dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This project has received support from the  Norwegian Agency for International Cooperation and Quality Enhancement in Higher Education (DIKU) , which supports the  Center for Computing in Science Education , from the  Research Council of Norway  through the project  MAMMAMIA  (Grant No. 301837 ) and from the  Faculty of Mathematics and Natural Sciences at the University of Oslo  through the strategic research initiative EarthFlows. We thank  Dr. PiM Lefeuvre  and  Dr. Simon Filhol  for constructive insights. The authors declare no conflict of interest. The authors thank reviewer  Martin Truffer , one anonymous reviewer, the Associate Editor and the Editor,  Olga Sergienko , for thorough reviews that improved the manuscript.
We choose to focus on the well-sampled period since 2005, where the Argo data coverage is almost global, in order to assess the ability of the current in-situ observing system to capture ocean intrinsic variability. The 50 sets of synthetic profiles were mapped using the In-Situ Analysis System (ISAS), using its standard configuration for mapping real in-situ profiles  (Gaillard et al 2016). This approach allows us to investigate the hydrographic coverage (down to 2000 m depth) needed to estimate the TSL and OHC changes and the imprint of intrinsic ocean variability as simulated by the ensemble numerical model and as seen by the in-situ network. The paper is organized as follows. Section 2 describes the data and methods used in this study. Section 3 presents the imprints of atmosphericallyforced and intrinsic variability on regional OHC and TSL trends.
ISAS ensemble analyses First, synthetic temperature and salinity profiles, corresponding to the observed profiles contained in the EN4 database (excluding eXpendable BathyThermographs profiles as the latter do not reach 2000 m depth and do not record salinity; Good et al 2013) have been extracted online from each member during the OCCIPUT ensemble integration. It is worth noting that the synthetic profiles are representative of the ocean state averaged over ¼ • model grid boxes rather than real point-wise longitude/latitude profiles as in real-world observations. We obtain 50 sets of insitu-like synthetic profiles of temperature and salinity from the surface to 2000 m depth over 2005-2015. A corresponding set of in-situ-like profiles has been extracted from the OCCICLIM run to correct for any possible model drift in the synthetic OCCIPUT profiles.
Each set of synthetic profiles is remapped on a regular 0. 5 • × 0. 5 • grid over 152 standard depths using the ISAS tool  (Gaillard et al 2016). ISAS is based on optimal interpolation (OI; Bretherton et al 1976), and is designed to map in-situ profiles onto a regular grid on a monthly basis to fill the spatio-temporal gaps between missing data and provide an associated error estimate. We will refer to this remapped set of 50-member simulations as the ISAS OCCIPUT ensemble. We also remapped the in-situ-like profiles from OCCICLIM, and will refer to this as the ISAS OCCICLIM solution. The ISAS configuration was the same as used for ISAS15 configuration  (Kolodziejczyk et al 2019 , Kolodziejczyk et al 2021). For mapping each member of ISAS OCCIPUT and ISAS OCCICLIM, we follow four steps described in Gaillard et al (2016): • The synthetic temperature and salinity profiles are standardized onto 152 levels.
We also computed the OHC and TSL fields from OCCICLIM and ISAS OCCICLIM simulations to assess any spurious model drift. At each grid point of these two data sets, we estimated the decadal OHC and TSL linear trends. We remove these decadal trends from each member of the OCCIPUT ensemble simulation and from the ISAS OCCIPUT ensemble gridded products for each field. We thus obtain two 50-member ensembles for OHC and TSL, both with gridded outputs, corrected for any spurious model drift.
The differences are locally large, especially in the western boundary currents and the ACC, with absolute values exceeding 10 mm yr -1. The subtropical gyres also exhibit large differences between the ensemble mean and member #1, with values exceeding 5 mm yr -1 and zonal extensions corresponding to oceanic jets. We find similar results for regional OHC trend maps based on OCCIPUT (see figures S2(a) for the atmospherically-forced trend map and S1(a) for member #1 in supplementary material). Figure  2 (b) shows the atmospherically-forced regional TSL trends estimated from ISAS OCCI-PUT. As previously mentioned, this trend map looks like member #1 presented in figure  1(b ). Again, large-scale decadal trends of regional TSL are captured by the in-situ data distribution, albeit with lower amplitude.
