dataesr/scientific-paragraphs-categorization huggingface dataset:
We included sex, platform, the first 15 genetic principal components and standardised full-scale IQ as covariates. In addition, for the analysis of ADOS-G, we included the ADOS-G module as a covariate. Linear regression was conducted in R. A total of 135,233 SNPs were included in the polygenic score analyses after clumping and thresholding. To validate the polygenic scores, we conducted additional polygenic score analysis using data combined from the AGRE, EU-AIMS LEAP and Paris cohorts. We followed similar quality control and imputation procedures to the SSC cohort. Given that this dataset was a mix of related and unrelated individuals, we chose unrelated individuals using a genomic relationship matrix (GRM) as provided in GCTA (grm-cutoff 0. 05)  125. To calculate GRMs, we included only SNPs with minor-allele frequency > 1%.
Scripts are provided here: https://github. com/ vwarrier/PARIS_LEAP_analysis. Polygenic scores were calculated using PRSice2 as described for the SSC data. Given the differences in dataset, polygenic scores were calculated separately for the AGRE dataset, and the EU-AIMS LEAP and Paris datasets combined. For each regression, we included sex and the first ten genetic principal components (standardised). The dependent variables were standardised scores on the RBS-R (N = 426) and the ADOS-G social and communication subscale (N = 475). IQ information was unavailable for most individuals, and hence we did not include IQ as a covariate. We combined the results of the EU-AIMS LEAP and Paris cohorts, and the AGRE dataset using inverse-variance weighted fixed-effect meta-analysis using the formula below: w i ¼ 1=SE 2 i SE meta ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1=Σ i w i p Beta meta ¼ Σ i β i w i =Σ i w i ; where β i is the standardised regression coefficient of the polygenic scores, SE i is the associated standard error, and w i is the weight.
Bivariate GREML. We conducted bivariate genetic correlation using GCTA GREML to test the genetic correlation between the ADOS social and communication domains and the RBS-R scores. We created a GRM after including autistic individuals from the SSC, AGRE, EU-AIMS LEAP, and Paris cohorts. We excluded SNPs and individuals using the same quality control pipeline as applied to the SSC dataset outlined in the section above. We further restricted our analysis only to SNPs with a minor-allele frequency > 1%. We excluded related individuals (-grm-cutoff 0. 05) resulting in a total of 2989 individuals. Of this, 2652 individuals had scores for the ADOS social and communication domain and 2550 individuals had scores on the RBS-R. We included sex and the first ten genetic principal components as covariates.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. (project no. : 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. The project leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement no. 777394. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. We thank the iPSCH-Broad Autism Group and the EU-AIMS LEAP group for sharing data. A full list of the authors and affiliations in the iPSYCH-Broad autism group and the EU-AIMS LEAP group is provided in the Supplementary Information.
Acknowledgements We are grateful to  Michel Nivard  and  Beate St. Pourcain  for their help with the analytical methods. We thank the research participants and employees of 23andMe for making this work possible. We are grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. V. W. was funded by  St. John's College, Cambridge , and the  Cambridge Commonwealth Trust. This study was funded by grants to SBC from the  Medical Research Council , the  Wellcome Trust , the  Autism Research Trust , the  Templeton World Charity Foundation , and to T. B. from the  Institut Pasteur , the  CNRS , the  INSERM, The Fondamental Foundation , the  APHP , the  BioPsy Labex  and the  University Paris Diderot.
Data availability The SQ-R GWAS results are available from 23andMe. The full set of summary statistics can be made available to qualified investigators who enter into an agreement with 23andMe that protects participant confidentiality. Interested investigators should email dataset-request@23andme. com for more information. Top SNPs (n = 10,000) can be visualised here: https://ghfc. pasteur. fr. Data. for ALSPAC can be requested here: http:// www. bristol. ac. uk/alspac/researchers/access/. Data from the Simons Simplex Collection can be requested here: https://www. sfari. org/resource/sfari-base/. Summary GWAS statistics were downloaded from the PGC consortium: http://www. med. unc. edu/pgc/ results-and-downloads. Data for chronotype was downloaded from http://www. t2diabetesgenes. org/data/.
