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917 results for “Theorie”
Literature review on the theories of collateral learning and cultural border-crossing
<p>This database was used to conduct a literature review of the theories of collateral learning and cultural boundary crossing (see CrossRef file). These theories relate to science learning in science education. The database of seminal articles on theories of science learning from a cultural perspective is also presented (see WOS and Scopus review archive). These papers are publicly accessible and can be used by interested readers, provided that the author and the database concerned are cited.</p>
Data from: No early warning signals for stochastic transitions: insights from large deviation theory
[No abstract entered]
Figure 2 from: Albratty M, Thangavel N, Chandrasekaran B, Meraya AM, Alhazmi HA, Muthumanickam S, Boomi P, Bhagavan NB, Saleh SF (2024) Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e118949
Figure 2 Benchmarking docking binding energy scores distribution: (a) AutoDock, (b) AutoDock Vina.
Data for "Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad84af" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citations is:</p> <p>[1] Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 225014</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Tabulated Data</li> <li>Misc</li> <li>Entanglement Entropy</li> <li>Appendix Data</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Tabulated Data" contains the data used in Table 1 and Table 2 in the paper).</p> <p>Each of these parent directories contain within them several sub-directories (child directories) corresponding to different produced data. The raw data can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>. The dictionary will have a parent key called "Energy", which in itself is yet another dictionary which will always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code>. The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p>Additionally, some child directories will include a <code>.npy</code> file, which holds the amplitudes of the variational state for the given simulation. These files should be loaded using numpy in python. For example:</p> <p><code>import numpy as np</code></p> <p><code>filePath = ...</code></p> <p><code>varState = np.load(filePath)</code></p> <p>This will load the amplitudes as an array into the <code>varState</code> variable.</p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></p> <p> </p>
Leveraging Human and Non-Human Actors to Advance Integrated Healthcare Delivery: Unpacking the Role of Actor-Network Theory, a Systematic Literature Review and Future Research Agenda
<p>This dataset includes the articles screened for inclusion/exclusion based on title, abstract and key words. It also includes the 197 articles screened for inlcusion/exclusion following a full-text screen. </p>
The Unified Geometric Wave Theory: Updated Summary and mathematical model and process
<p>The Unified Geometric Wave Theory (UGWT) proposes that the universe's fundamental interactions are interconnected through waves, vibrations, and frequencies. This theory integrates concepts from quantum mechanics, general relativity, and sacred geometry, aiming to bridge the gap between quantum phenomena and macroscopic gravitational effects. The UGWT is currently in the empirical validation stage and seeks peer review for further refinement.</p>
PhaseSplit-FH3: A dataset of ternary separation per Flory-Huggins theory.
<p>This dataset holds 1036 ternary phase diagrams and how points on the diagram phase separate if they do. The data is provided as a serialized object using the `pickle' Python module. The data was compiled using Python version 3.8. </p> <p><strong>References<br></strong>The specific applications and analyses of the data are described in <br>1. Dhamankar, S.; Jiang, S.; Webb, M.A. "Accelerating Multicomponent Phase-Coexistence Calculations with Physics-informed Neural Networks"</p> <p><strong>Usage</strong><br>To access the data in the .pickle file, users can execute the following:</p> <blockquote> <p><br># LOAD SIMULATION DATA<br>DATA_DIR = "your/custom/dir/"</p> <p>filename = os.path.join(DATA_DIR, f"data_clean.pickle")<br>with open(filename, "rb") as handle:<br> (x, y_c, y_r, phase_idx, num_phase, max_phase) = pickle.load(handle)</p> </blockquote> <ul> <li>x: Input x = (χ_AB, χ_BC, χ_AC, v_A, v_B, v_C, φ_A, φ_B) ∈ ℝ^8.</li> <li>y_c: Output one-hot encoded classification vector y_c ∈ ℝ^3.</li> <li>y_r: Output equilibrium composition and abundance vector y_r = (φ_A^α, φ_B^α, φ_A^β, φ_B^β, φ_A^γ, φ_B^γ, w^α, w^β, w^γ) ∈ ℝ^9.</li> <li>phase_idx: A single integer indicating which unique phase system it belongs to.</li> <li>num_phase: A single integer indicates the number of equilibrium phases the input splits into.</li> <li>max_phase: A single integer indicates the maximum number of equilibrium phases the system splits into.</li> </ul> <p><strong>Help, Suggestions, Corrections?</strong><br>If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu</p> <p><strong>GitHub</strong><br>Additional data and code relevant for this study is additionally accessible at <a href="https://github.com/webbtheosim/gcgnn">ht</a><a href="https://github.com/webbtheosim/ml-ternary-phase">https://github.com/webbtheosim/ml-ternary-phase</a></p>