e. SNR < 2). Very interestingly, figure 3(b) shows similar spatial patterns, but with coarser spatial resolution. The degraded spatial resolution comes from the OI mapping. SNR > 2 values are found in the tropical oceans, the center of subtropical gyres of the South Pacific Ocean and the North Atlantic Ocean, and the rim of subtropical gyres of the North Atlantic Ocean. Therefore, the in-situ spatial distribution remapped using the ISAS tool preserves -with a coarser spatial resolution-the SNR spatial patterns found in the OCCIPUT ensemble simulation. SNR patterns are however not well reproduced in the Arctic and Southern Oceans, mainly due to the limited number of in-situ measurements in these regions. We thus find that, outside the high latitudes, the in-situ data coverage is dense enough to preserve the atmosphericallyforced and intrinsic ocean variability ratios after binning the individual profiles with the ISAS tool.
Conclusions and discussion In-situ data and, more recently, Argo floats have remodeled our knowledge of ocean circulation and dynamics, and revealed large regional variability in decadal trends of TSL and OHC. Using synthetic profiles extracted from climate model output,  Allison et al (2019)  showed that the introduction of Argo profiles reduces the mapping method uncertainty in estimating the variability and spatial structures of OHC anomalies. We adopt the same strategy by assessing TSL and OHC trends using synthetic profiles extracted from an ensemble of forced ocean simulations sharing the same atmospheric forcing but with different initial conditions. By remapping an ensemble of in-situ-like synthetic profiles onto a regular grid using the ISAS tool, we show that the current in-situ coverage is dense enough to capture the regional decadal TSL and OHC trends simulated by the NEMO ocean model over 2005-2015.
The atmosphericallyforced response has been investigated using the OCCIPUT and ISAS OCCIPUT ensemble datasets. Atmospherically-forced trends are rather similar in the OCCIPUT and ISAS OCCIPUT ensembles; however, differences exist, especially in western boundary currents and basin interiors. This general resemblance supports the hypothesis that large scale TSL and OHC regional trends are a direct response to the atmospheric forcing. We also find that the OCCIPUT ensemble indicates that intrinsic ocean variability has a significant influence on both TSL and OHC regional trends over 2005-2015, especially in the western boundary currents and the ACC. These patterns are consistent with the imprints of intrinsic ocean variability on sea level trends over the same period (Llovel et al 2018). Again, this confirms that temperature changes are the main driver of regional sea level decadal trends, as was previously shown with ocean models (Fukumori and Wang 2013, Griffies et al 2014) and with in-situ-based gridded products (Llovel and Lee 2015).
We also show the presence of regions where the atmospherically-forced response dominates the intrinsic variability and vice versa for regional TSL and OHC trends over 2005-2015. Unsurprisingly, the tropics show a strong direct response to the atmosphere, along with the continental shelves. We find that TSL trends cannot be unambiguously attributed to the atmospheric forcing over 44% and 40% of the global ocean area in the OCCIPUT and ISAS OCCI-PUT datasets, respectively. Concerning the OHC trends, these fractions reach 45% and 39% from OCCIPUT and ISAS OCCIPUT datasets. This means that the intrinsic variability introduces uncertainty in attributing these decadal trends to an atmospheric influence. These fractions are large and consistent with decadal sea level trends over  2005 -2015 (see Llovel et al 2018)).
Acknowledgments This work is a contribution of the  CRATERE  project supported by the  French national program LEFE/INSU. This work is a contribution to the  OCCIPUT  and PIRATE projects. PIRATE (https://sealevel. jpl. nasa. gov/science/ostscienceteam/scientistlinks/scientificinvestigations2017/penduff/) is funded by  CNES  through the  Ocean Surface Topography Science Team (OSTST). OCCIPUT (http://meom-group. github. io/projects/occiput)has been funded by  ANR  through contract  ANR-13-BS06-0007-01. We acknowledge that the results of this research have been achieved using the  PRACE Research Infrastructure resource CURIE based in France at TGCC.
