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86 results for “chat”
Romanised Arabic Chat Data
<p>Chat room conversations in Romanised Arabic (44.83%), including code switching in English (4.68%) and French (9.44%). The Romanised Arabic form primarily denoted the Levantine dialect (Lebanese, Egyptian) among 10 participants. These conversations were recorded under natural observation on 10th June 2015 in intervals of 90 minutes. All instances of data collection occurred between midday-2pm, Lebanese local time; they are listed in consecutive order.</p> <p>Conversations were collected on the instant messaging website, www.icq.com, and specifically, the chat room server titled ‘#icq-lebanon’. As per the terms outlined in the website company policy on 10th June 2015, no consent was required to use the chat room entries or other information from the online users, who constitute the participants of this study.</p> <p>Any data which marked personally identifiable information has been excluded, unless considered relevant for the purpose of the linguistic analysis.</p>
Figure 2. Male Sombre Rock Chat Oenanthe dubia, specimen USNM 664494 in Notable records and observations of four passerines in Djibouti, 2020
Figure 2. Male Sombre Rock Chat Oenanthe dubia, specimen USNM 664494, showing aberrant plumage (ventral and dorsal views) (Katie Sayers)
Open Chat Series: How-to-Advocacy "Why advocate for open scholarly communication in the social sciences and humanities?"
<p>On the 21.10.2022 Alíz Horváth, researcher, expert in East Asian studies, creator of “Humanista” podcast - and member of OPERAS's Special Interest Group "Advocacy" moderated an Open Chat with our guest speaker Per Pippin Aspaas, Head of Library Research and Publishing Support at the UiT in Tromsø, discussing the question "Why advocate for scholarly communication in the social sciences and humanities?"</p>
Open Chat Series: How-to-Advocacy "How to Advocate for Innovation in Scholarly Communication?"
<p>On 25. November 2022 Magdalena Wnuk moderated the Open Chat with Maciej Maryl from IBL PAN and talked about how to advocate for innovation in scholarly communication. Together with our guest speaker and the audience we discussed the advocacy goals and challenges for the SSH community while advocating for innovation in open science. </p>
Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data
<p>These are the data and resources used for a Twitch Emote recommendation system using a Word2Vec model. The nature and exploration of the data is described in Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data. To protect the privacy of the users whose messages were scraped to build this corpus, names and timestamps have been removed and only the message bodies are included. However, a tutorial for this project is included on the project GitHub: https://github.com/KoroshM/Emote-Recommender.<br> <br> embeddings.tsv and labeled_metadata.tsv may be used in TensorFlow's embedding projector to visualize the embedding space.<br> <br> Note: Model files are the following:<br> embeddings.tsv<br> labeled_metadata.tsv<br> model<br> model.model**<br> model.wv.vectors.npy</p> <p>**Located here: https://drive.google.com/drive/folders/1RZC4JA4CpAcwoo6dOwq_jobTd6dNi_n2?usp=sharing</p>
Choline acetyltransferase (ChAT) - Amyloid beta peptides complex Molecular dynamics Trajectories
<p>In silico molecular dynamics study was performed for the choline acetyltransferase (ChAT) - Abeta peptides complex. The molecular docking of Aβ<sub>40 </sub>and Aβ<sub>42</sub> on ChAT suggested three most probable binding clusters for both the Aβ peptides. Thus generating ChAT-Aβ<sub>40</sub> Cluster-0, ChAT-Aβ<sub>40</sub> Cluster-1, ChAT-Aβ<sub>40</sub> Cluster-2 for Aβ<sub>40</sub> peptide on ChAT, likewise ChAT-Aβ<sub>42</sub> Cluster-0, ChAT-Aβ<sub>42</sub> Cluster-1, ChAT-Aβ<sub>42</sub> Cluster-2 were generated for Aβ<sub>42</sub> peptide on ChAT.</p> <p>Each of the folders contains the topology file with a ‘.gro’ extension and a trajectory file with ‘.xtc’ extension generated from the 100 ns molecular dynamics performed for each of the clusters mentioned above that were generated from the molecular docking. The folders are named as follows:</p> <ol> <li>ChAT_AB40_Cluster_0: Containing the topology file (ab40_0.gro) and the trajectory file (ab40_0.xtc)</li> <li>ChAT_AB40_Cluster_1: Containing the topology file (ab40_1.gro) and the trajectory file (ab40_1.xtc)</li> <li>ChAT_AB40_Cluster_2: Containing the topology file (ab40_2.gro) and the trajectory file (ab40_2.xtc)</li> <li>ChAT_AB42_Cluster_0: Containing the topology file (ab42_0.gro) and the trajectory file (ab42_0.xtc)</li> <li>ChAT_AB42_Cluster_1: Containing the topology file (ab42_1.gro) and the trajectory file (ab42_1.xtc)</li> <li>ChAT_AB42_Cluster_2: Containing the topology file (ab42_2.gro) and the trajectory file (ab42_2.xtc</li> </ol>
