dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018 and references therein). Unfortunately, VOC measurements are not available at the Uccle surface site. Secondly, although tropospheric ozone is mainly produced from the photolysis of NO 2 , NO destroys ozone especially during night-time, implying that reductions in NO x emissions might adversely result in more ozone, especially in highly polluted areas such as cities  (Yan et al. , 2018). Chang et al. (2017)  also noted that in Europe, the NO 2 column amount tends to be negatively correlated with ozone in urban sites. Moreover, they mention that in the warm season NO x emissions tend to produce ozone, while in the cold season fresh emissions tend to destroy ozone in urban areas, which is also observed on a European scale  (Tørseth et al. , 2012). For Uccle, however, we do not find substantial differences between summertime and wintertime ozone trends in both datasets.
5. 1), and for the scientific evaluation of both stratospheric (Sect. 5. 2) and tropospheric (Sect. 5. 3) ozone profile retrievals by satellite instruments. In these latter two sections, we also illustrate that a consistent and homogenous ozonesonde dataset like the Uccle one is crucial to determine the long-term stability of (merged) satellite ozone retrievals. https://doi. org/10. 5194/acp-2020-724 Preprint. Discussion started: 2 December 2020 c Author(s) 2020. CC BY 4. 0 License.
e. by 15%, in particular in the lower stratosphere (compare the full lines with dashed lines in Fig. 8 ). Moreover, as the GOME-2 ozone profile product is based on UV measurements, it is sensitive to degradation of the UV sensor  (van Peet et al. , 2014 , Munro et al. , 2016). Therefore, a degradation correction has been developed for the MetOp-A data, already launched in 2007, and applied to the data for the relative differences with the Uccle data in Fig. 8  (in grey). From this figure, it should be clear that this degradation correction improves significantly the agreement with the Uccle ozonesonde data compared to the operational product (in red), resulting in relative differences between GOME-2 ozone profiles and the Uccle data within the target error range of 15% (marked by the vertical red lines).
Validation of AURA-MLS stratospheric ozone profiles The Microwave Limb Sounder (MLS,  Froidevaux et al. , 2008)   validated to be very accurate and stable  (Jiang et al. , 2007 , Froidevaux et al. , 2008) , and have been used in many studies involving ozonesonde measurements (e. g. Witte et al. , 2017 , Stauffer et al. , 2020). Here, we have implemented the latest MLS v4. 2 data, screened according to the v4. 2 Level 2 MLS Data Quality document  (Livesey et al. , 2020)  and compared the satellite overpass measurements, without averaging kernel, with coincident ozonesonde profiles at Uccle. Because there are multiple profiles crossing over Uccle at a fixed time, the profile closest in distance is used for the validation. Both the noon and night overpasses have been used, as we did not find significant differences between those.
As a result, ~3000 profiles were included into the validation. To account for the difference in resolution, Uccle ozonesonde data are linearly interpolated to the MLS vertical resolution (between 3-6 km, depending on the altitude). The mean annual relative differences between MLS and Uccle ozonesondes are shown in Fig. 9. Different conclusions can be drawn from this figure. First, MLS and the Uccle ozonesondes compare very well, within ±5% between 10 and 70 hPa (grey shading in Fig. 9 ). At pressures smaller than 10 hPa, ozonesonde measurements are known to be less accurate (see also Sect. 2. 2), while at pressures larger than 70 hPa, the MLS ozone retrieval is more challenging because of the longer atmospheric path and the lower ozone volume mixing ratios increasing the relative differences.
, 2013). By applying all these constraints (coinciding criteria and the TES ozone master flag) 191 suitable coincidences or data pairs for the full time range from 2004 to 2018 were collected. Figure  10  indicates that TES is generally positively biased within the troposphere by up to ~10 ppbv, corresponding to relative differences up to ~15 %. The TES bias slightly varies as a function of pressure. TES appears to be almost unbiased with respect to the sondes in the lower troposphere, but this actually reflects the non-sensitivity of TES to ozone in the lower atmosphere for situations with lower brightness temperature as encountered at higher latitudes. Since the TES signal in the troposphere has typically 1-2 degrees of freedom, analysing the TES bias for two vertical regimes -the lower troposphere (LT, 1000 to 500 hPa) and the upper troposphere (UT, 500 hPa to tropopause)-might be meaningful  (Nassar et al.
