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2,212 results for “spacing”
QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
<p>Here, we introduce QM7-X, a comprehensive dataset of > 40 physicochemical properties for ~4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures---comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-trans-and conformational isomers)---as well as 100 non-equilibrium structural variations thereof to reach a total of ~4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly converged dataset of quantum-mechanically computed physical and chemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing <em>in silico</em> design of molecules with targeted properties.</p> <p>The dataset is provided in eight HDF5 based files (compressed in .XZ files). One can also find here a README file with technical usage details and examples of how to access the information stored in the dataset (see createDB.py). </p> <p>*The paper explaining the generation of data stored in QM7-X can be found in <em>Sci Data</em> 8, 43 (2021). DOI: 10.1038/s41597-021-00812-2 . arXiv: https://arxiv.org/abs/2006.15139 .</p>
New fault slip distribution for the 2010 Mw 7.2 El Mayor Cucapah earthquake based on realistic 3D finite element inversions of coseismic displacements using space geodetic data
<p>The .csv files included in this repository contain the data used in the numerical model as input, while the .txt file is the output (slip on a regular grid of points on the fault planes from the joint inversion of the geodetic datasets.</p>
Motor adaptation distorts visual space
<p>The folder Visual Adaptation contains the data for the visual adaptation experiment</p> <p>Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1<sup>st</sup>: Number of trial</li> <li>2<sup>nd</sup>: Test size</li> <li>3<sup>rd</sup>: Subject response on test</li> <li>4<sup>th</sup>: Condition </li> <li>5<sup>th</sup>: Test side</li> </ul> <p>The folder Motor Adaptation contains the data for the visual adaptation experiment</p> <p>Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1<sup>st</sup>: Number of trial</li> <li>2<sup>nd</sup>: Test size</li> <li>3<sup>rd</sup>: Subject response on test</li> </ul> <p>The structure “Resp” contains one matrix for each trial with the hand coordinates for the motor adaptation</p>
Space, time and beyond
<p>During the third Project Presentation Session on <strong>Monday</strong> <strong>23.07.2018</strong> 14:15 - 15:45<strong> </strong> the following 3 projects were presented:</p> <ul> <li><strong>Victor Westrich</strong> (Johannes Gutenberg University Mainz, Germany): "A spatial approach to the digital visualization of medieval sources"</li> <li><strong>Giovanni Pietro Vitali</strong> (Université de Poitiers, France): "Rethinking Rome as an Anthology: The Poeti der Trullo's Street Poetry"</li> <li><strong>Stefan Jänicke</strong> (Universität Leipzig, Germany): "The Value of Infographics for Timeline Visualizations"</li> </ul>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
<p>Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for post-processing, or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose global ocean temperature data. The uses of the method range from supporting decision making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterisation of the El Niño Southern Oscillation. </p>
Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing
<p>Simulation output data from COSMO and IFS used to produce the figures in the model intercomparison paper (https://doi.org/10.5194/gmd-2021-31), as well as the data used for initializing the soil in COSMO.</p> <p>The output data is partitioned into different parts:</p> <ol> <li>cosmo_hordiff.tar:<br> Model output from COSMO for the horizontal diffusion experiment.</li> <li>cosmo_standard.tar:<br> Model output from COSMO for the standard experiment.</li> <li>ifs_standard.tar:<br> Model output from IFS for the standard experiment.</li> <li>soil_cosmo_ini_vergara2021_avg_mayjune_12km.nc:<br> Initial conditions for the soil model used in COSMO.</li> </ol>
Space Debris: the origin dataset
<p>The <strong>"Space debris: the origin"</strong> dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition </strong>of the same name. The objective of this challenge is to trace the origin of space debris based on some sparse observations back to one of 100 potential originator satellites. The observations of the space debris consist of an epoch (JD2000) and the corresponding orbital elements in the following order:</p> <ul> <li>Semimajor axis (in km)</li> <li>Eccentricity (0 to 1)</li> <li>Inclination (in degrees)</li> <li>Mean anomaly (in degrees)</li> <li>Argument of the perigee (in degrees)</li> <li>RAAN (in degrees)</li> </ul> <p>For each debris in the subfolder <em>deb_train </em>the id of the originator satellite is given in the first column of <em>labels_train.dat</em>, followed by its area over to mass ratio. The competition asks its participants to recover these two values for all debris observations in the <em>deb_test </em>subfolder. The orbital elements of the satellites are denoted in the subfolder <em>sat</em>.</p> <p>For a detailed description (including the equations of motion) on the challenge and this dataset, visit <a href="https://kelvins.esa.int/space-debris-the-origin/">https://kelvins.esa.int/space-debris-the-origin/</a>.</p>
