{"Name":"Alzheimer’s disease Neuroimaging Initiative (ADNI)","N":"2,215","Age.range":"55-90","Country":"USA & Canada","Design":"Longitudinal (up to 9 years of follow-up, ongoing)","Clinical.Data":"Neurological (Alzheimer’s’), cognition, lumbar puncture","Neuroimaging":"MRI (T1w, T2, DWI, rsfMRI) PET (18F-FDG, FBB, AV45, PiB)","Genetics":"Genotyping, WGS","Genomics":"Methylation (subset of 653 individuals – 3 time points) ","Other.Omics":"Transcriptomics, CSF proteomics, metabolomics, lipidomics","Data.access":"http://adni.loni.usc.edu/data-samples/access-data/","Reference.article":"(Petersen et al., 2010; Vasanthakumar et al., 2020)","Specificities":"Four waves (ADNI1, 2, Go, 3) with different inclusion and protocols. "}
{"Name":"Australian Imaging Biomarkers and Lifestyle Study of Aging (AIBL)","N":"726","Age.range":">60","Country":"Australia","Design":"Longitudinal (up to 6 years of follow-up) ","Clinical.Data":"Neurological (Alzheimer’s’), cognition, lumbar puncture","Neuroimaging":"MRI (T1w, T2, DWI, rsfMRI) PET (PiB, AV45, Flute)","Genetics":"Genotyping","Genomics":"Methylation ","Smartphones.sensors":"ActiGraph activity (10% of sample)","Data.access":"https://ida.loni.usc.edu/collaboration/access/appLicense.jsp  https://aibl.csiro.au/ ","Reference.article":"(Ellis et al., 2009)","Specificities":"Neuroimaging and selected clinical data available via the LONI platform. Full sample (N~1200), incl. extended clinical, genotyping or methylation available via application to CSIRO."}
{"Name":"Open Access Series of Imaging Studies v3 (OASIS3)","N":"1,096","Age.range":"42-95","Country":"USA","Design":"Longitudinal (up to 12 years of follow-up)","Clinical.Data":"Neurological (Alzheimer’s’), cognition","Neuroimaging":"MRI (T1w, T2w, FLAIR, ASL, SWI, time of flight, rsfMRI, and DWI). PET (PIB, AV45, FDG)","Data.access":"https://www.oasis-brains.org  https://www.oasis-brains.org/#access ","Reference.article":" (LaMontagne et al., 2018)","Specificities":"Retrospective dataset from imaging projects collected by WUSTL Knight ADRC over 30 years. Two other (non-independent) datasets available: OASIS1 and OASIS2. "}
{"Name":"Adolescent Brain Cognitive Development (ABCD)","N":"~11,878","Age.range":"44904","Country":"USA","Design":"Longitudinal (up to 2 years follow-up, ongoing)","Clinical.Data":"Self and parental rating. Substance use, mental health (psychiatry), cognition, physical health.","Neuroimaging":"MRI (T1w, T2, rsfMRI, tfMRI).","Genetics":"Genotyping, pedigree (twinning)","Smartphones.sensors":"iPAD tasks and testing","Data.access":"https://abcdstudy.org https://nda.nih.gov/abcd/request-access ","Reference.article":"(Barch et al., 2018; Casey et al., 2018)","Specificities":"Objective of 10 years follow-up. "}
{"Name":"Enhancing NeuroImaging Genetics using Meta-analyses (ENIGMA)","N":">50,000 indiv. incl. 9,572 (SCZ) 6,503 (BD) 10,105 (MDD) 1,868 (PTSD) 3,240 (SUD) 3,665 (OCD) 4,180 (ADHD) 18,605 (lifespan)","Age.range":"3-90 (No restriction)","Country":"World-wide (43+ countries)","Design":"Cross-sectional and longitudinal","Clinical.Data":"Psychiatry, Neurology,  Addiction, Suicidality, Brain injury, HIV, Antisocial behaviour.","Neuroimaging":"T1w, DWI, rsfMRI, tfMRI","EEG.MEG":"Resting state EEG","Genetics":"Genotyping, CNVs, pedigree (twinning)","Genomics":"Methylation ","Data.access":"https://enigma.ini.usc.edu/join/       http://enigma.ini.usc.edu/research/download-enigma-gwas-results/   ","Reference.article":"Neuroimaging projects (Thompson et al., 2020)     (Grasby et al., 2020; Sønderby et al., 2021)","Specificities":"Consortium organized around genetics and/or disease/trait working groups as well as Non-clinical working groups with focus on sex, healthy ageing, plasticity… Imaging and genetic protocols and genome-wide association statistics are available for download on the ENIGMA web site"}
{"Name":"Chinese Brain PET Template (CNPET)","N":"116","Age.range":"27-81","Country":"China","Design":"Cross sectional","Clinical.Data":"Healthy subjects","Neuroimaging":"PET (18F-FDG)","Data.access":"https://www.nitrc.org/doi/landing_page.php?table=groups&id=1486&doi= https://www.nitrc.org/projects/cnpet","Reference.article":"(Wang et al., 2021)","Specificities":"Dataset made to build a Chinese specific SPM PET brain template"}
