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BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 5. Image down sample
<p>Our proposal is to store each column and row bits count in a separate file and used that to reproduce the image using genetic algorithm.If we take 10% of an image size and the row and column image hamming bit count our total size will be approximately below 15% of the actual image size. We proposed a method to reproduce original image from using this 15% information.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
Sample ACC Data Set for LOFAR Station IE613 LBA whole sky observation
<p>This is a large sample of data from Station IE613 suitable for use with <a href="https://github.com/creaneroDIAS/beamModelTester">beamModelTester</a> and <a href="https://github.com/2baOrNot2ba/iLiSA">iLiSA</a>. To use with these systems, transfer it to a directory of the form :</p> <p><em>{STN_ID}_YYYYMMDD_HHMMSS_rcu{RCU_MODE}<em>dur{DURATION}</em>{SOURCE}_acc</em><br> e.g. IE613_20180406_091321_rcu3_dur85635_CasA_acc</p>
Supporting data for "Snap happy: camera traps are an effective sampling tool when compared to alternative methods"
<p>Author recommendations and response ratios extracted from studies comparing camera traps to another survey method. These data underlie the analyses in a the journal article 'Snap happy: camera traps are an effective sampling tool when compared to alternative methods', published in the journal Royal Society Open Science (https://doi.org/10.1098/rsos.181748). </p>
Single-crystal X-ray diffractometry data for a sample of NiCl₂-dppe collected on beamline I19-2 at Diamond Light Source
<p>Single-crystal X-ray diffractometry data for a sample of [1,2-Bis(diphenylphosphino)ethane]dichloronickel(II) (NiCl<sub>2</sub>-dppe, [(C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>PCH<sub>2</sub>CH<sub>2</sub>P(C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>]NiCl<sub>2</sub>).</p> <p>Data collected at Diamond Light Source I19-2 on 2015-05-18, publicly available for users to test data reduction routines. Data are known to produce good merging statistics and final refinements.</p> <p>The sample was prepared as follows:<br> Nickel chloride (II) hexahydrate (1 g, 2 mmol) was heated under vacuum to produce anhydrous nickel chloride (II) with a visible colour change from green to yellow. The resulting solid was taken up in ethanol (5 ml) and added to 1,2-bis(dimethylphosphine)ethane (dppe) (0.837 g, 2 mmol) in ethanol (10 ml). The solution was refluxed for 3 hour after which the solvent was evaporated. The small red crystals were purified by recrystallisation in acetone (70% yield).</p> <p>The sample was held at an approximate temperature of 150 K and the illuminating beam had a wavelength of 0.68890 Å (17.997 keV). The detector was held at 2θ = 25° throughout.</p> <p>Inventory of data:<br> <strong>010_Ni_dppe_Cl_2_150K01</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = 160°.<br> <strong>010_Ni_dppe_Cl_2_150K02</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = 40°.<br> <strong>010_Ni_dppe_Cl_2_150K03</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = -80°.<br> <strong>010_Ni_dppe_Cl_2_150K04</strong> — 198° ω scan, 0.4° images, 0.4s per image, 495 images; κ = 0°, φ = -80°.</p>
Sample data for beamModelTester
<p>This sample data for use with <a href="https://github.com/creaneroDIAS/beamModelTester">beamModelTester</a>. The data consists of a HBA observation of CasA taken at LOFAR Station IE613 on the 16th of March 2018 and a model of that observation generated using <a href="https://github.com/2baornot2ba/dreamBeam">dreamBeam</a>. </p>
gwastro/o2-bbh-pe: v2.2 data release of O2 Binary Black Hole posterior samples
<p>This is the v2.2 data release associated with the parameter estimation analysis of the binary black-hole signals from Advanced LIGO-Virgo's second observing run, using the PyCBC Inference toolkit : <a href="https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b">https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b</a> . A companion paper presenting our parameter estimation analysis and the data release is available here : <a href="https://arxiv.org/pdf/1811.09232.pdf">https://arxiv.org/pdf/1811.09232.pdf</a>.</p> <p>The analysis was performed using the PyCBC v1.12.3 code on the gravitational-wave data available at <a href="https://www.gw-openscience.org/catalog/GWTC-1-confident/html/">https://www.gw-openscience.org/catalog/GWTC-1-confident/html/</a> . Descriptions of the gravitational-wave data can be found in the paper <a href="https://arxiv.org/abs/1811.12907">https://arxiv.org/abs/1811.12907</a> .