dataesr/scientific-paragraphs-categorization huggingface dataset:
AC KNOWLEDGEMENT S We would like to thank  Oscar Gonzalez-Pelayo ,  Angeles Garcı ´a-Mayor ,  Peter de Ruiter  and  Florian D. Schneider  for their comments and suggestions. The authors are also grateful to  Ton Markus  for editing the final figures and to two anonymous reviewers as well as to the journal editors for their comments, which greatly contributed to improve the manuscript. FUNDING This research has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under the grant agreement no. 283068  ( CASCADE  project). Thanks are due to  FCT/MCTES  for the financial support to CESAM ( UIDP/50017/2020+UIDB/50017/2020 ), through national funds and the post-doctoral research contract of  Jacob Keizer  ( FCT-IF/01465/2015 ), and to the  Spanish Ministry of Science and Innovation for financial support  ( CGL2017-89804-R ) of the work of  Susana Bautista.
Fisheries Landings data were obtained from the International Council for the Exploration of the Sea (ICES; http://ices. dk/marine-data/Pages/default. aspx) for the period 2007 to 2016. To obtain a more realistic Ecopath model, we integrated the 10 main French fleets operating in the area: bottom trawlers targeting demersal fishes, purse seiners, bottom trawlers targeting Norway lobster, gillnetters larger than 15 m, pelagic trawlers targeting small pelagic fishes, gillnetters smaller than 15 m, pelagic trawlers targeting demersal fish, long-liners and line vessels, pelagic trawlers targeting tuna, and Danish seine. Other European fleets were also included, mostly from Spain (29% of catches from foreign ships), the United Kingdom (10%), and Belgium (6%). This information was included in the ICES data.
The proportions contributing to the landings by each fleet were calculated from OBSMER reports  (Fauconnet et al. , 2011; Dubé et al. , 2012; Cornou et al. , 2013 Cornou et al. , , 2015a Cornou et al. , , 2015b Cornou et al. , , 2016 Cornou et al. , , 2017 Cornou et al. , , 2018)). ICES data provided the total biomass caught for each species per year. We applied the percentage calculated from OBSMER to the ICES data and obtained for the total biomass of each Ecopath group caught by every fleet from 2010 to 2016. The landings inputs were annual means of these results. Discards were calculated similarly to landings. Indeed, OBSMER reports include the discard rates for each species and each fleet, from 2010 to 2016. These rates were applied to the ICES catches to obtain annual mean discards over this period.
ENA Ecological network analysis (ENA) indices, which reveal the hidden properties of food webs, were used to highlight the effect of climate change and arrivals of new species in the Bay of Biscay. A set of five indices currently calculated in the Matlab routine ENATool  (Guesnet et al. , 2015)  were selected: Mean Trophic Level 2 (MTL 2 ), Total System Throughput (TST), Finn Cycling Index (FCI), relative ascendency (A/C), and System Omnivory Index (SOI). In addition, three new indices (Averaged Mutual Information, AMI; Mean Trophic Level 3. 25, MTL 3. 25 ; and Detritivory/Herbivory ratio, D/H) added to this routine were calculated to provide a detailed description of food web structure and functioning (Table  2 ). The Matlab routine ENATool takes into account Ecopath input uncertainties. It runs Monte-Carlo simulations to create a set of different versions of one Ecopath model, whose input parameters vary according to the Ecopath pedigree.
A compact representation of the ToA data is computed using the detected photon timestamps in an online manner for each individual pixel. The size of this representation scales with the parameters of the ToA model (i. e. the positions and intensities of the objects) and is independent of both the temporal resolution and the Fig. 2 : 3D reconstructions obtained by the proposed sketched RT3D algorithm for different sketch sizes m and other competing algorithms. The proposed SRT3D method incorporates spatial regularization, providing stable reconstructions in settings with low SBR or low number of measured photons. number of photons. However, the reconstruction methods presented in  [16]  do not incorporate any spatial regularization (pixels are processed independently), and thus can provide unstable reconstruction when the signal-to-background ratio (SBR) is low (see fig.
2 ). In this paper, we extend the sketched lidar framework to spatially regularized reconstruction algorithms. In particular, we present a novel sketched-based real-time algorithm whose complexity is agnostic of the number of recorded photons. The proposed algorithm paves the way for real-time 3D reconstruction of complex scenes for any number of recorded photons. The main contributions of the paper are: • We propose a novel approach to regularized 3D reconstruction with memory and computation requirements that are independent of the total number of observed photons. • We evaluate the robustness of the sketched reconstruction algorithm using real lidar datasets, demonstrating that the proposed approach achieves the same reconstruction performance as in the full data case. The paper is organised as follows.