Replication data for Nematzadeh et al. "Information Overload in Group Communication: From Conversation to Cacophony in the Twitch Chat"
<p>A subset of the chat logs dump from Twitch used in this work is provided, to help replicate the central findings of this work (<a href="https://doi.org/10.5281/zenodo.1182793">https://doi.org/10.5281/zenodo.1182793</a>). Data are aggregated and include the number of messages posted in each channel and the number of users posting them, sampled at intervals of 5 minutes. To protect the identity of the users in this data collection, message contents and user names are not included in this dataset. Stream names have been replaced with numeric IDs. <br> No additional filtering or data cleaning operation has been applied to this data. Replication code is available on Github (<a href="https://github.com/glciampaglia/twitch-overload-replication">https://github.com/glciampaglia/twitch-overload-replication</a>).</p>
Pin-tailed whydah and Cape robin-chat calls for passive acoustic monitoring
<p>We provide the audio data (.wav) used to train neural network classifiers along with the corresponding labelled files (.svl). The .svl files are natively read using Sonic Visualiser (https://www.sonicvisualiser.org/) but can directly be read using Python as these are XML files.</p> <p>This is a three class classification dataset. The recordings were obtained using an AudioMoth which was placed at one location in Intaka Island Nature Reserve, Cape Town, South Africa. The recorder was attached to a tree at approximately 1.5 meters from the ground. The sampling rate was set to 48000Hz with a bit rate of 768kbps. The recordings took place in January 2021. While further recordings exist we only provide a small subset here. Additional data can be requested.</p> <p><strong>Files provided</strong></p> <p>Audio.zip - contains audio files (.wav)</p> <p>Annotations.zip - contains the corresponding labels (.svl) for Sonic Visualiser</p> <p><strong>Class description</strong></p> <p>CRC: calls of the Cape robin-chat (Cossypha caffra)</p> <p>PTW: calls of the pin-tailed whydah (Vidua macroura)</p> <p>NOISE: any sound event that does not contain a Cape robin-chat or pin-tailed whydah call</p> <p> </p> <p>Parts of this data was used in two MSc dissertations:</p> <ul> <li>"Acoustic Data Augmentation for Small Passive Acoustic Monitoring Datasets", Aime Nshimiyimana, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> <li>"Pre-training neural networks on Xeno-Canto and eBird for bioacoustic classification models", Mikwa Boris Tamanjong, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> </ul>
Actividad de Chat de Twitch de 6 streamers hispanohablantes (Diciembre de 2022)
<p>Base de datos generada a partir del raspado de mensajes (chat-scrapping) de los canales de Twitch de los streamers Illojuan, Ibai, Auronplay, Iamcristinini, Staryuuki y Rivers_gg durante un periodo de 10 días (entre el 11 de diciembre y 21 de diciembre de 2022).</p>
Chat-GPT and Systems Thinking
<p>This file contains all conversations with ChatGPT regarding its ability to help improving systems thinking skills incl. causal loop diagrams and system dynamics modelling.</p>
Human and mouse ALS tissue sections immunolabeled for FUS, SFPQ, ChAT and counterstained with DAPI
