dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Claire Naylies  and  Yannick Lippi  for their contribution to microarray fingerprints acquisition and microarray data analysis carried out at  GeT-TRiX Genopole Toulouse Midi-Pyrénées facility ,  Dr. J. J. Peroua ,  Dr. M. Salzmann-Lovato , and the medical assistants for their help with patient recruitment, and the patients for their participation in the study. This work was supported by funds from the  French National Research Agency ,  ANR  ( ANR-11-JSV1-0006 , LC), and the  Fondation pour la Recherche Médicale  ( DPP20151033943 , LC).
2 mm yr -1 ). This extra convergence is induced by the compression imposed by the motion of the Anatolian micro plate, itself resulting in the rotation of the Pindos region  (Valkaniotis et al. 2020). In consequence, we expect a larger rate of convergence to the south of this zone, around Preveza and a lower rate of convergence to the north around Corfu and the continuation of this decrease until Vlora. Valkaniotis et al. (2020)  evaluated at 60 km the width of the deforming zone (defined as the zone where 80 per cent of the total convergence is accommodated), a value consistent with that found by  Kassaras et al. (2016)  from their analysis of a large database of focal mechanisms (see top section in their fig. 11 ). The thickness of the brittle (locked/seismic) area found by Valkaniotis et al.
A C K N O W L E D G E M E N T S We are grateful to the EUREF, IGS and several academic institutions from Greece (NOA, NTUA, TEIC, AUTH, TUC, NKUA, U. Patras), France (CNRS), Bulgaria (BAS), Italy (INGV), Czech Republic (Charles University), UK (COMET) and USA (UNAVCO) for distributing their GPS data through open channels. We are grateful to the companies TreeComp and Metrica SA (HxGN SmartNet) who provided us with the GPS data of their stations. The data from Albania are distributed by ALBPOS. The time-series of the Turkish stations (GCM network) are those distributed by the Nevada Geodetic Laboratory  (Blewitt et al. 2018). We had access to data from three stations of the Greek KTIMATOLOGO SA network. We thank JPL/NASA for the GIPSY 6. 4 software that was used to process the GPS data. We thank Nicolas Chamot-Rooke, Simon Bufféral, Hélène Lyon-Caen, Ioannis Kassaras, Sotiris Valkaniotis and Antonio Avallone for fruitful discussions.
We thank Vassilis Zorapas (IGME) for providing us borehole water level data. For several figures we used the Generic Mapping Tool  (Wessel & Smith 1998)  and SRTM and EMODNET topography data. Author contribution statement: The four authors have been deeply involved for 20 yr in the deployment, maintenance and data management of the academic permanent GPS stations from Greece and Bulgaria contributing to this paper. PB and AG gathered the RINEX data. PB processed the GPS data, analysed the time-series, made the blocks model and wrote the paper with the help from co-authors. All authors contributed to the edition and review of the manuscript. We are very grateful to the Editor, one anonymous reviewer and Wayne Thatcher for their very detailed and constructive reviews.
DATA AVA I L A B I L I T Y The GPS data acquired by the academic partners is available through their web portals. The time-series of the GPS station coordinates are provided in the Supporting Information. The computer code HelVel2020 that calculates velocities in Greece is provided in the Supporting Information. The ITRF2014 coordinates of all GPS stations at the epoch 2020. 0 are provided in the Supporting Information (Table  S1 ). 1  and 2 ). After the earthquake the velocity of the station has changed. The transient can be modelled with a sum of two exponential terms of time constant 65 and 1295 d, respectively. Figure  S3. Additional toponymy for features not mentioned in the main text but in the Supporting Information (coordinates and description of the sites in Table  S2 ). At AIGU and ATHI we measure an offset at the date of the earthquake with amplitudes that are reported in Table  S4.
