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2,359 results for “online”
Simulating and Evaluating the Global Aerosol Distributions with the Online Aerosol Coupled CAS-FGOALS Model
<p>We implement an existing aerosol module named Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) in the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System (CAS-FGOALS) model and simulate the global aerosol properties over 2002-2014. The CAS FGOALS modeling outputs associated with the work are stored here.</p>
PhD-delay Dataset for Online Stats Training
<p>This is a dataset used for the online stats training website (<a href="https://www.rensvandeschoot.com/tutorials/">https://www.rensvandeschoot.com/tutorials/</a>) and is based on the data used by <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0068839">Van de Schoot, Yerkes, Mouw and Sonneveld 2013</a></p> <p> </p> <p>Among many other questions, the researchers asked the Ph.D. recipients how long it took them to finish their Ph.D. thesis (n=333). It appeared that Ph.D. recipients took an average of 59.8 months (five years and four months) to complete their Ph.D. trajectory. The variable B3_difference_extra measures the difference between planned and actual project time in months (mean=9.97, minimum=-31, maximum=91, sd=14.43). For the the exercises we are interested in the question whether age (M = 31.7, SD = 6.86) of the Ph.D. recipients is related to a delay in their project. The relation between completion time and age is expected to be non-linear. This might be due to that at a certain point in your life (i.e., mid thirties), family life takes up more of your time than when you are in your twenties or when you are older. So, in our model the gapgap (<em>B3_difference_extra</em>) is the dependent variable and ageage (<em>E22_Age</em>) and age2age2(<em>E22_Age_Squared </em>) are the predictors.</p> <p>For more information on the sample, instruments, methodology and research context we refer the interested reader to <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0068839">Van de Schoot, Yerkes, Mouw and Sonneveld 2013</a>.</p> <p> </p>
Online Career Counselling for Students
<p>Are you looking for a <a href="https://www.collegeaims.com/for-school-students">career counselor online</a>? Our agency has a mentorship program for school students, we help students to choose the right placement for their career.</p>
Prototype for Collaboration using Augmented Reality in the Setting of Guided Virtual City Tours - Online Survey Videos
<p>The videos present in the online survey to give an inside of the prototype.</p>
Online Companion: A Preference Informed Energy Sharing Framework for a Centralized Energy Community
<p>This manuscript serves as an electronic companion to [1]. We present the complementary input data, including prosumers target demand, PV power generation, and electricity market prices.</p>
A Mediation Model of the Usability and Intergroup Relation for Online Project Management Community Effectiveness with Microsoft Teams
<p>A Mediation Model of the Usability and Intergroup Relation for Online Project Management Community Effectiveness with Microsoft Teams</p>
Online repository for Paper "GTE: A Framework for Learning Code AST Representation Efficiently and Effectively"
<p>The online repository for the under review IJCAI2024 paper "<strong>GTE: A Framework for Learning Code AST Representation Efficiently and Effectively</strong>"</p><p><strong>GTE-main.zip</strong> contains the source code of GTE, please see <strong>README.md</strong> in GTE-main.zip<strong> </strong>for more guidance.</p><p><strong>Appendix.pdf </strong>contains more<strong> </strong>details about the dataset and probing task design.</p>
Online Hostility towards UK MPs
<p>This is a dataset with tweets from X. Each tweet mentions one or more UK MPs from a subset selected for our study to give a diverse representation of political leanings. Each tweet is labelled for hostility and the identity characteristic it targets (religion, race, gender). Each annotator also provides a confidence score for each label. Three annotators annotate each tweet. Annotators are UK-based students from Computer Science and Politics.</p>
e One Week Online Short Term Course on "Library and Information Science"
<p><strong>e One Week Online Short Term Course on “Library and Information Science” </strong></p>
Online appendices for the paper Ugeskr Læger 2021;183:V20213
<p><strong>Online appendices for the paper Ugeskr Læger 2021;183:V20213</strong></p> <p>Original title (Danish): "Er nissehuer og rensdyrgevir lige så effektive som sorte bjælker over øjnene til anonymisering af fotos? Et randomiseret, kontrolleret studium"</p> <p>English title: "Do Christmas hats and reindeer antlers provide the same anonymity in photos of persons as black bars covering the eyes? A randomized controlled study"</p> <p>Author: Martin Rune Hassan Hansen<sup>1-3</sup></p> <ol> <li>Randers Regional Hospital, Department of Medicine</li> <li>Aarhus University Hospital, Department of Infectious Diseases</li> <li>Aarhus University, Department of Public Health</li> </ol> <p> </p> <table> <caption>Descriptions of individual files</caption> <thead> <tr> <th scope="col">Filename</th> <th scope="col">File format</th> <th scope="col">Contents</th> </tr> </thead> <tbody> <tr> <td>LICENSE.txt</td> <td>Plain