Code availability Genomic-SEM: https://github. com/MichelNivard/GenomicSEM, GWIS: https://sites. google. com/site/mgnivard/gwis, Plink: https://www. cog-genomics. org/plink2/, PRSice2: https://choishingwan. github. io/PRSice/, CAVIAR: http://genetics. cs. ucla. edu/caviar/, Michigan Imputation Server: https://imputationserver. sph. umich. edu/index. html, custom code for quality control of the SSC and the other cohorts can be downloaded from https://github. com/autism-research-centre/SSC_liftover_imputation (https://doi. org/10. 5281/zenodo. 3342561) and from https://github. com/vwarrier/ PARIS_LEAP_analysis (https://doi. org/10. 5281/zenodo. 3342569).
We then combine these data-derived profiles with a magnetic field model to acquire the value of the source function throughout the magnetospheric model volume, under the force balance assumption. More details are in  Caudal (1986)  and  Achilleos et al. (2010a) , who also describe "averaging" methods that they apply to data to acquire realistic profiles for the axisymmetric model formalism. The method of calculation of these magnetodisc solutions requires that various parameters of the plasma be initially specified on an appropriate reference surfaceusually the equatorial plane. Reference values for plasma ion flux tube content, temperature, ion mass and angular velocity are acquired from appropriate spacecraft observations. For the energetic ("hot") particle population, the plasma pressure P h is computed through the use of a simplified equatorial profile of the product P h V a , where V a denotes unit flux tube volume.
Cross-sectional study (Time 1) Participants Participants were recruited as part of a larger study, which entailed psychophysical and electrophysiological measures of auditory processing, as well as psychometric assessments of language and cognitive functioning (see  Halliday et al. , 2017a; Halliday et al. , 2017b). The study was conducted with the verbal assent of the participants and the written informed consent of their parents/guardians, and was approved by the UCL Research Ethics Committee. Unidentifiable data is available on GitHub  (Calcus, 2019 ; copy archived at https://github. com/elifesciences-publications/MMHL). Children with MMHL were approached via Hearing Services in Local Educational Authorities across Greater London and the South East of England. Inclusion criteria were: (a) a diagnosis of MMHL, defined as a BEPTA threshold of 21-40 dB HL (mild) or 41-70 dB HL (moderate) across octave frequencies 0.
Stimuli Stimuli were taken from three continua, constructed for the psychophysical tests that were undertaken as part of the wider study (see  Halliday et al. , 2017a; Halliday et al. , 2019). Stimuli are available on GitHub  (Calcus, 2019 ; copy archived at https://github. com/elifesciences-publications/ MMHL). Stimuli were (a) sinusoidally frequency-modulated pure tones (nonspeech), (b) complex periodic tones with a vowel-like formant structure whose second formant was sinusoidally frequencymodulated (speech-like), and (c) consonant-vowel (CV) speech syllables (speech; see Figure  2 ). All stimuli were 175 ms in duration, and both nonspeech and speech-like stimuli had a 15 ms linear onoff ramp. For all conditions, the difference between both the large deviant and the standard and the small deviant and the standard was clearly detectable during pilot testing (NH child listeners, n = 4).
Data recording and analysis EEG activity during audio presentation was recorded using a NuAmps 40-channel monopolar digital amplifier and NeuroScan 4. 4 Acquire software system (Neuromedical Supplies) at a sampling rate of 500 Hz. The EEG was recorded from 28 scalp electrodes, positioned in the standard 10/20 configuration. Additional electrodes were placed on the mastoids, and above and below the left eye (Vertical EOG) and on the outer canthi of both eyes (Horizontal EOG) to monitor eye movements. Skin-toelectrode impedances were below 5 kW at the start of the recording and monitored to ensure they remained below 5 kW. The EEG signal was amplified with a gain of 20000 and band-pass filtered at 0. 1-70 Hz. EEG data were analysed offline using the EEGLAB toolbox  (Delorme and Makeig, 2004 ) and custom Matlab software.