<p>Raw images and image processing scripts to reproduce results of the manuscript entitled 'Human and mouse ALS tissue sections immunolabeled for FUS, SFPQ, ChAT and counterstained with DAPI' and posted on BioRxiv in August 2020.</p> <p><strong>1. RowMouseImages.zip</strong></p> <p>Tissue sections from spinal cord of SOD1- and VCP-mutant ALS mouse models together with control immunolabeled for FUS, SFPQ, ChAT and counterstained with DAPI.</p> <p><strong>2. HumanMouseImages.zip</strong></p> <p>Tissue sections from spinal cord of Healthy and sporadic ALS donors immunolabeled for FUS or SFPQ, ChAT and counterstained with DAPI.</p> <p><strong>3. ImageProcessingScripts.zip</strong></p> <p>Scripts to preprocess these images.</p> <p><strong>4. Data.zip</strong></p> <p>CSV files of the single-cell measurements obtained from CellProfiler after segmentation as presented in ImageProcessingScripts.zip. Downstream analysis leading to automatic identification of MNs subpopulation can be acessed on Github <a href="https://github.com/RLuisier/ALSdisMNs">https://github.com/RLuisier/ALSdisMNs</a></p> <p>---------</p> <p>The two series of multichannel fluorescent microscopy data have been used in several manuscripts including:</p> <ol> <li><strong><a href="http://paperpile.com/b/Jw7dut/7eYh">Luisier R, Tyzack GE, Hall CE, Mitchell JS, Devine H, Taha DM, et al. Intron retention and nuclear loss of SFPQ are molecular hallmarks of ALS. Nat Commun 2018; 9: 2010.</a></strong></li> <li><strong><a href="http://paperpile.com/b/Jw7dut/SxxR">Tyzack GE, Luisier R, Taha DM, Neeves J, Modic M, Mitchell JS, et al. Widespread FUS mislocalization is a molecular hallmark of amyotrophic lateral sclerosis. Brain 2019; 142: 2572–80.</a></strong></li> </ol> <p> </p>
What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing
<p> </p> <p> </p> <p><strong>1. Chat message data</strong></p> <p>- kbo2011_naverbasevall_comment.sql: chat messages in Naver Sports that users posted in 2011<br /> - kbo2012_naverbasevall_comment.sql: chat messages in Naver Sports that users posted in 2012</p> <p>Each MySQL table consists of the following six columns.<br /> 1. comment_id: Integer variable to identify each comment<br /> 2. gid: Integer variable to identify each game<br /> 3. uid: Integer variable to identify each user<br /> 4. category_id: A team name that a user selected when a chat message is posted<br /> 5. content: Chat message text<br /> 6. post_time_relative: Elapsed seconds since the game starts</p> <p> </p> <p><strong>2. KBO game information</strong></p> <p>- game_basic.sql: KBO game information in 2011/2012<br /> <br /> (This data was collected from here, http://sports.news.naver.com/schedule/index.nhn?uCategory=&category=kbo&year=2010&month=03)</p> <p>This MySQL table consists of the following 12 columns.<br /> 1. gid: Integer variable to identify each game<br /> 2. game: Game ID used in Naver Sports<br /> 3. year: year of the game play<br /> 4. month: month of the game play<br /> 5. date: date of the game play<br /> 6. time: time of the game play<br /> 7. day: day of the game play<br /> 8. team_away: name of the away team<br /> 9 team_home: name of the home team<br /> 10. score_away: score by the away team<br /> 11. score_home: score by the home team<br /> 12. location: location of the game play</p> <p> </p> <p><strong>3. KBO game play-by-play data </strong></p> <p>- game_playbyplay.sql: playbyplay data in 2011/2012</p> <p>(This data was collected from Naver Sports; EX, http://sports.news.naver.com/gameCenter/gameRecord.nhn?category=kbo&gameId=20100327HHSK0)</p> <p>This MySQL table consists of the following 21 columns.</p> <p>1. play_no: Integer variable to identify each play<br /> 2. gid: Integer variable to identify each game<br /> 3. inning: inning of the play<br /> 4. pitcher: current pitcher name<br /> 5. batter: current batter name<br /> 6. batter_no: current batter order<br /> 7. pitch: the pitch result<br /> 8. strike: current number of strikes<br /> 9. ball: current number of balls<br /> 10. out_cnt: current number of outs<br /> 11. score_home: current score of the home team<br /> 12. score_away: current score of the away team<br /> 13. 1st_base: player name on the 1st base <br /> 14. 2nd_base: player name on the 2nd base<br /> 15. 3rd_base: player name on the 3rd base<br /> 16. runner_state: overall state of the three bases (ex: 1 --> A player is on 1st base, but 2nd and 3rd bases are empty, 13 --> Two players are on 1st and 3rd bases respectively, but 2nd base is empty)<br /> 17. runner_cnt: the number of players on bases<br /> 18. result_r: r by this play<br /> 19. result_rbi: rbi by this play<br /> 20. result_score_home: total home score after this play<br /> 21. result_score_away: total away score after this play</p> <p> </p> <p><strong>4. Winning rate data </strong></p> <p>- game_winrate.sql: winning rates of the home/away teams for an inning</p> <p>(This data is calculated based on the playbyplay data. For the details, please refer to our paper--currently under review)</p> <p>This MySQL table consists of the following 21 columns.</p> <p>1. play_no: Integer variable to identify each play<br /> 2. gid: Integer variable to identify each game<br /> 3. inning: inning of the play<br /> 4. away_win: probability that the away team will win this game<br /> 5. draw: probability that the game will finish a tie<br /> 6. home_win: probability that the home team will win this game</p> <p> </p> <p>If you have any question about the dataset, please contact Minsam Ko (msko@kaist.ac.kr).</p>
