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3,688 results for “computer”
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.325 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169065_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.8125 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169066_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 2.6 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169068_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Graphics for implanted brain-computer interfaces for communication and sensorimotor control applications.
<p>Updated information from Nature Reviews Bioengineering, doi: 10.1038/s44222-024-00239-5. Current as of 27 September 2024. Please reference the original publication if using these graphics. As the field moves into the "Translational Era", the original publication reviews the clinical trials up to December 2023. </p>
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
Bibliographic Data from the Computational Methods Applied to Earthen Historical Structures Review
<p>This database contains all the bibliographic information about the 293 records found after applying the Search Strategy used for the Computational Methods Applied to Earthen Historical Structures Review. Such strategy consisted on using relevant keywords grouped into three different search queries within ”TITLE-ABS-KEY”, for the years 2019-2023:</p> <ol> <li>(”earthen heritage” OR ”earthen historical building*” OR ”earthen historical structure*” OR ”earthen architect*” OR ”earthen monument*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (”computational method*” OR ”numerical analy*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (fem OR dem OR la OR ”finite element” OR ”discrete element” OR ”limit analysis”).</li> </ol> <p>The search was conducted on April 7, 2023.</p>
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>
Data for: Adaptive P300-Based Brain-Computer Interface for Attention Training
<p>The dataset contains EEG and behavioral data of 47 participants who completed 9 runs (i.e. copy-spelled 9 words) in a P300 speller task, as well as a random dot motion (RDM) task and questionnaires in a single experimental session. Details of the experimental protocol can be found here:</p> <p>Noble SC, Woods E, Ward T, Ringwood JV. “Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.” <em>JMIR Res Protoc</em> 2023, 12:e46135, doi: <a href="https://doi.org/10.2196/46135">10.2196/46135</a></p> <p>A journal article describing the results of the study can be found here:<br><br>Noble SC, Woods E, Ward T, Ringwood JV. “Accelerating P300-Based Neurofeedback Training for Attention Enhancement Using Iterative Learning Control: A Randomised Controlled Trial.” <em>J Neural Eng</em> 2024, 21(2), doi: <a href="https://doi.org/10.1088/1741-2552/ad2c9e" target="_blank" rel="noopener">10.1088/1741-2552/ad2c9e</a></p> <p>Please cite the results paper when using the data.</p> <p>Each participant folder contains:</p> <ul> <li>[xxx]-raw.[xxx] – unprocessed EEG signals (<strong>in</strong> <strong>mV</strong>) from 32 electrodes for all 9 P300 speller runs in Openvibe (.ov) and Matlab (.mat) file formats, see details of the runs below</li> <li>[xxx]-processed.[xxx] – contains 3 xDAWN components extracted by the xDAWN spatial filter according to the weights in “spatial-filter.cfg”</li> <li>classifier.cfg - LDA classifier weights</li> <li>spatial-filter.cfg - xDAWN spatial filter weights</li> <li>log.txt - contains the group assignment, start and end time of the experiment, and performance in the P300 speller and RDM tasks</li> </ul> <p>The file “Subject Information.csv” contains the age and gender of all participants.</p> <p>The file “Questionnaire scores.csv” contains the responses to the questionnaire described in the experimental protocol and the NASA Task Load Index (TLX) for all participants.</p> <p>The .ov and .mat files contain data from the following runs:</p> <table> <tbody> <tr> <th>Filename</th> <th>Word to be copy-spelled</th> <th>Number of flashes per row and column</th> <th>Feedback given to participant</th> </tr> </tbody> <tbody> <tr> <td>calibration-signal1</td> <td>THE</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signal2</td> <td>QUICK</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signals</td> <td>Concatenation of calibration-signal1 and calibration-signal2</td> </tr> <tr> <td>eval</td> <td>DOG</td> <td>12</td> <td>yes</td> </tr> <tr> <td>training-run-1</td> <td>BEAUTIFUL</td> <td>10</td> <td>yes</td> </tr> <tr> <td>training-run-2 to training-run-5</td> <td>BEAUTIFUL</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>post-training-run</td> <td>DANCE</td> <td>12</td> <td>yes</td> </tr> </tbody> </table> <p> </p> <p>This research is supported by the Irish Research Council under project ID GOIPG/2020/692 and Science Foundation Ireland under grant number 12/RC/2289_P2.</p>
