dataesr/scientific-paragraphs-categorization huggingface dataset:
To assess the impact on the flux estimate, we compared the transports calculated with and without the RREX mooring data during the 2-year RREX period. The comparisons show that using the RREX data yields a small increase in the mean IC transport (0. 8 Sv or ~10% of the mean transport). Because the IC comprises waters in both the upper and lower MOC limbs, the corresponding impact on the MOC estimate is smaller. Over the 2015-2017 time period, the inclusion of the RREX mooring data results in an increase in the time-mean MOC by 0. 2 Sv. Modified reconstruction of the deep current in the Iceland basin. The deep current in the western Iceland basin contains the densest overflow waters in the subpolar North Atlantic flowing southward across the OSNAP East section. The deep current has a bottom velocity core above the eastern flank of the Reykjanes Ridge with a clear intrusion farther into the basin interior (Fig.
It effectively minimizes the bottom triangle below the maximum common depth between the moorings M2 and IB3. To assess the impact from this change, we compared the transport of bottom waters (σ θ > 27. 80 kg m -3 ) between M2 and IB3 for the whole 46-month time period. It shows that the new method yields a small reduction in the time-mean transport in the bottom layer (0. 5 Sv) which leads to a reduction in the time-mean MOC by 0. 2 Sv. Updated objective analysis (OA) product. Away from the boundary arrays, temperature and salinity fields are created based primarily on the Argo and mooring data in the vicinity, using an OA technique. In Lozier et al. 10   depth surfaces (OA depth ), which inevitably tends to mix waters with different densities. In the current calculations, we have used OA gridded products where OA is performed on density surfaces (OA dens ) to best preserve water properties.
This updated product was used throughout the 2014-2018 time period. Input for the OA dens comprises the Argo data, OSNAP mooring data as well as the WOA18 climatology. Note that OA dens is limited to the upper water column owing to the limitation of the maximum sampling depth of Argo floats (2000 m). For the deeper layers, we use hydrographic data from the 2014 and 2016 summer OSNAP cruises. Density fields are calculated from gridded temperature and salinity fields using Gibbs Seawater Oceanographic Toolbox (version 3. 06. 11; http://www. teos-10. org/ software. htm#1) of the International Thermodynamic Equation of Seawater-2010 (TEOS-10). The OA dens impact on the MOC estimate mainly arises from the use of density profiles for calculating the geostrophic flow in the glider survey domain above the Hatton-Rockall Plateau (Fig.
6 ). However, due to multiple instrument losses, there has been only ~8 months of good data returned (30-Oct-2014 to 19-June-2015) during the whole 4-year deployment. To accommodate this data loss, in Lozier et al. 10  velocities from the nearest current meter mooring (RTEB1) to the ADCP were used to fill the wedge when the ADCP data are not available. To assess its impact (e. g. , to what extent it captures the shelf-break current), the transports were calculated using the RTEB1 data following Lozier et al. 10  during the 8-month time period when the ADCP data are available. Comparing it to the actual ADCP-derived transport suggests that using the RTEB1 data does not resolve the 1 Sv mean wedge transport. Because the transport is within the upper MOC limb, such an underestimation leads to an underestimation in the MOC of 0.
8 Sv in the mean MOC during the same 8-month period. Thus, using the time-mean ADCP velocity for all the times when no ADCP data are returned leads to an increase in the time-mean MOC of 0. 8 Sv. Updated time-mean altimetry reference velocity. As part of the velocity calculations, the time-mean surface velocity over the observational record from satellite altimetry is used to provide the surface reference for calculating the absolute geostrophic velocities in the basin interior  10. With the extension of the length of our records, we have now used 4-year mean altimetry velocity (23-Aug-2014 to 26-May-2018) in the current calculations. To assess the impact from this update, we compare the MOC estimates for the 2014-2018 time period using the timemean surface velocity averaged over the first 21 months and over the whole 46 months, respectively.
