Find research datasets worth reusing

opencc-by-4.0May 2011View details →

Figure 2. Hierarchical access to the image DB-Access Management in Medical Image Databases Based on New Format and Contents Protection with Inverse Pyramid Decomposition

The structure of the hierarchical access to the image database contents is shown on Fig. 2.

opencc-by-4.0Dec 2014View details →

CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms

The CID2013 Camera Image Database consists of real images taken by consumer cameras and mobile phones. It is developed to provide useful tool to allow researchers target more commercially relevant distortions when developing processes of objective image quality assessment algorithms. The CID2013 database consists of 480 evaluated images captured by 79 imaging devices (mobile phones, DSC, DSLR) in six Image Sets. Note that the actual number of images in the database is 474. In Image Set II, Device 6 is evaluated twice as we wanted to test inter-observer reliablity. The scores are later combined into a single MOS value as the two evaluations correlated strongly. If you use this database in your research, we kindly ask that you follow the copyright notice bellow and cite the following paper: Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 390-402, Jan. 2015. <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6975172">[pdf]</a> Method The images are evaluated by 188 observers using Dynamic Reference (DR-ACR) method (explained below). A separate scale realignment ACR data consisting evaluations from 34 observers is also included that allows to combine the data from the six image sets In other respects the DR-ACR method resembles very much a basic Absolute Category Rating (ACR) method (ITU-R 500-11), except the observers saw a slideshow of all the other images in the test depicting the same scene before every evaluation (See DR_demo.mp4). By seeing the other images in the test setup as reference the observers were more aware of the total variation of quality represented within a single image set. This improved their evaluation as they didn’t need to save the far ends of the scale in case there would be even more better or worse image later on the experiment. The DR-ACR method is explained in detail in: Mikko Nuutinen, Toni Virtanen, Tuomas Leisti, Terhi Mustonen, Jenni Radun, Jukka Häkkinen (2014)  A new method for evaluating the subjective image quality of photographs : dynamic reference Multimedia Tools and Applications 75:  4.  2367-2391 Dec. Database contains consumer camera images and their subjective evaluations in mean opinion score (MOS), sharpness, graininess, lightness and color saturation scales. It includes the complete raw data and background information from the naïve observers used to evaluate the images. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward. Outlier removal is made for mean opinion score (MOS) evaluations using ITU-R 500-11 recommendations to ease out the implementation of the database. Material The images in CID2013 are intended to represent typical photographs that consumers might capture with their cameras. The photographed scenes were based partly on the Photospace approach described by I3A (CPIQ Initiative Phase 1 White Paper: Fundamentals and review of considered test methods, I3A, 2007) The I3A CPIQ project has migrated under IEEE. The test environment The room has been covered with medium gray curtains to diffuse the ambient illumination. Fluorescent lights (5800K) were positioned behind the monitors and reflected from the back wall covered with grey curtain to create dim and uniform ambient illumination in the room. The light hitting the monitors measured below 20 lx. The subject’s viewing distance (approximately 80 cm) was controlled by a line hanging from the ceiling, and they were instructed to keep their forehead steady next to the line. Because of the display size, images were scaled to a size of 1600 x 1200 pixels using the bicubic interpolation method. Eizo ColorEdge CG241W, with 1920x1200 pixel resolution, monitors in was calibrated to sRGB having target values of: 80 cd/m2, 6500K and gamma 2.2 using EyeOne Pro calibrator (X-rite co.).   -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ Copyright (c) 2014 The University of Helsinki All rights reserved. Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission. The database and our paper are to be cited in the bibliography as: ----------------------------------------------------------------------------- Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, 2014, In press. ----------------------------------------------------------------------------- LIMITATION OF LIABILITY UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE. THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW. -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------

opencc-zeroSep 2019View details →

AVT Image Database

Raw Image Dataset for the Paper: Göring, Raake; "EVALUATION OF INTRA-CODING BASED IMAGE COMPRESSION" 2019. Consisting of 1133 images downloaded from wesaturate.com; all images are CC0 licenced.