Dataset_submission_Prospecting_behaviours_of_immature_eagles_support_the_theory_of_informed_dispersal
<p>This dataset is the one used to produce all the figures and models presented in the submitted paper entitled : <span>Prospecting behaviours of immature eagles support the theory of informed dispersal </span></p>
The role of mind theory in patients affected by neurodegenerative disorders and impact on caregiver burden
<p><strong>Background: </strong>Theory of Mind (ToM) is defined as the ability to understand mental and emotional state. This ability is assessed also in neurodegenerative disease. Few studies have investigated the impact that social cognition of patients could have on caregiver burden. The aim of this study was to investigate a possible correlation in level of social cognition impairment between patients with different neurodegenerative disorders and their caregivers with possible impact on caregivers burden.</p> <p><strong>Methods: </strong>we enrolled 48 patients with dementia divided in different groups: Fronto-Temporal Dementia (FTD), Alzheimer Disease (AD), and Mild Cognitive Impairment (MCI) and also the three groups of their respective caregivers. All subjects were submitted to ToM tests, and the caregiver groups also to Caregiver Burden Inventory (CBI) to evaluate level of burden.</p> <p><strong>Results: </strong>Our results showed that ToM was more impaired in FTD patients and in their caregivers In addition, FTD group showed more impaired performances in tasks related to emotional skills.</p> <p><strong>Conclusions: </strong>We suggested that ToM impairment of patients are related to ToM impairment of caregivers with differences of scores in caregiver groups. The caregiver difficulties to understand, attribute and describe emotional and mental states of their relatives develop distress and inability in burden management and disorders relative to neurodegenerative disease.</p> <p> </p>
Acceptance Behavior Theories and Models in Software Engineering
<p><strong>Electronic supplement for a mapping study on acceptance behavior theories and models in SE</strong></p> <p>Please refer to the following paper when you cite/use this data:</p> <p>Jürgen Börstler, Nauman bin Ali, Kai Petersen, Emelie Engström (2024).<br>Acceptance behavior theories and models in software engineering — A mapping study.<br><em>Information and Software Technology</em>.<br>https://doi.org/10.1016/j.infsof.2024.107469</p> <p><strong>Overview of the electronic supplement</strong></p> <ol> <li>An overview-file (README.docx) comprising the following: <ol> <li>An overview of the supplements.</li> <li>A plain list of the theories and models of acceptance behavior used for constructing the search string.</li> <li>A plain list of the software engineering venues used for constructing the search string.</li> <li>A ready-to-use search string for Scopus (plain text).</li> <li>A plain list of the 47 included primary studies.</li> </ol> </li> <li>A separate Bibtex-file (p1-p47.bib) with all 47 included primary studies.</li> <li>A separate Excel-file (data extraction.xlsx) comprising the following sheets: <ol> <li>The data extracted for the 47 included primary studies.</li> <li>The 27 primary studies excluded during full-text reading and data extraction.</li> </ol> </li> </ol>
Finding Small Proofs for Description Logic Entailments: Theory and Practice - LPAR20 - Resources
<p><strong>experiments-LPAR-2020 </strong>contains all datasets and scripts used in the experiments described in the paper, and a README file with instructions on how to rerun the experiments.</p>
DATA SET OF 'SOCIAL AWARENESS OF THE CAUSES, SYMPTOMS OF BIPOLAR DISORDER AND THE REVIEW OF LAY THEORIES'
<p>It is the dataset of the article titled ''SOCIAL AWARENESS OF THE CAUSES, SYMPTOMS OF BIPOLAR DISORDER AND THE REVIEW OF LAY THEORIES'. </p>
The Effect of Education Given With Creative Drama Method Based on Leininger's Theory on the Cultural Sensitivity and Effectiveness Levels of Nursing Students
ClinicalTrials.gov study NCT06973369. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Kolcaba Comfort Theory Training on Dyspnea, Function, and Comfort in COPD Patients
ClinicalTrials.gov study NCT06588361. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Self-Determination Theory and Motivational Interviewing
ClinicalTrials.gov study NCT04611737. IPD Sharing: NO. Countries: 1. Publications: 0.
Theory Based Integrated Program on Medication Adherence Among Community Dwelling Schizophrenia
ClinicalTrials.gov study NCT05835583. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Novel Measures and Theory of Pediatric Antiretroviral Therapy Adherence in Uganda
ClinicalTrials.gov study NCT01140633. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Construct and Evaluate the Effectiveness of a Theory-based mHealth for Overweight and Obese Women During Pregnancy
ClinicalTrials.gov study NCT04553718. IPD Sharing: NO. Countries: 1. Publications: 0.
Weak Pulse at Yang and Wiry Pulse at Yin Theory
ClinicalTrials.gov study NCT07378228. IPD Sharing: NO. Countries: 1. Publications: 0.
Digital Technology Combined With Blood Marker Screening and Diagnosis for Community Populations Based on Classification Theory: an Integrated Study
ClinicalTrials.gov study NCT07286812. IPD Sharing: YES. Countries: 1. Publications: 0.
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.