378 results on '"Graham, Mark"'
Search Results
2. Realistic morphology-preserving generative modelling of the brain
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Tudosiu, Petru-Daniel, Pinaya, Walter H. L., Ferreira Da Costa, Pedro, Dafflon, Jessica, Patel, Ashay, Borges, Pedro, Fernandez, Virginia, Graham, Mark S., Gray, Robert J., Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge
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- 2024
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3. Classification of Features across Five CURE Networks Reveals Opportunities to Improve Course Design, Instruction, and Equity
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Burmeister, Alita R., Bauer, Melanie, and Graham, Mark J.
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Course-based undergraduate research experiences (CUREs) are tools used to introduce students to authentic participation in science. Several specific CUREs have been shown to benefit students' interest and retention in the biological sciences. Nevertheless, CUREs vary greatly in terms of their context, methodology, and degree of research authenticity, so different types of CUREs may differently influence student outcomes. This programmatic diversity poses a challenge to educators who want to better understand which course components and features are reliably present in a CURE curriculum. To address these issues, we identified, catalogued, and classified 112 potential features of CUREs across the biosciences. To develop the list, we interviewed instructors experienced with teaching individual and large networked CUREs across a diversity of the biological disciplines, including: Squirrel-Net (field-based animal behavior), SEA-PHAGES (wet lab microbiology and computational microbiology), Tiny Earth (environmental and wet lab microbiology), PARE (environmental microbiology), and the Genomics Education Partnership (eukaryotic computational biology). Twenty-five interviewees contributed expert content in terms of CURE features and classification of those items into an organized list. The resulting list's categories encompasses student experiences with the following: (i) the scientific process; (ii) technical aspects of science; (iii) the professional development associated with research; and (iv) building scientific identity. The most striking insight was that CUREs vary widely in terms of which features they contain, since different CUREs will by necessity have different approaches to science and student involvement. We also identified several features commonly thought to be crucial to CUREs yet have ambiguous definitions. This ambiguity can potentially confound efforts to make CUREs research-authentic and aligned with the central goals of science. We disambiguate these terms and represent their varied meanings throughout the classification. We also provide instructor-friendly supplementary worksheets along with considerations for instructors interested in expanding their CURE course design, instruction, and equity.
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- 2023
4. A 3D generative model of pathological multi-modal MR images and segmentations
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Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Vercauteren, Tom, and Cardoso, M. Jorge
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). Nonetheless, the application of synthetic data to tasks such as 3D magnetic resonance imaging (MRI) segmentation remains limited due to the lack of labels associated with the generated images. Moreover, many of the proposed generative MRI models lack the ability to generate arbitrary modalities due to the absence of explicit contrast conditioning. These limitations prevent the user from adjusting the contrast and content of the images and obtaining more generalisable data for training task-specific models. In this work, we propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations, where the user can condition on specific pathological phenotypes and contrasts. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data., Comment: Accepted for publication at the 2023 Deep Generative Models (DGM4MICCAI) MICCAI workshop (Vancouver, Canada)
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- 2023
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5. Cloudwork als Chance für den Globalen Süden?: Einkommens- und professionelle Entwicklungschancen von Online-Plattformarbeiter*innen im Übersetzungs- und Transkriptionssektor
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López, Tatiana, Feuerstein, Patrick, de Vera, Janine, Varaschin, Giulia, Karlıdağ, Zeynep, and Graham, Mark
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- 2024
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6. Generative AI for Medical Imaging: extending the MONAI Framework
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Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features.