MCR LTER: Coral Reef: Computer Vision: Moorea Labeled Corals
The Moorea Labeled Corals dataset is a subset of the MCR LTER packaged for computer vision research. It contains 2055 images from three habitats IDs: fringing reef outer 10m and outer 17m, from 2008, 2009 and 2010. It also contains random point annotation (row, col, label) for the nine most abundant labels, four non coral labels: (1) Crustose Coralline Algae (CCA), (2) Turf algae, (3) Macroalgae and (4) Sand, and five coral genera: (5) Acropora, (6) Pavona, (7) Montipora, (8) Pocillopora, and (9) Porites. These nine classes account for 96% of the annotations and total to almost 400,000 points. These nine classes are the ones analyzed in (Beijbom, 2012); less-abundant genera not treated in the automation are also present in the dataset. These data were published in Beijbom O., Edmunds P.J., Kline D.I., Mitchell G.B., Kriegman D., 'Automated Annotation of Coral Reef Survey Images', IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, 2012. [BibTex] [pdf] These data are a subset of the raw data from which knb-lter-mcr.4 is derived. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Repetition of Computer Security Warnings Results in Differential Repetition Suppression Effects as Revealed with Functional MRI
Open the record for dataset details and reuse information.
A dataset recorded during development of a tempo-based brain-computer music interface
Open the record for dataset details and reuse information.
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).
<p>Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).</p> <p>This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2.</p> <p>The models identifiers in the paper correspond to the following terms in the code:</p> <p>Substrate: "13_5_coa"</p> <p>Product: "13_5_coa_desat_fe3"</p> <p>Apoprotein: "13_5_no_ligand"</p> <p>Saturated Lipid: "13_5_nocoa"</p> <p>Desaturated Lipid: "13_5_nocoa_desat_fe3"</p> <p>CoA model: "13_5_coa_nolipid"</p> <p>Substrate-waterbox model: "13_5_coa_waterbox"</p> <p>Saturated Lipid-waterbox: "13_5_nocoa_waterbox"</p>
Computed Light Fields Within a Sea Ice Pressure Ridge
<p>Calculated light fields in and around a sea-ice pressure ridge. The dataset contains total scalar irradiance and downwelling planar irradiance calculated in horizontal slices at the given distance form the ice surface. Calculations were performed using Monte-Carlo ray-tracing using Zemax Optic-Studio. In addition horizontal slices through the ridge geometry, as well as total and partial ice thickness in each point of the ridge are given. The fields are provided in python and matlab readable formats.</p> <p>For details please refer to the respective publication "The three-dimensional light field within sea ice ridges" by C. Katlein et al.</p>
Self-Assessment Questions - Operating Systems and Computer Networks
<p><strong>What does the dataset contain?</strong><br> The dataset contains a list of XML files and JPG images. The XML files contain a description of a certain type of self-assessment questions to be load and processed by a plugin called <em>qtype_selfassess</em> [1] for the Moodle learning management system.</p> <p>The XML files were names as follow:</p> <pre><code><number of course unit>-<question-ID>-selfassess-<short name of the qestion>.xml</code></pre> <p>The file unit_1-262-selfassess-Caching.xml for instance, is about a self-assessment question of the first course unit ("unit_1") that has the ID 263, and is about the topic "Caching". Most of the short names are in German since the content of the files is German as well.<br> The JPG files are referenced in the XML files.</p> <p><strong>What is the purpose and origin of the data?</strong><br> In order to give students the chance of iterative improvement, the SelfAssess-plugin [1], that allows students to create and assess solutions unlimited times, was implemented in Moodle. Students can open a question to start work. The plugin shows the question and asks the student to create and upload a solution. Solutions may be uploaded as image (e.g. photo, scan) or PDF file. Thereafter, the plugin presents a list of instructor-defined assessment criteria and a link to the sample solution, and asks to assess the uploaded solution. The plugin then presents feedback to the students based on their self-assessment. For this purpose, instructors defined an error tree for each question that maps typical errors to detect one or a combination of not considered criteria on one feedback text. Feedback texts help the student to improve their solution, e.g. by providing learning goal feedback (such as misconceived concepts) and learning process feedback (such as links to relevant resources, things to improve, or activities to do), until they self-assess their solution as correct or as good enough. After receiving the feedback, students could either improve their solution by performing a new iteration of the process (create, upload, self-assess the improved solution again, receive feedback, and take or decline another iteration) or finish the exercise.