ΠðσÞ ¼ 1 for½σ À σ * ≤ Δσ The outcropping area of each σ for each month is determined from the EN4 gridded subsurface salinity  51  and ERA5 sea surface temperature. Uncertainty for the monthly F is obtained as the ensemble standard error by taking into account the estimates using a variety of Δσ with surface buoyancy fluxes from either NCEP/NCAR 50 or ERA5. Uncertainty in the time-mean F is obtained by combining the uncertainty in individual months randomly following standard error propagation theory. Argo data. Temperature and salinity sampled by profiling floats as part of the Argo program (http://www. argo. ucsd. edu) are used to produce density profiles and to calculate the UNADW layer thickness in the Labrador and Irminger basin interiors. Argo data between 2014 and 2018 were downloaded by the U.
S. Global Ocean Data Assimilation Experiment (USGODAE) Argo Data Assembly Center. We used delayed-mode data with quality flag of 1 (good) or 2 (probably good) and rejected all problematic Argo profiles according to the Argo floats gray list (www. nodc. noaa. gov/argo/grey_floats. htm). Typically, there were 40 and 35 profiles in the Labrador and Irminger basin interior, respectively, each month during 2014-2018. Here the Labrador basin interior is defined as the region north of the OSNAP West line and deeper than 3000 m; the Irminger basin interior is the region north of the OSNAP East line and deeper than 2000 m. All profiles were linearly interpolated onto a uniform vertical grid with 20 m intervals, and then used for calculating layer thickness. The interior thickness is computed as the average of the 20% largest values of all available profiles.
Acknowledgements The authors thank  Kevin Balem  for processing the data from the French RREX moorings. Argo data are provided by  USGODAE GDAC  (https://nrlgodae1. nrlmry. navy. mil/;accessed on 5-March-2020). Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it (http://www. argo. ucsd. edu,http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System (https://doi. org/10. 17882/42182 ). We acknowledge funding from the  Physical Oceanography Program of the U. S. National Science Foundation  ( OCE-1259398 ,  OCE-1756231 ,  OCE-1948335 ); the  U. K. Natural Environment Research Council (NERC) National Capability programs the Extended Ellett Line  and  CLASS  ( NE/R015953/1 ), and  NERC  grants  UK-OSNAP (NE/K010875/1 ,  NE/K010875/2 ,  NE/K010700/1 ) and  U.
K. OSNAP Decade  ( NE/T00858X/1 ,  NE/T008938/1 ). Additional support was received from the  European Union   7th Framework Program  ( FP7 2007-2013 ) under grant  308299  (NACLIM), the  Horizon 2020 research and innovation program  under grants  727852  ( Blue-Action ), 862626 (EuroSea). We also acknowledge support from the  Royal Netherlands Institute for Sea Research , the  Surface Water and Ocean Topography-Canada (SWOT-C) ,  Canadian Space Agency , the  Aquatic Climate Change Adaptation Services Program (ACCASP) ,  Fisheries and Oceans Canada , an  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grant , and from the  China's national key research and development projects  ( 2016YFA0601803 ), the  National Natural Science Foundation of China  ( 41925025 ) and the  Fundamental Research Funds for the Central Universities  (201424001).
One participant was excluded from analyses for not completing the tasks correctly. The final sample included fifty-two participants: 12% underweight, 67% normal weight, 15% overweight, and 6% obese. The participants' median BMI was 22. 2. Shapiro-Wilk's tests results showed that participants' BMI and EAT-26 scores were not normally distributed (W = 0. 834, p < 0. 001 and W = 0. 845, p < 0. 001, respectively). Participants also rated their hunger level on a Likert scale from 1 to 7 and reported how much time had passed since their previous meal. Table  1  summarizes the demographical data of the experimental sample in participants with a BMI higher than the median ( "Higher BMI group ") and lower than the median ( "Lower BMI group "). Participants were tested at Udine's Santa Maria della Misericordia Hospital.
This task had the same instructions and stimuli as the bCFS task, but stimuli and distractors were superimposed and shown to both eyes, and therefore they did not elicit binocular rivalry and were immediately visible. Participants performed 40 trials of training, during which a dedicated set of images was displayed, and 320 trials of the bCFS task and NoCFS task each, with a break every 30 trials. The total testing time was of about one hour and 15 minutes. Example trials of the bCFS and NoCFS tasks are shown in Fig. 1  A and 1 B. The tasks were programmed in and presented with Python using the Psychopy library  ( Peirce, 2007 ). Stimuli consisted of 20 images of food and 20 images of nonfood items (e. g. animals, man-made objects, etc. ), selected from the FRIDa database  ( Foroni et al. , 2013 ).
