dataesr/scientific-paragraphs-categorization huggingface dataset:
OCT acquisition protocol and analysis parameters We used the Spectralis OCT2 (Heidelberg Engineering, Heidelberg, Germany) for OCT acquisition. This instrument uses an 870-nm central wavelength at an 85-kHz A-scan rate with SP 6. 7a software. We used an OCT cube of 30° × 25° (8. 7 × 7. 3 mm) centered on the fovea, which was formed by 121 horizontal B-scans. The interval between the B-scans was 60 µm and the lateral resolution was 5. 64 µm/pixel; the axial resolution was 3. 87 µm/ pixel, and the frame rate was 10 per B-scan. We generated the thickness map using OCT software that displayed the 6-mm classic ETDRS grid from the acquired OCT images. The ETDRS grid consists of three concentric circles that form the central, inner, and outer regions. The inner and outer regions were divided into four fields to further form nine fields.
Thus, we divided the enface thickness map into two areas: the "ETDRS area" and the "outside ETDRS area. " The latter included the retina part located outside the 6-mm circle till the acquisition limits (7. 3 mm in height and 8. 7 mm in length). This region was also divided into four areas (Fig. 1 ). Retinal edema was defined as retinal thickening and/ or presence of hard exudates and/or presence of retinal cysts. Depending on the localization, we used the terms "main DME" to designate retinal edema with the highest point located within the ETDRS area and "peripheral retinal edema" to designate retinal edema with the highest point located outside the ETDRS area. We collected the following data concerning the main DME from the ETDRS area: central macular thickness (CMT), DME surface expansion, greatest retinal thickness and its field localization, size and retinal localization of cysts (if present), and the presence of hard exudates and/or microaneurysms around the retinal thickening area.
We observed the presence of "peripheral events" outside the ETDRS area which were defined as the presence of an eventual extension of the main DME outside the ETDRS grid and/or the presence of peripheral retinal edemas. Subsequently, we counted the number of peripheral retinal edemas per eye and collected the following data for each lesion: field localization, highest retinal thickness, size and retinal localization of cysts, presence of hard exudates and/or microaneurysms within the retinal edema, and eventual extension of the peripheral retinal edema into the ETDRS grid. Finally, these features were also collected from the follow-up visits when they were available for the treated eyes using the same acquisition protocol. Thus, the data were analyzed at the baseline and at the first visit after treatment initiation: 3 weeks after five consecutive monthly intravitreal injections of aflibercept or twelve weeks after one intravitreal injections of Dexamethasone implant (Ozuredex).
Demographic and clinical data In total, 375 eyes (192 right eyes; 183 left eyes) of 218 patients (127 men, 91 women) presenting with retinal edema detected using this OCT protocol were included in this study. The mean age was 60. 62 ± 13. 02 years, while the mean duration of diabetes was 16. 75 ± 9. 34 years. The mean HbA1C level was 8. 99% ± 2. 2%. Chronic blood high pressure was observed in 160 patients (77. 3%). The demographic and clinical data are presented in Table  1.
d-f Right eye of a 61-year-old patient with VA of 20/20. Thickness map shows no main DME, but an isolated peripheral retinal edema is detected outside the ETDRS area that minimally overflow into the grid. B-scan displays a preserved foveola, and the retinal edema contains a small cyst with hard exudates and microaneurysm Interestingly, the OCT patterns of the main DME and peripheral retinal edemas were well matched for almost all the collected features. This protocol yielded supplementary findings from outside the ETDRS area in 74. 4% of eyes. Admittedly, data from the ETDRS area are crucial in the diagnosis and management of DME, since the CMT of the 500-μm-radius ETDRS central ring is mostly used, and the central pattern during assessment of disease activity, progression, and treatment response has been examined previously  [17, 17]  However, there are different manner in which data collected from outside the ETDRS area could contribute to better characterization of the DME and may support its management in the same way of the previous reported information from the non-central-ETDRS fields  [9, [18] [19] [20].
Discussion and Conclusions Here we have only begun to scratch the surface of the results becoming available from the CovidMIP simulations. We stress that this work has been the result of a very rapid response of the Earth system modeling community. It often takes several years to design and perform coordinated MIP experiments, and process the data for publication in a community archive. This activity has taken place in only a matter of months. This paper is just the very first analysis of initial results and therefore serves only as a first indication of how the climate system has responded to the perturbations to emissions in response to the COVID-19 pandemic. It is not possible at this stage to analyze all of the responses, nor the processes responsible for changes across the whole system.
