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566 results for “analytics”
Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> <a href="https://doi.org/10.1007/s10639-022-11093-6">https://doi.org/10.1007/s10639-022-11093-6</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2307.08582">https://arxiv.org/abs/2307.08582</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <p><strong>Attached content</strong></p> <p>The files included in the ZIP archive are:</p> <ul> <li>`All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search.</li> <li>`Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator.</li> <li>`Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator.</li> <li>`Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review.</li> <li>`Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers.</li> <li>`Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.</li> </ul>
DateLife: leveraging databases and analytical tools to reveal the dated Tree of Life
<p>Achieving a high-quality reconstruction of a phylogenetic tree with branch lengths proportional to absolute time (chronogram) is a difficult and time-consuming task. But the increased availability of fossil and molecular data, and time-efficient analytical techniques has resulted in many recent publications of large chronograms for a large number and wide diversity of organisms. Knowledge of the evolutionary time frame of organisms is key for research in the natural sciences. It also represent valuable information for education, science communication, and policy decisions. When chronograms are shared in public, open databases, this wealth of expertly-curated and peer-reviewed data on evolutionary timeframe is exposed in a programatic and reusable way, as intensive and localized efforts have improved data sharing practices, as well as incentivizited open science in biology. Here we present DateLife, a service implemented as an R package and an R Shiny website application available at www.datelife.org, that provides functionalities for efficient and easy finding, summary, reuse, and reanalysis of expert, peer-reviewed, public data on time frame of evolution. The main DateLife workflow constructs a chronogram for any given combination of taxon names by searching a local chronogram database constructed and curated from the Open Tree of Life Phylesystem phylogenetic database, which incorporates phylogenetic data from the TreeBASE database as well. We implement and test methods for summarizing time data from multiple source chronograms using supertree and congruification algorithms, and using age data extracted from source chronograms as secondary calibration points to add branch lengths proportional to absolute time to a tree topology. DateLife will be useful to increase awareness of the existing variation in alternative hypothesis of evolutionary time for the same organisms, and can foster exploration of the effect of alternative evolutionary timing hypotheses on the results of downstream analyses, providing a framework for a more informed interpretation of evolutionary results.</p>
Supplementary data for Plutniak, S. 2022. "What makes the identity of a scientific method? A history of the 'Structural and analytical typology' in the growth of evolutionary and digital archaeology in southwestern Europe (1950s–2000s)", Journal of Paleolithic Archaeology, vol. 5, 10.
<p>Supplementary data for Plutniak, S. 2022. “What makes the Identity of a Scientific Method? A History of the ‘Structural and analytical typology’ in the Growth of Evolutionary and Digital Archaeology in Southwestern Europe (1950s–2000s)”, <em>Journal of Paleolithic Archaeology</em>. vol 5, 10. DOI: <a href="https://doi.org/10.1007/s41982-022-00119-7">10.1007/s41982-022-00119-7</a>.</p> <p> </p> <p> </p> <p> </p>
Supplemental materials for "Benchmarking magnetized three-wave coupling for laser backscattering: Analytic solutions and kinetic simulations"
<p>Place the unzipped Data and Programs folders in the same directory. The contents of these folders are as follows:</p> <ul> <li>Data<br> Post processed data underlying each figure in the paper. The data files are .txt files with self-contained explanations. The files are organized in subfolders according to their purposes.<br> </li> <li>Programs <ul> <li>./PlotFigures<br> Contains python scripts for reading and plotting Data</li> <li>input.deck<br> Example input for EPOCH PIC code that generates raw data </li> <li>setup_batch.csh<br> Linux/Unix shell script for setting up batch simulations</li> <li>submit_batch<br> Slurm script for submitting jobs on computing clusters</li> </ul> </li> </ul>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Anonymized Responses to the Student Expectation of Learning Analytics Questionnaire (SELAQ) in 2022 and 2023
<p>Contains responses of 566 bachelor students to the Student Expectation of Learning Analytics Questionnaire (SELAQ) conducted at Bochum University of Applied Sciences in Germany in fall 2022 and summer 2023. The Questionnaire consists of 12 statements (in the dataset: enumerated from 1-12) which are evaluated by students regarding both their desire and their expectation (in the dataset: D and E). This results in 24 items (in the dataset: 1D, 1E, 2D, 2E, ..., 12D, 12E). Each item is evaluated by the students on a Likert-scale from 1 (strongly disagree) to 7 (strongly agree). Additionally, the students' study track (in the dataset: Study_Track) and their current semester (in the dataset: Semester) were also recorded. The questionnaires were carried out in paper form at the beginning of lectures. Statements 1, 2, 3, 5 and 6 deal with ethical and privacy expectations, while statements 4, 7, 8, 9, 10, 11 and 12 deal with service feature expectations regarding learning analytics. For a text version of the items, please refer to the paper related to this dataset (DOI 10.1145/3636555.3636923), the additional descriptions below or the attached description file.</p>
Data from: Meta-analytical evidence for frequency-dependent selection across the tree of life
<p>Explaining the maintenance of genetic variation in fitness related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency-dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta-analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non-significant in mesocosms and field-based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long-standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics.</p>
Dataset: Verisk Analytics, Inc. (VRSK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
A network of countries collaborating on learning analytics research