Summarised point data Following standardisation, the CSV data (see Section 3. 1) were converted to shapefile and geopackage lines and points. Lines were calculated automatically from the point data and split each time a gap of more than 5 km between two data points was found. For Bedmpa1, due to the difficulty in extracting the different campaign data and the sparsity of points, it was not possible to convert Bedmap1 data to shapefile or geopackage lines: only the Bedmap1 shapefile points are provided as part of this data release. The spatial distribution of the full-resolution survey point data is extremely heterogeneous with, for example, dense, metre-scale sampling along modern flight-lines that are often separated across-track by kilometres to hundreds of kilometres, and this heterogeneity varies between campaigns and data providers.
https://antarctica. github. io/PDC_GeophysicsBook, last accessed: 29 July 2022) or via the BAS GitHub repository (https://github. com/antarctica/PDC_GeophysicsBook, last accessed: 29 July 2022). A specific library called xcsv (https://github. com/paul-breen/xcsv, Paul Breen, 2022, last access: 29 July 2022) allows to read and plot data in the extended CSV format as described in Section 3. 2. 8. Author contributionsAlice Frémand. , Peter Fretwell, and Julien Bodart co-led this data release. A. Frémand and J. Bodart standardised the data. The Jupyter Notebook was developed by A. Frémand. P. Fretwell and H. Pritchard initiated the collaboration and P.
Acknowledgements We would like to dedicate this paper to the many scientists who have collected geophysical field data in harsh and extreme conditions over the Antarctic ice sheets over the last sixty years. Their commitment, dedication and drive has populated this dataset and has advanced Polar science beyond measure. Funding for the  British Antarctic Survey  staff has come from  Natural Environment Research Council  core funds. Funding for the data collection has come from many grants, institutions and projects. These are individually cited where appropriate in the metadata of the datasets.
The MOREPOC database is publicly available on the Zenodo repository at https://doi. org/10. 5281/zenodo. 7055970  (Ke et al. , 2022). 1 ), and 299 with aluminum-to-silicon mass ratios (Al / Si). In addition, reported analytical uncertainties for POC content, δ 13 C, and 14 C are included in MOREPOC. Note that riverbed or bank sediments are not included in this database. We selected studies reporting at least one carbon isotopic datum and those with paired elemental and dual carbon isotopic values. Studies reporting only POC contents were not compiled into MOREPOC v1. 1. Potential mistakes generated during the compilation of data entries were carefully checked, and duplicate data were removed. A supplementary table, "MORE-POC_RM", is provided to give additional information on the references, sampling method of SPM, filtration strategy, and carbonate removal methods and detailed information for the types of acid used to remove carbonate, etc.
, 2008b). POC contents are usually positively correlated with proportions of fine-grained fractions  (Mayer, 1994; Galy et al. , 2008b; Bouchez et al. , 2014). Although MOREPOC v1. 1 features data from river systems worldwide, it does not offer the same degree of representativeness for all the continents, with, for instance, an overrepresentation of Asian rivers and an underrepresentation of rivers draining Europe and Oceania (Table  1 ). It can be noticed that there are relatively abundant POC studies on North American fluvial systems. However, the MOREPOC database also indicates the lack of studies on POC in fluvial systems in the cryosphere regions such as Antarctica and Greenland as well as in arid regions, including Australia and vast areas spanning from northern Africa to Middle Eastern Asia (Fig.
Acknowledgements. The development of the MOREPOC database was funded by the  Agence Nationale de la Recherche (ANR) SEDIMAN  (grant no. ANR-15-CE01-0012 ). Yutian  Ke was supported by the  China Scholarship Council  with a  PhD scholarship  (grant no. 201706180008 ). Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-15-CE01-0012 ) and the  China Scholarship Council  (grant no. 201706180008 ). Review statement. This paper was edited by  Attila Demény  and reviewed by  Jin Wang  and three anonymous referees.
An external fan blows the air on the chamber and an ultrasonic anemometer WindSonic 2D (Gill Instruments Limited, Lymington Hampshire, UK) allows monitoring and recording on the data logger CR1000 the resulting wind speed 5cm above 135 the sample soil surface. It should be noted that 2ms -1 measured 5cm above the surface equals to about 6. 5ms -1 measured 2m above the surface (logarithmic profile). The data logger CR1000 was programmed to command closure (chamber head deployment) or opening of the chamber, to record the measured humidity and temperature along with the pressure, and the embedded fan rotation speed inside the chamber 140 head as well as the wind speed before and during the deployment. Before each chamber deployment, a prior 120s flushing with a 100% duty operated embedded fan was performed.
