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49 results for “distance measurements”
Consistent measures of oxidative balance predict survival but not reproduction in a long-distance migrant
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Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
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Measuring the distance and mass of galactic core-collapse supernovae using neutrinos
<p>This is the dataset and analysis scripts for the manuscript "Measuring the distance and mass of galactic core-collapse supernovae using neutrinos" (submitted to PRL, arXiv link to follow). </p>
Measurement report: On the contribution of long-distance transport to the secondary aerosol formation and aging
<p>(1) Chemical composition of PM2.5 in four transport sectors.<br> (2) Time series of organic aerosol factors resolved by PMF&ME-2 in four transport sectors.<br> (3) Meteorological data corresponded to the ACSM and PMF&ME-2 data.<br> (4) f44 and f43 ratio in four transport sectors.</p>
Data from: Consistency in the flight and visual orientation distances of habituated chacma baboons after an observed leopard predation: Do flight initiation distance methods always measure perceived predation risk?
<p>Flight initiation distance (FID) procedures are used to assess the risk perception animals have for threats (e.g., natural predators, hunters) but it is unclear whether these assessments remain meaningful if animals have habituated to certain human stimuli (e.g., researchers, tourists). Our previous work showed that habituated baboons displayed individually distinct and consistent responses to human approaches, a tolerance trait, but it is unknown if the trait is resilient to life-threatening scenarios. If it were consistent, it would imply FIDs might measure specific human threat perception only and not generalise to other threats such as predators when animals have experienced habituation processes. We used FID procedures to compare baseline responses to the visual orientation distance, FID, and individual tolerance estimates assessed after a leopard predation on an adult male baboon (group member). All variables were consistent despite the predation event, suggesting tolerance to observers was largely unaffected by the predation and FID procedures are unlikely to be generalisable to other threats when habituation has occurred. FID approaches could be an important tool for assessing how humans influence animal behaviour across a range of contexts, but careful planning is required to understand the type of stimuli presented.</p>
Preoperative Corneal Measurements Estimate the Corrected Distance Visual Acuity After Corneal Cross-linking
ClinicalTrials.gov study NCT06522789. IPD Sharing: NO. Countries: 1. Publications: 1.
Reliability of the Subacromial Distance Measurements With Standard Radiographic Imaging
ClinicalTrials.gov study NCT04759027. IPD Sharing: NO. Countries: 1. Publications: 19.
Measurement of the Distance Between the Corresponding Anatomical Landmarks in the Thoracic Cavity and the Incisors
ClinicalTrials.gov study NCT03720405. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Measurement of the Distances of the Lower Airway in Pediatric Population
ClinicalTrials.gov study NCT04533334. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Consistency in the flight and visual orientation distances of habituated chacma baboons after an observed leopard predation: Do flight initiation distance methods always measure perceived predation risk?
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Data from: Measures of oxidative state are primarily driven by extrinsic factors in a long-distance migrant
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Distance measurements between trityl radicals by pulse dressed electron paramagnetic resonance with phase modulation: Raw Data, Processing Scripts, Simulations
<p>These files include raw data and corresponding processing scirpts for a paper submitted to Magnetic Resonance (https://www.magnetic-resonance-ampere.net/)</p>
Figure 1 from: Zou Y (2020) Distance measurement using the spherical wave fact of astronomical objects. Research Ideas and Outcomes 6: e60713. https://doi.org/10.3897/rio.6.e60713
Figure 1 A schematic figure shows the spherical wave observed with 3 telescopes located at positions A, O, and B. The 3 telescopes are settled at the spherical wavefront. The source S is the center of the circle. With the measuring of distance \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} b \end{equation*} \end{varwidth} \end{document} =OO', one is able to get the distance \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} D = {d^2 \over 2b} \end{equation*} \end{varwidth} \end{document} , where \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} d \end{equation*} \end{varwidth} \end{document} is the distance between B and O'.