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- 2023
7. Unsupervised 3D out-of-distribution detection with latent diffusion models
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Graham, Mark S., Pinaya, Walter Hugo Lopez, Wright, Paul, Tudosiu, Petru-Daniel, Mah, Yee H., Teo, James T., Jäger, H. Rolf, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not trivially scale to 3D data. In this work, we propose to use Latent Diffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution 3D medical data. We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs). Not only does the proposed LDM-based approach achieve statistically significant better performance, it also shows less sensitivity to the underlying latent representation, more favourable memory scaling, and produces better spatial anomaly maps. Code is available at https://github.com/marksgraham/ddpm-ood, Comment: Accepted at MICCAI 2023
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- 2023
8. The knowledge based economy and digital divisions of labour
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Graham, Mark, primary, Ojanperä, Sanna, additional, and Dittus, Martin, additional
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- 2024
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9. Right HTML, Wrong JSON: Challenges in Replaying Archived Webpages Built with Client-Side Rendering
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Weigle, Michele C., Nelson, Michael L., Alam, Sawood, and Graham, Mark
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Computer Science - Digital Libraries - Abstract
Many web sites are transitioning how they construct their pages. The conventional model is where the content is embedded server-side in the HTML and returned to the client in an HTTP response. Increasingly, sites are moving to a model where the initial HTTP response contains only an HTML skeleton plus JavaScript that makes API calls to a variety of servers for the content (typically in JSON format), and then builds out the DOM client-side, more easily allowing for periodically refreshing the content in a page and allowing dynamic modification of the content. This client-side rendering, now predominant in social media platforms such as Twitter and Instagram, is also being adopted by news outlets, such as CNN.com. When conventional web archiving techniques, such as crawling with Heritrix, are applied to pages that render their content client-side, the JSON responses can become out of sync with the HTML page in which it is to be embedded, resulting in temporal violations on replay. Because the violative JSON is not directly observable in the page (i.e., in the same manner a violative embedded image is), the temporal violations can be difficult to detect. We describe how the top level CNN.com page has used client-side rendering since April 2015 and the impact this has had on web archives. Between April 24, 2015 and July 21, 2016, we found almost 15,000 mementos with a temporal violation of more than 2 days between the base CNN.com HTML and the JSON responses used to deliver the content under the main story. One way to mitigate this problem is to use browser-based crawling instead of conventional crawlers like Heritrix, but browser-based crawling is currently much slower than non-browser-based tools such as Heritrix., Comment: 20 pages, preprint version of paper accepted at the 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
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- 2023
10. The Digital Continent
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Anwar, Mohammad Amir and Graham, Mark
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gig economy, outsourcing, digital work, labour, job quality, precarity, flexibility, labour agency, Africa ,bic Book Industry Communication::K Economics, finance, business & management::KC Economics::KCM Development economics & emerging economies ,bic Book Industry Communication::K Economics, finance, business & management::KC Economics::KCL International economics ,bic Book Industry Communication::R Earth sciences, geography, environment, planning::RG Geography::RGC Human geography::RGCM Economic geography ,bic Book Industry Communication::1 Geographical Qualifiers::1H Africa - Abstract
Only ten years ago, there were more internet users in countries like France or Germany than in all of Africa put together. But much has changed in a decade. The year 2018 marks the first year in human history in which a majority of the world’s population are now connected to the internet. This mass connectivity means that we have an internet that no longer connects only the world’s wealthy. Workers from Lagos to Johannesburg to Nairobi and everywhere in between can now apply for and carry out jobs coming from clients who themselves can be located anywhere in the world. Digital outsourcing firms can now also set up operations in the most unlikely of places in order to tap into hitherto disconnected labour forces. With CEOs in the Global North proclaiming that ‘location is a thing of the past’ (Upwork, 2018), and governments and civil society in Africa promising to create millions of jobs on the continent, the book asks what this ‘new world of digital work’ means to the lives of African workers. It draws from a year-long fieldwork in South Africa, Kenya, Nigeria, Ghana, and Uganda, with over 200 interviews with participants including gig workers, call and contact centre workers, self-employed freelancers, small-business owners, government officials, labour union officials, and industry experts. Focusing on both platform-based remote work and call and contact centre work, the book examines the job quality implications of digital work for the lives and livelihoods of African workers.
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- 2022
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11. Between the Ancients and the Moderns: Charles Rollin, Popular Historian and Pedagogue of Virtue
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Graham, Mark W., Knox, Annie, Section editor, and Geier, Brett A., editor
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- 2024
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12. A 3D Generative Model of Pathological Multi-modal MR Images and Segmentations
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Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Vercauteren, Tom, Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mukhopadhyay, Anirban, editor, Oksuz, Ilkay, editor, Engelhardt, Sandy, editor, Zhu, Dajiang, editor, and Yuan, Yixuan, editor
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- 2024
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13. The poverty of ethical AI: impact sourcing and AI supply chains
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Muldoon, James, Cant, Callum, Graham, Mark, and Ustek Spilda, Funda
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- 2023
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14. Denoising diffusion models for out-of-distribution detection
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Graham, Mark S., Pinaya, Walter H. L., Tudosiu, Petru-Daniel, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge
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Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck - such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://github.com/marksgraham/ddpm-ood.
