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2,359 results for “Online”
Supporting Online Toxicity Detection with Knowledge Graphs: Data
<p>This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022).</p> <p> </p> <p>The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO).</p> <p>We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).</p>
Online survey of needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector
<p>The present dataset was generated as part of the "Needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector" of the H2020 <a href="http://innorbit.eu">InnORBIT project</a>.</p> <p>The aim of this study was to identify and explore the available and missing skills of innovation intermediaries to provide business support services to innovators within their local ecosystems to develop commercial activity in space. The assessment of skills was based on a baseline framework encompassing a wide array of skills and competencies innovation intermediaries are supposed to possess in order to provide effective business support services to space innovators. The skills of the baseline framework are grouped into five broad categories: (i) space industry knowledge, (ii) business assessment knowledge, (iii) business support skills, (iv) organisational and digital skills and (v) soft skills. The baseline framework was originally developed by the InnORBIT consortium through research in related works of EU funded projects and publications and validated through a series of 15 interviews with top-level executives of organisations across the CEE and SEE area, belonging to the two target groups of the study (i.e., innovation intermediaries and innovators). An online survey was deployed from May 26th to June 18th using the EU Survey tool, to innovation intermediaries and innovators across the EU and CEE/SEE countries in particular. Two online questionnaires were developed building on the baseline skills framework - the first intended for innovation intermediaries asking them to perform a self-assessment of their skills in terms of providing business support services and the second targeting innovators, asking them to state their perception on how innovation intermediaries they have worked with, perform in each of the skills.</p> <p>The dataset contains four files:</p> <p>1. Zip file including the transcripts from 6 interviews with space innovators in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>2. Zip file including the transcripts from 9 interviews with innovation intermediaries in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>3. a pdf file of the digital questionnaires developed in EU Survey deployed to innovation intermediaries and innovators in the region</p> <p>4. An excel file with 104 valid responses collected from the online survey (56 innovation intermediaries and 48 innovators) across 16 EU countries / 21 countries total.</p> <p>The dataset contains only non-sensitive anonymised information and is in full compliance with the GDPR provisions. Any information leading to the identification of participants in activities (interviews, survey) is either modified or omitted and deonted with brackets.</p>
Public WhatsApp groups from the Brazilian online repositories
<p>This repository contains two gzip files with public WhatsApp groups collected from two Brazilian repositories, they are:</p> <ul> <li><a href="https://gruposdezap.com/">Grupos de Zap</a> - db_grupos_whats.json.gz</li> <li><a href="https://gruposwhats.app/">Grupo de Whats</a> - db_grupos_zap.json.gz</li> </ul> <p>The groups were collected on 01/2022.</p> <p>The files contain five properties: </p> <ul> <li><strong>title</strong>: <em>group title</em></li> <li><strong>description</strong>: <em>description of the group informed by the administrator</em></li> <li><strong>created_date</strong>: <em>creation date when the group was registered in the repository</em></li> <li><strong>num_vizualization</strong><em>: times the group was seen on the site - only for the zap groups repository</em></li> <li><strong>category</strong>: <em>group category</em></li> </ul> <p>If you use this dataset cite your paper, please:</p> <ul> <li><a href="https://doi.org/10.1145/3539637.3557056"><em>"Click Here to Join": A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp</em> </a></li> </ul> <p><em>Daniel Kansaon, Philipe Melo, and Fabrício Benevenuto. 2022. “Click Here to Join”: A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp. In Brazilian Symposium on Multimedia and Web (WebMedia ’22), November 7–11, 2022, Curitiba, Brazil. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3539637.3557056</em></p>
