dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This research was funded by the  NATIONAL SCIENCE CENTRE, POLAND , grant number  2016/23/N/NZ1/02960  to J. J. The research was partly done in the  Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw  (grant numbers  G07-13 ,  GA69-31 , and  GA71-27 ). Acknowledgments: J. J. and S. F. participate in COST grant  CA18133  (ERNEST).
, 2015). Here we use the IAGOS dataset over the period 1 July to 31 October 2016 to investigate the influence of typhoons passing near Taiwan on ozone content and tropospheric dynamics. Section 2 gives information on the data and the methods used to analyze them. Section 3 presents the mean profiles of ozone, carbon monoxide and relative humidity from the surface to the upper troposphere from July to October, deduced from IAGOS measurements made during take-off from and landing to Taipei and from ERA-5 re-analyses. Section 4 discusses the vertical profiles of ozone and carbon monoxide from IAGOS data, in relation with PV, relative humidity, and vertical velocity fields from ERA-5 re-analyses. We show examples for three typhoons (typhoons Nepartak on 6-7 July 2016, Nida on 30-31 July 2016 and Megi on 25-26 September 2016) where the IAGOS data allow to identify a clear subsidence of ozone rich air coming from the tropopause region Section 5 puts these results in the context of similar observations over other basins.
IAGOS Data Full details of the IAGOS system and its operation can be found in  Petzold et al. (2015)  and  Nédélec et al. (2015). Ozone is measured using a dual-beam Ultra-Violet -absorption monitor with a response time of 4 seconds and an accuracy estimated at about ±2 parts per billion by volume (ppbv)  (Thouret et al. , 1998). Carbon monoxide is measured with an infrared analyser with a time resolution of 30 seconds (7. 5 km at cruise speed of 900 km h -1 ) and a precision estimated at ±5 ppbv  (Nédélec et al. , 2003). Test flights showed the stability of the measurements at concentrations above 40 ppbv and a minimum detectable concentration of 10 ppbv. Water vapor mixing ratio is measured by the IAGOS Capacitive Hygrometer  (Neis et al. , 2015). IAGOS data provide values of relative humidity with respect to liquid water (RHL).
Discussion The structures of relative humidity, vertical velocity and potential vorticity were also deduced from ERA-5 data for typhoons Meranti, Malakas, Sarika and Haima (not shown) and were broadly similar to those observed for Nepartak, Nida and Megi. The less favorable coverage by IAGOS aircraft for these typhoons which passed at a larger distance from Taiwan did not permit such detailed comparisons of the O3 and CO profiles. Generally, a tongue of high PV air and ozone richer air is observed ahead of the typhoons. Dry zones (RH < 40%), which could also be identified on Himawari-8 images in the water vapor channels and were associated with downward motion (< -1 cm s -1 ), were systematically found 500 to 1000 km ahead or to the northwest of the storms when they approach Taiwan over the Philippine Sea, the Luzon Strait and the South China Sea.
Code availability The MATLAB scripts used to analyze the ERA-5 data (Net-CDF files formatted by CLIMSERV) are available at https://mycore. core-cloud. net/index. php/s/vi2SmsVJNVnM4w0)  "coupe_zt_era5. txt" and "coupe_zt_era5. m" were used to obtain Fig. 3  The MATLAB scripts used to analyze the IAGOS data (Net-CDF files formatted by IAGOS Data Portal) are available at https://mycore. core-cloud. net/index. php/s/JWmep21ghplwUG1  "juloct. m" was used to obtain Fig. 2   "tra_rho3co. txt" and "tra_rho3co. m" were used to obtain  Figs. 6, 9 and 12  The MATLAB scripts used to simultaneously analyze ERA-5 and IAGOS data (in Net-CFDF formats) are available at https://mycore. core-cloud. net/index. php/s/dscfB8XUqLaw3Pv  "rhpvw_era5_sv_meanbox. txt" and "rhpvw_era5_sv_meanbox.
