Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
156
datasets available to search
ShareScore release 0.9.0
Dataset results
156 results for “Video dataset”
Video-Trajectory Robot Dataset
<p>This dataset consists of color and depth videos of Panda robot motions and their corresponding joint and Cartesian trajectories. The dataset also includes the trajectories of a receiver robot for the purpose of an object handover. Each motion sample comprises 6 files (RGB video, depth video and 4 giver/receiver trajectories in time series form). Total number of motion samples: 38393.</p> <p>Structure: MPEG-4 videos of robot motion and corresponding Python serialized (or “pickled”) files, containing joint and Cartesian trajectories. Dataset is divided into four parts: simulation dataset (PandaHandover_Sim.zip), real train dataset (PandaHandover_Real_Train.zip), real validation dataset (PandaHandover_Real_Val.zip), real test dataset (PandaHandover_Real_Test.zip). Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .pkl files can be loaded using Python (>=3.7) and the Python library Pandas (>=1.1.3).</p>
Video-Trajectory Hand Dataset
<p>This dataset consists of color and depth videos of human worker motions and their corresponding Cartesian trajectories of the worker’s hand, captured using OptiTrack motion capture system. Each motion sample comprises 4 files (RGB video, depth video, a Cartesian trajectory of worker’s hand and a label of the motion, representing one of four possible goals from 1 to 4). Total number of motion samples: 811.</p> <p>Structure: MPEG-4 videos of human worker motion and corresponding NumPy array files, containing Cartesian trajectories and a label of the motion. Dataset is in a .zip archive. Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .npy files can be loaded using Python (>=3.7) and the Python library NumPy (>=1.19.2).</p>
SCoRe - Student Crowd Research. A german dataset of university students' written reflections on their participation in collaborative research-based learning focused on sustainability topics and using videos as a research tool.
<p>Schriftliche Reflexionen von 57 Studierenden als Prüfungsleistung im Rahmen des forschenden Lernens mit Video zu Nachhaltigkeitsthemen. Die Reflexionen wurden durch vorgegebene Fragen angeleitet. Die hier vorliegenden Texte sind, von den Studierenden selbst niedergeschriebene, Transkripte von Sprechtexten eines Self-Video-Casts. Es handelt sich dabei um eine benotete Prüfungsleistung einer universitätsübergreifenden Wahlpflicht-Lehrveranstaltung mit 1-3 Credit-Points.</p> <p>A dataset containing written reflections of 57 university students from several German universities and fields of study on their participation in collaborative research-based learning focused on sustainability topics and using videos as a research tool. These reflections were guided by given questions. The texts presented here are transcripts of spoken texts of self-video-casts, written down by the students themselves. The texts were graded as part of an inter-university elective course worth 1-3 credit points.</p>
Video Game Bad Smells: What they are and how Developers Perceive Them - Online dataset
<p>This artifact contains the online replication package of the manuscript "Video Game Bad Smells: What they are and how Developers Perceive Them"</p>
SignBD-Word: Video-Based Bangla Word-Level Sign Language Dataset
<p>Bangla sign language (BdSL) is a complete and independent natural sign language with its own linguistic characteristics. While there exists video datasets for well-known sign languages, there is currently no available dataset for word-level BdSL. In this study, we present a video-based word-level dataset for Bangla sign language, called SignBD-Word, consisting of 6000 sign videos representing 200 unique words. The dataset includes full and upper-body views of the signers, along with 2D body pose information. This dataset can also be used as a benchmark for testing sign video classification algorithms.<br><br>Official Train Test Spllit (for both RGB and bodypose) can be found from the following link: <br>https://sites.google.com/view/signbd-word/dataset<br><br>This dataset is part of the following paper:<br>A. Sams, A. H. Akash and S. M. M. Rahman, "SignBD-Word: Video-Based Bangla Word-Level Sign Language and Pose Translation," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-7, doi: 10.1109/ICCCNT56998.2023.10306914.<br><br>Download the corresponding paper from this link:<br>https://asnsams.github.io/Publications.html</p>
