dataesr/scientific-paragraphs-categorization huggingface dataset:
cgi). The Adolescent Brain Cognitive Development (ABCD) is an ongoing longitudinal study of younger individuals, recruited aged 9-10 years and who will be followed over a decade  [16, 17]. The ABCD focuses on cognition, behavior, and physical and mental health (e. g. , substance use, autism, ADHD) of adolescents. It includes self-and parental rating of the adolescents as well as a description of the familial environment  [17]. ABCD data is hosted on the NIMH data archive and requires obtaining and maintaining an NDA Data Use Certification, which requires action from a signing official (SO) from the researcher institution, as defined in the NIH eRA Commons (https://era. nih. gov/files/eRA-Com mons-Roles-10-2019. pdf). The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) disease working groups have stemmed from the ENIGMA genetics project (see Subheading 5.
3) to perform worldwide neuroimaging studies for a wide range of disorders (e. g. , major depressive disorder  [18] , attention-deficit hyperactivity disorder  [19] , autism  [20] , post-traumatic stress disorder  [21] , obsessive-compulsive disorder  [22] , substance dependence  [23] , schizophrenia  [24] , bipolar disorder  [25] ) as well as traits of interest (e. g. , sex, healthy variation  [26] ); see  [27]  for a review. Each working group may conduct simultaneously several research projects, proposed and led by its members. Each site of the working group choses the project(s) they contribute to and performs the analyses. Of note, most ENIGMA working groups still rely on a meta-analytic framework, even if recent projects (e. g. , machine learning) now require sharing data onto a central server.
Interested researchers can contribute new data and propose analyses or new image processing pipelines to the different working groups. The ENIGMA samples typically comprise thousands of participants (controls and/or cases; see Table  1 ), and data are inherently heterogeneous, each site having specific recruitment and protocols. Other neuroimaging MRI datasets have focused on twins and siblings (see Subheading 5. 1) and include the Queensland Twin Imaging (QTIM) study, the Queensland Twin Adolescent Brain Project, the Vietnam Era Twin Study of Aging (VETSA)  [28] , and the Human Connectome Project (HCP)  [29]  (see Subheading 5. 1). In addition, there are many more datasets available on neurological disorders, which may be explored via XNAT, LONI, or the Dementias Platform UK (DPUK), to name a few, PPMI (Parkinson' Progression Markers Initiative)  [30] , MEMENTO (deterMinants and Evolution of AlzheiMer's disEase aNd relaTed disOrder)  [31] , EPAD (European Prevention of Alzheimer's Dementia)  [32] , and ABIDE (Autism Brain Imaging Data Exchange)  [33, 34].
Several tracers are used for brain PET imaging, one of the most common ones being the 18 F-fluorodeoxyglucose ( 18 F-FDG). 18 F-FDG concentrates in areas that consume a lot of glucose and will thus highlight brain metabolism. In practice, 18 F-FDG PET images are often used to study neurodegenerative disease by revealing hypometabolism that characterizes some dementia  [36, 37]. Other diseases such as epilepsy and multiple sclerosis can be studied through this modality, but since it is not part of clinical routine, data are rare, and we are not aware of publicly available datasets. In whole-body PET scans, 18 F-FDG is used to detect tumors, which consumes a lot of glucose. However, the brain consumes a lot of glucose as part of its normal functioning, and brain tumors are not noticeable using this tracer.
We have made a non-exhaustive list of publicly available datasets containing PET scans with different tracers. Most datasets focused on neurodegenerative disorders and also collected brain MRI (see previous section). The Alzheimer's Disease Neuroimaging Initiative (ADNI) is one of the largest datasets with PET images for Alzheimer's disease  [11]. ADNI used F-FDG-PET as well as PET amyloid tracers: FBB, AV45, and PiB. The Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) only collected amyloid tracers of PET images: PiB, AV45, and Flute  [12]. The Open Access Series of Imaging Studies v3 (OASIS3) includes PET imaging from three different tracers, PIB, AV45, and 18 F-FDG  [13]. In addition to those neurodegenerative datasets, PET is available in the Lundbeck Foundation Centre for Integrated Molecular Brain Imaging (CIMBI) database and biobank established in 2008 in Copenhagen, Denmark  [38].
