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opencc-by-4.0Jul 2023View details →

A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument: Data Repository

This repository contains the synthetic catalogue for the DESI Bright Galaxy Survey produced wit the N-body code gevolution, which is analysed in the manuscript "<a href="https://arxiv.org/abs/2306.04213">A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument</a>", as well as the raw data of the analysis results. The catalogue "catalogue.csv.bz2" is in the CSV format and can be directly read using the pandas library of python, for example. The columns in the catalogue contain the following information: 0. Column index 1. Comoving coordinate x (in units of Mpc/h) 2. Comoving coordinate y (in units of Mpc/h) 3. Comoving coordinate z (in units of Mpc/h) 4. Observed redshift 5. Cosine of the observed polar angle measured with respect to the axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with \(\phi_1 = \pi/4\), then rotating along the new y axis with \(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\) and setting the final rotation angle to zero, \(\phi_3 = 0\); hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix) 6. Observed azimuthal angle phi measured with respect to axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with \(\phi_1 = \pi/4\), then rotating along the new y axis with \(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\) and setting the final rotation angle to zero, \(\phi_3 = 0\); hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix) 7. Logarithm of the luminosity in units of solar luminosity \(L_\odot\) 8. Observed flux (in units of \(L_\odot/\mathrm{Mpc}^2\)) 9. Number of particles in each object, plus a uniform noise between 0 and 1. This quantity is the proxy of the mass that was used to assign luminosity to the objects. 10. Flag that identifies the selected objects within each redshift bin. The Flag is 0 for objects not included in the catalogue, and equal to the mean redshift of the bins \(\bar{z} = 0.25, 0.35, 0.45\) for the selected objects.  11. Flag that identifies the bright and faint objects for case 1 (50% bright, 50% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 12. Flag that identifies the bright and faint objects for case 2 (90% bright, 10% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 13. Flag that identifies the bright and faint objects for case 3 (50% bright, 50% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 14. Flag that identifies the bright and faint objects for case 4 (90% bright, 10% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. The example script "example-script.ipynb" demonstrates how to query the catalogue to extract e.g. the redshift distribution of the objects for the different cases considered in Table 4 of the manuscript. Additionally, the measured dipole data vectors with the jackknife covariance matrices (\(\mathrm{cov}^\mathrm{JK}_{ij}\)), as well as the theoretical data vectors with the theoretical measurement covariance (\(\mathrm{cov}^\mathrm{th}_{ij}\)) and the theoretical prediction covariance (\(\mathrm{cov}^\mathrm{pred}_{ij}\)) are provided within this repository: <ul> <li>In the measurements.tar.gz archive, the measured data for the flux-limited case can be found in the /flux-limit subdirectory, while the data for the case without flux-limit is in /no-flux-limit. The data vectors are named "dipole_<redshift bin>_<% of bright galaxies>.txt. The first column in each of those files is the separation bin \(d\) in \(\mathrm{Mpc}/h\), the second column is the mean two-point correlation function dipole of the 100 jackknife subsamples, and the third column is the square root of the diagonal part of the jackknife covariance matrix (\(\mathrm{cov}^\mathrm{JK}_{ij}\)). The corresponding jackknife covariance matrices are named "cov_<redshift bin>_<% of bright galaxies>.txt.</li> <li>In the theory.tar.gz archive, the theoretical predictions are found in /flux-limit for the case with flux limit and in /no-flux-limit for the case without flux limit. The theoretical data vectors are named "dipole_<% of bright galaxies>B_z<redshift bin>_gevol.dat". The first column in each of those files is the separation bin \(d\) in \(\mathrm{Mpc}/h\), the second column the theoretical two-point correlation function dipole and the third column is the square root of the diagonal part of the theoretical prediction covariance matrix (\(\mathrm{cov}^\mathrm{pred}_{ij}\)) . The theoretical measurement covariance matrices are named "covariance_Lp6_<% of bright galaxies>B_z<redshift bin>_gevol.dat", and the theoretical prediction covariance matrices are named "covtheo_<% of bright galaxies>B_z<redshift bin>_gevol.dat". </li> </ul> The example script also demonstrates how to use these data files to reproduce plots of the dipole measurement vs the theoretical prediction like in Figures 6, 7, C1 and C2. The archives need to be unpacked before using the example script to access the data.

opencc-by-4.0Jul 2023View details →

Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"

This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper. The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks. Where to Start The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'.  The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file. For more information, please refer to 'README.txt'.