opencc-by-4.0May 2021View details →

ISIEA: An image database of social inclusion and exclusion in Asian young adults

Here we introduce an open-access, free, and standardized set of image stimuli, the image database of social inclusion/exclusion in Asian young adults (ISIEA), which is developed and validated by our lab for academic purposes in the field of social & affective processing especially social inclusion/exclusion studies.   This database contains a set of 164 images depicting social interaction scenarios under three categories of social contexts (social exclusion, social neutral, and social inclusion). Standardized assessments are provided for each image, including the traditional emotional dimensions of arousal and valence, the inclusion score which evaluates the level of perceived social inclusion, and the vicarious feeling scale which evaluates the level of affective feeling when you imagine yourself as the highlighted person in the image. Visual physical properties of each image are also provided, including luminance, contrast, complexity, and color parameters. Additionally, we offer the ratings of face component and context component for each image (see Zheng et al., 2021 below for details). These parameters (Appendix 1) would be helpful for image selection and control of confounding factors.   Note: This database is freely provided for only academic purpose (including laboratory research, journal publication, academic posters, conference exhibitions, etc.). Any commercial or other non-academic use is not allowed. It is allowable to make appropriate post-processing of the images (e.g., adjustment of brightness, contrast, color, clarity, and size) for research purpose, but not allowable to maliciously modify or deface the portraits in these images. We require to cite the paper of Zheng et al. (2021) properly when using the ISIEA. You are also welcomed to cite our other studies that have already used part of the images in the database as experimental materials.   Users in Mainland China can also download from the following link using Baidu Netdisk. This link allows you to select a specific category of image to download according to your needs. If you have any problems with download or application of the database, please feel free to contact Mr. Li via <a href="mailto:liyw07@outlook.com">liyw07@outlook.com</a>. You are also welcome to contact Prof. Zhang via <a href="mailto:zhangdd05@gmail.com">zhangdd05@gmail.com</a> for suggestions and collaboration.    Baidu Netdisk link: https://pan.baidu.com/s/1ekNEUxNk4ZmoZgqIWBOHNA Code: ISIE   Database instructions and citation: Zheng, Z., Li, S., Mo, L., Chen, W., & Zhang, D. (2022). ISIEA: An image database of social inclusion and exclusion in young Asian adults. Behavior research methods, 54(5), 2409–2421. <a href="https://doi.org/10.3758/s13428-021-01736-w">https://doi.org/10.3758/s13428-021-01736-w</a>   Previous related articles using these images: Zhao J, Mo L, Bi R, He Z, Chen Y, Xu F, Xie H, Zhang D. The VLPFC versus the DLPFC in downregulating social pain using reappraisal and distraction strategies. The Journal of Neuroscience, 2021, 41(6):1331-9.  <a href="https://doi.org/10.1523/JNEUROSCI.1906-20.2020">https://doi.org/10.1523/JNEUROSCI.1906-20.2020</a> Zhenhong He, Sijin Li, Licheng Mo, Zixin Zheng, Yiwei Li, Hong Li, Dandan Zhang. The VLPFC-engaged voluntary emotion regulation: Combined TMS-fMRI evidence for the neural circuit of cognitive reappraisal. The Journal of Neuroscience, 2023, 43(34):6046-6060. <a href="https://doi.org/10.1523/JNEUROSCI.1337-22.2023">https://doi.org/10.1523/JNEUROSCI.1337-22.2023</a> He Z, Liu Z, Zhao J, Elliott R, Zhang D. Improving emotion regulation of social exclusion in depression-prone individuals: A tDCS study targeting right VLPFC. Psychological Medicine, 2020, 50(16):2768-79.   <a href="https://doi.org/10.1017/S0033291719002915">https://doi.org/10.1017/S0033291719002915</a> Sijin Li, Jingxu Chen, Kexiang Gao, Feng Xu, Dandan Zhang. Excitatory brain stimulation over the left dorsolateral prefrontal cortex enhances voluntary distraction in depressed patients, Psychological Medicine, 2023, 53:6646-55. <a href="https://doi.org/10.1017/S0033291723000028">https://doi.org/10.1017/S0033291723000028</a> He Z, Lin Y, Xia L, Liu Z, Zhang D, Elliott R. Critical role of the right VLPFC in emotional regulation of social exclusion: A tDCS study. Social Cognitive and Affective Neuroscience, 2018, 13(4):357-66. <a href="https://doi.org/10.1093/scan/nsy026">https://doi.org/10.1093/scan/nsy026</a> Licheng Mo, Sijin Li, Si Cheng, Yiwei Li, Feng Xu, Dandan Zhang. Emotion regulation of social pain: Double dissociation of lateral prefrontal cortices supporting reappraisal and distraction. Social Cognitive and Affective Neuroscience, 2023,18(1), 1-10. <a href="https://doi.org/10.1093/scan/nsad043">https://doi.org/10.1093/scan/nsad043</a> He Z, Zhao J, Shen J, Muhlert N, Elliott R, Zhang D. The right VLPFC and downregulation of social pain: A TMS study. Human Brain Mapping, 2020, 41:1362-71. <a href="https://doi.org/10.1002/hbm.24881">https://doi.org/10.1002/hbm.24881</a>  Wenwen Yu, Yiwei Li, Xueying Cao, Licheng Mo, Yuming Chen, Dandan Zhang. The role of ventrolateral prefrontal cortex on voluntary emotion regulation of social pain. Human Brain Mapping, 2023, 44(13): 4710-4721. <a href="https://doi.org/10.1002/hbm.26411">https://doi.org/10.1002/hbm.26411</a> Cheng S, Qiu X, Li S, Mo L, Xu F, Zhang D. Different Roles of the Left and Right Ventrolateral Prefrontal Cortex in Cognitive Reappraisal: An Online Transcranial Magnetic Stimulation Study. Frontiers in human neuroscience, 2022, 16:928077. <a href="https://doi.org/10.3389/fnhum.2022.928077">https://doi.org/10.3389/fnhum.2022.928077</a> 王妹, 程思, 李宜伟, 李红, 张丹丹. 背外侧前额叶在安慰剂效应中的作用:社会情绪调节研究. 心理学报, 2023, 55(7):1063-3. <a href="https://doi.org/10.3724/sp.J.1041.2023.01063">https://doi.org/10.3724/sp.J.1041.2023.01063</a> 莫李澄, 郭田友, 张岳瑶, 徐锋, 张丹丹. 激活右腹外侧前额叶提高抑郁症患者对社会疼痛的情绪调节能力:一项TMS研究. 心理学报, 2021, 53(05):494-504. <a href="https://doi.org/10.3724/sp.J.1041.2021.00494">https://doi.org/10.3724/sp.J.1041.2021.00494</a> 于文汶, 王妹, 仇秀芙, 高可翔, 陈伟茂, 张丹丹. 腹外侧前额叶经颅磁刺激对社会疼痛的影响:拒绝敏感性和抑郁的调节作用. 中国临床心理学杂志, 2022, 30(04):985-990+972. <a href="https://doi.org/10.16128/j.cnki.1005-3611.2022.04.045">https://doi.org/10.16128/j.cnki.1005-3611.2022.04.045</a> Last Updated: 2023.12.14

