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957 results for “conference”
An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021
<p>The dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).</p> <p> </p> <p><strong>For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.</strong></p>
BIP! NDR (NoDoiRefs): a dataset of citations from papers without DOIs in computer science conferences and workshops
<h2>Overview</h2> <p>In the field of Computer Science, conference and workshop papers serve as important contributions, carrying substantial weight in research assessment processes, compared to other disciplines. However, a considerable number of these papers are not assigned a Digital Object Identifier (DOI), hence their citations are not reported in widely used citation datasets like OpenCitations and Crossref, raising limitations to citation analysis. While the Microsoft Academic Graph (MAG) previously addressed this issue by providing substantial coverage, its discontinuation has created a void in available data.</p> <p>BIP! NDR aims to alleviate this issue and enhance the research assessment processes within the field of Computer Science. To accomplish this, it leverages a workflow that identifies and retrieves Open Science papers lacking DOIs from the DBLP Corpus, and by performing text analysis, it extracts citation information directly from their full text.</p> <p>The current version of the dataset contains <em>~4.3M citations</em> made by approximately <em>211K open access Computer Science conference or workshop papers</em> that, according to DBLP, do not have a DOI. The DBLP snapshot used for this version was the one released on <em>September 2025</em>. </p> <h2>Dataset files</h2> <h3>1. Core Non-DOI Citation Dataset - bip_ndr_{version}.tar.gz</h3> <p>The dataset is formatted as a JSON Lines (JSONL) file (one JSON Object per line) to facilitate file splitting and streaming. </p> <p>Each JSON object has three main fields:</p> <ul> <li> <p>“_id”: a unique identifier,</p> </li> <li> <p>“citing_paper”, the “dblp_id” of the citing paper,</p> </li> <li> <p>“cited_papers”: array containing the objects that correspond to each reference found in the text of the “citing_paper”; each object may contain the following fields:</p> <ul> <li> <p>“dblp_id”: the “dblp_id” of the cited paper. Optional - this field is required if a “doi” is not present.</p> </li> <li> <p>“doi”: the doi of the cited paper. Optional - this field is required if a “dblp_id” is not present.</p> </li> <li> <p>“bibliographic_reference”: the raw citation string as it appears in the citing paper.</p> </li> </ul> </li> </ul> <p>Changes from previous version:</p> <ul> <li>Added more papers from DBLP.</li> </ul> <h3>2. Citation Intents Dataset - bip_ndr_ci_{version}.tar.gz</h3> <p>This file enriches the BIP! NDR dataset with citation-level intent classification.<br>It preserves the same base structure of the previous file, while adding a nested array of "citations" with each element of "cited_papers".</p> <p>Each "citation" provides the local textual context, section, and intent of the citation in the following format:</p> <ul> <li>"citation_id": Unique identifier in the format {citing_id}>{cited_id}_CIT{index} linking the citing and cited entities.</li> <li>"section": The section of the citing paper where the citation occurs (e.g., Introduction, Methods, Results).</li> <li>"intent": Inferred purpose of the citation based on textual context (see classification schema below).</li> </ul> <p>The "intent" field follows the SciCite classification schema, which categorizes citations into three high-level functional types:</p> <ol> <li>background information: The citation states, mentions, or points to the background information giving more context about a problem, concept, approach, topic, or importance of the problem in the field.</li> <li>method: Making use of a method, tool, approach or dataset.</li> <li>results comparison: Comparison of the paper's results/findings with the results/findings of other work.</li> </ol> <p>The classification is done with the <a href="https://huggingface.co/sknow-lab/Qwen2.5-14B-CIC-SciCite">Qwen2.5-14B-CIC-SciCite fine-tuned Large Language Model, published by Athena RC</a>. </p> <p>Changes from previous version: </p> <ul> <li>Added more papers with intent</li> </ul>
Peripheral MC1R activation modulates immune responses and confers neuroprotection in a mouse model of Parkinson's disease
