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GENEA Challenge 2023 user-study video stimuli
<p>This Zenodo repository contains video stimuli in mp4 format from the user studies in the GENEA Challenge 2023.</p> <p> </p> <p><strong>Contents:</strong></p> <p>The file "monadic_videos.zip" contains the video stimuli used in the monadic evaluations of the challenge (human-likeness and speech appropriateness) and the file "dyadic_videos.zip" the video stimuli from the dyadic evaluation (appropriateness for the interlocutor).</p> <p>Video stimuli with mismatched motion are in the corresponding subfolders with the prefix "mismat_".</p> <p>The file "attention_check_examples.zip" contains examples of the attention-check videos used in the challenge.</p> <p> </p> <p>The release contains all videos used for the challenge evaluation, except for the attention-checks, where only a few examples are provided. Except for the attention-check examples for the human-likeness studies, all videos contain speech audio. This audio needs to be removed to replicate the human-likeness evaluation.</p> <p> </p> <p><strong>Attribution:</strong></p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-08-01) this is our ACM ICMI 2023 paper:</p> <p>Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2023. The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI ’23). ACM.</p> <p>Also, please cite the paper about the original dataset from Meta Research:</p> <p>Gilwoo Lee, Zhiwei Deng, Shugao Ma, Takaaki Shiratori, Siddhartha S. Srinivasa, and Yaser Sheikh. 2019. Talking With Hands 16.2M: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV ’19). IEEE, 763–772.</p> <p> </p> <p>Condition NA in the data contains motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These stimuli, and the audio, are licensed under a CC BY NC 4.0 international license. The motion for all other conditions is released under a CC BY 4.0 international license, whose license text is reproduced in the file LICENSE.txt.</p> <p> </p> <p><strong>More information:</strong></p> <p>To find more GENEA Challenge 2023 material on the web, please see:</p> <ul> <li> <p><a href="https://genea-workshop.github.io/2023/challenge/">https://genea-workshop.github.io/2023/challenge/</a></p> </li> </ul> <p>If you have any questions or comments, please contact:</p> <ul> <li> <p>The GENEA Challenge organisers <genea-challenge@googlegroups.com></p> </li> </ul>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Study Dataset: Shedding Light on CVSS Scoring Inconsistencies: A User- Centric Study on Evaluating Widespread Security Vulnerabilities
<p>This record contains the <strong>study datasets, descriptive results and questionnaires</strong> from the paper "Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security Vulnerabilities" by Julia Wunder, Andreas Kurtz, Christian Eichenmüller, Freya Gassmann and Zinaida Benenson to appear in Proceedings of the 45th IEEE Symposium on Security and Privacy (2024).</p> <p>The pseudonymous <strong>datasets</strong> contain data from the online surveys (main study with 196 participants and follow-up study with 59 participants). The first row gives the question codes and questions, the following rows gives the answers from the participants (see also README.md).</p> <p>We also provide <strong>descriptive results</strong> from the online surveys as PDF and the questionnaires.</p> <p>Please refer to the README.md file and our paper for further details about the data set and study.</p>
Data from: A user-friendly guide to using distance measures to compare time series in ecology
<p>Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored.</p> <p>Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology.</p> <p>Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.</p> <p>The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.</p>
Forschungsprojekt: Digitalisierung, Klassifikationen und Gesundheits-Apps. Dataset B - Document analysis for health app users and developers