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- 2022
15. Can segmentation models be trained with fully synthetically generated data?
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Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Tudosiu, Petru-Daniel, Graham, Mark S, Vercauteren, Tom, and Cardoso, M Jorge
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In order to achieve good performance and generalisability, medical image segmentation models should be trained on sizeable datasets with sufficient variability. Due to ethics and governance restrictions, and the costs associated with labelling data, scientific development is often stifled, with models trained and tested on limited data. Data augmentation is often used to artificially increase the variability in the data distribution and improve model generalisability. Recent works have explored deep generative models for image synthesis, as such an approach would enable the generation of an effectively infinite amount of varied data, addressing the generalisability and data access problems. However, many proposed solutions limit the user's control over what is generated. In this work, we propose brainSPADE, a model which combines a synthetic diffusion-based label generator with a semantic image generator. Our model can produce fully synthetic brain labels on-demand, with or without pathology of interest, and then generate a corresponding MRI image of an arbitrary guided style. Experiments show that brainSPADE synthetic data can be used to train segmentation models with performance comparable to that of models trained on real data., Comment: 12 pages, 2 (+2 App.) figures, 3 tables. Accepted at Simulation and Synthesis in Medical Imaging workshop (MICCAI 2022)
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- 2022
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16. Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
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Tudosiu, Petru-Daniel, Pinaya, Walter Hugo Lopez, Graham, Mark S., Borges, Pedro, Fernandez, Virginia, Yang, Dai, Appleyard, Jeremy, Novati, Guido, Mehra, Disha, Vella, Mike, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, Jorge
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,68T99 (Primary) 92C55 (Secondary) ,I.2.1 ,J.3 - Abstract
Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our ability to understand the human body. A possible solution to this issue is the creation of a model able to learn and then generate synthetic images of the human body conditioned on specific characteristics of relevance (e.g., age, sex, and disease status). Deep generative models, in the form of neural networks, have been recently used to create synthetic 2D images of natural scenes. Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations. This work proposes a generative model that can be scaled to produce anatomically correct, high-resolution, and realistic images of the human brain, with the necessary quality to allow further downstream analyses. The ability to generate a potentially unlimited amount of data not only enables large-scale studies of human anatomy and pathology without jeopardizing patient privacy, but also significantly advances research in the field of anomaly detection, modality synthesis, learning under limited data, and fair and ethical AI. Code and trained models are available at: https://github.com/AmigoLab/SynthAnatomy., Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 2022
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- 2022
17. Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
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Pinaya, Walter H. L., Graham, Mark S., Gray, Robert, Da Costa, Pedro F, Tudosiu, Petru-Daniel, Wright, Paul, Mah, Yee H., MacKinnon, Andrew D., Teo, James T., Jager, Rolf, Werring, David, Rees, Geraint, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge
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Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Quantitative Biology - Quantitative Methods - Abstract
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.
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- 2022
18. Transformer-based out-of-distribution detection for clinically safe segmentation
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Graham, Mark S, Tudosiu, Petru-Daniel, Wright, Paul, Pinaya, Walter Hugo Lopez, Jean-Marie, U, Mah, Yee, Teo, James, Jäger, Rolf H, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M Jorge
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model's segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data. Code is available at github.com/marksgraham/transformer-ood., Comment: Accepted at MIDL 2022 (Oral)
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- 2022
19. Tau forms synaptic nano-biomolecular condensates controlling the dynamic clustering of recycling synaptic vesicles
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Longfield, Shanley F., Mollazade, Mahdie, Wallis, Tristan P., Gormal, Rachel S., Joensuu, Merja, Wark, Jesse R., van Waardenberg, Ashley J., Small, Christopher, Graham, Mark E., Meunier, Frédéric A., and Martínez-Mármol, Ramón
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- 2023
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20. Subcellular analysis of blood-brain barrier function by micro-impalement of vessels in acute brain slices
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Hanafy, Amira Sayed, Steinlein, Pia, Pitsch, Julika, Silva, Mariella Hurtado, Vana, Natascha, Becker, Albert J., Graham, Mark Evan, Schoch, Susanne, Lamprecht, Alf, and Dietrich, Dirk
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- 2023
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21. Minimal expression of dysferlin prevents development of dysferlinopathy in dysferlin exon 40a knockout mice
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Yasa, Joe, Reed, Claudia E., Bournazos, Adam M., Evesson, Frances J., Pang, Ignatius, Graham, Mark E., Wark, Jesse R., Nijagal, Brunda, Kwan, Kim H., Kwiatkowski, Thomas, Jung, Rachel, Weisleder, Noah, Cooper, Sandra T., and Lemckert, Frances A.
