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2,359
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2,359 results for “Online”
QGIS dataset of the Kunbaja online resource
<p>QGIS dataset of the Kunbaja online resource model built in QGIS as dataset in the .qgz file format</p>
Online repository for `sceptrehub` package
<p>This repository contains the example data used within the `sceptrehub` package. The `sceptrehub` package is a companion package to the R package `sceptre`, providing examples for the vignettes and man pages.</p>
Voter Autrement 2022 - The Online Experiment ("Un Autre Vote'')
<p> In April 2022, we have run a voting experiment during the French presidential election. During this experiment, participants were asked to test several alternative voting methods to elect the French president, like scoring methods, instant-runoff voting, Borda with partial rankings, majority judgement and pairwise comparisons. The experiment was both carried out <em>in situ </em>in polling stations during the first round of the presidential election (using paper ballots), and online between April 8th (two days before the first round of the election) and May 7th (using a web application). A total of 2308 participants took part in the online experiment. This dataset contains the answers provided by the participants to the online experiment, with no other processsing than a basic transformation to a set of CSV files.</p> <p>The companion paper available on this repository describes the experimental protocol, the format of the files, and summarizes the precise conditions under which this dataset is available.</p>
FOKUSPLAY | AGEN TARUHAN ONLINE GAMPANG WEDE
<p><a href="https://heylink.me/fokusplay/">FOKUSPLAY</a> Agen situs taruhan game online terbaik dan terpercaya dengan sistem pembayaran paling adil , cepat dan sangat akurat. Memberikan pelayanan tempat bermain terbaik yang akan Anda nikmati disetiap harinya . Menyediakan pelayanan pengaduan 24jam yang dapat Anda akses di Whatsapp dan Livechat yang telah kami sediakan.</p>
Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"
<h1>Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"</h1> <p>DOI: https://doi.org/10.1038/s41467-024-54520-7</p> <h2>Authors</h2> <ul> <li>Franco De Palma</li> <li>Fabian Oppliger</li> <li>Wonjin Jang</li> <li>Stefano Bosco</li> <li>Marián Janík</li> <li>Stefano Calcaterra</li> <li>Georgios Katsaros</li> <li>Giovanni Isella</li> <li>Daniel Loss</li> <li>Pasquale Scarlino</li> </ul> <h2>Description</h2> <p>The data for all figures in the main text can be found in csv files in ASCII format in the corresponing folders. For Figures 4-6, the panels are numbered from top to bottom.</p>
Data archive for the peer-reviewed journal article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions"
<p>This data archive accompanies the article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions", which was accepted in November 2024 in the peer-reviewed journal Environmental Science and Technology Letters. The data archive contains the processed OP_DTT, PM loading, oxidant level, and elemental ratio measurements presented in this journal article. </p>
Province of Cremona - Environment and Territory Department - Online digital library database
<p>The province of Cremona lies in Northern Italy, along the left bank of the River Po floodplain. This is the database of the online digital library of the Province Administration Environment and Territory Department. At present the library comprises 610 contributions concerning the environment, ecology, geography, history and landscape of the province, of the Po basin at large and of other areas as well. Most contributions have a short or extended abstract in English. Each publication in the library is freely downloadable as a PDF file from the library website http://bibliotecadigitale.provincia.cremona.it.</p> <p>The database fields are as follows: Publication code, Publication series, Publication title in Italian, Publication title in English, Publication type (magazine or monograph), ISSN number (if assigned), Issue number, Issue year, Contribution type (article, report or short note, monograph etc.), Title in Italian<em>,</em> Title in English, Topic, Subtopic 1, Subtopic 2, Pages, Author 1, Author 2, …, Author 16, Link to the file of each publication issue on the digital library website, Notes.</p> <p>Further information on the digital library and the database can be found in the open access article (in English) in V. 2 N. 1 (2022) of BORNH (Bullettin of Regional Natural History of the Società dei Naturalisti in Napoli): <a href="https://serena.sharepress.it/index.php/bornh/issue/view/653">https://serena.sharepress.it/index.php/bornh/issue/view/653</a></p> <p>Please note that upon opening the file with LibreOffice (and perhaps other softwares) it might be necessary to select Windows 1252 as the character type and the comma as the separator.</p>
