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42 results for “science learning”
Improving machine-learning models in materials science through large datasets
<p>1. Image of the <a href="https://alexandria.icams.rub.de/"><strong>Alexandria database </strong></a> state corresponding to the paper "<strong>Improving machine-learning models in materials science through large datasets</strong>".</p> <ul> <li>Static pbe calculations for 1D, 2D, 3D compounds can be found in 1D_pbe.tar.gz, 2D_pbe.tar.gz, 3D_pbe.tar.gz in batches of 100k materials. The latter also contains a separate convex hull pickle with all compounds on the pbe convex hull (convex_hull_pbe_2023.12.29.json.bz2) and a list of prototypes in the database (prototypes.json.bz2). The systematic 3D calculations performed for the article <strong>Improving machine-learning models in materials science through large datasets </strong>(in the paper referred to as round 2 and 3) can be found by the location keyword in the data dictionary of each ComputedStructureEntry containing "<strong>cgat_comp/quaternaries</strong>" (round 2) and "<strong>cgat_comp2/</strong>" (round 3). Round 1 (10.1002/adma.202210788) can be found under "cgat_comp/ternaries", ""cgat_comp/binaries".</li> <li>Static pbesol calculations for 3D compounds can be found in 3D_ps.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the pbesol convex hull (convex_hull_ps_2023.12.29.json.bz2). </li> <li>Static scan calculations for 3D compounds can be found in 3D_scan.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the scan convex hull (convex_hull_scan_2023.12.29.json.bz2). </li> <li>Geometry relaxation curves for 1D and 2D and 3D compounds calculated with PBE can be found in geo_opt_1D.tar.gz, geo_opt_2D.tar.gz. and geo_opt_3D.tar. Each file in each folder contains a batch of up to 10k relaxation trajectories.</li> <li>PBESOL relaxation trajectories for 3D compounds can be found in geo_opt_ps.tar</li> </ul> <p>2. Crystal graph attention networks to predict the volume (<a href="https://zenodo.org/api/records/12582650/draft/files/volume_round_3.tar.gz/content" target="_blank" rel="noopener noreferrer">volume_round_3.tar.gz</a>) and distance to the convex hull (<a href="https://zenodo.org/api/records/12582650/draft/files/e_above_hull_round_3.tar.gz/content" target="_blank" rel="noopener noreferrer">e_above_hull_round_3.tar.gz</a>) trained for the paper "Improving machine-learning models in materials science through large datasets".</p> <p>Can be used with the code at https://github.com/hyllios/CGAT/tree/main/CGAT.<br><strong>Note will predict the distance to the convex hull not normalized per atom when using the code on the github.<br></strong></p> <p>3. Alignn models as well as m3gnet and mace models corresponding to the publication can be found in <a href="https://zenodo.org/api/records/12582650/draft/files/alexandria_v2.tar.gz/content" target="_blank" rel="noopener noreferrer">alexandria_v2.tar.gz</a></p> <p>4. scripts.tar.gz Some scripts used for generating CGAT input data/ performing parallel predictions and for relaxations with m3gnet/mace force fields</p>
Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)
In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.
Dataset: Whole blood count, used in: "AIDeveloper: deep learning image classification in life science and beyond"
<p>Real-time deformability cytometry (RT-DC) data of whole blood measurements.<br> Data was used to train and validate a neural net to perform a blood count based on brightfield images of RT-DC.</p> <p>01_Model: Contains the final model as well as an AIDeveloper meta-file that allows to reproduce the training procedure. The metafile preciesely defines which dataset was used for training and which for validation as well as all parameters that were set in AIDeveloper.</p> <p>The following folders contain data that was used for training (and validation):</p> <ul> <li>Cambr</li> <li>KIK</li> <li>20190306_DextranBlood_AI_DataSet</li> <li>Gs_Blood_Train</li> </ul> <p>Testing data is stored on figshare:<br> https://figshare.com/articles/Krater_et_al_2020_Data_zip/9902636</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
MULTIPLIERS_WP5_Science learning project on Forest use vs. forest protection_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Forest use vs. forest protection</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with OSC members, and student groups (</span><span>Pseudo-/Anonymised)</span></li> </ul>
MULTIPLIERS_WP5_Science learning project on Biodiversity and agriculture_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Biodiversity and agriculture</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers, and OSC members (</span><span>Pseudo-/Anonymised)</span></li> </ul>
Data from: Fun surveys? Developing an innovative approach to assessing learning through citizen science
Open the record for dataset details and reuse information.