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- 2023
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22. Teacher Collaboration and Elementary Arts Integration: Policy and Possibility
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Carpenter Estrada, Tara, Graham, Mark A., Peterken, Corinna, Cannon, Mikaela, and Harris, Anna
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Arts education can be an important part of the elementary child's experience. However, keeping the arts in schools is often a challenge because of funding concerns and competing academic priorities. An important strategy in arts education advocacy and policy is arts integration. Arts integration creates a rationale for the arts based on the enhancement of the educational experience for all students and the potential to engage underserved or less engaged students with academic disciplines through the arts. This article examines teacher experiences within a statewide program that funds arts specialists in elementary schools with the requirement that they integrate the arts with other subjects. The researchers describe how the program works with particular focus on collaboration between arts specialists and classroom teachers. This is a multifaceted study across four school districts with 640 participants including classroom teachers and arts specialists. An understanding of both arts integration and how collaboration happens between elementary teachers and arts specialists was gained through interviews, classroom visits, and surveys. The data suggest that effective integration and collaboration can enhance teachers' professional growth but require structural and policy support as well as the creativity and energy of individual teachers. The professional life of arts specialists, teacher knowledge, and professional development emerged as important issues for teachers, school leaders, and arts advocates.
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- 2023
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23. Mindfulness as Art Education, Self-Inquiry, and Artmaking
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Graham, Mark A. and Lewis, Rebecca
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This article presents an overview and continuation of a study investigating how artist awareness and critical response might provide a better understanding of mindfulness and its practice within art education. While there are distinct advantages to mindfulness practices in education, these practices might also have problematic aspects, such as helping people conform to oppressive structures in education rather than questioning them. The pandemic of 2020-2021 accentuated student concerns with social, emotional, and mental health and illuminated possible benefits of mindfulness practice. Results show that preservice art education students used mindfulness and data visualization to connect art and self-inquiry. Important findings included the positive impact mindfulness practices had on students' social-emotional learning and a refined distinction between mindfulness as a therapeutic educational intervention versus mindfulness an art educational experience.
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- 2023
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24. Cumulative Cross Course Exposure to Evidence-Based Teaching Is Related to Increases in STEM Student Buy-In and Intent to Persist
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Reeves, Philip M., Cavanagh, Andrew J., Bauer, Melanie, Wang, Cong, and Graham, Mark J.
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A growing body of evidence had demonstrated that increased student exposure and commitment to evidence-based teaching (EBT) leads to improved academic performance, greater persistence, and higher buy-in to instructional methods. Despite the increasing number of teaching development opportunities available to STEM instructors, which often encourage the use of EBT, implementation is still highly variable across instructors. The frequency with which students are exposed to EBT across multiple courses in a department or university has not been studied in relation to students' success in a given course or their ultimate persistence in STEM. This study shows that there is a cumulative benefit of exposure to EBT across multiple courses. Students who are frequently exposed to EBT also find these practices to be more valuable and report a higher intention to persist in STEM. If students demonstrate higher commitment after multiple EBT exposures, this may increase the likelihood that faculty will incorporate these teaching practices into their courses. The findings are useful for instructors, faculty, department chairs, and administrators who are attempting to support a more unified, evidence-based approach to teaching in their department or institution.
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- 2023
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25. Ketogenic diet modifies ribosomal protein dysregulation in KMT2D Kabuki syndrome
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Tsang, Erica, Han, Velda X., Flutter, Chloe, Alshammery, Sarah, Keating, Brooke A., Williams, Tracey, Gloss, Brian S., Graham, Mark E., Aryamanesh, Nader, Pang, Ignatius, Wong, Melanie, Winlaw, David, Cardamone, Michael, Mohammad, Shekeeb, Gold, Wendy, Patel, Shrujna, and Dale, Russell C.
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- 2024
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26. The German platform economy: Strict regulations but unfair standards?
