dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation results In this section, we use various metrics to evaluate and compare the quality of the modelled variables. The Pearson correlation coefficient is the most used measure in this work for the following reasons: 1. It is well-known and widely used; 2. It is scale-independent and thus very useful in comparing variables with different scales (e. g. capacity factor or power output) and is thereby less sensitive to error on the installed capacity; 3. It measures the covariance of two variables, so it is particularly suitable to assess the capability of the proposed models to reconstruct and capture the variability of the energy variables, one of the aims of the C3S-E dataset. We have also applied the Mean Absolute Error (MAE) to describe the error using the same unit of measure as the energy variable.
It is important to show the performance of a model with respect to the average generation (thus the installed capacity) because there is a clear link between performances and amount of installed capacity measured by the ENTSO-E dataset. Figure  6  shows that, overall, the correlation coefficient between the reconstructed variables and the ENTSO-E data is good. For demand, Luxembourg is an exception, due to issues in the data available to train the model. Hydropower shows better results for run-of-river (HRO) than reservoirs based (HRE). This is expected as HRO generation is more directly driven by climate variability than HRE, which embeds the management of reservoirs in supply-demand balance operations. A general rule is that the models perform better when the installed capacity is bigger.
Overall, there is a very good fit between reconstructed and reported values in the ENTSO-E database. This very good performance is mainly due to the fact that demand depends foremost on calendar information due to its seasonal, weekly and daily variations; the climate dependance varies from country to country. Results for France are excellent because of a very good knowledge of the weather related demand behavior and its associated predictors by the project team. Results are a little less accurate for other countries (DE and ES for instance), but could most likely be improved by further iterations and interactions with the national TSOs. For countries like UK, as mentioned above, the lower accuracy comes mainly from ENTSO-E data issues, which cannot be solved simply. Direct interactions with TSOs would be required to get better quality data or fix issues in ENTSO-E data, but was out of scope of the project.
Overall, the good performance of the GAM models lies in practice in three main aspects: i) the quality of the training data from the ENTSO-E database; ii) the degree of dependence of demand on the climate parameters and iii) the expert knowledge put in the model parameters definition. Point ii) refers to the fact that some countries have more direct dependence of their demand on climate parameters, the most important one being temperature. France is the perfect example of this, as winter peak demand dependance is around 2,400 MW per degree Celsius. This is the highest dependance in Europe, where the total sensitivity of demand on temperature is around 4,000 MW per degree Celsius. For those countries where climate data play a significant role, point iii) above relates to the degree of sophistication that was put into each model.
Discussion and conclusion The objective of C3S Energy service is to produce a dataset of coherent weather-dependant energy variables for all European countries for different temporal scales: covering the last four decades, for the next 6-month using seasonal forecasts and until the end of the century using climate projections. In this paper, we describe the energy conversion models developed to generate this dataset. These are trained and validated against power data from the ENTSO-E databases. This choice was made to cover as many countries as possible, also considering the need to use publicly available data to comply with C3S open access policy. The validation is conducted using reanalysis data as input to the models. Although not presented here, the same models have also been applied to seasonal forecasts from three of the C3S models (ECMWF, Météo-France and the UK Met Office) as well as to ten climate projections models from the EURO-CORDEX experiment  [41, 42]  for IPCC scenarios RCP4.
5 and RCP8. 5. The extended evaluation that was performed shows that the energy conversion models produce plausible demand and supply data, and in particular they reproduce fairly well the effects of climate variability on electricity demand and generation, noting that validation is hindered by uncertainty in the reference data. Several causes have been identified to explain the model limitations, and improvement options have been listed, which might be implemented in the near future. Firstly, energy observations are a key issue in developing and validating such models. The ENTSO-E TP database provides uniform access to data from all European countries only since 2015. The record length is relatively short especially with respect to the amount of data needed to calibrate statistical models when the interannual variability is large, as is the case for hydropower.