A systematic review uses a rigorous and transparent approach for research synthesis, with the aim of assessing and, where possible, minimizing bias in the findings. While many systematic reviews are based on an explicit quantitative meta-analysis of available data, there are also qualitative reviews and other types of mixed-methods reviews which adhere to standards for gathering, analyzing and reporting evidence. [9] stematic reviews of quantitative data or mixedmethod reviews sometimes use statistical techniques (meta-analysis) to combine results of eligible studies. Scoring levels are sometimes used to rate the quality of the evidence depending on the methodology used, although this is discouraged by the Cochrane Library. [10]  Several reporting guidelines exist to standardise reporting about how systematic reviews are conducted.
Searching for relevant data sources Planning how the review will search for relevant data from research that matches certain criteria is a decisive stage in developing a rigorous systematic review. Relevant criteria can include only selecting research that is good quality and answers the defined question. [37] The search strategy should be designed to retrieve literature that matches the protocol's specified inclusion and exclusion criteria. The methodology section of a systematic review should list all of the databases and citation indices that were searched. The titles and abstracts of identified articles can be checked against pre-determined criteria for eligibility and relevance. Each included study may be assigned an objective assessment of methodological quality, preferably by using methods conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement,  [18]  or the highquality standards of Cochrane.
[39] mmon information sources used in searches include scholarly databases of peer-reviewed articles such as MEDLINE, Web of Science, Embase, and PubMed as well as sources of unpublished literature such as clinical trial registries and grey literature collections. Key references can also be yielded through additional methods such as citation searching, reference list checking (related to a search method called 'pearl growing'), manually searching information sources not indexed in the major electronic databases (sometimes called 'handsearching'),  [40]  and directly contacting experts in the field. [41]  be systematic, searchers must use a combination of search skills and tools such as database subject headings, keyword searching, Boolean operators, proximity searching, while attempting to balance the sensitivity (systematicity) and precision (accuracy).
'Extraction' of relevant data Relevant data are 'extracted' from the data sources according to the review method. It is important to note that the data extraction method is specific to the kind of data, and data extracted on 'outcomes' is only relevant to certain types of reviews. For example, a systematic review of clinical trials might extract data about how the research was done (often called the method or 'intervention'), who participated in the research (including how many people), how it was paid for (for example funding sources) and what happened (the outcomes). [37] Figure  1  illustrates relevant data being extracted and 'combined' in a Cochrane intervention effect review, where a meta-analysis is possible.
History of systematic reviews in medicine A 1904 British Medical Journal paper by Karl Pearson collated data from several studies in the UK, India and South Africa of typhoid inoculation. He used a meta-analytic approach to aggregate the outcomes of multiple clinical studies. [55] In 1972 Archie Cochrane wrote: 'It is surely a great criticism of our profession that we have not organised a critical summary, by specialty or subspecialty, adapted periodically, of all relevant randomised controlled trials'. [56] Critical appraisal and synthesis of research findings in a systematic way emerged in 1975 under the term 'meta analysis'. [57][58] Early syntheses were conducted in broad areas of public policy and social interventions, with systematic research synthesis applied to medicine and health.
The Cochrane Library is a collection of databases that contains different types of independent evidence to inform healthcare decision-making. It contains a database of systematic review and meta-analyses which summarize and interpret the results of multi-disciplinary research. The library contains the Cochrane Database of Systematic Reviews (CDSR), which is a journal and database for systematic reviews in health care. The Cochrane Library also contains the Cochrane Central Register of Controlled Trials (CENTRAL) which is a database of reports of randomized and quasi-randomized controlled trials. [74] The Cochrane Library is also available in Spanish. [75] e Cochrane Library is owned by Cochrane. It was originally published by Update Software and now published by the share-holder owned publisher John Wiley & Sons, Ltd.
Limitations Out-dated or risk of bias While systematic reviews are regarded as the strongest form of evidence, a 2003 review of 300 studies found that not all systematic reviews were equally reliable, and that their reporting can be improved by a universally agreed upon set of standards and guidelines. [104] A further study by the same group found that of 100 systematic reviews monitored, 7% needed updating at the time of publication, another 4% within a year, and another 11% within 2 years; this figure was higher in rapidly changing fields of medicine, especially cardiovascular medicine. [105] A 2003 study suggested that extending searches beyond major databases, perhaps into grey literature, would increase the effectiveness of reviews. [106] me authors have highlighted problems with systematic reviews, particularly those conducted by Cochrane, noting that published reviews are often biased, out of date and excessively long.