, 2008). From a linear regression of all TES vs. sonde ozone data pairs for Uccle in the lower troposphere, we find a slope of 0. 90 with an intercept of 7. 98 (R = 0. 60) with a bias of +2. 96 ppbv. For the upper troposphere the bias is a bit higher (7. 80 ppbv), the correlation (R) is 0. 89 and the slope and intercept are 0. 99 and 8. 75 respectively. These values are in line with reported ones for data pairs collected for the whole northern mid-latitudes  (Verstraeten et al. , 2013). The temporal stability of the TES sensor for tropospheric ozone can be assessed by applying the Theil Sen trend statistics  (Theil 1950a (Theil , 1950b (Theil , 1950c;; Sen, 1968)  on the time series of the TES-sonde data pairs for each pressure level in the troposphere (surface to 300 hPa). Analysis shows that all p-values are larger than 0.
, 1998a,b)  to guarantee the data homogeneity. Another distinct feature of the Uccle ozonesonde dataset is the correction for urban SO 2 interference with the chemical reactions in the ozone cells in the first half of the period. Although satellites provide global routine measurements of ozone profiles with increasing accuracy and spatial resolution, ozonesondes are the only technique that can provide, since 50 years, accurate (around 5-10%), vertically resolved observations from the surface up to the lower stratosphere, unaffected by clouds or precipitation. Furthermore, they can resolve strong gradients in the UTLS (upper troposphere/lower stratosphere), while precisely locating the thermal tropopause  (Thompson et al. , 2011). In this paper, we illustrated the importance of the Uccle ozonesonde dataset in two specific application areas: for the assessment of the long-term vertical ozone trends and for the validation of satellite retrievals of ozone profiles.
For the operational validation of the GOME-2 and IASI ozone profiles within the EUMETSAT AC-SAF, the role of ozonesonde profiles is crucial. We showed how the Uccle dataset can be used to evaluate the performance of a degradation correction for the GOME-2 UV sensors. The Uccle ozonesondes are also used to assess the accuracy and stability of satellite ozone retrievals. Here, we showed that the AURA-MLS overpass ozone profiles agree very well with the ozonesonde profiles, within ±5% between 10 and 70 hPa. Another instrument on the same AURA satellite platform, TES, has its largest vertical sensitivity for ozone in the troposphere, and is generally positively biased with respect to the Uccle ozonesondes in the troposphere by up to ~10 ppbv, corresponding to relative differences up to ~15 %. Using the Uccle ozonesonde data series as reference, we also found that the temporal stability of both satellite retrievals is excellent.
for tropospheric ozone:  Flemming et al. , 2015) , analyses (e. g. for stratospheric ozone:  Lefever et al. , 2015)  and reanalyses (e. g. Inness et al, 2019). Those services require a near real-time delivery of the ozonesonde measurements, with an operational quality assessment/quality control tool, as the total column ozone drop-off in a third of the ozonesonde stations  (Stauffer et al. , 2020)  made obvious. These are the challenges for operational applications of ozonesondes. For the assessment of the longterm variability of ozone concentrations at different atmospheric altitudes and the interaction between climate change and ozone (also studied in coupled chemistry-climate and chemistry-transport models, see e. g. Morgenstern et al. , 2017) , the availability of a long-term homogeneous dataset is crucial.
Homogenization efforts of ozonesonde networks and/or datasets  (Tarasick et al. , 2016; Van Malderen et al. , 2016; Thompson et al. , 2017; Witte et al. , 2017 Witte et al. , , 2018 Witte et al. , , 2019;; Sterling et al. , 2018)  should therefore be continued and extended. With these developments in mind, we aim at continuing the pioneering role that the Uccle time series had in some of the research areas during its half a century lifetime.
from GOME-2 and Infrared Atmospheric Sounder Interferometer (IASI) instruments on board the MetOp A/B/C satellite platforms. Those different instruments give us the opportunity to obtain a unique dataset, retrieved with an identical technique, from the beginning of the MetOp-A/GOME-2 instrument in 2007, until the end of the third, MetOp-C/GOME-2, foreseen in 2022. GOME-2 ozone profiles are given as partial ozone columns, expressed in Dobson Units, on 40 varying pressure levels between the surface level and 0. 001 hPa and are calculated by the Ozone Profile Retrieval Algorithm(OPERA, van Peet et al. , 2014). The a-priori information used for the retrieval is obtained from McPeters and Labow (2012).