Methodology In a first step, I used historical data to quantify the cement and steel consumption for each country to provide high access levels to different infrastructure services in 2030 and until 2050 in case of population changes. Subsection 2. 1 describes the data and the global historical patterns of infrastructure access along the material stocks increase. Subsection 2. 2 details the modelling approach used in this step. In a second step, I quantified the CO 2 emissions corresponding to the material consumptions obtained in the previous step. Subsection 2. 3 describes how I represented in the modelling framework country-specific determinants of CO 2 emissions related to cement and steel consumption. 2. 1. Analysing trends in infrastructure access levels along the in-use material stocks build-up 2.
The reduction of emissions in the cement sector is more related to a change in technological level than to a choice of production route as in the steel sector  (van Ruijven et al. , 2016). The second production stage is the blending and grinding of clinker with other materials to produce cement  (Branger and Sato, 2017). The conversion of primary energy into electricity used during this process also generates CO 2 emissions, which I refer to as indirect emissions. I estimated process, direct and indirect emissions to produce one ton of cement in each producing country. The World Business Council for Sustainable Development (WBCSD) developed the Getting the Numbers Right (GNR) database synthesizing from 2005 to 2018 technical informations for more than 900 individual cement plants in different world regions.
I extracted for the year 2015 the regional average values of clinker ratio, carbon intensity of the fuel mix, thermal energy consumption and cement plant power consumption (WBCSD, 2016). I applied these values to each country included in the related world regions. Process emissions factor for clinker production was taken from  Eggleston et al. (2006)  and national CO2 intensity of grid from IEA (2017a). Data and sources are synthesized in Table  5.
I assumed the related trade effects associated on emissions for these products are negligible. I considered here one ton of cement produced in a country is consumed in the same one following  Hache et al. (2020)  and  Denis-Ryan et al. (2016). Conversely, the 2015 value of trade ratio for crude steel is 30% highlighting potential imported or exported emissions. To represent the steel trade structure, I first extracted from BACI database  (Gaulier and Zignago, 2010)  the imports and exports flows in weight of steel primary products for the year 2015 and for 207 countries. The commodity HS codes considered are from 7206 to 7306 following the World Steel Association guidelines  (World Steel Association, 2011). I then summed all the steel flows from one country to another and built a bilateral trade matrix where each element (i,j) represent the aggregated weight of steel primary products imported by the country i from the country j.
I then incorporated in the matrix diagonal the domestic consumption assuming it as equal to the steel production minus the steel exports (in case of negative values, it was considered as null). I finally divided each line i by the apparent consumptiondomestic production minus exports plus importsof the country i. Steel production data was obtained from World Steel Association (2016). In each cell (i,j), the obtained value represents the share of country i consumption of steel primary products coming from the country j.
To project the evolution of the electricity carbon intensity, I use the average values in 2050 extracted from the low carbon scenario database ADVANCE  (Luderer et al. , 2018; Vrontisi et al. , 2018) , which is described in more detail in the next section. Data are disaggregated across 5 major world regions and for different levels of climate ambition. Following  Audoly et al. (2018) , I defined the carbon content of electricity as the ratio between the emissions from the power sector and the total electric energy produced by the power sector. Values are based on the Med2C and the WB2C scenarios of the ADVANCE database for respectively the median2C and the below2C alternatives. In these scenarios, the carbon content can be negative because the emissions reported for the electricity sector are those related to fossil combustion as well as the negative emissions related to the use of bio-energy with carbon capture and storage (BECCS).
Composition of the regions can be found in the description of the Advance Scenarios Database (https://db1. ene. iiasa. ac. at/ ADVANCEDB/dsd?Action=htmlpage&page=about). Most of the material demand is concentrated in the developing regions ASIA and MAF which represent more than 90% of global demand regardless of the infrastructure considered (Fig. 3 ). Within these regions, material demand is unevenly distributed across countries (Fig. 4 ). Some countries contribute particularly to the global demand such as India, Nigeria, Indonesia, Pakistan, Bangladesh, Ethiopia, Congo Dem. Rep, the Philippines and Tanzania where material needs are greater than 1 Gt to reach high access levels to transportation infrastructure. One factor is the high population growth over the period 2015-2030, ranging from 21 million inhabitants for Tanzania to 235 million inhabitants for India.
