dataesr/scientific-paragraphs-categorization huggingface dataset:
Mobility Data Before the simulations could be carried out, it was necessary to prepare the model, specifically the three files described above. For this purpose, the following methodology was followed as detailed in this section. We started from the mobility study conducted by the Cabildo de Tenerife in October 2018  [23]  to analyze travel on the island and to allow access to citizenship, through a series of applications, of the data obtained, after processing and debugging them. Specifically, the information obtained from the geolocation records of mobile terminals, travel data from the Ten+ island public transport card, capacity data, and surveys of both residents and non-residents were used. For this purpose, the island was divided into 150 zones, called transport zones, which can be seen in Figure  8.
These previous maps have been elaborated after applying a data treatment in Python to the results provided by the MATSim simulator, which are given in xml format. In total, with a budget of 30,000,000 euros, 6294 EV charging points with a power of 7 kW and 144 points with a power of 22 kW were obtained, corresponding to slow and semi-fast charging points, respectively. These points have been grouped by municipality by means of data processing to obtain Table  2 , which shows the exact number of EV charging points obtained in each municipality of the island of Tenerife and in each step of the simulation. It should be noted that these results do not correspond to census population criteria, but to the simulation built by synthetic agents from the mobility data available in the study conducted by the Cabildo de Tenerife in October 2018  [26].
The municipalities of the Orotava Valley, i. e. , La Orotava, Puerto de la Cruz, and Los Realejos, also stand out, occupying in this case the sixth, seventh, and eighth positions, all of them with more than 200 charging points in the year 2037. The rest of the municipalities of Tenerife have a lower concentration of charging points, although it should be noted that of the 31 municipalities, the simulation results show more than 10 charging points in 29 municipalities, with Los Silos and Vilaflor being the only municipalities with a lower concentration of points, with nine and eight chargers, respectively. This information can be seen more graphically in Figure  17 , where the two municipalities of the metropolitan area clearly dominate: Santa Cruz de Tenerife and La Laguna. These previous maps have been elaborated after applying a data treatment in Python to the results provided by the MATSim simulator, which are given in xml format.
In total, with a budget of 30,000,000 euros, 6294 EV charging points with a power of 7 kW and 144 points with a power of 22 kW were obtained, corresponding to slow and   These previous maps have been elaborated after applying a data treatment in Python to the results provided by the MATSim simulator, which are given in xml format. In total, with a budget of 30,000,000 euros, 6294 EV charging points with a power of  also stand out, occupying in this case the sixth, seventh, and eighth positions, all of them with more than 200 charging points in the year 2037. The rest of the municipalities of Tenerife have a lower concentration of charging points, although it should be noted that of the 31 municipalities, the simulation results show more than 10 charging points in 29 municipalities, with Los Silos and Vilaflor being the only municipalities with a lower concentration of points, with nine and eight chargers, respectively.
Conclusions In this study, we first obtained the most efficient geographical distribution of electric vehicle charging points to be installed on the island of Tenerife for a budget of 30,000,000 euros and with a horizon until 2037. For this purpose, a simulation of the island's traffic was carried out using MATSim, a multi-agent traffic simulation software. For the elaboration of such simulation, a synthetic population elaborated in Python was used from the data obtained from a mobility study carried out by the Cabildo de Tenerife in October 2018. Prior to the elaboration of the simulation, the different governmental plans and.
Conclusions In this study, we first obtained the most efficient geographical distribution of electric vehicle charging points to be installed on the island of Tenerife for a budget of 30,000,000 euros and with a horizon until 2037. For this purpose, a simulation of the island's traffic was carried out using MATSim, a multi-agent traffic simulation software. For the elaboration of such simulation, a synthetic population elaborated in Python was used from the data obtained from a mobility study carried out by the Cabildo de Tenerife in October 2018. Prior to the elaboration of simulation, the different governmental plans and studies carried out to date in the Canary Islands on the preparation of the network of charging points for the growing number of EVs expected in the coming years were analyzed.
