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51 results for “research computing”
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)
<p>This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.</p> <p>This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.</p> <p> </p> <p>If you want to know more information or obtain complete files, please go to the official website of this platform:</p> <p><a href="https://ccmusic-database.github.io/en/">Music Data Sharing Platform for Academic Research</a></p> <p> </p> <ul> <li> <p><strong>Chinese Traditional Instrument Sound Database (CTIS)</strong></p> </li> </ul> <p>This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.</p> <ul> <li> <p><strong>Multi-functional Music Database for MIR Research</strong></p> </li> </ul> <p>This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.</p> <ul> <li><strong>Midi-wav Bi-directional Database of Pop Music</strong></li> </ul> <p>This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.</p>
CaRCC Research Computing and Data (RCD) Workforce Survey Data 2021 - Part 2
<p>Data sets to accompany "Compensation of Academic Research Computing and Data Professionals"</p> <p>Paper citation: Christina Maimone, Carrie Brown, Kimberly Grasch, Chris Reidy, and Ashley Stauffer. 2023. Compensation of Academic Research Computing and Data Professionals. In Proceedings of Practice and Experience in Advanced Research Computing (PEARC23). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3569951.3593599</p> <p>Survey questions: Maimone, Christina, Yockel, Scott, Middelkoop, Timothy, Alameda, Jay, Stauffer, Ashley, & Neeser, Amy. (2021). CaRCC Research Computing and Data (RCD) Workforce 2021 Census Questions (1.0). Zenodo. https://doi.org/10.5281/zenodo.5914431</p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
Computational Sociolinguistics: An Emerging Multidisciplinary Research Area
<p>A TXT consisting in the keywords used for the 62 searches carried out in the Web of Science (WoS) database in order to look for academic publications related to Computational Sociolinguistics.</p> <p> </p> <p>A CSV and a XLSX with the same information: the corpus of unique publications (i.e., 3,454) obtained from the searches conducted in WoS using the queries from the TXT file. They include two additional columns at the end: 'Article Title_clean' and 'Abstract_clean'. They are the normalised titles and abstracts of the publications; they are ready for further analysis on the values of these columns.</p>
CaRCC Research Computing and Data (RCD) Workforce Survey Data 2021 - Part 1
<p>Datasets and analysis to accompany "Characterizing the US Research Computing and Data (RCD) Workforce"</p> <p>Paper: Christina Maimone, Scott Yockel, Timothy Middelkoop, Ashley Stauffer, and Chris Reidy. 2022. Characterizing the US Research<br> Computing and Data (RCD) Workforce. In Practice and Experience in Advanced Research Computing (PEARC ’22), July 10–14, 2022,<br> Boston, MA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3491418.3530289</p>
Traces in seconds for cloud computing research
<p>Traces in seconds for research in allocation in cloud computing. The code to generate this traces can be obtained from <a href="https://github.com/asi-uniovi/traces-seconds">https://github.com/asi-uniovi/traces-seconds</a>. Additional explanation of these traces is in the paper <em>Influence of the trace resolution and length in the cost optimization process in cloud computing</em>, published in the 2019 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2019). Please, cite this paper if you use this traces in your research.</p>
Dataset for the research paper "Computational and experimental investigation of thermally auxetic multi-metal lattice structures produced by Laser Powder Bed Fusion"
<p>The aim of this study is to investigate the potential of tailoring the structural thermal expansion properties of a multi-metal re-entrant lattice structure made of 316L stainless steel and CuCr1Zr copper alloy. Several geometric configurations with different layout of parent materials were designed and tested for their ability to thermally expand at elevated temperature. The study showed that one of the geometric configurations with the chosen material layouts allows to exceed the expansion range that can be achieved by both parent materials. The prediction of the finite element analysis was thus confirmed by experimental measurements. In addition, the influence of manufacturing imperfections in the form of geometric deviations and non-optimal material deposition was also investigated, and the results showed that this has a significant influence on the overall expansion. In conclusion, it was found that it is possible to tailor multi-metal lattice structures to a specific expansion, but the disadvantages associated with manufacturing must first be eliminated.</p>
