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Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.</p>
Online Tone Manipulation in Violin Performance: An ERP and ERSP study
<p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Data and Software used in <br> Online Tone Manipulation in Violin Performance: An ERP and ERSP Study.<br> <em>Ángel David Blanco, Jordi Costa-Faidella, Alfonso Pérez, David Dalmazzo, Rafael Ramirez, Iria SanMiguel</em><br> (not published at this moment)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>FILES:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1. Online_Tone_Manipulation_Violin_DATA.rar</strong></p> <p>In this compressed file we found 3 folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.1 Raw_data</strong>: raw data of the participants of the experiment.</p> <p>Inside we find 16 folders. Each one contains the raw data of each participant: SXX (where XX is the code assigned to each subject).<br> Data from participants S01 and S11 are missing due to technical problems.</p> <p>Each folder contains:</p> <p>audio: This folder contains the audio recorded during each block of the session.<br> sXX: This folder contains the EEG files recorded during each block of the session.<br> tony: This folder contains the tony and excel files with the information about the audio onsets and the onsets of corrective movements.</p> <p>We also find 2 matlab scripts:</p> <p>main_final.m: This script creates one *.set file per block with the EEG data and the audio markers for each event. <br> main_EEG.m This script creates the Merged_Datasets.set file with the data from all the blocks. It also cleans the data from noise artifacts that were previously visual inspected.<br> It also computes the average reference, filters the Data, computes ICA and removes those components related with ocular activity. <br> It also creates te SXX_MergedDatasets_filt25_ICprun.set and the SXX_MergedDatasets_filt50_ICprun_TF.set</p> <p>Those files can already be found inside each folder. </p> <p>SXX_MergedDatasets_filt25_ICprun.set: This file contains the data for the ERPs already processed (pass band filter 1-25Hz). <br> SXX_MergedDatasets_filt25_ICprun_TF.set: This file contains the data for the ERSPs already processed (pass band filter 1-50Hz).</p> <p>RECODED TRIGGERS <br> (Based on audio onsets and logfiles)<br> Hundreds: TASK<br> Tens: FEEDBACK<br> Units: ORDER<br> 0: Reference<br> 100: Active<br> 200: Replayed<br> 300: Manipulated Active<br> 400: Post-error manipulation Active<br> 500: Non-manipulated active<br> 600: Manipulated Replayed<br> 700: Post-error manipulated Replayed<br> 800: Non-manipulated Replayed<br> 900: Onset End Correction Active<br> 1000: Onset End Correction Passive<br> 10: Open-String Note<br> 20:In Tune ONSET<br> 30: Mistuned ONSET <br> 40: In Tune STABLE<br> 50: Mistuned STABLE<br> 60: Notes with correction ONSET (All)<br> 70: Mistuned notes with correction ONSET<br> 80: Mistuned notes without correction ONSET<br> 1: Low (15-30c)<br> 2: LowHigh(30-50c)<br> 3: Middle (50-70c)<br> 4: MiddleHigh(70-100)<br> 5: High (>100)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>In Raw_data we can also found two scripts</p> <p>load_participants.m: This script executes the main_final.m script for each participant.<br> load_participants_EEG.m: This script executes the main_EEG.m script for each participant</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.2 ERPs:</strong></p> <p>Inside this folder we find 3 more folders:</p> <p>Active: Contains the *.set files with the ERPs for each event of interest inside the Active condition.<br> Replayed (Passive): Contains the *.set files with the ERPs for each event of interest inside the Replayed condition.<br> Reference Melody: Contains the *.set files with the ERPs of the reference melody.</p> <p>Events of interest in the names of each Folder:<br> XXXX_Tuned: tuned notes<br> XXXX_Mistuned: notes with an error higher than 30 cents.<br> XXXX_nonman: nonmanipulated<br> XXXX_man: manipulated<br> XXXX_postman: postmanipulated<br> XXXX_Corr_Low: Trials with slow corrective movements (>350 ms) <br> XXXX_Corr_Medium: Trials with medium corrective movements (250-350ms)<br> XXXX_Corr_High: Trials with fast corrective movements (<250ms)<br> XXXX_Low: low error (15-30 cents)<br> XXXX_Medium: Medium error (30-50 cents)<br> XXXX_Medium_High: Medium High error (50-70 cents)<br> XXXX_High_High: High errors (>70 cents)</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> <br> <strong>1.3 ERSPs:</strong></p> <p>ERSPs of Active Tuned and Mistuned and Replayed Tuned and Mistuned in MATLAB Data files.</p> <p>Inside each file we can find the ERSPs and the ITC for different electrodes:</p> <p>ersp_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..<br> itc_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..</p> <p>both the ersp_XX and the itc_XX are three-dimensional matrices:</p> <p>frequencies (30 points) X time (200 points) X participants (15 subjects).</p> <p>The frequencies and times variables contain an array with the information of the frequency value (Hz) and time value (ms) for each point.