Half of the food stimuli depicted highly caloric food (i. e. cookies), and the other half low-calorie foods (i. e. strawberries); in each group, half of the depicted foods were highly transformed (ice cream, fruit salad) and the other half was comprised of natural foods (almonds, cherries). Food and nonfood items were matched for brightness [ p = 0. 82] and spatial frequency [ p = 0. 09] since low-level visual features can influence suppression times  ( Gayet et al. , 2014 ; Gray et al. , 2013 ; Yang and Blake, 2012 ). Food and nonfood items also did not differ in valence, familiarity, typicality, or arousal according to the databaseindependent ratings [ ps > 0. 15]. A subset of the stimuli is depicted in Supplementary Figure  1. The distractor images consisted of a set of 100 pictures with colorful oval shapes of different sizes creating random patterns.
Analyses Breaking continuous flash suppression: We investigated the effect of the participants' BMI on RTs in the bCFS and NoCFS tasks using linear mixed-effects models (LMMs). All analyses were run using the software R (version 3. 6. 2, R Core Team, 2017 ). LMMs were tested using the "lmer " function (lme4 package,  Bates et al. , 2015 ). Effect sizes of the LMMs were calculated using the marginal and conditional R2 statistics  ( Draper and Smith, 1998  ) using the r. squaredGLMM function of the MuMIn package  ( Barton, 2020 ). The effect size of individual factors was calculates using the effectsize package  ( Ben-Shachar et al. , 2020 ). Marginal R2 statistics quantify the amount of variance explained by the fixed effects of the models, while conditional R2 quantifies the variance explained by both fixed and random effects.
To verify that all predictors and all interactions significantly improved the model fit, non-significant interactions and predictors were removed from the model, and the resulting model was compared to the initial model on the basis of the AIC criterion  ( Bolker et al. , 2009 ) , using the "anova " function (lmerTest package,  Kuznetsova et al. , 2017 ). All factors and interactions that did not significantly improve the initial model were pruned. To confirm that high-level interactions explained more variance than the combination of the lower-level interactions, we compared the AIC of models with and models without the high-level interactions, and only included highlevel interactions that improved the models' fit. To make sure our predictors were not correlated among themselves, we computed the variance inflation factor (VIF) for the predictors of the final model using the VIF function of the car package  ( Fox and Weisberg, 2019 ).
Data acquisition and preprocessing A 3 Tesla Philips Achieva scanner was used to acquire the anatomical and functional images. Anatomical images consisted of high-resolution In the VBM analyses, preprocessing was done using the SPM12 (version 7219,  Friston et al. , 2017 )  and CAT12 (version 12. 3, r1318;  Gaser and Danhke, 2016 )  toolboxes in Matlab (version 2017a, 9. 2. 0, www. mathworks. com/products/matlab. html ). Scans were spatially normalized using the DARTEL algorithm, and SPM12 tissue probability maps were used to segment the scans into grey matter (GW), white matter (WM), and cerebrospinal fluid (CSF), as well as for spatial registration, after which the Total Intracranial Volume (TIV) was calculated. Data were then smoothed with an 8 mm full width at half maximum (FWHM) gaussian kernel.
The final voxel size was of 1. 5 × 1. 5 × 1. 5 mm. For the resting state analyses, data were preprocessed and analyzed using the CONN toolbox (version 17; Whitfield-Gabrieli and Nieto-Castanon, 2012 ). The preprocessing was done following CONN's default processing pipeline. Raw functional images were realigned and unwarped, time-slice corrected, and coregistered to T 1 weighted images. Images were then normalized in MNI space and spatially smoothed with an 8 mm FWHM kernel. Data were processed by removing movement parameters by covarying the 6 rotation/translation movement, as well as regressing motion outliers using the ART package (Artifact Detection Tools, https://www. nitrc. org/projects/artifact_detect ). Following the standard settings implemented in CONN, no participant was discarded from the analysis, since acquisition's frame-wise displacements were below 0.