<p><strong>Allgemeine Hinweise Data Set B</strong></p> <ol> <li>Titel des Forschungsprojekts</li> </ol> <p>Digitale Gesundheitsklassifikationen in Apps - Praktiken und Probleme ihrer Entwicklung und situativen Anwendung. Projektleitung: Prof. Dr. Rainer Diaz-Bone. Bearbeitung: Valeska Cappel Dipl. Soz., Miriam Kutt (Hilfsassistenz). Laufzeit: 2019-2023. Finanzierung: Schweizer Nationalfonds.</p> <p>2. PrimärforscherInnen:</p> <ul> <li>Rainer Diaz-Bone</li> <li>Valeska Cappel</li> <li>Miriam Kutt</li> </ul> <p>3. Publikationsjahr:</p> <ul> <li>2023</li> </ul> <p>4. Hinweise zur Verfügbarkeit</p> <p>Die Daten werden über LORY (Lucerne Open Repository) dauerhaft zugänglich gemacht.</p> <ul> <li>Alle Dateien die sich auf die App-Entwicklung beziehen beginnen mit E</li> <li>Alle Dateien die sich auf die App-Nutzung beziehen beginnen mit N</li> </ul> <p>5. Fachgebiet</p> <ul> <li>Soziologie</li> </ul> <p>6. Kategorie und Schlagwörter</p> <ul> <li>Gesundheitswesen</li> <li>Selbstvermessung</li> <li>Gesundheits-Apps</li> <li>Klassifikationen</li> <li>Pragmatismus</li> <li>Economics of convention</li> <li>Soziologie der Konventionen</li> <li>Digitalisierung</li> </ul> <p>7. Abstract, wozu die Daten erhoben wurden</p> <p>Die Daten wurden für eine Dokumentenanalyse erhoben. Ausgewählt wurden (1) Daten von forschungsrelevanten Akteuren oder Institutionen, die nicht für ein Interview gewonnen werden konnten und (2) Medienberichte, Anleitungen, Beschreibungen von präventiven Gesundheits-Apps, Wissenschaftliche Berichte (White Papers, Ausschreibungen) sowie Bewertungen von diesen Gesundheits-Apps aus dem „App-Store“ und „Google-Play-Store“ (Plattformen zum Download von Apps). Ziel war es mit diesen Dokumenten weitere Analysen durchzuführen, die diskursanlytische Aussagen über die Entstehung und Nutzung von präventiven Gesundheits-Apps sowie Entwicklungen im Feld der digitalen Gesundheit zulassen. Gleichzeitig wurden auch Dokumente, wie Nutzer-Bewertungen erhoben, um die Interviews zu ergänzen aus einer pragmatischen Perspektive Aushandlungs- und Problemlösungsprozesse im Umgang mit Gesundheits-Apps und der damit verbundenen Technologie zu untersuchen.</p> <p>8. Untersuchungsgebiet</p> <p>Das Untersuchungsgebiet liegt im Bereich der digitalen Gesundheit und beschränkt sich im Speziellen auf die Prozesse der Entwicklung von Gesundheits-Apps, sowie die Nutzung der Gesundheits-Apps. Untersuchungsgebiet waren öffentlich zugängliche Medien sowie die Gesundheits-App selbst. Dabei wurden Unternehmen, Zeitschriften, Blogs und Apps, die sich konkret mit der App-Entwicklung, der App-Nutzung oder der Berichterstattung über präventive Gesundheits-Apps beschäftigen ausgewählt zur Dokumentenerhebung ausgewählt. Bei der Auswahl wurden der Fokus darauf gelegt Inhalte auszuwählen, die sich nicht auf Gesundheits-App als Medizinprodukte konzentrieren, sondern auf präventive Gesundheits-Apps, die kein spezifisches Krankheitsbild adressieren, sondern einen allgemeinen positiven Gesundheitszustand herstellen oder erhalten sollen. </p> <p>9. Gesamtheit auf die generalisiert werden könnte („Grundgesamtheit“)</p> <p>Insgesamt wurden ca. 300 Dokumente erhoben.</p> <p>10. Auswahlverfahren und Stichproben</p> <p>Die Daten im Projekt wurden anhand qualitativer Methoden gewonnen. Die Fälle und Daten wurden über die Methode der „Theoretical-Sampling-Technik“ ausgewählt. Die genauen Begründungen zur Auswahl der Fälle wurden im Verlauf des Projektes theoretisch erarbeitet. Dabei wurde sich dem Forschungsfeld der präventiven Gesundheits-Apps mit heuristischen Vermutungen angenähert, die anhand der Konzepte der Theorie der Konventionen und einer machttheoretischen Perspektive Foucaults entwickelt wurden. Gesundheit und Gesundheitshandlungen wurden dabei aus einer pragmatischen Perspektive als ein Ergebnis von Koordinationsbemühungen zwischen Akteuren, Gegenständen, Technologien und Machtstrukturen verstanden. Die Auswahl der Dokumente stützte sich besonders auf die Memos und Inhalte der vorher geführten Interviews und den daraus entwickelten heuristische Fragestellungen während des Forschungsprozesses.