Remote Sensing Satellite Video Dataset for Super-resolution
<p>This is a satellite video super-resolution dataset generated from "Jilin-1" video satellite.</p> <p>Training set: 189 clips; Test set: 12 clips.</p> <p>More details can be found in our paper published in IEEE TGRS: https://ieeexplore.ieee.org/document/9530280</p> <p>If you find our work helpful, please cite our paper. Thank you very much!</p>
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and other sources about the 2024 outbreak of Measles
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: <a href="https://doi.org/10.48550/arXiv.2406.07693" rel="nofollow">https://doi.org/10.48550/arXiv.2406.07693</a>)</p> <p><strong>Abstract</strong></p> <p>This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.</p>
[LS2N_IPI_YouTube_UGC] A DATASET FOR UNDERSTANDING OPEN UGC VIDEO DATASETS
<p>User Generated Content (UGC) video streaming is a major application on the Internet. Even small bitrate savings can have large network impacts at this scale. In order to achieve improvements without sacrificing experience, the quality of UGC videos needs to be better understood. In recent years video quality evaluation models designed for the evaluation of UGC videos have received a lot of attention. However, considering that these models are learning-based models, they heavily depend on the training data that has been used. </p> <p>In this paper, a new dataset is introduced that allows studying the differences in characteristics between existing UGC video datasets. It reveals the range of quality that was covered by existing UGC video datasets, and the implication of these quality ranges on training and validation performance of UGC video quality prediction models. Furthermore, this work demonstrates that dataset alignment enables existing UGC models to achieve higher performance.</p>
YA Domain Dataset: Dataset of scholarly bibliographic references on YouTube videos
<p><strong>Abstract</strong></p> <p>Scholarly communication through YouTube videos has been increasing. Although Altmetric (<a href="https://altmetric.com/">https://altmetric.com/</a>) provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Considering this background, we built and published a dataset of scholarly bibliographic references on YouTube videos by using YouTube Data API v3, targeting six types of domain names: "doi.org," "ncbi.nlm.nih.gov," ieeexplore.ieee.org," "link.springer.com," "onlinelibrary.wiley.com," and "sciencedirect.com." As a result, we identified approximately 480,000 references associated with Crossref DOIs among 230,000 videos published by December 31, 2023, posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. This dataset is helpful for identifying and analyzing scholarly references on YouTube.<br>As for the original paper related to this dataset, please refer to the references section.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. The data is split into files by DOI Registration Agencies. A sample of the record is as follows:</p> <table> <tbody> <tr> <td>{<br> "channel_id": "UCEfEi-IMiB87UsxY3765P6w",<br> "video_id": "e7YmyVd4uOE",<br> "is_covered_by_altmetric_com": false,<br> "youtube_data_api_search": [<br> {<br> "query": "doi.org",<br> "uri": "http://dx.doi.org/10.1145/2807442.2814654"<br> }<br> ],<br> "doi": "10.1145/2807442.2814654",<br> "doiRA": "Crossref"<br>}</td> </tr> </tbody> </table> <ul> <li>channel_id (String) -- Channel ID of the YouTube channel that uploaded the video.</li> <li>video_id (String) -- Video ID.</li> <li>is_covered_by_altmetric_com (Boolean) -- Whether this reference is covered by altmetric.com or not.</li> <li>youtube_data_api_search (Array) <ul> <li> query (String) -- The query used in the search:list of YouTube Data API v3. (<a href="https://developers.google.com/youtube/v3/docs/search/list?hl=en">https://developers.google.com/youtube/v3/docs/search/list?hl=en</a>)</li> <li> uri (String)-- The original external links written in the description text or video title in each video.</li> </ul> </li> <li>doi (String) -- DOI corresponding to the bibliographic reference in the video.</li> <li>doiRA (String) -- DOI registration agency for the DOI. We obtained this data using the WhichRA? API (<a href="https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra">https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra</a>).