The sketch is equivalent to the empirical characteristic function sampled at frequencies ω , where Ψ θ (ω) = E x∼π e iωx is the corresponding expected characteristic function (CF)  [18]. The CF has the special property that it exists for all probability distributions and captures all the information of the distribution, providing a one-to-one correspondence. The CF of the observation model in (1) by Ψ θ (ω) = K k=1 α k ĥ(ω)e iωt + α 0 sinc(ωT /2), (4) where ĥ denotes the (discrete) Fourier transform of the impulse response function h. It is well documented in the empirical characteristic function (ECF) literature e. g. [19] , that a sketch z computed over a finite dataset X = {x 1 ,. , x n }, satisfies the central limit theorem. Formally, the sketch z converges asymptotically to a Gaussian random variable z dist --→ N Ψ θ , n -1 Σ θ , (5) where Σ θ ∈ C m×m depends on Ψ θ (ω).
EXPERIMENTS We evaluate the proposed SRT3D algorithm on two real datasets: a polystyrene head at a distance of 40 metres  [7]  and a scene with two people walking behind a camouflage net at a distance of 320 metres  [13]. We compare the proposed method with 3 other algorithms: traditional crosscorrelation  [5]  (XCORR), pixelwise sketched reconstruction  [16]  (SMLE) which doesn't exploit spatial regularization (also with W set as the identity), and RT3D which accesses the full fine-resolution histogram data. All the experiments were performed using an NVIDIA RTX 3070 laptop GPU. The polystyrene head dataset has size of 141 × 141 pixels with T = 4613. Most of the pixels in this scene contain exactly K = 1 surface. A ground-truth reference was obtained using using the standard cross-correlation algorithm on the raw ToA information (with high number of photons per pixel and high SBR).
Using this reference and the observation model in eq. (  1 ), we synthesized multiple datasets for different mean photons per pixel n and SBR levels. Figure  2  shows the 3D reconstructions obtained for SBR levels of 10, 1 and 0. 1. All methods perform similarly when the number of photons and SBR are large. Interestingly, only m = 5 sketches are enough to provide good reconstructions. However, when the scene contains a low number of photons or low SBR, pixelwise methods fail to provide good reconstructions, whereas both RT3D and SRT3D provide good reconstructions. In this challenging setting, a sketch of size m = 10 sufficiently provides a reconstruction that has the same quality as the ones obtained in the full data case. True and false detections, depth absolute error (DAE), and normalised intensity absolute error (IAE) are presented in fig.
3  for an SBR of 1 and different number of photons per pixel. Figure  1  shows the execution time of RT3D and SRT3D as a function of the mean number of photons per pixel in the polystyrene head datasets. The GPU memory requirements of RT3D become prohibitive if the number of observed photons is in the order of hundreds per pixel per frame, whereas the sketched version has a complexity which is agnostic of the number of photons, and can potentially handle very large numbers of detections in real-time. Table  1  shows the execution time of SRT3D for increasing array sizes, demonstrating that the proposed method can process up to 705 × 705 arrays at 14 frames per second on a laptop computer. The datasets were generated by upsampling the head reference before the synthesis of photon detections.
, 2012). This software allows to highlight homology and evolutionary relationships between the studied sequences and thus, to select rRNA molecule regions specific of the digestive symbiont lineages. Literature data on molecular probes design  (Behrens et al. , 2003)  were used to select 16S rRNA molecule regions known to be accessible for probe hybridization, i. e. avoiding complex 3D in situ conformations of the rRNA molecule or regions showing interactions with ribosomal proteins. Then, the physical properties of the designed probes were estimated to avoid hairpin structure formation, cross-hybridization, self-dimer formation, and to check their melting temperatures (TM), using the Geneious Primer Design tool and the Oligo Calc software  (Kibbe, 2007). Finally, the complementarity of these designed probes with rRNA gene sequences of non-targeted microorganisms was evaluated more largely using BLAST  (Altschul et al.