Data Availability Statement This work used JASMIN, the UK's collaborative data analysis environment (http://jasmin. ac. uk, Lawrence et al. , 2013). The authors are extremely grateful to the help and support of Martin Juckes, Alan Iwi, Ruth Petrie, Ag Stephens, Charlotte Pascoe at the Center for Environmental Data Analysis, Science and Technology Facilities Council, UK who facilitated the data sharing on JASMIN. CESM1 data can be accessed at https://zenodo. org/record/4521767. All other model data is published on the CMIP6 archive available via the Earth System Grid Federation. https://esgf-index1. ceda. ac. uk/search/cmip6-ceda/.
The topography of the study area is dominated by Mount Meru, which is a steep stratovolcano with a summit culminating at 4565 m summit  [23] , but the highest elevation in the relief map in Figure  1  is 4532; this is due to the limitation of the ASTER GDEM 2 dataset. Our study area covers most of its slopes. The eastern flank of Mount Meru is incised by a deep valley formed by a catastrophic sector collapse that left a horseshoeshaped valley now occupied by an ash cone that last erupted in 1910  [20]. Multiple parasitic cones are notable features in the vicinity  [20, 23, 24]. Rainfall data from four existing rainfall stations-Agricultural Seed Agency (ASA) Farm at Ngaramtoni with 50 years data trend , Arusha Airport with 51 years data trend , Arusha Urban Water Supply and Sanitation Authority (AUWSA) with 27 years data trend (1992-2018), and Tengeru Livestock Institute with 29 years data trend (1990-2018)-were used to analyse long-term trends and the amount of rainfall in the study area.
[23]  with updates from Delcamp et al. [20] , who re-interpreted the deposits that had originally been mapped as lahar, as debris avalanche deposits (DADs). The deposits that had originally been mapped as tholoids by Wilkinson et al. [23]  are now re-interpreted as lava domes in this study. [23]  with updates from Delcamp et al. [20] , who re-interpreted the deposits that had originally been mapped as lahar, as debris avalanche deposits (DADs). The deposits that had originally been mapped as tholoids by Wilkinson et al. [23]  are now re-interpreted as lava domes in this study. Existing borehole completion reports and field stratigraphic descriptions of subsurface sediments during the digging of new hand-dug wells were used to characterise the geology of the aquifers. On the northeastern flank, data from one borehole at Mkuru (depth of around 65 m) described by Ghiglieri et al.
Data Processing and Analysis The hydrochemical characterisation of groundwater in the study area has been investigated based on the analysis of a Schoeller diagram, Piper diagram, bivariate diagrams, and statistical analyses. The concentrations of F -determined in water samples were compared to the maximum permissible limits for drinking water set by the WHO (1. 5 mg/L) and Tanzanian standards (4. 0 mg/L). Major physicochemical parameters were tested for normality using IBM SPSS Statistics 27 software. Considering that all were not normally distributed, the Spearman's correlation was used for analysis. Since the IBM SPSS Statistics 27 reported all the significant p-values for this test as 0. 000 (only in three decimal places), the calculation of p-values in more decimal places was done using Microsoft Excel 2019.
Acknowledgments: The authors thank the  Tanzania Commission for Science and Technology (COSTECH)  and  Tanzania National Parks Authority (TANAPA)  for providing research permits. In addition, the authors thank  Laura Segers  and  Stefanie Rombaut  for their assistance during fieldworks. Thanks to  Martine Leermakers  and  Natacha Brion  ( AMGC, Vrije Universiteit Brussel ) for their help with chemical analysis of some water samples. Ines Tomašek  acknowledges the support received from the  VUB Strategic Research Program (SRP)  and the  Agence Nationale de la Recherche  of the  French government through the program  " Investissements d'Avenir " ( 16-IDEX-0001 CAP [20][21][22][23][24][25 ]. Karen Fontijn  acknowledges support from  F. R. S. -FNRS MIS  grant  F. 4515. 20. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of  VLIR-UOS.