</li> </ul> <p>We note that the altmetric dataset obtained from Altmetric Explorer in this study is not included in this dataset.</p> <p><strong>References</strong></p> <ul> <li>Kikkawa, Jiro; Takaku, Masao; Yoshikane, Fuyuki: "Enhancing Identification of Scholarly Reference on YouTube: Method Development and Analysis of External Link Characteristics", <em>Proceedings of the 28th International Conference on Theory and Practice of Digital Libraries (<a href="https://tpdl2024.nuk.si/">TPDL 2024</a>)</em>, Ljubljana, Slovenia, Lecture Notes in Computer Science (LNCS), Vol.15178, 2024.09. (in press).</li> </ul> <p><strong>Fundings</strong></p> <p>JSPS KAKENHI Grant Numbers <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147/">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-23K11761">JP23K11761</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-24K15652">JP24K15652</a>.</p>
Video Dataset of 101 Cataract Surgeries
<p>This dataset contains 101 videos of cataract surgeries annotated with two kinds of information:</p> <ul> <li>(Anonymous) ID and experience level of operating surgeon</li> <li>Starting points of quasi-standardized operation phases in videos</li> </ul> <p>The cataract surgeries have been performed by four different surgeons of two levels of experience (low, high).</p> <p>The dataset has been collected and annotated by ophthalmic surgeons of<br> Klinikum Klagenfurt, and has been prepared and provided by the Institute of<br> Information Technology (ITEC) of Alpen-Adria-Universitaet (AAU) Klagenfurt, Austria.</p>
Artificially-generated Lecture Video Fragmentation Dataset and Ground Truth
<p>We provide a large-scale lecture video dataset consisting of artificially-generated lectures, and the corresponding ground-truth fragmentation, for the purpose of evaluating lecture video fragmentation techniques.</p> <p>For creating this dataset, 1498 speech transcript files (generated automatically by ASR software) were used from the world's biggest academic online video repository, the VideoLectures.NET. These transcripts correspond to lectures from various fields of science, such as Computer science, Mathematics, Medicine, Politics etc. In order to create the synthetic video lectures, all transcripts were randomly split in fragments, the duration of which ranges between 4 and 8 minutes. Each synthetic lecture was then assembled by combining (stitching) exactly 20 randomly selected fragments. 300 such artificially-generated lectures are included in the released dataset. Each such lecture file has a mean duration of about 120 minutes, thus the dataset contains altogether about 600 hours of artificially-generated lectures. Every pair of consecutive fragments in these lectures originally comes from different videos, consequently the point in time where such two fragments are joined is a known ground-truth fragment boundary. All these boundaries form the dataset's ground truth. We should stress that we do not generate the corresponding video files for the artificially-generated lectures (only the transcripts), and one should not try to reverse-engineer the dataset creation process so as to use in some way the visual modality for detecting the fragments in this dataset.</p> <p><strong>File format</strong></p> <p>After you download the provided .zip and unpack it, the extracted folder will contain two sub-folders:</p> <pre><code>1. ALV_srt 2. ALV_srt_GT </code></pre> <p>Each of them contains 300 files.</p> <p>The <strong>ALV_srt</strong> folder contains the transcripts of every artificially-generated lecture, in the standard SRT format:</p> <pre><code>1. A numeric counter identifying each sequential subtitle 2. The time that the subtitle should appear on the screen, followed by --> and the time it should disappear 3. Subtitle's text itself on one or more lines 4. A blank line containing no text </code></pre> <p>The <strong>ALV_srt_GT</strong> folder contains the ground truth (GT) fragments corresponding to the lectures (transcripts) of the <strong>ALV_srt</strong> folder. Each GT file consists of 3 tab-separated columns and 20 rows, in the following format:</p> <pre><code><Fragment_ID_1> <StartTime_1> <EndTime_1> <Fragment_ID_2> <StartTime_2> <EndTime_2> <Fragment_ID_3> <StartTime_3> <EndTime_3> . . . <Fragment_ID_20> <StartTime_20> <EndTime_20> </code></pre> <p>Each row indicates a fragment. The first column indicates the ID of a fragment while the second and the third column indicate the start and the end time of the fragment respectively.