5], 0. 005M EDTA [pH 8] and 0,01% [w/v] SDS]. Then, they were briefly rinsed twice with distilled water, once at washing temperature then at room temperature. Finally, sections were mounted on slides with SlowFade™ Gold antifade reagent with DAPI (Invitrogen). Observations on hybridized tissues were made using a Zeiss Imager. Z2 microscope equipped with the Apotome. 2 sliding module and Colibri. 7 light technology (Zeiss, Oberkochen, Germany). The micrographs were analyzed using the Zen software (Zeiss). DIC (Differential interference contrast Differential interference contrast) was used to better visualize the host tissues. Probes Def1229 Def1229 Myco378-1 Myco378-2 Myco378-3 Myco378-1 Myco378-2 Myco378-3.
Bacterial pathogens in surface waters may threaten human health, especially in developing countries, where untreated surface water is often used for domestic needs. The objective of the long-term multiscale monitoring of Escherichia coli ([E. coli]) concentration in stream water, and that of associated variables (temperature (T ), electrical conductance (EC), dissolved oxygen concentration ([DO]) and saturation (DO%), pH (pH), oxidation-reduction potential (ORP), turbidity (Turb), and total suspended sediment concentration ([TSS])), was to identify the drivers of bacterial dissemination across tropical catchments. This data description paper presents three datasets (see "Data availability" section) collected at 31 sampling stations located within the Mekong River and its tributaries in Lao PDR (0.
6-25 946 km 2 ) from 2011 to 2021. The 1602 records have been used to describe the hydrological processes driving in-stream E. coli concentration during flood events, to understand the land-use impact on bacterial dissemination on small and large catchment scales, to relate stream water quality and diarrhea outbreaks, and to build numerical models. The database may be further used, e. g. , to interpret new variables measured in the monitored catchments, or to map the health risk posed by fecal pathogens.
, 2017)  in a climate change context. The 31 sampling stations (Fig. 1 , Table  1 ) of this dataset are located in Lao PDR, within the Mekong River basin. The choice of sampling stations in the Mekong River tributaries was made to encompass a broad range of catchment sizes (0. 6-25 946 km 2 ), and a large range of geological, topographical, and land-use features. In Lao PDR, the tropical climate (i. e. , wet and dry (Aw) climate) is under the influence of the monsoon regime, dividing the year into two seasons: a dry season lasting from October to April, and a rainy season lasting from May to October. The average annual rainfall in Lao PDR varies from 1300 to 2500 mm and can exceed 3500 mm in central and southwestern Lao PDR  (Nakhle et al. , 2021b). The 0. 6 km 2 Houay Pano headwater catchment (station S4) is located in northern Lao PDR, 10 km south of Luang Prabang city (Fig.
Data collection The dataset comprises E. coli concentrations ([E. coli]) with lower and upper limits of the confidence interval ([E. coli] LL and [E. coli] UL , respectively), and physicochemical measurements in stream water, recorded from 25 May 2011, to 25 May 2021. Physicochemical measurements include T , EC at 25 • C,  [DO] and DO%, pH, ORP, Turb, and [TSS]. The units of the 11 variables are given in Table  2. For the spatial survey during both the dry and the rainy seasons, we chose sampling sites so as to ensure a broad geographical coverage of Lao PDR, and to represent a large range of geological, topographical, and land-use features. We also chose the sampling sites for being accessible from the road, in order to achieve the sampling campaign within a relatively short time. For the temporal monitoring, we initiated the sampling at stations S4, NK20, NK26, and A6, in May 2011 with a biweekly time interval, changed to a 10 d time interval in 2017.
e. , 1 : 2, 1 : 20, 1 : 200, and 1 : 2000) in a 96-well microplate (MUG/EC, BIOKAR DIAGNOSTICS) for 48 h at 44 • C. Ringers' lactate solution was used for the dilutions and one plate was used per sample. We tested the sterility of the Ringers' lactate solution and found no E. coli. The number of positive wells for each microplate was noted and the most probable number (MPN) per 100 mL was determined using the Poisson distribution. Given the four dilution ratios, the detection limit of [E. coli] was 38 MPN 100 mL -1 with a lower confidence limit at 5. 4 MPN 100 mL -1 and an upper confidence limit at 270 MPN 100 mL -1. Among the three datasets, the number of water samples with [E. coli] below the limit of detection was 5. 9 %. We measured  [TSS]  in the laboratory after the filtration of 100 mL of sample water on 0.