1 years. In both scans, foam pads were used to reduce head movement and scanner noise. The anatomical data were used to normalize functional data. All preprocessing steps were carried out using the Data Processing & Analysis for (Resting-State) Brain Imaging (DPABI v4. 1,  (Yan et al. , 2016) ) and Matlab scripts. Consistent with our previous study  (Dong et al. , 2018a) , functional images were (1) discarded in the first five volumes, (2) slice-time corrected, (3) realigned, (4) co-registered to the high-resolution 3D anatomic volume, (5) warped into MNI152 standard space (resampling the voxel size into 3×3×3 mm 3 ), (6) underwent wavelet despiking of head motion artifacts  (Patel et al. , 2014) ), (7) underwent regression of motion and non-relevant signals, including linear trend, Friston 24 head motion parameters  (Friston et al.
Data and code availability The preprocessing software is freely available (DPABI v4. 1, http://rfmri. org/dpabi). The code for gradient analysis is openly available via the BrainSpace toolbox (http://brainspace. readthedocs. io) (Wael et al. , 2020). The code for SFC analysis is available via a direct request to Jorge Sepulcre. Results were visualized with BrainNet Viewer v1. 7 (https://www. nitrc. org/projects/bnv/) (Xia et al. , 2013). The imaging and clinical data are made available via a direct request to the corresponding author (Cheng Luo). Sharing and re-use of imaging and clinical data need the expressed written permission of the authors and clearance from the relevant institutional review boards.
Acknowledgments The authors declare no conflicts of interests. This work was supported by the grant from  National Key R&D Program of China  ( 2018YFA0701400 ), The grants from the  National Nature Science Foundation of China  (grant number:  61933003 ,  81771822 ,  81861128001 , and  81771925 ), The  Project of Science and Technology Department of Sichuan Province  ( 2019YJ0179 ), and the  CAMS Innovation Fund for Medical Sciences (CIFMS)  (No. 2019-I2M-5-039 ). We are grateful to all the participants in this study. Our thanks also go.
Specific modelling of the spectral reflectance of sea foam, affecting the transmittance across the air-sea interface, is also included in OASIM (please refer to Appendix of GC2009). In addition to the cloud properties necessary for the Environmental Prediction  (NCEP 1979 -July 2002; Kalnay et al. , 1996)  reanalysis dataset. In the present implementation, most of these input data are extracted from the ECMWF ERA-Interim dataset (please refer to Section 2. 4). The spatial and temporal resolutions of OASIM are determined by the forcing datasets (i. e. , aerosol optical data, cloud property data and atmospheric surface level data). The standard configuration presented in GC2009 is hereby maintained, with a 1degree horizontal resolution, thereby increasing the temporal frequency to 15 minutes to resolve the diurnal variability and properly compare the output to the temporal resolution of the in situ data considered in the present study.
e. , from 400 to 700 nm. Quality-controlled Chl, T and S NRT data are currently available from the Coriolis data centre (Argo Data Management Team, ADMT, manual). The photosynthetically available radiation (PAR) must be generally derived by spectrally integrating the scalar irradiance (i. e. , the radiance integrated across the complete solid angle) because phytoplankton utilize light from all directions. In the present case, BGC-Argo sensors measure the downward irradiance integrated in the 400 to 700 nm range: to avoid confusion, the measured quantity is referred to as DPAR. QC of radiometric data (DPAR and Ed), specifically designed for in situ and remote sensing ocean colour applications, has been implemented by  Organelli et al. (2016). The delayed mode (DM) QC approach to identify data corruption by biofouling and instrument drift based on tests and procedures has been developed by  Organelli et al.
(2017). In the present work, a BGC-Argo dataset was adopted covering the period between 2012 and 2017 with 3800 profiles (Fig. 2 ). Before comparing model values to observations, the irradiance profiles obtained from floats were extrapolated to the surface with a nonlinear fitting procedure only for profiles at a depth shallower than 1. 5 metres, with at least 4 measurements in the first 10 metres. In addition, any regions and months containing fewer than 5 profiles were discarded.
