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6,452 results for “Application”
Dataset for "Quantitative evaluation of probabilistic hazard mapping with polynomial chaos quadrature and its practical application"
<p>Dataset for the manuscript entitled "Quantitative evaluation of probabilistic hazard mapping with polynomial chaos quadrature and its practical application" by T. Tanabe, K. Tsunematsu, and K. Nishimura submitted to Journal of Geophysical Research: Earth Surface.</p> <p> </p>
Figure 2 from: Takano A, Horiuchi Y, Fujimoto Y, Aoki K, Mitsuhashi H, Takahashi A (2019) Simple but long-lasting: A specimen imaging method applicable for small- and medium-sized herbaria. PhytoKeys 118: 1-14. https://doi.org/10.3897/phytokeys.118.29434
Figure 2 Birds-eye-view of imaging equipment. Numbers indicate size (cm).
Figure 3 from: Takano A, Horiuchi Y, Fujimoto Y, Aoki K, Mitsuhashi H, Takahashi A (2019) Simple but long-lasting: A specimen imaging method applicable for small- and medium-sized herbaria. PhytoKeys 118: 1-14. https://doi.org/10.3897/phytokeys.118.29434
Figure 3 Employees working on the imaging system.
Data set for training a ML model to predict duration of MPI application phases (HPC system) - with previous phase info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 10 different data sets corresponding to different HPC applications.</p> <p>These data sets contain information regarding the previous MPI call with same ID and type.</p>
Data set for training a ML model to predict duration of MPI application phases (HPC system) - without previous phases info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 11 different data sets corresponding to different HPC applications.</p> <p>These data sets do not contain information regarding previous MPI calls</p>
The application performance of the ANN model in the ECS shelf
<p>In order to retrieve pH on the ECS shelf, the monthly T, S, DO, N, P and Si from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data were applied to the ANN model as the input variables. Monthly pH for the period 2000-2016 was obtained at the spatial resolution of the Changjiang Biology FVCOM output: 1-10 165 km in the horizontal, 10 depth levels in the vertical, and 12 months. We compared retrieved pH with the corresponding observations at some sites repeated sampling for 3 to 4 years in order to evaluate the application performance of the ANN model. These sites were A1-5 (123.0140°E, 32.2145°N), A1-6 (123.2750°E, 32.2679°N), A6-7 (122.9880°E, 30.7050°N), A6-9 (123.4990°E, 30.5723°N), A7-5 (123.4990°E, 30.2523°N), and A8-5 (123.4930°E, 29.9940°N). Additionally, we further compared retrieved pH using the Changjiang Biology FVCOM Data with retrieved pH using measured T, S and DO, and in situ measured pH values.</p>
For wireless applications, design and analysis of patch antenna at 2.45 GHz
Open the record for dataset details and reuse information.
Supplementary material for the preprint "GLOSSA: a user-friendly R Shiny application for Bayesian machine learning analysis of marine species distribution"
<p>In this repository we present the code and data for the case studies in "GLOSSA: a user-friendly R Shiny application for Bayesian machine learning analysis of marine species distribution". The GLOSSA website can be accessed at https://jmestret.github.io/glossa/. Occurrence data for <em>Thunnus albacares</em> was obtained from the OBIS database (https://obis.org/taxon/127027), for <em>Caretta caretta</em> from GBIF (https://doi.org/10.15468/dl.es7562), and for <em>Siganus luridus </em>from the GreekMarineICAS geodataset (https://doi.org/10.25607/t2smha), created as part of the ALAS (Aliens in the Aegean – A Sea Under Siege) project.</p>
A large synthetic dataset for machine learning applications in power transmission grids
<p>With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. </p> <p>This dataset contains long time series for production, consumption, and line flows, amounting to 20 years of data with a time resolution of one hour, for several thousands of loads and several hundreds of generators of various types representing the ultra-high-voltage transmission grid of continental Europe. The synthetic time series have been statistically validated agains real-world data.</p> <h2>Data generation algorithm</h2> <p>The algorithm is described in a <a href="https://doi.org/10.1038/s41597-025-04479-x">Nature Scientific Data paper</a>. It relies on <a href="https://zenodo.org/records/2642175" target="_blank" rel="noopener">the PanTaGruEl model of the European transmission network</a> -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated data gathered from <a href="https://transparency.entsoe.eu/" target="_blank" rel="noopener">the ENTSO-E transparency platform</a>, such as power consumption aggregated at the national level.