{"Name":"Center for Integrated Molecular Brain Imaging (CIMBI)","N":"~2000","Age.range":"17-93","Country":"Denmark","Design":"Cross sectional","Clinical.Data":"Mental and physical state, personality and background. Neuropsychological measures (memory, language…)","Neuroimaging":"MRI (T1w, T2w, DWI, fMRI) PET (11C-5-HT)","Genetics":"Genetic polymorphisms relevant for the 5-HT system","Data.access":"https://www.cimbi.dk/index.php","Reference.article":"(Knudsen et al., 2016)","Specificities":"Data sharing only within the European Union (EU). Visiting access for non-EU researchers.  "}
{"Name":"Motor Imagery dataset from Cho et al 2017","N":"52","Age.range":"mean=24.8; SD=3.86","Country":"South Korea","Design":"Cross-sectional","Clinical.Data":"Healthy subjects","EEG.MEG":"EEG (64 channels); real hand movements, motor imagination hand movement tasks","Data.access":"http://moabb.neurotechx.com/docs/generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017","Reference.article":"(Cho et al., 2017)","Specificities":"BCI experiments  "}
{"Name":"EEG Alpha Waves dataset","N":"20","Age.range":"19-44","Country":"France","Design":"Cross-sectional","Clinical.Data":"Healthy subjects","EEG.MEG":"EEG (16 channels); Resting state, eyes open/closed","Data.access":"https://zenodo.org/record/2605110#.YTeT5Z4zZhE","Reference.article":"(Cattan et al., 2018)","Specificities":"Alpha waves dataset. BCI experiments."}
{"Name":"Multitaper spectra recorded during GABAergic anesthetic unconsciousness","N":"55","Country":"USA","Design":"Cross-sectional","Clinical.Data":"Healthy participants and patients receiving an anaesthesia care in an operating room context","EEG.MEG":"EEG (64 for healthy volunteers, 6 for patients under anaesthesia)","Data.access":"https://physionet.org/content/eeg-power-anesthesia/1.0.0/","Reference.article":"(Abel et al., 2021)","Specificities":"Patients under anesthesia during surgery."}
{"Name":"A multi-subject, multi-modal human neuroimaging dataset","N":"19 (16 for MEG and EEG, 3 for fMRI)","Age.range":"23-37","Country":"UK","Design":"Longitudinal (2 visits over 3 months)","Clinical.Data":"Healthy subjects","Neuroimaging":"fMRI","EEG.MEG":"MEG, EEG (70 channels); resting, sensory-motor tasks","Data.access":"https://legacy.openfmri.org/dataset/ds000117/ ","Reference.article":"(Wakeman & Henson, 2015)","Specificities":"OpenfMRI database, accession number ds000117 "}
{"Name":"Motor imagery, uncued classifier application","N":"10","Age.range":"26 - 46 year old","Country":"Germany","Design":"Cross-sectional","Clinical.Data":"Healthy subjects","EEG.MEG":"EEG (59 channels); Hands, feet and tongue motor imagination tasks","Data.access":"http://www.bbci.de/competition/iv/desc_1.html","Reference.article":"(Blankertz et al., 2007)","Specificities":"Data used for a BCI competition. Also include simulated/synthetic data."}
{"Name":"BCI Competition 2008 – Graz data set A","N":"9","Country":"Austria","Design":"Cross-sectional","Clinical.Data":"Healthy subjects","EEG.MEG":"EEG (22 channels); Hands, feet and tongue motor imagination tasks.","Data.access":"http://www.bbci.de/competition/iv/desc_2a.pdf","Reference.article":"(Tangermann et al., 2012)","Specificities":"Data used for a BCI competition."}
{"Name":"BCI Competition 2008 – Graz data set B","N":"9","Country":"Austria","Design":"Cross-sectional","Clinical.Data":"Healthy subjects","EEG.MEG":"EEG (3 channels); Hands motor imagination tasks.","Data.access":"http://www.bbci.de/competition/iv/desc_2b.pdf","Reference.article":"(Tangermann et al., 2012)","Specificities":"Data for a BCI competition. "}
{"Name":"EEG dataset from Mumtaz et al., 2017","N":"64 (34 MDD, 30 healthy)","Age.range":"40.3 ±12.9 year old","Country":"Malaysia","Design":"Longitudinal (multiple visits to the clinic)","Clinical.Data":"Case control for Major Depressive Disorder","EEG.MEG":"EEG (19 channels)","Data.access":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171409","Reference.article":"(Mumtaz et al., 2017)","Specificities":"EEG (resting task), MDD based on medical history of patients"}