Note that ℓ p is always greater than ℓ/2 so that the two length scales do not differ very much in the low-frequency regime. The formula analogous to (Eq. 8) for the transport mean free path is given by: (ℓ*) -1 ≈ k 3 β a 2 πΓ(κ + 1) 4 + 8] + 5v 2 Γ(κ) k β a → 0. ( 16 ) When one approaches the stochastic regime, the scattering pattern becomes more and more strongly peaked in the forward direction. In the high-frequency limit, g approaches 1 and the transport mean free path can largely exceed the scattering mean free path. The simple method used to obtain an asymptotic expansion of ℓ in the stochastic regime cannot be applied to obtain an estimate of ℓ p because the (1-cos θ) term in the numerator of (Eq. 15) evaluates to 0 in the forward direction. In the exponential case used for the numerical simulations, it is possible to obtain closed form expressions for the transport mean free path using symbolic computation softwares such as Mathematica ™ but the final formulas are not particularly illuminating.
Data Analysis and Synthesis Thematic analysis was conducted through a line-by-line analysis of included papers coding relevant text. The analysis of the data was carried out using a mainly inductive approach, without a pre-established thematic grid. The identified articles were analyzed using a thematic analysis that incorporated the following themes: identification of how PR was measured in the various works, a comprehensive approach to PR in BD, and how care may contribute to PR. Through these three themes, we presented the results of different types of work: qualitative studies that described this process, as well as a number of observational and interventional studies exploring the measurement of PR and the factors involved in this process. Additionally, we presented the various interventional studies that targeted PR as an outcome to better understand the evidence for care tools that might contribute to it.
Study Types Among the 24 articles selected, we found seven qualitative studies, including one on a core outcome set (COS); 12 observational studies; and five interventional studies, including two randomized controlled trials (RCTs). Qualitative studies consisted of the systematic analysis of the verbatim narratives of people suffering from BD, collected during individual interviews or focus groups; this analysis allowed us to group the elements of the discourse into themes and subthemes to draw out their meaning. The techniques used were in-depth individual interviews  (12, 14, 16, 17)  or focus groups  (13, 15). The number of subjects included varied from 11 (12) to 56  (15). Observational studies involved different data collected through objective clinical or sociodemographic indicators and/or subjective psychometric scales at a defined time.
Limitations Our search strategy included three bibliographic databases, which may have limited the number of articles selected. As a result, it is possible that some data of interest could not be collected. We excluded all articles that were not specifically focused on people with BD. In this way, we were able to collect very specific information in a very complex area of research. This choice may also have limited the number of articles selected. The literature search was limited to peer-reviewed studies available in English or French, which may not represent all the evidence and may have introduced a language bias. The characteristics of PR in people with BD are in the early stages of exploration. The small number of articles to date may limit the scope of the evidence. Moreover, our work found that there is a lack of RCTs and works with a high level of scientific evidence.
7 Calculated according to the method in NEJM 2017; 377: 419-430. Data were collected for the first 28 days, up to and including the day of discharge from the ICU. For each co-intervention, N refers to the number of patients that ever received it. The mean hours of data on day 1, which included randomization to 23h59 on the same calendar day, was xx (SD xx). The ICEMAN tool (Instrument to assess the Credibility of Effect Modification Analyses) is available at CMAJ 2020; 192: E901-6. The questions are as follows: 1: Was the direction of the effect modification correctly hypothesized a priori? 2: Was the effect modification supported by prior evidence? 3: Does a test for interaction suggest that chance is an unlikely explanation of the apparent effect modification? 4: Did the authors test only a small number of effect modifiers or consider the number in their statistical analysis? 5: If the effect modifier is a continuous variable, were arbitrary cut points avoided? 6 Optional: Are there any additional considerations that may increase or decrease credibility? 7: How would you rate the overall credibility of the proposed effect modification? 1 The p-value for the interaction between baseline vitamin C and treatment group was 0.