It could also benefit from direct interactions with national TSOs, in order to refine the models equations based on their expertise of the demand behovior in their country. The C3S-E models and dataset are the first to provide climate related energy indicators for electricity demand and generation from wind, solar and hydro power for most countries in Europe, in a homogenized way, including open and free access via the C3S Climate Data Store, for three different time streams. This is a first stone in building a common framework for climate related energy modelling activities. Rather than an end-product, the dataset should be seen as the demonstration that it is possible to combine all the necessary elements to provide relevant information to help energy modelers and decisions makers better integrate the effects of climate variability and climate change in long-term energy prospective studies, as well as seasonal outlooks.
Data Availability All processed C3S-E datasets are passed to a publicly accessible drive. Then, as part of a monthly automated procedure, integrated into the Climate Data Store (CDS). The dataset is freely available to download from the CDS  8  to anyone who has a registered account (DOI: 10. 24381/cds. 4bd77450). In addition to downloading the data from the CDS, users are able to access and plot data via the online visualisation tool 9 , made available as part of the CDS toolbox.
The Subpolar North Atlantic is known for rapid reversals of decadal temperature trends, with ramifications encompassing the large-scale meridional overturning and gyre circulations, Arctic heat and mass balances, or extreme continental weather. Here, we combine datasets derived from sustained ocean observing systems (satellite and in situ), idealized observationbased modelling (advection-diffusion of a passive tracer), and a machine learning technique (ocean profile clustering) to document and explain the most-recent and ongoing cooling-towarming transition of the Subpolar North Atlantic. Following a gradual cooling of the region that was persisting since 2006, a surface-intensified and large-scale warming sharply emerged in 2016 following an ocean circulation shift that enhanced the northeastward penetration of warm and saline waters from the western subtropics.
It is found that the first of these contributions largely dominates the others, confirming that shifts in the large-scale circulation projecting onto the time-mean ocean thermal structure is the primary mechanism triggering basin-scale reversals of temperature trends in the SPNA (Fig. 4b ). The advection of remote temperature anomalies from both source regions remain nonnegligible and consistently tend to oppose themselves-most especially since 2013-in line with the well-known dipole pattern of temperature anomalies between the western subtropical and subpolar regions  [38] [39] [40]. Statistical clustering of water masses. An alternative confirmation of the importance of circulation shifts in driving the recent cooling-to-warming reversal in the eastern SPNA is provided by applying a machine learning algorithm-namely a profile classification model (PCM)-to our three-dimensional observational dataset  41.
Conclusion and discussion This study documents an ongoing large-scale hydrographic change occurring within the upper layer of the SPNA: the decadelong cooling trend prevailing since 2006 rapidly reversed in 2016 and a large-scale warming settled in the central and eastern portions of the domain. The responsible mechanism-also at play for the previous warming-to-cooling reversal in 2006-was revealed with observational datasets by solving a simple altimetry-based advective model for a passive tracer and by performing a statistical clustering of ocean in situ profiles. This combined approach provided two independent and observation-based decompositions of eastern SPNA waters into subtropical-origin and subpolar-origin waters, and both depicted the changing relative proportion of these two source waters as a predominant cause for the 2016 trend reversal.
Methods ISAS-15 temperature and salinity gridded fields. The observational in situ dataset used is the In Situ Analysis System, which provides gridded threedimensional fields of temperature and salinity during 2002-2019  58. Originally based on the Argo network of profiling floats, ISAS is now optimally interpolating all types of vertical profile (e. g. , XBTs, CTDs) and time series. The version used here is ISAS15 and is available online  17. Data were quality-controlled in delayed mode for the years 2002-2016 and in near-real-time mode for 2017-2019, with this difference in data treatment having no impacts on the large-scale temperature anomalies investigated herein. The field of eddy diffusivities was computed by  26  from observational data and theory and is available online at https://figshare.