{"Name":"EEG data for ADHD / Control children","N":"121","Age.range":"7-12","Country":"South Korea","Design":"Cross-sectional","Clinical.Data":"ADHD  (61 children) and healthy controls (60) ","EEG.MEG":"EEG (19 channels) ; visual attention tasks","Data.access":"https://ieee-dataport.org/open-access/eeg-data-adhd-control-children","Reference.article":"(Mohammadi et al., 2016)","Specificities":"Visual cognitive tests on children with ADHD, using videos"}
{"Name":"EEG and EOG data from Jaramillo-Gonzalez et al., 2021","N":"4","Country":"Germany","Design":"Longitudinal (2 to 10 visits)","Clinical.Data":"Amyotrophic lateral sclerosis, locked-in state","EEG.MEG":"EEG (116 channels)","Data.access":"https://doi.org/10.6084/m9.figshare.13148762","Reference.article":"(Jaramillo-Gonzalez et al., 2021)","Specificities":"Spelling task with eye movements–based answers. Electro-oculography (EOG)."}
{"Name":"Temple University Hospital (TUH) EEG Corpus","N":"10,874","Age.range":"32874","Country":"USA","Design":"Cross-sectional","Clinical.Data":"Epilepsy, stroke, concussion, healthy","EEG.MEG":"EEG (20-41 channels)","Data.access":"https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml","Reference.article":"(Shah et al., 2018)","Specificities":"Extensive dataset of 30k clinical EEG recordings collected at TUH from 2002 to today. Epilepsy subset : TUEP dataset. "}
{"Name":"Bern Barcelona EEG data base","N":"5","Country":"Spain","Design":"Longitudinal","Clinical.Data":"Epilepsy","EEG.MEG":"EEG (64 channels) ; rest","Data.access":"https://repositori.upf.edu/handle/10230/42829 ","Reference.article":"(Andrzejak et al., 2012)","Specificities":"Intracranial EEG samples before and after surgery"}
{"Name":"CHB-MIT Scalp EEG Database","N":"22","Age.range":"1-22","Country":"USA","Design":"Longitudinal","Clinical.Data":"Seizures and  intractable seizures","EEG.MEG":"EEG (21 channels) ; Resting state","Data.access":"https://www.physionet.org/content/chbmit/1.0.0/","Reference.article":"(Goldberger et al., 2000)","Specificities":"Paediatric subjects with intractable seizures"}
{"Name":"Brain/Neural Computer Interaction (BNCI) Horizon","N":"2","Country":"Austria","Design":"longitudinal","Clinical.Data":"Chronic stroke","EEG.MEG":"EEG (2 channels) ; Eye staring task","Data.access":"http://bnci-horizon-2020.eu/database/data-sets ","Reference.article":"(Brunner et al., 2015)","Specificities":"The BNCI is an opensource project with several datasets. Stroke is “6. SCP training in stroke (006-2014)”"}
{"Name":"Queensland Twin Adolescent Brain (QTAB)","N":"422 (baseline)","Age.range":"9-14 (baseline)","Country":"Australia","Design":"Longitudinal","Clinical.Data":"Population sample. Parental and/or self-report mental health, cognition, social behaviour measures","Neuroimaging":"MRI (T1w, T2w, FLAIR, DWI, rsfMRI, tfMRI, ASL)","Genetics":"Pedigree (twin/sibling), genotyping","Smartphones.sensors":"Wrist-worn accelerometer","Other.Omics":"Gut microbiome","Data.access":"https://imaginggenomics.net.au/ ","Reference.article":"(O’Callaghan et al., 2021) ","Specificities":"Includes participants from Queensland Twin Registry and Twins Research Australia"}
{"Name":"Queensland Twin Imaging Study (QTIM)","N":"1,200+","Age.range":"12-30","Country":"Australia","Design":"Cross-sectional","Clinical.Data":"Population sample (as part of BATS). Self-report mental health, cognition, substance use, personality measures","Neuroimaging":"MRI (T1w, DWI, rsfMRI, tfMRI)","Genetics":"Pedigree (twin/sibling), genotyping","Data.access":"https://imaginggenomics.net.au/ ","Reference.article":"(de Zubicaray et al., 2008)","Specificities":"Include participants from Brisbane Adolescent Twin Study (BATS)"}
{"Name":"Human Connectome Project, young adults (HCP-YA)","N":"~1,200","Age.range":"22-35","Country":"USA","Design":"Cross-sectional","Clinical.Data":"Population sample. Self-report mental health, cognition, personality, substance use measures","Neuroimaging":"MRI (T1w, T2w, DWI, rsfMRI, tfMRI)","EEG.MEG":"MEG (n = 95)","Genetics":"Pedigree (twin/sibling). Genotyping","Data.access":"https://db.humanconnectome.org","Reference.article":"(Van Essen et al., 2013)","Specificities":"Expanded to development and aging projects (similar imaging protocols, but extensions do not include twins)."}
