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174 results for “online data”
Superadditive Communications with the Green Machine: Online Data Repository
<p>This online repository contains selected datasets and scripts for data processes in the paper "Superadditive Communications with the Green Machine: A Practical Demonstration of Nonlocality without Entanglement". arXiv.2310.05889</p>
Using a Hybrid Kano-Importance Questionnaire in the Acquisition of Data Related to Students' Expectations from Online Educational Platforms
<p>This dataset contains the data collected for the assessment of the quality attributes of a new online educational platform. The questionnaire used for data collection the Kano methodology and was designed as a hybrid Kano-importance questionnaire. The purpose of this data collection consists of the analysis of the students’ expectations regarding the features proposed for a new online educational platform. This analysis facilitates the identification of student needs during times of COVID-19 pandemic and post-pandemic times, while a transition to an online educational system was used throughout the world. </p>
WP2 DESIRA_Online survey_Data
<p>The online survey complements the key-informants interviews and participatory workshop (or focus group discussions) organized in the living labs (LL) to provide a thorough and holistic socio-economic impact assessment to co-create knowledge on possible impact and shared transition pathways toward digitalization. The survey was designed jointly by UNIPI and KIT-ITAS and addresses all stakeholders involved in the LL.</p>
Data for: Image-based evaluation of beers at an online Pint of Science festival using Projective Mapping, Check-All-That-Apply and Acceptability
<p>Data obtained from n=67 untrained attendants at an outreach Pint of Science festival, online because of the COVID-19 pandemic but usually held at bars. The participants used images of brand logos to evaluate eight beers among the most commonly consumed in Spain. Three sensory analysis techniques were used: Projective Mapping, Acceptability and Check-All-That-Apply (CATA).</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach
<p>In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example
<p>Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript. </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews (data repository)
<p>These are the data we generated in our evaluation of the graphical user interface.</p> <p>Please see our publication on Wellcome Open Research for information about the evaluations.</p>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
Educational data collected from parents - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The responses of the 784 parents were collected through the questionnaire available at: <a href="https://forms.gle/Km8WE5QamrYYgXJi7" target="_new" rel="noopener"><strong>https://forms.gle/Km8WE5QamrYYgXJi7</strong></a></p> <p>It was designed with various types of responses, including binomial (yes/no), polynomial (multiple options), and open-ended responses, to capture a comprehensive range of data. This combined approach allows for both quantitative analysis of fixed-response questions and qualitative insights from open-ended questions. Patterns, correlations, and differences between various demographic groups and their experiences and attitudes toward online education can be identified.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
An Online Integrated Development Environment for Automated Programming Assessment Systems Open Source Data
<p>This dataset accompanies the paper <em>"An Online Integrated Development Environment for Automated Programming Assessment Systems"</em>. It contains data from the usability evaluation of a feature-rich online IDE designed for integration into Automated Programming Assessment Systems (APASs). The dataset includes survey responses from 27 participants based on the Technology Acceptance Model (TAM), performance metrics such as memory usage, and qualitative user feedback. The study highlights challenges in integrating online IDEs with APASs, such as memory efficiency, load balancing, and user experience. The dataset supports further research in developing scalable, effective, and user-friendly programming education tools.<br><br>Here you can find the code changes required for the online IDE in Artemis: <a href="https://github.com/ls1intum/Artemis/pull/6706/files" target="_blank" rel="noopener">Github</a></p>
Saving carbon emissions through online learning for overseas students [Data]
<p>We estimated the savings in CO<sub>2</sub> emissions by a cohort of master’s students who studied fully online from their home countries, rather than traveling to the UK and living there while attending university.</p> <p>Data come from International Civil Aviation Organization (ICAO) carbon emissions calculator <a href="https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx">https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx</a>; and CO₂ and Greenhouse Gas Emissions <a href="https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions">https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions</a></p>
Dataset literatur review online business AND data protection
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "online business "dan "data protection"</p>
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
<p>The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees.</p> <p>We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (<a href="https://github.com/evolbioinfo/bdei">PyBDEI</a>) using a combination of numerical analysis methods for efficient equation resolution. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in <a href="http://www.beast2.org/">BEAST2</a> (mtbd package) and a deep learning estimator <a href="https://github.com/evolbioinfo/phylodeep">PhyloDeep</a>: the parameter values estimated by the 3 tools.<br><br>The PyBDEI and the theoretical findings behind it are described in A Zhukova, F Hecht, Y Maday, and O Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023. This dataset contains the online Appendix (Fig S1-S3 and Table S1).</p>
Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"
<p>Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform".</p> <p>The R code containsboth code for simulation and code for estimation.</p>
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques - Dataset
<p>This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "<strong>Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques</strong>"</p>
Supplementary data for "WebQUAST: online evaluation of genome assemblies"
<p>Supplementary data for A. Mikheenko, V. Saveliev, P. Hirsch, A. Gurevich. WebQUAST: online evaluation of genome assemblies.</p> <p><br> Reference genomes of two <em>Escherichia coli</em> K-12 substrains (MG1655 and W3110) and genome annotation of MG1655.</p> <p><em>De novo</em> assemblies of the <em>Escherichia coli</em> K-12 MG1655 short-read Illumina dataset (<a href="https://trace.ncbi.nlm.nih.gov/Traces/index.html?view=run_browser&acc=ERR008613&display=metadata">ERR008613</a>) with ABySS, MEGAHIT, SPAdes, and Velvet.</p> <p>WebQUAST reports in three evaluation modes: <br> * Use Case 1: reference-free evaluation (<em>sample_data_no_ref</em>)<br> * Use Case 2: reference-based evaluation (<em>sample_data_true_ref</em>) <br> * Use Case 3: evaluation based on a close reference (<em>sample_data_close_ref</em>)</p> <p>The snapshot (<a href="https://github.com/ablab/quast/commit/2bd50600bcf63ee826965c10f6ca4cdc2aa046e5">commit 2bd5060</a>) of the QUAST command-line version used by WebQUAST for generating the reports.</p>
Online Data: Evolution of dusty quiescent galaxies over the last six billion years from the hCOSMOS survey
<p>The dataset contains the physical and structural parameters of 545 dusty quiescent galaxies presented in Donevski et al. (2023, A&A accepted, arXiv:2304.05842). The galaxies are observed at intermediate redshifts (0.1 < z < 0.6) as part of the hCOSMOS spectroscopic survey. The dataset contains the physical parameters (redshift, D4000, gas-phase metallicities) estimated from optical spectra, as well as SED-derived specific dust masses and stellar masses. </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.