CIMBI shares structural MRI, PET, genetic, biochemical, and clinical data from 2000 persons (around 1600 healthy subjects and almost 400 patients with various pathologies). Tracer used for PET is the 11 C-5-HT which is relevant to study the serotonergic transmitter system. Applications to access the data can be made on their website by completing a form (see Table  2 ). The ChiNese brain PET Template (CNPET) dataset has been developed by the Medical Imaging Research Group (https:// biomedimg-dlut-edu. cn/), from Dalian University of Technology (China)  [39]. The database contains 116 records of 18 F-FDG-PET from healthy patients, which has been used to make a Chinese population-specific statistical parametric mapping (SPM, i. e. , average template used for PET processing). The data used to build the PET brain template have been released and are available on Neuro-Imaging Tools and Resources Collaboratory (NITRC, https:// www.
Main Existing Datasets 775 Another approach is to study the interactions across sources (inferring connection between two electrodes by means of temporal dependency between the registered signals), which is known as functional connectivity. Multiple connectivity estimators have been developed to quantify this interaction  [58]. Through these functional interactions, complex network analysis can also be implemented, where sensors are modeled as nodes and connectivity interactions as links  [59] [60] [61]. EEG and MEG are essential to evaluate several types of brain disorders. One of the most documented is epilepsy, based on seizure detection and prediction  [62] [63] [64]. Other neurological conditions can be characterized like Alzheimer's disease, associated with changes in signal synchrony  [65, 66].
Furthermore, motor task decoding in brain-computer interfaces (BCI) offers a promising tool in rehabilitation  [67]. This type of data, from healthy to clinical cases, can be found on multiple open-access repositories, such as Zenodo (https://zenodo. org) or PhysioNet ((1)), as well as via collaborative projects such as the BNCI Horizon 2020 (http://bnci-horizon-2020. eu), which gathers a collection of BCI datasets (see Table  2 ). These repositories are also valuable in that they contribute to establishing harmonization procedures in processing and recording. All dataset-collected informed consent and data were anonymized to protect the participants' privacy. Moreover, regulations may vary from one country to another, which require, for example, studies to be approved by ethics committees.
Additionally, licensing (that define copyrights of the dataset) must be considered depending on the intended use of the open-access datasets. Data come in different formats according to the acquisition system or the preprocessing software. The most common formats for EEG are. edf,. gdf,. eeg,. csv, or. mat files. For MEG, it is very often. fif and. bin (see Table  1 ). The different formats can create challenges when working with multiple datasets. Luckily, some tools have been developed to handle this problem, for example, FieldTrip  [68]  or Brainstorm  [69]  implemented in MATLAB, or the Python modules mne  [70]  and moabb  [71]. Of note, these tools also contain sets of algorithms and utility functions for analysis and visualization.
g. , saliva, blood, hair, urine). Datasets derived from twin studies are text-based and include phenotypic data and background variables (e. g. , individual and family IDs, sex, zygosity, age). Notably, the correlated nature of twin data (i. e. , the non-independence of participants) should be considered during analysis as it may violate statistical test assumptions  [72, 73]. Raw data is typically stored locally by the data owner, with de-identified data available upon request. In larger studies, data is stored and distributed through online repositories. Recently, the sharing of publicly available de-identified data with accompanying publications has become commonplace. Several extensive twin studies combine imaging, behavioral, or biological data (see Table  2 ). These studies cover the whole life span (STR) as well as specific age periods, for example, children/adolescents (QTAB), young (QTIM, HCP-YA), middle-aged (VETSA), or older (OATS) adults.
The Swedish Twin Registry (STR) was established in the late 1950s with the primary aim to explore the effect of environmental factors (e. g. , smoking and alcohol) on disorders  [74]. Data were first collected through questionnaires and interviews with the twins and their parents. Later, the STR incorporated data from biobanks, clinical blood chemistry assessments, genotyping, health checkups, and linkages to various Swedish national population and health registers  [74]. The STR is now one of the largest twin registers in the world  [75]  with information on more than 87,000 twin pairs (https://ki. se/en/research/the-swedish-twin-registry). It has been used extensively for the research of health and illness, including various neurological disorders, including dementia  [76] , Parkinson's disease  [77] , and motor neuron disease  [78].