<p>Raw data sets for the manuscripts</p> <p>This work was supported by NIH grants R01NS102735 and R01NS110879, the Farmer Family Foundation Initiative for Parkinson’s Disease Research and the MJFF and ASAP [ASAP-000312].</p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
ImProDiReT Final Conference Movie
<p>The video-clip captures the ImProDiReT project and its results (for the area and for the wider disaster risk reduction field) will be presented in a way understandable for the general public. The video is produced with 3 distinct objectives and motivations: (1) the documentation of the project, as it provides an (high-level) overview of the objectives and motivations for the project, the stakeholders involved, activities conducted and the results and outcomes.(2) The video helps partners of the project, including the commission, to bring the project and its results to the attention of others. The video provides an overview and introduction to people not familiar with the project, and a recap of the results for those who are familiar. (3) Finally, the project video aims to spread awareness of the project, the approach and the results to a wide audience. The video aims to not only raise awareness about the project, but specifically about the approach taken by the project.</p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
Annual Conference in Global Energy Transition Law and Policy
<p>The Environment Energy and Natural Resources (EENR) Center in association with the Center for U.S. and Mexican Law of University of Houston Law Center will be hosting a virtual symposium on Friday, April 17<sup>th</sup>, 2020, 9:00 a.m.- 12:30 p.m. (CDT), by way of our 1st Annual Conference in Global Energy Transition Law and Policy.</p> <p><strong>Topic</strong>: <strong>THE ENERGY TRANSITION IN A CLIMATE CONSTRAINED WORLD. AN INTEGRATIVE APPROACH IN GLOBAL ENERGY LAW AND POLICY ISSUES?</strong></p> <p><strong>Date</strong>: Friday, April 17<sup>th</sup>, 2020, from 9:00 to a.m.-12:30 p.m. (CDT)</p> <p>The conference, which is designed for all (policy-makers, researchers, professionals, students, etc.), will feature an outstanding faculty roster who will address the current energy transition issues.</p> <p><strong>Highlights</strong>:</p> <p>· Recent Developments in Energy Transition Law and Policy;</p> <p>· Energy Policy in Citizens’ Climate Assemblies;</p> <p>· Energy Communities in the European Union;</p> <p>· Europeanisation of the Development of Renewable Energy in Transition;</p> <p>· Finance and Risk Policy for The Just Transition to a Low-Carbon Economy;</p> <p>· A Sustainable and Prosperous Future: The Role of Climate Clubs and International Trade;</p> <p>· Decarbonization Options for Gas and Electricity Systems: Power-to-Gas and Carbon Capture Utilization and Storage;</p> <p>· Incorporation of DMDU decision-making under deep uncertainty) Framework into Energy Policy;</p> <p>· COVID-19 provides Warning about the Transition from Fossil Fuels. </p>
Analysis of Virtual Conferences
<p>This project contains data set and code accompanying a paper titled "Virtual conferences raise standards for accessibility and interactions" eLife (2020) by the author on virtual conferences of 2020.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Allopatric divergence of cooperators confers cheating resistance and limits the effects of a defector mutation
<p>Studies of microbial social defectors that 'cheat' on cooperative genotypes generally focus on interactions with their cooperative parents, yet in nature defectors may meet diverse cooperators. Genotype-by-genotype interactions may constrain the ranges of cooperators upon which particular defectors can cheat, limiting the cheaters' spread and potentially the overall equilibrium frequency of cheaters. The bacterium Myxococcus xanthus undergoes cooperative multicellular development upon starvation, but some developmental defectors can cheat on cooperators, outcompeting them within mixed groups. We show that a defector disrupted at the signaling gene csgA has a narrow cheating range among diverse natural cooperators owing to antagonisms not specifically targeted at defectors. More strikingly, lab-evolved cooperators only slightly differentiated from the defector have allopatrically evolved beyond its cheating range by accumulating fewer than 20 mutations when development was not directly under selection. Cooperators might diversify not only with respect to which defectors cheat on them, but also in the potential for a particular mutation to reduce expression of cooperative trait or generate a cheating phenotype. We tested this by constructing a new csgA mutation in several highly diverged cooperators. The mutation generated very different sporulation phenotypes – from a complete defect to no defect – indicating that genetic background effects can limit the set of genomes for which a given mutation creates a defector and potentiates cheating. Our results suggest that natural populations feature geographic mosaics of cooperators diversified in susceptibility to cheating by any given defector and in the social phenotypes generated by any given mutation in a cooperation gene.</p>
