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1,184 results for “conversion”
UVC Up-Conversion and Vis-NIR Luminescence Examined in SrO-CaO-MgO-SiO2 Glasses Doped with Pr
<p><span>This work was supported by the National Science Centre, Poland, under grant number DEC-2021/41/B/ST5/03792 entitled: Phosphors for UVC LEDs: Self-Disinfecting Surfaces.</span> </p>
Catalyst sites and active species in the early stages of MTO conversion over cobalt AlPO-18 followed by IR spectroscopy
<p>Supplementary material: Ex-situ DR-UV-visible spectroscopy, Ex-situ FT-IR Spectroscopy. In-situ FT-IR Spectroscopy, In continuo FT-IR Spectroscopy, Brønsted acidity of SAPO-18 </p>
Supported PdZn nanoparticles for selective CO2 conversion, through the grafting of a heterobimetallic complex on CeZrOx
<p>Supplementary material: IR, PXRD, N2 adsorption, EDS, TEM, XPS, EXAFS, testing data</p>
PdZn/ZrO2+SAPO-34 bifunctional catalyst for CO2 conversion: Further insights by spectroscopic characterization
<p>Supplementary material: atomic concentrations calculated from XPS, XPS spectra</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Data for "Temperature dependence of charge conversion during NV-center relaxometry in nanodiamond"
<p>In this Zenodo repository, the data as plotted in "Temperature dependence of charge conversion during NV-center relaxometry in nanodiamond" is uploaded. The file consists of folders named after the figures in the manuscript, where each folder contains csv files and a readme file in which additional information can be found. If a fit function is plotted in a figure, the fit data is also given in a csv file.</p>
On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past
<p>Replication Package for "On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past".</p>
Supplementary Data for "Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"
<p>This dataset contains quality assessment results for 26 vocabularies. The assessment was conducted using the <a href="https://skos-play.sparna.fr/skos-testing-tool/">qSKOS vocabulary quality assessment tool</a>.</p> <p>The 26 assessed vocabularies were converted from their original formats into the Simple Knowledge Organization System (SKOS) data model using the approach described in our paper titled <a href="https://doi.org/10.1007/978-3-031-62362-2_9">"Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"</a>, presented at the <a href="https://doi.org/10.1007/978-3-031-62362-2">24th International Conference on Web Engineering (ICWE 2024)</a>.</p> <p>The dataset contains a quality assessment for the following vocabularies:</p> <ol> <li>A Taxonomy of Evaluation Towards Standards</li> <li>Cross-Device Taxonomy</li> <li>What Makes a Data-driven Business Model? A Consolidated Taxonomy</li> <li>DDI Aggregation Method</li> <li>DDI Mode of Collection</li> <li>Building a New Taxonomy for Data Discretization Techniques</li> <li>Demopaedia</li> <li>Data Science Glossary</li> <li>A Taxonomy of Evaluation Approaches in Software Engineering</li> <li>Evaluation Thesaurus</li> <li>The Glossary of Human Computer Interaction</li> <li>Human-Factors Taxonomy</li> <li>A Taxonomy to Structure and Analyze Human–Robot Interaction</li> <li>A Taxonomy of Interaction for Instructional Multimedia</li> <li>A Taxonomy of Interrogation Methods</li> <li>Design Vocabulary for Human–IoT Systems Communication</li> <li>Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors</li> <li>Thesaurus Mass Communication</li> <li>Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey</li> <li>A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction</li> <li>A Human-Centered Taxonomy of Interaction Modalities and Devices</li> <li>A Taxonomy of Spatial Interaction Patterns and Techniques</li> <li>A Taxonomy of Social Errors in Human-Robot Interaction</li> <li>Taxonomy of Digital Research Activities in the Humanities</li> <li>Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions </li> <li>Cross-Device Interaction</li> </ol>
Data for Analysis for "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"