</p> <p>11. Erhebungszeitraum</p> <p>Die Dokumente wurden in dem Zeitraum 2019-2023 erhoben. Das Datum in den Dateinamen bezieht sich immer auf den Erhebungszeitpunkt</p> <p>Sprache</p> <ul> <li>Deutsch und Englisch</li> </ul> <p>12. Größe des Datensatzes</p> <ul> <li>ca. 45 MB</li> </ul> <p>13. Verwendete Dateiformate und notwendige Software</p> <ul> <li>Dateiformat: PDF (Portable Document Format) und RTF (Rich Text Format)</li> </ul> <p>Software:</p> <ul> <li>RTF Standard-Textprogrammen auf unterschiedlichen Betriebssystemen (Bspw. Word, Wordpad, LibreOffice, OpenOffice)</li> <li>PDF: PDF-Programme/Reader oder auch ATLAS</li> </ul> <p> </p>
Forschungsprojekt: Digitalisierung, Klassifikationen und Gesundheits-Apps. Dataset A - Transcript of interviews with health app users and developers
<p><strong>Allgemeine Hinweise Data Set A</strong></p> <p>1. Titel des Forschungsprojekts in dessen Rahmen die Daten entstanden sind:</p> <p>"Digitale Gesundheitsklassifikationen in Apps - Praktiken und Probleme ihrer Entwicklung und situativen Anwendung. Projektleitung: Prof. Dr. Rainer Diaz-Bone. Bearbeitung: Valeska Cappel Dipl. Soz., Miriam Kutt (Hilfsassistenz). Laufzeit: 2019-2023. Finanzierung: Schweizer Nationalfonds."</p> <p>2. PrimärforscherInnen:</p> <ul> <li>Rainer Diaz-Bone</li> <li>Valeska Cappel</li> <li>Miriam Kutt</li> </ul> <p>3. Publikationsjahr:</p> <ul> <li>2023</li> </ul> <p>4. Hinweise zur Verfügbarkeit</p> <p>Die Daten werden über LORY (Lucerne Open Repository) dauerhaft in Form von Transkripten zugänglich gemacht.</p> <p>5. Fachgebiet</p> <ul> <li>Soziologie</li> </ul> <p>6. Kategorie und Schlagwörter</p> <ul> <li>Gesundheitswesen</li> <li>Selbstvermessung</li> <li>Gesundheits-Apps</li> <li>Klassifikationen</li> <li>Pragmatismus</li> <li>Economics of convention</li> <li>Soziologie der Konventionen</li> <li>Digitalisierung</li> </ul> <p>7. Abstract, wozu die Daten erhoben wurden</p> <p>Ziel der Datenerhebung war es die Bewusstseinsstrukturen, Meinungen, Einstellungen und Aushandlungs- und Problemlösungsprozesse der Versuchsteilnehmer (Experten aus dem Gesundheitswesen und der App-Nutzenden) zu präventiven Gesundheits-Apps zu erheben. Für ein kontrolliertes Erhebungsverfahren wurde dazu die qualitative Methode des Interviews durchgeführt. Mit dieser Methode wurden die Daten als Teil-Aspekte der Realität der Befragten verstanden und erhoben. Die Daten beinhalten Interpretations- und Deutungs-, und Bewertungsschemata, Motivationsstrukturen und Aushandlungsprozesse sowie Sachinformationen zu dem Umgang und der Entwicklung von Gesundheits-Apps.</p> <p>8. Untersuchungsgebiet</p> <p>Das Untersuchungsgebiet liegt im Bereich der digitalen Gesundheit und beschränkt sich im Speziellen auf die Prozesse der Entwicklung von Gesundheits-Apps, sowie die Nutzung der Gesundheits-Apps. Untersuchungsgebiet waren damit Personen, die in einem Unternehmen im Gesundheitsfeld arbeiten und an der App-Entwicklung beteiligt sind, sowie Personen die Gesundheits-Apps in ihrem Alltag aktiv benutzen.</p> <p>9. Gesamtheit auf die generalisiert werden könnte („Grundgesamtheit“)</p> <p>Im Rahmen der qualitativen Interviews wurden 20 Personen befragt.</p> <p>10. Auswahlverfahren und Stichproben</p> <p>Die Daten im Projekt wurden anhand qualitativer Methoden gewonnen. Die Fälle und Daten wurden über die Methode der „Theoretical-Sampling-Technik“ ausgewählt. Die genauen Begründungen zur Auswahl der Fälle wurden im Verlauf des Projektes theoretisch erarbeitet. Dabei wurde sich dem Forschungsfeld der präventiven Gesundheits-Apps mit heuristischen Vermutungen angenähert, die anhand der Konzepte der Theorie der Konventionen und einer machttheoretischen Perspektive Foucaults entwickelt wurden. Gesundheit und Gesundheitshandlungen wurden dabei aus einer pragmatischen