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.
197
datasets available to search
ShareScore release 0.9.0
Dataset results
197 results for “time estimation”
Figure 1 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences
Figure 1. Sliding window analysis. The red curve shows the value of nucleotide diversity (Pi). Pi value of each PCG is shown below the arrows.
Figure 4 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences
Figure 4. BI analysis (MrBayes) based on PCG12R–ATP8. Numerals at nodes are Bayesian posterior probabilities (PP). '–' indicates different clades.
Figure 2 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences
Figure 2. Genetic distance (on average) and ratio of non-synonymous (Ka) to synonymous (Ks) substitution rates.
Figure 3 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences
Figure 3. ML analysis based on PCG12R–ATP8. Numerals at nodes are bootstrap values (BS). '–' indicates different clades.
Dataset for "Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study"
<p>The dataset (EPANET file) which accompanies the publication </p> <p>Vrachimis, S. G., Eliades, D. G., and Polycarpou, M. M.: Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study, Drink. Water Eng. Sci., 2018</p>
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
<p><strong>License + Attribution</strong></p> <p>This dataset is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>. Commercial usage is not permitted. If you use this dataset or the code in a scientific publication, please cite the following <a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.html">paper</a>:</p> <blockquote> <p>@inproceedings{FischerECCV2018,<br> author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},<br> title = "{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}",<br> booktitle = {European Conference on Computer Vision},<br> year = {2018},<br> month = {September},<br> pages = {339--357}<br> }</p> </blockquote> <p>This work was supported in part by the Samsung Global Research Outreach program, and in part by the EU Horizon 2020 Project PAL (643783-RIA).</p> <p>More information can be found on the Personal Robotic Lab's website: <a href="https://www.imperial.ac.uk/personal-robotics/software/">https://www.imperial.ac.uk/personal-robotics/software/</a>.</p> <p><strong>Overview</strong></p> <p>The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix <em>_glasses</em>), and 2) One with natural appearances (no eyetracking glasses are worn; suffix <em>_noglasses</em>). The <em>_noglasses</em> images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the <em>_glasses</em> images.</p> <p>There is code accompanying this dataset: <a href="https://github.com/Tobias-Fischer/rt_gene">https://github.com/Tobias-Fischer/rt_gene</a>. Please use the issue tracker in the code respository if you have questions regarding the dataset.</p> <p><strong>Subjects / 3-Fold evaluation</strong></p> <p>15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used).</p> <p>We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups):</p> <ol> <li>'s001', 's002', 's008', 's010'</li> <li>'s003', 's004', 's007', 's009'</li> <li>'s005', 's006', 's011', 's012', 's013'</li> </ol> <p>The validation set consists of sessions 's014', 's015' and 's016'.</p> <p>While the MATLAB script (<em>prepare_dataset.m</em>; see code repository) creates train and test images for each subject, all images were used for the evaluation (see <em>evaluate_model.py</em>).</p> <p><strong>Labeled dataset (sXYZ_glasses)</strong></p> <p>The file for each subject contains the following information:</p> <ul> <li>label_combined.txt This is the main file containing labels. The formatting is as follows:<br> seq_number, [head pose: right(pos) / left(neg), up (pos) / down(neg)], [gaze: right(pos) / left(neg), up(pos) / down(neg)], timestamp</li> <li>label_headpose.txt This file contains more detail about the head pose of the subject.<br> seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp</li> <li>kinect2_calibration.yaml<br> The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"original" folder <ul> <li>The face_before_inpainting folder contains the face with a large margin to the left and right.</li> <li>The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder.</li> <li>The overlay folder contains images where the mask was overlaid on the face_before_inpainting images.</li> <li>The face folder contains the face image extracted using MTCNN with a tighter margin.</li> <li>The left and right folders contain the left and right eye image areas.</li> </ul> </li> <li>The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting).</li> <li>"inpainted" folder <ul> <li>The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting.</li> <li>Then, the images contained in the face, left and right folders were extracted using MTCNN as above.</li> </ul> </li> </ul> <p><strong>Unlabeled dataset (sXYZ_noglasses)</strong></p> <ul> <li>kinect2_calibration.yaml<br> This file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"face" folder<br> This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn).</li> </ul>
Volcanic ash source inversion data for paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals"
<p>This dataset consists of volcanic ash source inversion data for the paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals" by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort, Atmosphere, 2021, 12, 1573, https://doi.org/10.3390/atmos12121573. Satellite retrievals, dispersion model simulations and inversion calculations are included for the eruptions of Eyjafjallajokull in 2010 and Grimsvotn in 2011.</p>
Fig. 3 Chronogram showing the relationships and divergence times for 80 bears, estimated from a concatenated mitochondrial dataset comprising all 13 protein coding and 2 in Examining the sensitivity of molecular species delimitations to the choice of mitochondrial marker
Fig. 3 Chronogram showing the relationships and divergence times for 80 bears, estimated from a concatenated mitochondrial dataset comprising all 13 protein coding and 2 ribosomal RNA genes. Groups delimited as species by the GMYC analysis are shown as triangles. The horizontal axis shows the timescale, measured in millions of years.