Using NGS clonality analysis, the composition of the sample can be analyzed in much more depth, thus allowing detection of these small clones, which may assist in refining the diagnosis. For data interpretation, the ARResT/Interrogate Q19 platform was used, which was designed specifically within EuroClonality-NGS to analyze Ig and T-cellereceptor sequence data, and the data are presented in the form of interactive visualizations. 5  Each sequence was analyzed for the presence of V, D, or J genes, and the IgK intronRSS and KDE elements and the junctional region containing the complementary determining region 3, including a determination of the deletion of germline Q20 and addition of random nucleotides, referred to as segmentation hereafter. The sequences were attributed to different clonotypes, which in the context of this study are defined by the involved 5 0 and 3 0 genes, the amino acid sequence of the junction, and, for some, the functionality of the full nucleotide sequence.
Materials and Methods To develop the algorithm for scoring, the data set of the multicenter biological validation study was used, 2 in which individual Ig gene rearrangement targets (IgHVeIgHDeIgHJ, IgHDeIgHJ, IgKVeIgKJ, and IgKVeKDE/intronRSS-KDE) were evaluated and the results were combined into a molecular conclusion. This data set included data obtained from 209 samples derived from B-cell neoplasias (n Z 124), reactive lymphoproliferations (n Z 82), and samples inconclusive between neoplasia and a reactive condition (n Z 3). This data set was based on DNA isolated from FFPE tissue (n Z 150), frozen tissue (n Z 41), or blood (n Z 18). All samples were tested in two different laboratories. Additionally, the information obtained from the 2020 EuroClonality external qualityassurance scheme, which included an educational scheme for scoring NGS-ampliconebased clonality detection via provided data files, was used.
The ARResT/Interrogate software shows these products at 23 and 24 amino acid length. There are rare cases with a very high ratio that still are considered polyclonal. Therefore, even though the cut-off value for calling this target clonal is higher (Figure  5 ), particular caution should be taken when scoring an intronRSS-KDE product as clonal, especially if it is the only target in which a clonal product is detected. When using these ratios to distinguish clonal from polyclonal results, a single cut-off value will always be a trade-off between sensitivity and specificity. In fact, when the observed ratio is very close to the cut-off point, it is important to recognize that the result is in a gray zone. Assessment of ratios as a quantitative measure thus always should be considered as a tool to support target evaluation and scoring, but not as an absolute truth.
Acknowledgments We Q41 thank  Jos Rijntjes  and  Jeroen Luijks  ( Radboud University Medical Center ) for technical assistance;  Diede van Bladel  ( Radboud University Medical Center ) for input of the Hodgkin sample; and  Blanca Scheijen  ( Radboud University Medical Center ),  Michèle van der Klift  and  Kim Heezen  ( Erasmus MC ) for fruitful discussions.
Samples collection PM10 sampling and measurement campaign was carried out on 24-hour basis every three days from 1 st of January to 31 st of December 2013. During the sampling period, PM10 concentrations were monitored using a MP101 beta gauge analyzer (Environment SA®). Moreover, PM10 filters were collected using an automated high-volume sampler (DA80, DIGITEL®, Switzerland) operating at 30 m 3 /h onto 150 mm Pall® QAT-UP filters. Filters were pre-heated for 4 hr at 450 ˚C before sampling to decrease the impurities. Over the sampling period, 122 samples have been collected and stored at -20 ˚C until analysis. Field blanks (two per month) were also considered by placing a blank filter in sampling conditions but without pumping. Additionally, meteorological data (temperature, wind speed and direction) were recorded on site using the WMT 52 ultrasonic wind sensor (Vaisala Windcap) coupled to the DIGITEL® DA80.
Concerning the uncertainties, it was calculated by summing errors related to the analytical and sampling procedures. For trace elements, the calculated uncertainty was majored of 10% as it is usually reported  (Prendes et al. , 1999). In the case of species concentration below the detection limit (D. L. ), it was replaced by D. L. /2 and given an uncertainty of 100%. As for missing data, it was replaced by the mean value and was assigned an uncertainty of 400%  (Polissar et al. , 1998; Kim et al. , 2004). Additionally, the stability of the solution was evaluated by bootstrap analysis. It is based on the same principles of the bootstrap analysis used in EPA PMF 5. 0. The profile of a source was validated if more than 80% of the bootstrap profiles were correlated (with r 2 >0. 6) to the reference one (the one obtained by considering the original dataset).
