Find research datasets worth reusing

opencc-by-4.0Nov 2011View details →

Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

Based on what we stated above, we need to extract 28 features, which describe the distances between certain points explained in the previous stage, these features are classified into six groups, and each group describes the features of one face element. All features are a vertical distances between two points. Group one contains seven features for mouth, groups two and three contains 14 features for eyes, groups four and five contain six features for eyebrows, and the last group has one feature only which is the distance between the beginning of the eyebrow and the beginning of the eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance of eyebrow from the eye. This feature is shown in Figure 4 by a line.

opencc-by-4.0Aug 2015View details →

Figure 3. (Top): Illustration of the therapy selection main menu. This enables the user to select one of three options for the therapy. Stimuli sequence selectors; (Bottom): (a) Short distance – complete visual field; (b) Short distance – macular; (c) Middle-long distance.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy

distance therapies for the prescribed time suggested by the ophthalmologist. Note that the complete visual field therapy stimulates different parts in the entire visual field whereas macular therapy stimulate only a small part of the visual field, only the first 10° of vision range. In contrast, middle-long distance therapies are not developed inside the device; instead the patient must sit watching a wall, where the stimuli will be presented. Figure 3 (Bottom) shows the sequence selectors for the three different cases. The therapist will choose a desired number of sequences according to the results of the examination to each patient; hence it is completely patient dependent. Once the therapist finishes the particular design of the stimuli sequence, the software automatically displays a window where he can save the customized patient-specific details for future use as a text file.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 17. The results of calculating the Hausdorff distances

The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the Corpus Calosum segmentation.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 16. Results of calculating the Hausdorff distances

Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a subject Normal following segmentation of the Corpus Calosum.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 15. The results of calculating the Hausdorff distances

The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the Corpus Calosum segmentation.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 12. Results of calculating the Hausdorff distances

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 11. Results of calculating the Hausdorff distance

The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.  Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) .and the ground truth about a MCI subject following segmentation of the hippocampus.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods

The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 5. Weekly session stats for 2016

On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5).

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 3. Monthly user/session stats for 2016

It is also interesting to observe that in Figure 3, where the usage for 2016 is presented, that in the non-promotional period, the site is mostly used in January (before January exam term), in April, May, and June (before the June exam session and during colloquial exams) or in July and August (before the September exam session).

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 2. Monthly user/session stats for 2015

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 1. Components of EduWebCast System

The aim of this partnership would be to implement an infrastructure for live and on-demand video streaming of learning material for the targeted groups and, to this purpose, to establish a long and fruitful cooperation between teachers, pupils, and students on both sides of the border. The joint creation and administration of the webcast project is the ground stone of the partnership between the two universities and will result in more common projects based on the materials obtained through the project, contests between pupils and students, possible periodic educational exchanges.

opencc-by-4.0Mar 2018View details →

Data and results for the paper "On the Rank-Distance Median of 3 Permutations"

The uploaded files contain: (1) the list of 13 real genomes of the campanulaceae family (in "signed gene permutation" format); (2) the raw input files obtained from simulating triplets using SCJ and DCJ operations (the folder name and the first number in the filename indicates the size of the genome; the second number in the filename indicates the rearrangement rate r; the third number in the filename indicates the iteration, from 0 to 9); each text file contains the three genomic matrices (the rawest data is in "Matlab files"); (3) the results of running our algorithms on the data obtained from simulating triplets using SCJ and DCJ operations (in .RData files); the Results element contains, for each size above, the performance of the algorithms on inputs of that size as well as the outputs of the algorithm for finding the median being tested; the Times element contains 10 groups of 5 times produced by timing the algorihtm on the inputs of the given size.

openother-atFeb 2018View details →

Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (3rd version)