User evaluation results for a Wikidata-centric tool for temporal data in Humanities and Cultural Heritage (June 2024): raw tabular result data and web forms for two questionnaires from five online focus group workshops
<p><strong>Introduction</strong></p> <p>This resource is created for the article: "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" in Semantic Web Journal Special issue on the Semantic Web and Ontology Design for Cultural Heritage. It contains materials use for the user evaluation (June 2024) of a Wikidata visualization tool (<a title="ReKisstory" href="https://rekisstory.labs.vu.nl/" target="_blank" rel="noopener">ReKisstory</a>) described in the article. </p> <p><strong>Summary of the user evaluation</strong></p> <p>The structrue of the user evalution is summarized in the table below:</p> <table style="border-collapse: collapse; width: 99.9708%;"><colgroup><col style="width: 28.0622%;"><col style="width: 26.893%;"><col style="width: 27.0309%;"><col style="width: 17.9856%;"></colgroup> <tbody> <tr> <td> </td> <td>Objectives</td> <td>Setup </td> <td># of people participating</td> </tr> <tr> <td>Pre-workshop survey</td> <td>Understanding potential user profiles before workshop</td> <td>Online questionnaire (Q1)</td> <td>51</td> </tr> <tr> <td>Workshop (Focus Group)</td> <td>Introducing and testing the tool. Obtaining feedback about it</td> <td>Online demo, testing, discussion</td> <td>16</td> </tr> <tr> <td>Post-workshop survey</td> <td>Understanding user needs after testing</td> <td>Online questionnaire (Q2)</td> <td>11</td> </tr> </tbody> </table> <p> </p> <p><strong>The structure of the data is as follows:</strong></p> <p>1. The PDF and PPTX files, containing materials prepared for the pre-workshop survey, workshop, and post-workshop survey:</p> <ul> <li>Pre-workshop survey: Questionnaire (Q1) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/1stQuestionnaire.pdf?download=1&preview=1">1stQuestionnaire.pdf</a></li> <li>Post-workshop survey: Questionnaire (Q2) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/2ndQuestionnaire.pdf?download=1&preview=1">2ndQuestionnaire.pdf</a></li> <li>Documents distributed to the participants of the Focus Group Workshop: <ul> <li>ReKisstory Compare section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_compare_manual.pdf?download=1&preview=1">rekisstory_compare_manual.pdf</a></li> <li>ReKisstory Find section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_find_manual.pdf?download=1&preview=1">rekisstory_find_manual.pdf</a></li> <li>ReKisstory Find example search patterns <a href="https://zenodo.org/records/14960584/files/rekisstory_group_search_example_search_patterns.pdf?download=1&preview=1">rekisstory_group_search_example_search_patterns.pdf</a></li> <li>Workshop slides <a href="https://zenodo.org/records/14960584/files/Focus_Group_Workshop_slides.pptx?download=1&preview=1">Focus_Group_Workshop_slides.pptx</a></li> </ul> </li> </ul> <p>2. The Excel spreadsheets consists of the results of two questionnaires (Q1 and Q2) (<a href="https://zenodo.org/records/14960584/files/Two_questionnaires_online_workshops.xlsx?download=1&preview=1">Two_questionnaires_online_workshops.xlsx</a>): </p> <ul> <li>Pre-workshop survey: Questionnaire (Q1), containing information about potential users: <ul> <li>Demographics</li> <li>Experience of <ul> <li>Time-related data</li> <li>Wikidata</li> <li>SPARQL</li> </ul> </li> </ul> </li> <li>Post-workshop survey: Questionnaire (Q2), containing information about feedback from the Workshop participants : <ul> <li>Comparison with Q1</li> <li>Questions about time-related functionalities</li> <li>Questions about Compare and Find searches</li> <li>Overall comment</li> </ul> </li> </ul> <p><em>Please see "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" for more details</em></p>
Voter Autrement 2024 - The Online Experiment
