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OMICS is an extensive collection of knowledge for indoor service robots gathered from internet users. Currently, it contains 48 tables capturing different sorts of knowledge. Each tuple of the Help table maps a user desire to a task that may meet the desire (e.g., ⟨ “feel thirsty”, “by offering drink” ⟩). Each tuple of the Tasks/Steps table decomposes a task into several steps (e.g., ⟨ “serve a drink”, 0. “get a glass”, 1. “get a bottle”, 2. “fill class from bottle”, 3. “give class to person” ⟩). Given this, OMICS offers useful knowledge about hierarchism of naturalistic instructions, where a high-level user request (e.g., “serve a drink”) can be reduced to lower-level tasks (e.g., “get a glass”, ⋯). Another feature of OMICS is that elements of any tuple in an OMICS table are semantically related according to a predefined template. This facilitates the semantic interpretation of the OMICS tuples. Source: Understanding User Instructions by Utilizing Open Knowledge for Service Robots Image Source: https://www.aaai.org/Papers/AAAI/2004/AAAI04-096.pdf

These are 10 synthetic genomics datasets generated with NEAT v3 (based on TP53 gene of Homo Sapiens) for the use case of benchmarking somatic variant callers. To find more about our generating framework please visit synth4bench GitHub repository. The datasets explore intrinsic NGS data parameters for the use case of observing their effect on tumor-only somatic variant calling algorithms. From the 10 datasets, there are 5 of them with different coverage (while keeping all other parameters fixed) and 5 with varying read length. The reads in all datasets are paired-end .

We have characterized 1000 human cancer cell lines and screened them with 100s of compounds. On this website, you will find drug response data and genomic markers of sensitivity. The Genomics of Drug Sensitivity in Cancer Project - http://www.cancerrxgene.org/ - was part of a Wellcome Trust-funded collaboration between The Cancer Genome Project at the Wellcome Sanger Institute (UK) and the Center for Molecular Therapeutics, Massachusetts General Hospital Cancer Center (USA). This collaboration integrated the expertise at both sites toward the goal of identifying cancer biomarkers that can be used to identify genetically defined subsets of patients most likely to respond to cancer therapies. We screened >1000 genetically characterized human cancer cell lines with a wide range of anti-cancer therapeutics. These compounds included cytotoxic chemotherapeutics as well as targeted therapeutics from commercial sources, academic collaborators, and the biotech and pharmaceutical industries. The sensitivity patterns of the cell lines were correlated with extensive genomic and expression data to identify genetic features that are predictive of sensitivity. This large collection of cell lines enabled us to capture much of the genomic heterogeneity that underlies human cancer, and which appears to play a critical role in determining the variable response of patients to treatment with specific agents.

The dataset contains multi-omics data, incuding mRNA, miRNA, and DNA methylation. The dataset comprises 8,464 samples involving 2,794 omics features and covers 31 cancer types and normal tissues.

This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available.

Unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, segmentation maps of tumors in the CT scans, and quantitative values obtained from the PET/CT scans. Imaging data are also paired with gene mutation, RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes.

10x Genomics dataset of sequenced TCRs barcoded by a panel of pMHCs (arranged on a dextramer) Source: A new way of exploring immunity: linking highly multiplexed antigen recognition to immune repertoire and phenotype

The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets. Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals. These data are intended to be re-used, and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments. The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons. The initial release includes derived data from from two seasons in which the sorghum diversity panel was evaluated. Future releases will include data from additional seasons and locations. The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering.