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2,373 results for “Platform”
Stone Platform with Buddha's Feet
Stone platform etched with an image of Buddha's feet found at Kantharodai, North Sri Lanka. Source: Objaverse 1.0 / Sketchfab
Platform with a Celtic Knot
This is a platform I am using in my puzzle/platformer demo *Eye of Enigma*. I have eight other texture variations of this platform and I will likely upload them as seperate models. The original Maya file I was working in got corrupted so thats why they're not in one file Source: Objaverse 1.0 / Sketchfab
A public mid-density genotyping platform for alfalfa (Medicago sativa L.)
<p>Small public breeding programs have many barriers to adopting technology, particularly creating, and using genetic marker panels for genomic-based decisions in selection. Here we report the creation of a DArTag panel of 3,000 loci distributed across the alfalfa genome for use in molecular breeding and genomic prediction. The creation of this marker panel brings cost-effective and rapid genotyping capabilities to public breeding programs. The open access provided by this platform will allow genetic data sets generated on the marker panel to be compared and joined across projects, institutions, and countries. This genotyping resource has the power to bring genotyping equity to breeders in alfalfa. This is the first installment of a series of papers on creating affordable public genotyping resources for underserved agricultural plant and animal species.</p>
Managing competition between legacy television services and video streaming platforms in Hungary in the early 2020s – A case study [Secondary documentary sources]
<p>Secondary documentary sources used in the paper entitled "Managing competition between legacy television services and video streaming platforms in Hungary in the early 2020s – A case study"</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>
Generative AI in Crowdwork: A Survey of Workers at Three Platforms
<p>Dataset surveying the use and opinions on Generative AI by crowdworkers over three crowdsourcing platforms and across three continents. </p>
Generative AI in Crowdwork for Web and Social Media Research: A Survey of Workers at Three Platforms
<p>Data generated for the paper "<span>Generative AI in Crowdwork for Web and Social Media Research:</span><br><span>A Survey of Workers at Three Platforms</span>" presented at ICWSM 2024.</p>
HawkEar: A bird-borne visual and acoustic platform for eavesdropping the behavior of mobile animals
<p>UAVs (drones) offer mobile platforms for ecological investigation, but can be impractical in some environments and the resulting noise can disturb wildlife.</p> <p>We developed a mobile alternative using a bird-borne platform to record the behavior of other animals in the field. This unit consists of a lightweight audio and video sensor that is carried by a trained Harris's hawk <em>Parabuteo unicinctus</em>.</p> <p>We tested the hypothesis that our bird-borne platform is a viable option for collecting behavioral data from mobile animals. We recorded acoustic and video data as the hawk flew through a dense group of Brazilian free-tailed bats <em>Tadarida brasiliensis</em> emerging from a cave, with a test case of investigating how echolocation calls changes depending on spatial position in the bat group.</p> <p>The HawkEar platform is an alternative for collecting behavioral data when a mobile platform that is less noisy and restrictive than traditional UAVs is needed. The design and software are open source and can be modified to accommodate additional sensor needs.</p>
Data Package for "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns"