Summary This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2018, ApJ, DOI: <a href="https://doi.org/10.3847/1538-4357/aac88f">10.3847/1538-4357/aac88f</a>, <a href="https://arxiv.org/abs/1604.04631">arXiv:1604.04631</a>). The compressed archive file "ddmc-nosample-v3.1.tar.xz" contains only the compressed SNIa data, the BAO measurements, and 3rd-party data files used in this work.  The random samples can be re-created by the tools included in the package.  This is the file suitable for low-speed download. The file "ddmc-v3.1.tar.xz" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v3.1.tar.xz" file. This is the archive containing all the data and figure files used directly in the paper. To uncompress the files, the XZ Utils software package is required. The file "CHECKSUM.asc" is a GPG-clearsigned text file containing the SHA-512 checksum values for file integrity verification.  The text file itself is signed with the GPG key 0xE977A6E990102402 available from keyservers. Please read the README files in each package for more details and instructions. Release notes for version 3.1 Version 3.1 is a minor revision with the addition of some alternative input parameter distributions. Release notes for version 3 This is the 3rd version representing a re-written analysis of the distance-duality test. This new version updated and renamed the complementary parameter (CP) sets to match the ones used in the paper. New results concerning the interpretation of results as a diagnostics of distance measurement systematics are presented. Also included are updated utility scripts, new tests for Gaussian approximation to the results, and new data-visualization scripts. Earlier versions Earlier versions are available from Zenodo. Links: <a href="https://doi.org/10.5281/zenodo.49825">v1</a>, <a href="https://doi.org/10.5281/zenodo.57982">v2</a>.

opencc-by-4.0Oct 2018View details →

Common marmosets are sensitive to simple dependencies at variable distances in an artificial grammar

Video data of each trial in a study with common marmoset monkeys. Its current title is "Common marmosets are sensitive to simple dependencies at variable distances in an artificial grammar". Abstract of the publication is as below. Recognizing that two elements within a sequence of variable length depend on each other is a key ability in understanding the structure of language and music. Perception of such interdependencies has previously been documented in chimpanzees in the visual domain and in human infants and common squirrel monkeys with auditory playback experiments, but it remains unclear whether it typifies primates in general. Here, we investigated the ability of common marmosets (Callithrix jacchus) to recognize and respond to such dependencies. We tested subjects in a familiarization-discrimination playback experiment using stimuli composed of pure tones that either conformed or did not conform to a grammatical rule. After familiarization to sequences with dependencies, marmosets spontaneously discriminated between sequences containing (‘consistent’) and lacking dependencies (‘inconsistent’), independent of stimulus length. Marmosets looked more often to the sound source when hearing sequences consistent with the familiarization stimuli, as previously found in human infants. Crucially, looks were coded automatically by computer software, avoiding risk of human bias. Our results support the hypothesis that the ability to perceive dependencies at variable distances was already present in the common ancestor of all anthropoid primates (Simiiformes).

opencc-by-4.0Oct 2018View details →

Fig. 2. Potentially diagnostic distances and angles. 1 in Cochlostoma revised: the subgenus Lovcenia Zallot et al., 2015 (Caenogastropoda, Cochlostomatidae)

Fig. 2. Potentially diagnostic distances and angles. 1 = shell height (H). 2 = height of aperture at the columellar side. 3 = height of the first whorl. 4 = width of body whorl (Wbw). 5 = width of the suture above the body whorl. 6 = width of the penultimate whorl. 7 = width of the suture above the penultimate whorl. 8 = distance between the 5 most central ribs on the 4th whorl up. 9 = diameter of the 4th whorl up. 10 = aperture width (Wa). 11 = width of the lip at the columellar side. 12 = aperture height (Ha). 13 = rib inclination. 14 = suture inclination. 15 = aperture inclination. 16 = protoconch diameter in upper view (Dp). 17 = end of the smooth part of the protoconch. 18 = end of the protoconch.