</p> <p>For the mentioned plugin 43 self-assessment questions including assessment criteria and feedback have been created for fundamental computer science course about operating systems and computer networks. The course was offered at FernUniversität in Hagen, Germany, in winter term 2019/20 and 2020/21 by Professor Dr. Jörg M. Haake.</p> <p>Further information about the plugin and the proposed self-assessment question type can be found in [2] and [3].</p> <p><strong>References</strong><br> [1] Steinkohl, K., Burchart, M., Haake, J.M., Seidel, N. (2020). SelfAssess Question Type Plugin for<br> Moodle. https://github.com/D2L2/qtype_selfassess<br> [2] Haake, J. M.; Seidel, N.; Karolyi, H.; Ma, L.: Self-Assessment mit High-Information Feedback. DELFI 2020–Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 2020.<br> [3] Haake, J. M., Seidel, N., Karolyi, H., & Burchart, M. (2021). Accuracy of self-assessments in higher education. In DELFI 2020 – Die 19. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. Bonn.</p> <p> </p> <p><strong>Acknowledgments.</strong></p> <p>This research was supported by the Research Cluster "Digitalization, Diversity and Lifelong Learning – Consequences for Higher Education" (D2L2) of the FernUniversität in Hagen, Germany.</p>
Computer Scientists on Twitter
<p>This dataset contains the data used in the paper<br> <em>Identifying and Analyzing Researchers on Twitter</em> (http://dx.doi.org/10.1145/2615569.2615676).<br> At the moment, this includes computer scientists, though an extension to other disciplines is planned.<br> <br> The data can be cited as follows:<br> <em>Asmelash Teka Hadgu and Robert Jäschke. 2014. Identifying and<br> Analyzing Researchers on Twitter. In Proceedings of the 6th Annual ACM<br> Web Science Conference (WebSci '14). 23-30. ACM, New York, NY, USA. DOI: 10.1145/2615569.2615676</em></p>
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
Data and Computer scripts for 'Place recognition using batlike sonar' (eLife)
<p>The paper ‘Place recognition using batlike sonar’ can be freely accessed online at http://dx.doi.org/10.7554/eLife.14188. Contents, including text, figures, and data, are free to reuse under a CC BY 4.0 license. </p> <p>The uploaded files contain all data (including raw data), scripts and supporting files used in preparing the manuscript</p> <ul> <li>360panoramas.tar: 360 panoramic pictures taken at the locations at the St Andrews site.</li> <li>PhotosSites.tar: Additional pictures taken at the different ensonification sites</li> <li>ProcessData.tar: This contains all Matlab code for processing and visualizing the data. Also, it contains the templates for all locations as used in the paper.</li> <li>RawData.7z.0xx: These files contain the raw acoustic data as recorded from the microphones for all locations at each of the three sites. This data is provided as Matlab arrays. Due to the file size limitation of Zenodo, the archive has been split into 17 parts. Your archive manager should be able to open all data by accessing RawData.7z.001.</li> <li>Latex.tar: The latex source code and images for the final manuscript.</li> </ul>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 671,304 tweet-URL pairs (<em>tweets_2014_sample_6271_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,271 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 605,080 tweet-URL pairs (<em>tweets_2014_sample_6694_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,694 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul> <p>In addition, the following rankings (based on the odds ratio) of domains, hosts, and URLs that appear in both the researcher dataset and the sample are included:</p> <ul> <li><em>domains_by_odds_ratio.tsv.bz2</em> - a ranking of 61,860 domains,</li> <li><em>hosts_by_odds_ratio.tsv.bz2</em> - a ranking of 80,384 hosts,</li> <li><em>publisher_domains_by_odds_ratio.tsv.bz2</em> - a ranking of 924 publisher domains,</li> <li><em>publisher_urls_by_odds_ratio.tsv.bz2</em> - a ranking of 4,227 publisher URLs.</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.