{"Name":"Vietnam Era Twin Study of Aging (VETSA)","N":"1,237 (baseline; 500+ with MRI)","Age.range":"51-60 (baseline)","Country":"USA","Design":"Longitudinal","Clinical.Data":"US veterans, males only.","Neuroimaging":"MRI (T1w, DWI, ASL (subset of sample))","Genetics":"Pedigree (twin). Genotyping","Data.access":"https://medschool.ucsd.edu/som/psychiatry/research/VETSA/Researchers/Pages/default.aspx","Reference.article":"(Kremen et al., 2019)","Specificities":"Subset of the Vietnam Era Twin Registry, males only. "}
{"Name":"Older Adults Twin Study (OATS)","N":"623 (baseline)","Age.range":">65","Country":"Australia","Design":"Longitudinal","Clinical.Data":"Population sample. Self-report medical and mental health, neuropsychological measures","Neuroimaging":"MRI  (T1w, DWI, tfMRI) ; PET","Genetics":"Pedigree (twin/sibling). Genotyping","Data.access":"https://cheba.unsw.edu.au/research-projects/older-australian-twins-study","Reference.article":"(Sachdev et al., 2009)","Specificities":"Recruited through the Australian Twin Registry."}
{"Name":"Swedish Twin Registry (STR)","N":"87,000 twin pairs","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since the late 1950s)","Genetics":"Genome wide single nucleotide polymorphisms array genotyping","Genomics":"Methylation is not available in the STR yet, but is available in the Swedish Adoption/Twin Study of Aging (SATSA), a sub-study of the STR.","Data.access":"https://ki.se/en/research/the-swedish-twin-registry ","Reference.article":"(Zagai et al., 2019)","Specificities":"Twin Registry. "}
{"Name":"UK Biobank (UKB)","N":"~502,000     ~50,000 (imaging subset)    ~100,000 (actigraphy subset)","Age.range":"37-73     44-82 (imaging)       40 – 69 (actigraphy)","Country":"UK","Design":"Longitudinal","Clinical.Data":"Self-reported and EHR medical history (incl. cancer, neurology, COVID-19)","Neuroimaging":"MRI (T1w, T2w, FLAIR, DWI, SWI, rsfMRI, tfMRI). ","Genetics":"Genotyping, Exome, WGS, pedigree","Smartphones.sensors":"Actigraphy","Other.Omics":"Metabolomics (future release)","Data.access":"https://bbams.ndph.ox.ac.uk/ams/resApplications ","Reference.article":"(Bycroft et al., 2018; Miller et al., 2016; Williamson et al., 2021)","Specificities":"Population based sample (volunteers), healthy bias, ongoing resource and data collection. Target MRI sample 100K, retest 10K. Also available: whole body MRI, Haematological assays, serological antibody responses assay, telomere length"}
{"Name":"Avon Longitudinal Study of Parents and Children (ALSPAC): Accessible Resource for Integrated Epigenomic Studies (ARIES)","N":"2,044","Age.range":"Average age: Mothers (antenatal = 28.7, follow-up = 47.5), Offspring (Birth = 40 weeks, Childhood = 7.5, Adolescence = 17.1)","Country":"UK","Design":"Longitudinal (2 timepoints for mother, 3 for offspring)","Clinical.Data":"Clinical evaluations, Obstetric data, Cognition, questionnaires","Neuroimaging":"MRI (T1w, DWI, mcDESPOT (subset of offspring cohort))","Genetics":"Genotyping","Genomics":"Methylation ","Other.Omics":"Transcriptomics","Data.access":"http://www.ariesepigenomics.org.uk/       https://github.com/MRCIEU/aries","Reference.article":"(Relton et al., 2015)","Specificities":"General population study (health and development) following 1,022 mother-offspring pairs; 2 timepoints for mother, 3 for offspring."}
{"Name":"Biobank-based Integrative Omics Studies (The BIOS Consortium)","N":"~4,000 ","Age.range":"18 - 87","Country":"Netherlands","Design":"Longitudinal","Clinical.Data":"Clinical information and bioassays (depending on the sub-cohort)","Genetics":"Genotyping, pedigree","Genomics":"Methylation ","Other.Omics":"Transcriptomics, Metabolomics","Data.access":"https://www.bbmri.nl/node/24 ","Reference.article":"(Bonder et al., 2017)","Specificities":"Population study including various sub-cohorts, covering differing research designs (Life Lines, Leiden Longevity Study, Netherlands Twin Registry, Rotterdam Study, CODAM, and the Prospective ALS Study Netherlands); access via European Genome-phenome Archive (EGA) and SURFsara High Performance Computing cloud"}
