Dataset

This dataset was created by Yaseen Abdulghany

Released under CC0: Public Domain

Contents

Solaar Array

## Overview

Solaar Array is a dataset for object detection tasks - it contains Array annotations for 283 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

This data set is a NumPy derivative of the HuBMAP competition dataset

Content

This dataset consists of 10 NumPy arrays of shape : [number of data,Channels(4),width(256),height(256)] there is 4 channels in the data set : 0 to 3 are the RGB channel of original image and the 4th channel is the mask for that image first I divided images to 1024*1024 tiles and then I resized them to 256*256images

Dataset for "Deep Crustal Imaging Across the Chenghai fault zone in Binchuan, China, from wavelet-based Wavefield Decomposition on the Ultra-dense Array Datasets" sumitted to Journal of Geophysical Research - Solid Earth. It contains the Binchuan dense array dataset and synthetic seismic data. The waveform data processed by wavelets are also included.

Primers (5′→3′) used for qPCR validation of targets from array dataset.

Measurements from the ships visiting the TOA (Tropical Atmosphere Ocean) buoy array.

This data set contains measurements from a 2D-sensitive fluid flow sensor array in response to 5 different objects moving in different directions and different distances with respect to the array.

Dataset

This dataset was created by Awsaf

Released under CC0: Public Domain

Contents

Assessment of DNA methylation as obtained through DNA methylation arrays. In NTR projects YC_ACTIONBB2 and YC_ACTIONBB3, genome-wide methylation data in buccal DNA samples was performed by the Human Genotyping facility (HugeF) of ErasmusMC (the Netherlands, http://www.glimdna.org/) on the Infinium MethylationEPIC BeadChip Kit (Illumina, San Diego, CA, USA). Recently, additional EPIC array data became available for NTR ACTION twins and siblings (generated by the Avera Institure of Human Genetics, Sioux Falls, South Dakota, USA). For information on the measurement of DNA methylation with the EPIC array, cleaning of DNA methtylation data, and estimation of proportion of cell counts see: https://doi.org/10.1038/s41380-020-00987-x.

This dataset contains ~50,000 single nucleotide polymorphisms (SNPs, DNA mutations) for Scots pine (Pinus sylvestris) and closely related members of the Pinus mugo complex, which were selected for inclusion on a 50K SNP Axiom array Full details about this dataset can be found at https://doi.org/10.5285/cbaa464a-ac18-42bf-8518-c746d8d97270

The Global Ocean Biogeochemistry (GO-BGC) Array is a project funded by the US National Science Foundation (NSF Award 1946578 ) to build a global network of chemical and biological sensors that will monitor ocean health. This grant is being used to build and deploy 500 robotic ocean-monitoring floats around the globe as part of NSF’s Mid-scale Research Infrastructure-2 program. This network of floats is collecting data on the chemistry and the biology of the ocean from the surface to a depth of 2,000 meters, augmenting the existing Argo array that monitors ocean temperature and salinity. The GO-BGC Array is led by Director Ken Johnson and administered by the Monterey Bay Aquarium Research Institute. For questions specific to the HPLC/POC/PON data submitted to SeaBASS please contact Josh Plant at jplant@mbari.org.

58 3-component short period seismometers were deployed in a grid pattern covering an area about 250 meters square near the Pinon Flats Observatory. The goal was to record the unaliased seismic wavefields from local and regional events. A total of 325 seismic events were recorded.

A structured array-based version of the FER2013 dataset, widely used for emotion recognition training, deep learning experiments, and facial expression classification applications.

Epigenome-wide association studies seek to identify DNA methylation sites associated with clinical outcomes. Difference in observed methylation between specific cell-subtypes is often of interest; however, available samples often comprise a mixture of cells. To date, cell-subtype estimates have been obtained from mixed-cell DNA data using linear regression models, but the accuracy of such estimates has not been critically assessed. We evaluated linear regression performance for cell-subtype specific methylation estimation using a 450K methylation array dataset of both mixed-cell and cell-subtype sorted samples from six healthy males. CpGs associated with each cell-subtype were first identified using t-tests between groups of cell-subtype sorted samples. Subsequent reduced panels of reliably accurate CpGs were identified from mixed-cell samples using an accuracy heuristic (D). Performance was assessed by comparing cell-subtype specific estimates from mixed-cells with corresponding cell-sorted mean using the mean absolute error (MAE) and the Coefficient of Determination (R2). At the cell-subtype level, methylation levels at 3272 CpGs could be estimated to within a MAE of 5% of the expected value. The cell-subtypes with the highest accuracy were CD56+ NK (R2 = 0.56) and CD8+T (R2 = 0.48), where 23% of sites were accurately estimated. Hierarchical clustering and pathways enrichment analysis confirmed the biological relevance of the panels. Our results suggest that linear regression for cell-subtype specific methylation estimation is accurate only for some cell-subtypes at a small fraction of cell-associated sites but may be applicable to EWASs of disease traits with a blood-based pathology. Although sample size was a limitation in this study, we suggest that alternative statistical methods will provide the greatest performance improvements.