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31 results for “Online Communities”
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</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>
FlyWire: Online community for whole-brain connectomics
<p>A ground truth dataset for 3D neuron reconstruction from electron microscopy (EM) images of the fly whole-brain, created for our project FlyWire: A human-AI collaboration to map the fly connectome. For more information, please visit <a href="https://flywire.ai/">https://flywire.ai/</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p><em>FlyWire: Online community for whole-brain connectomics</em><br> Dorkenwald et al.<br> bioRxiv 2020.08.30.274225; doi: https://doi.org/10.1101/2020.08.30.274225</p> <p> </p> <p><strong>Dataset description</strong></p> <ul> <li><strong>cremi_realigned.tar.gz</strong> <ul> <li>cremi_{a,b,c}_realigned.h5: re-aligned CREMI volumes (<a href="https://cremi.org/">https://cremi.org/</a>)</li> <li>cremi_b_realigned_new.h5: re-aligned CREMI B volume with de-novo annotation</li> </ul> </li> <li><strong>focused.tar.gz</strong> <ul> <li>A set of densely annotated volumes covering diverse structures in the fly brain that are underrepresented in the CREMI volumes.</li> </ul> </li> <li><strong>sparse.tar.gz</strong> <ul> <li>A set of sparsely annotated volumes covering tricky failure modes in the initial segmentation attempt. Here intracellular structures are often oversegmented conservatively to prevent merge errors at the expense of introducing some split errors. </li> </ul> </li> <li><strong>glia.tar.gz</strong> <ul> <li>Semi-automatically generated ground truth for glia detction. A set of subvolumes from the FlyWire segmentation was sampled from which human experts classified automatically generated segments into either neurons or glia, while existing merge errors were excluded from annotation.</li> </ul> </li> </ul>
Effects of community management on user activity in online communities
<p>Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft <a href="https://www.overleaf.com/13551865bzgswwbvkpgq#/52358582/">here</a>.</p> <p>Instructions:</p> <ol> <li>Unzip the files.</li> <li>Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.</li> <li>Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.</li> <li>Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file</li> <li>Launch Stata and open each flat csv files with it, then save it in Stata format.</li> <li>Use the provided Stata .do scripts to replicate results.</li> </ol> <p>Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.</p> <p> </p>
The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities - Supplementary Tables
<p><strong>Supplementary Table 1:</strong></p> <p>A map between each of the genomes in mOTUs-db (3’747’151), the associated study and its metagenomic sample (in case of MAGs).</p> <p>Columns:</p> <p><code> GENOME → Unique mOTUs-db name of the genome</code><br><code> STUDY → Unique mOTUs-db name of the study</code><br><code> IS_MAG → True if genome is a MAG, otherwise False </code><br><code> METAGENOMIC_SAMPLE → Unique name of the metagenomic sample or NA in case of non-MAG genome</code></p> <p>Example:</p> <p><code> GENOME STUDY IS_MAG METAGENOMIC_SAMPLE</code><br><code> ---------------------------------------------------------------------------------------------</code><br><code> ACIN21-1_SAMN05421555_MAG_00000001 ACIN21-1 True ACIN21-1_SAMN05421555_METAG</code><br><code> RSGB23-1_GCA-006096615-V1_GENO_10000001 RSGB23-1 False NA</code></p> <p><strong>Supplementary Table 2:</strong></p> <p>A map between all non-MAG genomes (919’090) and their source (e.g. Refseq or JGI).</p> <p>Columns:</p> <p><code> GENOME → Unique mOTUs-db name of the genome</code><br><code> SOURCE_SAMPLE_LINK → Link to the original location of this genome</code></p> <p>Example:</p> <p><code> #GENOME SOURCE_SAMPLE_LINK</code><br><code> --------------------------------------------------------------------------------------------------------</code><br><code> JGIG23-1_GA0055041_GENO_10000001 https://gold.jgi.doe.gov/analysis_project?id=Ga0055041</code><br><code> RSGB23-1_GCA-006717865-V1_GENO_10000001 https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_006717865.1</code></p> <p><strong>Supplementary Table 3:</strong></p> <p>A list of all metagenomic studies processed for the mOTUs-db, their number of samples, the number of reconstructed MAGs and the associated publication.