opencc-by-4.0Dec 2017View details →

For MACHINE LEARNING DATABASE evaluation: old-version SEM images of TiO2 particles UNITO

Test images recorded with old ZEISS software, metadata version could differ to the up-to-date version.

opencc-by-4.0Apr 2023View details →

3D images of fossil planktonic foraminifera from the western Pacific Ocean: a database concerning two biostratigraphic events during the Early Pleistocene

Here we present planktonic foraminifera X-ray images dataset during 1.72-2.15 million years ago using Microfocus X-ray CT (MXCT) and Projection X-ray Microscopy (PXM) technologies in a sedimentary core ODP Hole 1115B (9 11'S, 151 34E, water depth 1149 m) in the Solomon Sea. The species Globigeerinoideseela fistuolsa, Trilobatus sacculifer, and Pulleniatina spp. tests were hand-picked and gently cleaned for X-ray images. In total, there are 20 individuals with 20 images are presented in this dataset.-

opencc-by-4.0Jul 2023View details →

ATLAS Database — 2D Images

We need affect-based stimuli specifically conceived to investigate architectural spaces. The RESONANCES project crafted ATLAS, a dATabase of visuaL Atmospheric Stimuli. It collects a series of spatial patterns born from a systematic selection of generators of atmosphere. Generators of atmosphere are architectural features designed to afford atmospheric effects (such as lights, colors, materials, and proportions). ATLAS is an open-access tool that supports researchers interested in studying emotional reactions to architectural features by providing reliable, standardized, and reproducible stimuli. In this dataset, ATLAS stimuli are presented as 2D images.

dryad36/100

Data from: Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database

Open the record for dataset details and reuse information.

publicOct 2022View details →

opencc-by-4.0Mar 2020View details →

Image Databases for Facial Analysis Coded for Race and Gender Features

This document consists of the corpus of image databases examined for race and gender information as published in: Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R. Brubaker. 2020. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Proc. ACM Hum.-Comput. CSCW. This code book includes: 1. Whether race/gender is present implicitly (as descriptive, but not annotated) or explicitly (annotated/labeled).  2. What categories or descriptions of race/gender are used. 3. Whether those categories/descriptions use underlying source material to justify or motivate their descriptions of race/gender. 4. Whether explicitly annotated databases describe the process of annotating race/gender.

opencc-by-nc-4.0Nov 2016View details →

The DIsgust-RelaTed-Images (DIRTI) Database: Validation of a novel standardized set of disgust pictures