5 ). I use as reference low-carbon scenarios from the ADVANCE database  (Luderer et al. , 2018; Vrontisi et al. , 2018). In this database, nine integrated assessments models have produced a set of global climate policy pathways consistent with limiting temperature increase in the 1. 5-2 • C range with different levels of short-term ambition. This database has also the advantage of giving quantification of CO 2 emissions in the industrial sectorboth process and direct emissions from energy consumptionat the global scale and at a 5 world regions disaggregation (see Section 3. 1 for regions description). To be able to compare my results with the ADVANCE scenarios database, I first excluded in this part the indirect emissionsemissions induced by the conversion of primary energy into electricity used-which are aggregated to the electricity sector emissions in the ADVANCE database.
Secondly, in order to have an order of magnitude of cement and steel sectors emissions in the database scenarios, I assumed that their share in the total industry sector emissions were similar over time and between the five world regions. The cumulative emissions of the industry sector extracted from the ADVANCE database scenarios were therefore multiplied by 0. 55 following  IEA (2017b). Finally, to be consistent with the decarbonisation scenarios I applied for the cement and steel sectors in the modelling framework, I selected in the ADVANCE database (i) the 2020 Med2C and 2030 Med2C scenarios as comparison references for the median2C estimations and (ii) the 2020 WB2C and 2030 WB2C scenarios as comparison references for the below2C estimations. In the 2020 Med2C scenarios, mitigation efforts are strengthened after 2020 to limit cumulative 2011-2100 CO2 emissions to 1600 GtCO2, leading to more than 50% to stay below 2 • C.
As for all Argo physical and biogeochemical variables, radiometry quality-control (QC) must be provided in real-time (RT) and delayed-mode (DM). The RT-QC is mainly devoted to operational oceanography (e. g. , assimilation in forecast models of ocean state) and consists in a number of automatic procedures that target the evaluation of a single profile at a time and QC data distribution within 12 h from sampling. The DM-QC aims to make data available within 12 months from the acquisition, after human control and exploiting all measured profiles together  [31]. The resulting DM-QC dataset is expected to have the highest quality requested for scientific analysis and, ultimately, for climate studies. The RT-QC procedure for radiometry, accepted by the Argo Data Management Team, aims to check and flag measurements outside the range of expected values  [32].
The Biogeochemical-Argo Database Biogeochemical-Argo data used to develop and assess the DM-QC procedure for radiometric profiles were acquired by 55 no longer profiling PROVOR-CTS4 floats, for a total of 12,867 measured radiometry profiles. This fleet has operated since 2012 across a variety of trophic environments and regional seas (Figure  1 ). All floats were configured and deployed according to standard procedures  [35]. The data were downloaded from the Coriolis Global Data Assembly Center (GDAC) and stored in the Argo B and trajectory files (ftp://ftp. ifremer. fr/ifremer/argo (accessed on 1 November 2020)). temperature on BGC-Argo radiometric dark signals according to the material used to build the instrument, and account for sensor dark drift with time (hereinafter referred to as aging).
The Biogeochemical-Argo Database Biogeochemical-Argo data used to develop and assess the DM-QC procedure for radiometric profiles were acquired by 55 no longer profiling PROVOR-CTS4 floats, for a total of 12867 measured radiometry profiles. This fleet has operated since 2012 across a variety of trophic environments and regional seas (Figure  1 ). All floats were configured and deployed according to standard procedures  [35]. The data were downloaded from the Coriolis Global Data Assembly Center (GDAC) and stored in the Argo B and trajectory files (ftp://ftp. ifremer. fr/ifremer/argo (accessed on 1 November 2020)). Floats were programmed to drift at a parking depth of 1000 dbar and acquire vertical profiles up to the sea surface every 1 to 10 days. Pressure and water temperature data were collected every 2 s by a SBE-41 CP conductivity-temperature-depth sensor (Sea-Bird Scientific, Bellevue, WA, USA), and quality-controlled according to standard, internationally-accepted protocols  [36].
