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2,212 results for “Space”
Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011
The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.
Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of two parts. The first part of the data set is measurements for six different specimens with wave type impact – short sweep signal with duration 0.05 s. The second part is the measurements during splice connection degradation of one of the specimens with short impulse. The degradation of a connection is presented by four different states of joints. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the second part of the data set is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Database of measurements for damage detection of steel beam splice connections by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of measurements for six different specimens with two types of impact – sweep signal with duration 0.5 s and short impulse, during degradation of the splice connections realised by unbolting the bolts in the connections. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>This database is a continuation of the database Kurtenoks, V., Buka-Vaivade, K., Serdjuks, D., Lapkovskis, V., Mironovs, V., & Podkoritovs, A. (2023). Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10077332<br>Suggested by authors data post-processing is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Shared neural codes for visual and semantic information about familiar faces in a common representational space
Open the record for dataset details and reuse information.
Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
Dataset of "Marcus cross relation in the space of H-atom abstraction reactions boosted through off-diagonal thermodynamics"
<p>Proton-coupled electron transfer (PCET) and hydrogen-atom transfer (HAT) reactions play critical roles in biological processes and modern organic synthesis. The kinetics of these processes can align with the principles described in the renowned Marcus cross relation (MCR), a framework initially formulated to describe electron transfer mechanisms. The MCR provides an outstanding link between the kinetics of PCET/HAT reaction involving two distinct reactants and two related auxiliary self-exchange reactions – each between a molecule of one of the reactants and its coupled radical. In this study, we investigate the applicability and limitations of the canonical MCR across over 300 PCET and HAT reactions, providing a comprehensive theoretical analysis. Our findings reveal the need for an enhanced framework that incorporates ‘off-diagonal’ thermodynamic factors—asynchronicity and frustration. Of these factors, asynchronicity, which quantifies the imbalance between the proton vs. electron transfer components of the reaction, is identified as the dominant contributor to the improved predictive accuracy of the MCR. Notably, the incorporation of off-diagonal thermodynamics yields a more pronounced enhancement for HAT reactions than for PCET reactions. This advancement offers a refined theoretical basis for understanding H-atom abstraction mechanisms and underscores the importance of off-diagonal effects in PCET/HAT chemistry.</p>
GIXD data of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), processed q-space maps
<p>This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space and polar projection. The underlying raw data is published in <a href="https://doi.org/10.5281/zenodo.6683616">10.5281/zenodo.6683616</a> and processed with <a href="https://doi.org/10.5281/zenodo.6683658">10.5281/zenodo.6683658</a>. This data describes a time series of diffraction images acquired with 10 Hz.</p> <p> </p> <p>Parameters of the provided data:</p> <ul> <li> <p>Q-space-maps</p> </li> </ul> <p> </p> <ul> <li> <ul> <li> <p>Horizontal axis (Q<sub>xy</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (Q<sub>z</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Resolution: 1350x1350 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> <li> <p>Polar data</p> <ul> <li> <p>Horizontal axis (||<strong>q</strong>||) range: (0, 4.53) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (ф) range: (0, 90) deg</p> </li> <li> <p>Resolution: 512x1024 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> </ul>
Vascular Territory template and atlases in MNI space