<p>This Zenodo repository contains the data supporting the findings of the journal paper, titled "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns", published on IEEE Transactions on Software Engineering, including:</p> <ol> <li><strong>Heuristic Linguistic Pattern Set</strong>: <span>we listed the 80 HLP we derived from </span><span>Apache's JIRA issue tracking system</span><span>. Column "</span><span>Category" </span><span>lists the type of each pattern. Namely, LEX represents lexical pattern, STR represents structural pattern, SEM represents semantic pattern, and PRF represents profiling pattern. Column "Name" is a descriptive name we give to each pattern. Column "Definition" defines the detailed content in each pattern.</span></li> <li><strong>Manual Tagging Results</strong>: manual_tagging.xlsx spreadsheet <span>comprises both sentence-level and issue-level manually tagging results for three datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', '</span><span>Dataset-</span><span>2: Apache Jira's Heterologous Evaluation', and '</span><span>Dataset-</span><span>3: Other Platform's Evaluation'. The tagging results are segmented into sentence-level tabs ("Dataset-1 Sen", "Dataset-2 Sen", "Dataset-3 Sen") and issue-level tabs ("Dataset-1 Issue", "Dataset-2 Issue", "Dataset-3 Issue").</span></li> <li><span><strong>RQ Findings</strong>: </span> <p><span>This section contains detailed data findings from six research questions (RQ1 to RQ6).</span></p> <ul> <li> <p><span>The RQ1 tab provides an evaluation of our HLP-based approach, showing the precision, recall, and F1-Score of eight classifiers. These results are juxtaposed with the corresponding values from baseline methods, at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ2 tab illustrates the precision, recall, and F1-Score of eight classifiers under two training conditions: a balanced training dataset (BT+HLP) and an imbalanced training dataset (UBT+HLP). These outcomes are contrasted with the equivalent values from baseline methods, also trained under balanced (BT+BLM) and imbalanced (UBT+BLM) conditions. The results are shown at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ3 tab evaluates the dataset transferability of our HLP-based approach in comparison to baseline methods. It achieves this by analyzing the precision, recall, and F1-Score metrics for eight classifiers under two different "training/testing" dataset conditions, i.e., 'D1/D1' and 'D1/D3'. These conditions allow for a direct comparison of performance when applied to the same dataset ('D1/D1') versus when transferred to a different dataset ('D1/D3'). Additionally, the tab includes an 'Avg Change' and 'p-value' section, summarizing the statistical change in performance metrics between the two dataset conditions. </span></p> </li> <li> <p><span>The RQ4 tab presents a direct comparison between strict and fuzzy HLP matching approaches, assessed through precision, recall, and F1-Score metrics across eight issue classifiers.</span></p> </li> <li> <p><span>The RQ5 tab examines the influence of sentence order on the accuracy of eight classifiers within our approach. It shows the change in precision, recall, and F1-Score when the sentence order feature is taken into consideration versus when it is not.</span></p> </li> <li> <p><span>The RQ6 tab explores the impact of feature selection algorithms on both issue and sentence-level tagging accuracy. This tab presents the average precision, recall, and F1-Score for three experiments: Boruta, Recursive Feature Elimination (RFE), and the usage of all 80 features. </span></p> </li> </ul> </li> <li><strong>Qualitative Analysis</strong>: <p><span>This spreadsheet offers a comprehensive examination of the data supporting Section 6.1, which focuses on Qualitative Analysis. It is organized into several tabs, each dedicated to specific research questions (RQs) as outlined below:</span></p> <ul> <li> <p><span>Tab "RQ-1" showcases performance issue reports accurately detected by our High-Level Performance (HLP) approach's top model, XGBoost, which were not identified by the benchmark method's leading model, BERT. This highlights the comparative advantage of our approach in identifying nuanced performance issues.</span></p> </li> <li> <p><span>Tab "RQ-2" continues the exploration of performance issue reports, presenting cases with specific details (to be added).</span></p> </li> <li> <p><span>Tab "RQ-3" delves into the unique capabilities of XGBoost, the leading model in our HLP approach, showcasing its ability to detect performance issues missed by the baseline's top model, BERT. This comparison is drawn under distinct conditions: with pre-training (Dataset 1) and without pre-training (Dataset 3), illustrating the robustness and adaptability of our model.</span></p> </li> <li> <p><span>Tab "RQ-4" focuses on performance issue reports uniquely identified through the implementation of Fuzzy HLP Matching within our HLP approach. This method underscores the innovative matching techniques that enhance issue detection.