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376 results for “Causality”
Data from: Quantification of collective behaviour via causality analysis
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Data from: Forest tree breeding using genomic Markov causal models: A new approach to genomic tree breeding improvement
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Data for: "Generative prediction of causal gene sets responsible for complex traits"
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SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals
<p><strong>SemEval-2020 Task 5</strong></p> <p> </p> <p><strong>Subtask-1:</strong> Recognizing Counterfactual Statements (RCS) -- Determine whether a given sentence is counterfactual or not.</p> <p><strong>Subtask-2: </strong>Detecting Antecedent and Consequent (DAC) -- Extract the antecedent and consequent part in a given counterfactual sentence.</p> <p> </p> <p>The released dataset consists of train/test data of both subtask-1 and subtask-2. In our competition, participants could only use the corresponding dataset in each subtask.</p> <p> </p> <p><strong>Task 5 Codalab Website:</strong> <a href="https://competitions.codalab.org/competitions/21691">https://competitions.codalab.org/competitions/21691</a></p>
The cultural transmission of causally (ir)relevant actions in the laboratory: Does modelling or verbal instruction lead to greater copying fidelity?
<p>This study examines the fidelity with which adults vs. children transmit a set of actions within diffusion chains (generations 1 to 3). The set consisted of causally relevant and causally irrelevant actions. Half of the adult and half of the child chains transmitted the actions via demonstration (next participant saw the previous perform the actions on video); the other half transmitted them verbally (next participant listened to audio file the previous participant describe his/her actions). We measured whether the actions were retained (i.e. re-produced by each next participant in the chain).</p>
Visual perception of shape altered by inferred causal history
<p>Dataset and stimuli relative to the following publication:</p> <p>Spröte, P., Schmidt, F., & Fleming, R. W. (2016). Visual perception of shape altered by inferred causal history. <em>Scientific Reports, 6</em>, 36245. <a href="http://dx.doi.org/10.1038/srep36245"> http://dx.doi.org/10.1038/srep36245</a></p> <p>Each folder contains the data and stimuli relative to one experiment and a text file with comments.</p> <p> </p>
Population impact of fine particulate matter on tuberculosis risk in China: A causal inference
<p>Supplementary to "Population impact of fine particulate matter on tuberculosis risk in China: A causal inference"</p>
Datasets for Insights into prismatic loop formation in irradiated Fe-Cr alloys from hypothesis-driven active learning and causal analysis
<p>Datasets for irradiated Fe-Cr alloys are collected from the experimental reports on dislocation loop type and dislocation density. We have constructed a data set from experimental literature containing 182 data points. To address such challenges to predict dislocation density, we have implemented a three-step ML approach as listed in the following:</p> <div> <div> <div> <ul> <li> <p>impute dataset to fill in the missing data to construct a predictive model using the RF regression algorithm. </p> </li> <li> <p>generate functionalized features and evaluate feature importance using the predictive model</p> </li> <li> <p>use the physics-based important functionalized features as hypotheses (physics-augmented GP models) in a hypothesis-driven active learning scheme to learn and predict dislocation density for all alloys. </p> </li> </ul> </div> </div> </div>
Causal functional maps of brain rhythms in working memory
<p>Results of the meta-modeling of transcranial alternating current stimulation (tACS) studies in working memory. Two files are for theta and gamma maps in MNI brain space. The files accompany the paper "Causal functional maps of brain rhythms in working memory" by Miles Wischnewski*, Taylor A Berger, Alexander Opitz, and Ivan Alekseichuk**. Correspondance: *m.wischnewski@rug.nl or **ialeksei@umn.edu</p> <p> </p>
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization
<p>Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization</p>
Dataset for "Towards Centrality and Causality based Vulnerability Propagation Analysis"
<ul> <li> <p><strong><code>aggregated_data.json</code></strong>: This file contains the processed data generated after loading the Goblin Weaver tool. It includes all CVEs, CWEs, and other relevant attributes. Statistical analyses, particularly vulnerability analyses, are performed on this dataset.</p> </li> <li> <p><strong><code>graph_nodes_edges.pkl</code></strong>: This file stores the parsed GraphML-format graph, divided into chunks of nodes and edges to optimize memory usage during computations. All centrality-based measurements are conducted using this file.</p> </li> <li> <p><strong><code>cve_data.csv</code></strong>: This file captures features of nodes along with their one-hop neighbors. It includes attributes such as <code>whether_cve_exists</code>, <code>cve_exists</code>, and <code>cve_num</code>, as well as the source and target nodes involved.</p> </li> <li> <p><strong><code>cve_2_siblings_data.csv</code></strong>: This file documents features of nodes with their two-hop neighbors. It includes attributes like <code>whether_cve_exists</code>, <code>cve_exists</code>, and <code>cve_num</code>, along with details of source nodes and their two-hop neighbor nodes.</p> </li> <li><code><strong>fea_matrix.csv</strong>:</code><code>This file records the matrix of every node based on transformed five types of attributes, including missrelease (freshness missrelease), outdays(freshness outdatedTimeinMs), popularity, speed, and severity. It has been used for correlation analysis. </code></li> </ul>