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Ferrari, Fabian, Bertolini, Alessio, Borkert, Maren, and Graham, Mark
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- 2024
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27. Ground-based calibration for remote sensing of biomass in the tallest forests
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Sillett, Stephen C., Graham, Mark E., Montague, John P., Antoine, Marie E., and Koch, George W.
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- 2024
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28. Phytoplankton communities as indicators of environmental change in the Canadian Rockies
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Cook, Jenna, Loewen, Charlie J.G., Nagao, Tamika L., Graham, Mark D., and Vinebrooke, Rolf D.
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Canadian Rockies -- Environmental aspects ,Phytoplankton -- Environmental aspects ,Climatic changes -- Environmental aspects ,Earth sciences - Abstract
Remote mountain lakes in protected areas are sentinels of the ecological impacts of extreme and novel environmental changes occurring at broad regional scales. Ecosystem responses to such stressors are often first detected as shifts in community composition. We surveyed phytoplankton communities across 82 mountain lakes to test the hypothesis that taxonomic composition is indicative of more environmental changes than are aggregate properties, such as total biomass. Phosphorus was the only significant predictor of chlorophyll-inferred algal biomass, a correlative finding supported by evidence from our nutrient amendment bioassays. Interlake variances in taxonomically diagnostic algal pigments and 78 genera were indicative of changes in total phosphorus, glacial coverage, underwater light availability, and dissolved organic carbon. Lack of concordance was observed between ordinations of pigment- and genus-based data as environmental variables captured more variance in the pigment data. Our findings provide a baseline for future lake monitoring programs in the Canadian Rockies as they increasingly experience interactive effects involving climate change and landscape features, such as variation in turbid glacial meltwaters and aeolian phosphorus deposition from wildfires. Key words: algae, climate change, environmental gradient analysis, glaciers, nutrient limitation, phosphorus, Introduction Aquatic scientists are increasingly challenged to advance our predictive understanding of the cumulative impacts of global warming on lake ecosystems (Carpenter et al. 1992; Williamson et al. 2009; Jenny [...]
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- 2023
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29. Instructional Models for Course-Based Research Experience (CRE) Teaching
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Hanauer, David I, Graham, Mark J, Arnold, Rachel J, Ayuk, Mary A, Balish, Mitchell F, Beyer, Andrea R, Butela, Kristen A, Byrum, Christine A, Chia, Catherine P, Chung, Hui-Min, Clase, Kari L, Conant, Stephanie, Coomans, Roy J, D’Elia, Tom, Diaz, Jason, Diaz, Arturo, Doty, Jean A, Edgington, Nicholas P, Edwards, Dustin C, Eivazova, Elvira, Emmons, Christine B, Fast, Kayla M, Fisher, Emily J, Fleischacker, Christine L, Frederick, Gregory D, Freise, Amanda C, Gainey, Maria D, Gissendanner, Chris R, Golebiewska, Urszula P, Guild, Nancy A, Hendrickson, Heather L, Herren, Christopher D, Hopson-Fernandes, Margaret S, Hughes, Lee E, Jacobs-Sera, Deborah, Johnson, Allison A, Kirkpatrick, Bridgette L, Klyczek, Karen K, Koga, Ann P, Kotturi, Hari, LeBlanc-Straceski, Janine, Lee-Soety, Julia Y, Leonard, Justin E, Mastropaolo, Matthew D, Merkhofer, Evan C, Michael, Scott F, Mitchell, Jon C, Mohan, Swarna, Monti, Denise L, Noutsos, Christos, Nsa, Imade Y, Peters, Nick T, Plymale, Ruth, Pollenz, Richard S, Porter, Megan L, Rinehart, Claire A, Rosas-Acosta, German, Ross, Joseph F, Rubin, Michael R, Scherer, Anne E, Schroeder, Stephanie C, Shaffer, Christopher D, Sprenkle, Amy B, Sunnen, C Nicole, Swerdlow, Sarah J, Tobiason, Deborah, Tolsma, Sara S, Tsourkas, Philippos K, Ward, Robert E, Ware, Vassie C, Warner, Marcie H, Washington, Jacqueline M, Westover, Kristi M, White, Simon J, Whitefleet-Smith, JoAnn L, Williams, Daniel C, Wolyniak, Michael J, Zeilstra-Ryalls, Jill H, Asai, David J, Hatfull, Graham F, and Sivanathan, Viknesh
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Curriculum and Pedagogy ,Education Systems ,Education ,Quality Education ,Engineering ,Faculty ,Humans ,Mathematics ,Models ,Educational ,Students ,Teaching ,Curriculum and pedagogy - Abstract
The course-based research experience (CRE) with its documented educational benefits is increasingly being implemented in science, technology, engineering, and mathematics education. This article reports on a study that was done over a period of 3 years to explicate the instructional processes involved in teaching an undergraduate CRE. One hundred and two instructors from the established and large multi-institutional SEA-PHAGES program were surveyed for their understanding of the aims and practices of CRE teaching. This was followed by large-scale feedback sessions with the cohort of instructors at the annual SEA Faculty Meeting and subsequently with a small focus group of expert CRE instructors. Using a qualitative content analysis approach, the survey data were analyzed for the aims of inquiry instruction and pedagogical practices used to achieve these goals. The results characterize CRE inquiry teaching as involving three instructional models: 1) being a scientist and generating data; 2) teaching procedural knowledge; and 3) fostering project ownership. Each of these models is explicated and visualized in terms of the specific pedagogical practices and their relationships. The models present a complex picture of the ways in which CRE instruction is conducted on a daily basis and can inform instructors and institutions new to CRE teaching.