{"Name":"Framingham Heart Study","N":"4,241","Age.range":"Offspring cohort mean age = 66, Third generation cohort = 45","Country":"USA","Design":"Cross-sectional (multi-generational)","Clinical.Data":"Extensive clinical evaluations and bioassays, original focus on cardiovascular diseases","Neuroimaging":"MRI (T2w)","Genetics":"Genotyping, WGS","Genomics":"Methylation ","Other.Omics":"Transcriptomics, Metabolomics","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000724.v9.p13","Reference.article":"(Huan et al., 2019)(Sarnowski et al., 2018)","Specificities":"Data collected over two generations of individuals; cardiovascular MRI. A subset of individuals are used to identify rare variants influencing brain imaging phenotypes. Data included in NHLBI TOPMed"}
{"Name":"Women's Health Initiative","N":"2,129","Age.range":"50-79","Country":"USA","Design":"Cross-sectional","Clinical.Data":"Clinical evaluations and bioassays; cardiovascular diseases, women only","Genetics":"Genotyping","Genomics":"Methylation","Other.Omics":"Transcriptomics, Metabolomics, miRNA","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001335.v2.p3 ","Reference.article":"(Westerman et al., 2019)","Specificities":"Women only."}
{"Name":"Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC)","N":"1,395","Age.range":"Predicted mean age (from DNA methylation) 60.4 (controls), 62.9 (cases)","Country":"Australia","Design":"Cross-sectional","Clinical.Data":"Case control of Amyotrophic Lateral Sclerosis ","Genetics":"Genotyping","Genomics":"Methylation","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002068.v1.p1 ","Reference.article":"(Nabais et al., 2020)"}
{"Name":"System Genomics of Parkinson’s Disease (SGPD)","N":"2,333","Age.range":"Predicted mean age (from DNA methylation), mean range","Country":"Australia & New-Zealand","Design":"Cross-sectional","Clinical.Data":"Case control of Parkinson’s disease","Genetics":"Genotyping","Genomics":"Methylation","Data.access":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145361 ","Reference.article":"(Vallerga et al., 2020)"}
{"Name":"Genetics of DNA Methylation Consortium (goDMC)","N":"32,851","Age.range":"0-91","Country":"World-wide","Design":"Cross-sectional","Clinical.Data":"Multiple diseases, but also healthy-ageing cohorts","Genetics":"Genotyping","Genomics":"Methylation","Data.access":"http://www.godmc.org.uk/cohorts.html ","Reference.article":"(Wright & Martin, 2004)","Specificities":"Consortium gathering 38 independent studies; data access to be obtained from each sub-sample."}
{"Name":"Psychiatric Genomics Consortium (PGC)","N":"By 2025, ~2.5 million cases of psychiatric disorders","Age.range":"All ages","Country":"World-wide","Design":"Cross-sectional","Clinical.Data":"Psychiatric disorders, substance use disorders and neurology: Major Depressive Disorder, Cannabis Use Disorder, Alcohol Use Disorder, Schizophrenia, Anorexia, Bipolar, ADHD, Alzheimer’s","Genetics":"Genotyping and sequencing","Genomics":"Expression and methylation data available in some working groups","Data.access":"https://www.med.unc.edu/pgc/shared-methods/open-source-philosophy/ ","Reference.article":"(Sullivan, 2010; Sullivan et al., 2018)","Specificities":"Consortium organized around disease/trait working groups"}
{"Name":"The Genetic Epidemiology of Asthma in Costa Rica","N":"4,347","Age.range":"44901","Country":"Costa Rica","Design":"Cross-sectional","Clinical.Data":"Asthma cases and controls","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA295246 ","Reference.article":"(Mak et al., 2018) ","Specificities":"A subset of individuals from this sample/accession is used in Pharmacogenetic Drug Response in Racially Diverse Children with Asthma. Data included in NHLBI TOPMed"}
{"Name":"Coronary Artery Risk Development in Young Adults (CARDIA)","N":"3,425","Age.range":"18-30","Country":"USA","Design":"Longitudinal","Clinical.Data":"Coronary Artery Risk","Genetics":"WGS","Data.access":"https://anvilproject.org/ncpi/data/studies/phs001612  ","Reference.article":"(Kurniansyah et al., 2021)","Specificities":"A subset of longitudinal dataset of 3,087 self-identified Black and White participants from the CARDIA study were used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression"}
{"Name":"Genetic Epidemiology Network of Arteriopathy (GENOA)","N":"1,854","Age.range":">60","Country":"USA","Design":"Longitudinal","Clinical.Data":"elucidate the genetics of target organ complications of hypertension","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001345.v3.p1 ","Reference.article":"(Kurniansyah et al., 2021)","Specificities":"Study was used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression"}