Selecting appropriate stimuli is a major challenge of affective research. Although several standardized databases for affective pictures exist, none of them focus on discrete emotions such as disgust. Validated pictures inducing discrete emotions are still limited, and this presents a problem for researchers interested in studying different facets of disgust. In this paper, we introduce the DIsgust-RelaTed-Images (DIRTI) picture set. The set consists of 240 disgust-inducing pictures divided into six categories (food, animals, body products, injuries/infections, death, and hygiene). Additionally, we included 60 matched neutral pictures (10 per category). All pictures were rated by 200 participants on nine-point rating scales measuring disgust, fear, valence, and arousal. The present validation study covered a wide age range (18–75 years) with a balanced number of participants in each decade of life. For each picture, we provide separate ratings on the four scales for men and women. In addition to the original pictures, we also provide a luminance-matched version for experiments that require control of the physical properties of the pictures. The standardized DIRTI picture set allows researchers to chose from a wide set of disgust-inducing pictures and may enhance researchers’ ability to draw comparisons between studies on disgust. (Download DIRTI picture set: http://dx.doi.org/10.5281/zenodo.167037). Picture sets and supplementary material relative to the following publication: Haberkamp, A., Glombiewski, J. A., Schmidt, F., & Barke, A. (submitted). The DIsgust-RelaTed-Images (DIRTI) Database: Validation of a novel standardized set of disgust pictures. Behaviour Research and Therapy. The three files contain the following content. DIRTI Database.zip → original picture set DIRTI luminance matched images.zip → luminance-matched version of the picture set DIRTI Supplementary Material.zip → excel file containing separate worksheets for all ratings (disgust, valence, arousal, fear), for the physical properties of the original and luminance-matched disgust pictures, and for a luminance-matched subset of the original disgust pictures

opencc-zeroJan 2018View details →

Supplementary material 1 from: Báthori F, Pfliegler WP, Zimmerman C-U, Tartally A (2017) Online image databases as multi-purpose resources: discovery of a new host ant of Rickia wasmannii Cavara (Ascomycota, Laboulbeniales) by screening AntWeb.org. Journal of Hymenoptera Research 61: 85-94. https://doi.org/10.3897/jhr.61.20255

Online image databases as multi-purpose resources: Rickia wasmannii Cavara (Ascomycota, Laboulbeniales) on a new host ant from a new country by screening AntWeb.org :

opengpl-3.0-or-laterAug 2024View details →

PMcardio ECG Image Database (PM-ECG-ID): A Diverse ECG Database for Evaluating Digitization Solutions

The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions. PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below: <ul> <li>metadata.csv: This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns: <ul> <li>Image name: image name with extension,</li> <li>ECG ID: this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the leads.npz and rhythms.npz,</li> <li>Image relative path: relative path to the image in question,</li> <li>Image page: page number of the particular image (starting from 0),</li> <li>ECG number of pages: number of pages in the whole ECG,</li> <li>ECG number of columns per page: number of columns per page in the ECG,</li> <li>ECG number of rows per page: number of rows in the ECG,</li> <li>ECG number of rhythm leads:  number of rhythms in the ECG,</li> <li>ECG format: short version of the ECG format.</li> </ul> </li> <li>data folder:  <ul> <li>leads.npz: NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.</li> <li>rhythms.npz: NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.</li> </ul> </li> <li>visual_data folder:   This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The metadata.csv file provides a direct link to each image and its associated ECG information.</li> </ul>

opencc-by-4.0Jan 2023View details →

Open Plant Phenotyping Database Seedling Images

The Open Plant Phenotyping Database [OPPD] is a public dataset for visual recognition tasks on images of plant seedlings. The dataset consists of 7,590 images with 315,038 plant objects, representing 64,292 individual plants from 47 different species. Each plant species has been cultivated using three growth conditions (ideal, drought and natural) and tracked temporally to achieve high intra-species variability. This is a subset of the .jpg images available at https://gitlab.au.dk/AUENG-Vision/OPPD/-/archive/master/OPPD-master.zip in the folder: /DATA/images_plants/1COMF/

opencc-by-4.0May 2023View details →

IAVRS - INTERNATIONAL AFFECTIVE VIRTUAL REALITY SYSTEM: database of validated 360° images

IAVRS database contains 46 360° images validated for emotion emotion validation.

Proximal Femur Image Database Validation

ClinicalTrials.gov study NCT06351943. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.

Constitution of a Standardized Neural Imaging Database in Healthy Subjects

ClinicalTrials.gov study NCT00484523. IPD Sharing: Not stated. Countries: 1. Publications: 3.

The Application of Non-invasive and Cellular Level Resolution Fullfield Optical Coherence Tomography: Establishment and Analysis of Subcutaneous Cellular Level Image Database of Anatomical Locations i

ClinicalTrials.gov study NCT04406454. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

Creation of an Image Database for the Development of a Computer Aided Diagnostic (CAD) System in Patients With Prostate Cancer

ClinicalTrials.gov study NCT02618200. IPD Sharing: Not stated. Countries: 1. Publications: 2.