Results The spatial survey  (Ribolzi et al. , 2021c)  conducted in both the dry and the rainy seasons in 2016 resulted in 58 records (Fig. 2 ). The dataset shows contrasted values of [E. coli] depending on the season  (Nakhle et al. , 2021b). Median values of [E. coli] are higher during the rainy season, similar to [TSS], Turb, ORP, and T , while EC, DO%, and pH show smaller median values during the rainy season compared to the dry season. The temporal monitoring at the outlet of the six catchments  (Ribolzi et al. , 2021a) , initiated in May 2011 at stations NK20, NK26, and A6 (Fig. 3 ), and in July 2017 at stations Nou_1, Nse_1, and MK_17 (Fig. 4 ), resulted in 1131 records until May 2021. The dataset shows seasonal variahttps://doi. org/10. 5194/essd-14-2883-2022 Earth Syst. Sci. Data, 14, 2883-2894, 2022 tions of [E.
coli] and of the other variables  (Boithias et al. , 2016). In general, T and EC were increasing throughout the dry season and decreasing throughout the rainy season. The highest and lowest values of Turb, [TSS], and [E. coli], were measured during the rainy and the dry seasons, respectively. The temporal dynamics of DO%, [DO], pH, and ORP, are less clear. At station NK26 the number of water samples with [E. coli] below the detection limit increased after 2016 (Fig. 3 ). The data gap between 12 March and 20 June 2020, is due to the COVID-19 lockdown and traffic restrictions. The temporal monitoring at station S4, outlet of the Houay Pano headwater catchment  (Ribolzi et al. , 2021b) , initiated in May 2011, resulted in 413 records until May 2021 (Fig. 5 ). The dataset shows seasonal variations of [E.
Technical validation We calibrated the multi-probe system each day before measurement, i. e. , every day during the spatial survey  (Ribolzi et al. , 2021c) , and at a biweekly or 10 d frequency for the two other datasets  (Ribolzi et al. , 2021b, a). The EC probe was calibrated with a 1413 µS cm -1 solution, and the [DO] probe was calibrated following the air-calibration chamber in air method  (USGS, 2006). The DO% was then automatically calibrated based on [DO] and the barometric pressure. The pH probe was calibrated using a three-point calibration (pH = 4. 01, 7. 01, and 9. 18). The ORP probe was calibrated with a 240 mV solution. For the turbidity meter, each day before measurement, we verified that the turbidity measured using the 100 NTU calibration solution was correct. If a discrepancy was observed, we calibrated the turbidity meter with 4 calibration solutions at 0.
, 2022). Before laboratory analysis, water samples were stored in clean plastic bottles. These bottles were new empty bottles, supplied by a plastic bottle plant producing bottles for mineral water packaging. We regularly verified that the bottles were free from E. coli, and we triple rinsed the bottles with stream water before each water sampling. Records were collated, curated, and cross-checked. If the information recorded in the databases was ambiguous or did https://doi. org/10. 5194/essd-14-2883-2022 Earth Syst. Sci. Data, 14, 2883-2894, 2022 not match between records, we traced the sample back using the original paper records as far as possible. Samples which could not be verified in this way were excluded from publication in the current dataset.
Usage note We published the three datasets as CSV files so that they can be accessed and processed with any data-processing software. The three datasets are open access, licensed under a Creative Commons Attribution 4. 0 International License. For the proper functioning and the sustainability of the CZO, M-TROPICS asks data users to (1) acknowledge the M-TROPICS CZO and its financing institutions in their publications, (2) cite both the appropriate DOI of the data they used and the related publications, and (3) send a copy of any produced material based on the data to the corresponding authors of the present data description paper (LB and/or OR). The corresponding authors, who are intimately familiar with the background of these datasets, are at the disposal of the researchers wishing to reuse the datasets.
Conclusions These three datasets together present a unique long-term spatiotemporal and multiscale surface water quality monitoring within the Mekong River basin. So far, the datasets have been used: (1) to describe the hydrological processes driving instream E. coli concentration during flood events  (Boithias et al. , 2021a; Ribolzi et al. , 2016b) , (2) to understand the role of land use in bacterial dissemination on small and large catchment scales, e. g. , E. coli  (Causse et al. , 2015; Rochelle-Newall et al. , 2016; Nakhle et al. , 2021b; Ribolzi et al. , 2011)  and Burkholderia pseudomallei  (Ribolzi et al. , 2016a; Zimmermann et al. , 2018; Liechti et al. , 2021) , (3) to relate stream water quality and diarrhea outbreaks  (Boithias et al. , 2016) , and (4) to build catchment-scale numerical models focused on water quality  (Kim et al.