Data for: Citizen science as an ecosystem of engagement: Implications for learning and broadening participation
<p>The bulk of research on citizen science participants is project-centric, based on an assumption that volunteers experience a single project. Contrary to this assumption, survey responses (n=3,894) and digital trace data (n=3,649) from volunteers, who collectively engaged in 1,126 unique projects, revealed that multi-project participation was the norm. Only 23% of volunteers were singletons (who participated in only one project), and multi-project participants split evenly between disciplines specialists (39%) and discipline spanners (38% joined projects with different disciplinary topics), and unevenly between mode specialists (67%) and mode spanners (33% participated in online and offline projects). Public engagement was narrow: multi-project participants were eight times more likely to be white, and five times more likely to hold advanced degrees, than the general population. We propose a volunteer-centric framework that explores how the dynamic accumulation of experiences in a project ecosystem can support broad learning objectives and inclusive citizen science. </p>
Families in Science Learning – The Video
<p>This 30 minutes-long movie has been co-created by the participating students and their families. The movie explains in the most authentic way possible how the students learnt in interaction with their family when contextualising open science schooling, and how this learning is different from traditional science teaching.</p>
MULTIPLIERS_WP5_Science learning project on AMR_UAB_Public data_v1
<p><span>The datasets contain the following data related to the science learning project on ARM: </span></p> <ul> <li><span>Summary of transcripts from interviews with OSC members, and student groups (Pseudo-/Anonymised).</span></li> </ul>
Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".
<p>This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper:</p> <p>Ponti, M., Seredko, A. <a href="http://doi.org/10.1057/s41599-022-01049-z">Human-machine-learning integration and task allocation in citizen science.</a> <em>Humanit Soc Sci Commun</em> <strong>9, </strong>48 (2022). https://doi.org/10.1057/s41599-022-01049-z</p> <p>The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.</p> <p> </p> <p> </p>
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels.</p> <p> </p> <p>The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. </p> <p> </p> <p>The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.</p> <p><br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (<a href="https://github.com/ai2es/miles-guess">https://github.com/ai2es/miles-guess</a>).</p>
Data for: Citizen science as an ecosystem of engagement: Implications for learning and broadening participation
Open the record for dataset details and reuse information.
Data from: Graduate student reflections on learning and implementing community science methods to tackle compounding climate extremes
Open the record for dataset details and reuse information.
The impact of learning modality on team-based learning (TBL) outcomes in anatomical sciences education
<p>Team-based learning (TBL) is an instructional methodology that has been increasingly used in anatomy and physiology education in recent years. The appropriateness of TBL methods for students with diverse preferred sensory learning modalities has not been adequately examined. This study aimed to show the influence of students preferred sensory modality for learning on TBL and traditional academic outcome measures (tests, assignments, etc.). 157 American undergraduate Communication Sciences and Disorders students taking anatomy and physiology courses took the VARK, a questionnaire quantifying their preferred learning modality. These students traditional and TBL measures of academic success were compared by preferred learning modality. A one-way MANOVA failed to find any difference in TBL or other academic outcomes by preferred learning modality (<em>p</em> = .252). The results of this study support the effectiveness of TBL methods for undergraduate students regardless of what sensory mode they prefer to receive information</p>
A Transformative Edge: How Transformative Learning Can Benefit Open Science
<p>In this episode we talk to Ursel Biester and Marylin Mehlmann, two experts in adult education, about what Transformative Learning is and how it can be applied to creating meaningful change in the context of trainings, including those in the Open Science and RRI movement. </p> <p><strong>Episode Links</strong></p> <p><a href="http://www.hostingtransformation.eu/a-transformative-edge/">The Transformative Edge Book</a></p> <p><a href="http://www.hostingtransformation.eu/about/">Hosting Transformation Project</a></p>
Nunataryuk Session "Indigenous Peoples, science and climate change in the Arctic: lessons learned and a way forward" at ICASS X
<p>Permafrost coasts in the whole Arctic represent 34% of the world's coasts (Lantuit et al., 2012) and a key interface for human-environmental interactions. These coasts provide essential ecosystem services, exhibit high biodiversity and productivity, and support indigenous lifestyles. At the same time, this coastal zone is a dynamic and vulnerable zone of expanding infrastructure investment and growing health concerns. Climate change is affecting this fragile environment by triggering coastal landscape instability and increased hazard exposure (Forbes et al., 2011). Permafrost thaw in combination with increasing sea level and changing sea-ice cover expose the Arctic coastal and nearshore areas to rapid changes (Fritz et al. 2017). Since 2017, scientists from the Nunataryuk Project are working in cooperation with local communities in order to identify the impacts of thawing land, coast and subsea permafrost on the global climate and on humans in the Arctic and to develop targeted and co-designed adaptation and mitigation strategies.</p>
ScienceDex guides
Understand access before you commit
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.