<div> <div> <div>The election of the French representative to the European Parliament is based on a proportional representation system with a 5% threshold. Due to this threshold, votes for parties that do not reach this threshold are lost, and the corresponding voters are not represented, and not taken into account for the allocation of seats. In this context, we wanted to test alternative voting methods that could mitigate this issue by allowing voters to rank more than one list. We conducted an online voting experiment during the election of the French representative to the European Parliament in June 2024. In this experiment, participants were asked to vote with the alternative voting methods, and give their opinion. We ran the experiment on two sample of participants, a self-selected one in which every voter could participate, and a representative one, collected through a poll institute.</div> <div> </div> <div> <div> <div>If you are using this dataset in a scientific paper, please cite our paper <em><strong>Reallocating Wasted Votes in Proportional Parliamentary Elections with Thresholds</strong></em> <em>(Théo Delemazure, Rupert Freeman, Jérôme Lang, Jean-François Laslier, Dominik Peters).</em></div> </div> </div> </div> </div> <div> <div> </div> <ul> <li><em>V2.0:</em> Added the representative dataset and updated the documentation.</li> <li><em>V1.2:</em> same bug but in "participants.csv" for voting intentions.</li> <li><em>V1.1: </em>there was a bug in the data of the "vote_two_choices.csv" file => NaN values were replaced by "ESPERANTO LANGUE COMMUNE".</li> </ul> </div>
Personal Online Dosimetry Using Computational Methods: The PODIUM Project and the Future of Active Dosimetry
<p>Individual monitoring of workers exposed to external ionizing radiation is essential to allow application of the ALARA principle and follow up of the official dose limits. However, large uncertainties still exist in personal dosimetry. Also, many practical problems exist for personal dosimetry, with many dosemeters getting lost and the reluctance of many workers to wear one or more dosemeters.</p> <p>Most legal dosimetry is done with passive dosemeters, which are analyzed after the wearing period in an accredited lab. Active dosemeters are also widely used, although mostly only for ALARA purposes or for specific exposure situations. Although the active dosemeters are dosimetrically and technically at least equivalent to passive dosemeters, their higher cost limits their use as only legal dosemeters.</p> <p>In an attempt of reinventing dosimetry by using the modern evolutions in simulations, artificial intelligence and computer vision, the PODIUM project was set up. PODIUM was a short feasibility project, funded by the EC CONCERT programme.</p> <p>The objective of the PODIUM project was to improve personal dosimetry by an innovative approach: the development of an online dosimetry application based on computer simulations without the use of physical dosemeters. Operational quantities, protection quantities and radiosensitive organ doses (e.g. eye lens, brain, heart, extremities) can be calculated based on the use of modern technology such as personal tracking devices, flexible individualized phantoms and scanning of geometry set-up. When combined with fast simulation codes, the aim was to perform personal dosimetry in real-time.</p> <p>We applied and validated the methodology for two situations where improvements in dosimetry are urgently needed: neutron workplaces and interventional radiology. Several validation and test measurements were done in different hospitals, and in 2 workplace fields with significant neutron exposure. Personal doses could be calculated within acceptable simulation times, just based on captured movements of the workers and information of the radiation fields. These doses agreed with the results from physical dosemeters within the standard uncertainties that are accepted in personal dosimetry.</p> <p>This PODIUM dosimetry method can also be used to visualize the radiation in near real time. This will increase awareness of radiation protection among workers and will improve the application of the ALARA principle, and it can also be used in training modules. The use of neural networks and big data will help in further reducing simulation time, making real time simulations and dosimetry without physical dosemeters possible in the near future.</p>
Zum Unterricht: Online-Vorlesungen, synchron und als "Konserve"
<p>"Warum eine synchrone online-Vorlesung mitmachen, wenn man danach die 'Konserve' anhören und ansehen kann?" - eine Antwort und Unterrichtsreflexion im COVID-Zeitalter.</p>
Self-Directed Online Machine Learning for Topology Optimization
<p>Code and results of the paper "Self-Directed Online Machine Learning for Topology Optimization".</p> <p>See latest updates at https://github.com/deng-cy/deep_learning_topology_opt</p>