Online Appendix for PhD Thesis Titled "Dissecting Causal Relationships and Molecular Mechanisms in Disease using Genetic Risk Profiles"
<p>This repository contains 23 tables and two figures, which are too big to be included in the Appendix section of my thesis document.</p> <p>The second version includes additional summary statistics of metabolite-PGS associations which can be found at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.</p>
Conversational Networks For Automatic Online Moderation
<p><strong>Description. </strong>This repository contains several datasets of conversational networks, extracted from the chat messages exchanged by players of the <a href="https://www.spaceorigin.fr/">SpaceOrigin</a> MMORPG. Each graph represents a specific conversation, and belongs to one of two classes: <em>Abusive</em> (1) or <em>Non-abusive</em> (0). Vertices represent users, and edges represent the fact that the connected users exchanged message during the considered time period. Edges are weighted and directed: weights represent the intensity of the message exchanges, and directions represent who sent messages to whom.</p> <p>We provide two types of graphs: unsigned and signed. Unsigned graphs were extracted using the method described in paper [1], below. Version 1.0 of this dataset contain only a part of the conversations, subsampled to get balanced classes. Version 1.1 is extended to contain all available conversations, and there are much more <em>Non-abusive</em> than <em>Abusive</em> conversations. Signed graphs were extracted later, using the method described in publication [9] below. Each edge is described by an additional sign, that indicates the polarity of the messages exchanged by two users; friendly (positive) vs. hostile (negative). </p> <p>These datasets were used to train a classifier into automatically recognizing abusive messages. See the below papers for more details. The repository also contains some figures that appear in these papers.</p> <p><strong>Publications. </strong>The following papers used the unsigned version of the conversational networks. The extraction method is described in paper [1].</p> <ul> <li>[1] É. Papégnies, V. Labatut, R. Dufour & G. Linarès, “Conversational Networks for Automatic Online Moderation,” <em>IEEE Transactions on Computational Social Systems </em>6(1):38–55, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01999546">hal-01999546</a>⟩ DOI: <a href="https://doi.org/10.1109/TCSS.2018.2887240">10.1109/tcss.2018.2887240</a></li> <li>[2] É. Papegnies, R. Dufour, V. Labatut & G. Linarès. “Détection de messages abusifs au moyen de réseaux conversationnels,” in <em>8ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et informatiques</em> (MARAMI), 2017. ⟨<a href="https://hal.science/hal-01614279">hal-01614279</a>⟩</li> <li>[3] É. Papegnies, V. Labatut, R. Dufour, & G. Linares. “Graph-based Features for Automatic Online Abuse Detection,” in <em>International Conference on Statistical Language and Speech Processing</em> (SLSP), Springer, Lecture Notes in Computer Science 10583:70-81, 2017. ⟨<a href="https://hal.science/hal-01571639/">hal-01571639</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-68456-7_6">10.1007/978-3-319-68456-7_6</a></li> <li>[4] N. Cécillon. “Exploration de descripteurs de plongements de graphes pour la détection de messages abusifs,” MSc Thesis, <em>Université d'Avignon, </em>2019. ⟨<a href="https://dumas.ccsd.cnrs.fr/dumas-04073337">dumas-04073337</a>⟩</li> <li>[5] N. Cécillon, V. Labatut, R. Dufour, and G. Linarès, “Abusive Language Detection in Online Conversations by Combining Content- and Graph-based Features,” in <em>International Workshop on Modeling and Mining Socia-Media Driven Complex Networks</em>, Frontiers in Big Data 2:8, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02130205">hal-02130205</a>⟩ DOI: <a href="http://doi.org/10.3389/fdata.2019.00008">10.3389/fdata.2019.00008</a></li> <li>[6] N. Cécillon, V. Labatut, R. Dufour, & G. Linarès. “Tuning Graph2vec with Node Labels for Abuse Detection in Online Conversations,” in <em>11ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et</em> informatiques (MARAMI), 2020. <a href="https://hal.science/hal-02993571" target="_blank" rel="noopener">⟨hal-02993571⟩</a> <a href="http://ceur-ws.org/Vol-2750/paper8.pdf">Official