</p> <p><strong>License and Citation</strong></p> <p>This dataset is provided for academic, non-commercial use only. If you find this dataset useful in your work, please cite the following publication where the dataset is introduced:</p> <p><em>D. Galanopoulos, V. Mezaris, “Temporal Lecture Video Fragmentation using Word Embeddings”, Proc. 25th Int. Conf. on Multimedia Modeling (MMM2019), Thessaloniki, Greece, Jan. 2019.</em></p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the EU’s Horizon 2020 research and innovation programme under grant agreement No 693092 MOVING. We are grateful to JSI/VideoLectures.NET for providing the lectures’ transcripts.</p>
Headcam: Cylindrical Panoramic Video Dataset for Unsupervised Learning of Depth and Ego-Motion
<p>This dataset contains panoramic video captured from a helmet-mounted camera while riding a bike through suburban Northern Virginia. We used the videos to evaluate an unsupervised learning method for depth and ego-motion estimation, as described in our paper:</p> <p>Alisha Sharma and Jonathan Ventura. "Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video." Proceedings of the 2019 IEEE Artificial Intelligence & Virtual Reality Conference, San Diego, CA, 2019.</p> <p>If you make use of this dataset, please cite this paper.</p> <p> </p> <p>The videos are stored as .mkv video files encoded using lossless H.264. To extract the images, we recommend using ffmpeg:</p> <blockquote> <p>mkdir 2018-10-03 ;</p> <p>ffmpeg -i 2018-10-03.mkv -q:v 1 2018-10-03/%05d.png ;</p> </blockquote> <p>Associated code can be found in our <a href="https://github.com/jonathanventura/cylindricalsfmlearner">GitHub repository</a>. </p>
Grape Bunch Video Dataset with Berry Annotations and Tracking Data
<p>The dataset consists of videos of Bonarda grape bunches in a mature state. A total of 100 grape bunches were collected from five consecutive rows of the same plot at the "Finca de Sancho" located in Lavalle, Mendoza, Argentina, on March 21, 2023. Each bunch was assigned a unique identifier after collection.</p> <p>For video capture, two setups were built, referred to as setup 1 and setup 2. Each consisted of a stand to hold a bunch against a smooth white background. This background was carefully chosen to facilitate precise segmentation of the grapes in the images, ensuring more efficient and accurate object detection. Additionally, a curved structure with a 10x7 grid of QR codes was placed behind the bunch. These codes were not used in this dataset but were included for potential future studies. The setups were mounted outdoors, taking advantage of natural lighting to obtain a more realistic representation of the grape bunches. Video capture sessions were conducted over three consecutive days, lasting between 4 to 7 hours each day. The first session took place on the same day the bunches were harvested. Throughout the sessions, natural light fluctuated due to intermittent cloud cover, introducing variations in ambient lighting.</p> <p>Video recording was performed by two individuals referred to as capturer A and capturer B. The recording devices were the cameras of two smartphones, a Samsung Galaxy S20 FE and a Motorola G200. The Open Camera application (<a href="https://opencamera.org.uk/" target="_new" rel="noopener">https://opencamera.org.uk/</a>) was used, configured to automatically capture 5-second videos at a resolution of 720 pixels wide by 1280 pixels high (portrait orientation), at 30 frames per second. A fixed focal distance of 30 cm was maintained, and the white balance was manually adjusted according to the natural light variations to maintain good image quality. </p> <p>Three camera movements were defined for video capture: two systematic movements named "horizontal 180º" and "vertical 180º", and a third movement called "freestyle." The horizontal and vertical movements involved sweeping the camera from left to right and from bottom to top, respectively, while keeping the bunch centered in the frame and at a distance of approximately 30 cm. The freestyle movement consisted of random movements, maintaining the bunch centered as best as possible within the frame.