</span></p> </li> <li> <p><span>Tab "RQ-5" presents performance issue reports pinpointed exclusively by applying the Issue HLP Matrix within our approach. This tab demonstrates the effectiveness of our matrix-based analysis in isolating and identifying specific performance concerns.</span></p> </li> <li> <p><span>Tab "RQ-6" is dedicated to performance issue reports uniquely detected by incorporating feature selection techniques into our HLP approach. This illustrates the value of advanced feature selection in improving the precision of performance issue identification.</span></p> </li> </ul> </li> <li><strong>LLM Experiment Data</strong>: presents the tagging outcomes of Large Language Models (LLMs), specifically ChatGPT-3.5 and ChatGPT-4, across three distinct datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', 'Dataset-2: Apache Jira's Heterologous Evaluation', and 'Dataset-3: Evaluation on Other Platforms'. The results are organized into three separate tabs: 'Dataset-1 Issue', 'Dataset-2 Issue', and 'Dataset-3 Issue'.</li> <li><strong>ChatGPT Operation Python Script</strong>: crafted for automating the evaluation and tagging of issue reports in Excel using Large Language Models (LLMs) like ChatGPT-3.5 and ChatGPT-4. It underscores the importance of administrative rights for file modifications and outlines procedures for reading from and writing responses to Excel files. Key functions include querying LLMs with issue descriptions, processing their responses, and updating the spreadsheet with 'Yes' or 'No' labels and explanatory reasons, thereby facilitating an organized review of LLM performance across different datasets.</li> </ol>
Dataset for "Reconfigurable spin-wave platform based on interplay between nanodots and waveguide in hybrid magnonic crystal"
<p>The dataset consist of the data used to prepare the figures for the manuscript: </p> <p>Krzysztof Szulc, Mateusz Zelent, Maciej Krawczyk<br><em>Reconfigurable spin-wave platform based on interplay between nanodots and waveguide in hybrid magnonic crystal.</em></p> <p>together with animated version of two figures: Figure 4 and Figure S1.</p> <p>Please read README.txt file to see the description of the data in the files.</p>
BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
<p>The use of patient-derived material and immune cell co-cultures in modeling immune-oncology has gained significant interest for understanding and manipulating immune cell tumor targeting in a patient-specific context. However, current protocols have limitations in visualizing and analyzing the dynamic cellular features of these living culture systems. We recently developped a workflow names BEHAV3D, that combines multi-color live 3D imaging and computational tools to analyze cell death dynamics, classify T cell behavior, and generate data-informed 3D images and videos. Here we provide some example pre-processed dataset of videos of two co-culture set ups: breast cancer Patient Derived Organoids with αβ T cells engineered to express a γδ TCR (TEGs) and Acute Lymphoblastic Leukemia cells with CD19 CART cells.</p>
Testing nectar price effect on bumblebee feeding by automatized computer-controlled laboratory platform
Open the record for dataset details and reuse information.
Dataset Scooping review digital platforms and data management in Education
<p>Dataset used for a scoping review about digital platforms and data management in education</p>
InnoRate_Platform_Services_Data_Dataset12_2021.12.14_v1
<p>This dataset has been collected in the context of deploying and pilot testing the technology assessment and rating platform (http://platform.innorate-project.eu/#!/homepage) of the InnoRate project (H2020 GA 821518). It represents the anonymized outcome of the InnoRate services that are available via the platform, including the user business profiles, the rates of the Pre-screening tool, and the status of the Deep Assessment of companies.</p>
Supplementary Material on "Early timing analysis based on scenario requirements and platform models"