Perspektive als ein Ergebnis von Koordinationsbemühungen zwischen Akteuren, Gegenständen, Technologien und Machtstrukturen verstanden. In den Analyseschritten der Codierung und Auswertung der Interviews und Dokumente, wurden zur Qualitätssicherung Memos angelegt. Diese Memos bildeten eine theoretische Grundlage für weitere Auswahlprozesse des Materials. Diese Methode des Theoretical-Samplings wurde zudem durch die Methode des Schneeball-Systems ergänzt, indem interviewte Personen immer nach weiteren Kontakten befragt wurden und neu genannte Kontakte vor dem Hintergrund des Theoretical-Samplings ausgewählt wurden. </p> <p>11. Erhebungszeitraum</p> <ul> <li>2019-2022</li> </ul> <p>12. Sprache</p> <ul> <li>19 Interviews deutsch</li> <li>1 Interview englisch</li> </ul> <p>13. Größe des Datensatzes</p> <ul> <li>1,07 MB</li> </ul> <p>14. Verwendete Dateiformate und notwendige Software</p> <ul> <li>Dateiformat: RTF</li> </ul> <p>Software: RTF Standard-Textprogrammen auf unterschiedlichen Betriebssystemen (Bspw. Word, Wordpad, LibreOffice, OpenOffice)</p>
JusBrasilRec: A large-scale dataset of user sessions for recommendations on the legal domain
<p><strong>JusBrasilRec: A large-scale dataset of user sessions for recommendations on the legal domain</strong></p> <p>The proliferation of legal documents in various formats and their dispersion across multiple courts present a significant challenge for users seeking precise matches to their information requirements. Despite notable advancements in legal information retrieval systems, research into legal recommender systems remains limited. A plausible factor contributing to this scarcity could be the absence of extensive publicly accessible datasets or benchmarks.</p> <p>Jusbrasil (<a href="https://www.jusbrasil.com.br">https://www.jusbrasil.com.br</a>) is known as the largest legal search portal in Brazil. It provides an online environment where users can find the legal documents that best match their information needs. With millions of user interactions to billions of documents containing different artifacts related to law in Brazil, Jusbrasil appears as a large-scale test bed for advancing research on the still scarce area of legal recommender systems. </p> <p>Therefore, we collected and made available the <strong>JusBrasilRec</strong>, a dataset containing user sessions from Jusbrasil for recommendations on the legal domain. Additionally, we also computed and made available a TF-IDF matrix from the textual content of the documents in Jusbrasil. The following files are available for download from JusBrasilRec:</p> <ul> <li><strong>jusbrasilrec_dataset.zip:</strong> a compacted file containing the user sessions;</li> <li><strong>jusbrasilrec_tfidf_matrix.zip:</strong> a compacted file containing the TF-IDF matrix;</li> <li><strong>readme.txt:</strong> a text file explaining the content and format of the previous files.</li> </ul> <p><strong>How to cite the dataset:</strong> Marcos Aurélio Domingues, Edleno Silva de Moura, Leandro Balby Marinho and Altigran da Silva. A Large Scale Benchmark for Session-based Recommendations on the Legal Domain. Artificial Intelligence and Law. 2023.</p>
User preference optimization for control of ankle exoskeletons using sample efficient active learning
<p>A major challenge to the widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are ``tuned'' to optimize a physiological or biomechanical objective. However, these approaches are resource-intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is derived from many factors, including comfort and stability, among others. This work introduces an approach to conveniently tune four parameters of the exoskeleton controller that maximize user preference. We use an evolutionary algorithm to recommend potential parameters, which are ranked by a neural network that is pre-trained with previously collected preference data. The controller parameters that have the highest preference ranking