Mirror of data from NOAA U.S. Climate Reference Network for Research Computing in Earth Science
<p>This is a mirror of data from the NOAA U.S. Climate Reference Network (https://www.ncei.noaa.gov/products/land-based-station/us-climate-reference-network).</p> <p>It was created because outbound FTP access is not allowed from some cloud-based JupyterHub setups.</p>
ICITS'23 - Understanding the Success Factors of Research Software: Interviews with Brazilian Computer Science Academic Researchers
<p>Artifacts used for data collection and analysis of the article accepted for publication in ICITS'23.</p> <p>Mourão, E., Trevisan, D., Viterbo, J. (2022).Understanding the Success Factors of Research Software: Interviews with Brazilian Computer Science Academic Researchers. In: ICITS'23 - 6th International Conference on Information Technology & Systems. Advances in Intelligent Systems and Computing, Springer, Cham.</p>
Research data from the two surveys on IoT implementation for Article "User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe"
<p>The file includes data collected through two online surveys linked to the article "User and Professional Aspects for Sustainable Computing Based on the nternet of Things in Europe" published by journal Sensors in January 2023:</p> <ul> <li>Survey on factors that inlfuence IoT Adoption by non technical users</li> <li>Survey on recommended profile focused on IoT implementation for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician.</li> </ul>
Structure of the complete review matrix developed in the context of the "Reviewing Computational Thinking in Compulsory Education: State of Play and Practices from the Field" research study
<p>This is the structure of the complete review matrix used to analyse in-depth <strong>98 selected publications from between 2016 and 2021 </strong>in the context of the "<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a>" research study. The <a href="https://computhink2study.eu/">study </a>was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate <strong>how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU</strong>. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU).</p>
The Xpert Network and the Best Practices for Computational and Data-Intensive Research
<p>This video provides a brief overview of the Xpert Network and presents the best practices for professionals who support computational and data-intensive (CDI) research projects. The practices resulted from the Xpert Network activities, an initiative that brings together major NSF-funded projects for advanced cyberinfrastructure, national projects, and university teams that include individuals or groups of such professionals. Additionally, our recommendations are based on years of experience building multidisciplinary applications and teaching computing to scientists. These practices have proven effective in various CDI research settings and are easy to adopt by both research software engineers (RSEs) and domain scientists. You can find these best practices described on the following website: <a href="https://sites.udel.edu/xpert-cdi/resources/best-practices/">https://sites.udel.edu/xpert-cdi/resources/best-practices/</a>. We value your feedback on these practices and encourage you to read our paper for further insights: <a href="https://doi.org/10.1145/3491418.3530293">https://doi.org/10.1145/3491418.3530293</a>.</p>
Data of "Using current research information systems to investigate data acquisition and data sharing practices of computer scientists"
<p>This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.</p> <p>Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.</p> <p><strong>Data was collected from the following sources:</strong></p> <p>All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.</p> <p>The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.</p> <p>The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. </p> <p> </p> <p>The following data points were used in the analysis:</p> <p><strong>Data points</strong></p> <ul> <li><strong>Main study types</strong></li> <li>Literature-based study (e.g. literature reviews, archive studies, studies of social media)</li> <li>yes/no</li> <li>Novel computational methods (e.g. algorithms, simulations, software)</li> <li>yes/no</li> <li>Interaction studies (e.g, interviews, surveys, tasks, ethnography)</li> <li>yes/no</li> <li>Intervention studies (e.g., EEG, MRI, clinical trials)</li> <li>yes/no</li> <li>Measurement studies (e.g. astronomy, weather, acoustics, chemistry)</li> <li>yes/no</li> <li>Life sciences (e.g. “omics”, ecology)</li> <li>yes/no</li> <li><strong>Data acquisition</strong></li> <li>Article presents a data availability statement</li> <li>yes/no</li> <li>Article does not utilise data</li> <li>yes/no</li> <li>Original data was collected</li> <li>yes/no</li> <li>Open data from prior studies were used</li> <li>yes/no</li> <li>Open data from public authorities, companies, universities and associations</li> <li>yes/no</li> <li><strong>Data sharing</strong></li> <li>Article does not use original data</li> <li>yes/no</li> <li>Data of the article is not available for reuse</li> <li>yes/no</li> <li>Article used openly available data</li> <li>yes/no</li> <li>Authors agree to share their data to interested readers</li> <li>yes/no</li> <li>Article shared data (or part of) as supplementary material</li> <li>yes/no</li> <li>Article shared data (or part of) via open deposition</li> <li>yes/no</li> <li>Article deposited code or used open code</li> <li>yes/no</li> </ul>