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2. Online_Tone_Manipulation_Violin_STIMULI_and_MAX_software.rar</strong></p> <p>In this compressed file we found two folders:</p> <p><br> <strong>2.1 Online_Tone_Manipulation_System_in_Max folder</strong></p> <p>This folder contains the system in Max that allows us to manipulate the pitch of the played note in the melody.</p> <p>recording_session.maxpat: open this file to access the system.<br> random_file.csv: file which contains the order of the melodies reproduced to the participants, the note which has to receive the manipulation, and the direction of the manipulation (1 up, 0 down).</p> <p>We can also find two folders:</p> <p>audio: the audio of the participant for each block is recorded and saved inside this folder<br> New_generated_melodies: This folder needs to contain the melodies reproduced to the participant during the experiment</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2 Stimuli folder</strong></p> <p>This folder contains three folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.1 Generated_Scores folder</strong></p> <p>This folder contains the code and which generates the score images used during the experiment.</p> <p>Inside the folder we can find:</p> <p>generate_scores.m: Script used to generate the score images</p> <p>New_generated_scores folder: This folder contains the XML code and the *.jpg file for each score.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.2 Screen</strong></p> <p>This folder contains the code used to deliver the visual information to the participant during the session and also the clicks sent to the DSP computer and the markers to the EEG computer via parallel port.<br> The random_file.csv inside this folder has to be the same that the one contained inside the Online_Tone_Manipulation_System_in_Max.</p> <p>Inside this folder we can find:</p> <p>Violin_screen_Brainlab.m: script with the code which has to be executed to start delivering the visual instructions to the participants</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.3 Violin_Sample_sounds</strong></p> <p>Inside this folder we can find two folders:</p> <p>New_generated_melodies: contains the final generated melodies of the experiment<br> Original_Sounds: contains the original sounds used to generate the rest of the melodies of the experiment</p> <p>We can also find two important scripts:</p> <p>Generate_audios: this script generates the different melodies of the experiment from the original sounds.<br> Randomize_audios_new: this script generates the random_file.csv with the random order of the melodies together</p> <p><br> </p>
Appendix-A: Online Repositories Available for Text Mining
<p>Appendix A is associated with Chapter 2: Text data and where to find them of the book: Manika Lamba and Margam Madhusudhan (2021) Text Mining for Information Professionals: An Uncharted Territory, SpringerNature.</p>
A Pragmatic Trial of Interactive Online Statistical Webtools for Teaching Biostatistics to First Year Medical Students: A Constructivism-Informed Approach
<p>Underlying quantitative and anonymised qualitative data for the study, "A Pragmatic Trial of Interactive Online Statistical Tools for Teaching Biostatistics to First Year Medical Students: A Constructivism-Informed Approach"</p>
Dataset on an online collaborative learning situation in acomputer networks course
<p>Here you can find a dataset of a collaborative learning situation. Students were enrolled in two undergradute courses on computer networks where they were required to carry out a set of learning activities supported by Moodle and an online collaborative environment called CoTrackV2. The data collected includes logs of the writing process of shared documents, logs of the chat messages between the group members, and logs from Moodle with coarser-grained information about course-level interactions. This dataset has been generated with the aim of allowing researchers to study self-and socially-shared regulation in online environments.</p> <p>There will be 6 files:</p> <ul> <li>document_logs.csv</li> <li>chat_logs.csv</li> <li>moodle_logs.csv</li> <li>individual_submissions.csv</li> <li>learning_design.csv</li> <li>final_questionnaire.csv</li> </ul>
Online Package for the manuscript "Continuous Integration and Delivery for Cyber-Physical systems: A Grounded-Theory"
<p>This package contains the material of the (grounded theory) study related to the paper</p> <p>"Continuous Integration and Delivery for Cyber-Physical systems: A Grounded-Theory"</p> <p>The content of the various files and directories is detailed in the following.</p> <p>InterviewStructure.pdf: complete interview structure (the paper reports an overview in Table 1)</p> <p>open_coding/: this directory contains details from the open coding procedure. In particular:<br> - AllCodesUsedWhileCodingAndMapping.csv contains the list of all codes generated during the open coding, with a symbol near each one (first column) then used to compute the inter-rater agreement<br> - first_round.csv, second_round.csv, third_round.csv, fourth_round.csv: files used to compute the inter_rater agreement</p> <p><br> CodesContributingToMindMap.csv: final set of codes, that contributed to the taxonomy (see below)</p> <p>FinalCodingTraceability.csv: this file describes how the final set of codes is traced onto the ten interviews. Note that, at this stage, the transcripts have been redacted for confidentiality purposes.