Seismological Methods We examined the seismicity recorded in the WGoC between 23 December 2020 and 28 February 2021. We analyzed 2960 events, with P-and S-wave arrival-time data manually determined at the Seismological Laboratory of NKUA and the Geodynamic Institute of NOA. Initial locations were assessed using HYPOINVERSE  (Klein, 2002) , tuning distance weighting by excluding stations located 40+ km away from the epicenters. We employed the 1D velocity model of  Rigo et al. (1996) ; therefore, our locations are consistent with those of previous works using the same model (e. g. ,  Duverger et al. , 2018). With a mean root mean square travel-time residual of 0. 11 s, the average absolute location uncertainties reported by HYPOINVERSE are 0. 31 km in horizontal and 0. 86 km in vertical, supposing an average arrival-time reading error (i.
Geodetic Methods GNSS data were processed using software GIPSY (version 6. 4), developed by Jet Propulsion Laboratory-National Aeronautics and Space Administration. Following  Briole et al. (2021) , the time series were corrected for the secular velocities and filtered with a Gaussian filter. The residual time series are plotted in Figure  3 , and the coseismic displacements are listed in Table  S2. For the two main events (12 January 2021 M w 5. 0 and 17 February 2021 M w 5. 3), Sentinel-1 SAR interferometry (InSAR) shows no deformation within a quarter of fringe (7 mm) in the line-of-sight direction. Indicatively, an interferogram of Sentinel-1 Track 80, spanning the dates 12 and 24 January 2021, is shown in Figure  S5. Modeling was performed with Inverse6 code (Briole, 2017), using the method described by  Briole et al.
Data and Resources Corinth Rift Laboratory network (CRLnet) comprises seismological, geodetic and geophysical subnetworks from various educational and research entities. The first instruments were installed in 2000. Seismological data were acquired with 20 broadband and short-period velocimeters and six accelerometers installed and operated by the following institutions: (1) the Corinth Rift Laboratory (CRL) team (CL network, data hosted at RESIF, DOI: 10. 15778/RESIF. CL), (2) the National and Kapodistrian University of Athens (NKUA; HA network, DOI: 10. 7914/SN/HA), (3) the University of Patras (HP network, DOI: 10. 7914/SN/HP), which operates certain stations jointly with Charles University, Prague, and (4) the National Observatory of Athens (NOA; HL network, DOI: 10. 7914/SN/HL) (Fig. 1 ).
Acknowledgments The authors thank the personnel of  Corinth Rift Laboratory network (CRLnet) and Hellenic Unified Seismological Network (HUSN)  who worked for the installation, operation, and maintenance of stations used in this article. George Kaviris ,  Vasilis Kapetanidis ,  Anna Serpetsidaki ,  Andreas Karakonstantis ,  Efthimios Sokos ,  Ioannis Kassaras ,  Vassilis Sakkas ,  Ioannis Spingos ,  Christos Evangelidis ,  Ioannis Fountoulakis ,  Olga-Joan Ktenidou ,  Panayotis Papadimitriou , and  Nicholas Voulgaris  acknowledge financial support by the  HELPOS  project, " Hellenic Plate Observing System" (MIS 5002697 ). Maps and cross sections were drawn using the Generic Mapping Tools (GMT) software (Wessel and Smith, 1998).
Data Availability Statement and Declaration Data from seismometers and accelerometers can be retrieved from European Plate Observing System (EPOS) nodes (https:// www. orfeus-eu. org/data/eida/nodes/), European Integrated Data Archive (EIDA) nodes at RESIF, and National Observatory of Athens (NOA; Evangelidis et al. , 2021). Global Navigation Satellite Systems (GNSS) data and positioning solutions are available on the Corinth Rift Laboratory (CRL) portal (http://crlab. eu). Phase and focal mechanism data of Seismological Laboratory of National and Kapodistrian University of Athens (NKUA-SL) are available at http:// www. geophysics. geol. uoa. gr/stations/gmapv3_db/index. php? lang=en and of GI-NOA at http://bbnet. gein. noa. gr/HL/ databases/database. The focal mechanism of the 25 April 2012 event is available at https://www.
The seven major challenges outlined here will help interdisciplinary researchers to facilitate collective goals and direct future systematic analyses of MP fate and transport controls on potential entry points and uptake mechanisms into aquatic food webs. The resulting knowledge will enable confirmation of the major propagation and biomagnification routes and subsequent organismal and ecosystem impacts of MPs in freshwater ecosystems and determination of the relative risks to human health, allowing targeted mitigation strategies with maximum impact to be developed. 734317). Berta Bonnet was funded through the EU H2020 Marie Curie Individual Fellowship (NanoTOX -706172) and Jen Drummond by the Royal Society International Newton Fellowship (NIF\R1\180935).