Brain imaging was collected in a large genetically informative population sample of young adults (18-30 years, N > 1200) for whom a range of behavioral traits, including cognitive function, were already characterized (as a component of the Brisbane Adolescent Twin Study, QIMR Berghofer Medical Research Institute  [79] ). Notably, the dataset includes a test-retest neuroimaging subsample (n = 75) to estimate measurement reliability. Data is available from the project owners upon request. The Human Connectome Project Young Adult (HCP-YA, 2010-2015) study, funded by the NIH, is based at Washington University, University of Minnesota, and Oxford University. Investigators spent 2 years developing state-of-the-art imaging methods  [29]  before collecting high-quality neuroimaging, behavioral, and genotype data in ~1200 healthy young adult twins and non-twin siblings  (22-35 years).
Foremost is the Netherlands Twin Registry  [86] , a substantial data resource with dedicated projects investigating neuropsychological, biomarker, and behavioral traits. In addition, several extensive family/pedigree imaging studies exist, including the Genetics of Brain Structure and Function study  [87]  and the Diabetes Heart Study-Mind Cohort  [88]. Further, the previously mentioned ABCD study  [89]  includes embedded twin subsamples. Twin datasets have been used to estimate the heritability (the proportion of observed variance in a phenotype attributed to genetic variance) of phenotypes derived through machine learning, such as brain aging  [90] [91] [92]  and brain network connectivity  [93]. Further, machine learning models have been trained to discriminate between MZ and DZ twins based on dynamic functional connectivity  [94]  and psychological measures  [95].
Molecular Genetics The UK Biobank (UKB) is one of the largest population-based cohorts, comprising nearly half a million adult participants (aged over 40 years at the time of recruitment), recruited across over 20 assessment centers in the UK. The UKB resource is accessible to the research community through application (https://www. ukbiobank. ac. uk/enable-your-research/apply-for-access) and, as of the end of 2021, counted more than 28,000 registered approved researchers worldwide. In 2021, UKB launched a cloud-based Research Analysis Platform (RAP), which provides computational tools for data visualization and analysis, thereby aiming to democratize access for researchers lacking such infrastructure. The associated fees for using the UKB resource include the yearly tier-based access fees, depending on the type of data accessed, as well as the cost of running the analyses and storing the generated data, while the storage of the UKB dataset itself is provided free of charge.
The core software is the RICOPILI data analytic pipeline  [116]. This pipeline has explicit written protocols for uploading data to the cluster in the Netherlands that one uses for quality control, imputation, analysis, meta-analysis, and bioinformatics. The actual mega-analyses are conducted by PGC analysts under the direction of a senior statistical geneticist, geneticist, or highly experienced analyst. The PGC has a proven commitment to open-source, rapid progress science. All PGC results are made freely available as soon as a primary paper is accepted (GWAS summary statistics available at https://www. med. unc. edu/pgc/download-results/). The researchers can obtain access to the individual-level data either through controlled-access repositories (e. g. , the Database of Genotypes and Phenotypes, dbGaP, or the European Genome-phenome Archive) or via the PGC streamlined process for secondary data analyses (https://www.
" Individual-level data is accessible to researchers with an approved dbGaP data access request (https://topmed. nhlbi. nih. gov/data-sets), via Google and Amazon cloud services. More information about data availability and how to access it can be found on the dataset page (https:// topmed. nhlbi. nih. gov/data-sets). As of September 2021, TOPMed consists of ~180 K participants from >85 different studies with varying designs. Prospective cohorts provide large numbers of disease risk factors, subclinical disease measures, and incident disease cases; case-control studies provide improved power to detect rare variant effects. Most of the TOPMed studies focus on HLBS (heart, lung, blood, and sleep) phenotypes, which leads to 62 K (~35%) participants with heart phenotype, 50 K (~28%) with lung data, 19 K (~11%) with blood, 4 K (~2%) with sleep, and 43 K (~24%) for multi-phenotype cohort studies.