</p> <h2>Network</h2> <p>The network information is encoded in the file <a href="https://zenodo.org/records/13378476/files/europe_network.json">europe_network.json</a>. It is given in <a href="https://lanl-ansi.github.io/PowerModels.jl/stable/" target="_blank" rel="noopener">PowerModels format</a>, which it itself derived from <a href="https://matpower.org/" target="_blank" rel="noopener">MatPower</a> and compatible with <a href="https://www.pandapower.org/" target="_blank" rel="noopener">PandaPower</a>. The network features 7822 power lines and 553 transformers connecting 4097 buses, to which are attached 815 generators of various types.</p> <h2>Time series</h2> <p>The time series forming the core of this dataset are given in CSV format. Each CSV file is a table with 8736 rows, one for each hourly time step of a 364-day year. All years are truncated to exactly 52 weeks of 7 days, and start on a Monday (the load profiles are typically different during weekdays and weekends). The number of columns depends on the type of table: there are 4097 columns in load files, 815 for generators, and 8375 for lines (including transformers). Each column is described by a header corresponding to the element identifier in the network file. All values are given in per-unit, both in the model file and in the tables, i.e. they are multiples of a base unit taken to be 100 MW.</p> <p>There are 20 tables of each type, labeled with a reference year (2016 to 2020) and an index (1 to 4), zipped into archive files arranged by year. This amount to a total of 20 years of synthetic data. When using loads, generators, and lines profiles together, it is important to use the same label: for instance, the files <em>loads_2020_1.csv</em>, <em>gens_2020_1.csv</em>, and <em>lines_2020_1.csv</em> represent a same year of the dataset, whereas <em>gens_2020_2.csv</em> is unrelated (it actually shares some features, such as nuclear profiles, but it is based on a dispatch with distinct loads).</p> <h2>Usage</h2> <p>The time series can be used without a reference to the network file, simply using all or a selection of columns of the CSV files, depending on the needs. We show below how to select series from a particular country, or how to aggregate hourly time steps into days or weeks. These examples use Python and the data analyis library <em>pandas</em>, but other frameworks can be used as well (Matlab, Julia). Since all the yearly time series are periodic, it is always possible to define a coherent time window modulo the length of the series.</p> <h3>Selecting a particular country</h3> <p>This example illustrates how to select generation data for Switzerland in Python. This can be done without parsing the network file, but using instead <a href="https://zenodo.org/records/13378476/files/gens_by_country.csv">gens_by_country.csv</a>, which contains a list of all generators for any country in the network. We start by importing the <em>pandas</em> library, and read the column of the file corresponding to Switzerland (country code CH):</p> <pre><code>import pandas as pd CH_gens = pd.read_csv('gens_by_country.csv', usecols=['CH'], dtype=str)</code></pre> <p>The object created in this way is Dataframe with some null values (not all countries have the same number of generators). It can be turned into a list with:</p> <pre><code>CH_gens_list = CH_gens.dropna().squeeze().to_list()</code></pre> <p>Finally, we can import all the time series of Swiss generators from a given data table with</p> <pre><code>pd.read_csv('gens_2016_1.csv', usecols=CH_gens_list)</code></pre> <p>The same procedure can be applied to loads using the list contained in the file <a href="https://zenodo.org/records/13378476/files/loads_by_country.csv">loads_by_country.csv</a>.</p> <h3>Averaging over time</h3> <p>This second example shows how to change the time resolution of the series. Suppose that we are interested in all the loads from a given table, which are given by default with a one-hour resolution:</p> <pre><code>hourly_loads = pd.read_csv('loads_2018_3.csv')</code></pre> <p>To get a daily average of the loads, we can use: </p> <pre><code>daily_loads = hourly_loads.groupby([t // 24 for t in range(24 * 364)]).mean()</code></pre> <p>This results in series of length 364. To average further over entire weeks and get series of length 52, we use: </p> <pre><code>weekly_loads = hourly_loads.groupby([t // (24 * 7) for t in range(24 * 364)]).mean()</code></pre> <h2>Source code</h2> <p>The code used to generate the dataset is freely available at <a href="https://github.com/GeeeHesso/PowerData" target="_blank" rel="noopener">https://github.com/GeeeHesso/PowerData</a>. It consists in two packages and several documentation notebooks. The first package, written in Python, provides functions to handle the data and to generate synthetic series based on historical data. The second package, written in Julia, is used to perform the optimal power flow. The documentation in the form of Jupyter notebooks contains numerous examples on how to use both packages. The entire workflow used to create this dataset is also provided, starting from raw ENTSO-E data files and ending with the synthetic dataset given in the repository.</p> <h2>Funding</h2> <p>This work was supported by the <a href="https://www.cydcampus.admin.ch">Cyber-Defence Campus of armasuisse</a> and by an internal research grant of the Engineering and Architecture domain of <a href="https://www.hes-so.ch">HES-SO</a>.</p>