Finally, to assess whether in participants with a higher BMI, compared to participants with a lower BMI, performance in the behavioral tasks was connected to GM density in different areas, we formed two groups of participants, one with higher BMIs and one with lower BMIs, based on a median split. We ran two full factorial models with suppression times in the CFS task and the mean difference between nonfood and food RTs in Go trials in the Go/No-Go trials as behavioral covariates to assess whether a significantly different regression slope predicted GM density in the two groups, based on their behavioral performance. The regression slopes' beta values were extracted using the REX Matlab toolkit  ( Duff et al. , 2007 ). Corrections for multiple comparisons based on uncorrected voxel-level (alpha = 0.
Resting state analyses The resting-state analyses aimed at establishing if the regions with altered GM density also showed connectivity patterns that correlated with participants' BMI and task performance. The peaks of each resulting cluster of the VBM analyses were used as seeds for Seed-to-Voxel connectivity analyses. Custom spherical ROIs with a radius of 8 mm centered on each peak were created using the MarsBaR toolbox in Matlab (MARSeille Boîte À Région d'Intérêt,  Brett et al. , 2002 ). The behavioral variables that had led to the identification of each peak were used as regressors in the corresponding ROI connectivity analyses (e. g. participants' performance on the bCFS task was used as a regressor in the analysis of the connectivity of the ROIs derived from the VBM on the bCFS task).
Following the same approach, the peaks resulting from the VBM analysis, whose regression slope significantly differed in the two groups depending on participants' performance in the bCFS task, were used as seeds in the Seed-to-voxel analysis of the difference in connectivity between high-and low-BMI groups, as predicted by participants' performance at the bCFS task. The same was done for ROIs resulting in the VBM analysis of Go/No-Go task performance. Beta values of the regression slopes were calculated using the REX Matlab toolkit  ( Duff et al. , 2007 ) , and correction for multiple comparisons was applied to all regressions (uncorrected peak level alpha = 0. 001, cluster level FDR alpha = 0. 05). All analyses included age and gender as covariates of no interest, as done in previous works (see e.
The eluted fraction was collected and stored. Afterward, the columns were washed with 10 mL 50 mM Tris-HCl, pH 8. 0, 200 mM NaCl. The second elution step was achieved by using a high concentration of imidazole, therefore 1 mL 50 mM Tris-HCl, pH 8. 0, 200 mM NaCl, 50 mM DTT, and 500 mM imidazole were applied to the column and incubated at RT for 15 min. The second elution step was also collected and stored. The samples of the first and second elution were trypsinated after chloroform/methanol precipitation  [25]  and dissolved in 0. 1% formic acid, 1% acetonitrile. Peptides were separated by reverse-phase nano-LC and analyzed by electrospray ionization-mass spectrometry (ESI-QTOF-MS) as described  [26]. Data were searched against the entries of UP000006548 3702 ARATH A. thaliana of the UniProt database using ProteinLynx Global Server 3.
A wealth of confidential data available for research in France Twenty years ago, in France, the practice of granting researchers access to anonymized microdata from public sources was just starting. In 1986, a first agreement was signed between the National Statistical Office, INSEE (Institut National de la Statistique et des Études Économiques) and CNRS (Centre National de la Recherche Scientifique), entrusting one of the CNRS research units specialized in quantitative sociology 1 with the responsibility of making a set of anonymized datasets available to all CNRS researchers, prior to any legislation in this regard. This process kept going for a decade, allowing access to an increasing number of survey data, including data provided by statistical departments in various ministries. However, significant difficulties remained to extend access beyond CNRS to all university-affiliated researchers.