{"Name":"Hispanic Community Health Study/Study of Latinos (HCHS/SOL)","N":"8093","Age.range":"18-74 ","Country":"USA","Design":"Longitudinal","Clinical.Data":"A multicenter prospective cohort study for asthma patients","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001395.v1.p1 ","Reference.article":"(Kurniansyah et al., 2021)","Specificities":"Study was used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression"}
{"Name":"Women's Health Initiative (WHI)","N":"11,357","Age.range":">65 ","Country":"USA","Design":"Longitudinal","Clinical.Data":"Women's Health Initiative cohort involved study on  ischemic stroke, 900 cases of hemorrhagic stroke ","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000200.v12.p3 ","Reference.article":"(Kurniansyah et al., 2021)","Specificities":"Study was used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression"}
{"Name":"Atherosclerosis Risk in Communities (ARIC)","N":"8,975","Age.range":"45-64","Country":"USA","Design":"Cross-sectional/longitudinal ","Clinical.Data":"Red blood cell phenotype","Genetics":"WGS","Data.access":"https://anvilproject.org/data/studies/phs001211 ","Reference.article":"(Hu et al., 2021)","Specificities":"WGS association study of red blood cell phenotypes, GWAS statistics available. Data included in NHLBI TOPMed"}
{"Name":"Rare Variants for Hypertension in Taiwan Chinese (THRV)","N":"2,159","Age.range":">35","Country":"Taiwan/China & Japan","Design":"Longitudinal","Clinical.Data":"Insulin resistant cases and controls","Genetics":"WGS","Data.access":"https://anvilproject.org/ncpi/data/studies/phs001387 ","Reference.article":"(Wu et al., 2002)","Specificities":"Clustering and heritability of insulin resistance in Chinese and Japanese hypertensive families. Data included in NHLBI TOPMed"}
{"Name":"My Life, Our Future initiative (MLOF)","N":"7,482","Age.range":">18","Country":"USA","Design":"Cross-sectional","Clinical.Data":"Haemophilia cases and controls","Genetics":"WGS","Data.access":"https://athn.org/what-we-do/national-projects/mlof-research-repository.html ","Reference.article":"(Johnsen et al., 2017)","Specificities":"Summary statistics, different types of DNA variants detected in haemophilia. Data included in NHLBI TOPMed"}
{"Name":"Genetic Epidemiology of COPD (COPDGene)","N":"19,996","Age.range":"45-80","Country":"USA","Design":"Cross-sectional","Clinical.Data":"pulmonary functions","Genetics":"WGS","Other.Omics":"gene expression (eQTL) and methylation (mQTL): eQTLs in 48 tissues from GTEx v7","Data.access":"https://anvilproject.org/data/studies/phs001211","Reference.article":"(Zhao et al., 2020)","Specificities":"multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":" Cardiovascular Health Study (CHS)","N":"4,877","Age.range":">65","Country":"USA","Design":"Longitudinal","Clinical.Data":"Cardiovascular health","Genetics":"WGS","Data.access":"https://ega-archive.org/studies/phs001368  DOI:10.1038/s41467-020-18334-7","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":"Cleveland Family Study (CFS)","N":"3,576","Age.range":">65","Country":"USA","Design":"Longitudinal","Clinical.Data":"epidemiological data on genetic and non-genetic risk factors for sleep disordered breathing","Genetics":"WGS","Data.access":"https://ega-archive.org/studies/phs000954    DOI:10.1038/s41467-020-18334-7","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":" Framingham Heart Study (FHS)","N":"4,241","Age.range":"<65","Country":"USA","Design":"Longitudinal","Clinical.Data":"Assess risk of cardiovascular disease study","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000724.v9.p13 ","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":"Jackson Heart Study (JHS)","N":"3,596","Age.range":">55","Country":"USA","Design":"Longitudinal","Clinical.Data":"Assess cardiovascular disease ","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000964.v5.p1","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":"Multi-Ethnic Study of Atherosclerosis (MESA)","N":"6,814","Age.range":"45-84","Country":"USA","Design":"Longitudinal","Clinical.Data":"Assess