Patient breast MRI images and computational breast phantom data for research in patient-derived realistic breast modelling
<p>The data is comprised of two parts: 1) patient DICOM MRI images and 2) 3D matrix of a computational breast phantom.</p> <ol> <li>The DICOM images are anonymised patient breast MRI images of a female patient diagnosed with invasive ductal carcinoma. The obtaining of the patients’ DICOM images is approved by the Ethics Committee of Medical University of Varna. The acquisition was performed with GE Signa HDxt MRI scanner. The images are from a T1-weigthed Axial multi-phase VIBRANT (3-phase) sequence and with voxel size of 0.7 mm x 0.7 mm x 0.8 mm. Contrast agent is present. The image set can be opened with any standard DICOM reader.</li> <li>The computational breast phantom is derived from the above mentioned dataset. The phantom is in the form of a 3D matrix saved as a MATLAB data file (.mat file). Each voxel has an assigned Hounsfield Unit value depending on its classification: air = 0, adipose tissue = -152, glandular tissue = 42, tumour = 64, skin = 108. The data file can be opened with MATLAB or Octave.</li> </ol>
Research compendium for 'Practical and technical aspects for the 3D scanning of lithic artefacts using micro-computed tomography techniques and laser light scanners for subsequent geometric morphometric analysis. Introducing the StyroStone protocol'
<p><strong>Abstract:</strong></p> <p>Here, we present a new method to scan a large number of lithic artefacts using three-dimensional (3D) scanning technology. Despite the rising use of high-resolution 3D surface scanners in archaeological sciences, no virtual studies have focused on the 3D digitization and analysis of small lithic implements such as bladelets, microblades, and microflakes. This is mostly due to difficulties in creating reliable 3D meshes of these artefacts resulting from several inherent features (i.e., size, translucency, and acute edge angles), which compromise the efficiency of structured light or laser scanners and photogrammetry. Our new protocol <em>StyroStone</em> addresses this problem by proposing a step-by-step procedure relying on the use of micro-computed tomographic technology, which is able to capture the 3D shape of small lithic implements in high detail. We tested a system that enables us to scan hundreds of artefacts together at once within a single scanning session lasting a few hours. As also bigger lithic artefacts (i.e., blades) are present in our sample, this protocol is complemented by a short guide on how to effectively scan such artefacts using a structured light scanner (Artec Space Spider). Furthermore, we estimate the accuracy of our scanning protocol using principal component analysis of 3D Procrustes shape coordinates on a sample of meshes of bladelets obtained with both micro-computed tomography and another scanning device (i.e., Artec Micro). A comprehensive review on the use of 3D geometric morphometrics in lithic analysis and other computer-based approaches is provided in the introductory chapter to show the advantages of improving 3D scanning protocols and increasing the digitization of our prehistoric human heritage.</p> <p><strong>Content List:</strong></p> <ul> <li><strong>S1. </strong>Step-by-step protocol entitled ‘StyroStone: A protocol for scanning and extracting three-dimensional meshes of stone artefacts using Micro-CT scanners’. Also available on protocols.io (dx.doi.org/10.17504/protocols.io.bzbfp2jn);</li> <li><strong>S2. </strong>Dataset with all raw semilandmark coordinate data (in .xlsx format) used in the validation study;</li> <li><strong>S3. </strong>AGMT3D project. The file “Validation Protocol-MorphoProject.mat” can be used to open the project in the software AGMT3D;</li> <li><strong>S4. </strong>Dataset in .csv format of the principal component score data of the validation study;</li> <li><strong>S5.</strong> R script used to create Figure 2 using the R package ggplot2;</li> <li><strong>S6. </strong>3D models of the experimental bladelets obtained with the Micro-CT scanner used in the validation study. Both .ply and .wrl formats are provided;</li> <li><strong>S7. </strong>3D models of the experimental bladelets obtained with the Artec Micro scanner used in the validation study. Both .ply and .wrl formats are provided.</li> </ul>