com/ articles/Groeskamp_et_al_2020_-_mixing_diffusivities/12554555. Satellite altimetry. The dataset for the sea surface height (SSH) and associated geostrophic velocities used to derive the mean kinetic energy (MKE) is the DUACS DT2018 multimission satellite altimetry  59. The data is on a 1/4°grid resolution and spans the period from January 1993 until December 2019. Since the delayed time data is only available until May 2019, we extended the data until December 2019 using with the near-real-time fields. NCEP atmospheric fluxes. The atmospheric fluxes used here are derived from the 1948 to 2019 NCEP/NCAR reanalysis  60. The net heat flux was derived by adding the shortwave, longwave, sensible and latent heat flux. For the momentum fluxes, we used the zonal and meridional wind stress to derive the wind-stress curl.
2 to account for the appropriate advection timescales of anomalies from their source regions to the eastern SPNA. Profile classification model. A PCM with K = 2 classes was trained on vertical temperature and salinity profiles from 2002 to 2019 time mean of the ISAS dataset (described above). Only profiles where depth does exceed 2000 m were included in the PCM. Each variable was first normalized by vertical levels (e. g. , all-temperature values at 200 m are centered and standardized, this allows for deep structures to be preserved against the more diverse surface values) and secondly compressed using a principal component analysis (to reduce the number of vertical dimensions to a few eigen values). Then a Gaussian Mixture Model with 2 modes is fitted on reduced profiles using a classic Expectation-Maximization algorithm  62  (See  41  for full details of the PCM methodology and  43  for the presentation of a PCM with two variables).
Measurements of the spectrum of the atmospheric emission in the far-infrared (FIR) range, between 100 and 667 cm -1 (100-15 µm) are scarce because of the detection complexity and of the strong absorption of air at ground level preventing the sounding of the FIR from low altitude. Consequently, FIR measurements need to be made from high-altitude sites or on board airborne platforms or satellites. This paper describes the dataset of FIR spectral radiances of the atmosphere and snow surface emission measured in the 100-1000 cm -1 range by the Far-Infrared Radiation Mobile Observation System (FIRMOS) instrument during a 2-month campaign carried out from the ground at about 3000 m of altitude on the top of Mt. Zugspitze in the German Alps in 2018-2019. This campaign is part of the preparatory activity of a new space FIR mission, named Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is under development by the European Space Agency (ESA).
The dataset acquired during the campaign also includes all the additional measurements needed to provide a full characterisation of the observed atmospheric state and the local atmospheric and surface conditions. It includes co-located spectral measurements in the infrared range from 400 to 1800 cm -1 ; lidar backscatter profiles; radio soundings of temperature, humidity and aerosol backscatter profiles; local weather parameters; and snow/ice microphysical properties. These measurements provide a unique dataset that can be used to perform radiative closure experiments to improve modelling parameters in the FIR that are not wellcharacterised, such as water vapour spectroscopy, scattering properties of cirrus clouds, and the FIR emissivity of the surface covered by snow.
56 µm) using both FIRMOS, covering the 100-1000 cm -1 spectral range, and the first channel of E-AERI, covering the 400-1800 cm -1 range. Spectra are provided without apodisation, with a sampling resolution corresponding to the sinc response function of σ = 0. 3 cm -1 for FIRMOS (maximum optical path difference (MOPD) = 1. 66667 cm) and σ = 0. 48215 cm -1 for E-AERI (MOPD = 1. 03703 cm). The instrument line shape (ILS) of FIRMOS is a linear combination of sinc and sinc 2 (as in the case shown in  Bianchini et al. , 2019)  with a full width at half maximum (FWHM) of about 0. 36 cm -1 , whereas for E-AERI the ILS is an "ideal" sinc with FWHM = 0. 58 cm -1  (Knuteson et al. , 2004). More information about ILSs is provided with the dataset. Figure  2  shows FIRMOS' installation close to the E-AERI instrument, which is permanently installed on the top of the shelter, 4 m above FIRMOS, and routinely performs measurements of spectral DLR when the weather conditions allow operation.