Fig. 2 Chronogram showing the relationships and divergence times for 357 cetaceans, estimated from a concatenated mitochondrial dataset comprising all 13 protein coding and 2 in Examining the sensitivity of molecular species delimitations to the choice of mitochondrial marker
Fig. 2 Chronogram showing the relationships and divergence times for 357 cetaceans, estimated from a concatenated mitochondrial dataset comprising all 13 protein coding and 2 ribosomal RNA genes. Groups delimited as species by the GMYC analysis are shown as triangles. The horizontal axis shows the timescale, measured in millions of years.
Fig. 2 in Divergence time estimation in Cichorieae (Asteraceae) using a fossil-calibrated relaxed molecular clock
Fig. 2 Chronogram of Cichorieae produced by the program BEAST based on ITS1 and ITS2 sequences (unconstrained topology; maximum clade credibility tree with mean node heights obtained by stem group node calibration). Posterior probabilities of nodes are shown
Fig. 2 in Estimating range disjunction time of the Palearctic Admirals (Limenitis L.) with COI and histone H1 genes
Fig. 2 The consensus Bayesian tree for Limenitis spp. based on 699 bp of the histone H1 gene sequence. Posterior probabilities are indicated at nodes; mean substitution rates (sites/mya) are indicated above branches. Designations of specimens are as in Table 1
Fig. 3 Haplotypic networks for the studied histone H1 in Estimating range disjunction time of the Palearctic Admirals (Limenitis L.) with COI and histone H1 genes
Fig. 3 Haplotypic networks for the studied histone H1 gene (a–c) and COI fragment (d) of L. camilla (a–b) and L. helmanni (c–d) from the eastern and western parts of their ranges; b shows representation of combinations of histone H1 gene haplotypic variants in individuals of L. camilla
Fig. 1 in Estimating range disjunction time of the Palearctic Admirals (Limenitis L.) with COI and histone H1 genes
Fig. 1 Disjunction of the Limenitis spp. ranges in the Palearctic. The borders of the ranges of L. camilla, L. helmanni and L. sydyi are marked with purple, green and red lines, respectively; the dots indicate sampling localities
Figure 1 in Using photo by-catch data to reliably estimate spotted hyaena densities over time
Figure 1: Hluhluwe-iMfolozi Park's geographic position within Africa and South Africa. The park is separated into two sections, Hluhluwe and iMfolozi. These sections are separated by a paved road known as the R618 (Corridor Road).
FIGURE 3 in Review of the systematic status of Sceloporus arenicolus Degenhardt and Jones, 1972 with an estimate of divergence time
FIGURE 3. Consensus tree from Bayesian phylogenetic analysis. Bayesian posterior probabilities (PP) and ML bootstrap support (BS) are noted at nodes with high support (> 95% PP and 75 BS) and at basal nodes. Nodal support of PP = 1 and BS = 100 are indicated with a solid circle at the branch. Shaded symbols correspond to collection localities on Figure 1.
FIGURE 2. Minimum spanning haplotype networks for all S in Review of the systematic status of Sceloporus arenicolus Degenhardt and Jones, 1972 with an estimate of divergence time
FIGURE 2. Minimum spanning haplotype networks for all S. graciosus group samples sequenced at each of three nuclear loci. Size of each circle corresponds to the frequency of that haplotype. Shading corresponds to clade membership in Figure 3.
FIGURE 1 in Review of the systematic status of Sceloporus arenicolus Degenhardt and Jones, 1972 with an estimate of divergence time
FIGURE 1. Collection localities for samples from the Sceloporus graciosus group included in this study. Colored symbols correspond to clade membership on Figure 3; three individuals for which we only have sequence data at R35 are indicated with stars. Putative species and subspecies boundaries are shaded for the members of the Sceloporus graciosus group. Sceloporus graciosus graciosus: dark gray distribution, S. g. gracilis: light gray distribution, S. g. vandenburgianus: brown distribution, and S. arenicolus: black distribution.
FIGURE 5 in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 5. Present distributions of Syncordulia species in South Africa. Uppermost box shows the distributions of all Syncordulia species, lower boxes show individual species distributions: S. gracilis (top and top left); S. legator (top right); S. serendipator (bottom left); S. venator (bottom right).
FIGURE 3. R8S in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 3. R8S analysis on a 26-taxon tree; Geological maps adapted from figures on rst.gsfc.nasa.gov.
FIGURE 4 in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 4. Ancestral distributions; DIVA analysis optimized with 2 regions; larger letters indicate the scenarios discussed in the text
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.