cardiovascular disease ","Genetics":"WGS","Data.access":"https://www.omicsdi.org/dataset/dbgap/phs001416","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":"Boston Early-Onset COPD (EOCOPD)","N":"80","Age.range":"<53","Country":"USA","Design":"Longitudinal","Clinical.Data":"Chronic obstructive pulmonary disease (COPD) ","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000946.v5.p1 ","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":"Genetic Epidemiology of COPD (COPDGene)","N":"10,647","Age.range":"45–80","Country":"USA","Design":"Longitudinal","Clinical.Data":"Chronic obstructive pulmonary disease (COPD) ","Genetics":"WGS","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000951.v5.p5 ","Reference.article":"(Zhao et al., 2020)","Specificities":"potential molecular mechanisms underlying four of the 22 novel loci. Data included in NHLBI TOPMed"}
{"Name":"Genotype-Tissue Expression (GTEx)","N":"948 individuals, 17,382 samples (across 54 tissues)","Age.range":"20-70","Country":"USA","Design":"Cross-sectional","Clinical.Data":"General medical history, HIV status,  potential exposures, medical history at time of death, death circumstances, serology results,  smoking status","Genetics":"WGS","Genomics":"Gene expression (bulk RNA seq)","Data.access":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/dataset.cgi?study_id=phs000424.v8.p2&phv=169091&phd=3910&pha=&pht=2742&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1","Reference.article":"(GTEx Consortium, 2020)","Specificities":"Post-mortem samples"}
{"Name":"Developmental Genotype-Tissue Expression (dGTEx)","N":"120 individuals, 3,600 samples (across 30 tissues)","Age.range":"0-18","Country":"USA","Design":"Cross-sectional","Clinical.Data":"General medical history","Genetics":"WGS","Genomics":"Gene expression (bulk RNA seq)","Data.access":"https://dgtex.org/","Specificities":"Expected data release by late 2022  Four developmental timepoints: postnatal (0-2 years), early childhood (2-8 years), pre-pubertal (8-12.5 years) and post-pubertal (12.5-18 years)"}
{"Name":"Greater-Paris Area Hospital (AP-HP)","N":"Several millions.  ~130K patients with brain MRI (~200K images)","Age.range":"All ages","Country":"France","Design":"longitudinal","Clinical.Data":"ICD10 codes for diagnoses, medical reports, wide range of physiological variables","Neuroimaging":"All medical imaging modalities and all organs: MRI , PET, SPECT, CT, Ultrasounds.","Data.access":"https://eds.aphp.fr/ ","Reference.article":"(Daniel & Salamanca, 2020)","Specificities":"All patients visiting Greater-Paris Area Hospital (AP-HP). The figures correspond to a specific project approved on the clinical data warehouse (CDW)."}
{"Name":"Swedish Total Population Register (TPR)","N":"Nationwide coverage (N=9,775,572 on April 30, 2015)","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since 1968)","Data.access":"www.scb.se/vara-tjanster/bestall-data-och-statistik/bestalla-mikrodata/vilka-mikrodata-finns/individregister/registret-over-totalbefolkningen-rtb/ ","Reference.article":"(Ludvigsson et al., 2016)","Specificities":"Demographic information"}
{"Name":"Swedish Longitudinal Integrated Database for Health Insurance and Labour Market Studies (LISA)","N":"All individuals ≥16 years in Sweden","Age.range":"≥16 years (15 since 2010)","Country":"Sweden","Design":"Longitudinal (Since 1990)","Data.access":"www.scb.se/lisa","Reference.article":"(Ludvigsson et al., 2019)","Specificities":"Demographic and socioeconomic information."}
{"Name":"Swedish Multi-Generation Register (MGR)","N":"Over 11 million ","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since 1961)","Genetics":"Pedigree","Data.access":"www.scb.se/en/finding-statistics/statistics-by-subject-area/other/other/other-publications-non-statistical/pong/publications/multi-generation-register-2016/","Reference.article":"(Ekbom, 2011)","Specificities":"Family relation"}
{"Name":"Swedish National Patient Register (NPR)","N":"Nationwide coverage","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since 1964)","Clinical.Data":"Nationwide inpatient care since 1987 and outpatient care since 2001","Data.access":"www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/the-national-patient-register/","Reference.article":"(Ludvigsson et al., 2011)","Specificities":"Clinical diagnoses from inpatient and outpatient care"}