<p>This dataset includes, the data files for validating the statistical analysis from "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"</p> <p> </p> <p>The dataset is composed of 1500 files named following the pattern `Test-R-N-User-C-P.csv` where</p> <ul> <li>R is the n-th repetition. From 0 to 50</li> <li>N is the number of concurrent users. From 100 to 1000</li> <li>C is the treatment. chat for the framework version. chat-session for the legacy version.</li> <li>P is the problem number. 16 or 352.</li> </ul> <p>The data files corresponding to chat and problem 16 are those that in the paper are identified as Framework. The files por problem 352 are the collaborative version with students grouped.</p> <p>Each csv, is composed following the standard formate by Apache JMeter, and contains XX columns:</p> <ul> <li>timeStamp - UNIX timestamp of the request</li> <li>elapsed - Time taken to finish the request</li> <li>label - which step</li> <li>responseCode - HTTP response code</li> <li>responseMessage</li> <li>threadName</li> <li>dataType</li> <li>success - true|false</li> <li>failureMessage</li> <li>sentBytes</li> <li>grpThreads</li> <li>allThreads - Threads running</li> <li>URL - Endpoint URL</li> <li>Latency</li> <li>SampleCount</li> <li>ErrorCount - Cumulative amount of errors</li> <li>IdleTime </li> <li>Connect - Connection time</li> </ul> <p> </p>
Evolution and the quasistationary state of collective fast neutrino flavor conversion in three dimensions without axisymmetry
<p>This repository contains the simulation data used in the study titled `Evolution and the quasistationary state of collective fast neutrino flavor conversion in three dimensions without axisymmetry`. All the necessary data are provided in the hdf5 file. Please check the article and the README enclosed herewith for more details.</p>
Unveiling Energy Conversions of the Venus Atmosphere by the Bred Vectors
<p>This is the data for the paper: Unveiling Energy Conversions of the Venus Atmosphere by the Bred Vectors</p> <p><a href="../api/records/13790212/draft/files/BV-energy-equation.ipynb/content" target="_blank" rel="noopener noreferrer">BV-energy-equation.ipynb</a>: script for plotting</p> <p><span><a href="../api/records/13790212/draft/files/solar-position.csv/content" target="_blank" rel="noopener noreferrer">solar-position.csv</a></span>: solar positions</p> <p>control-run.tar.gz: control run data</p> <p>perturbed-run.tar.gz: perturbed run data</p>
Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using DAS
<p>This repository contains codes and data used to reproduce the figures in the paper <em>Vernet, C. et al, "Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using distributed acoustic sensing", 2025, (<a href="https://doi.org/10.1029/2024JB030507">https://doi.org/10.1029/2024JB030507</a>).</em></p>
QReCC - Question Rewriting in Conversational Context
<p>QReCC contains 14K conversations with 81K question-answer pairs and a collection of 54M passages.</p> <p>See the README for more information.</p> <p>Additional data used in the SCAI-QReCC 2021 challenge: <a href="https://doi.org/10.5281/zenodo.5749472">https://doi.org/10.5281/zenodo.5749472</a></p>
Standardization of Methodology of Light-to-Heat Conversion Efficiency Determination for Colloidal Nanoheaters
<p>Localized photothermal therapy (PTT) has been demonstrated to be a promising method of combating cancer, that additionally synergistically enhances other treatment modalities such as photodynamic therapy or chemotherapy. PTT exploits nanoparticles (called nanoheaters), that upon proper biofunctionalization may target cancerous tissues, and under light stimulation may convert the energy of photons to heat, leading to local overheating and treatment of cancerous cells. Despite extensive work, there is, however, no agreement on how to accurately and quantitatively compare light-to-heat conversion efficiency (ηQ) and rank the nanoheating performances of various groups of nanomaterials. This disagreement is highly problematic because the obtained ηQ values, measured with various methods, differ significantly for similar nanomaterials. In this work, we experimentally review existing optical setups, methods, and physical models used to evaluate ηQ. In order to draw a binding conclusion, we cross-check and critically evaluate the same Au@SiO2 sample in various experimental conditions. This critical study let us additionally compare and understand the influence of the other experimental factors, such as stirring, data recording and analysis, and assumptions on the effective mass of the system, in order to determine ηQ in a most straightforward and reproducible way. Our goal is therefore to contribute to the understanding, standardization, and reliable evaluation of ηQ measurements, aiming to accurately rank various nanoheater platforms.</p>
