacher insight and supporting their implementation needs.Co‐designing learning analytics and support technologies with teachers is difficult due to differences in design and development goals, workplace norms, and AI‐literacy and learning analytics background of teachers.What this paper addsWe provide a co‐design workflow for middle school teachers that centres on co‐designing and developing actionable insights to support problem‐based learning (PBL) by systematic development of responsive teaching practices using AI‐generated learning analytics.We adapt established human‐computer interaction (HCI) methods to tackle the complex task of classroom PBL implementation, working with experienced and novice teachers to create a learning analytics dashboard for a PBL curriculum.We demonstrate researcher and teacher roles and needs in ensuring co‐design collaboration and the co‐construction of actionable insight to support middle school PBL.Implications for practice and/or policyLearning analytics researchers will be able to use the workflow as a tool to support their PBL co‐design processes.Learning analytics researchers will be able to apply adapted HCI methods for effective co‐design processes.Co‐design teams will be able to pre‐emptively prepare for the difficulties and needs of teachers when integrating middle school teacher feedback during the co‐design process in support of PBL technologies.more » « less
-
Abstract The field of ecology has undergone a molecular revolution, with researchers increasingly relying on DNA‐based methods for organism detection. Unfortunately, these techniques often require expensive equipment, dedicated laboratory spaces and specialized training in molecular and computational techniques; limitations that may exclude field researchers, underfunded programmes and citizen scientists from contributing to cutting‐edge science.It is for these reasons that we have designed a simplified, inexpensive method for field‐based molecular organism detection—FINDeM (Field‐deployableIsothermalNucleotide‐basedDetectionMethod). In this approach, DNA is extracted using chemical cell lysis and a cellulose filter disc, followed by two body‐heat inducible reactions—recombinase polymerase amplification and a CRISPR‐Cas12a fluorescent reporter assay—to amplify and detect target DNA, respectively.Here, we introduce and validate FINDeM in detectingBatrachochytrium dendrobatidis, the causative agent of amphibian chytridiomycosis, and show that this approach can identify single‐digit DNA copies from epidermal swabs in under 1 h using low‐cost supplies and field‐friendly equipment.This research signifies a breakthrough in ecology, as we demonstrate a field‐deployable platform that requires only basic supplies (i.e. micropipettes, plastic consumables and a UV flashlight), inexpensive reagents (~$1.29 USD/sample) and emanated body heat for highly sensitive, DNA‐based organism detection. By presenting FINDeM in an ecological system with pressing, global biodiversity implications, we aim to not only highlight how CRISPR‐based applications promise to revolutionize organism detection but also how the continued development of such techniques will allow for additional, more diversely trained researchers to answer the most pressing questions in ecology.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/2041-210X.14327