{"Name":"Swedish Cancer Register (SCR)","N":"Around 60, 000 malignant cases are included annually (statistics in 2020)","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since 1958)","Clinical.Data":"Clinical diagnoses of cancer","Data.access":"www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/swedish-cancer-register/","Reference.article":"(Barlow et al., 2009)"}
{"Name":"Swedish Medical Birth Register (MBR)","N":"Around 80,000-120,000 deliveries annually ","Age.range":"Infants at birth","Country":"Sweden","Design":"Longitudinal (Since 1973)","Clinical.Data":"Yes","Data.access":"https://www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/the-swedish-medical-birth-register/","Reference.article":"(Källén & Källén, 2003)","Specificities":" Register of medical birth"}
{"Name":"Swedish Causes of Death Register (CDR)","N":"Nearly 100,000 deaths annually","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since 1952)","Clinical.Data":"Cause of death","Data.access":"https://www.socialstyrelsen.se/statistik-och-data/register/alla-register/dodsorsaksregistret/","Reference.article":"(Brooke et al., 2017)","Specificities":"Register of cause of death"}
{"Name":"Swedish Prescribed Drug Register (PDR)","N":"More than 100 million records annually","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since July 2005)","Clinical.Data":"Prescribed drug(s)","Data.access":"https://www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/the-swedish-prescribed-drug-register/","Reference.article":"(Wallerstedt et al., 2016)","Specificities":"Register of prescribed drug"}
{"Name":"Swedish Dementia Registry (SDR)","N":"More than 100,000","Age.range":"27-103 (statistics between 2007-2012)","Country":"Sweden","Design":"Longitudinal (Since 2007)","Clinical.Data":"Dementia","Neuroimaging":"MRI, PET, CT","EEG.MEG":"EEG","Data.access":"www.svedem.se","Reference.article":"(Religa et al., 2015)","Specificities":"Register of Dementia"}
{"Name":"Swedish Neuro-Register (SNR)","N":"Around 16,000 multiple sclerosis, over 1,500 Parkinson's disease and over 600 myasthenia gravis (numbers from 2015)","Age.range":"All ages","Country":"Sweden","Design":"Longitudinal (Since 2004)","Clinical.Data":"Motor neuron disease, MS","Neuroimaging":"MRI, PET and CT","EEG.MEG":"MEG","Genetics":"Genotyping","Data.access":"www.neuroreg.se","Reference.article":"(Hillert & Stawiarz, 2015)","Specificities":"Register of MND (especially MS) "}
{"Name":"Swedish Stroke Register (Riks-Stroke)","N":"Around 29, 000 cases (around 21,000 stroke and 8,000 TIA) annually (statistics in 2020)","Age.range":"Mean age 75 for stroke; mean age 74 for TIA","Country":"Sweden","Design":"Longitudinal (Since 1994)","Clinical.Data":"Stroke and transient ischaemic attack (TIA)","Neuroimaging":"MRI and CT scan ","Data.access":"www.riksstroke.org/ ","Reference.article":"(Asplund et al., 2011)","Specificities":"Register of stroke and TIA; includes Electro-cardiograms"}
{"Name":"Brisbane Adolescent Twin Sample (BATS)","N":"Up to 4000","Age.range":"≥12","Country":"Australia","Design":"Cross-sectional and longitudinal","Clinical.Data":"Population sample. Self-report mental health, cognition, substance use, personality measures","EEG.MEG":"EEG (15 channels, eyes closed resting; N ~1000) ","Genetics":"Pedigree (twin/sibling), genotyping","Smartphones.sensors":"Wrist-worn accelerometer (N ~ 130)","Data.access":"https://imaginggenomics.net.au/   ","Reference.article":"(Sletten et al., 2013; Wright & Martin, 2004)(Mitchell et al., 2019; Zietsch et al., 2007)","Specificities":"Also known as the Brisbane Longitudinal Twin Study (BLTS). Includes participants from the Queensland Twin Registry. "}
{"Name":"mPower","N":"~8000","Age.range":">18","Country":"USA","Design":"Longitudinal","Clinical.Data":"Parkinson’s disease (subsample self-identified professional diagnosis)","Smartphones.sensors":"iPhone application","Data.access":"https://parkinsonmpower.org/team https://www.synapse.org/#!Synapse:syn4993293/wiki/247860 ","Reference.article":" (Bot et al., 2016)","Specificities":"Sample size varies across surveys and tasks completed"}