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29 results for “Learning elements”
Learning Elements in Learning Management Systems (LMSs)
<p>Results of a survey in the higher education area. Participants are professors, lecturers, and tutors.</p> <p> </p> <p>The final definitions for the elements are:</p> <ul> <li>Brief Overview (BO): Short summary or recap without details of the actual learning material</li> <li>Quiz (QU): Quiz questions related to the content taught</li> <li>Learning Goal (LG): Description of the competences, skills or abilities that the learners should acquire in relation to a specific learning content</li> <li>Manuscript (MS): Complete or brief elaboration of a speech, a lecture, a course, or similar</li> <li>Exercise (EX): Opportunity to apply and deepen the learned. Varied tasks are possible beside the classic exercise sheet</li> <li>Summary (SU): Elementalization (reduction to the essentials) of the actual content with details</li> <li>Auditory additional material (AAM): Material with the aim of applying and deepening the learned with audio files</li> <li>Textual additional material (TAM): Material with the aim of applying and deepening the learned with textual further information (also named additional literature)</li> <li>Visual additional material (VAM): Material with the aim of applying and deepening the learned with videos or similar</li> <li>Collaboration Tool (CT): Cooperative and interactive communication medium with the aim of knowledge sharing between learners and learners and/or lecturers, and is used for collaborative work</li> </ul> <p>The corresponding scientific paper can be found via ORCID as of December 2023.</p> <p> </p> <p>The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).</p>
Global mapping of lunar refractory elements: multivariate regression vs. machine learning
<p>The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images<br> of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity.<br> We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps<br> and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to<br> sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the<br> two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.</p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
Fruit flies can learn non-elemental olfactory discriminations
Associative learning allows animals to establish links between stimuli based on their concomitance. In the case of Pavlovian conditioning, a single stimulus A (the conditional stimulus, CS) is reinforced unambiguously with an unconditional stimulus (US) eliciting an innate response. This conditioning constitutes an 'elemental' association enabling to elicit a learnt response from A+ without US presentation after learning. However, associative learning may involve a 'complex' CS composed of several components. In that case, the compound may predict a different outcome than the components taken separately, leading to an ambiguity and requiring the animal to perform a so-called 'non-elemental' discrimination. Here we focus on such a non-elemental task, the negative patterning (NP) problem, and provide the first evidence of NP solving in Drosophila. We show that Drosophila learn to discriminate a simple component (A or B) associated to electric shocks (+) from an odour mixture composed either partly (called 'feature-negative discrimination' A+ vs. AB-) or entirely (called 'NP' A+B+ vs. AB-) of the shock associated components. Furthermore, we show that conditioning repetition results in a transition from an elemental to a configural representation of the mixture required to solve the NP task, highlighting the cognitive flexibility of Drosophila.
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 1000 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed.</p>
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 100 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed</p>
Fruit flies can learn non-elemental olfactory discriminations
Open the record for dataset details and reuse information.
Tyche Algorithm and Learning Elements Preferences
<p>This dataset contains the result of the survey to learning styles and their learning element preferences.</p> <p>The survey was executed in summer term of 2023 in German universities.</p> <p> </p> <p>The zip folder "implementation.zip" contains the code to the Tyche algorithm with some examples.</p> <p>The Python script runs with Python 3.10.5. Before executing, you need to install the packages of requirements.txt (pip install -r requirements.txt)</p> <p>For executing static and generic Tyche run "run_tyche.py"</p> <p> </p> <p>Refering to Data_local_view.xlsx</p> <p>Learning element01[1] means probability for first learning element after the lecture for Learning Goal.</p> <p>Coding for the numbers in brackets is the following:</p> <table> <tbody> <tr> <td>1</td> <td>Learning goal</td> </tr> <tr> <td>2</td> <td>Brief overview</td> </tr> <tr> <td>3</td> <td>Manuscript</td> </tr> <tr> <td>4</td> <td>Quiz</td> </tr> <tr> <td>5</td> <td>Exercise</td> </tr> <tr> <td>6</td> <td>Summary</td> </tr> <tr> <td>7</td> <td>Auditory additional material</td> </tr> <tr> <td>8</td> <td>Textual additional material</td> </tr> <tr> <td>9</td> <td>Visual additional material</td> </tr> </tbody> </table> <p> </p> <p>The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).</p>