Acknowledgements The Uccle ozone sounding time series could only be built up thanks to the efforts and dedication of the ozone sounding operators over the past 50 years ( Martin Lebrun ,  Jean-Claude Grymonpont ,  André Massy ,  Jozef Bartholomees ,  Daniel Wattez ,  Eli Weerts ,  Kevin Knockaert , and  Roger Ameloot ) and the technical support by  Geert Desadelaer. Since 2007, the ozone sounding program in Uccle and  R. Van Malderen  are funded by the  Solar-Terrestrial Centre of Excellence (STCE) , a research collaboration established by the  Belgian Federal Government  through the action plan for reinforcement of the federal scientific institutes. We are grateful to WOUDC and the  NDACC  for archiving the (Uccle) ozone data and making them publicly available. The MOZAIC/CARIBIC/IAGOS data were created with support from the  European Commission, national agencies in Germany (BMBF), France (MESR) , and the  UK (NERC) , and the IAGOS member institutions (http://www.
Code/Data availability The ozonesonde and total column ozone data used in this paper is publicly available through the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) and the Network for the Detection of Atmospheric Composition Change (NDACC). The MOZAIC/CARIBIC/IAGOS data are available at http://www. iagos. fr and the surface ozone and ozone precursor data at Uccle can be found at http://www. irceline. be, the website of IRCEL-CELINE (Belgian Interregional Environment Agency). The AURA MLS v4. 2 Uccle overpass data were obtained at http://avdc. gsfc. nasa. gov/pub, the TES data at https://search. earthdata. nasa. gov/. The source code of the LOTUS regression model is publicly available at https://arg. usask. ca/docs/LOTUS_regression.
Measurements for the spring and summer periods started 1 month after the establishment of the recent root exclusion plots, late October 2017 and mid-January 2018, respectively. The measurements for the summer period were done on a subset of 4 randomly selected collars (out of the 9) in each treamtent. Soil temperature (T s ) was measured every hour throughout the experiment using a series of six thermocouples at a depth of 100 mm, one in each of the root exclusion plots established in spring (T s , Type E, Omega engineering Ltd. , Stamford, CT, USA). Soil volumetric water content was also monitored continuously using three moisture probes (h s , Theta Probe model ML1, Delta-T Devices Ltd. , Cambridge, UK). The data were recorded on a data-logger (CR1000, Campbell Scientific Inc. , Logan, UT, USA).
Statistical analyses To test for the effect of the treatments (1 month and long-term root exclusion) on the temperature sensitivity of R, the model from  Lloyd and Taylor (1994)  was fitted as R ¼ R 10 e E 0 ð 1 56:02 À 1 Ts À227:1 Þ ð1Þ where R 10 is a basal respiration rate at 10 °C, and E 0 is related to the relative temperature sensitivity of respiration and T s is expressed in K. The data collected in spring and summer were first analysed separately. Each measurement of R was treated as a sample. For each season, non-linear mixed-effect models were used, including spatial replicates (collar number) as a random effect to account for the non-independence of measurements made on the same collars over time. For analysing the temperature response on a seasonal time scale, values of R from both seasons were combined.
Acknowledgements We are grateful to  Louis Schipper  for his suggestions on the experimental design and  David Whitehead  for proof reading the manuscript. We also thank  Graeme Rogers ,  Aliia Gilmullina  and  Jonathan Nuñez  for their help with field set up and sampling, and  Keith Cameron  from  Lincoln University  and the  Ashley Dene Research Station  staff for facilitating access to the site. This work was funded by the  New Zealand Government  to support the objectives of the  Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases. CNRS  is acknowledged for funding the internship of  Matthias Moinet.
Bill of materials The required airflow for a tight-fitting PAPR (by NIOSH standards) is 4 cubic feet per minute (115 L per minute). A significant amount of literature exists to aid in understanding the process of selecting a fan/blower  [62] [63] [64] [65] [66] [67] [68]. In general, axial fans (e. g. computer cooling fans) are good for large airflow rates against very low resistance (or static pressure), while centrifugal fans perform better against airflow resistance/static pressure  [61, 64]. Centrifugal fans are further divided in performance characteristics by the shape and layout of their blades  [65]. Ultimately, a forward-curved centrifugal fan was selected for this purpose, based on the availability of such fans made for low voltages. The data sheet for the Sanyo Denki 12 V centrifugal blower indicates that the blower can provide sufficient airflow at the expected static pressure  [69].