</p> <p>Columns:</p> <p><code> STUDY --> Unique mOTUs-db study identifier</code><br><code> BIOPROJECT --> Public identifier (NCBI/JGI) of metagenomic sequencing project</code><br><code> SAMPLES --> Number of metagenomic samples</code><br><code> MAGs --> Number of reconstructed MAGs</code><br><code> PUBLICATION --> Link to publication</code></p> <p>Example:</p> <p><code> STUDY BIOPROJECT SAMPLES MAGs PUBLICATION</code><br><code> -------------------------------------------------------------------------------------------------</code><br><code> ACIN21-1 PRJEB44456 58 1,110 https://www.nature.com/articles/s42003-021-02112-2</code></p> <p><strong>Supplementary Table 4:</strong></p> <p>Mapping between mOTUs-db sample identifier, the associated biosample and the environment.</p> <p>Columns:</p> <p><code> SAMPLE --> Unique mOTUS-db sample identifier</code><br><code> BIOSAMPLE --> Public identifier (NCBI/JGI) of metagenomic sample</code><br><code> STUDY --> Unique mOTUs-db study identifier</code><br><code> ENVIRONMENT --> Environment of metagenomic sample</code><br><code> SOURCE_SAMPLE_LINK --> Link to the original location of this sample</code></p> <p>Example:</p> <p><code> #SAMPLE BIOSAMPLE STUDY ENVIRONMENT SOURCE_SAMPLE_LINK</code><br><code> ---------------------------------------------------------------------------------------------------------------------</code><br><code> ACIN21-1_SAMN05421555_METAG SAMN05421555 ACIN21-1 marine https://www.ncbi.nlm.nih.gov/biosample/SAMN05421555/</code></p> <p><strong>Supplementary Table 5:</strong></p> <p>A list of environments covered in the mOTUs-db mapped to the respective NCBI taxonomy (if possible)</p> <p>Columns:</p> <p><code> TERM --> Unique environment name</code><br><code> NCBI TAXONOMY ID --> Link to the NCBI taxonomy</code></p> <p>Example:</p> <p><code> TERM NCBI TAXONOMY ID</code><br><code> ----------------------------------------------</code><br><code> activated sludge metagenome NCBI:txid942017</code><br><code> air metagenome NCBI:txid655179</code></p>
Full dataset for: Diversifying environmental volunteers by engaging with online communities
<p>1. Environmental volunteering can benefit participants and nature through improving physical and mental wellbeing while encouraging environmental stewardship. To enhance achievement of these outcomes, conservation organisations need to reach different groups of people to increase participation in environmental volunteering. This paper explores what engages communities searching online for environmental volunteering.</p> <p>2. We conducted a literature review of 1032 papers to determine key factors fostering participation by existing volunteers in environmental projects. We found the most important factor was to tailor projects to the motivations of participants. Also important were: promoting projects to people with relevant interests; meeting the perceived benefits of volunteers and removing barriers to participation.</p> <p>3. We then assessed the composition and factors fostering participation of the NatureVolunteers's online community (n = 2216) of potential environmental volunteers and compared findings with those from the literature review. We asked whether projects advertised by conservation organisations meet motivations and interests of this online community.</p> <p>4. Using Facebook insights and Google Analytics we found that the online community were on average younger than extant communities observed in studies of environmental volunteering. Their motivations were also different as they were more interested in physical activity and using skills and less in social factors. They also exhibited preference for projects which are outdoor based, and which offer close contact with wildlife. Finally, we found that the online community showed a stronger preference for habitat improvement projects over those involving species-survey based citizen science.</p> <p>5. Our results demonstrate mis-matches between what our online community are looking for and what is advertised by conservation organisations. The online community are looking for projects which are more solitary, more physically active and more accessible by organised transport. We discuss how our results may be used by conservation organisations to better engage with more people searching for environmental volunteering opportunities online.</p> <p>6. We conclude that there is a pool of young people attracted to environmental volunteering projects whose interests are different to those of current volunteers. If conservation organisations can develop projects that meet these interests, they can engage larger and more diverse communities in nature volunteering.</p>
Models for Building Relationships and Refining Approaches for Collaborating with Indigenous Communities: Reporting on Five Years of Online Work with the Nuer.