are provided to the exoskeleton, and the wearer provides feedback as forced-choice comparisons. Our approach was able to converge on controller parameters preferred by the wearer compared to randomized parameters with an accuracy of 88% on average. The result indicates that the proposed algorithm was able to identify users' preferences while requiring less than 50 queries to users. This work demonstrates user preference can be used to tune high-dimensional controller spaces easily and accurately, which shows the potential of translating lower-limb wearable technologies into our daily lives.</p>
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
<p>Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and robot pet. Interaction behaviors included activities like playing, petting, talking, cooking, etc.</p>
Deaf Weight Wise: Community-engaged Implementation Research to Promote Healthy Lifestyle Change With Deaf ASL Users
ClinicalTrials.gov study NCT05211596. IPD Sharing: YES. Countries: 1. Publications: 2.
User-Led Meaningful Activity and Early-Stage Dementia
ClinicalTrials.gov study NCT05159869. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Data from: A user-friendly guide to using distance measures to compare time series in ecology
Open the record for dataset details and reuse information.
User preference optimization for control of ankle exoskeletons using sample efficient active learning
Open the record for dataset details and reuse information.
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
Open the record for dataset details and reuse information.
Cardio PyMEA: A user-friendly, open-source Python application for cardiomyocyte microelectrode array analysis
Open the record for dataset details and reuse information.
Franks Tract User Survey map-based responses
This data includes point, line, and polygon map-based responses to the Franks Tract public user survey administered by UC Davis researchers for the Franks Tract Futures project. The survey was conducted in the Fall of 2019 and concerned current recreational activities in the tract as well as perspectives on potential marsh placement and areas of improvement.
Supplement material for the paper "On the Relationship between User Churn and So!ware Issues"
<p>The data represented as CSV files containing the alternatives, the comments, the reviews, and the issue reports studied in the paper.</p>
#cdnpoli and the Twittersphere: User mentions during the 2019 Federal Election
<p>This is a raw dataset containing the @mention (username) frequencies within tweets that contained one of 150 various keywords and hashtags related to Canadian politics. The dataset begins on October 1 2019 and ends on November 15 2019, providing a roughly 6-week window around the October 21 election. Data was captured using the Digital Methods Initiative's DMI-TCAT toolkit (github.com/dmi-tcat). Part of a larger project supported by a grant from the Canadian Heritage Fund.</p>
IoT device identification - Multi user data (No fading)
<p>Artificial multi user observations without added fading generated as described in the associated paper Section III.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
Understanding Users' Choices and Constraints when Positioning Loudspeakers in Living Rooms
<p>Dataset pertaining to an experiment concerning positions of ad-hoc loudspeakers and mobile phones in domestic living rooms.</p> <p>This forms part of the PhD research of Craig Cieciura. This was experiment-based research to determine how to render object-based audio in the domestic environment using ad-hoc, audio-capable devices.</p> <p><strong>References</strong></p> <p>AES148 (2020): Cieciura, C., Mason, R., Coleman, P. and Francombe, J. 2020. Understanding users’ choices and constraints when positioning loudspeakers in living rooms, Audio Engineering Society Preprint, 148th Convention, Engineering Brief (number tbc).</p>
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.