Tunable and state-preserving frequency conversion of single photons in hydrogen
<p>Dataset for Tyumenev <em>et al</em>., "Tunable and state-preserving frequency conversion of single photons in hydrogen".</p> <p>The files include all raw data and numerical simulation codes used for the figures displayed in the main text and the supplementary materials.</p>
Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity; Image data
<p>Lists of image sets included in <strong>"Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity"</strong></p> <p> </p> <p>Duplex-1 and Duplex-2 Images</p> <p>Amyloid Fibril TIRFM Images (SNCA-A53T ImagesTIRF Images)</p> <p>TEM Fibril Images</p> <p>DLS Images </p> <p>SMLM Images 1 & 2</p> <p> </p> <p>CLEM Images</p> <ul> <li>FIB SEM Images (videos)</li> <li>TEM Images </li> </ul> <p> </p> <p>Live-cell imaging</p> <ul> <li>Superoxide Images</li> <li>MitoTracker® Red Images</li> <li>Membrane Potential (TMRM) Images </li> <li>Ca 2+ Images</li> <li>NADH Autofluorescence Images</li> <li>Cell Death Images</li> <li>Amytracker Images</li> <li>Cardiolipin Images</li> <li>FRET Images</li> </ul>
2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage
<p>Raw data of published journal article "2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage", DOI: 10.1016/j.apmt.2021.101301</p>
US Hetero–Homo conversion test
<p># US Hetero–Homo conversion test</p> <p> </p> <p>## Paper information</p> <p>(under review)</p> <p>Deep Learning for Hetero–Homo Conversion in Channel-Domain for Phase Aberration Correction in Ultrasound Imaging</p> <p>Tatsuki Koike, Naoki Tomii, Yoshiki Watanabe, Takashi Azumaa, Shu Takagi</p> <p> </p> <p>## Required</p> <p>+ Matlab 2018b</p> <p> + Matlab Signal Processing Toolbox version 8.1</p> <p> + Matlab Image Processing Toolbox version >= 9.3</p> <p>+ Docker version 20.10.14</p> <p> </p> <p>## How to test</p> <p> </p> <p>### RF Data Cropping</p> <p>[Shell]</p> <p>> cd code/rfdata_cropping</p> <p>> matlab ./RFDataCropping</p> <p> </p> <p>### RF Data Conversion Using Deep Neural Network</p> <p>[Shell]</p> <p>> cd code/prediction</p> <p>> ./build.sh</p> <p>> ./run.sh</p> <p> </p> <p>### B-Mode Image Reconstruction</p> <p>[Shell]</p> <p>> cd code/analysis</p> <p>> matlab ./BModeReconstruction\(true\)</p> <p>\# boolean flag is true if image reconstruction is performed using rf data processed by DNN</p> <p> </p> <p>## Contents</p> <p> </p> <p>+ code</p> <p> + analysis : Matlab scripts for B-mode image reconstruction</p> <p> + item : Matlab matrices</p> <p> + prediction : python scripts for Hetero–Homo conversion test</p> <p> + rfdata_cropping : Matlab scripts for rf data cropping</p> <p>+ data</p> <p> + test</p> <p> + hetero : cropped rf data for Hetero–Homo conversion</p> <p>+ result</p> <p> + dnn_result : trained model</p> <p> + images : B-mode images of test data</p> <p> + sim_result : K-wave simlation results</p> <p> </p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</p>
CONVERSE 2022 Distributed Volcanism Scenario Exercise materials
<p>The CONVERSE research coordination network, aimed at organizing the US volcano science community towards better organization and collaboration, ran an eruption scenario exercise in February 2022. The exercise simulated an unrest and eruption event in a distributed volcanic field in the southwestern US (Arizona). During the activity, the organizers shared synthetic and re-purposed data and "official" information statements with the participants. Data types included seismic, geodetic (GPS / InSAR), gas, remote-sensing, and imagery. </p> <p>This dataset accompanies the publication "Lessons Learned from the 2022 CONVERSE Monogenetic Volcanism Response Scenario Exercise", by Yolanda Lin et al. </p>
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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.
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DANDI Archive for NWB datasets
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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.