</p> <p>The capture protocol was as follows:</p> <p>First, a grape bunch was placed in each setup. Capturer A at setup 1 captured five videos for each of the three defined camera movements, while capturer B did the same at setup 2. Afterward, the capturers switched positions, with capturer B taking videos at setup 1 and capturer A at setup 2. The bunches were then replaced with new ones, and the process was repeated until all 100 bunches were captured.</p> <p>As a result, the dataset contains five videos for each of the three camera movements, captured by both individuals for each of the 100 bunches. This produces a dataset consisting of 3,000 videos: 1,000 videos for each camera movement and 30 videos per bunch. </p> <p>It is worth noting that, due to the manual nature of the capture process, some variations occurred in the number of videos recorded per bunch. In some cases, 9 videos were recorded instead of 10, in one instance only 8 were captured, and in one case 11 videos were obtained. These minor discrepancies are mainly due to human error in counting the recorded videos, but they do not affect the overall quality or integrity of the dataset.</p> <p> </p> <p>The dataset also includes berry annotations for each video, provided in JSON files. These files specify, for each frame of the video, the pixel coordinates of each berry's center and the radius it occupies in the image, also in pixels. The berry detections were obtained through inference using a deep learning architecture called CircleNet, which was specifically trained for this dataset.</p> <p>In addition, the dataset contains berry tracking data, provided in CSV files. These files indicate which berries are the same across different frames of the video, through a unique berry identifier. The tracking was generated using a custom algorithm developed specifically to produce these tracks.</p> <p> </p>
DIVERSE: Deciphering Internet Views on the U.S. Military Through Video Comment Stance Analysis: A Novel Benchmark Dataset for Stance Classification
<p>Paper citation: Cruickshank, Iain J., and Lynnette Hui Xian Ng. "DIVERSE: Deciphering Internet Views on the US Military Through Video Comment Stance Analysis, A Novel Benchmark Dataset for Stance Classification." <em>arXiv preprint arXiv:2403.03334</em> (2024).</p> <p>Link to paper: https://arxiv.org/abs/2403.03334</p>
NANCY SNS JU Project - VR Video Streaming & iPerf3 on O-RAN 5G Testbed Dataset
<p>This dataset was developed in the context of the NANCY project and it is the output of the experiments involving streaming a virtual reality (VR) video in a 5G coverage expansion scenario. Additionally, iPerf3 experiments in both TCP and UDP modes were carried out. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services.</p> <p>The dataset includes network traffic, which was captured and stored in a .pcap files, as well as various performance metrics that were collected by an xApp running in the near-real-time Radio Access Network Intelligent Controller.</p> <p>The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.</p>
Dataset: Combining video telemetry and wearable MEG for naturalistic imaging
<p>OPM-MEG and Openpose keypoint data from the study "Combining video telemetry and wearable MEG for naturalistic imaging".</p> <p><strong>Changelog</strong></p> <p><strong>v1.10</strong></p> <ul> <li>Subject 004 from v1.01 has been renamed 005 (to reflect addition of new subject recorded prior to 005 during acquisition).</li> <li><strong>NEW </strong>sub-004</li> <li>Subjects 003-004 have a proof-of-principle motor paradigm added.</li> <li>README changes</li> </ul> <p><strong>v1.01</strong></p> <ul> <li>Corrected sub-004 *_channel.json files to include bad channel identifiers</li> <li>Telemetry data zipped prior to uploading to zenondo</li> <li>Updates to README</li> </ul>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<pre>#A fMRI dataset in response to large number of short natural dynamic facial expression videos<br>Natural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.<br>The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data.<br>To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047<br><br>## Preprocess procedure<br>The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/**<br>Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.<br>## GLM of main experiment<br>We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).