The expanding set of agreements triggered new possibilities for matching these datasets via the national individual identifier, for research purposes. In addition, thanks to its certification as a highly secure remote access system, CASD was able to sign agreements to host data from several epidemiological cohorts (panels) supervised by academics, including one of the largest in Europe, CONSTANCES. Moreover, due to the legal obligation to comply with the rigid security baseline meant to protect privacy for all analyses based on the medico-administrative data of the the National Health Data System (SNDS, Système national de données de santé), many organizations active in the health sector now rely on CASD in order to pursue health data analysis, and to carry out data matching operations with SNDS data for clinical studies, or for enriching registers and cohorts.
A first provision, in connection with the Archives Act (Loi Archives) which covers all government data, authorizes data holders to provide access to their administrative data for research purposes, even when such data are protected by professional confidentiality rules. This presents an interesting attempt to address the issue of potentially having to change regulatory texts one by one for each body or service. Two other provisions, one for the statistical system coordinated by INSEE, the second for the researchers, authorizes the possibility to match these data using the national identification number (NIR, Numéro d'inscription au répertoire de l'INSEE ). The matching procedure requires the involvement of trusted third parties, the use of a irreversible hashing process, and strict requirements regarding the destruction of the matching keys.
Data requirements for the assessment of public policies (for which researchers are strongly mobilized, some of these being in think tanks attached to Prime Ministerial services, such as the Centre d'Analyse Economique or France Stratégie) contributed substantially to changing the legal framework. In its recent annual report 6  , the Conseil d' État thus underlined the decisive role of access to confidential data played by CASD for improving the assessment of public policies in France. Another factor has been the pressure exerted by the Open Data initiative. Despite the fact that it primarily concerns anonymized data available to the general public, it nevertheless sheds light on the necessity to design a framework enabling access to confidential data for authorized users. Significantly, provisions which facilitate access to and matches between administrative datasets are implemented in the 2018 Digital Republic Law.
Security concerns were a key driver while opening access to confidential data. Creating highly secure access has been at the heart of this process. It has fostered the confidence of producers and citizens alike and greatly facilitated legislative development. The creation of the CASD Secure Data Hub within INSEE in 2007 thus preceded an amendment to the 1951 Statistics Law by a few months. As a trusted third party between producers and users, CASD became an independent public entity in 2019. Set up relatively late, compared to several other countries, where onsite access has been implemented since the early 2000s, CASD benefited from the technological advances made since then, opting without further delay for remote access as the sole method of access. Many remote access systems rely on software which needs to be installed on the user's workstation.
The solution designed and fully controlled by CASD is an integrated system, involving a dedicated access device (the "SD-Box") and biometric authentication. This integration enables end-to-end control of security, offering both greater security for the data provider, and ease of deployment and usage for the users. Likewise, the CASD architecture facilitates the recommended security certifications, in particular ISO 27 0001 and RGPD certifications, because each component of the chain is fully controlled by CASD, without any additional constraint for the user. This high security level is combined with great ease of work for the user until the final drafting of the articles inside the "secure bubble" of the project. Strong and transparent governance has been a third element in France to facilitate the opening of access to confidential data.
For Secure Use Files, authorizations are submitted for each project and research team by the CSS, ensuring projects and users are "safe" in coordination with the bodies in charge of data privacy protection (CNIL and National Archives). The principles are broadly comparable to those upheld by other countries: applicants have to provide a detailed description of the project, including the need for confidential data, the research or study purpose of the work, the methodology, and the list of data sources required. Secure settings are mandatory and secure remote access is provided for most data sources via CASD. User awareness of confidentiality issues is an important dimension, contributing to "safe" behavior among data users. Authorized users are required to attend CASD training sessions 8  on security covering legal aspects (user responsibility and sanctions in case of breach of confidentiality) and the anonymization rules applied to research outputs (safe outputs).
9 Transparency, which is important for producers, users and citizens alike, is also an element of the governance. All research projects are posted on the CASD website with links between data, projects, and publications. CASD has also set up a cooperation with the cascad (Certification Agency for Scientific Code and Data Agency, a collaboration between UMS 2007, École des Hautes Études Commerciales de Paris-HEC Paris, Université d'Orléans, and CNRS) to assess, prior to submitting a paper to a journal, the reproducibility of research results based on confidential data hosted by CASD. Such verifications would normally take a long time to obtain if reviewers have to go through the procedure for accessing confidential data. 10.