Acknowledgements This work was supported by the  Birmingham Institute of Global Innovation  and funded through the  Leverhulme Trust  research grant ( RPG-2017-377 ) and the  EU H2020 ITN Grants HypoTrain  ( H2020-MSCA-ITN-2014 -641939 ) and  HiFreq  ( H2020-MSCA-RISE-2016  -.
Samples preparation The rapeseed leaves were acclimatized for about 2 h at 20 • C before being inserted into the multiphase simulation chamber. In this way, leaves reached room temperature (20 • C), which corresponds to the average temperature in the North of France during summertime. This was necessary for reproduction of the real-time conditions under which the rapeseed leaves start their decomposition. Once acclimatized, leaves were weighted and spread out to cover the whole surface of a FEP (fluorinated ethylene propylene) film (with a surface of 0. 64 m 2 ) (Fig. 1a ). After 6 d of measurement, the surface covered by the rapeseed litter was estimated to be 0. 45 m 2 (Fig. 1b ) using Adobe Photoshop software (V 21. 1. 1). Photoshop allowed for the manual selection of the pixels containing the litter; the pixels were converted into surface area (m 2 ) using the following formula: A litter = P x litter P x × m 2 , ( 1 ) where A litter is the area covered by the rapeseed litter 6 d after the beginning of the experiment, P x litter is the number of pixels in the litter area, and P x × m 2 is the number of pixels per m 2.
Particle measurements Particles were detected by means of an SMPS consisting of a differential mobility analyser (DMA, model 3085, TSI) and an ultrafine condensation particle counter (UCPC model 3776 high flow, TSI, d 50 >2. 5 nm). During the experiments, the scanning particle size ranged from 2. 5 to 79. 1 nm, and both the sheath and sample flow rates were settled at 3 and 0. 3 L min -1 , respectively. The SMPS inlet was positioned at 180 cm above the rapeseed surface to observe the particle formation and growth. The density of the measured particles was assumed to be 1 g cm -3. The particle loss due to the impact of the chamber walls was calculated based on data from previous experiments performed on the same multiphase simulation chamber  (Alpert et al. , 2017; Bernard et al. , 2016). The estimations of the particle loss used for the correction of the SMPS data are shown in Supplement Fig.
VOCs measurement VOCs were detected using the PTR-TOF-MS technique, which has been already described in detail by  Müller et al. (2014). Ionization of the VOCs was carried out using the H 3 O + mode. The pressure and voltage of the drift tube were respectively set to 2. 2 mbar and 500 V with a temperature of 80 • C. Consequently, the E/N ratio was about 123 Td (Townsend, where 1 Td = 10 -17 V cm 2 ). These parameters were maintained constant during the whole experiment to avoid different ionization conditions of the VOCs within the drift tube. The sample inlet of the PTR-TOF-MS was constantly heated at 60 • C to avoid product loss by absorption in the inlet tube. The instrument sampled every 30 s with a flow rate of 100 mL min -1 , and the raw data were recorded using TofDaq software (Tofwerk AG, Switzerland).
Moreover, the calibration of the spectra was performed via both an oxygen isotope of the ion source H 18 3 O + (21. 022 m/z) and an ionized acetone molecule C 3 H 7 O + (59. 0449 m/z) as described by  Cappellin et al. (2011). Those compounds were chosen for the calibration because their identification was straightforward for all the kinds of samples used in this study. After calibrating the spectra, a peak table was created including the largest number of detected compounds. The threshold for the automatic research feature of the peak was set to 0. 1 cps. Even if the peaks were automatically identi- Furthermore, the mixing ratio (ppb) was calculated using the PTR viewer software (V3. 2. 8, Ionicon, Analytik GmbH) which used the equation described in  Cappellin et al. (2011) , and the VOC emissions fluxes (E VOC , in µg m -2 h -1 ) were calculated as follows: E VOC = F air × ([VOC] litter -[mVOC] blank ) × M VOC V air mol × ((S litter-S + S litter-E ) /2) × 1000 (ng/µg) (2) where F air is the net airflow (F air = 240 L h -1 ),  [VOC]  litter is the concentration (ppb) of the VOC emitted in the chamber with the samples, and [VOC] blank is the concentration (ppb) of the VOC measured in the empty chamber.