Similarly, a large set of multi-ethnic samples from freeze 5, 8, and 9 were used to develop comprehensive tools such as the STAAR and SCANG pipelines, which are used to identify noncoding rare variants  [121]  and to build predictive models for protein abundances  [122]  and discovery of causal genetic variants for different phenotypes  [123, 124]. Overall, the Trans-Omics for Precision Medicine (TOPMed) program has the potential to help in improving diagnosis, treatment, and prevention of major diseases by adding WGS and other "omics" data to existing studies with deep phenotyping. Main Existing Datasets 785 6 Genomics 6. 1 Methylation DNA methylation (DNAm) is a covalent molecular modification by which methyl groups (CH 3 ) are added to the DNA. In vertebrates-and eukaryotes in general-the most common methylation modification occurs at the fifth carbon of the pyrimidine ring (5mC) at cytosine-guanine dinucleotides (CpG).
e. , genomic regions with high CpG frequency), 92% of the CpG islands' shores  [125, 126]  (<2 kb flanking CpG Islands), and 86% of the CpG islands' shelves (<2 kb flanking outward from a CpG shore), which have been shown to be more dynamic than CpG islands  [127]. Although most current studies have used the 450 K array [128], the EPIC array covers >90% of the 450 K sites plus additional CpG sites in the enhancer regions identified by the ENCODE and FANTOM5 projects  [129]. After probe hybridization and extension steps, the array is scanned, and the intensities of the unmethylated and methylated bead types are measured. DNAm values are then represented by the ratio of the intensity of the methylated bead type to the combined locus intensity. These are known as beta (β) values and are continuous variables between 0 and 1 (Equation  1 ), although a value of 1 is impossible to achieve in practice, due to the addition of a stabilizing α offset (to handle low-intensity signals): Equation 1 DNA methylation β values as measured by the Illumina Infinium® methylation arrays M = methylated intensity, U = unmethylated intensity, α = arbitrary offset to handle signals with low readings (usually 100) β = M M þ U þ α ð1Þ These raw intensities are then stored in binary IDAT files (one for each of the red and green channels).
Main Existing Datasets 787 In Table  2 , we have compiled a list of the largest and/or most used DNAm array datasets-including the Genetics of DNA Methylation Consortium (goDMC), an international collaboration of human epidemiological studies that comprises >30,000 study participants with genetic and DNAm array data  [139]. These samples are usually integrated in larger genetic/epidemiological studies, except for perhaps the NIH Roadmap Epigenomics Mapping Consortium  [140] , which was launched with the goal of producing a public resource of human epigenomic data to catalyze basic biology and disease-oriented research, and the BLUEPRINT project  [141, 142] , which aims to generate at least 100 reference epigenomes of distinct types of hematopoietic cells from healthy individuals and of their malignant leukemic counterparts.
Gene Expression Data: GTEx Launched in 2010, the Genotype-Tissue Expression (GTEx) project is an ongoing effort that aims to characterize the genetic determinants of tissue-specific gene expression  [144]. It is a resource database available to the scientific community, which is comprised of multi-tissue RNA sequencing (RNA-seq: gene expression) and whole genome sequence (WGS) data collected in 17,382 samples across 54 tissue types from 948 postmortem donors (version 8 release). Sample size per tissue ranges from n = 4 in kidney (medulla) to n = 803 in skeletal muscle. The majority of donors are of European ancestry (84. 6%) and male (67. 1%) with ages ranging from 20-70 years old. The primary cause of death for donors 20-39 years old was traumatic injury (46. 4%) and heart disease for donors  60-70 years (40.
9%). Data is constantly being added to the database using sample data from the GTEx Biobank. For example, recent efforts have focused on gene expression profiling at the single-cell level to achieve a higher resolution understanding of tissue-specific gene expression and within tissue heterogeneity. As a result, single-cell RNA-seq (scRNA-seq) data was generated in 8 tissues from 25 archived, frozen tissue samples collected on 16 donors. Further, the Developmental Genotype-Tissue Expression (dGTEx) project (https://dgtex. org/) is a relatively new extension of GTEx that was launched in 2021 that aims to understand the role of gene expression at four developmental time points: postnatal (0-2 years of age), early childhood (2-8 years of age), pre-pubertal (8-12. 5 years of age), and post-pubertal  (12.