Such characteristics reinforce previously published scientific studies restricted to the first optical depth or the mixed layer  [4, 6, 20] , and joint applications with remote sensing observations  [8, 11]. Yet a newly generated radiometric database enhanced with sensor dark's aging and temperature-dependence corrections will surely open to the possibility of re-analysis studies. However, the applied DM procedure correctly resolves artificial features such as steps in the profiles due to a significant increase of the dark counts which respond to the sudden changes in water temperature (Figure  7g-i ). The developed protocols remove these features and shift to zero dark values at depth, so that the resulting radiometric profiles show the monotonic decrease with depth as expected. The DM-QC procedure we developed has been implemented over a total of 12,867 measured profiles each band.
The procedure returned profiles that monotonically decreased as expected from theory and reached greater depths (Figure  7 ). A total of 11,824 profiles (from 47 floats), i. e. , about 92% of the tested database for bands at 412 and 490 nm, and PAR was corrected (Figure  8 ). In the case of E d (380), correction was successful for 11,597 profiles (from 46 floats), i. e. , 90% of the tested database. In particular, the DM successfully corrected profiles derived from 45 floats made with PEEK components (44 floats for E d (380)), and two floats with aluminum components. The uncorrected 227 E d (380) profiles (all from one float) were corrected with alternative procedures (see Supplementary Materials Section S2). The developed QC procedure demonstrated high and similar performances for all radiometric channels.
e. , about 5% of the tested database) were corrected with alternative procedures specifically developed for the array of 76 floats with an insufficient number of night profiles or drift measurements (Supplementary Materials Section S2), while 461 profiles from five floats (i. e. , about 4% of the tested database) were not corrected. Correction was made with alternative procedures when the ancillary data (most often night profiles) were not good enough to confidently apply the procedure described here, correction was abandoned when the alternative methods also failed. Overall, the DM-QC procedure to correct the sensor dark signal systematically succeeded for all tested floats with at least four night profiles collected over the float lifetime (Figure  9 ). Overall, the DM-QC procedure to correct the sensor dark signal systematically succeeded for all tested floats with at least four night profiles collected over the float lifetime (Figure  9 ).
We must notice that the joint use of the DM-QC method here proposed with the one presented by Organelli et al. [28]  represents an opportunity to generate a unique highquality and interoperable radiometric dataset free of clouds and wave focusing/defocusing. Given the potential for the BGC-Argo network to expand  [2, 46] , it can be expected that the resulting dataset, potentially increasing in near real-time, would allow addressing or readdressing key topics of applications in ocean optics the investigation of which was up to now suffering from limited data availability. The quality of the data could be further enhanced when also the impact of instrument tilt on measured values as well as the effect of bio-fouling that can occur  [8]  will be taken into account. Among these ocean optics science topics, the understanding of regional and seasonal variability of K d with a higher degree of confidence must be refined along the water column  [4].
Additionally, comparing such in-situ BGC-Argo float products with their satellite counterparts would allow the identification of the locations where bio-optical anomalies or nuances exist. This would represent a preliminary step to understand the causes of discrepancies and, as a consequence, possibly refine the retrieval algorithms for satellite products in some areas. The possible derivation of radiometry with depth over the whole vertical dimension is expected to provide high resolution K d profiles that will be useful to address the link between surface remotely-sensed properties and their vertical variability according to region and season. Such data could in turn allow to re-evaluate and possibly improve methods developed to retrieve the vertical profile of chlorophyll-a from simultaneous measurement of chlorophyll fluorescence and radiometry from floats, methods that were initially developed on a very small float dataset  [47].