, 2015). The Summit station is served with standard pressure, temperature, relative humidity (RH), and wind (PTHW) meteorological observations, provided by Deutscher Wetterdienst (DWD) at the website https://opendata. dwd. de/climate_ environment/CDC/observations_germany/climate/, last access: 28 February 2019, which for completeness were also included in the available dataset for the period covered by the field campaign. Useful standard atmospheric soundings with operational radiosondes available near Zugspitze can be freely downloaded via the University of Wyoming website (http://weather. uwyo. edu/upperair/sounding. html, last access: 26 October 2020) for the station of Muenchen-Oberschleissheim (Station No. 10868, daily soundings at 00:00 and 12:00 UTC), 100 km north of the Summit, and the Innsbruck-Flughafen station (Station No.
Field campaign The aim of the Zugspitze field campaign was to collect FIR measurements of the DLR to be used to provide evidence of the FIR capability to retrieve vertical profiles of water vapour, temperature, and cirrus cloud properties, as well as to perform a side-by-side validation of the FIRMOS measurements in the spectral range in common with the E-AERI instrument permanently installed on the site. The campaign took place from 29 November to 18 December 2018 and from 21 January to 20 February 2019. Measurements were performed when the weather conditions allowed operations; this occurred for a total of 33 d. The main specifications of the measurements, which are provided in the dataset, are summarised in Table  1. During the first part of the campaign in November-December 2018, FIRMOS was installed and made operational.
The weather conditions during that period were not very good, and only a few days of clear sky to test acquisitions occurred (only 43 useful sky observations). This  first part is considered an engineering campaign for the deployment and preparation of the full suite of instruments. For completeness, these measurements were included in the dataset, but their usefulness is quite limited. The second part of the campaign in January-February 2019 was dedicated to the acquisition of the DLR spectra, both in clear sky and in cirrus cloud conditions, and of all the ancillary information provided by lidars, radio soundings, and meteorological stations. Lidar measurements can be used to retrieve cirrus properties such as cloud geometry (cloud bottom and top heights), extinction profile, and optical depths.
FIRMOS validation The FIRMOS dataset includes calibrated spectra and noise estimates, calculated starting from the FTS-acquired interferograms and following the procedure described in Bianchini and  Palchetti (2008). Each spectrum is the average of four sky observations, and it is calibrated with four calibration measurements (two looking at a hot back body reference source and two looking at a cold one). The noise is char- acterised by a spectrally uncorrelated component (independent from one spectral channel to the other), called noiseequivalent spectral radiance (NESR) due to the detector error, and a spectrally correlated component, called calibration error (CalErr) mainly due to the temperature uncertainty of the calibration sources. The NESR is obtained from the error propagation through the calibration procedure of the 1σ detector random error, whereas the CalErr is calculated through the error propagation of the temperature accuracy error measured on each reference blackbody  (Bianchini and Palchetti, 2008).
The noise and the calibration accuracy for E-AERI were calculated, using the standard procedure provided by the manufacturer and outlined in  Knuteson et al. (2004) , from the variance of measurements observing constant sources and taking the average over 25 cm -1 wavenumber bins across the spectrum. To compare these features with the analogous features of FIRMOS, the same 25 cm -1 average was also operated. The comparison is shown in Fig. 8  in brightness temperature error at 280 K. The FIRMOS noise equivalent delta temperature (NEDT) was calculated from the STD using a similar approach as for the E-AERI noise estimate. More information about the spectral and radiance calibration of the two instruments is provided with the dataset. A measurable error of the spectral frequency scale due to the uncertainty of the metrology laser was found for  both FIRMOS and E-AERI by fitting the measurements with synthetic spectra.