Demonstration of Digital Twin-Enabled Collision Mitigation for Path Constrained Applications
<p>This video presents a depiction of each robot and their surrounding environment in <em>RViz</em>, along with a video recording of both robots. It also features a monitor of the output from the <em>Collision Zones FMU</em>.</p>
Data from: The effect of mood state on visual search times for detecting a target in noise: an application of smartphone technology
The study of visual perception has largely been completed without regard to the influence that an individual's emotional status may have on their performance in visual tasks. However, there is a growing body of evidence to suggest that mood may affect not only creative abilities and interpersonal skills but also the capacity to perform low-level cognitive tasks. Here, we sought to determine whether rudimentary visual search processes are similarly affected by emotion. Specifically, we examined whether an individual's perceived happiness level affects their ability to detect a target in noise. To do so, we employed pop-out and serial visual search paradigms, implemented using a novel smartphone application that allowed search times and self-rated levels of happiness to be recorded throughout each twenty-four-hour period for two weeks. This experience sampling protocol circumvented the need to alter mood artificially with laboratory-based induction methods. Using our smartphone application, we were able to replicate the classic visual search findings, whereby pop-out search times remained largely unaffected by the number of distractors whereas serial search times increased with increasing number of distractors. While pop-out search times were unaffected by happiness level, serial search times with the maximum numbers of distractors (n = 30) were significantly faster for high happiness levels than low happiness levels (p = 0.02). Our results demonstrate the utility of smartphone applications in assessing ecologically valid measures of human visual performance. We discuss the significance of our findings for the assessment of basic visual functions using search time measures, and for our ability to search effectively for targets in real world settings.
Artifact for paper: "SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications"
<p>Artifact for IEEE S&P 2022 paper: "SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications"</p>
Characterization and impact of fiber size variability on the mechanical properties of fiber networks with an application to paper materials
<p>This repository contains data to</p> <p>Characterization and impact of fiber size variability on the mechanical properties of fiber networks with an application to paper materials</p> <p>In prep. 2021.</p> <p> </p> <p>There are a few key files to note:</p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/mainArticleE.m?versionId=e434d0da-db30-4588-a807-de72c4187468">mainArticleE.m </a><br> Generates a number of plots from the manuscript, and also contains calls to the functions that can be used to study how the copula formulation can be used and worked with.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/calculationSequenceForArticle.m?versionId=be372e6e-6eda-4ba5-b78b-2c8966de45f0">calculationSequenceForArticle.m </a><br> Shows the steps performed in the fitting and sampling that are presented in the manuscript.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/data.zip">data.zip </a><br> Contains all of the data. Raw machine outputs, condensed outputs (several runs collected into a single file), the ESI simulations and the result files used to generate the plots. The full solution files are not included as they would have taken way too much space. However, the input files can be found in each repository.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/v1.0.zip?versionId=8134396c-687b-4c31-a220-d27676f391cf">v1.0.zip </a><br> Output from <a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/calculationSequenceForArticle.m?versionId=be372e6e-6eda-4ba5-b78b-2c8966de45f0">calculationSequenceForArticle.m </a>just before submission.</li> </ul> <p> </p> <p>The FEM files are not included (MyPacking.exe - an internal tool I do not own the rights to, and the linked ANSYS executable which I likewise do not own the rights to).</p> <p>Also absent is the "pulpDistr" package (will be a seperate ZENODO submission)-</p> <p> </p> <p> </p> <p>Should something not work, please do get in touch.</p> <p> </p> <p>/A. Brandberg, 2021-08-10</p> <p> </p> <p> </p>