For example, several computer-aided diagnosis tools have been developed for the classification of neurodegenerative diseases. One of their main limitations is that they are typically trained and validated using research data or on a limited number of clinical images  [149] [150] [151] [152] [153] [154]. It is still unclear how these algorithms would perform on large clinical dataset, which would include participants with multiple diagnoses and more generally heterogeneous data (e. g. , multiple scanners, hospitals, populations). One of the first CDW in France was launched in 2017 by the AP-HP (Assistance Publique -Hopitaux de Paris), which gathers most of the Parisian hospitals  [155]. They obtained the authorization of the CNIL (Commission Nationale de l'informatique et des Libertes, the French regulatory body for data collection and management) to share data for research purposes.
The aim is to develop decision support algorithms, to support clinical trials, and to promote multicenter studies. The AP-HP CDW keeps patients updated about the different research projects through a portal (as authorized by CNIL), but, according to French regulation, active consent was not required as these data were acquired as part of the routine clinical care of the patients. Accessing the data is possible with the following procedure. A Environment, R, MATLAB) and provides computational power (CPUs and GPUs) to analyze the data. An example of the research possible using such CDW is the APPRIMAGE project, led by the ARAMIS team at the Paris Brain Institute. The project was approved by the Scientific and Ethics Board of the AP-HP in 2018. It aims to develop or validate algorithms that predict neurodegenerative diseases from structural brain MRI, using a very large-scale clinical dataset.
The dataset provided by the AP-HP gathers all T1w brain MRI of patients aged more than 18 years old, collected since 1980. It therefore consists of around 130,000 patients and 200,000 MRI which were made available via the Big Data Platform of the AP-HP. Of note, clinical data was available for only 30% of the imaged participants (>30,000 patients) as it relies on the ORBIS Clinical Information System (Agfa HealthCare), installed more recently in the hospitals. The sheer size of the data poses obvious computational challenges, but other difficulties include harmonizing clinical reports collected in the different hospitals or handling the general heterogeneity of the data (e. g. , hospitals, acquisition software, populations). To tackle this issue, we have developed a pipeline for the quality control of the MR images [156].
Swedish National Registries In Sweden, a unique 10-digit personal identification number has been assigned to each individual at birth or migration since 1947, which allows linkages across different Swedish population and health registers with almost 100% coverage  [157]. The Swedish Total Population Register (TPR) was established in 1968 and is maintained by Statistics Sweden to obtain data on major life events, such as birth, vital status, migration, and civil status  [158]. TPR is a key source to provide basic information in medical and social research in Sweden. The Swedish Population and Housing  Censuses (1960 Censuses ( -1990) )  and the Swedish Longitudinal Integrated Database for Health Insurance and Labour Market Studies (Swedish acronym LISA) (since 1990) provide information on demographic and socioeconomic status for the Swedish population, including the highest attained educational level and household income  [159].
Wearable sensor devices (e. g. , wrist-worn accelerometers) can collect data on sleep, activity, and physiology without burdening participants or influencing their behavior. Datasets derived from smartphone and sensor studies are typically text-based, though raw data may be proprietary. The analysis of smartphone and sensor data typically requires complex algorithms/machine learning approaches due to the complexity of data collected (in the frequency of hundreds of observations per second, from many different sensors collecting data simultaneously). Raw data is typically stored locally by the data owner, with de-identified data available upon request. In more extensive studies, data is stored and distributed through online repositories. Several studies have collected real-world behavioral and physiological data using smartphone and sensor devices (see Table  2 ), including community twin studies (BATS, QTAB), large-scale biomedical databases (UK Biobank), and studies focusing on specific disorders (mPower).
The Brisbane Adolescent Twin Study (BATS) and the Queensland Twin Adolescent Brain (QTAB) projects are twin studies sourced from the Queensland Twin Registry (QTwin). The BATS project, enabled through funding from the NHMRC, was a longitudinal study of adolescent twins, which collected accelerometry data over three waves between 2014 and 2018 (ages 12, 14, and 16 years). The Queensland Twin Adolescent Brain study  (QTAB, 2015-present) , previously discussed in Subheading 5. 1, collected accelerometry data over two waves (age 9-14 years at baseline). In both studies, participants wore a wrist-mounted accelerometry recording device for 2 weeks (day and night, removed only for bathing) and completed a daily sleep diary. Raw accelerometry data were processed and consolidated with sleep diary data to produce sleep onset, wake, and sleep duration estimates.