OASIM input data for Mediterranean Sea applications The input data required by the OASIM model in the present Mediterranean Sea application are shown in Fig. 3 , together with the datasets used for the validation (BOUSSOLE and BGC-Argo). In the setup specific for the Mediterranean Sea, developed within the framework of the BIOPTIMOD project, the variables of the cloud and atmospheric properties are extracted from the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset  (Dee et al. , 2011) ; further details on their pre-processing are provided in Appendix A. The investigated period ranges from 2004 to 2017, with the input data provided as daily averages and with a spatial resolution of 1 degree. The use of ERA-Interim to force OASIM is motivated by increasing the coherence between the input cloud properties and surface level atmospheric properties, which, in the GC2009 configuration, were provided by two different datasets (ISCCP and NCEP, respectively).
OASIM model validation in the Mediterranean Sea Data available from the BOUSSOLE mooring station and BGC-Argo floats deployed in the Mediterranean Sea were employed to validate the OASIM model outputs. To visualize the comparison periods between the in situ data and model outputs, the data availability time windows are shown in Fig. 3. The analysis is performed using datasets from BOUSSOLE (the downward planar irradiance  ; (0 > ) at  412. 5, 442. 5, 490, 510, 555, 560, 665, 670, and 681. 25 nm, as well as DPAR) and BGC-Argo floats (the downward planar irradiance  ; (0 > ) at 380, 412, and 490 nm and DPAR). The notation 0 -indicates quantities just below the sea surface with any reflections at the air-sea interface removed, and 0 + is applied for the radiances just above the air-sea interface (the light measured before air-sea transmission occurs).
In the case of January, at each wavelength and each 15' interval, there exists a distribution consisting of 31x9 data points (including the data reduction due to sensor failure episodes). In principle, these distributions should be constrained by quite homogeneous conditions, with the same daily zenith angle component, and similar seasonal conditions, i. e. , with all the data grouped by the month. Therefore, assuming that a large number of small cumulative perturbations affects the variability, the data should be log-normally distributed. However, the Kolmogorov-Smirnov test revealed that the distributions are not lognormal, neither the BOUSSOLE nor the model data. In fact, the distances between the accumulated empirical distributions and the reference log-normal distribution were almost always larger than the critical distance (Dcrit=1.
Validation of the OASIM model with the BGC Argo float network The quality-controlled BGC-Argo float dataset adopted in this work contains radiometric measurements acquired from 10:00 to 14:00 local time. To compare OASIM with these BGC-Argo measurements, a point-by-point match-up analysis was performed, where the closest model output to the float measurement at the surface was selected. The individual match-ups were then spatiotemporally aggregated based on the climatological months and the defined 16 subbasins, as shown in Fig. 2 , at each of the three wavelengths and for DPAR separately (Figs. 8 to 11). At 380 nm, the mean values of the model outputs are overall higher than the observations. Apart from the seasonal cycle, a west-east gradient is also observed, with high values in the Eastern Mediterranean, most likely due to the low cloud cover (Fig.
Summary of the OASIM model skills in the Mediterranean Sea The absolute bias of the comparison of the OASIM outputs to the BOUSSOLE data (Tab. 1, upper part) is generally lower than 0. 1 W m -2 nm -1 , and the regression slope approaches 1. In particular, the model vs the observations reveals a total negative bias below 20% of the average measured signal, and on average, the RMSD is approximately 30-40%. The regression slopes are lower than 1, indicating a slight underestimation of the model with respect to the observations in terms of the irradiance maxima, particularly at 412 nm (slope=0. 66), 670 nm (slope=0. 63) and 681. 25 nm (slope=0. 62). A lower agreement is observed between OASIM and the BGC-Argo floats (Tab. 1, lower part) than that between OASIM and BOUSSOLE, especially in terms of DPAR.
Moreover, in this case, the bias is not always negative, being positive only at the 380-nm wavelength and for DPAR. The correlation for (, 0 > ) indicates a general good agreement between the model and BGC-Argo data (r~0. 8), but the slope is below 0. 8. Moreover, similar to OASIM vs BOUSSOLE, (, 0 > ) reveals the highest discrepancy at 412 nm, with an RMSD of 0. 3 W m -2 nm -1 , a bias of -0. 13 W m -2 nm -1 and a regression slope of 0. 51. The comparison to DPAR indicates a positive bias higher than 300 µmol quanta m -2 s -1 , which is high with respect to the BGC-Argo floats than in the BOUSSOLE case.