</p> <p>MindMap_Complete.pdf: complete taxonomy of codes, in the form of a mind map. The one reported in the paper (Figure 2) cuts leaves, unless (as for benefits, for example) they are necessary to properly describe and understand the category. Also, note that the complete mind map separates the benefits into "actual" and "expected" (based on what was collected from the interviews) whereas the summary mindmap shown in the paper (Figure 2) does not make this distinction.</p> <p>6C_Diagrams/ : This directory contains the detailed 6C diagrams (i.e., each box related to a "C" contains the list of codes pertaining to it) for the three dimensions investigated in the paper and addressed in RQ1, RQ2, and RQ3.</p>
WATER: AN ONLINE TOOLKIT TO SUPPORT THE RESEARCH FOR STANDARDS
<p>Within <a href="https://www.eu-project-o.eu/">Project Ô</a>, the European Commission-funded Horizon 2020 project, <a href="https://www.uni.com/">UNI</a> - Italian Standardization Body has realized a <strong>first version of an online <a href="https://www.eu-project-o.eu/standardization-toolkit/">Standardization Toolkit</a></strong><a href="https://www.eu-project-o.eu/standardization-toolkit/"> </a>to support the research over a dataset of more than 100 standardization documents in the field of water and its management.</p> <p>The Standardization Toolkit, finalized in May 2021, is based on the vision of Project Ô.</p> <p>Specifically, the toolkit consists of an online tool, which let you select standards choosing among several filters. For example, it is possible to select standards starting from technical committees at national (UNI), European (CEN) and international (ISO) level. In addition, it is possible to make a deeper search by selecting standards according to specific areas and/or keywords.</p> <p>The list of standards will be the result of what emerges by searching in a panel of more than 100 different standardization documents identified within the activity of mapping that was carried out for Project Ô. The standards are related to the theme of water, its reuse also in a circular economy perspective, and to the environmental management.</p> <p>Standardization is a key tool for technology transfer, bridging the gap between research and the international market.</p> <p>Standards codify innovation through a shared language understood by companies, researchers, stakeholders and citizens worldwide, based on the principles of openness and transparency. The co-creation process of standardization supports open innovation and cross-sectoral innovation.</p> <p>Standards ensure comparability, compatibility and interoperability, building trust with customers and suppliers, promoting sustainability in terms of processes, products and business.</p> <p>On Zenodo it has been uploaded the dataset behind this toolkit. </p> <p> </p>
CAPRI ASPHALT ONLINE WEBINAR | 20 October 2022
<p>CAPRI organised the 1st online webinar, dedicated to Asphalt Use Case, on October 20, 2022.</p> <p>This is a recording of the webinar.</p> <p>For more information visit the website: https://www.capri-project.com/.</p> <p>The project receives funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement Number 870062.</p>
Dataset Literature Review on Online Business and commitment
<p>Dataset Literature Review on Online Business and commitment</p>
Emotional and Cognitive Changes Surrounding Online Depression Identity Claims
<p>The repository includes data files containing anonymized user IDs, timestamps, identity claim time, LIWC variables, post vs. comment (boolean), and mental health vs. other subreddit (boolean) for our paper <em>Emotional and Cognitive Changes Surrounding Online Depression Identity Claims </em>to replicate LIWC analysis. These files are named ic_liwc.csv (for users with identity claims) and control_liwc.csv (for users without identity claims). Because the identity claims themselves are excluded from these files but metadata about them is required to split users into groups, we also provide a file for doing so, ic_properties.csv.</p> <p>We have also included files with n-gram counts to reproduce our n-gram analysis, and files with the patterns used to collect the data.</p> <p>The Reddit posts themselves will not be made widely available, following the lead of Cohan et al. (the paper with the data collection process we follow) who only release raw text data to researchers upon request.</p>
Dataset Literature Review Online Business AND Hotels
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci online business dan hotels</p>
Dataset Literatur Review Online Business AND Social Media
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci Online Business AND Social Media</p>
Expert consensus on core topics of sustainable development online learning module for family physicians: A Delphi study