Page</a></li> <li>[7] N. Cécillon, V. Labatut, R. Dufour & G. Linarès. “Graph embeddings for Abusive Language Detection,” <em>Springer Nature Computer Science</em> 2:37,<em> </em>2021<em>. </em>⟨<a href="https://hal.science/hal-03042171/">hal-03042171</a>⟩ DOI: <a href="http://doi.org/10.1007/s42979-020-00413-7">10.1007/s42979-020-00413-7</a></li> <li>[8] N. Cécillon, R. Dufour & V. Labatut. “Approche multimodale par plongements de texte et de graphes pour la détection de messages abusifs,” <em>Traitement Automatique des Langues</em><em> </em>62:13-38, 2021. ⟨<a href="https://hal.science/hal-03527016">hal-03527016</a>⟩ <a href="https://aclanthology.org/2021.tal-2.2/">Official Page</a></li> </ul> <p>The following publications use the signed version of the graphs. The modified extraction method is described in publication [9].</p> <ul> <li>[9] N. Cécillon. “Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection,” PhD Thesis, <em>Université d'Avignon, </em>2024. ⟨<a href="https://theses.hal.science/tel-04441308">tel-04441308</a>⟩</li> </ul> <p><strong>Funding. </strong>Part of this work was funded by a grant from the <em>Provence-Alpes-Côte-d'Azur</em> region (PACA, France) and the <em>Nectar de Code</em> company.</p> <p><strong>Citation. </strong>If you use this dataset, please cite paper [1] for the unsigned networks:</p> <p><br><code>@Article{Papegnies2019,</code><br><code> author = {Papegnies, Étienne and Labatut, Vincent and Dufour, Richard and Linarès, Georges},</code><br><code> title = {Conversational Networks for Automatic Online Moderation},</code><br><code> journal = {IEEE Transactions on Computational Social Systems},</code><br><code> year = {2019},</code><br><code> volume = {6},</code><br><code> number = {1},</code><br><code> pages = {38-55},</code><br><code> doi = {10.1109/TCSS.2018.2887240},</code><br><code>}</code></p> <p>and [9] for the signed ones:</p> <p><br><code>@PhdThesis{Cecillon2024,</code><br><code> author = {Cécillon, Noé},</code><br><code> title = {Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection},</code><br><code> school = {Université d'Avignon},</code><br><code> year = {2024},</code><br><code> type = {PhD Thesis},</code><br><code> address = {Avignon, FR},</code><br><code> url = {https://theses.fr/2024AVIG0100},</code><br><code>}</code></p>
Supporting Online Data for 'Timber trade in the United States of America 1870 to 2017. A socio-metabolic analysis'
<p>This data file (.xlsx) contains all data used to create tables and figures of the study "Timber trade in the United States of America 1870 to 2017. A socio-metabolic analysis". Main article is available under: https://doi.org/10.1080/01615440.2024.2316039</p>
Dataset for the publication: Tactile Convolutional Networks for Online Slip and Rotation Detection
<p>Raw data recorded for the publication "Tactile Convolutional Networks for Online Slip and Rotation Detection" in rosbag format. Each .bag file contains recordings of two tactile sensors sampled with 1kHz as "sensor_msgs/Image" and a third channel for data labeling in the format "sr_robot_msgs/UBI0All". Detailed informations about the preprocessing and labeling are in the publication.</p>
Mapping practices of online community management
<p>Results of an online survey conducted during the period 13-31st March 2018. Responses were collected through a Google form; instructions and context were made available on a <a href="http://www.cottica.net/2018/03/13/mapping-online-community-management-practices-can-i-have-a-little-help-with-my-thesis/">web page</a>, the link of which was disseminated through Facebook, Twitter and on the e-mint mailing list (dedicated to professional online community managers on Yahoo. </p> <p>Each row of the file represents one questionnaire; each column represents one question.</p> <ul> <li>The first 9 questions are all of the format "To manage your online community, which of these courses of actions do you take, and how often?". The answers were given on a Likert-4 scale.</li> <li>The 10th question was "Do you want to add any other activity that uses up significant chunks of your community management time?". The answers were given in free form text.</li> <li>The 11th question was "How old is the community you manage? If you manage more than one, refer to the oldest." The answers were given as multiple choice, with three possible choices.</li> <li>The 12th question was "How large is the community you manage? If you manage more than one, refer to the largest.". The answers were given as multiple choice, with five possible choices.</li> </ul> <p>This work is part of my PhD Thesis.</p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Dataset of FEUTURE Online Paper No. 8 "Understanding the EU-Turkey Sectoral Trade Flows During 1990-2016: a Trade Gravity Approach"