Identification of the MMN and LDN Voltage maps confirmed that the MMN was strongest in the fronto-central region (at around 200 ms post-stimulus onset, see Figure  3 ), as is typical for this response (Na ¨a ¨ta  ¨nen et al. , 2007). Statistical analyses for the MMN and LDN were therefore conducted on data recorded at electrode Fz. To calculate the MMN/LDN, the responses evoked by standards were subtracted from responses evoked by deviants for each participant for each condition. Differential waves were obtained separately for the small and large deviants. The presence of a significant MMN or LDN was assessed in two ways. First, for each group and age band, point-to-point comparison of the differential wave amplitudes was performed in order to determine the latency period over which the waveforms were significantly different from zero, if any.
Statistical analyses A series of mixed-effects regression models was applied using the lme4 package of R  (Bates et al. , 2014). Two sorts of analyses were conducted. Logistic mixed-effects regressions were conducted to determine whether group (NH vs. MM), age band (Y vs. O), condition (nonspeech, speech-like, and speech), component (P1, N1, P2, N2) or response (MMN or LDN) predicted the presence of a component/response. Linear mixed-effects regressions were conducted to determine whether those same independent variables predicted the amplitude (P1-N2, MMN, and LDN) or latency (P1-N2 only) of these components/responses. Starting from saturated models with listener as random intercepts, group, age band, condition, and component as main effects, and all two-, three-and four-way interactions included (which were treated as fixed-effect terms), predictors were removed iteratively if their removal did not significantly worsen the fit of each model.
Data recording and analysis The experimental setup for stimulus presentation and EEG recording was identical to that used at Time 1, with two main differences. First, as the system was older, technical difficulties led to us to discard EEG activity recorded on the left mastoids. Therefore, data from the MM-YO subgroup was only re-referenced to the right mastoids. Second, some scalp channels appeared consistently faulty, hence increasing the noise floor at Time 2. Because the MMN typically displayed a fronto-central distribution for the cross-sectional study, we thus limited the artefact rejection to a threshold of ±100 mV criterion in either Cz or Fz. Data from the three other subgroups (MM-Y, MM-O, NH-O) were thus reprocessed using the same re-referencing and artefact rejection criterion as those applied to the MM-YO subgroup.
Acknowledgements The authors would like to thank all the children who participated in this study and their parents. We gratefully acknowledge funding from the  Economic and Social Research Council  ( RES-061-25-0440 ) and the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme FP7 /2007-2013/under REA grant agreement n˚FP7-607139 (iCARE). Thanks go to  Steve Nevard  and  Andrew Clark  for their technical help with this project, and to  Paraic Scanlon  and  Xiaowen Wang  for their help in recruiting and testing the children.
MOOSE also provides a large flow of real-time data to facilitate the validation of operational oceanographic models. The MOOSE strategy is based on an in-situ observing system, capable of capturing all scales of variability, thus avoiding any aliasing effects caused by sub-sampling. Thus, MOOSE aims to address scales of variability ranging from the very small (1 km horizontally, over a few days) to the basin scale (500 km, months/years), passing by the mesoscale (15 km, weeks/months). The MOOSE components are (Figure  3  Deepwater convection processes and their interaction with dense shelf water cascading events modify deepwater mass properties and deep sediment resuspension in the Gulf of Lion (Durrieu de  Madron et al. , 2013). Over the past decade, the MOOSE network has documented a slow increase in deepwater temperature, punctuated by very rapid warming, leading to even warmer and saltier deep waters.
The network includes satellite-tracked surface and profiling drifters (through an annual deployment of 3 Argo floats 4  and 8 SVP drifters), 16 autonomous fixed coastal stations deployed around the Balearic Islands, 2 metocean moorings located in the Ibiza channel and the bay of Palma, a 24 m coastal research vessel, 2 high-frequency radar stations overlooking the Ibiza Channel, a fleet of 7 autonomous underwater gliders and 2 beach monitoring stations. More specifically, SOCIB runs a glider endurance line in the Ibiza channel (a well-established biodiversity hot spot) to monitor the north-south exchanges and their relation to the variability of the circulation, of the different water masses and the associated ecosystem variability. Animal-borne instruments are a new addition to SOCIB and since 2015, tracked sea turtles complement the observing system with unique and cost-effective data  (Patel et al.