Peak identification method The spectra were analysed using the Spectra Analyser tool of the PTR viewer software (version 3. 2. 8, Ionicon). This tool allowed identification of the compounds corresponding to each peak in the spectra by searching for the possible combinations of elements leading to the closest molecular weight. This identification of the VOC was also double-checked with literature reviews. Even if this method accounted for the most precise identification of the VOCs, it does not provide a certain identification of the compounds since (1) it is not possi-ble to distinguish between two ion masses that are closer than the PTR-TOF-MS mass resolution, and (2) the PTR-TOF-MS does not distinguish between isomers (VOCs having the same molecular mass).
Data analysis The database was filled with 217 variables corresponding to the number of detected masses for the three different conditions UV, O 3 , and UV_O 3 (as mentioned previously). The statistical analysis of the entire dataset was performed using the R software (Version 1. 2. 5019 -© 2009-2019 RStudio). At first, we selected the variables that were normally distributed by the Shapiro-Wilk test (W >0. 9). Secondly, we tested the homogeneity of the variance by the Levene-test to perform the analysis of variance (ANOVA) test followed by the Tukey post hoc test. Furthermore, we tested the differences between the conditions using the principal component analysis (PCA -package FactormineR). The PCA allowed a graphical representation of the whole dataset differentiating the VOCs emission profiles for the different tested conditions without bias.
A table with the 30 most emitted compounds and their relative abundance at the three different conditions is presented in Table  1. Finally, the calculation of the Shannon index was performed. The Shannon index is a quantitative measure reflecting how many different VOCs were emitted from each sample. It was calculated with the diversity function of the vegan package (version 2. 4-3) in R software (version 3. 2. 3). The diversity index was calculated as H = VOC E VOC log (E VOC ), where the sum is over all VOCs recorded in the mass table. 3 Results.
Low emissions of isoprene were detected, even though the production of SOA was not negligible. The densest portion of particles produced by litter samples had a diameter lower than 20 nm, which might have caused an underestimation of the SOA formation from litter in other studies that detected a range of particles with a diameter higher than 20 nm. (RAVISA grant agreement no. 1762C0006) and the European Commission (EC) under the European Union's Horizon 2020 research and innovation program (Eurochamp 2020 grant agreement no. 730997). Financial support. This research has been supported by the Agence de l'Environnement et de la Maîtrise de l'Energie (grant no. RAVISA grant agreement no. 1762C0006) and the European Commission, Horizon 2020 Framework Programme (grant no. EUROCHAMP-2020 (730997)).
Code availability. The code is available under request at letizia. abis@tu-berlin. de. Data availability. Data are available for download at https://doi. org/10. 25326/0JXT-M252 (George, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-12613-2021-supplement. Author contributions. LA took charge of the conceptualization, data curation, investigation, formal analysis, methodology, visualization, and writing. CK handled the methodology, investigation, writing, review and editing. BL handled the methodology and investigation. TW, LZ, and JC took care of the data curation and investigation. SP took care of the investigation, methodology and resources. BL performed writing, review, editing, and investigation. RC performed the conceptualization, data curation, methodology, writing, review and editing, funding acquisition, and project administration, supervision, and validation.
64 m 2 ; concentration of ozone injected the chamber: 80 ppbs, surface covered after in the chamber of 80 ppbs and 7 h d -1 of irradiation 6 d: 0. 45 m 2 7 h d -1 of irradiation with UV with UV fied, a manual readjustment of every peak was performed to reduce the bias of the automatic peak research. The range of the detected masses was between 31 and 164 m/z. Masses deriving from the water cluster, such as 37. 03, 38. 03, 39. 03, and 55. 03 m/z, were not taken into account during the analysis of the dataset.
METHODOLOGY AND DATA COLLECTION We utilized the Bank Service Quality questionnaire to collect data. The questionnaire was modified to include the two additional items that we aimed to evaluate, namely e-banking and mobile e-baking. From March to May 2018, surveys were distributed to bank clients in Serbia. The bank ' s clients were randomly selected to take part in our research. The bank ' s clients were randomly contacted in a variety of methods, including (1) social media; (2) several organizations consented to distribute surveys to their employees; and (3) a few professors agreed to distribute questionnaires to their students. Because of the personal touch and regular follow-ups, the response rate was very high. The respondents's assessment of service quality was assessed using a seven-point Likert scale (1strongly disagree to 7-strongly agree).