<p><strong>Data</strong></p> <p>Sixteen subjects (mean age (sd): 69.6 (8.2); 37.5% female) were recruited to generate a high-resolution template. The cohort consists of twelve stroke-free, non-demented patients with the sporadic form of cerebral amyloid angiopathy (CAA), and similarly-aged healthy controls (n=4). Each participant underwent high-resolution MRI with a Siemens Magnetom Prisma 3T scanner (using a 32-channel head coil) as part of a separate study. The standardized protocol included a Multiecho T1-weighted (voxel size: 1x1x1 mm<sup>3</sup>; Repetition Time [TR]: 2510 ms), a 3D-FLAIR (voxel size: 0.9x0.9x0.9 mm<sup>3</sup>; TR: 5000 ms; TE: 356 ms), and a T2-weighted Turbo Spin Echo (voxel size: 0.5x0.5x2.0 mm<sup>3</sup>; TR: 7500 ms; TE: 84 ms) sequence. Scans were manually assessed to ensure no gross pathology was present, such as hemorrhage or silent brain infarcts.</p> <p><strong>Template and territorial map creation</strong></p> <p>We employed Advanced Normalization Tools (ANTs) for image processing (Avants et al., 2010, 2011) for creating a brain template based on multimodal information using T1, T2 and 3D-FLAIR sequences. After template creation, we smoothed the resulting templates (FSL; Gaussian smoothing, sigma = 1) and registered the resulting templates into MNI space, again using ANTs (Avants et al., 2011).</p> <p>Vascular territories were outlined on the right hemisphere in the T1-weighted atlas image and contain anatomically validated ACA, MCA, and PCA territories supratentorially. The right hemispheric map was then mirrored onto the left hemisphere to create a full-brain vascular territory map, which was manually assessed and corrected where necessary.</p> <p> </p> <p>For more details, please see the original publication that utilized the template. If you utilize this template, please also cite</p> <p>Schirmer, Markus D., et al. "Spatial signature of white matter hyperintensities in stroke patients." <em>Frontiers in neurology</em> 10 (2019): 208.</p> <p><a href="https://doi.org/10.3389/fneur.2019.00208">https://doi.org/10.3389/fneur.2019.00208</a></p> <p> </p> <p><strong>Files</strong></p> <p><strong>FLAIR template</strong>: caa_flair_in_mni_template_smooth.nii.gz<br> <br> <strong>FLAIR template after brain extraction and intensity normalization</strong>: caa_flair_in_mni_template_smooth_brain_intres.nii.gz<br> <br> <strong>T1 template</strong>: caa_t1_in_mni_template_smooth.nii.gz 27.7 Mb<br> <br> <strong>T2 template</strong>: caa_t2_in_mni_template_smooth.nii.gz 27.7 Mb<br> <br> <strong>Vascular territory map</strong>: mni_vascular_territories.nii.gz</p>
Plant aboveground biomass dry weight record for Space for Time plots in PIE LTER.
Aboveground biomass measurements were conducted annually near peak biomass to evaluate aboveground plant production and determine differences in relation to other biotic and abiotic factors. In a 0.053 m2 plot, aboveground biomass was clipped to the soil surface at Space For Time plots, dried, and weighed to capture dry weight.
Nearly optimal coverings of orientation space
<p>We give various sets of orientations which cover orientation space nearly optimally. These are suitable for searching orientation space and for integrating over orientation (together with the provided weights). The data is identical to that provided in March, 2006. The documentation has been updated for this release; this is available at https://github.com/cffk/orientation/tree/v1.1</p>
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
Simulation of the SLR Space Segment Evolution to Improve the Realization of Terrestrial Reference Frames and Determination of Low-Degree Gravity Field Parameters
<p>These are data obtained from simulation studies of the development of the space segment of the SLR technique. Detailed information can be found in Najder et al. (2025). Najder, J., Sośnica, K., Zajdel, R., & Kur, T. (2025). Simulation of the SLR space segment evolution to improve the realization of terrestrial reference frames and determination of low-degree gravity field parameters. <em>Journal of Geodesy</em>, <em>99</em>(6), 46. https://doi.org/10.1007/s00190-025-01971-5</p>
The Neanderthal Niche Space of Western Eurasia - Supplemental Material
<p>The data provided here are the supplemental information accompanying the journal article <strong>The Neanderthal Niche Space of Western Eurasia 145ka to 30ka ago</strong> by Yaworsky, Nielsen, & Nielsen. All analyses were performed in R v4.5.0 and are documented in the HTML document, <strong>Supplemental 9</strong>.</p> <p>List of Supplemental Files:</p> <ol> <li><strong>ROCEEH Archaeological Observations - File name: </strong><em><strong>FaunalData_52923_NOANIMALS.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. The zooarchaeological observations have been removed from the original PHP query.</li> </ol> </li> <li><strong>ROCEEH Dates Data - File name: </strong><em><strong>Dates_Geog.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. These are the Geolayer dates.</li> </ol> </li> <li><strong>ROCEEH Dates Data - File name: </strong><em><strong>Dates_Assem.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. These are the ArchLayer dates.