, 2018). The data, scientific production, outreach and engagement activities, as well as tools and products developed are a clear performance indicator of SOCIB achievements and innovations in the new era of ocean observation. The alignment of these elements is possible due to a dedicated data lifecycle management that is fully committed with the Findable, Accessible, Interoperable and Reusable (FAIR) data principles  (Wilkinson et al. , 2016)  and that also contributes to the IOC Ocean Best Practices System 5  (Pearlman et al. , 2019). From the SOCIB perspective, the real challenge for the next decade is the full integration of these technologies and multi-platform observing and forecasting systems. As a research infrastructure, SOCIB in partnership with CSIC and IEO scientists, combines operational, scientific and training activities.
SOCIB and similar infrastructures worldwide, due to their scientific excellence, critical mass, multidisciplinary, integrated and targeted approach, open data policy and sustained funding, are establishing new research ecosystems that facilitate missionoriented innovation. Further details are provided section SOCIB Innovation, Products and Services. The SOCIB observing system has contributed to, the understanding of the relationship between the north-south internannual exchanges and the water masses vertical structure  (Heslop et al. , 2012; Barcelo-Llull et al. , 2019; Juza et al. , 2019) , the understanding of the dynamics of meso and submesoscale eddies and their impact on the circulation  (Escudier et al. , 2016a)  and biogeochemical fluxes  (Cotroneo et al. , 2016; Pascual et al. , 2017; Aulicino et al.
The system is being developed in accordance with the policy frameworks suggested by GOOS, EuroGOOS, MonGOOS, and GEO, while maintaining a balance between the operational and research characteristics of the infrastructure through the integration of methodologies and tools developed in relevant EU initiatives and projects. The data provided by POSEIDON in the Aegean and Ionian Seas sample a wide area. The present (2018) status of the POSEIDON observatory includes multiple platforms  (Figure  5 ) operating at various spatiotemporal scales (Figure  6 ). The components are: -Three wavescan buoys are deployed in the S. Aegean (E1-M3A), N. Aegean (ATHOS) and Ionian waters (PYLOS) provide meteorological, physical and biochemical (O 2 , Chl-a) data  (Petihakis et al. , 2018). -A Ferrybox system (FB) operating on the route connecting the ports of Piraeus (Athens) and Heraklion.
The POSEIDON system will offer additional products to a wider range of society users, through its expanded NRT data delivery, proxy estimations, hazard mapping, warning systems and higher resolution, in addition to addressing specific scientific questions. For example, deep water ADCP data from the Cretan Sea provided insights into important processes in terms of ecosystem functioning, such as zooplankton migration  (Potiris et al. , 2018). The observation of the various patterns of vertical migration and of mesopelagic inhabitants, makes possible the investigation of the role of these components in organic carbon sequestration.
In ETS, a total of 9 stations between 20 and 500 m depths are monitored monthly for T, S, and DO (Figure  7 ). Further, monthly measurements for biological and chemical essential ocean variables (T, S, DO, Chl, Tur, Par, Secchi disk) are conducted at 4 selected stations at 20, 100, 200, and 500 m depths. In addition, monthly trawl surveys have been carried out since 2007 to investigate and monitor demersal fish stocks. A mooring system that has 7 underwater inductive sensors (T, S, DO) at different levels of the water column and meteorological sensors was deployed on the ETS at 100 m water depth. The data can be acquired daily from the mooring. DEKOSIM also supported Argo deployments in the region and 850 profile data have been acquired from 4 Argo floats in Black Sea and 2 in Mediterranean since 2013 within the DEKOSIM.
SELIPS-ISRAMAR The South Eastern Levantine Israeli Prediction System (SELIPS) and the Israel Marine Data Center (ISRAMAR, https://isramar. ocean. org. il) were established 20 years ago at the Israel Oceanographic & Limnological Research (IOLR) Center as a research and monitoring infrastructure. They provide scientific data and knowledge and effective management and protection programmes for the marine environment. Both systems were integrated in several national and international/EU initiatives dedicated to operational oceanography and data archiving. The SELIPS-ISRAMAR contain various forecasting and observational platforms operating at different scales. Since 2002 bi-annual monitoring cruises have been conducted in the SE Levantine basin along transects from 20 to 1,700 m water depths, and 70 km offshore by the R/V Shikmona until 2016 and further by the R/V Bat Galim.