, 2017 (Kim et al. , , 2018;; Abbas et al. , 2021 Abbas et al. , , 2022)). The dataset may be further used (1) to assess the role of headwater catchments as a source of E. coli in large tropical river basins, (2) to interpret new variables measured in the monitored catchments (e. g. , contaminants other than E. coli), (3) to assess the impact of dams on downstream E. coli concentration, (4) to map the health risk posed by fecal pathogens, and (5) to assess the relative contributions of both climate and land-use change on changes in instream E. coli concentration. Author contributions. OR designed the study. LB, OR, and APB coordinated the project. OR, RZ, SR, PO, and APB collected water samples and performed field measurements for dataset https://doi. org/10. 23708/ZRSBM4 (Ribolzi et al. , 2021c).
LB, OR, CT, BS, KL, NoS, PS, KX, JC, and THdT collected water samples and performed field measurements for dataset https://doi. org/10. 23708/1YZQHH (Ribolzi et al. , 2021a). LB, OR, ERN, CT, BS, KL, NoS, PS, KX, TP, OE, and SH collected water samples and performed field measurements for dataset https://doi. org/10. 23708/EWOYNK (Ribolzi et al. , 2021b). CT and APB performed microbial laboratory analysis. LB and OR validated the data and curated the database. LB, OR, ERN, and PN analyzed the data. OS and NiS provided institutional support. LB wrote the original draft of the manuscript. All other authors reviewed and edited the manuscript. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. These three datasets have been collected thanks to the long-term partnership with the Department of Agricultural Land Management (DALaM), Lao PDR, which granted the permission for field access, and to the financial, scientific, technical, and logistical support of the Multiscale TROPIcal CatchmentS Critical Zone Observatory (M-TROPICS CZO, previously MSEC; https://mtropics. obs-mip. fr/, last access: 21 June 2022). Review statement. This paper was edited by David Carlson and reviewed by two anonymous referees.
This research has been supported by the Consultative Group on International Agricultural Research (CGIAR; Humidtropics program; http://humidtropics. cgiar. org/, last access: 21 June 2022), the GIS-Climat (Pastek program, http://www. gisclimat. fr/projet/pastek. html, last access: 21 June 2022), the French National Research Agency (TecItEasy project ANR-13-AGRO-0007; https://anr. fr/, last access: 21 June 2022), the Institut de Recherche pour le Développement (IRD), including through the International Joint Laboratory on the impact of rapid Land-use change on Soil Ecosystem Services (LMI LUSES; ECOFILTER program; https://luses. ird. fr/, last access: 21 June 2022) and the regional pilot program Soils, Waters, Coastal Zones and Societies in Southern and Southeast Asia (SELTAR-RPP), the International Center for Tropical Agriculture (CIAT) through the Payment for Environmental Services (PES) program in Lao PDR, the French Centre National de la Recherche Scientifique (CNRS) through the national grants EC2CO-Biohefect/Ecodyn//Dril/MicrobiEn (Belcrue, Belkong, and NARIBACT projects), the US Defence Threat Reduction Agency's Cooperative Biological Engagement Program (contract HDTRA-16-C-0017), the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit funded by the Wellcome Trust of Great Britain (grant no.
Financial support. support This research has been supported by the Consultative Group on International Agricultural Research (CGIAR; Humidtropics program; http://humidtropics. cgiar. org/, last access: 21 June 2022), the GIS-Climat (Pastek program, http://www. gisclimat. fr/projet/pastek. html, last access: 21 June 2022), the French National Research Agency (TecItEasy project ANR-13-AGRO-0007; https://anr. fr/, last access: 21 June 2022), the Institut de Recherche pour le Développement (IRD), including through the International Joint Laboratory on the impact of rapid Land-use change on Soil Ecosystem Services (LMI LUSES; ECOFILTER program; https://luses. ird. fr/, last access: 21 June 2022) and the regional pilot program Soils, Waters, Coastal Zones and Societies in Southern and Southeast Asia (SELTAR-RPP), the International Center for Tropical Agriculture (CIAT) through the Payment for Environmental Services (PES) program in Lao PDR, the French Centre National de la Recherche Scientifique (CNRS) through the national grants EC2CO-Biohefect/Ecodyn//Dril/MicrobiEn (Belcrue, Belkong, and NARIBACT projects), the US Defence Threat Reduction Agency's Cooperative Biological Engagement Program (contract HDTRA-16-C-0017), the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit funded by the Wellcome Trust of Great Britain (grant no.