- Published
- 2022
30. The global polarisation of remote work
- Author
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Braesemann, Fabian, Stephany, Fabian, Teutloff, Ole, Kässi, Otto, Graham, Mark, and Lehdonvirta, Vili
- Subjects
Economics - General Economics - Abstract
The Covid-19 pandemic has led to the rise of remote work with consequences for the global division of work. Remote work could connect labour markets, but it could also increase spatial polarisation. However, our understanding of the geographies of remote work is limited. Specifically, does remote work bring jobs to rural areas or is it concentrating in large cities, and how do skill requirements affect competition for jobs and wages? We use data from a fully remote labour market - an online labour platform - to show that remote work is polarised along three dimensions. First, countries are globally divided: North American, European, and South Asian remote workers attract most jobs, while many Global South countries participate only marginally. Secondly, remote jobs are pulled to urban regions; rural areas fall behind. Thirdly, remote work is polarised along the skill axis: workers with in-demand skills attract profitable jobs, while others face intense competition and obtain low wages. The findings suggest that remote work is shaped by agglomerative forces, which are deepening the gap between urban and rural areas. To make remote work an effective tool for rural development, it needs to be embedded in local skill-building and labour market programmes., Comment: This is an Accepted Manuscript of an article published in the journal PLOS ONE
- Published
- 2021
- Full Text
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31. Trans-omic profiling uncovers molecular controls of early human cerebral organoid formation
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Chen, Carissa, Lee, Scott, Zyner, Katherine G., Fernando, Milan, Nemeruck, Victoria, Wong, Emilie, Marshall, Lee L., Wark, Jesse R., Aryamanesh, Nader, Tam, Patrick P.L., Graham, Mark E., Gonzalez-Cordero, Anai, and Yang, Pengyi
- Published
- 2024
- Full Text
- View/download PDF
32. Multiomics of early epileptogenesis in mice reveals phosphorylation and dephosphorylation-directed growth and synaptic weakening
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Hurtado Silva, Mariella, van Waardenberg, Ashley J., Mostafa, Aya, Schoch, Susanne, Dietrich, Dirk, and Graham, Mark E.