<br><br>The regressors in the GLMsingle toolbox mainly include the following categories:<br>1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response.<br>2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation.<br>3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal.<br>4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement.<br>5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level.<br><br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m**<br><br>#### retinotopic mapping<br><br>The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.<br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m**<br><br>#### fLoc experiment<br><br>We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m**<br><br><br>## Validation<br>### Basic quality control<br>The fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py**<br>### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment.<br>In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py**<br>### noise celling<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py**<br><br>### Correspondence between human brain and DCNN<br>The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/**<br><br>### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.<br>**code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/**<br><br>## results<br> The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".<br><br>## Whole-brain mapping<br>The whole-brain data mapped to the cerebral cortex as obtained from the technical validation.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m**<br><br>## Mannually prepared environment<br>We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.<br><br>## stimuli<br>The video stimuli used in the NFED experiment are saved in the "stimuli" folders.<br><br></pre>
Dataset for hydrate particle number and area calculation, and hydrate coarsening videos
<p>There are three series (24 files) of data on day 4, 14, 24, 34, 44, 54, and 64:</p> <p>1. Hydrate particle number in open area (7 files):</p> <p>· 4 days-particle number.pptx</p> <p>· 14 days-particle number.pptx</p> <p>· 24 days-particle number.pptx</p> <p>· 34 days-particle number.pptx</p> <p>· 44 days-particle number.pptx</p> <p>· 54 days-particle number.pptx</p> <p>· 64 days-particle number.pptx</p> <p> </p> <p>2. Hydrate saturation calculation in porous media (15 files):</p> <p>· 4 days-area pixels.xlsx</p> <p>· 4 days-area pixels.pptx</p> <p>· 14 days-area pixels.xlsx</p> <p>· 14 days-area pixels.pptx</p> <p>· 24 days-area pixels.xlsx</p> <p>· 24 days-area pixels.pptx</p> <p>· 34 days-area pixels.xlsx</p> <p>· 34 days-area pixels.pptx</p> <p>· 44 days-area pixels.xlsx</p> <p>· 44 days-area pixels.pptx</p> <p>· 54 days-area pixels.xlsx</p> <p>· 54 days-area pixels.pptx</p> <p>· 64 days-area pixels.xlsx</p> <p>· 64 days-area pixels.pptx</p> <p>· Particle number and SH variation.xlsx</p> <p> </p> <p>3. Videos of the hydrate coarsening and sintering process (2 files):</p> <p>· Hydrate coarsening and sintering in open area.mp4</p> <p>· Hydrate coarsening and sintering in porous media.mp4</p>
Atmospheric turbulence distorted video sequence dataset
<p>This contains the full version of the dataset utilized in our paper "Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning". Three main types of data are covered, which include algorithm simulated data, physical simulated data and real-world data. Specifically, the algorithm/physical simulated sequences are given with reference without turbulence distortion.</p>
PolarBearVidID: A Video-based Re-Identification Benchmark Dataset for Polar Bears
<p><em><strong>The peer-reviewed publication for this dataset has now been published in Animals, an MDPI journal, and can be accessed here: <a href="https://doi.org/10.3390/ani13050801">https://doi.org/10.3390/ani13050801</a>. Please cite this when using the dataset.</strong></em></p> <p><em>PolarBearVidID</em> includes 13 individual polar bears housed in six institutions. Each identity has 110 sequences on average. The maximum length of the sequences is 8 seconds, respectively 100 frames at a frame rate of 12.5 frames per second. The average length of the sequences is 96.69 images. In total, the dataset includes 1431 sequences. The resolution of the images is set to 256 x 128 pixels. Finally, <em>PolarBearVidID</em> is the first dataset to enable utilizing the movement of a non-human species as a feature for the task of re-identification.</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.