text (UTF-8 encoding)</td> <td>License information</td> </tr> <tr> <td>Online_appendix_1.pdf</td> <td>Portable Document Format</td> <td>Images used in tests</td> </tr> <tr> <td>Online_appendix_2.zip</td> <td>ZIP-compressed folder</td> <td>Electronic questionnaires</td> </tr> <tr> <td>Online_appendix_3.pdf</td> <td>Portable Document Format</td> <td>Categorization of answers</td> </tr> <tr> <td>Online_appendix_4.csv</td> <td>CSV = comma-separated values (UTF-8 encoding)</td> <td>Anonymized dataset with all participant responses</td> </tr> <tr> <td>Online_appendix_5.do</td> <td>Stata 15 do-file (UTF-8 encoding)</td> <td>Stata 15 syntax file for replication of all results presented in the paper</td> </tr> <tr> <td>Online_appendix_6.pdf</td> <td>Portable Document Format</td> <td>Results from all analyses, including sensitivity and secondary</td> </tr> </tbody> </table> <p> </p>
Code, benchmarks and experiment data for the IPC 2018 entry "Delfi: Online Planner Selection for Cost-Optimal Planning"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>katz-et-al-icp2018-delfi.zip and katz-et-al-icp2018-delf2.zip contain the implementation based on Fast Downward (the two folders only differ in how the planner selection works). It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018.</p> <p>katz-et-al-ipc2018-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models.</p> <p>katz-et-al-icp2018-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks).</p> <p>katz-et-al-icp2018-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab).</p> <p>katz-et-al-icp2018-raw-data.zip and katz-et-al-icp2018-parsed-data.zip contain the experimental data. Directories in katz-et-al-icp2018-raw-data.zip (without the "-eval" ending) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories in katz-et-al-icp2018-parsed-data.zip (with the "-eval" ending) contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>The image data set used for training can be found online: https://github.com/IBM/IPC-image-data</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p> <p> </p>
Data Sets for "Online Charge Measurement for Petawatt Laser-Driven Ion Acceleration" (submitted manuscript)
<p>The data presented in the paper titled "Online Charge Measurement for Petawatt Laser-Driven Ion Acceleration" is provided in this data repository.</p> <p>The data is sorted into subfolders according to their presentation in the figures of the paper.</p>
Supplementary Online Data for Characterizing the hologenome of L. pustulata and tracing genomic footprints of lichenization
<p>Processed data and scripts, as well as the the figures and tables for <em>Characterizing the hologenome of L. pustulata and tracing genomic footprints of lichenization</em>. The <em>zip</em> file contains three subfolders for <em>Chapter 3</em>, <em>Chapter 4</em>, <em>Chapter 5</em> of <em>Characterizing the hologenome of L. pustulata and tracing genomic footprints of lichenization</em>. Each subfolder comes with its own <em>README.md</em> further describing its contents.</p> <p>Furthermore the read mappings against <em>Lasallia pustulata</em> individually, as well as the ones used for the <em>anvi´o</em> visualization, which include <em>L. pustulata</em>, <em>Trebouxia sp.</em> as well as the 499 bacterial scaffolds are given. The <em>Trinity</em> RNAseq assemblies (guided and unguided) are given too.<br> </p>
Figure 2 from: Keshavan A, Klein A, Cipollini B (2017) Interactive online brain shape visualization. Research Ideas and Outcomes 3: e12358. https://doi.org/10.3897/rio.3.e12358
Figure 2 - Example of a selected region and its accompanying boxplot.
Figure 1 from: Keshavan A, Klein A, Cipollini B (2017) Interactive online brain shape visualization. Research Ideas and Outcomes 3: e12358. https://doi.org/10.3897/rio.3.e12358
Figure 1 - Example visualization
Figure 3 from: Keshavan A, Klein A, Cipollini B (2017) Interactive online brain shape visualization. Research Ideas and Outcomes 3: e12358. https://doi.org/10.3897/rio.3.e12358
Figure 3 - Example master/slave visualization
Figure 6 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 6 - The user interface field for uploading EML files into ARPHA.
Figure 4 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 4 - Download of an EML from the GBIF Integarted Publishuing Toolkit (IPT)
Figure 1 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 1 - Poll results about composition of audience during live participation.
Empathy and redemption: Exploring the narrative transformation of online support for mental health across communities before and after Covid-19
<p>Collect posts and comments from the Reddit community. </p> <p> </p> <p>Article Source: <a title="Back to original article" href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307042"><strong>Empathy and redemption: Exploring the narrative transformation of online support for mental health across communities before and after Covid-19</strong></a><br>Cai Y, Wei E, Cai X (2024) Empathy and redemption: Exploring the narrative transformation of online support for mental health across communities before and after Covid-19. PLOS ONE 19(7): e0307042. <a href="https://doi.org/10.1371/journal.pone.0307042">https://doi.org/10.1371/journal.pone.0307042</a></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.