Demonstrating a Bayesian Online Learning forEnergy-Aware Resource Orchestration in vRANs
<p>Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implemented our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).</p>
Example Solution for the Pickup an Delivery Problem with Online Transfers
<p>This is an example solution of our model for the pickup-and-delivery problem that utilizes quite a few online transfers.<br> </p>
Buy Pewter Posey Linen Embroidered Smock Top Online
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Building Interprofessional Learning in the Midst of the COVID-19 Pandemic: Implementation of Online Simulation for Students in Health Profession
<p><strong>Abstract</strong></p> <p>COVID-19 has significantly affected the learning process of health institutions. It is inevitable that face-to-face learning activities will shift to online learning, including Interprofessional Education (IPE). The purpose of this article is to describe our experiences in developing a simulation of a two-day interprofessional learning and teaching session for students in nursing, midwifery, pharmacy, environmental sanitation, medical laboratory technology, dental health, and nutrition. A total of 956 students, 112 lecturers, and 15 standardized patients (SP) took part in this research. The study conducted in 6 schools of health profession in Indonesia. Students were assigned to two simulation models. The data were collected using an online questionnaire with the variables readiness to cooperate, interprofessional group discussions, and facilitator role with a 5-point Likert scale. In the implementation of the two simulation models, significant results have been demonstrated and their uniqueness is revealed. The simulation models that make direct anamnesis of the SP results in a higher level of readiness for IPE learning, and is significantly different. Meanwhile, the simulation model that is preceded by a scenario discussion process before making anamnesis to the SP shows a significantly positive attitude to the interprofessional groups discussion and the role of the facilitator.</p> <p><strong>Keywords:</strong> interprofessional education, COVID-19 pandemic, online simulation, health professionals, standardized patient</p>
Buy Handmade Double-Sided 3in1 Wide Thin Wool Scarf Online
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Buy Lilac Embroidered Mix Layer Mini Dress Online
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Dataset for Understanding Unemployment during COVID-19 from Online and Offline Behaviors
<table> <tbody> <tr> <td>variable</td> <td>data</td> <td>source</td> <td>file</td> </tr> <tr> <td><em>Insurance</em></td> <td>Health Insurance Coverage Uninsured, Percent</td> <td>U.S. Census Bureau, 2008 to 2019 American Community Surveys (ACS).</td> <td>Health Insurance Coverage Percent uninsured by State.csv</td> </tr> <tr> <td><em>Income</em></td> <td>household Income</td> <td>United States Census Bureau’s Annual ASEC survey 2019</td> <td>Median Household Income by State.csv</td> </tr> <tr> <td><em>Diploma</em></td> <td>Percent of adults with less than a high school diploma plus Percent of adults with a high school diploma only</td> <td>U.S. Census Bureau 2014-18 American Community Survey 4-yr average county-level estimates</td> <td>Education by State.xls</td> </tr> <tr> <td><em>Death, Case</em></td> <td>COVID-19 deaths and cases</td> <td> Centers for Disease Control and Prevention </td> <td>United_States_COVID-19_Cases_and_Deaths_by_State_over_Time.csv</td> </tr> <tr> <td><em>Test</em></td> <td>Test</td> <td>The COVID Tracking Project</td> <td>COVID-19 Tests by State.xlsx</td> </tr> <tr> <td><em>Density</em></td> <td>population density/population</td> <td>US Census Bureau</td> <td>population-density-data-table.xlsx</td> </tr> <tr> <td><em>Nonwhite</em></td> <td>Non-white%</td> <td>US Census Bureau</td> <td>Bridged-Race Population Estimates 2019_by State .xlsx</td> </tr> <tr> <td><em>Gini</em></td> <td>Gini</td> <td>U.S. Census Bureau, American Community Survey .</td> <td>US State Gini.csv</td> </tr> <tr> <td><em>Grant</em></td> <td>total grants</td> <td>U.S. Department of Education</td> <td>U.S. Department of Education appropriations for major programs, by state or jurisdiction Fiscal year 2018.xls</td> </tr> <tr> <td><em>Unemployment</em></td> <td>unemployment rate</td> <td>US Census Bureau</td> <td>Unemployment rate by State.csv</td> </tr> </tbody> </table> <p> </p>
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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.