Conditional bivariate probability function (CBPF) The conditional bivariate probability function (CBPF) combines wind speed with the conditional probability function (CPF). The latter is calculated as the probability that the concentration of a species, in a certain wind direction, is greater than a specific concentration value (which is the 75 th percentile concentration of the different species in this study). CBPF will help to further understand the dependencies of the sources to the wind speed (Uria-Tellaetxe and Carslaw, 2014). These representations were drawn using the open source software R and the open air package  (Carslaw, 2015). The dataset used consisted of the species concentrations for each PM10 sample for the 24h period distributed in front of the 48 corresponding wind speeds and directions (one measurement every 30 min).
Concentration weighted trajectories (CWT) The Concentration Weighted Trajectories (CWT) approach investigates potential transport of pollutants and/or sources over large geographical scales  (Polissar et al. , 2001). It consists of combining species concentrations and/or source contributions with back-trajectories and use residence time information to identify air parcels that might be responsible of high concentrations observed at the receptor site  (Petit et al. , 2017). Air mass back-trajectories were calculated using the HYSPLIT model considering the Gridded Meteorological Data Archives (GDAS 1 degrees). For each sample, eight 72h backward trajectories were assigned and the source contribution values for the 24 hours period were combined with the corresponding trajectories. These representations were achieved through the Zefir 4.
Acknowledgment The authors would like to thank the  "Centre Commun de Mesures, ULCO " and specifically  Fabrice Cazier  and  Dorothée Dewaele , as well as the regional air quality monitoring network  Atmo Haut-de-France , for their contribution to this project. The authors would also like to thank  Amaury Kasprowiak  for his help in the ionic chromatography analysis.
Meteorological and ozone data sets We use a variety of meteorological covariates along with O 3 observations in this study (see Table  1 ). Air temperature (AT), sum of long wave and short wave downwelling ("global") radiation (GR), precipitation (PR), relative humidity (RH) and wind direction (WD) were obtained as 10-min data from the measurement station at the University of Natural Resources and Life Sciences, Vienna (BOKU; 48 Initially all data were averaged to hourly values. If not stated otherwise, for AT, daily maxima were used. For GR and PR, the daily sums were used, while for the WD the daily mean was used. The planetary boundary layer height (PBL) was obtained from the Austrian meteorological service (ZAMG) as hourly data. We use the daily maxima of PBL as they coincide with the daily maxima of AT.
Relative Soil Saturation (RSS) Within the ADA project (Agro Drought Austria, 2021) a daily operational drought monitoring and forecasting system (ARIS) was developed for the main agricultural crops grown in Austria (winter wheat, summer barley, corn, sugar beet and grassland) using different indicators. For ARIS the meteorological data are obtained from the Integrated Nowcasting through Comprehensive Analysis (INCA) data set  (Haiden et al. , 2011). ARIS uses further soil data from the digital soil map of Austria (eBOD; BFW, 2021a) and land cover information from CORINE  (Copernicus, 2021). ARIS products are calculated for two soil depths (0-40 cm and 40-100 cm for agricultural crops, 0-20 cm and 20-40 cm for grassland). RSS is a key output from ARIS, and we use this daily index in addition to SPI to gain information on soil moisture deficits on shorter time scales, which may lead to delayed or reduced leaf deployment and reduced BVOC emissions.
SURFEX is used in a wide range of applications, e. g. , river discharge prediction  (Fairbairn et al. , 2017) , drought monitoring  (Albergel et al. , 2019) , and urban climate studies  (Schoetter et al. , 2020). SURFEX can be configured to dynamically simulate changes in vegetation according to how meteorology impacts the growing season, which results in a dynamic representation of LAI. The use of this dynamic representation of vegetation has shown to be skillful at reproducing phenological changes and soil moisture on seasonal timescales when forced with state-of-the-art meteorological atmospheric forcing  (Albergel et al. , 2019). SURFEX can also perform data assimilation of satellite observations of land surface variables (soil moisture and LAI) using the Extended Kalman Filter  (Albergel et al.