Perception of shape and space across rigid transformations
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., Spröte, P., & Fleming, R. W. (2016). Perception of shape and space across rigid transformations. <em>Vision Research, 126</em>, 318-329. <a href="http://dx.doi.org/10.1016/j.visres.2015.04.011"> http://dx.doi.org/10.1016/j.visres.2015.04.011 </a></p> <p>Each folder contains the data relative to one experiment and a text file with comments.</p>
Interview guideline for manager of maker space
<p>In the framework of the EU funded project Make-IT, 10 case studies of maker spaces in different European countries have been compiled based on four interviews each (amongst other data). One interview was conducted with the manager of the maker space and three interviews with makers who regularly made use of the maker space. The results are publised in aggregated form in D3.1 and D3.2 on the project's website: http://make-it.io/</p>
Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."
<p>These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13.</p> <p>The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited.</p> <p>The following files are available for experiment 2 of the article:</p> <p>allData.mat</p> <p>Includes the datamatrix allData with the following columns:</p> <p>1) Line used for analysis in the article (0 - no, 1-yes).<br> Possible reasons for exclusion:<br> a. fixation duration smaller than 50ms or larger 1000ms<br> b. fixation adjacent to a blink (preceding or following)<br> c. fixation outside the image</p> <p>2) ID of observer (1-24)</p> <p>3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise)</p> <p>4) ID of image (48 unique numbers between 1 and 135)</p> <p>5) horizontal eye position</p> <p>6) vertical eye position</p> <p>7) fixation duration in ms</p> <p>8) value of empirical map generated from search condition of experiment 1 at fixated location</p> <p>9) value of empirical map generated from preference condition of experiment 1 at fixated location</p> <p>10) value of empirical map generated from memorization condition of experiment 1 at fixated location</p> <p>11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location</p> <p>12) value of empirical map generated from condition 1 at fixated location</p> <p>13) value of empirical map generated from condition 2 at fixated location</p> <p>14) value of empirical map generated from condition 3 at fixated location</p> <p>15) value of empirical map generated from condition 4 at fixated location</p> <p>16) value of empirical map generated from condition 5 at fixated location</p> <p>17) value of empirical map generated from condition 6 at fixated location</p> <p>18) value of empirical map generated from condition 1 at fixated location leaving out the current observer</p> <p>19) value of empirical map generated from condition 2 at fixated location leaving out the current observer</p> <p>20) value of empirical map generated from condition 3 at fixated location leaving out the current observer</p> <p>21) value of empirical map generated from condition 4 at fixated location leaving out the current observer</p> <p>22) value of empirical map generated from condition 5 at fixated location leaving out the current observer</p> <p>23) value of empirical map generated from condition 6 at fixated location leaving out the current observer</p> <p>24) luminance at fixation</p> <p>25) luminance contrast at fixation</p> <p>26) edge density at fixation</p> <p>27) eccentricity of fixation</p> <p> </p> <p>usedData.Rdata</p> <p>- for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above):</p> <p>obsNum: the ID of the observer (1-24)</p> <p>condNum: the ID of the condition (1-6)</p> <p>imgNum: the ID of the image (48 unique numbers between 1 and 135)</p> <p>fixDur: fixation duration</p> <p>LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation</p> <p>empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation</p> <p>empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location</p> <p>empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer</p> <p>x,y - coordinates of fixation</p> <p> </p> <p>modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p>modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p> </p>
Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023