<p>The dataset provides the following variables for 1990-2016 for Austria Belgium, Bulgaria, China, Denmark, France, Germany, Greece, Hungary, Iran, Ireland, Italy, Japan, Netherlands, Poland, Russia, Spain, Sweden, UK, and USA: </p> <p>- Total export and import (in USD and %)</p> <p>- Intermediate goods exports and imports (in USD and %)</p> <p>- Household goods exports and imports (in USD and %)</p> <p>- Capital goods exports and imports (in USD and %)</p> <p>- Mixed-end exports and imports (in USD and %)</p> <p>- Miscellaneous exports and imports (in USD and %)</p> <p> </p>
Dataset of FEUTURE Online Paper No. 9 "The Financial Flows and the Future of EU-Turkey Relations"
<p>The dataset provides the annual GDP (in US Dollar) and the annaul GDP growth (in %) of Turkey between 1960 and 2016.</p>
Methodological Appendix for FEUTURE Online Paper No. 28 "Narratives of a Contested Relationship: Unravelling the Debates in the EU and Turkey"
<p>This is the methodological appendix for the narrative analysis conducted by the researchers from the University of Cologne (UzK) and Middle East Technical University (METU) within the scope of the ongoing research project, which is entitled “The Future of EU-Turkey Relations: Mapping Dynamics and Testing Scenarios” (FEUTURE) and funded by the European Union’s Horizon 2020 Research and Innovation Programme. The appendix is designed to provide comprehensive information on the operationalization of the qualitative research that was carried out for the FEUTURE Online Paper No. 28 “Narratives of a Contested Relationship: Unravelling the Debates in the EU and Turkey” published in February 2019.</p> <p>The following sections present details on the selected actors, data sampling and collection, codebook and variables, and overall time span of the research.</p>
The World Atlas of Language Structures Online
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dryer, Matthew S. & Haspelmath, Martin (eds.) 2013. The World Atlas of Language Structures Online. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at https://wals.info)</p> </blockquote>
MaMo online Webinar Cycle "Materializing Modernity - Landscape, Architecture and Anthropology intersections in 20th-century rurality"
<p>A dataset (WP2-B_Materials_1) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_1 (PART 1) - Contents</p> <ul> <li>Programme of the MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" held in April-May 2021 on ZOOM online platform</li> <li>Banner of the MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" held in April-May 2021 on ZOOM online platform</li> <li>1st meeting - Introduction: MaMo - an introduction to Albanian Socialist and Post-Socialist rurality by Federica Pompejano, MSCA-IF Fellow, Department of Ethnology, Institute of Cultural Anthropology and Art Studies (IAKSA), Academy of Albanian Studies, Albania<br> Oral presentation: "The Albanian Village as an anthropological encounter of modernity" by Nebi Bardhoshi, Associate Professor and Director of the Institute of Cultural Anthropology and Art Studies (IAKSA), Academy of Albanian Studies, and Olsi Lelaj, Researcher, Department of Ethnology, IAKSA, Academy of Albanian Studies, Albania</li> <li>MaMo Webinar Cycle - 1st meeting banner</li> <li>MaMo Webinar Cycle - 1st meeting poster with oral presentation abstract</li> <li>MaMo Webinar Cycle - 1st meeting poster with oral presenters short bio</li> <li>MaMo Webinar Cycle - 1st meeting Instagram post</li> <li>2nd meeting - Oral presentation: "Exploring Rurality in Southern Italy: the experience of 'Sonic Ethnography'" by Nicola Scaldaferri, Associate Professor, Department of Cultural and Environmental Heritage, Università Statale di Milano, Italy, and Lorenzo Ferrarini, Lecturer in Social and Visual Anthropology, Granada Centre for Visual Anthropology, University of Manchester, United Kingdom</li> <li>MaMo Webinar Cycle - 2nd meeting banner</li> <li>MaMo Webinar Cycle - 2nd meeting poster with oral presentation abstract</li> <li>MaMo Webinar Cycle - 2nd meeting poster with oral presenters short bio</li> <li>MaMo Webinar Cycle - 2nd meeting Instagram post</li> </ul>
Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions
<p><strong>Looking into the Futures: Delphi Survey about the MENA region</strong></p> <p>In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled.</p> <p>The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).</p>
Analysis of Bussines online and Atificial Intellegence
<p>Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p>Corresponding dataset for the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The structure of the dataset is as follows:</p> <p><strong>Annotations</strong> </p> <p><em>List of annotations made per day for each of the analysed projects.</em></p> <p><code>annotations.csv </code></p> <p><strong>Comments </strong></p> <p><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></p> <p><code>comments.csv </code></p> <p><strong>Rolechanges</strong> </p> <p><em>List of roles per user to determine number of role changes </em></p> <p><code>478_rolechanges.csv</code></p> <p><code>1104_rolechanges.csv</code></p> <p><code>...</code></p> <p><strong>Totalnetworkdata</strong> </p> <p><em>Network data (edge and node sets) for the given projects (without time slices).</em></p> <p>Edges </p> <ul> <li> <p><code>478_edges.csv</code></p> </li> <li> <p><code>1104_edges.csv</code></p> </li> </ul> <p>Nodes </p> <ul> <li> <p><code>478_nodes.csv</code> </p> </li> <li> <p><code>1104_nodes.csv</code> </p> </li> </ul> <p><strong>Trajectories</strong> </p> <p><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></p> <p>478 </p> <ul> <li>Edges <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> </ul> </li> <li> <p>...</p> </li> <li>Nodes <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> <p> </p> <p>1104 </p> <ul> <li> <p>Edges </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p>Nodes </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><code>...</code></p> </li> </ul> <p> </p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
Additional online material for publication: Frames and Narratives in scientific press releases on ocean climate change and ocean plastic.
<p>This is the additional material for the publication of paper: Frames and Narratives in scientific press releases on ocean climate change and ocean plastic. The paper is currently under submission. </p> <p>Included with the material is a codebook used to code narrative- and frame variables in scientific press releases and a cross-tabulate showing the frame variables that were coded per press release. </p> <p>For questions about the material, or information about how to reference to the material, please contact Aike Vonk (a.n.vonk@uu.nl).</p>
Creating Safe Environments: Optimal Acoustic Alarming of Laypeople in Fire Prevention - Online Supplement
<p>This online supplement contains datasets (raw data) and study material collected in an experimental study by the University of Münster, Germany. The study is part of a larger research project (<a href="https://www.brawa.ovgu.de/en/">https://www.brawa.ovgu.de/en/</a>) and examined the perception of acoustic fire alarm signals.</p> <p>Hazards like fires occur regularly and can cost people’s lives. Optimal auditory alarm signals enable laypeople to recognize dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (<em>N</em> = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were most effective. In Study 2 (<em>N</em> = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction.</p> <p>The ethics committee of the University of Münster approved the studies (ID 2021-57-MT), and we preregistered both studies with AsPredicted.org under numbers #77031 and #81137 (see <a href="https://aspredicted.org/vx2rr.pdf">https://aspredicted.org/vx2rr.pdf</a> and <a href="https://aspredicted.org/mt9g3.pdf">https://aspredicted.org/mt9g3.pdf</a>). The studies were supported by the German Federal Ministry of Education and Research (grant numbers 13N15416 and 13N15419).</p> <p><strong>This online supplement includes: </strong></p> <ul> <li>Two codebooks describing all instructions and items in Study 1 and in Study 2</li> <li>Raw data (anonymized) and analysis scripts (Note: The raw data contains only the information of persons who were included in the analysis and who gave their informed consent. Some demographic information was deleted to ensure anonymity.)</li> <li>Study material: <ul> <li>Alarm signal example</li> <li>Hearing test implemented in both studies</li> </ul> </li> </ul>
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.