</li> </ol> </li> <li><strong>ROCEEH Dates Data - File name: </strong><em><strong>Dates_ArchLayer.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. These are the Assemblage dates.</li> </ol> </li> <li><strong>Spatiotemporal Archaeological Observations - File name: </strong><em><strong>ArchaeologicalData_V1.csv</strong></em> <ol> <li>Derived from Supplemental 1 after removing observations that dated outside of the 145ka to 50ka year range, all NA values, and observations duplicated in time and space (1000-year range).</li> </ol> </li> <li><strong>Spatiotemporal Background Points - File name: </strong><em><strong>AbsencePointData.csv</strong></em> <ol> <li>Randomly generated background points. 100 random points were generated in each millennium.</li> </ol> </li> <li><strong>High-Resolution Spatiotemporal Predictions - File name: </strong><em><strong>SDM_MainGIF.mp4</strong></em> <ol> <li>High-resolution mp4 file showing the predictions of the Neanderthal niche space from 145ka to 30ka ago.</li> </ol> </li> <li><strong>Neanderthal Niche Space 145ka to 30ka ago- File name: </strong><em><strong>Human_Niche_Size.csv</strong></em> <ol> <li>Quantification of the Neanderthal niche space for each millennium.</li> </ol> </li> <li><strong>Analysis Markdown Document - File Name: </strong><em><strong>NeanderEdgeMD_v5.html</strong></em> <ol> <li>Markdown illustrating step-by-step the methods used to organize and analyze the data.</li> </ol> </li> <li><strong>Delta O18 Record - File name: </strong><em><strong>LisieckiRaymod18O.csv</strong></em> <ol> <li>Delta O18 Record from Lisiecki & Raymo, 2005.</li> <li>Used in Figure 1 of the publication to illustrate the relationship between Neanderthal niche size and Delta O18 values.</li> </ol> </li> <li><strong>Neanderthal Niche Space 350ka ago to Present - File name: </strong><em><strong>Human_Niche_Size_350k.csv</strong></em> <ol> <li>Neanderthal niche space estimates from 350ka to the present based on the model constructed around the 145ka to 50ka ago archaeological observations.</li> <li>Not discussed in the main publication.</li> </ol> </li> </ol> <p>The NeanderEDGE Project is funded by the Independent Research Fund Denmark (Danmarks Frie Forskningsfond) case number 9062-00027B.</p>
The Human Niche Space of Post-LGM Late Upper Paleolithic Europe - Supplemental Material
<p>The data provided here are the supplemental information accompanying the journal article <strong>The Human Niche Space of Post-LGM Late Upper Paleolithic Europe: The Effects of Climate and Population Growth on Human Land Use</strong> by Yaworsky, Hussain, & Riede. All analyses were performed in R v4.5.0 and are documented in the HTML document, <strong>Supplemental 4</strong>.</p> <p>Version 1.2 of the Analysis Markdown Document incoporates changes made to functions within the package ENMeval.</p> <p>List of Supplemental Files:</p> <ol> <li><strong>Spatiotemporal Archaeological Observations - File name: <em>Archaeologicaldata_v1.csv</em></strong> <ol> <li>Archaeological observations derived from Kretschmer (2015) and supplemented with additional observations (see main paper for details).</li> </ol> </li> <li><strong>Summed Probability Estimate for Population Estimation - File name: <em>Population_SPD2.csv</em></strong><br> <ol> <li>Summed probability distribution estimating changes in relative population size across Europe from 22ka ago to 9.1ka ago using data from the P3K14C database (Bird et al, 2022; see <strong>Supplemental 4</strong> for details).</li> </ol> </li> <li><strong>Spatiotemporal Background Points - File name: </strong><em><strong>AbsencePointData.csv</strong></em><br> <ol> <li>Randomly generated background points. 100 random points were generated in each millennium.</li> </ol> </li> <li><strong>Analysis Markdown Document - File Name: </strong><em><strong>CLIOARCH_MD_v1.2.html</strong></em><br> <ol> <li>Markdown illustrating step-by-step the methods used to organize and analyze the data.</li> </ol> </li> <li><strong>High-Resolution Spatiotemporal Predictions - File name: </strong><em><strong>SDM_MainGIF.mp4</strong></em><br> <ol> <li>High-resolution mp4 file showing the predictions of the potential climate niche space for humans from 22ka to 9.1ka ago.</li> </ol> </li> <li><strong>Potential Niche Space 22ka to 9.1ka ago- File name: </strong><em><strong>Human_Niche_Size.csv</strong></em> <ol> <li>Quantification of the potential climate niche space for each century.</li> </ol> </li> </ol> <p>The Climate data are not provided due to their size but are sourced from Karger et al (2023) and are accessible <a href="https://chelsa-climate.org/">here (https://chelsa-climate.org/)</a>.</p> <p> </p>
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