<p>This dataset provides supplementary material on the submission “Early timing analysis based on scenario requirements and platform models” to the SoSyM theme issue on Model-Driven Requirements Engineering. It provides software and models for illustrating the paper's example application results as well as more detailed evaluation data.</p> <p>MSD-CCSL-TimingAnalysis.zip contains our approach and encompasses the following artifacts (Java 8 and not later required; if needed modify the GemocStudio.ini and point the vm to a corresponding Java version via "-vm <PathToJava8>\jre\bin"):</p> <ul> <li>Development workspace: <ul> <li>ECL specification under /de.fraunhofer.iem.swt.msd.tam.dse/ecl/MSDLanguage.ecl</li> <li>MoCCML constraints under /de.fraunhofer.iem.swt.msd.tam.mocc/mocc/MSDLanguageComplete.moccml</li> <li>TAM profile under /de.fraunhofer.iem.swt.msd.tam.tamProfile/model/tam.profile.uml</li> </ul> </li> <li>Runtime workspace: <ul> <li>Models under "01_ExampleModels"</li> <li>Exemplary traces under "02_ExampleTraces"</li> <li>QVT-O Transformations (e.g., Preprocessing) needed when modifying the models</li> </ul> </li> </ul> <p>Papyrus-CCSLEditor-Measurement.zip contains the plugins and artifacts that we used for measuring the particular modeling operations for the evaluation of the hypothesis H2 (see further documents below). It requires Java 11; if needed modify the eclipse.ini and point the vm to a corresponding Java version via "-vm <PathToJava11>\jre\bin". Contained plugins and artifacts:</p> <ul> <li>Development workspace: <ul> <li>is.ru.cs.PapyrusActivityLogger: Our adapted version of ModRec, particularly extended by an Xtext document listener</li> <li>org.eclipse.gemoc.moccml.*: MoCCML editor prerequisites for the CCSL runtime model</li> <li>org.scenariotools.msd.profile and de.fraunhofer.iem.swt.msd.tam.tamProfile: Profiles that we partially use in the Papyrus runtime model</li> </ul> </li> <li>Runtime workspace: <ul> <li>CCSL Measuring Project: Measuring project for CCSL models</li> <li>Papyrus Measuring Project: Measuring project for Papyrus models</li> </ul> </li> </ul> <p>Further documents:</p> <ul> <li>MSD-CCSL-TimingAnalysisTutorial.pdf: Tutorial on starting the simulative timing analysis</li> <li>EvaluationData_H1_TimingEffectTestResults: Test results for the particular timing effects based on several models for hypothesis H1</li> <li>Files for hypothesis H2: <ul> <li>EvaluationData_H2.xlsx: Spreadsheet containing the particular model element amounts of MSD-spec-1--4 and CCSL-model-1--4, the measurements for the categorized atomic model operation kinds, the multiplication scheme for predicting the raw overall effort, and the measured transformation execution times</li> <li>EvaluationData_H2_MSD-specification-effort.pdf: PDF extract of the spreadsheet contents for the MSD specification effort and computation</li> <li>EvaluationData_H2_CCSL-model-effort.pdf: PDF extract of the spreadsheet contents for the CCSL model effort and computation</li> <li>EvaluationData_H2_transformationExecTimes.pdf: PDF extract of the spreadsheet contents for the transformation execution times</li> <li>EvaluationData_H2_MSD-specification_measurement-timestamps.txt: Raw timestamp logs for the conducted measurements for model operations on MSD specifications</li> <li>EvaluationData_H2_CCSL-model_measurement-timestamps.txt: Raw timestamp logs for the conducted measurements for model operations on CCSL models</li> </ul> </li> </ul>
In-operando visualization of redox flow battery in membrane-free microfluidic platform
<p>Images are raw data of main figure 1.(B-D) from "In-operando visualization of redox flow battery in membrane-free microfluidic platform"</p>
Raw data for the article "N-terminal selective C-H azidation of proline-containing peptides: a platform for late-stage diversification"
<p>Raw NMR, IR and MS data for the article "N-terminal selective C-H azidation of proline-containing peptides: a platform for late-stage diversification" published in Chemistry- A European Journal, DOI: </p> <p><a href="http://dx.doi.org/10.1002/chem.202200368">http://dx.doi.org/10.1002/chem.202200368</a>.</p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Wave dissipation and mean circulation on a shore platform under storm wave conditions: the dataset