32 minutes of participants interactions at #i2k2020 virtual conference
<p>Anonymised dataset showing participants positions at 1s intervals for 32 minutes during the online conference #i2k2020.</p> <p>https://www.janelia.org/you-janelia/conferences/from-images-to-knowledge-with-imagej-friends</p> <p> </p> <p> </p>
The database of words and affiliations of the SEG Annual Conferences (1982 - 2019)
<p>The database of the SEG Annual meetings v2.0<br> This repository includes data for the words and phrases frequency of occurrence analysis "SEGgrams.sqlite" and the database "SEG_affiliations_data.sqlite" consisting of the industry companies and different countries academia that presented their research during the 38 Society of Explorational Geophysicists Annual Conferences (1982 - 2019) with the corresponding number of affiliations for the whole period of study.</p>
SFHH-conference
<h4><strong>Source of original data</strong></h4><ul><li><a href="http://www.sociopatterns.org/datasets/sfhh-conference-data-set/">SFHH conference data set</a></li></ul><h4><strong>References</strong></h4><p>If you use this data, please cite the following papers:</p><ul><li><a href="https://doi.org/10.1140/epjds/s13688-018-0140-1">Can co-location be used as a proxy for face-to-face contacts?</a> M. Génois and A. Barrat, EPJ Data Science (2018).</li><li><a href="https://doi.org/10.1371/journal.pone.0011596">Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks</a>. Cattuto et al., PLoS ONE (2010).</li><li><a href="https://doi.org/10.1186/1741-7015-9-87">Simulation of an SEIR Infectious Disease Model on the Dynamic Contact Network of Conference Attendees</a>. Stehlé et al., BMC Medicine (2011).</li></ul>
hypertext-conference
<h4><strong>Source of original data</strong></h4><ul><li><a href="http://www.sociopatterns.org/datasets/hypertext-2009-dynamic-contact-network/">Hypertext 2009 dynamic contact network</a></li></ul><h4><strong>References</strong></h4><p>If you use this data, please cite the following:</p><ul><li><a href="https://doi.org/10.1016/j.jtbi.2010.11.033">What's in a crowd? Analysis of face-to-face behavioral networks</a>. Isella et al., Journal of Theoretical Biology (2011).</li><li><a href="http://www.sociopatterns.org/">The SocioPatterns collaboration</a></li></ul><p> </p>
III PhasAGE International Conference - PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins - Lecture
<p>The III PhasAGE International Conference "Multiscale understanding of protein aggregation and biomolecular condensates in aging and disease" brought together members of the PhasAGE consortium as well as outstanding international speakers from multidisciplinary fields dedicated to unraveling the intricacies of protein aggregation and biomolecular condensates in the context of aging and disease. For details on the conference program please see https://phasage.eu/iii-phasage-international-conference/. </p>
UN 2023 Water Conference - LabGEA
<p>Local do Evento: Nova York Concepção e criação do produto: Anna Virgínia Muniz Machado, D.Sc. Apoio e difusão nacional: Escola de Engenharia da Universidade Federal Fluminense Apoio e difusão internacional: El link al video del webinar va a estar disponible en el sitio web de la Red Argentina de Capacitación y Fortalecimiento en Gestión Integrada de los Recursos Hídricos - Arg Cap-Net y en sus redes sociales de Instagram y Facebook. Convidados Especiais: Ana Maria Acevedo; Mirta Paes; Patrick Moriarty; Yasmina Rais LabGEA- Laboratório de Gestão Ambiental/ TDT/UFF CapNet/Brasil.</p>
Best of both worlds: Acclimation to fluctuating environments confers advantages and minimizes costs of constant environments
