Data files required to process Allen Human Brain Atlas data:A. Arnatkeviciute, B.D. Fulcher, A. Fornito.A practical guide to linking brain-wide gene expression and neuroimaging data (in submission).Matlab code for processing these data files and reproducing our analyses is in the github repository (link below).Please refer to the README_AHBAdata.txt file for further details.NOTE1: Data has been updated on the 28th August 2018 - in the previous version gene ordering in the ROI x gene matrices did not correspond to the gene information provided in the probeInformation structure. Now this issue is fixed.NOTE2: Data has been updated on the 24th October 2018 to comply with changes made during the article revision. NOTE3: Data has been updated on the 31st December 2018 to comply with changes made during the article revision.NOTE4: Data has been updated on the 7th April 2020. Two brain parcellations (and corresponding processed data) containing 100 and 250 regions per hemisphere were updated. NOTE5: Data has been updated on the 11th June 2020. Four Schaefer brain parcellations (and corresponding processed data) containing 100, 300, 500 and 1000 regions were added.

The code for Climate risk and higher moments time-frequency connectedness among carbon, energy and metals markets.

Data with coding.

Each tissue's gene expression profile was processed by experts to annotate clusters of cells with biological functions. These are the Robjects created using Seurat to normalize and cluster the single-cell RNA-seq expression data.Update 2018-03-27: Updated to resubmitted RobjUpdate 2018-09-20: Updated to accepted Robj

GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)

Transition1x - a dataset for building generalizable reactive machine learning potentialshttps://www.nature.com/articles/s41597-022-01870-wThis dataset is constructed by running NEB on 10.000 reactions with H, C, N and O using the wb97x functional and 6-31G(d) basis set. This resulted in DFT calculations for 9.6 million molecular configurations on and around minimal energy paths on the potential energy surface. The data is intended for training ML models to work in transition state regions of chemical space.Dataloaders and example scripts are availble in https://gitlab.com/matschreiner/T1xThe authors acknowledge support from the Novo Nordisk Foundation (SURE, NNF19OC0057822) and the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY2030PLUS). Ole Winther also receives support from Novo Nordisk Foundation through the Center for Basic Machine Learning Research in Life Science (NNF20OC0062606) and the Pioneer Centre for AI, DNRF grant number P1

source data

For single cell RNA sequencing dataset, the 10× Genomics platform was used to perform massive single cell mRNA profiling from mouse spinal cord samples collected at 6 different time points after Spinal Cord Injury (crush injury), i.e., 4h, 1d, 3d, 7d, 14d, and 38d post injury. Uninjured spinal cord samples are included as well. Both male and female C57BL/6 mice were used in the analyses, 10mm-long spinal cord segments encompassing the lesion site were dissociated into single cells through our proprietary method developed based on published studies1,2. For non-injury controls, male and female spinal cord samples were sequenced separately, and male and female scRNA-seq data overlapped rather well.

For bulk RNA sequencing, 1 cm long spinal cord segments centered on leison core, average of 20 mg, at 15min, 1d, 3d, 7d, 14d, 28d, 42d after injury, were homogenized in 1 ml TRIzol (Invitrogen), and mRNA was extracted and purified by RNeasy Mini Kit (QIAGEN) according to the manufacturer’s instructions. The qualities of RNA were determined by Agilent 2100. 1-3 μg qualified RNA were subjected to Illumina V2 RNAseq library construction, followed by Hiseq2000 SE50bp Sequencing.

This dataset contains a collection of Twitter rumours and non-rumours posted during breaking news.

The data is structured as follows. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet. Also each folder contains ‘annotation.json’ which contains information about veracity of the rumour and ‘structure.json’, which contains information about structure of the conversation.

This dataset is an extension of the PHEME dataset of rumours and non-rumours (https://figshare.com/articles/PHEME_dataset_of_rumours_and_non-rumours/4010619), it contains rumours related to 9 events and each of the rumours is annotated with its veracity value, either True, False or Unverified.

This dataset was used in the paper 'All-in-one: Multi-task Learning for Rumour Verification'. For more details, please refer to the paper.

Code using this dataset can be found on github (https://github.com/kochkinaelena/Multitask4Veracity).

License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.

This brain tumor dataset contains 3064 T1-weighted contrast-inhanced images with three kinds of brain tumor. Detailed information of the dataset can be found in the readme file.The README file is updated:Add image acquisition protocolAdd MATLAB code to convert .mat file to jpg images

Database of bat records for species located in Africa. It contains 17285 unique locality records of 266 species of bats.MetadataFamily: family of bat record;Genus: genus of bat record;Species: species of bat record;Museum_number: the museum accession number of the bat record;Date: the date on which the bat was recorded;Year: the year in which the bat was recorded;Country: the country in which the bat was recorded;Location: the name of the location or locality that the bat was recorded;Latitude: the latitude in decimal degrees of the record;Longitude: the longitude in decimal degrees of the record;Reference: the source of the record;Holotype: whether the record is a type specimen;Checked: whether the bat specimen was examined by the authors.R scripts1_african bats_maxent models_all_species.R provides code to run maxent models for each African bat species2_aoo-eoo.R script to calculate EOO (extent of occurrence) and AOO (area of occupancy) for African bats

A data set on hospitalized patients with COVID-19, which information recorded included demographic data, signs and symptoms, medical history, laboratory values, time of virus negative, anti-virus treatment, and chest computed tomographic (CT) scans. All data are extracted from electronic medical records.

Official data release for Tabula Muris Senis

Dataset, Tam et al. (2021) PNAS

Data used for analysis in "Plastic pollution fosters more microbial growth in lakes than natural organic matter"

PGC3 bipolar disorder GWAS summary statisticsThree sets of GWAS summary statistics are provided: 1. bipolar disorder (all cases): pgc-bip2021-all.vcf.tsv.gz2. bipolar I disorder: pgc-bip2021-BDI.vcf.tsv.gz3. bipolar II disorder: pgc-bip2021-BDII.vcf.tsv.gzPlease refer to original publication for detailed study info and phenotype definitions.

Raw data of subject A

Summary of the mortality (MR) models.

The processed data used for the real application section of the paper: Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method. real_data_results_Residual_Strata10: real data results using the residual stratification with 10 strata real_data_results_DoublyRanked_Strata10: real data results using the doubly-ranked stratification with 10 strata real_data_results_Residual_Strata77: real data results using the residual stratification with 77 strata real_data_results_DoublyRanked_Strata77: real data results using the doubly-ranked stratification with 77 strata

Data used in A History of Population Health