A hybrid approach to the small unannotated corpus-based language comparison and its application to the Old East Slavic charters - Supplementary material 5 (Corpus-based language distance measurement results)
<h1>General description</h1> <p>These are the results of the experiments with the use of <a href="https://doi.org/10.5281/zenodo.11395683" target="_blank" rel="noopener">corpus-based language distance measurement package</a> on the material of <a href="https://doi.org/10.5281/zenodo.14057668" target="_blank" rel="noopener">Old East Slavic</a>, <a href="https://doi.org/10.5281/zenodo.14148179" target="_blank" rel="noopener">modern East Slavic</a>, and <a href="https://doi.org/10.5281/zenodo.14148561">modern standard Slavic</a> lects. There are 40 possible experiments for each data set, divided by the usage of:</p> <ul> <li>topic antimodelling heuristic,</li> <li>Soerensen-Dice coefficient-based normalisation,</li> <li>the presence of hybridisation of frequency-based metric for coinciding units and combined frequency-based metric and string similarity measure for non-coinciding units,</li> <li>hybridisation type,</li> <li>exact type of string similarity measure used for combination,</li> <li>alphabet entropy-based normalisation for vector-based string similarity measures.</li> </ul> <p>In addition, modern standard Slavic dataset undergoes experiments 4 times that differ by the share of its size, used for measurements (0.1, 0.3, 0.6 and 1).</p> <p>For further information on each of the experiment parameters, refer to the documentation of the package.</p> <h1>Data set structure</h1> <h2>Executive summary</h2> <p>Data set consists of 240 folders that represent information on the experiments and 1 <code>.csv</code>-file that aggregates the resulting values into a single table.</p> <p>Folders with indices 1-20 and 121-140 contain experiments with 0.1 share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 21-40 and 141-160 contain experiments with 0.3 share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 41-60 and 161-180 contain experiments with 0.6 share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 61-80 and 181-200 contain experiments with the full share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 81-100 and 201-220 contain experiments with the full share of the modern East Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 101-120 and 221-240 contain experiments with the full share of the Old East Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <h2>.csv-file</h2> <p>Named <code>aggregated_results.csv</code>, lies in the root of the dataset. Separator is <strong>comma</strong> (<strong>,</strong>). Contains <strong>13 columns</strong> and <strong>241 row</strong>. The first row is <strong>header</strong>, the other 240 rows contain description for each conducted experiment and its resulting values, according to the columns. The columns are the following (in <strong>rtl</strong> order):</p> <ul> <li><strong>X.</strong> (<em>int</em>) - experiment ID; column is used as index.</li> <li><strong>Material </strong>(<em>string</em>) - the data set used for language distance measurement. The possible values are: <ul> <li>Slavic standard - Croatian, Slovenian, Slovak standard lects.</li> <li>Modern East Slavic - Northern Russian lect Megra, Central Russian lect Belogornoje, and Northern Belarusian lect Zialionka.</li> <li>Old East Slavic - Novgorod, Polack and Smolensk parts of the Old East Slavic continuum.</li> </ul> </li> <li><strong>Gensim </strong>(<em>int</em>) - the binary numeric indicator (0 or 1) of using the heuristic of <em>topic antimodelling</em>, namely, cleaning the words that were defined as a topic words by <em>gensim</em> Latent Dirichlet Association implementation (Rehurek & Sojka, 2010). The intention of using this heuristic is to remove the tokens that are characteristic for the genre of the texts presented in the corpus for the sake of increasing the presence of the tokens that are characteristic of the lects themselves.</li> <li><strong>Split</strong> (<em>float</em>) - the used share of the data set (from 0 to 1); required to check the influence of the data set size on the metric efficiency.</li> <li><strong>Hybridisation</strong> (<em>string</em>) - the indicator of implementation of the hybridisation between the frequency-based metric between the 3-shingles (character 3-grams) that coincide for the compared lect pair, and the combination of frequency-based metric and string similarity measure between the 3-shingles that do not coincide for the compared lect pair. The possible values are: <ul> <li>TRUE: the experiment utilises hybridisation</li> <li>FALSE: the experiment does not utilise hybridisation.</li> </ul> </li> <li><strong>Hybridisation_type</strong> (<em>string</em>) - the indicator of how the frequency metric between coinciding 3-shingles and the combined metric between non-coinciding 3-shingles undergo the hybridisation process. The values are:<br> <ul> <li>JOINED - the approach is to multiply the means of the two.</li> <li>ARRAY - the approach is to join all the values into a single list, and then to score the mean.</li> <li>NOT_USED - experiment does not employ hybridisation. (<strong>Hybridisation </strong>is FALSE).</li> </ul> </li> <li><strong>Soerensen_normalisation</strong> (<em>string</em>) - the indicator of whether the frequency-based metric value undergoes normalisation with the division by Soerensen-Dice coefficient (a measure of number of coincidences between two lists) (Soerensen, 1948), in order to compensate the skewing between the coinciding and non-coinciding 3-shingles of the lects. The values are: <ul> <li>NOT_USED - <strong>Hybridisation_type </strong>is ARRAY, so there are no values to use Soerensen-Dice coefficient on.</li> <li>TRUE - the frequency-based metric undergoes division by the Soerensen-Dice coefficient.</li> <li>FALSE - the algorithm does not apply the normalisation by the Soerensen-Dice coefficient.</li> </ul> </li> <li><strong>Alphabet_normalisation</strong> (<em>string</em>) - indicator of whether the algorithm applies normalisation with the alphabet entropy (Shannon, 1948), the measure of differences in the skewings of symbols distribution in the texts, between the given lects. The values are:<br> <ul> <li>NOT_USED - a heuristic may not be implemented; present either in the cases, when <strong>Hybridisation </strong>is FALSE, or when the next parameter, <strong>Auxiliary_metrics</strong> is not VDND or VWJDND.</li> <li>TRUE<strong> </strong>- the experiment employs the heuristic.</li> <li>FALSE - the experiment does not employ the heuristic.</li> </ul> </li> <li><strong>Auxiliary_metrics </strong>(<em>string</em>) - the string similarity measure, used for the combination with the frequency-based metric for non-coinciding 3-shingles between analysed lects. There are five possible values: <ul> <li>LDND (Levenshtein distance normalised between analysed 3-shingles) (Holman et al., 2008).</li> <li>WJWDND (weighted Jaro-Winkler distance normalised between analysed 3-shingles) (Gueddah et al., 2015).</li> <li>VDND (Euclidean distance between the sums of symbol vector values between 3-shingles).</li> <li>VWJDND (VDND multiplied by scoring Jaro (Jaro, 1989) distance between analysed 3-shingles).</li> </ul> </li> <li><strong>Outgroup.identification</strong> (<em>string</em>) - the indicator of whether the outgroup detected in the given experiment coincides with the lect that preliminary manual classification supposes to be the outgroup. There are two possible values:<br> <ul> <li>CORRECT - the detected outgroup coincides with the supposed one.</li> <li>INCORRECT - the detected outgroup does not coincide with the supposed one.</li> </ul> </li> <li><strong>Outer.distance.split </strong>(<em>float</em>) - the length of the outgroup branch.</li> <li><strong>Inner.distance.split</strong> (<em>float</em>) - the distance between the split between the outgroup and the ingroup, and the split between the two ingroup lects.</li> <li><strong>Split.difference</strong> (<em>float</em>) - the division of <strong>Outer.distance.split</strong> by <strong>Inner.distance.split</strong>.</li> </ul> <h2>Folders</h2> <p>Each folder contains 6 files, each named according to the used experiment setup:</p> <ul> <li>3 <code>.csv</code>-files that contain unit-by-unit comparison between each pair of the analysed lects. Each <code>.csv</code>-file is <strong>semi-colon</strong>-separated, and has <strong>4 </strong>columns, <strong>header row</strong>, and rows that describes each unit-to-unit comparison. The columns contain the following information (in <strong>rtl</strong> order):<br> <ul> <li><code>[Name of the first compared lect]</code> : unit (character 3-shingle, or just 3-shingle) of the [name of the first compared lect] that undergoes comparison with units of the [name of the second compared lect]; datatype: string.</li> <li><code>[Name of the second compared lect] </code>: unit (character 3-shingle, or just 3-shingle) of the [name of the second compared lect] that undergoes comparison with units of the [name of the first compared lect]; if units coincide, contains value <code>id.</code>; datatype: string.</li> <li><code>[Experiment setup]</code>: name of the metric, a combination of the [experiment setup](concatenated through <code><em>-</em></code> parameters) and its exact part, which compares the two units; datatype: string. The possible values are: <ul> <li><code>[experiment setup] - DistRank</code> - the frequency-based metric that compares identical units</li> <li><code>[experiment setup] - hybrid</code> - the string similarity measure for non-identical units, combined with the frequency-based metric </li> </ul> </li> <li><code>Distance</code>: value of the metric; datatype: float</li> </ul> </li> <li><code>.info</code>-file that contains data on branch lengths along with coincidence/non-coincidence of the detected outgroup with the manually defined one. The file is a <strong>tabular-separated plain text</strong> that always contains three values: coincidence (CORRECT)/non-coincidence (INCORRECT) of the yielded classification with the supposed one; outer distance split (the length of the outgroup branch; datatype: float) and inner distance split (the length of the ingroup branch before split of its lects; datatype: float).</li> <li><code>.newick</code> -file that contains the result of an experiment, the phylogenetic tree built by UPGMA classifier. One can read it with <a href="https://cran.r-project.org/web/packages/TreeTools/vignettes/load-trees.html">ape::read.tree</a> (R), or <a href="https://biopython.org/wiki/Phylo">Phylo.read</a> (Python).</li> <li><code>.png</code> -file that contains the phylogenetic tree visualisation.</li> </ul> <p> </p> <h1>How-to</h1> <p>For the analysis of the results, download and unpack the archive, and further refer to the <a href="https://doi.org/10.5281/zenodo.14169792" target="_blank" rel="noopener">companion R notebook</a>. </p>