<ol> <li><span>Thermal acclimation is often considered critical in organismal responses to novel thermal conditions. Our understanding of the physiological implications of acclimation is largely derived from lab studies with simplified thermal regimes that fail to account for any variation that animals would experience naturally (i.e. diel variation). As such, constant temperature acclimation experiments may produce a flawed understanding of acclimation in the wild. </span></li> <li><span>To fill this gap, we acclimated lizards (<em>Amphibolurus</em> <em>muricatus</em>) under three thermal regimes (Hot Constant, Cold Constant and Alternating) and compared their physiological responses (Metabolic Rate, Sprint Speed, Thermal Preferences and Thermal Limits). </span></li> <li> <span>We found that animals maintained constantly at hot temperatures (preferred temperature, 35</span>°<span>C) gained sprint performance increases, not seen in those maintained constantly at cold temperatures (20</span>°<span>C), yet suffered costs to growth (in younger animals) and maintenance (mass loss in older animals). Animals maintained at alternating temperatures (12 hr 20</span>°<span>C; 12 hr 35</span>°<span>C) had performance benefits matching animals in the hot treatment, without experiencing reductions in juvenile growth and adult mass. </span> </li> <li><span>Animals acclimated under hot temperatures showed a significant lower preferred and voluntary maximum temperatures compared to animals acclimated under a cold temperature regime. </span></li> <li><span>We found no impact of acclimation treatment on behavioural thermal limits or Standard Metabolic Rate. </span></li> </ol> <p><span>Overall, we show that alternating between access to preferred temperatures and having periods of energetic rest confer the greatest benefits for our animals. These results highlight the importance of natural body temperature variation for enhancing overall ectotherm performance and physiology, and the costs of novel thermal environments that fail to provide this variation.</span><span><br></span></p>
Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using LLMs
<p>This dataset provides the input data and intermediate results of the paper titled "Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using Large Language Models and Semantic Web Techniques". It contains the following resources.</p> <ul> <li>conference proceedings front matter links - these links can be used to download the pdf files of the conference proceeding front matters that include information about the number of submitted and accepted papers that can be used to calculate acceptance rates, names of all conference organization committee members, list of programme committee and senior programme member names for each track with other interesting facts such as the main topics of the submitted papers and emerging topics according to the editors, etc.</li> <li>web crawl of conference websites - this contains a set of crawled content from each conference website in both HTML and text formats. Each file contains web pages from a specific conference along with the page URL, page title, and page content. Information such as important dates (deadlines) and other announcements can be extracted from the content of the web sites. </li> <li>papers and paper-authors list for each conference in a given conference series - this contains the paper list along with their corresponding authors for each conference series extracted from DBLP. </li> <li>OpenRefine projects - this contains examples of open refile projects that were used to perform entity linking and reconciliation as well as the schemas that was used to map the tabular data columns to Wikidata properties, and qualifiers and cell values to Wikidata entities.</li> <li>evaluation benchmark - this contains the outputs of LLM generations for the tasks (a) extracting the number of submitted and accepted papers per each track at a given conference, (b) extraction of organizers with their roles for each conference, (c) extraction of programme committee members with track and their role (member, SPC member), and (d) extraction of important dates or deadlines for each activity (submission, notification, etc.) in each track. </li> </ul> <p>The corresponding source code is available at the <a href="https://github.com/scholarly-wikidata/scholarly-wikidata/">scholary-data repo</a>.</p>
Animated GIF - Utrecht MIDA Opening Conference - Badges
<p><strong>Animated GIF</strong></p> <ul> <li>Created with the badges of the participants. </li> <li>This animated GIF was used to promote the MIDA Opening Conference in Utrecht. </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.