<p>###</p> <p>Author: Laura Lavaud (laura.lavaud@univ-lr.fr or llavaud@orange.fr)</p> <p>Date: 11/02/2022</p> <p>Purpose: This repository provides the field observations presented in the paper referred below</p> <p>Reference: Lavaud, L., Bertin, X., Martins, K., Pezerat, M., Coulombier, T., Dausse, D. (2022). Wave dissipation and mean circulation on a shore platform under storm wave conditions. Submitted to Journal of Geophysical Research-Earth Surface</p> <p>###</p> <p>* The directory storm_conditions is relative to the field campaign conducted in storm conditions, it includes:</p> <p>(1) Wave bulk parameters, computed as described in the paper for the 8 sensors used in this study: (SENSOR_Date_WDepth_Hm0_Tm02_Tpc_Fmin_Fmax.dat files) : the offshore ADCP 600 kHz and PT2, the intertidal PT4, 5, 6, 8, 9 and 10. The field "WDepth" in the title of each file corresponds to the mean water depth above the seabed (i.e. corrected from sensor's elevation above the bed), Hm0 is the significant wave height, Tm02 the mean wave period and Tpc the continuous peak period. The fields "Fmin" and "Fmax" are the boundaries for the integration of the wave energy frequency spectra performed to compute the wave bulk parameters.</p> <p>(2) The file ADV7_Date_WDepth_horizontal_current_ENU_20minburst.dat contains the WDepth and the horizontal components of the velocity at the ADV in the ENU frame, averaged over bursts of 20 minutes. </p> <p>(3) The file sensors_coordinates_storm_cond.dat gathers the coordinates and the NGF (IGN69) elevation of the seabed at the location of each sensor.</p> <p>* The directory fair_weather_conditions is relative to the field campaign conducted in fair weather conditions, it includes:</p> <p>(1) Wave bulk parameters, computed for the 6 sensors used in this study (SENSOR_Date_WDepth_Hm0_Tm02_Tp_Fmin0pt04_Fmax0pt25.dat files) : the intertidal ADCP 2MHz, PT0, PT1, ADV, PT2 and PT3.</p> <p>(2) The file sensors_coordinates_fairweather_cond.dat gathers the coordinates of each sensor.</p> <p>* The directory model_input_files includes the files used to run the simulations presented in this study:</p> <p>(1) The SCHISM and WWM input files (param.nml and wwminput.nml) and the vertical grid (vgrid.in)</p> <p>(2) The file "ww3.bnd_spec_20200120_20200229.nc" which corresponds to directional wave energy spectra used to force WWM at the open boundary of the computational grid. These spectra were computed from a North Atlantic application of Wavewatch III model forced by CFSR wind fields. </p>
Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns (repository for Genome Research paper, 2022)
<p>Simulated ONT and PacBio RNA-Seq data for "Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns" paper (Mikheenko et al., Genome Research, 2022). All details can be found in the Methods section of the paper.</p> <p><strong>PacBio.simulated_uniform_coverage.fasta.gz</strong> and <strong>ONT.simulated_uniform_coverage.fasta.gz files</strong> were used in Supplemental Note “Benchmarking of the read-to-isoform assignment algorithm”.</p> <p><strong>ONT.simulated_real_expression.fasta.gz</strong> file and all GTF files were used in the Section "Splice site correction improves transcript discovery precision". <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> was used as the annotation file for all tools. <strong>mouse.gencode.M26.spatial.15percent.expressed.gtf </strong>contains the set of all expressed isoforms. <strong>mouse.gencode.M26.spatial.15percent.expressed_kept.gtf</strong> contains those of the isoforms that are in presented in the annotation file ("known" transcripts), <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> contains expressed isoforms that were removed from the annotation ("novel" transcripts).</p>
Supporting Information for: Microbial blooms triggered pyrite framboid enrichment and oxygen depletion in carbonate platforms immediately after the latest Permian extinction
<p>Supporting Information for:</p> <p><strong>Microbial blooms triggered pyrite framboid enrichment and oxygen depletion in carbonate platforms immediately after the latest Permian extinction</strong></p> <p>The supporting information includes: (1) conodont biostratigraphic and carbon isotopic chemostratigraphic correlations of the uppermost Permian to lowest Triassic successions from nine studied sections across South China and (2) pyrite framboid size distributions and redox variations throughout the Permian-Triassic boundary successions of the nine studied sections.</p> <p> </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.