<p>Models for Building Relationships and Refining Approaches for Collaborating with Indigenous Communities: Reporting on Five Years of Online Work with the Nuer.</p> <p>Presentation given by Tatiana Reid at the Language Documentation and Archiving conference on the 5. October 2022 at the Berlin-Brandenburg Academy of Sciences and Humanities.</p> <p>I report on my experience of working with the Nuer-speaking community. Nuer is a little studied West Nilotic language of South Sudan and Ethiopia. Over the past five years I have carried description, documentation and language development exclusively online through social media such as Messenger and WhatsApp, and more recently, via Zoom.</p> <p>I will highlight various aspects of this mode of work, showing what is possible to achieve working remotely and the practicalities of doing so. These include, but are not limited to: utilising google documents when working on recorded narratives with reference speakers; and collecting data using a hybrid mode where the researcher (online) and a community member (in-person) conduct interviews with speakers producing high quality recordings. I will also draw on my experience of outsourced documentation – where audio and video recordings are made by local organisations in East Africa. I will touch on the experience (together with the colleagues at the University of Surrey) of working towards literacy development. This work involved creating databases that the community members populated with data collected via our ‘Nuer Lexicon’ Facebook group; and running an online Nuer literacy development workshops attended by over 30 community members from South Sudan, Ethiopia and from around the world.</p> <p>I claim that the prerequisite for conducting remote work with a language community is the collaborative approach between the researchers and the speakers of the language, which places greater emphasis on the 1) ability of the community members to to carry out data collection and processing and 2) continuity. My experience shows that ELDP/ELAR’s advice for field linguists to strengthen capacity building within language communities is gaining even more relevance in the predominantly virtually connected world.</p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Replication Package for "The Double-edged Sword of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange"
<p>This is a replication package for "The Double-edged Sword of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange".</p>
Full dataset for: Diversifying environmental volunteers by engaging with online communities
Open the record for dataset details and reuse information.
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><p> </p><p> </p>
Producer Conflict Management Approaches in Online Peer Production Communities – Case Study of OpenStreetMap
<p>These files are supplementary materials to a paper: Youjin Choe, Martin Tomko, and Mohsen Kalantari. 2023. Producer Conflict Management Approaches in Online Peer Production Communities – Case Study of OpenStreetMap. In <em>Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. </em>ACM, New York, NY, USA, 19 pages. https://doi.org/10.1145/3544548.3581036:<br> <br> (1) finaldata_FILENAMES: Files that starts with "finaldata_" contain final data we used for the analysis in text file (binary data) with a codebook. It also contains the count data (with a codebook) that we initially used but failed to pass the linearity assumption.</p> <p>(2) LOGITM~1.R: It is R code that we used for logistic regression analysis.<br> <br> (3) osmcha-parser.py: This our Python code and the resulting files that extracted raw data from OSM Changeset Analyzer (OSM) website.<br> <br> (4) 20210920_AnnotatorsGuide.pdf: This file is the annotators guide that we shared with our two external annotators for introduction and training of our data annotation.</p>
Online companion -Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids
<p>Online companion for the article "Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids".</p>
Online Group-based Dual-task Training to Improve Cognitive Function of Community-dwelling Older Adults
ClinicalTrials.gov study NCT05573646. IPD Sharing: NO. Countries: 1. Publications: 1.
Community-based Study With 1st-year Secondary Students (n=1104) and Their Environment. It Evaluates a Complex Intervention (Community Actions, Classroom-based Mindfulness for Adolescents, and Online M
ClinicalTrials.gov study NCT07357792. IPD Sharing: NO. Countries: 1. Publications: 5.
Feasibility of an Online Exercise Community Among Individuals With Type 2 Diabetes
ClinicalTrials.gov study NCT05668442. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Randomized Controlled Trial Evaluating an Online Intervention in Increasing Seasonal Influenza Vaccination Among Community Dwelling People Aged ≥65 Years
ClinicalTrials.gov study NCT05155241. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Implementation of Mind-Body Practices in an Online Community of Caring (CaringBridge)
ClinicalTrials.gov study NCT05002842. IPD Sharing: NO. Countries: 1. Publications: 1.
Online machine learning algorithms to predict link quality in community wireless mesh networks
<p>FunkFeuer raw topology data from 15-01-2016 to 28-01-2016 retrieved on 17-02-2016 from http://opendata.confine-project.eu/dataset/funkfeuer-topology-data. This dataset was employed in "Miguel L. Bote-Lorenzo, Eduardo Gómez-Sánchez, Carlos Mediavilla-Pastor, Juan I. Asensio-Pérez, Online machine learning algorithms to predict link quality in community wireless mesh networks, Computer Networks, Volume 132, 2018, Pages 68-80, https://doi.org/10.1016/j.comnet.2018.01.005."</p>
Brief Online Music Intervention (BOMI) in Improving the Mental Well-being of Young People in the Community in Hong Kong
ClinicalTrials.gov study NCT04638244. IPD Sharing: NO. Countries: 0. Publications: 8.
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)
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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.