Space Weather ElectroMagnetic Database for Ireland (SWEMDI)
<p>This is a database containing electromagnetic (EM) data that can contribute to better understand and quantify the electric fields caused by space weather events at the Earth's surface, and the physical properties of Ireland’s lithosphere. The database is named Space Weather Electromagnetic Database for Ireland (SWEMDI).</p> <p>It contains measured electromagnetic time series using magnetotelluric equipment, electromagnetic tensor relationships, 3D electrical resistivity model of Ireland's lithosphere, modelled electric and magnetic time series for Ireland between 1991 and 2018, documents and publications that used parts of this database, and a series of scripts that were used to generate the database.</p>
6D phase space of charged beam in particle accelerator
<p>The dataset is collected from HPSim (https://github.com/apphys/hpsim), an advanced, open-source tool developed at LANL, enables rapid, online simulations of multipleparticle beam dynamics is used to collect data. HPSim solves Vlasov-Maxwell equations to calculate the effects of external accelerating and focusing forces on the charged particle beam as well as space charge forces within the beam. To generate the dataset from HPSim, the RF set points (amplitude and phase) for the first four modules are randomly sampled from a uniform distribution keeping the rest of the set points of 44 modules at a mean value. Other beam and accelerator parameters, like the initial beam condition, are also set to constant realistic values. Using the RF set points as inputs to the simulation, HPSim provides a six-dimensional phase space of the charged particle beam in the form of 15 unique projections at each of the 48 accelerating section/modules of LANSCE linear accelerator. </p>
Identifying the mechanisms by which irrigation can cool urban green spaces in summer
<p>This dataset contains the measured soil moisture and microclimate data from two (2021 and 2022) urban green space irrigation experiments conducted in Burnley, Melbourne, Australia. The experiments consisted of two treatments, irrigated turf and unirrigated turf. The purpose of the experiments was to provide testing (2021) and evaluation (2022) data for an urban ecohydrological model, UT&C. </p> <p><br>After evaluating the performance of UT&C in modelling soil moisture and microclimate, UT&C was used to model the surface energy balance and evapotranspiration processes of the irrigated and unirrigated turf. This dataset also contains the modelled soil moisture, microclimate, surface energy balance and evapotranspiration data, as well as the measured background climate data at the reference climate station and the forcing data for the model.</p> <p><br>The aims of this study were to:<br>i) identify the proportional contribution of different evapotranspiration processes to irrigation cooling effect, and <br>ii) quantify the impacts of different irrigation amounts (from 2 to 30 mm/d) on the cooling effect of irrigating turfgrass in Melbourne, Australia during normal summer conditions.</p> <p>This study was published in:<br>Pui Kwan Cheung, Naika Meili, Kerry A. Nice, Stephen J. Livesley (2024). Identifying the mechanisms by which irrigation can cool urban green spaces in summer. Urban Climate. 55,101914. https://doi.org/10.1016/j.uclim.2024.101914.</p>
Momentum space wave functions for the linear potential
<p>Normalized momentum space wave functions for the linear potential. The Schrödinger equation was solved with the methods described in "A simple high-accuracy method for solving bound-state equations with the Cornell potential in momentum space", Alfred Stadler, Elmar P. Biernat, Vasco Valverde. </p> <table> <tbody> <tr> <td><a href="https://arxiv.org/abs/2407.21789">arXiv:2407.21789</a> [hep-ph]</td> </tr> </tbody> </table> <p>(to be pulished in Physical Review D)</p> <p>The wave functions correspond to the energie eigenvalues shown in Table VI of this work.</p> <p>The name of each file indicates the orbital angular momentum and which eigenstates it contains. For instance, wf_n1-5_l=0_np=1000_NL=5.txt contains the wave functions of the states n=1, 2, 3, 4, 5 for l=0, and wf_n6-10_l=3_np=1000.txt the wave functions of the states n=6, 7, 8, 9, 10 for l=3. Furthermore, np=1000 means that 1000 momentum integration points were used for the solution of the Schrödinger equation, and NL=5 or NL=15 means that 5 or 15 points were used for the Lagrange interpolations.</p> <p>Each data file in text format contains 6 columns and 1000 lines. Column 1 ist the momentum (GeV), columns 2-6 the wave functions. The momenta were generated according to Eq. (4.3) of the article, with p_0=1.</p> <p> </p>
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.