Dataset Structure

This dataset contains clean simulated weak lensing maps without noise.

  Data Fields

input: 4D tensor of shape (N, 1, 66, 66) containing weak lensing maps, N=number of examples. label: 2D array of shape (N, 2) containing the label for cosmological parameters $\Omega_m$ and $\sigma_8$ for each examples.

  Data Splits

train: 90,000 examples validation: 10,000 examples test: 10,000 examples

  Usage

from datasets import load_dataset… See the full description on the dataset page: https://huggingface.co/datasets/BrachioLab/massmaps-cosmogrid-100k.

This repository contains: a) 100 samples of mass maps created from the Dark Energy Survey (DES) Year 3 (Y3) weak lensing data using the Kappa Reconstruction for Mass Mapping (KaRMMa) algorithm (desy3_karmma_maps.zip), b) The DES-Y3 data used for running KaRMMa (karmma_data.zip). KaRMMa is a Bayesian forward modelled mass mapping algorithm that produces mass map samples assuming a lognormal prior on the convergence field. - desy3_karmma_maps.zip: The files contain the masked HEALPIX maps as a numpy array. The repository contains the associated HEALPIX mask and a script to produce the HEALPIX maps from the masked array.

• karmma_data.zip: Contains the pixelized shear maps, associated C(l)'s, redshift distributions, and the mask files used in the KaRMMa run on DES-Y3 data.

A recent proteomics-grade (95%+ sequence reliability) high-throughput de novo sequencing method utilizes the benefits of high resolution, high mass accuracy, and the use of two complementary fragmentation techniques collision-activated dissociation (CAD) and electron capture dissociation (ECD). With this high-fidelity sequencing approach, hundreds of peptides can be sequenced de novo in a single LC−MS/MS experiment. The high productivity of the new analysis technique has revealed a new bottleneck which occurs in data representation. Here we suggest a new method of data analysis and visualization that presents a comprehensive picture of the peptide content including relative abundances and grouping into families. The 2D mass mapping consists of putting the molecular masses onto a two-dimensional bubble plot, with the relative monoisotopic mass defect and isotopic shift being the axes and with the bubble area proportional to the peptide abundance. Peptides belonging to the same family form a compact group on such a plot, so that the family identity can in many cases be determined from the molecular mass alone. The performance of the method is demonstrated on the high-throughput analysis of skin secretion from three frogs, Rana ridibunda, Rana arvalis, and Rana temporaria. Two dimensional mass maps simplify the task of global comparison between the species and make obvious the similarities and differences in the peptide contents that are obscure in traditional data presentation methods. Even biological activity of the peptide can sometimes be inferred from its position on the plot. Two dimensional mass mapping is a general method applicable to any complex mixture, peptide and nonpeptide alike.