The improved accuracy of radiometric measurements with depth will also enhance their use across the biogeochemical and ecosystem model community. An improved accuracy is expected to support studies that assimilate irradiance data to model phytoplankton photosynthesis  [26] , especially at the most elevated depths where the deep chlorophyll maxima are observed and supported by small quantities of light. When considering the DM corrected profiles over the whole tested database, the method we presented showed high and similar applicability for the three channels of downwelling irradiance as well as for PAR, thus suggesting potential applicability to hyperspectral radiometers. With the advent of future hyperspectral satellite missions  [48] , there is an increasing interest in in-situ hyperspectral optics.
Finally, it should be noticed that with the increasing development of robotic observation systems, a fleet of sensors can now be deployed and operated globally which definitely will change our way to look at data and qualify them. Working with a dense dataset acquired from multiple-a priori identical and interoperable-instruments will indeed allow us to identify sensor issues that would be difficult to discover on a case-by-case analysis  [43]. In this respect the BGC-Argo network represents a unique platform to help in improving sensor performances for the benefit of other observation systems. Computer Code and Software: The computed code to implement the protocols presented in this study is openly available at https://github. com/qjutard/radiometry_QC (version 1. 03, accessed on 13 September 2021).
Recruitment of Participants and Group Characteristics The online survey was approved by the ethics committee of the Medical University of Graz, Austria (No: EK 31-021ex 18/19). Data were acquired using an anonymous, selfrated questionnaire accessible via Google forms (https://www. google. com/forms/about/, accessed on 1 December 2018). The questionnaire was distributed via email to national and international colleagues using a combined snowball sampling approach. All psychiatrists of Austria received an invitation to participate via the Austrian medical chamber. Psychiatrists and psychologists worldwide were contacted through the early career psychiatrists (ECP) network of the World Psychiatric Association (WPA) and local professional psychiatric associations. Participants provided informed consent electronically before participation.
Statistical Evaluation and Data Management The analyses were conducted in SPSS v23. 0 (IBM, Armonk, New York, NY, USA). Data visualization was performed using Microsoft Excel 365 (Microsoft Corporation, Redmond, USA). Unless stated otherwise, descriptive results of continuous variables are expressed as mean and standard deviation (SD) for Gaussian distributed variables. Data of the Likert scales are expressed as Median (Mdn) and Interquartile Range (IQR). Depending on the distribution of data and type of data, we performed an ANOVA, Mann-Whitney U test, Kruskal-Wallis-Test, or χ 2 -test to identify differences between professional groups. Professional subgroup analyses were conducted for demographical data (sex, age, working experience), current perceptions of 'Nutritional Psychiatry', and current treatment practices.
Correlations between variables (current rated knowledge, work experience, rated importance of 'Nutritional psychiatry', and rated 'importance of discussion nutrition with patients') were calculated with Spearman's correlation coefficient. Unless otherwise specified, missing survey data were mostly treated as user-defined missing values in SPSS. Data were screened for plausibility and manually converted into missing values if they were not plausible. Results for post-hoc tests were corrected with the Bonferroni correction for multiple comparisons. Levels of statistical significance were set at p < 0. 05 (two-tailed).
Results Data were collected for a total of 22 months (from December 2018 to September 2020). Participants who did not agree to the terms and conditions (n = 22) and those who did not meet the inclusion criteria (n = 21) were excluded from the analysis. The remaining participants (n = 1056) were included in the data analysis. When someone had several professions (for example being a psychiatrist and a psychotherapist), the reported main profession (psychiatrist) was counted. Figure  1  gives an overview of the flow of participants through the study.
Demographical Data of Study Participants A final sample of 1056 participants, 354 psychiatrists, 511 psychologists, 147 psychiatry and psychology trainees, and 44 psychotherapists, from 52 countries, were included. The majority of the participants were female (71. 9%), and the mean age was 39. 9 (SD 10. 0) years. The majority of participants reported working in a hospital (n = 450, 42. 6%), followed by private practice (n = 233, 22. 1%), mental health outpatient services (n = 178, 16. 9%), rehabilitations centres (n = 64, 6. 1%), and day clinic (n = 20, 1. 9%). A further 10% reported working in institutions other than those listed above, and one participant did not specify their place of work.