opencc-by-4.0Jul 2023View details →

Matrix Profile of Seismological Data Repository

The Computed Matrix Profile (MP) repository for seismic data. Please see below the archived manuscript of Shakibay Senobari et al., (Submitted to JGR, under review) for more information regarding the Matrix Profile and data set: essopenarchive.org/604953/p1vlDMs-5b0zsrs7C033Ug https://doi.org/10.1002/essoar.10512525.1

opencc-by-4.0Aug 2023View details →

Sounding Covid-19 Repository

The Covid-19 pandemic has caused cities to become sparsely populated, significantly changing urban sound environments with different restrictions placed. By capturing audio recordings at various points during lockdowns, we can sonically observe the impact of Covid-19 constraints and how these places have transformed over time.  The repository holds 91 sound compositions from 27 separate locations, focusing on the effects of Covid-19 during Belfast, Northern Ireland's Lockdowns (1-3 in 2020-2021), Exit Strategies (Summer 2020 and 2021), Lifted Restrictions (2022), and a Lockdown visit to Montreal, Canada, in 2020-2021.  This project explores the auditory and sonic art perspectives of how cities sound without the presence of humans or the usual level of human density in popular public spaces in Belfast and a few areas in Montreal. It focuses on field recordings, soundwalking experiences, soundscape composition, and sound map contributions. Folders contain audio and images, categorised as: 1-Sounding Covid-19_Belfast-Northern Ireland <ul> <li>1-Lockdown 1 + Exit Strategies-2020</li> <li>2-Lockdown 2-2020</li> <li>3-Lockdown 3 + Exit Strategies-2021</li> <li>4-Lifted Restrictions-2022</li> </ul> 2-Sounding Covid-19_Montreal-Canada

opencc-by-4.0Aug 2023View details →

Data repository supporting the JGR article: A case study of the solar and lunar semidiurnal tide response to the 2013 sudden stratospheric warming

This dataset contains the meteor radar wind measurements and the background atmospheric data fields used to produce the results in the Journal of Geophysical Research - Space Physics article titled: A case study of the solar and lunar semidiurnal tide response to the 2013 sudden stratospheric warming. The nav_2013_ERAn_1dm_grid.dat file contains gridded latitude-longitude-altitude daily mean winds and temperatures for the composite atmosphere described in the article. The full range of dates span from the 1st of December 2012 to the 1st of April 2023. The composite atmosphere is constructed using ERA5 winds and temperatures below 85 hPa, NAVGEM-HA winds and temperatures between 85-0.001 hPa, and HWM14 + MSISE00 winds and temperatures above 0.001 hPa. The meteor_radars.zip file contains a folder containing the hourly meteor radar wind measurements for the Collm (Col), CMOR (CMO), and Kiruna (Kir) radars, as described in the supporting paper. The .hdf5 files contain both the horizontal and meridional wind measurements for December, January, February, and March (winter of 2012/2013).

opencc-by-4.0Aug 2023View details →

Data repository in support of the article: A case study of the solar and lunar semidiurnal tide response to the 2013 sudden stratospheric warming

opencc-by-4.0Jan 2023View details →

Data and Code Repository for Expanding non-target analysis methods to characterize the prenatal exposome

Data and Code Repository for the following manuscript: Expanding non-target analysis methods to characterize the prenatal exposome.

opencc-by-4.0Sep 2023View details →

RMT repository test data

This data set contains all of the test data which was previously housed in the R-matrix with time-dependence (RMT) repository at <a href="https://gitlab.com/Uk-amor/RMT/rmt">https://gitlab.com/Uk-amor/RMT/rmt</a>.  There are four separate directories: atomic_big, atomic_small and molecular_tests contain sample inputs and outputs for rmt calculations. field_tests contains inputs and outputs for the field_check utility.  Each test calculation contains an inputs and rmt_output directory. Calculations were performed with RMT compiled with the gnu compiler running on the ARCHER2 supercomputer.

opencc-by-4.0Jun 2023View details →

Raw data repository for the article: "Revealing the impact of polystyrene-functionalization of Au octahedral nanocrystals of different sizes on formation and structure of mesocrystals"

Raw data depository for the article in ChemNanoMat journal: <a href="https://doi.org/10.1002/cnma.202300336">DOI: 10.1002/cnma.202300336</a>. Details and the file description are given in the file "Lapkin_Dataset_Info.pdf"

opencc-by-4.0Oct 2023View details →

Data accompanying the GitHub repository bartonlab/paper-clonal-dynamics

This dataset contains data that accompany the GitHub repository bartonlab/paper-covariance-estimation. The GitHub repository contains code and scripts for reproducing results described in the manuscript "Correlated allele frequency changes reveal clonal structure and selection in temporal genetic data." See the GitHub repository for details on the interpretation and analysis of this data.

opencc-by-4.0Feb 2023View details →

Data repository accompanying "Many-Body Majorana Braiding without an Exponential Hilbert space"

This repository includes the data and notebooks to generate the figures published in "Many-body Majorana braiding without an exponential Hilbert space".

Swiss Frailty Network and Repository

ClinicalTrials.gov study NCT04516642. IPD Sharing: YES. Countries: 1. Publications: 1.

The Dystonia Coalition Natural History and Biospecimen Repository for Isolated Dystonias

ClinicalTrials.gov study NCT01373424. IPD Sharing: YES. Countries: 7. Publications: 1.

Biological Sample Repository for Gastrointestinal Disorders

ClinicalTrials.gov study NCT05874726. IPD Sharing: YES. Countries: 1. Publications: 15.

Pediatric Metabolism and Microbiome Repository

ClinicalTrials.gov study NCT02959034. IPD Sharing: YES. Countries: 1. Publications: 1.