SURFEX-MEGAN3 We have developed a coupling setup between the SURFEX land surface model and the MEGAN3 BVOC emission model, which from now on we term SURFEX-MEGAN3. The SURFEX-MEGAN3 has been continued and run within the SEEDS in the European CAMS domain for 2019 using the SURFEX land surface model outputs for root zone soil moisture (5th soil layer down) and LAI (Section 2. 5. 1) based both a freerunning model (termed open-loop) and that using the LAI assimilation. The coupling between the land-surface model SURFEX and MEGAN3 significantly improves the soil moisture and leaf area index information that is used within MEGAN3 to calculate BVOC emissions. MEGAN3 calculates the emission factors using the MEGAN3-EFP python code that is provided with the main MEGAN3 fortran code. MEGAN3 introduces updated emission factors and algorithms based on recent measurements, treatment for processes unrepresented in MEGAN2.
SURFEX-MEGAN2. 1 We also use an alternative coupling setup between SURFEX and MEGAN. The land surface model SURFEX was coupled online to the BVOC emission model MEGAN2. 1. This online coupling means that the MEGAN2. 1 model is embedded within SURFEX and it is executed when SURFEX is run. Similarly, to before, the motivation for this coupling was to try to improve the BVOCs flux estimation by using the ISBA scheme included in SURFEX to provide more precise, vegetation-type dependent input parameters (LAI and soil moisture) to MEGAN2. 1. In the coupled model, the estimation of the biogenic flux is done by using vegetation types extracted from ECOCLIMAP-II, which is a 1-km global database of land covers made by CNRM (Centre Nationale des Recherches Météorologiques)  (Masson et al. , 2003). ECOCLIMAP-II includes a description of 19 vegetation types also called patches in addition to land parameters associated to each type of vegetation: 3 soil depths (root, soil, ice), height of trees and LAI (Leaf area index) available at 10 days' time step.
A mapping was done in order to associate to each patch defined in ECOCLIMAP-II its corresponding vegetation type in CLM4. MEGAN uses the same meteorological driving variables used as input to SURFEX (temperature, precipitation rate, incident shortwave radiation, CO 2 concentration and wind speed). The incoming PAR at the top of the canopy was assumed to be 48% of the incoming shortwave radiation. SURFEX-MEGAN2. 1 uses a different approach to the vegetation parameters as compared to SURFEX-MEGAN3. In this case, the LAI is extracted from the ECOCLIMAP-II database for each vegetation type independently. As before, SURFEX-MEGAN2. 1 uses the soil moisture of the 5th soil layer (corresponding to the root zone) and uses the wilting point calculated from the fraction of sand and clay to estimate the soil moisture activity factor.
Periods of investigation Given limitations in the availability of long-term HCHO data our analysis focuses on the years 2018-2020, which includes distinct dry and regular moister conditions. As we are interested in characterizing the influence of dry vs. wet conditions on BVOC emission and their contribution to ambient O 3 burden during the vegetation season we select from the data available the driest and wettest spring (MAM) and summer (JJA) seasons based on meteorological drought characterized by SPI-3 (Supplement Fig. S3 ). This selection yielded spring 2020 (MAM20) and summer 2019 (JJA19) as the driest spring and summer, respectively. Spring 2018 (MAM18) and summer 2020 (JJA20) were selected as the reference wettest spring and wettest summer seasons, respectively, based on recorded precipitation and soil moisture.
Results We perform a comprehensive analysis of the datasets introduced in Section 2. 2 to 2. 4 for the period 2018 to 2020. First, in Section 3. 1 we show the timeseries of the analysed variables to illustrate the broad seasonal correlation of the abundance of O 3 and HCHO with meteorological, hydrological and vegetation parameters. In Section 3. 2 we investigate the changes in HCHO mixing ratios as a response to environmental parameters. In Section 3. 3 we focus on the influence of flow direction and anthropogenic influences. Section 3. 4 focuses on the vegetation and meteorological conditions that occurred during periods with elevated ozone concentrations. Finally, Section 3. 5 is focused on the study of the impacts of the 2019 summer drought on simulated isoprene emissions and HCHO.