<p><strong>Objectives</strong></p> <p>Medical institutions must provide learning experiences that enhance the knowledge and perspectives of Sustainable Development (SD) to prepare trainees of family medicine to become competent global citizens. The aim of this study was to develop an SD online learning module for trainees of family medicine.</p> <p><strong>Design</strong></p> <p>This mixed-methods study was conducted from January 2020 to May 2021, beginning with a literature review concerning Sustainable Development Goals (SDG) and Education for Sustainable Development (ESD) in medicine. In-depth interviews were held to assess the relevant needs of family medic<span>ine</span> training, followed by a two-round Delphi survey with experienced educators (n = 21) in family medicine to refine and achieve consensus on the appropriate SDG topics for family physicians.</p> <p><strong>Setting</strong></p> <p>All residency training programmes in Thailand.</p> <p><strong>Participants</strong></p> <p>Members of the Residency Training Committee, Royal College of Family Physicians of Thailand.</p> <p><strong>Results</strong></p> <p>The literature review and in-depth interviews identified 12 topics of SD that were required for family physicians. The first round of the Delphi survey was concluded by identifying 7 core topics with additional suggestions. In the second round, a consensus was obtained among the experienced educators regarding 7 core topics: 1) A definition of SD, 2) Principles of SD, 3) SDG, 4) A new concept of SD, 5) SD in the context of Thailand, 6) SD and principles of family medicine and 7) SD and family practice. These core topics were grouped within three main objectives and three sub-modules.</p> <p><strong>Conclusions</strong></p> <p>An online <span>learning module of SD for family physicians was developed using a modified Delphi method. This included three sub-modules: 1) Concept and principles of SD, 2) SDG and 3) SD and its integration with family practice. This online learning module will provide additional resources for trainees of family medicine and Thai family physicians to expand their knowledge and perspectives of sustainable development.</span></p>
Appendix for "How Programmers Find Online Learning Resources"
<p>This artifact contains the the documents needed to replicate our user study as well as the collected data to validate our observations presented in the paper "How Programmers Find Online Learning Resources" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.</p>
Increasing lexical engagement with motor-enhanced pictures: An online study using the Picture-Word Interference paradigm
<p>data, training materials & code </p>
Online Repository for "Sorting lithium-ion battery electrode materials using dielectrophoresis"
<p>Please see the readme file.</p> <p>The matlab script for evaluating the measurements is called “Eval_Fluoro.m” and can be found in this repository.</p> <p>The excel sheet “20221028_photometric_iron.xlsx” contaiins the data from the chemical analysis.</p> <p>The manufacturing data for the electrodes is provided in the zip folder: PCB_boards_Giesler.zip and can be uploaded to a manufacturer of choice.</p>
Global misinformation spillovers in the online vaccination debate before and during COVID-19 - ex
<p>The dataset includes all the ids of the tweets analysed in the paper "Global misinformation spillovers in the online vaccination debate before and during COVID-19", divided by lang.</p> <p>They are all the tweets containing vaccines keywords in 18 different European languages, spanning the period October 2019 - March 2021 (excluded 2020 Jan - 2020 Jun).</p> <p>Note that a large fraction of the tweets can't be retrieved because of the suspension of the accounts and the removal of the posts by the users, so the study is only partially reproducible.</p> <p>The unzipped dataset has a dimension of 8,0G.</p> <p>The authors of the paper are: Lenti J, Mejova Y, Kalimeri K, Panisson A, Paolotti D, Tizzani M, Starnini M.</p>
Analisis Sentimen Twitter terhadap Aplikasi Pinjaman Online Menggunakan Metode Naive Bayes dan tools Rapidminer
<p>Berikut adalah dataset berbentuk csv yang kami gunakan untuk training dan juga testing analisis sentimen twitter terhadap pinjaman online</p>
SHIFT OF ONLINE CONSUMER PURCHASING BEHAVIOR IN VIETNAM DURING THE COVID-19 AND IN THE NEW NORMAL CONTEXT
<p>The study’s findings contribute to clarify shift of online consumer purchasing behavior and factors affecting the frequency of consumers’ online purchases in pandemic and after the pandemic’s demise, in order to help business to developeffective marketing strategies and enhance their presence in the e-commerce sector. </p>
Supplementary Online Content to "Alterity marking and enhancing accessibility in lexical borrowing: Meta-information techniques in the use of incipient anglicisms in French and Italian"
<ul> <li>Overview of newspaper articles analysed in Sections 4 and 5</li> <li>Sources of the newspaper articles</li> <li>Sample analyses of the use of meta-information techniques in F2 and I3</li> </ul>
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.