ReportFlow: an application for EEG visualization and reporting using cloud platform
<p>Background</p> <p>The cloud is a promising resource for data sharing and computing. It can optimize several legacy processes involving different units of a company or more companies. Recently, cloud technology applications are spreading out in the healthcare setting as well, allowing to cut down costs for physical infrastructures and staff movements. In a public environment the main challenge is to guarantee the patients’ data protection. We describe a cloud-based system, named ReportFlow, developed with the aim to improve the process of reporting and delivering electroencephalograms.</p> <p>Methods</p> <p>We illustrate the functioning of this application through a use-case scenario occurring in an Italian hospital, and describe the corresponding key encryption and key management used for data security guarantee. We used the X<sup>2</sup> test or the unpaired Student <em>t</em> test to perform pre-post comparisons of some indexes, in order to evaluate significant changes after the application of ReportFlow.</p> <p>Results</p> <p>The results obtained through the use of ReportFlow show a reduction of the time for exam reporting (<em>t</em> =&thinsp;19.94; <em>p</em> < 0.001) and for its delivering (<em>t</em> =&thinsp;14.95; <em>p</em> < 0.001), as well as an increase of the number of neurophysiologic examinations performed (about 20%), guaranteeing data integrity and security. Moreover, 68% of exam reports were delivered completely digitally.</p> <p>Conclusions</p> <p>The application resulted to be an optimal solution to optimize the legacy process adopted in this scenario. The comparative pre-post analysis showed promising preliminary results of performance. Future directions will be the creation and release of certificates automatically.</p>
Figure 1 from: Sutton BD, Steck GJ, Norrbom AL, Rodriguez EJ, Srivastava P, Alvarado NN, Colque F, Landa EY, Sánchez JJL, Quisberth E, Peñaranda EA, Clavijo PAR, Alvarez-Baca JK, Zapata TG, Ponce P (2015) Nuclear ribosomal internal transcribed spacer 1 (ITS1) variation in the Anastrepha fraterculus cryptic species complex (Diptera, Tephritidae) of the Andean region. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 175-191. https://doi.org/10.3897/zookeys.540.6147
Figure 1 - Anastrepha fraterculus collection localities.
Figure 4 from: Sutton BD, Steck GJ, Norrbom AL, Rodriguez EJ, Srivastava P, Alvarado NN, Colque F, Landa EY, Sánchez JJL, Quisberth E, Peñaranda EA, Clavijo PAR, Alvarez-Baca JK, Zapata TG, Ponce P (2015) Nuclear ribosomal internal transcribed spacer 1 (ITS1) variation in the Anastrepha fraterculus cryptic species complex (Diptera, Tephritidae) of the Andean region. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 175-191. https://doi.org/10.3897/zookeys.540.6147
Figure 4 - Geographical distribution of Andean Anastrepha fraterculus ITS1 sequence types.
Figure 6 from: De Meyer M, Delatte H, Ekesi S, Jordaens K, Kalinová B, Manrakhan A, Mwatawala M, Steck G, Van Cann J, Vaníčková L, Břízová R, Virgilio M (2015) An integrative approach to unravel the Ceratitis FAR (Diptera, Tephritidae) cryptic species complex: a review. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 405-427. https://doi.org/10.3897/zookeys.540.10046
Figure 6 - Geographical distribution of Ceratitis anonae.
Figure 4 from: De Meyer M, Delatte H, Ekesi S, Jordaens K, Kalinová B, Manrakhan A, Mwatawala M, Steck G, Van Cann J, Vaníčková L, Břízová R, Virgilio M (2015) An integrative approach to unravel the Ceratitis FAR (Diptera, Tephritidae) cryptic species complex: a review. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 405-427. https://doi.org/10.3897/zookeys.540.10046
Figure 4 - Mid leg of male Ceratitis fasciventris, anterior view (photo I.M. White, NHM).
Figure 3 from: De Meyer M, Delatte H, Ekesi S, Jordaens K, Kalinová B, Manrakhan A, Mwatawala M, Steck G, Van Cann J, Vaníčková L, Břízová R, Virgilio M (2015) An integrative approach to unravel the Ceratitis FAR (Diptera, Tephritidae) cryptic species complex: a review. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 405-427. https://doi.org/10.3897/zookeys.540.10046
Figure 3 - Mid leg of male Ceratitis rosa, anterior view (photo I.M. White, NHM).
Figure 4 from: Mwatawala M, Virgilio M, Joseph J, De Meyer M (2015) Niche partitioning among two Ceratitis rosa morphotypes and other Ceratitis pest species (Diptera, Tephritidae) along an altitudinal transect in Central Tanzania. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 429-442. https://doi.org/10.3897/zookeys.540.6016
Figure 4 - Catches of Ceratitis rosa morphotypes along the transect (EGO lure).
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.