Snow sampling and NO - 3 determination The snow pit NO - 3 data stem from sampling that was done at and near Concordia between January 2009 and December 2010, and at ≈ 3 m distance from snow tower 2 in January 2014. Snow was collected in pre-cleaned 50 mL centrifuge tubes inserted directly on a newly scraped wall of a snow pit. Nitrate concentration in snow samples was measured directly in the field, at the wet chemistry laboratory of Concordia Station. Each sample was melted at room temperatures and NO - 3 concentrations were determined using a colorimetric method employed routinely at Concordia  (Frey et al. , 2009).
4 ). During the course of the ≈ 14 month study, a total of ≈ 50 pollution events were observed, although some events overlapped (Fig. 4 ). Most of these occurrences were relatively short, with elevated mixing ratios above the snowpack lasting from minutes to a few hours. We counted 15 events in total when there was a spike in the above-surface NO x measurements followed by an increase of at least 1 ppb of NO x in snowpack air. It took up to in excess of 7 d for NO x in the snowpack air to return to pre-event levels. Integrated over Figure  4. NO x measured from the above-surface inlets on the two snow towers and from all inlets on the meteorological tower. Circles represent 10 min averaged data. Plotted are data that were extracted from occurrences when NO x was above 1 ppb, well above the background mixing ratio, and any time when the wind direction was from the polluted wind sector.
Acknowledgements. We thank all staff and scientists who helped with the setup and maintenance of the experiment at  Concordia Station. Alexandra Mass ,  William Vicars , and  Albane Barbero  helped with some of the field experimental work and data processing. Albane Barbero  provided the photograph in Fig. 3. Financial support. The French partners recognize technical support from the  C2FN (French National Center for Coring and Drilling , handled by  INSU) , a grant from  Labex   OSUG@2020  (In-vestissements d'avenir - ANR10 LABX56 ), and the program 1011 SUNITEDC of the  Institut Polaire Paul Emile Victor (IPEV). The US scientists were supported through a grant from the  National Science Foundation (NSF) ,  PLR # 1142145. Review statement. This paper was edited by  Martin Schneebeli  and reviewed by two anonymous referees.
Data management Clinical images, associated data and metadata will be stored using the XNAT platform (www. xnat. org) hosted on the dedicated BEAt-DKD server at SIB. Clinical study data will be managed using REDCap (www. project-redcap. org), also installed on the dedicated BEAt-DKD server. All variables will be recorded on iBEAt central case report form (CRFssee additional files 3. 0-3. 8) and uploaded onto the central RedCap instance. It is envisaged that the iBEAt clinical study will be set up as a federated node enabling remote analysis of the data generated in the future, and integration of the iBEAt data with other datasets collected in BEAt-DKD.
Sample size There are insufficient a priori data available to perform a reliable formal power calculation for this study. Thus, a more pragmatic approach was adopted, with the sample size taking into account the feasibility aspects of the study, for example, the number of imaging facilities and the estimated rate of recruitment in each centre. This produced a target population of 500 patients, which will result in the largest quantitative imaging DKD study, to date, both in terms of patient numbers and regarding patient follow-up. Participants will be recruited across various stages of DKD, as measured by ACR (A1, A2, A3) and eGFR (G1 + G2, G3). This will produce a heterogeneous population enabling the evaluation of the associations between imaging biomarkers and DKD in T2D patients.
Statistical analysis plan Descriptive statistics of all covariates across the study centres will assess the bivariate relationships between sets of related covariates. A cross-sectional analysis will be performed using data collected at baseline, as well as a longitudinal analysis using the imaging data collected at baseline and blood and urine markers collected annually. Given the large number of covariates in the study, a variable selection and regularisation method such as LASSO  [28]  or the elastic net regularisation  [29]  will be used. Modelling will use linear models for the crosssectional analyses, and linear mixed effects models for the longitudinal analysis. The analyses will be performed separately in each ACR / eGFR stratum, and the modelling of the whole cohort will account, whenever possible, for differences in potential associations across the strata.
