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42 results for “science learning”
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Machine learning for Material Science 2022 - Neural Networks Assignment Dataset
<p>Data for the neural network assignment</p> <p> </p>
OQM9HK: A Large-scale Graph Dataset for Machine Learning in Materials Science
<p>This is a large-scale graph dataset of materials science based on the Open Quantum Materials Database (OQMD) v1.5 .</p> <p><a href="https://storage.googleapis.com/rimcs_cgnn/oqm9hk_dataset_Sep_30_2022.pdf" target="_blank" rel="noopener">Technical Report</a></p> <p><a href="https://www.rimcs.co.jp" target="_blank" rel="noopener">RIMCS Website</a></p> <p><strong>Data Loading</strong></p> <p>A Python code example:</p> <pre><code>import sys sys.path.append('/your/path/to/data/OQM9HK_BEL') import OQM9HK bel_path='/your/path/to/data/OQM9HK_BEL' config = OQM9HK.load_config(path=bel_path) print(config['atomic_numbers']) split = OQM9HK.load_split(path=bel_path) print(len(split['train']), len(split['val']), len(split['test'])) graph_data = OQM9HK.load_graph_data(path=bel_path) name = next(iter(graph_data)) # Frist entry's name graph = graph_data[name] # Graph object print(graph.nodes) print(graph.edge_sources) print(graph.edge_targets) dataset = OQM9HK.load_targets(path=bel_path) # Pandas dataframe print(dataset) train_set = dataset.iloc[split['train']] val_set = dataset.iloc[split['val']] test_set = dataset.iloc[split['test']]</code></pre> <p> </p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki. in Deep learning brings speed, accuracy to the life sciences.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki.
Researchers trained a in Deep learning brings speed, accuracy to the life sciences.
Researchers trained a neural network with random plant samples, shown here, from a large French herbaria data set. Next, the network was trained on a much smaller Costa Rican database. The French and Costa Rican species are different, but the model identified Costa Rican species with 80 percent accuracy. This training process, called transfer learning, can help taxonomists identify plants in countries that have small numbers of plant images in digital collections. Photograph: Reprinted from Carranza-Rojas J et al. 2017, Going deeper in the automated identification of herbarium specimens. BMC Evolutionary Biology.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
Open the record for dataset details and reuse information.
How Do Clearly Defined Learning Goals Enhance Science Performance? An Investigation for Sustainable Education
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"Reading" people to learn about new science careers: the Human Library method
<p>The "human library" method is frequently used to establish open conversations with people one would not meet in their daily routine, and this is what makes it so interesting in the context of science education. The MULTIPLIERS project is exploring the method to promote meaningful interactions between students and stakeholders involved in socio-scientific issues, while also eliciting interest in future career paths in science.</p> <p>Take a look at the expert interviews recorded by our students during the MULTIPLIERS awareness-raising campaign on Antimicrobial Resistance in Nicosia, Cyprus!</p> <p>More about the project: https://multipliers-project.org/</p>
M-learning to Improve Professional Competencies in Health Sciences
ClinicalTrials.gov study NCT03335397. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dataset of the study "Bridging cultural studies and learning science: An investigation of social media use for Holocaust memory and education in the digital age"
<p>This Zenodo item contains the dataset of the study: Manca, S. (2020). “Bridging cultural studies and learning science: An investigation of social media use for Holocaust memory and education in the digital age".</p> <p>Abstract</p> <p>Along with advances in communication technology that are making new forms of historical memorialization and education available, social media are researched as valuable tools for supporting forms of digital memory and for engaging students and teachers about historical knowledge and moral education. This study aims to map the current state of Holocaust remembrance and Holocaust education and to identify main topics of research in the two areas. It adopts a mixed-method approach that combines qualitative analysis with bibliometric approaches to review publications that use social media for digital memory and history education about the Holocaust. Results based on 28 publications reveal several research topics and that, despite some common theoretical references, the two subfields mostly rely on separate conceptual backgrounds. While Holocaust remembrance is a well-established research field, there are few studies and a lack of theoretical elaboration about social media use for teaching and learning about the Holocaust.</p>
PENERAPAN MODEL PEMBELAJARAN CHILDREN LEARNING IN SCIENCE (CLIS) UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK KELAS XI SMA KARTINI LUARI HALMERA UTARA PADA POKOK BAHASAN TEKANAN PADA ZAT CAIR
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Pedagogical innovation: dynamics of E-learning in the teaching of accounting sciences based on a bibliometric analysis.
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Data for Science Time Series: Deep Learning in Hydrology
<p>Processed CAMELS and Caravan data.</p>
How Well do Patients With Traumatic Brain Injury Learn New Material Using Learning Styles in Online Science Classrooms?
ClinicalTrials.gov study NCT01417468. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers
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Datasets corresponding to publication: AIDeveloper: deep learning image classification in life science and beyond
<p>Datasets and videos corresponding to publication:<br> AIDeveloper: deep learning image classification in life science and beyond</p>
Explore the Nature Factors Relevant to Learning-Pioneering Natural Factors That Influences Science Learning II
ClinicalTrials.gov study NCT00713570. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.