Research Dataset Catalog and ShareScore Ratings | ScienceDex

zenodo48/100

Data from: Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter

This dataset is associated with the paper “Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter” and includes recordings of improvisations by jazz duos over a network. The related paper is published in Music Perception and is accessible at <a href="https://doi.org/10.1525/mp.2024.42.1.48">doi:10.1525/mp.2024.42.1.48</a>  Introduction: This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in Cambridge, United Kingdom between April-July 2022 as part of a doctoral research project. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in Cheston, Cross, and Harrison (2023), "Trade-offs in Coordination Strategies for Networked Jazz Performances". Please refer to this publication for full details on the data collection procedure. Our codebook is <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">hosted on GitHub</a> and, in conjunction with this dataset, can be used to reproduce the analysis contained in the article. The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo. Data collected includes: <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> Repository structure: NB: please see <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">this section of the code documentation website</a> for a full description of how to recreate the analyses and models created in the paper. The files data.zip and data.z0* contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the data.zip file and all the corresponding volumes ending in .z0 and open the data.zip file using a tool for opening multi-part zip files, such as WinRAR. Do not try to open the files ending in .z0, otherwise you may get a message about the data being corrupted. Inside data.zip, you'll see the following folders and files: <ul> <li>avmanip_output: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li>midi_bpm_cleaning: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the avmanip_output folder, using the same conventions.</li> </ul> </li> <li>muxed_performances: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html#reproduce-combined-audio-visual-stimuli">see the linked section of the code documentation website.</a></li> </ul> </li> <li>questionnaire_anonymized: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> Alongside data.zip and the data.z0* archives, there are two further loose files, Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder perceptual_study_videos.rar, which contains the stimuli used in the perceptual experiment. To reproduce the analysis from the paper, all files should be unzipped into the \data\raw directory of the code repository created after <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">cloning this from GitHub</a>. For more detail and instructions on installation, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">see the section of the code documentation website linked here</a>. Usage: These recordings of live, improvised duo performances are unattributed and anonymised as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by Cambridge Digital Humanities (<a href="https://www.cdh.cam.ac.uk/research/projects/newmusicsoftwareplatform/">project page</a>). All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted. Citation: If you use this dataset in your research, please cite the paper it relates to: <pre><code>@article{10.1525/mp.2024.42.1.48, author = {Cheston, Huw and Cross, Ian and Harrison, Peter M. C.}, title = "{Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter}", journal = {Music Perception}, volume = {42}, number = {1}, pages = {48-72}, year = {2024}, month = {09}, issn = {0730-7829}, doi = {10.1525/mp.2024.42.1.48}, url = {https://doi.org/10.1525/mp.2024.42.1.48}, eprint = {https://online.ucpress.edu/mp/article-pdf/42/1/48/833292/mp.2024.42.1.48.pdf}, }</code></pre> Contact: Huw Cheston - <a href="http://twitter.com/huwcheston/">@huwcheston</a> - hwc31@cam.ac.uk

opencc-by-4.0Jul 2023View details →

zenodo44/100

Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"

Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.

opencc-by-4.0Jul 2021View details →

zenodo44/100

Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"

Please see README.md for a description of this package.  This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.

opencc-by-4.0Sep 2024View details →

zenodo40/100

A Queueing Network Model for Performance Prediction of Apache Cassandra

The dataset consists in several csv files containing Cassandra and ScyllaDB performance. The experiments are organized in folders. There are three main folders containing: - Cassandra 4 nodes: the files related to the Cassandra experiments conducted on a cluster composed of four nodes. - ScyllaDB 4 nodes: The files related to the ScyllaDB experiments conducted on a cluster composed of four nodes. - Cassandra QUORUM variant: the simulation data where a different kind of QUORUM is implemented in Cassandra. "Cassandra 4 nodes" and "ScyllaDB 4 nodes" include some subfolders, each one containing the files of the Consistency Level applied for those experiments. Each experiment is composed by three files (data*.csv) with the data reported by Yahoo! Cloud System Benchmark (YCSB) in the end of the experiment execution. Each folder contains also a sim.csv file with the data gathered from the simulation of the model inside Java Modeling Tool. The data*.csv files are composed by: -Number of threads or clients -Overall Throughput -Number of Read requests -Overall Read Response Time -95 percentile Read Response Time -99 percentile Read Response Time -99.9 percentile Read Response Time Differently, the sim.csv files are composed by: -Number of threads or clients -Overall Throughput -Overall Read Response Time

opencc-by-4.0Sep 2016View details →

zenodo40/100

Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies. The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study. The structure of the information shared in this repository is as follows: The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders. The material is distributed under a CC-BY 4.0 license.

opencc-by-4.0Mar 2024View details →

zenodo40/100

Impacts of centralized control on mixed traffic network performance: A strategic games analysis

This dataset contains the data that were used to assess the proposed framework within the context of the case study in the paper entitled "Impacts of centralized control on mdaixed traffic network per-formance: A strategic games analysis".

opencc-by-4.0Jun 2024View details →

zenodo40/100

Dataset: Gilat Satellite Networks Ltd. (GILT) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.