Ancillary study 3 To validate automated or semi-automated processing of multiparametric renal MRI. In its current form the generation of biomarkers from complex functional MRI scans involves significant manual intervention as well as automated but slow iterative optimisation methods. In this study, led by Sheffield University, a subset of the iBEAt data will be used as training data to develop an ideally automated approach for image processing, which will then be validated on the remaining test data against the manual results.
Also in 2018, the UK Renal Imaging network (UKRIN) received a 3-year partnership grant to create a national infrastructure for quantitative renal MRI. In 2020, UKRIN has received funding for a 10 year cohort study in 500 CKD patients starting 1 sept 2020 (AFiRM study; principal investigator: Nick Selby, University of Nottingham). iBEAt setup started in September of 2016 and is the first study to respond to the clinical need for systematically collected evidence at a larger scale and across institutions, with well-validated methods linking up the imaging findings with other sources of data so the added value can be identified. In that sense, iBEAt is inspired by the landmark study CRISP (Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease)  [36]  -the first multi-centre cohort study exploring a quantitative MRI biomarker (TKV) in CKD and a foundation for the aforementioned FDA qualification of TKV.
In turn, iBEAt is committed to a "pay-it-forward" philosophy and will aim to share its study documentation and procedures widely for use by other investigators. iBEAt collaborators are also committed to maximise the opportunities for data sharing in order to increase the lifetime value of their research data as assets for human health and to do so timely, responsibly, with as few restrictions as possible, in a way consistent with the law, regulation and recognised good practice. Beyond data, iBEAt will aim to form a powerful resource for future biomarker discovery sources by collecting a rich collection of blood and urine samples in its central biobank. These will be made available for external investigators subject to formal application and approval by the iBEAt Steering Committee. After a 2-year setup period the first study participant was recruited into iBEAt in October 2018.
Funding This project is principally funded by the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  115974. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA  with  JDRF. This study receives additional support (personnel support) by grants from the  Swedish Heart and Lung Foundation  [20160872]; the  Swedish Research Council  [ 2018-02837 ;  EXODIAB 2009-1039 ]; the  Swedish Foundation for Strategic Research  ( LUDC-IRC 15-0067 ) to MFG; and the  UK Medical Research Council  ( MR/R02264X/1 ) and  Kidney Research UK  ( RP55/2012 ) to SS. This project is also supported by the  National Institute for Health Research (NIHR) Exeter Clinical Research Facility  and the  NIHR Leeds Clinical Research Facility.
Material and methods occurrence data. We downloaded records of presence for both spider species from the GBIF  40  via the rgbif package (citations for R packages are provided in Supplementary Material 1) in R  41. The GBIF database gathers volunteer-based naturalist observations (Supplementary Material 2), which often require a quality check. We used the package CoordinateCleaner (Supplementary Material 1) to remove null or duplicate coordinates, and to flag the records requiring a subjective decision, such as old records or records located in urban areas, or at the centroid of a county. Urban records were not necessarily false presence, and we used aerial photography 42 accessed with packages leaflet and mapedit (Supplementary Material 1) to decide whether to keep these records or not. We visually checked, for instance, if a record was not in a recently modified areas in a city.
Some records suggesting co-occurrence of the two species were checked in the field during summer 2018 and 2019 (25 locations, including four actually syntopic locations). We retained 775 records for Dolomedes fimbriatus and 181 records for Dolomedes plantarius (Fig. 2 ), reflecting the GBIF data available until October 2019 in Fennoscandia. When several records fell in the same raster cell, we kept only one.
Species distribution modelling. Predictor variables. For the climatic component of the ecological niche, we included variables which were biologically relevant for spiders, and not too correlated  43. Using a correlation coefficient threshold of 0. 7  44  , we selected mean and maximum annual temperature, mean diurnal temperature range, mean temperature of the wettest quarter, and annual precipitation, which we extracted from the WorldClim database  45  at a spatial resolution of 30 arc-seconds (Supplementary Material Table  S2 ). To predict the future distribution of Dolomedes spiders in Fennoscandia, we used IPCC projections for 2050 and 2070, under multi-factors "representative concentration pathways" (RCP) 4. 5 and 8. 5  46. RCP4. 5 corresponds to medium-low greenhouse gas emissions and air pollution, whereas RCP8.