<p>Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023</p>
Expanding the chemical space using a Chemical Reaction Knowledge Graph
<p>This contains:</p><ul><li>the reaction graph dataset used to train the link prediction model</li></ul><p>Homepage: https://github.com/MolecularAI/reaction-graph-link-prediction</p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</p>
Five-dimensional phase space measurement at the Spallation Neutron Source Beam Test Facility
<p>This data set consists of 285,082 two-dimensional images which collectively describe the five-dimensional phase space distribution \(f(x, x', y, y', w)\) of a 2.5 MeV, -25.6 mA H\(^-\) ion beam in the <a href="https://neutrons.ornl.gov/sns">Spallation Neutron Source</a> Beam Test Facility (SNS-BTF). Here, \(x\) and \(y\) are the transverse positions, \(x' = dx/ds\) and \(y' = dy/ds\) are the transverse slopes, \(s\) is the position along the reference trajectory, and \(w\) is the deviation from the kinetic energy of the synchronous particle.</p><p>The measurement plane is located in the medium energy beam transport (MEBT) section of the SNS-BTF, 1.3 meters after a radio-frequency quadrupole (RFQ). The measurement apparatus consists of three transverse slits (one horizontal, two vertical) and a 90-degree dipole bend followed by a fluorescent screen. The horizontal slit selects \(y\); two vertical slits select \(x\) and \(x'\); \(y'\)is a function of \(y\) and the vertical position on the screen, \(w\) is a function of \(x\), \(x'\), and the horizontal position on the screen. Thus, the image on the screen gives the density \(f(y', w)\) within a small three-dimensional region in \(x-x'-y\) space. The five-dimensional density is obtained by scanning the slits in a nested loop.</p><p>The data set consists of 285,082 images (20 GB). Jupyter notebooks are included to interpolate the data on a regular grid in five-dimensional phase space, as well as to generate interactive figures. See 'README.md' for instructions. (The interpolated five-dimensional image is also included in a separate folder.)</p><p>More information can be found in a corresponding publication: https://doi.org/10.1103/PhysRevAccelBeams.26.064202</p>
Unraveling clonal trait space: Beyond aboveground and fine-root traits
<p>Plant trait variation is constrained by mechanical and energetic tradeoffs as attested by the global spectrum of plant form and function and the fine-root economics space for above- and belowground traits. However, traits that are key for fitness maintenance in some plant groups, such as clonal and bud bank traits, have not yet been integrated within the frameworks provided by the aboveground and the fine-root economics space.</p> <p>By using an extensive dataset encompassing aboveground, fine-root, clonal, and bud bank traits of 2000 species of Central European herbs, we asked whether clonal and bud bank traits correspond to the placement of species in the aboveground or fine-root trait spaces.</p> <p>Perennial clonal and non-clonal herbs show indistinct positioning within the aboveground and fine-root trait spaces. This extends and reinforces previous fragmentary evidence of weak correlations between clonal and bud bank traits and aboveground trait dimensions. Additionally, we identify for the first time a limited correlation between clonal and fine-root traits as well. This disconnection suggests that clonal traits operate independently from other trait spectra. For this reason, we introduce the concept of a "clonal trait space" for clonal herbs. The first dimension of this space is defined by bud bank size and the persistence of clonal connection, reflecting a gradient of species specialisation for on-spot persistence and tolerance to disturbance (persistence dimension). The second dimension, defined by multiplication rate and lateral spread, reflects a specialisation axis for clonal multiplication and horizontal size dimension (clonal multiplication dimension). Clonal trait dimensions add non-redundant information to the aboveground or fine-roots trait space.</p> <p><strong>Synthesis:</strong> We champion the integration of the persistence and clonal multiplication dimensions from the "clonal trait space" into the frameworks provided by the aboveground trait and the fine-root economics spaces, thereby enhancing our comprehension of the multifaceted trait strategies exhibited by plants.</p>
Effects of food supplementation and helminth removal on space use and spatial overlap in wild bank vole populations