Dogs' looking times and pupil dilation response reveal expectations about contact causality
<p>Contact causality is one of the fundamental principles allowing us to make sense of our physical environment. From an early age, humans perceive spatiotemporally contiguous launching events as causal. Surprisingly little is known about causal perception in nonhuman animals, particularly outside the primate order. Violation-of-expectation paradigms in combination with eye-tracking and pupillometry have been used to study physical expectations in human infants. In the current study, we establish this approach for dogs (Canis familiaris). We presented dogs with realistic 3D animations of launching events with contact (regular launching event) or without contact between the involved objects. In both conditions, the objects moved with the same timing and kinematic properties. The dogs tracked the object movements closely throughout the study but their pupils were larger in the No-contact condition and they looked longer at the object initiating the launch after the No-contact event compared to the Contact event. We conclude that dogs have implicit expectations about contact causality.</p>
Supporting data and code for: Thiophanate-methyl and carbendazim resistance in Fusicoccum amygdali, the causal agent of constriction canker of peach and almond
<p>This is the first release of the final data and code for the article accepted for publication in Plant Pathology journal. It contains all the necessary scripts to perform the data analysis and to produce the Figures. All the necessary data can be found in the 'data' folder.</p>
Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
<p>This contains data described in detail in our paper, "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery", where we develop a novel algorithm called RPath that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures.</p>
Data from: A causal role of anterior prefrontal-putamen circuit for response inhibition revealed by transcranial ultrasound stimulation in humans
<p>Stopping an inappropriate response requires the involvement of the prefrontal-subthalamic hyperdirect pathway. However, how the prefrontal-striatal indirect pathway contributes to stopping is poorly understood. In this study, transcranial ultrasound stimulation is used to perform interventions in a task-related region in the striatum. Functional magnetic resonance imaging (MRI) reveals activation in the right anterior part of the putamen during response inhibition, and ultrasound stimulation to the anterior putamen, as well as the subthalamic nucleus, results in significant impairments in stopping performance. Diffusion imaging further reveals prominent structural connections between the anterior putamen and the right anterior part of the inferior frontal cortex (IFC), and ultrasound stimulation to the anterior IFC also shows significant impaired stopping performance. These results demonstrate that the right anterior putamen and right anterior IFC causally contribute to stopping and suggest that the anterior IFC-anterior putamen circuit in the indirect pathway serves as an essential route for stopping.</p>
Wikidata Causal Event Triple Data
<p>This dataset contains triples curated from Wikidata surrounding news events with causal relations, and is released as part of our WWW'23 paper, "Event Prediction using Case-Based Reasoning over Knowledge Graphs".</p> <p>Starting from a set of classes that we consider to be types of "events", we queried Wikidata to collect entities that were an instanceOf an event class and that were connected to another such event entity by a causal triple (https://www.wikidata.org/wiki/Wikidata:List_of_properties/causality). For all such cause-effect event pairs, we then collected a 3-hop neighborhood of outgoing triples.</p>
Causal Relation Benchmarks
<p>We present the causal relation prediction benchmarks based on wiki data.</p> <p>If you use our benchmark for your work, please cite our workshop paper that presents the preliminary results.</p> <p>A. Khatiwada, S. Shirai, K. Srinivas and O. Hassanzadeh, "Knowledge Graph Embeddings for Causal Relation Prediction", in Deep Learning for Knowledge Graphs Workshop (DL4KG@ISWC), 2022.</p> <pre>Bibtex: @inproceedings{khatiwada2022knowledge, title={Knowledge graph embeddings for causal relation prediction}, author={Khatiwada, Aamod and Shirai, Sola and Srinivas, Kavitha and Hassanzadeh, Oktie}, booktitle={Workshop on Deep Learning for Knowledge Graphs (DL4KG)}, year={2022} }</pre>
Study: Notation of Causal Graphs
<p>This repository contains the material and obtained data of an eye tracking study on the topic "Notation of Causal Graphs".</p> <p>For more information, please feel free to contact <lisa.grabinger@oth-regensburg.de>.</p>
CLEVRER-Humans: Describing physical and causal events the human way
<p>Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of causal relationships. This design brings up two issues. First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels. We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models. We convert the collected CEGs into questions and answers to be consistent with prior work. Finally, we study a collection of baseline approaches for CLEVRER-Humans question-answering, highlighting the great challenges set forth by our benchmark.</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.