Supplementary initial and final MD configurations for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>Extended XYZ files for the initial and final configurations of the molecular dynamics simulations from the article 'General-purpose machine-learned potential for 16 elemental metals and their alloys' (https://arxiv.org/abs/2311.04732).</p> <p><span>The Supplementary Data is contained in the folder named:<br>1) Polycrystalline-MoTaVW: <span> </span>The plasticity MD simulations in multi-principal element alloys.</span></p> <p><span>2) MoTaVW-radiation: The primary radiation damage MD simulations in multi-principal element alloys.</span></p> <p><span>3) Goldene: Comparisons between UNEP-v1 and EAM models in MD simulations.</span></p> <p><span>4) NiAlMo: Comparisons between UNEP-v1 and EAM models in MCMD simulations.<br>5) AlCrCuNiV: Comparisons between UNEP-v1 and EAM models in MCMD simulations.</span></p>
Source Data for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>***Source Data***<br>This folder contains multiple .txt files that provide the source data for the figures and tables presented in the paper: "General-purpose machine-learned potential for 16 elemental metals and their alloys."</p> <p>The source data are organized in the following folders and files:</p> <p>1) Fig2<br>2) Fig3<br>3) Fig4<br>4) Fig5<br>5) Fig6<br>6) FigS1<br>7) FigS2<br>8) FigS3<br>9) FigS4-6-pure<br>10) FigS7-9-binary<br>11) FigS10-12-ternany<br>12) FigS13-15-quaternary<br>13) FigS16-17-quinary<br>14) FigS18-20<br>15) FigS21<br>16) FigS22<br>17) FigS23<br>18) FigS26<br>19) Table1-Element-atoms-GPU-Speed.txt<br>20) Table-S1-DFT-EAM-UNEP-Elastic.txt<br>21) Table-S2-DFT-EAM-UNEP-Mono-vacanc.txt<br>22) Table-s3-Surface-100-110-111-DFT-EAM-UNEP.txt<br>23) Table-S4-Melting-EAM-UNEP-Exp.txt</p>
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
<p>This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"<br> It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.</p>
Learning Brushing Using Game Elements in Mobile Phones Apps
ClinicalTrials.gov study NCT03935009. IPD Sharing: YES. Countries: 1. Publications: 12.
Implementation of a learning healthcare system for Sickle Cell disease: List of smart data elements contained in the Epic Smartform and their SmartData types
Open the record for dataset details and reuse information.
Evidence of cognitive spezialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees
<p>Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help responding flexibly in scenarios with different resource availability. Individual l<span>earning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. D</span>etermining if learning abilities correlate between tasks of different complexity or between sensory modalities has a high interest for research on brain modularity and task-dependent specialisation of neural circuits. <span>The honeybee <i>Apis mellifera</i> constitutes an attractive model to address this question due to its capacity to successfully learn a large range of tasks in various sensory domains.</span> <span>Here </span><span>we studied </span>whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relations between objects) or an elemental olfactory task (a discrimination between two odorants)<span>. </span>We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialisation within the hive, which may contribute to ecological social success.</p>
Would you detour with me? Association between functional breed selection and social learning in dogs sheds light on elements of dog-human cooperation
<p>Raw dataset for the research article.</p> <p>Dog Owner Consent form</p>
Instrumentation Neutron Activation Analysis & Proton Induced X-RAY Emission techniques supported with Machine learning analysis for rare earth/macro/micro elements correlation from O. Sativa Rice varieties in Senegal River valley
<p>data sheet INAA;results</p>
Evidence of cognitive spezialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees
Open the record for dataset details and reuse information.
Analysis of the Drosophila and Human DPR Elements Reveals a Distinct Human Variant Whose Specificity Can Be Enhanced by Machine Learning
GEO Series GSE225570. Drosophila melanogaster. 2 samples. Type: Expression profiling by high throughput sequencing.
Learning the cis sequence elements that determine AP-1 monomer specificity
GEO Series GSE111856. Mus musculus. 51 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other.
Learning the cis sequence elements that determine AP-1 monomer specificity (ChIP-seq data sets)
GEO Series GSE111854. Mus musculus. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.