Data availability The full dataset of the 2-month campaign, including infrared spectra (FIRMOS and E-AERI) and all the additional information (lidars, local PTHW, dedicated RS, snow SSA), is available via the ESA campaign dataset website https://earth. esa. int/eogateway/campaigns/ firmos (last access: 20 May 2021)  (Palchetti et al. , 2020a , https://doi. org/10. 5270/ESA-38034ee). ESA requires a free registration to inform users about issues concerning data quality and news on reprocessing. Information about the data formats are reported in README files within each data subdirectory.
Conclusions In summary, the unique spectral measurements provided by the combination of FIRMOS and E-AERI covering the relevant spectral region of the thermal emission of the atmosphere from 100 to 1800 cm -1 , together with the other supporting measurements (lidars, radio soundings, atmospheric state, and surface properties), provide a complete dataset that can be used to constrain radiative properties of water vapour, cirrus ice particles, and snow/ice emissivity over almost all the infrared emission, including the under-explored FIR spectral range.
Acknowledgements. The authors acknowledge the reviews from  Xianglei Huang  and the anonymous referees who provided valuable comments to improve the paper. Financial support. The study has been supported by the  European Space Agency (ESA)  and the  FIRMOS  project ( ESA-ESTEC  contract no. 4000123691/18/NL/LF ) for the development and the deployment of FIRMOS, as well as the  Italian Space Agency (ASI)  and the research projects  SCIEF (Italian acronym of Development of the National Competencies  for the FORUM experiment -ASI contract no. 2016-010-U. 0 ) for the preliminary development of a few subsystems. The dedicated balloon activities were also partly supported by funding from the  Helmholtz Association  in the framework of  MOSES (Modular Observation Solutions for Earth Systems ). Review statement.
Sample description The brain structural sample comprises a total of 39 cohorts with genotyping and magnetic resonance imaging (MRI) data-38 from the ENIGMA-CNV consortium in addition to a subsample of the UK Biobank  49  (project ID #27412). Demographic characteristics for each cohort are described in Supplementary Table  1  with a reference to participants' collection and datasets including individual inclusion and exclusion parameters. Extended information on diagnosis and family information can be found in Supplementary Note 1 and age distribution of the cohorts in Supplementary Fig. 1. All participants gave written informed consent and sites involved obtained ethical approvals. The main 1q21. 1 distal sample consisted of 28 deletion carriers, 22 duplication carriers and 37,088 noncarriers (Table  1 ) from 13 different datasets and 15 scanner sites with various ascertainments (family, clinical and population studies, case-control study for psychiatric disease) collected up until 30 September 2019.
Statistical analysis Imaging data processing and CNV calling were performed locally and de-identified CNV and imaging data were provided for a central mega-analysis. One of a pair of duplicates was kept. Relatives were removed from the sample used for the main analysis. In addition, we conducted a number of sensitivity analyses to test the robustness of the results (Supplementary Note 5 and Supplementary Tables  5 6 7 8 ). Individuals with a minimum overlap of 0. 4 to regions with known pathogenic CNVs (Supplementary Table  2 ) were excluded from the analysis regardless of copy number status as were individuals from scanner sites without 1q21. 1 distal CNV carriers. Brain measures were normalized in R v3. 3. 2 by an inverse normal transformation of the residual of a linear regression on the phenotype correcting for covariates as done previously  15.
For the primary analysis, covariates were age, age 2 , sex, scanner site and ICV. In the analysis of ICV, ICV was not included as a covariate. These final covariance-corrected values were used in downstream analysis and are reported for each measure. For comparison between groups, normalization was carried out including only the groups addressed (deletion and noncarriers, duplications and non-carriers) except for the deletion versus duplication comparison, where values from normalization of the entire dataset were used due to the low numbers. For the copy number dosage effect analysis (i. e. the effect on brain structure of 1q21. 1 distal copy number variation), a linear regression on the copy number status of the individuals (deletion = 1, normal = 2, duplication = 3) was performed using the following model: covariance-corrected, normalized brain measure ~copy number (deletion = 1, non-carrier = 2, duplication = 3).