Data from: Comparative analysis of 2D and 3D distance measurements to study spatial genome organization
The spatial organization of genomes is non-random, cell-type specific, and has been linked to cellular function. The investigation of spatial organization has traditionally relied extensively on fluorescence microscopy. The validity of the imaging methods used to probe spatial genome organization often depends on the accuracy and precision of distance measurements. Imaging-based measurements may either use 2 dimensional datasets or 3D datasets which include the z-axis information in image stacks. Here we compare the suitability of 2D vs 3D distance measurements in the analysis of various features of spatial genome organization. We find in general good agreement between 2D and 3D analysis with higher convergence of measurements as the interrogated distance increases, especially in flat cells. Overall, 3D distance measurements are more accurate than 2D distances, but are also more susceptible to noise. In particular, z-stacks are prone to error due to imaging properties such as limited resolution along the z-axis and optical aberrations, and we also find significant deviations from unimodal distance distributions caused by low sampling frequency in z. These deviations are ameliorated by significantly higher sampling frequency in the z-direction. We conclude that 2D distances are preferred for comparative analyses between cells, but 3D distances are preferred when comparing to theoretical models in large samples of cells. In general and for practical purposes, 2D distance measurements are preferable for many applications of analysis of spatial genome organization.
Using DNA origami nanorulers as traceable distance measurement standards and nanoscopic benchmark structures
<p>In recent years, DNA origami nanorulers for superresolution (SR) fluorescence microscopy have been developed from fundamental proof-of-principle experiments to commercially available test structures. The self-assembled nanostructures allow placing a defined number of fluorescent dye molecules in defined geometries in the nanometer range. Besides the unprecedented control over matter on the nanoscale, robust DNA origami nanorulers are reproducibly obtained in high yields. The distances between their fluorescent marks can be easily analysed yielding intermark distance histograms from many identical structures. Thus, DNA origami nanorulers have become excellent reference and training structures for superresolution microscopy. In this work, we go one step further and develop a calibration process for the measured distances between the fluorescent marks on DNA origami nanorulers. The superresolution technique DNA-PAINT is used to achieve nanometrological traceability of nanoruler distances following the guide to the expression of uncertainty in measurement (GUM). We further show two examples how these nanorulers are used to evaluate the performance of TIRF microscopes that are capable of single-molecule localization microscopy (SMLM).</p> <p>Here we show the raw data the publication is based on.</p>
Dataset for the paper "A boundary-guided transformer based method for measuring distance from rectal tumor to anal verge on magnetic resonance images"
<p>A sagittal MR rectal image dataset for the field of DTAV measurement.</p>
Supplementary material 1 from: Casarin N, Hasbroucq S, López-Mercadal J, Miranda MÁ, Bragard C, Grégoire J-C (2023) Measuring the threat from a distance: insight into the complexity and perspectives for implementing sentinel plantation to test the host range of Xylella fastidiosa. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 47-80. https://doi.org/10.3897/neobiota.84.90024
Administrative burdens and delays, a comparative view
Data from: Comparative analysis of 2D and 3D distance measurements to study spatial genome organization
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Cross-validation of distance measurements in proteins by PELDOR/DEER and single-molecule FRET
<p>Pulsed electron-electron double resonance spectroscopy (PELDOR/DEER) and single-molecule Förster resonance energy transfer spectroscopy (smFRET) are frequently used to determine conformational changes, structural heterogeneity and inter probe distances in biological macromolecules. They provide qualitative information that facilitates mechanistic understanding of biochemical processes and quantitative data for structural modeling. To provide a comprehensive comparison of the accuracy of PELDOR/DEER and smFRET, we use a library of double cysteine variants of four proteins that undergo large-scale conformational changes upon ligand binding. With either method, we use established standard experimental protocols and data analysis routines to determine inter-probe distances in the presence and absence of ligands. The results are compared to distance predictions from structural models. Despite an overall satisfying and similar distance accuracy, some inconsistencies are identified, which we attribute to the use of cryoprotectants for PELDOR/DEER and label-protein interactions for smFRET. This large-scale cross-validation of PELDOR/DEER and smFRET highlights the strengths, weaknesses, and synergies of these two important and complementary tools in integrative structural biology.</p>
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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.