opencc-by-4.0Feb 2019View details →

Distances for the Gaia RV set with corrected parallaxes

The datasets are described in <a href="https://arxiv.org/abs/1902.02355">https://arxiv.org/abs/1902.02355</a> , which is submitted to MNRAS. Each file contains a distance derivation under different assumptions for the Gaia RV dataset. Please note the following: to allow for freedom in choice of cuts, we have limited ourselves to just minimal cuts: parallax/parallaxerr > 3, detected G,G_BP,G_RP magnitudes > 0 mag, vlos_err < 10 km/s, n_vis > 5, reasonably measured RV (<5550 km/s), distance and 1/parallax < 10 kpc Note that these cuts are NOT sufficient to ensure quality of the sample. We strongly advise to use the quality criteria laid out in Schoenrich, McMillan and Eyer (2019), and test for the effects of a cut in galactic latitude b > 10 degrees for most applications. The file format is .csv (comma separated) with the following columns (a key is in row 1) index: running number in our sample sourceid: Gaia Source ID E_dist: expectation value for the distance, unit is kpc distm2: second moment of distance probability distribution (use the usual formula to get the variance/dispersion) distm3: third moment of distance probability distribution distm4: fourth moment """" parallax: The Gaia parallax _including_ the offset correction chosen, unit is mas parallax/parallaxerr : ratio of parallax to parallax uncertainty. Again, both parallax and parallaxerror contain the corrections. Use this and the last column to obtain the parallax uncertainty for quality cuts x: x coordinate (radial coordinate in local cartesian frame), unit is kpc y: y coordinate in the azimuthal direction, unit is kpc z: z coordinate (perpendicular to the plane), relative to the Sun, unit is kpc. Please add the solar offset from the plane that you favor (0.02 kpc, Joshi et al., or the Gerhard & Bland-Hawthorn review are good values) R: galactocentric radius (in-plane, cylindrical coordinates), unit is kpc U_g: cylindrical radial velocity in a galactocentric frame (positive taken inwards), unit is km/s v_phi: azimuthal velocity (V_g, azimuthal direction), unit is km/s v_z: velocity component upwards perpendicular to the plane, unit is km/s vlos: line-of-sight velocity as provided in the Gaia RV datasets vloserr: line-of-sight velocity error as provided in the Gaia RV datasets gl: Galactic longitude l (degrees) gb: Galactic latitude b (degrees) pml: proper motion in l (mas/yr) pmb: proper motion in b (mas/yr) pml_err: proper motion error/uncertainty in l (mas/yr) pmb_err: proper motion error in b (mas/yr) E_bprp: BP-RP excess flux factor (provided in Gaia catalogue) excessnoise: astrometric excess noise Gmag: Gaia G magnitude Rmag: Gaia G_RP magnitude B-Rcolour: G_BP - G_RP  (colour in mag) nvis: number of visibility periods eclat: ecliptic latitude (degrees) Each file contains a different set of assumptions that result in a near-zero average distance bias, as discussed in the paper: We recommend using either: gaiaRVdelp54delsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for parallax offset of 0.054 mas gaiaRVdelpeqspdelsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for a parallax offset = modified parallax error For comparison and applications that need long-range in distance s, it may be interesting to use: gaiaRVdelp48delsp00 : only increased parallaxes by an offset of 0.048 mas. Distance bias on average is stable out to 4kpc or more, but you have to be wary of random errors. All distances were derived with the standard prior described in Schoenrich, McMillan and Eyer (2019); the parameter for the additional exponential function in the prior at very large s > 4 kpc is 0.06/kpc If you need specific subsamples of stars, we advice to draw a new prior with the method of Schoenrich & Aumer (2017) and then re-derive the distances. We are also happy to help with that and provide you with the necessary code. Please note that our definition (angular distance per time in the direction of l, b) of pml and pmb is frequently noted with a * in other derivations. Please also note that why we used z_Sun = 0.02 kpc in the underlying analysis and distance prior, the value in the catalogue is given relative to the Sun (i.e. add the Solar offset that you favour).

opencc-by-4.0Feb 2018View details →

Segmentation of Nuclei in Histopathology Images by deep regression of the distance map

This dataset has been annonced in our accepted paper "Segmentation of Nuclei in Histopathology Images by deep regression of the distance map" in Transcation on Medical Imaging on the 13th of August. This dataset consists of 50 annotated images, divided into 11 patients.   v1.1 (27/02/19): Small corrections to a few pixel that were labelled nuclei but weren't.

opencc-by-4.0Dec 2013View details →

Figure. The phylogenetic tree showing the relationship among Brevibacillus parabrevis strains SA2.2 and TJ2.3, Bacillus licheniformis MG4.2, and their phylogenetically closest type strains. The GenBank accession numbers of the type strains and studied strains are shown following species names. Distance matrix was calculated by Kimura's 2-parameter model. The scale bar indicates 0.02 substitutions per nucleotide position. Alicyclobacillus pohliae AJ564766 served as an out-group. in Distribution of extracellular enzyme-producing bacteria in the digestive tracts of 4 brackish water fish species

Figure. The phylogenetic tree showing the relationship among Brevibacillus parabrevis strains SA2.2 and TJ2.3, Bacillus licheniformis MG4.2, and their phylogenetically closest type strains. The GenBank accession numbers of the type strains and studied strains are shown following species names. Distance matrix was calculated by Kimura's 2-parameter model. The scale bar indicates 0.02 substitutions per nucleotide position. Alicyclobacillus pohliae AJ564766 served as an out-group.