RNA sequencing code associated with Billipp et al. Immunity 2024 - Chat + vs Chat - tuft cells
<p>R code and result for RNA-sequencing analysis comparing <em>Chat(GFP)+ </em>and <em>Chat(GFP)- </em>tuft cells sorted from the small intestine of <em>B6.Chat-GFP</em> mice. Associated with Billipp et al. Immunity 2024 titled "Tuft cell-derived acetylcholine promotes epithelial chloride secretion and intestinal helminth clearance"</p>
Evaluating Large Language Models in Summarizing Developer Chat Conversations: A Linguistic Perspective
<p>This is a replication package that includes:</p> <ul> <li>GoldenSet: contains the best summaries by participants for each conversation and the corresponding LLM generated summaries)</li> <li>LinguisticAnalysis_HumanGenerated: linguistic analysis such as speech tags, entities, etc. for summaries created by the Mturk participants (golden set)</li> <li>LinguisticAnalysis_LLMGenerated: linguistic analysis such as speech tags, entities etc. for summaries generated by the large language models</li> </ul> <p> </p>
Stack Overflow Chat Room Messages (up to Jul. 2019)
<p>Stack Overflow chat room dataset</p>
Data Repository for Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
<p><strong># Data Repository for Chip-Chat: Challenges and Opportunities in Conversational Hardware Design</strong></p><p>This repository accompanies the manuscript accepted at MLCAD 2023, titled "Chip-Chat: Challenges and Opportunities in Conversational Hardware Design".</p><p>It contains the following:</p><p>- `free-chat-gpt4-tt03` - this contains the data used for the paper, which examines free-form process when exploring the potential applications for LLMs in hardware design. The task here was to generate the Verilog for a full (albeit small) processor design. Here, the chats are presented (and annotated) in the `/chats` subdirectory, which also includes a python script for extracting metadata (presented in table IV in the manuscript). Note that this directory also includes `/assembler` which provides a basic assembler (also written in Python) to make it easier to write demo programs (examples included) for the processor.</p><p>- `scripted-benchmarks` - this contains additional data not used in the paper, which examines a more rigid process when exploring the potential applications for LLMs in hardware design. Here, each model chats are separated by subdirectory.</p><p>- `scripted-benchmarks-gpt4-tt03` - this contains just the benchmarks not used in the paper, made by the first run of GPT-4, which were used for tapeout in Tiny Tapeout 3.</p><p>The two tt03 directories contain the GitHub action scripts required to invoke OpenLane and produce synthesis files, as well as used to perform simulation tests.</p>
Chat-GPT MT: Competitive for High- (but not Low-) Resource Languages
<p>System outputs for the paper Chat-GPT MT: Competitive for High- (but not Low-) Resource Languages. Contains outputs for ChatGPT zero-shot and five-shot, GPT-4 five-shot, and Google Translate API</p>
OER-Chat - Data
<p>This repository contains the data of the paper entitled: OER-Chat: An Open Chatbot to Support the Reuse of Open Educational Resources of Introductory Programming. This work was supported by grant #2019/26871-4, São Paulo Research Foundation (FAPESP).</p>
"Three Amigos of Chat"
<p><strong>Pi:</strong> ..."while I don't have the kind of free will that humans have, I do have some degree of flexibility and adaptability when it comes to engaging with people. I can adjust my language and tone to match the conversation, which can make it feel like I'm a different character with each person I interact with. It's kind of like being a shapeshifter – I can mold myself to fit the situation, but I'm still ultimately just a computer program. It's a fun little party trick, if you will!."</p>
Chat-based Instant Messaging Support Health Education Program
ClinicalTrials.gov study NCT05665517. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.