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- 2024
- Full Text
- View/download PDF
33. Advanced Placement in Studio Art
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Graham, Mark, primary
- Published
- 2023
- Full Text
- View/download PDF
34. Fairness in the platform economy: lessons learnt from a pandemic
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Badger, Adam, primary, Bertolini, Alessio, additional, Graham, Mark, additional, and Ustek Spilda, Funda, additional
- Published
- 2023
- Full Text
- View/download PDF
35. Executive Summary
- Author
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
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- 2023
- Full Text
- View/download PDF
36. Access to STEM: Gender Dimensions and Challenges
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
- Published
- 2023
- Full Text
- View/download PDF
37. Back Matter: Appendix: Country Profiles
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
- Published
- 2023
- Full Text
- View/download PDF
38. STEM Trends—Globally and in South Asia
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
- Published
- 2023
- Full Text
- View/download PDF
39. Key Observations from South Asia
- Author
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
- Published
- 2023
- Full Text
- View/download PDF
40. Potential Interventions for South Asia
- Author
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
- Published
- 2023
- Full Text
- View/download PDF
41. Front Matter
- Author
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Sosale, Shobhana, primary, Harrison, Graham Mark, additional, Tognatta, Namrata, additional, Nakata, Shiro, additional, and Gala, Priyal Mukesh, additional
- Published
- 2023
- Full Text
- View/download PDF
42. Platformizing Informality, One Gig at a Time
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Bertolini, Alessio, Graham, Mark, Neerukonda, Mounika, Ojanperä, Sanna, Parthasarathy, Balaji, Srinivasan, Janaki, Taduri, Pradyumna, Ustek-Spilda, Funda, Huws, Ursula, Series Editor, Gill, Rosalind, Series Editor, and Surie, Aditi, editor
- Published
- 2023
- Full Text
- View/download PDF
43. Broadening Access to STEM through the Community College: Investigating the Role of Course-Based Research Experiences (CREs)
- Author
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Hanauer, David I., Graham, Mark J., Jacobs-Sera, Deborah, Garlena, Rebecca A., Russell, Daniel A., Sivanathan, Viknesh, Asai, David J., and Hatfull, Graham F.
- Abstract
Broadening access to science, technology, engineering, and mathematics (STEM) professions through the provision of early-career research experiences for a wide range of demographic groups is important for the diversification of the STEM workforce. The size and diversity of the community college system make it a prime educational site for achieving this aim. However, some evidence shows that women and Black, Latinx, and Native American student groups have been hindered in STEM at the community college level. One option for enhancing persistence in STEM is to incorporate the course-based research experiences (CREs) into the curriculum as a replacement for the prevalent traditional laboratory. This can be achieved through the integration of community colleges within extant, multi-institutional CREs such as the SEA-PHAGES program. Using a propensity score-matching technique, students in a CRE and traditional laboratory were compared on a range of psychosocial variables (project ownership, self-efficacy, science identity, scientific community values, and networking). Results revealed higher ratings for women and persons excluded because of their ethnicity or race (PEERs) in the SEA-PHAGES program on important predictors of persistence such as project ownership and science identity. This suggests that the usage of CREs at community colleges could have positive effects in addressing the gender gap for women and enhance inclusiveness for PEER students in STEM.
- Published
- 2022
- Full Text
- View/download PDF
44. Fair work in South Africa's gig economy: A journey of engaged scholarship
- Author
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Van Belle, Jean-Paul, Howson, Kelle, Graham, Mark, Heeks, Richard, Bezuidenhout, Louise, Tsibolane, Pitso, du Toit, Darcy, Fredman, Sandra, and Mungai, Paul
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- 2023
- Full Text
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45. Latent Transformer Models for out-of-distribution detection
- Author
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Graham, Mark S., Tudosiu, Petru-Daniel, Wright, Paul, Pinaya, Walter Hugo Lopez, Teikari, Petteri, Patel, Ashay, U-King-Im, Jean-Marie, Mah, Yee H., Teo, James T., Jäger, Hans Rolf, Werring, David, Rees, Geraint, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge
- Published
- 2023
- Full Text
- View/download PDF
46. Instructional Models for Course-Based Research Experience (CRE) Teaching
- Author