</p> <p>The changes in this release are</p> <ul> <li>An update to the plotting code in <code>data_release_o2_bbh_pe.ipynb</code> for generating Figs. 1, 2, and 3 in the companion paper to take into account cases where a boundary bias may be introduced for plotting probability contours.</li> <li>Addition of a plotting code in <code>data_release_o2_bbh_pe.ipynb</code> that generates a corner plot showing estimates (median and 90% credible interval) and posterior distributions for all the parameters presented in Table 1 of the companion paper.</li> <li>Addition of a notebook <code>o2_bbh_pe_skymaps.ipynb</code> that demonstrates the method for visualizing sky location posteriors as presented in Fig. 4 of the manuscript.</li> </ul> <p>The data and configuration files included remain the same as in the v2.1 release.</p> <p>This release includes :</p> <ul> <li>posterior and prior samples from parameter estimation analyses of the seven binary black-hole events---GW170104, GW170608, GW170729, GW170809, GW170814, GW170818, and GW170823.</li> <li>PSDs used in each of the analyses</li> <li>configuration files and run scripts for running the analyses and generating the data.</li> <li>tutorials for manipulating the data and reconstructing the figures in the companion paper.</li> </ul>
Data sample from Israel Electric Corporation (IEC)
<p>The dataset contains logs from IT and OT systems collected from a testbed setup of the IEC.<br> The content is specifically related to:</p> <p>·CCTV and PTZ cameras.</p> <p>·Intrusion detection system (IDS).</p> <p>·Access control system.</p> <p>·Proximity cards and sensors.</p>
Geochemical evidence for high volatile fluxes from the mantle at the end of the Archean: Sample data
<p>This file describes the origin of samples and gives the original xenon data published in refs. 4, 5 and 8. The slopes of the fractionation lines are given in refs. 8 (Table S1), 5 (Tables 1 and S1) and 4 (Table S1). For the barite sample (Ref. 6, Table 1), Xe data were normalized to 132Xe since 130Xe could have been contributed by radioactivity products. In this case Δ129Xe was computed from 128Xe and 131,132Xe data. The Δ129Xe values were computed from the difference between the isotope fractionation slope (‰/u) and the 129Xe/130Xe values in deviation permil (‰) relative to the corresponding atmospheric isotope composition.</p>
Data & Sample Chess
<p>Data & Sample Chess is a game-like figure to enable all project partners of the Horizon 2020 project "MEET" to mark for each work package, which task-related data or sample type has to be provided to other work packages. This figure was used within an interactive session during a DMP-workshop held in May 2019 in Zagreb.</p> <p>The outcomes of this game-like session were summarized and combined into one general figure to identify the internal data paths and dependencies between work packages.</p>
Neutron diffraction data of Sikkim and West Bengal samples
<p>This is the data set for the quartz pole figures for six quartzite samples from Sikkim and West Bengal, Indian Himalayas. The data for each sample starts with the sample label: SK222, WB03, WB10, WB11, WB41, and WB76.</p>
EEG data for generating reference rdFC patterns and sample rdFC patterns
<p>The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.</p>
Fig. 6 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 6. Percent similarity among bins averaged to 1 degree bins. A. om7 interval. B. om8 interval. C. om9 interval. The thicker the line, the greater the similarity between the two cells connected by the line.
Fig. 4 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 4. Correlations between richness per map and number of localities. A. om7 interval. B. om8 interval. C. om9 interval. The gap in the distribution of points for the om8 interval highlights the discontinuity between a group of maps with few taxa at a few localities and other maps with a large number of localities and high richness.
Fig. 5 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 5. Rarefaction curves for each interval, based on number of occurrences. The confidence envelope of the species richness for om9 departs significantly from those of om7 and om8 above 50 occurrences, but the significantly higher species−richness of om8 only becomes apparent at sample sizes of around 250 specimens, indicating that a few, rare taxa are boosting richness in the om8 interval.
Fig. 2 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 2. Distribution of Muschelkalk ammonoid localities used in this study plotted on a map of modern Germany. The overall geographic spread of localities does not change greatly over time.
Fig. 3 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 3. Correlations between richness per map and number of occurrences. A. om7 interval. B. om8 interval. C. om9 interval.
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.