IOLR has operated the Deep Levantine Marine Station (DeepLev) since November 2016, which is the first deep-moored research and monitoring station in the SE Levantine Basin (LB), located 50 km offshore of Haifa (Israel) at a water depth of 1,500 m. It carries an array of instruments, including various current meters (ADCPs), CTDs, sediment traps, fluorometers and turbidity sensors, aiming to produce a long-term, highresolution characteristic of the physical, chemical, and biological dynamics of the water column. In addition, IOLR operates ocean gliders for monitoring activities, which conduct bi-annual transects along N-S and E-W at the SE LB. Finally, since 2001 ISRAMAR has been a repository for oceanographic data (physical, chemical, biological) and acquires, archives and distributes data and information about the marine environment.
It also contain a marine barcoding database consisting of DNA-barcode tagging, identification and taxonomy with the purpose of promoting taxonomic knowledge for marine biodiversity (https://isramar. ocean. org. il/IsraelBarcoding). Since 2007, a synoptic database of ocean color parameters offshore of Israel using Earth Observation has been generated by SISCAL (Satellite-based Information System on Coastal Areas and Lakes, operated by IOLR). These include maps of the Sea Surface Temperature (SST), Chlorophyll-a concentration, Total Suspended Matter and Secchi depth, obtained from operational ocean observing satellites. The SELIPS-ISRAMAR has acquired 30 years' worth of hydrographic observations, showing that the south-eastern Mediterranean Sea is getting saltier and warmer at rates in agreement or higher than the IPCC 2014 high end prediction  (Ozer et al.
PORTUS (Puertos del Estado System) The observational component of the Portus system in the Mediterranean Sea consists of 7 deep water buoys (able to measure currents, SST, salinity, wind, atmospheric pressure, air temperature and waves), 6 coastal buoys (waves and SST), 2 HF radar systems (Catalan coast and straits of Gibraltar) and 16 tide gauges. This comprehensive observing system is fully integrated into the modeling component of the Portus (see the section below) and serves data for multiple socio-economic sectors with the main customer being the Spanish Port system, via the SAMOA service (see section Downstream Services in Response to Societal Challenges and Stakeholders). Real-time data access is provided via Portus web site 7.
Among various applications, Argo float data have been used to study convection and dense water formation in the Adriatic Sea (see for instance  Bensi et al. , 2013). MAOS also regularly operates several types of surface drifters to monitor the surface currents and temperature, and maintains a historical drifter database  (Menna et al. , 2018). It uses four gliders to measure water mass properties in the Mediterranean, with the main focus on the Italian seas and the Levantine Basin. MAOS also runs a glider endurance line across the southern Adriatic Sea to monitor dense water formation processes.
CMEMS Thematic Assembly Centers The CMEMS data assembly structure is organized around in-situ and satellite Thematic Assembly Centers. The CMEMS in situ Thematic Assembly Center (in situ TAC) (Petit de la  Villeon et al. , 2018; Le Traon et al. , 2019)  integrates near real-time in-situ observational data. These data are collected from the European members and complemented by the observations collected through the GTS in the area. The details of the physical parameters and data management procedures can be found in Copernicus Marine in situ TAC Data Management Team (  2018 ). The data are quality controlled using automated procedures and the database is updated continuously, providing observations within 24-48 h from acquisition. CMEMS Mediterranean in-situ TAC works in coordination with MONGOOS (section MONGOOS Collaborative Framework) and EMODnet to access almost all the real-time data collected in the basin by the European Members States.
mongoos. eu/hf-radars. Additionally, HF radar datasets can also be found in the new product (INSITU_GLO_UV_NRT_OBSERVATIONS_013_048) for near real-time surface current data in the CMEMS Catalog (V5), included in the recently updated CMEMS service release. Since the early 2010s over 60 Argo floats have been established in the Mediterranean basin, with 78 instruments in June 2019 (Figure  10B ) and 7 Glider facilities (Figure  10C ). More information can be found at the Mongoos web site  17. In summary, for some variables and in certain regions, the monitoring system is satisfactory. For example, the number of tide gauges on the western European Mediterranean coast is sufficient for most of the applications (study of sea level trends, storm surge monitoring, etc. ). Similar situations can be found for wave buoys.