<p>Animal space use and spatial overlap can have important consequences for population-level processes such as social interactions and pathogen transmission. Identifying how environmental variability and inter-individual variation affect spatial patterns and in turn influence interactions in animal populations is a priority for the study of animal behavior and disease ecology. Environmental food availability and macroparasite infection are common drivers of variation, but there are few experimental studies investigating how they affect spatial patterns of wildlife. Bank voles (<em>Clethrionomys glareolus</em>) are a tractable study system to investigate spatial patterns of wildlife and are amenable to experimental manipulations. We conducted a replicated, factorial field experiment in which we provided supplementary food and removed helminths in vole populations in natural forest habitats and monitored vole space use and spatial overlap using capture-mark-recapture methods. Using network analysis, we quantified vole space use and spatial overlap. We compared the effects of food supplementation and helminth removal and investigated the impact of season, sex, and reproductive status on space use and spatial overlap. We found that food supplementation decreased vole space use while helminth removal increased space use. Space use also varied by sex, reproductive status, and season. Spatial overlap was similar between treatments despite up to three-fold differences in population size. By quantifying the spatial effects of food availability and macroparasite infection on wildlife populations, we demonstrate the potential for space use and population density to trade off and maintain consistent spatial overlap in wildlife populations. This has important implications for spatial processes in wildlife including pathogen transmission.</p>
funspace: an R package to build, analyze and plot functional trait spaces
<p>Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes-troublesome steps needed to build and analyze functional trait spaces.</p> <p>To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate (PCA-based) functional trait space analyses. The six functions that constitute the package can be grouped in three modules: 'Building and exploring', 'Mapping', and 'Plotting'. The building and exploring module defines the main features of a functional trait space (e.g., functional diversity metrics) by leveraging kernel density-based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and high-quality figures representing the outputs obtained from previous modules. We provide a worked example to demonstrate a complete funspace workflow.</p> <p>funspace will provide researchers working with functional traits across the tree of life with an indispensable asset to easily explore: (i) the main features of any functional trait space, (ii) the relationship between a functional trait space and any other biological or non-biological factor that might contribute to shaping species' functional diversity.</p>
Data and code from: SIDERITE: Unveiling Hidden Siderophore Diversity in the Chemical Space Through Digital Exploration
<h1>TMAP of COCONUT database</h1> <p>The script and data used in SIDERITE paper to generate TMAP picture (Figure S3 in supplementart material).</p> <p>Requirement: tmap</p> <p>You can install tmap by conda.</p> <blockquote> <p>conda create -n tmap python=3.7</p> <p>conda activate tmap</p> <p>conda install -c tmap tmap</p> <p>pip install faerun</p> <p>pip install matplotlib</p> <p>conda install scipy</p> <p>conda install -c rdkit rdkit</p> <p>conda install -c conda-forge mhfp</p> </blockquote> <p> </p> <p>Usage: python plot_COCONUT.py</p> <p>Then it will use COCONUT.csv to generate index.html and index.js. Open index.html to see result.</p> <h1>Other large input files</h1> <p>Tanimoto_COCONUT_SIDERITE.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/predicted_new/Clustering_coconut.m">SIDERITE/predicted_new/Clustering_coconut.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>Sid_structure_output3.xlsx is used in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/statistics/Figure1.m" target="_blank" rel="noopener">SIDERITE/statistics/Figure1.m at main · RuolinHe/SIDERITE</a>, <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a> and <a href="https://github.com/RuolinHe/SIDERITE/blob/main/siderophore_process/Sid_process_code.m" target="_blank" rel="noopener">SIDERITE/siderophore_process/Sid_process_code.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>COCONUT4MetFrag_Canonical.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>COCONUT_r.txt is the output file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a>. and the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/predicted_new/isSiderophore1.py" target="_blank" rel="noopener">SIDERITE/predicted_new/isSiderophore1.py at main · RuolinHe/SIDERITE.</a></p> <p> </p> <p>COCONUT4MetFrag.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/CheckSMILES2.py" target="_blank" rel="noopener">SIDERITE/TAMP/CheckSMILES2.py at main · RuolinHe/SIDERITE</a>.</p> <p> </p>
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet.
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.