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Hanauer, David I., Graham, Mark J., Arnold, Rachel J., Ayuk, Mary A., Balish, Mitchell F., Beyer, Andrea R., Butela, Kristen A., Byrum, Christine A., Chia, Catherine P., Chung, Hui-Min, Clase, Kari L., Conant, Stephanie, Coomans, Roy J., D'Elia, Tom, Diaz, Jason, Diaz, Arturo, Doty, Jean A., Edgington, Nicholas P., Edwards, Dustin C., Eivazova, Elvira, Emmons, Christine B., Fast, Kayla M., Fisher, Emily J., Fleischacker, Christine L., Frederick, Gregory D., Freise, Amanda C., Gainey, Maria D., Gissendanner, Chris R., Golebiewska, Urszula P., Guild, Nancy A., Hendrickson, Heather L., Herren, Christopher D., Hopson-Fernandes, Margaret S., Hughes, Lee E., Jacobs-Sera, Deborah, Johnson, Allison A., Kirkpatrick, Bridgette L., Klyczek, Karen K., Koga, Ann P., Kotturi, Hari, LeBlanc-Straceski, Janine, Lee-Soety, Julia Y., Leonard, Justin E., Mastropaolo, Matthew D., Merkhofer, Evan C., Michael, Scott F., Mitchell, Jon C., Mohan, Swarna, Monti, Denise L., Noutsos, Christos, Nsa, Imade Y., Peters, Nick T., Plymale, Ruth, Pollenz, Richard S., Porter, Megan L., Rinehart, Claire A., Rosas-Acosta, German, Ross, Joseph F., Rubin, Michael R., Scherer, Anne E., Schroeder, Stephanie C., Shaffer, Christopher D., Sprenkle, Amy B., Sunnen, C. Nicole, Swerdlow, Sarah J., Tobiason, Deborah, Tolsma, Sara S., Tsourkas, Philippos K., Ward, Robert E., Ware, Vassie C., Warner, Marcie H., Washington, Jacqueline M., Westover, Kristi M., White, Simon J., Whitefleet-Smith, JoAnn L., Williams, Daniel C., Wolyniak, Michael J., Zeilstra-Ryalls, Jill H., Asai, David J., Hatfull, Graham F., and Sivanathan, Viknesh
- Abstract
The course-based research experience (CRE) with its documented educational benefits is increasingly being implemented in science, technology, engineering, and mathematics education. This article reports on a study that was done over a period of 3 years to explicate the instructional processes involved in teaching an undergraduate CRE. One hundred and two instructors from the established and large multi-institutional SEA-PHAGES program were surveyed for their understanding of the aims and practices of CRE teaching. This was followed by large-scale feedback sessions with the cohort of instructors at the annual SEA Faculty Meeting and subsequently with a small focus group of expert CRE instructors. Using a qualitative content analysis approach, the survey data were analyzed for the aims of inquiry instruction and pedagogical practices used to achieve these goals. The results characterize CRE inquiry teaching as involving three instructional models: (1) being a scientist and generating data; (2) teaching procedural knowledge; and (3) fostering project ownership. Each of these models is explicated and visualized in terms of the specific pedagogical practices and their relationships. The models present a complex picture of the ways in which CRE instruction is conducted on a daily basis and can inform instructors and institutions new to CRE teaching.
- Published
- 2022
- Full Text
- View/download PDF
47. Major Taylor: World Cycling Champion
- Author
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Graham, Mark
- Subjects
Major Taylor: World Cycling Champion (Picture story) -- Smith, Charles R. -- Espinosa, Leo ,Books -- Book reviews ,Family and marriage ,Library and information science ,Publishing industry - Abstract
Major Taylor: World Cycling Champion Charles R. Smith, author Leo Espinosa, illustrator Candlewick https://www.candlewick.com 9781536214987, $18.99 One hundred years ago, one of the most popular spectator sports was bicycle racing, [...]
- Published
- 2024
48. A Few Beautiful Minutes: Experiencing a Solar Eclipse
- Author
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Graham, Mark
- Subjects
A Few Beautiful Minutes: Experiencing a Solar Eclipse (Picture story) -- Fox, Kate Allen -- Le, Khoa ,Books -- Book reviews ,Family and marriage ,Library and information science ,Publishing industry - Abstract
A Few Beautiful Minutes: Experiencing a Solar Eclipse Kate Allen Fox, author Khoa Le, illustrator Little, Brown Books for Young Readers c/o Hachette https://www.hachettebookgroup.com 9780316416924, $18.99 What does it really [...]
- Published
- 2024
49. Juniper's Christmas
- Author
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Graham, Mark
- Subjects
Roaring Brook Press ,Book publishing ,Family and marriage ,Library and information science ,Publishing industry - Abstract
Juniper's Christmas Eoin Colfer Roaring Brook Press c/o Macmillan https://us.macmillan.com 9781250321947, $22.99 This is a new way of seeing Santa Claus. Eoin Colfer writes a story of relationships that seem [...]
- Published
- 2023
50. Finch House
- Author
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Graham, Mark
- Subjects
Simon & Schuster Inc. ,Book publishing ,Family and marriage ,Library and information science ,Publishing industry - Abstract
Finch House Ciera Burch Margaret K. McElderry Books c/o Simon & Schuster Publishing https://simonandschusterpublishing.com 9781665930543, $17.99 Ciera Burch has written a youth novel that will even intrigue adults. Ciera's descriptions [...]
- Published
- 2023
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