<p>A network of countries collaborating on learning analytics research. It includes original research articles published in Scopus Database up to 16 January 2018. The file is an un-directed Graphml network of 76 countries. It can be opened in Social Network Analysis applications such as Gephi, or Igraph R package.</p>
Figs_tables_for_Tang&Sun2018_analytical_GreenFun
<p>We shear the data and code of Figs. 3-6 used in the manuscript "Closed-form expressions of seismic deformation in a homogeneous Maxwell Earth model". The Figs. 1-2 are shared here as well. Figs. 3-4 are the displacement vector Green's functions due to four point dislocations with a depth of 32 km. Figs. 5-6 are the displacement vector due to a left-lateral finite strike-slip fault.</p> <p>For the data *.txt for Figs. 3-4, the columns denote the angular distance (theta), the vertical component, the south-north component, the east-west component, respectively.</p> <p>For the data for Figs. 5-6, the columns of *.xy file denotes <em>the distance along longitude, the distance along latitude, direction (rotation from east to north), horizontal displacement, north displacement, east displacement, vertical displacement</em>. The columns of vector2.txt for Fig. 5 denote the <em>distance along longitude, the distance along latitude, the unit direction vector along the east, the unit direction vector along the north.</em></p>
A National Forum on Web Privacy and Web Analytics — Participant Survey Instrument
<p>This survey instrument was administered to participants of the <em>National Forum on Web Privacy and Web Analytics</em>. Results informed the Forum event and Forum deliverables.</p> <p>The <em>National Forum on Web Privacy and Web Analytics </em>was held September 2018 in Bozeman, Montana, where 40 librarians, technologists, and privacy researchers collaborated in producing a practical roadmap for enhancing our analytics practice in support of privacy.</p> <p>More information is available on our project site: <a href="https://osf.io/gnfpu/">https://osf.io/gnfpu/</a>. </p> <p>This project is made possible in part by the Institute of Museum and Library Services, through grant <a href="https://www.imls.gov/grants/awarded/lg-73-18-0100-18"># LG-73-18-0100-18</a>.</p>
pieproject.eu website analytics 1-11-2006_30-6-2019
<p>CSV analytics form Google Analytics of the COMMONFARE (PIE News) project website for the period 1-10-2016 _ 30-6-2019</p>
Analytical Protocols for Quantitative Evaluation of [4Fe-4S]-maquette Spectra
<p>A step-by-step tutorial is presented for the quantitative analysis of UV-vis spectra of <em>'as isolated'</em> [4Fe-4S]-maquettes that contain diamagnetic, [4Fe-4S]<sup>2+</sup> clusters, as chromophores. The UV-vis model is justified by the schematic molecular orbital diagram of Fe-S(sulfide) and Fe-S(thiolate) bonding. The spectral analysis is based on integrated peak intensities, which is superior to using absorption maxima (<span class="math-tex">\(\lambda _{max}\)</span>), when the chemical composition of the chromophores are not identical, but similar. Three spectra (two model cluster complexes and a metalloprotein active site) provide the reference for converting spectral intensities to concentrations.</p> <p>The UV-vis modeling is demonstrated for a prototypical <span class="math-tex">\([Fe_4S_4(CIACGAC)(\beta ME)]^{2-}\)</span> complex (FdM-7 maquette) and the <span class="math-tex">\([Fe_4S_4(\beta ME)_4]^{2-}\)</span> complex formed under identical conditions, but without the presence of a peptide.</p>
Multi-Source Distributed System Data for AI-powered Analytics
<p><strong>Abstract:</strong></p> <p>In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.<br> The major contributions have been materialized in the form of novel algorithms.<br> Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.<br> Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance. <br> Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.<br> Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.</p> <p><strong>General Information:</strong></p> <p>This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.</p> <p>You may find details of this dataset from the original paper:</p> <p><em>Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". </em></p> <p><strong>If you use the data, implementation, or any details of the paper, please cite!</strong></p> <p> </p> <p>BIBTEX:</p> <p>_________________________________________</p> <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> <p>___________________________</p> <p>The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The <em><strong>sequential_data</strong> </em>is generated via executing workload of sequential user requests. The <strong><em>concurrent_data </em></strong>is generated via executing workload of concurrent user requests.</p> <p>The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). <strong>In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.</strong></p> <p><strong><strong>Important:</strong> The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.</strong></p> <p>Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a></p>
Figure 5 in A standardised analytical framework for sampling fish communities: relevance to Turkish riverine ecosystems
Figure 5. Trend profiles in total richness (a–e) and nativeness (f) recorded over six years of TLM monitoring in four habitats at the six Icon Sites in the River Murray system (codes in Table 1). See also Table 5.
Figure 1 in A standardised analytical framework for sampling fish communities: relevance to Turkish riverine ecosystems
Figure 1. Map of the River Murray system in south-eastern Australia with indication of the six Icon Sites part of The Living Murray initiative (see Table 1).
Figure 7 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 7. Separated (a) and aggregated (b) comparison of the distribution areas of the beetle and its major host plants based on von Raab-Straube's (2014) study.
Figure 5 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 5. Seasonal activity of Lamprodila festiva as a function of Köppen-Geiger climate zones (Peel et al. 2007) based on the online observation records. The grey zones indicate the centre of seasonal flight activity (15 -15 percent intervals from the arithmetic mean of all observation records).
Figure 4 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 4. Different European propagation zones of Lamprodila festiva and the main directions of their escalation
Figure 3 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 3. Occurrence data, distribution, and the theoretical spreading of Lamprodila festiva in Europe based on the data of Table 1. Explanation: the hatched area represents the assumed distribution area in the given period.
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.