Comparison of OASIM, BOUSSOLE and BGC-Argo floats in the North-Western Mediterranean Sea Intercomparison of the model outputs and data obtained from the radiometric sensors of the BOUSSOLE buoy and BGC-Argo floats was possible only when the latter were located in the vicinity of the BOUSSOLE buoy in the NW Mediterranean Sea. Different spatial aggregations of profiles surrounding the fixed buoy were tested, ranging from 1 degree (+/-0. 5 degree from the location of the buoy, as shown in Fig. 12 ) to the whole northwestern Mediterranean sub-basin (NWM), as shown in Fig. 13. In regard to the 1-degree aggregation, up to 10 BGC-Argo profiles were available per month (Fig. 12 , number not shown), while for the sub-basin analysis, the number of available profiles ranged from 8 in October to up to 100 in March (Fig.
13 , number not shown). Monthly climatologies were calculated from 2004-2012 for BOUSSOLE and from 2012-2017 for BGC-Argo, with the model values corresponding to the locations of the instruments limited to the model spatial resolution. The BGC-Argo data were extrapolated to the surface, and the considered profiles followed the required conditions, as described in the previous section. The mean values and standard deviations are therefore shown for 4 different sets of results (Figs. 12 and 13, respectively): the data from the BOUSSOLE buoy (B) and BGC-Argo floats (F) with the corresponding OASIM (BM and FM, respectively) model outputs at 412 and 490 nm (expressed in W m -2 nm -1 ) and DPAR (expressed in µmol quanta m -2 s -1 ). A separate comparison of the two different data sources conveyed an overall good agreement, with higher standard deviation values for the floats (Figs.
2 W m -2 nm -1 , as shown in Fig. 13 ). Consistent with the results shown in Fig. 10 , major discrepancies arose when comparing DPAR, where the model values resulted in much higher values than those obtained from the floats, increasing especially during summer (up to 600 µmol quanta m -2 s -1 in August, as shown in Fig. 13 ). Such inter-and intra-comparisons of the atmospheric radiative transfer model and available radiometric measurement platforms could also serve as a useful tool to estimate the range of variability when considering optical data from different sources. The spatial aggregation of measurements to the sub-basin level (i. e. , a range of up to 10 degrees) reveals the preservation of the irradiance seasonal variations. However, much remains to be explained in terms of the sources of both variabilities, both between the model and float data, especially when considering DPAR, as well as between the two different sources, such as the floats and fixed buoy.
Discussion An extensive comparison of the OASIM model's temporal and spatial variabilities was performed using the BOUSSOLE and BGC-Argo optical sensor data as references. The results indicated that, in general, the model reproduced the variability in the spectral downward irradiance in the Mediterranean Sea, which depends on the spatiotemporal scale. In particular, considering the OASIM applications within biogeochemical models equipped with a multispectral in-water optical module, the impact will be different according to the specific scale under investigation. In terms of fine temporal scales, it was observed that a large part of the discrepancy expressed by the RMSD occurred due to the day-to-day variability, similar to the findings reported by  Somayajula et al. (2018). When this temporal scale is filtered out, the RMSD decreases by half or more.
The low skill at a specific wavelength could also be explained in terms of the wavelength discretization in the OASIM model: the current model spectral resolution of 25 nm could be refined near 412 and 682. 25 nm to investigate whether this would reduce the BIAS. In addition to the seasonal indicators discussed above, the interannual trends of  ; (, 0 > ) and DPAR were investigated both for the measured data and model results. Given the strong seasonal cycle present in all the properties considered, low-pass filters (i. e. , moving averages) were applied to the data before the regression. The results for both datasets were biased by the gaps in the acquisitions, which introduced spurious trends. Therefore, the focus was on the model inputs instead (ECMWF products and MODIS aerosol data) and the outputs corresponding to a 14-year gapfree time series both in spatial and temporal terms.