Data collection A structured facility assessment questionnaire (Additional le 1) was used to collect data, which was adapted from the World Health Organization package of essential non-communicable diseases (WHO-PEN) interventions tool  [19]. At each facility, one questionnaire was completed on the availability of indicator items speci c to the diseases. Where feasible, direct observations were conducted to verify the interview responses. Prior to the main survey, a pilot study was conducted in Nairobi following the design. Its aims were: (i) to test and evaluate the instrument design, (ii) to assess the feasibility of the study in local settings, (iii) to a rm the validity and reliability of the instruments, and (iv) to familiarize the study data collection team with the study questionnaires and procedures.
Data from health facilities eligible for the main study were collected electronically on tablets using SurveyCTO (Dobility, Inc. Cambridge, MA, USA), a digital data collection platform. In total, 258 (86%) of the 301 initially targeted health facilities were successfully sampled. This level of response is acceptable according to SARA methodology guidelines, which specify a response rate of 80% as reasonably acceptable level of response  [18]. The other targeted facilities did not participate either due to security concerns, inaccessibility of the facility, voluntary non-consent, or the facility was no longer operational at the time of the study.
Sample weights The sampling scheme was self-weighting within strata (region, county, healthcare level, and facility type), but the probability of being sampled differed between strata, hence sampling weights were computed for each stratum. The probability of facilities being sampled was calculated as the number of facilities in the sample divided by the total number of facilities in the stratum. Thus, weights were calculated as the inverse of the probability of being sampled and assigned to the tracer items indicator variables in the nal dataset for analyses.
Characteristics of health facilities Of the 258 surveyed facilities, the majority (68%; n = 175) were publicly managed. The survey consisted mostly of primary health facilities; for which 54% (n = 140) were level 2 and 34% (n = 88) were level 3 facilities. In addition, the sample included three of the ve national referral hospitals (level 6) in the country. Furthermore, more than half of the facilities (53%; n = 138) were located in urban areas, with a proportionate distribution among the different regions (Table  1 ). Considering the small sample sizes of level 5 and level 6 facilities, data from them was combined and analyzed together.
Strengths and weaknesses of the study One of the strengths of this study is that it examined a sample of facilities spanning across all geographic regions of Kenya, ensuring national representativeness in terms of general health facilities characteristics within the country's health system. A further strength was that the data were collected using an adapted WHO-PEN questionnaire, nevertheless, with a focus on NCDs. This tool is increasingly being used in other LMICs, allowing our results to be comparable with other studies. However, some limitations should be acknowledged when interpreting the ndings of this study. Firstly, like any other survey as opposed to a census, there is an inherent risk of sampling bias. In the present study, steps were taken to ensure that the sampling design was robust and sample weights were applied to account for disproportionate selection of facilities.
Acknowledgements The authors acknowledge collaborative support from the  Amsterdam Institute for Global Health and Development (AIGHD), Netherlands  and the  African Population and Health Research Center (APHRC) in Nairobi, Kenya. We would like to thank all the participants for their valuable contributions to this study. We would also like to thank  Nina Schat  ( AIGHD ) and  Phyllis Mungai  ( APHRC ), our project management team for providing support and administrative coordination of the project activities. We are grateful to the  Ministry of Health Kenya , its partners, local county authorities and stakeholders that allowed us to carry out this facility health assessment survey.
These modifications were evaluated with a direct mock-up in the full-mouth prior to the initial preparation to evaluate aesthetics and function (Figure  5 ). The patient's comfort, speech and appearance were assessed after 2 weeks. Subsequently, an optical impression of the two arches with the composite resin mock-up was taken with intra-oral scanner Trios 3 (3Shape A/S, Copenhagen, Denmark). The Trios intra-oral scanner was operated on the confocal principle, with the video bitrate image capturing method based on the real-time rendering technique. The software 3Shape Dental Designer was used for modelling veenerlays and crowns. Once the volume of the final restoration was defined by the complete mock-up, the preparation of teeth for the definitive adhesive restoration could be performed with calibrated burs to achieve an occlusal reduction of 1.