We hypothesize that the phenological changes reflected by the reduced LAI in July-August 2019 presented in Fig. 2b , and the reduced SIF and fAPARa over the same period, are induced by the exceptionally warm AT and high VPD values occurring around mid and late June 2019. In other words, we hypothesize that these hot dry spells in June 2019 induce a drought, which is in turn represented by the changes seen in LAI, SIF, and fAPARa. The JJA19/20 case, thus, presents a rather more complex picture than for the spring periods since there is some evidence of droughtdriven phenological changes in the SIF, LAI, and fAPARa observations, but little apparent change in observed HCHO mixing ratios. For this reason, we now look at the land surface and BVOC model data from these JJA periods in order to look at the drought-driven processes in more detail.
In contrast, the locations Lobau and Schwarzenberg Park show greatly reduced isoprene emissions over this same period later in July as each of these locations lies within the region most affected by the low root zone soil moisture conditions that develop during July, i. e. , <0. 2 m 3 m -3. The results from SURFEX-MEGAN3 are broadly consistent with those from SURFEX-MEGAN2. 1 (see Fig. S6 ) for these same five locations. SURFEX-MEGAN2. 1 reproduces the decline in isoprene emissions from that occurred in July 2019 in all locations, and that the decline was stronger in some locations compared to others. The differences between the different locations arises due to spatial variations in the emission factors used in MEGAN2. 1 (calculated from ECOCLIMAP-II plant functional type maps) and MEGAN3 (calculated using the MEGAN3-EFP python code).
Further, we are grateful to the  ESA/Copernicus Sentinel 5 P Mission  and the preparation and release of the TROPO SIF dataset by  Philipp Koehler  (pkoehler@ca ltech. edu)and  Christian Frankenberg  (cfranken@caltech. edu) of the  Caltech University , to NASA and the  Orbiting Carbon Observatory   OCO-2  and  Bernard Heinesch  and  Valerie Gros  for the supply of isoprene flux measurement and concentration data of the flux stations at  Vielsalm, Belgium  and the  Oak Observatory -Observatoire de Haute Provence  ( O3HP ), France for comparison and better understanding. Finally, we thank  Bradley Matthews  and  Ramiro Checa-Garcia  for discussion. Thomas Karl received additional support from the  Austrian Science Fund (FWF , grant no. P30600 ).
Funding, acknowledgements, and data Funding: This work was supported by  Wiener Wissenschafts-, Forschungs-und Technologiefonds (WWTF)  within the  Environmental System Research Call 2017  [grant number  ESR17-027 , project  UOZONE ]. The BVOC and land surface modelling was funded by the  SEEDS  project ( EU/H2020  project number  101004318 ). Safae Oumami  was funded by  Meteo France PhD  scholarship grant number  76229706993504831700. The funding source had no influence on the study design, in the collection, analysis and interpretation of data, in the writing of the report and in the decision to submit the article for publication.
For example, the simulation-based inference, which leverages the power of numerical simulations and statistical inference, is a novel direction for cytoarchitecture measurements  [10]. Concerning the current numerical simulation methods for diffusion MRI applications, two popular groups of approaches are Monte-Carlo methods and Bloch-Torrey PDE-based methods. Monte-Carlo methods use random walkers to mimic the diffusion process in a geometrical configuration. Software packages include Camino  [4] , DIFSIM  [1] , and a GPU-based Monte-Carlo simulator  [22]. The Bloch Torrey PDEbased methods solve the Bloch-Torrey partial differential equation, which describes the evolution of the complex transverse water proton magnetization under the influence of diffusion-encoding magnetic field gradient pulses.
XANES analysis To obtain the information on sulfur species for examining the reduction/oxidation condition in peatland, Sulfur K-edge X-ray absorption near edge structure spectra (XANES) were collected and analyzed at Beamline 4B7A in Beijing Synchrotron Radiation Facilities (BSRF)  7, 92. Briefly, spectra were obtained from 12 freeze-dried samples from hummock profile in fluorescence mode at ambient temperature under high vacuum (10 -8 -10 -6 mbar). The storage ring was operated at 2. 5 GeV with a ring current of 250 mA. A fixed double-crystal monochromator with Si(111) crystals was used to monochromatize the white beam. Scans were taken at the energy range of 2462-2500 eV with a step size of 0. 2 eV. Data averaging, normalization, and Gaussian curve deconvolution were conducted using Athena, WinXAS, and Microsoft Excel (Supplementary Figs.