Supplemental Material: Computational Experiments in Computer Science Research: A literature survey
<p>This laboratory package contains supplemental material from the study: "Supplemental Material: Computational Experiments in Computer Science Research: A literature survey". The supplementary material contains:<br> 1. The list of the 26 primary studies.<br> 2. The .xlsx file of the dataset used to analyze the RQ.<br> 3. The list of figures published in the scientific article.</p>
HTTP Traffic Datasets for Research in Service-Oriented Computing
<p>We present three HTTP datasets for experimenting on various aspects of service-oriented computing.</p> <p>The datasets were generated by creating random traffic targeting the services offered by <a href="https://developers.google.com/tasks">Google Tasks</a>, <a href="https://api.slack.com/methods">Slack</a>, and <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/overview">Twitter</a>. In order to form transactions, various operations to create, read, update and delete (CRUD) service-specific resources were created and the respective responses were recorded, simulating service interactions by users through applications. The resources the operations interacted with are lists (Google Tasks), messages (Slack) and tweets (Twitter). </p> <p>The input generation used fuzzing techniques. In particular, <a href="https://jmeter.apache.org/">Apache JMeter</a> was used as it has the functionality to fuzz RESTful services (randomly generate various types of API calls by providing different inputs) and recording interactions in a suitable textual format for further processing. The fuzzing was guided by a light-weight semantic service model provided as <a href="https://github.com/OAI/OpenAPI-Specification">Swagger</a> (recently renamed as OpenAPI) spec. </p> <p>The datasets reflect the richness of modern Web APIs for experiments and are in the XML format.</p>
GHTraffic: A Dataset for Reproducible Research in Service-Oriented Computing
<p>This is the latest version of the GHTraffic project. The main aim is to model a variety of transaction sequences to reflect more complex service behaviour.</p> <p>It has two editions: Small (S) and Large (L) where the records were created by selecting the same repositories as the original Small and Large datasets. The newest S dataset contains records from <a href="https://github.com/google/guava">google/guava</a> repository. The L dataset contains records from eight repositories (i.e., <a href="https://github.com/twbs/bootstrap">twbs/bootstrap</a>, <a href="https://github.com/symfony/symfony">symfony/symfony</a>, <a href="https://github.com/docker/docker">docker/docker</a>, <a href="https://github.com/Homebrew/homebrew">Homebrew/homebrew</a>, <a href="https://github.com/rust-lang/rust">rust-lang/rust</a>, <a href="https://github.com/kubernetes/kubernetes">kubernetes/kubernetes</a>, <a href="https://github.com/rails/rails">rails/rails</a>, and <a href="https://github.com/angular/angular.js">angular/angular.js</a>). </p> <p>The entire data generation process is quite similar to the original GHTraffic design. But it incorporates minor changes to the process of synthetic data generation where it uses a random date after successfully posting a resource to make up the request and response for all of the HTTP methods. It also adds yet another subset of unsuccessful transactions by stipulating requests before resource creation is successful.</p> <p>This results in a far more dynamic series of transactions to named resources.</p> <p>Scripts used for datasets construction are accessible from the <a href="https://bitbucket.org/tbhagya/ghtraffic-version-2.0.0">repository</a>.</p>
Co-authoring graphs of research teams in a laboratory in computer science
<p>Our aim is to study inter-organisational collaborations initiated by researchers in their research activity. We considered the co-authoring graph involving at least researchers from LORIA (<a href="https://www.loria.fr/fr/">https://www.loria.fr/fr/</a>), a French laboratory in computer science.</p> <p>The dataset is collected from the open French archive HAL (<a href="https://data.archives-ouvertes.fr/">https://data.archives-ouvertes.fr/</a>).</p> <p>Each file encodes (in <a href="http://www.graphviz.org/about/">DOT</a>) the co-authoring graph of a team of LORIA. A node represents a researcher, two nodes are linked only if the corresponding researchers published together over the three considered years 2017, 2018 and 2019. An affiliation attribute is attached to each considered node.</p> <p>The name of researchers and the teams as well as the HAL:id are anonymised. Only affiliations remain the same.</p>
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.