dataesr/scientific-paragraphs-categorization huggingface dataset:
Timing of the Fires The timing of the fire can be derived from the active fire product derived from MODIS data and complemented by the S2 maps. As illustrated in Figure  8 , coherence between the two in term of temporal pattern is visible. Burnt areas are observed on the S2 image of 22 January in three distinct locations shown in dark blue in Figure  8A. Two of these areas are located in a previously logged area, close to old logging roads. The third one is located along the main road. From the MODIS active fires data, it can be deduced that these areas started burning from 16 January. The fire continued spreading in these three locations but also started in new ones. In 10 days, from 22 January to 1 February, the estimated burnt area increased from less than 2000 ha to more than 18,000 ha.
As no observations are available between those two dates, it is impossible to determine from the S2 maps if the fire spread from these places to the other ones or if there were new sources of fire in the meantime. However, from the active fire data, some fires are detected between 27 and 29 January in the northeast of Figure  8A  (yellow dots), close to the logging road. Following this, the fire spread to the rest of the area 30-31 January (orange dots in Figure  8A ). The active fire product gives us the precise date of the fire observation with a daily temporal resolution. The timing detected by S2 strongly depends on the time of acquisition of the image, theoretically every 10 days, and the absence of clouds at the time of acquisition. From the MODIS data active fires, it is observed that fires occur from the 26 December 2015 to the 10 April 2016.
Potential of Sentinel 1 and Sentinel 2 for Burnt Areas Monitoring To monitor and map fire events in tropical forests, the sensors installed on the S1 and S2 satellites show some potential. The arrival of the S2 satellite provides a far higher frequency of Earth Observation data than was previously possible at this fine (10-20 m) spatial resolution. Due to its novelty, literature about the use of time series of S2 images is scarce and mainly covers the use of single or pair of images but first experiences confirm a good potential for land cover mapping capabilities  [59, 60]. The frequent observations by S1 over the Congo Basin and its independence from cloud cover also make the mission an indispensable asset. The use of the two sensors (supported by the active fires from MODIS) allows a fuller picture of what has actually occurred.
Spatial Distribution of the Burnt Areas In reviewing the spatial distribution of the fires in the forest domain, it is clear that the fires occurred mainly in open forests, more specifically in the very open Marantaceae forests. From the density of roads, it is deducted that while mainly located inside a logging concession, fires occurred in old logged areas and in areas that were never exploited. Around 80% of the burnt areas detected with S1 and S2 data occur in the core of the very open Marantaceae forest. Another 13% is observed in the open forests situated at the border of the very open forests formations. According to the analysis, the dense forest was only slightly affected, with less than 2% of this class mapped as burnt. However, this low percentage is only indicative as the Clearly, the combination of the Sentinel data with that of Landsat would further improve the temporal coverage.
Spatial Distribution of the Burnt Areas In reviewing the spatial distribution of the fires in the forest domain, it is clear that the fires occurred mainly in open forests, more specifically in the very open Marantaceae forests. From the density of roads, it is deducted that while mainly located inside a logging concession, fires occurred in old logged areas and in areas that were never exploited. Around 80% of the burnt areas detected with S1 and S2 data occur in the core of the very open Marantaceae forest. Another 13% is observed in the open forests situated at the border of the very open forests formations. According to the analysis, the dense forest was only slightly affected, with less than 2% of this class mapped as burnt. However, this low percentage is only indicative as the vegetation map has a coarser spatial resolution compared to the burnt area maps.
Conclusions This paper demonstrates the potential for S2 and S1 data to detect and monitor fire outbreaks in forest ecosystems at finer temporal and spatial resolution than was previously possible. Burnt areas totalizing an area of 36,000 ha were detected in the north of the Republic of Congo thanks to the combined used of S1 and S2 time series. At its peak in early 2016, the fire was spreading at a rate of 1600 ha per day. Indications of the forthcoming event were in evidence in late December, both from the high detection of active fires in the region and the low SPI rainfall values. The fifteen S2 images analyzed from December 2015 to May 2016 represent the first time series of S2 data released over the study area and show a great potential for tropical forest monitoring applications. The burnt area maps derived from each S2 image are adding a precise spatial extent to the point location of the MODIS active fire product and provide a better documentation of the fire propagation in the Marantaceae forests.
The two burnt areas maps derived from S1 bi-monthly composites provides a gap filling capacity (in case of cloud cover) and brings confidence to the S2 results, with over 60% concurrence between the two data sets. The arrival of the S1 and S2 satellites, soon supported by the Sentinel 3 SLSTR sensor with bands optimized for fire monitoring, can provide forest managers and national agencies with a rapid and accurate assessment of fire events. From the methodology proposed here, some points have to be addressed to move from a case study to an operational monitoring tool. The combination of the radar and optical information is robust in terms of burnt area detections but the respective S1 and S2 methodologies should evolve into algorithms that are independent from locally tuned thresholds. A review of climatic data and vegetation susceptibility to fire could also be employed to highlight anomalies which may lead to fire outbreaks and provide managers with an early warning of the possibility of such events.
Benchmark The second part of the study uses observations from a subset of the FLUXNET sites to assess systematically or benchmark the suite of ISBA versions developed at Météo-France. The FLUXNET database has been used for LSM evaluation by several widely used LSMs  (Stöckli et al. , 2008; Blyth et al. , 2010; Ukkola et al. , 2016). Many sites are available within the FLUXNET database; however, in the current study, only those sites and years with an energy balance closure of 20 % (or less) before adjustment of the turbulent fluxes are retained. Ground heat flux was assumed to represent 3 % of net radiation when it is not available (this is close to the average value over the three French sites). After screening, 42 forested sites remain, which give a total of 179 years of observations (see Fig.
, 1998; Gibelin et al. , 2006) , which simulates photosynthesis and its coupled to the stomatal conductance in response to atmospheric CO 2. Parameters of the stomatal resistance scheme are vegetation-type dependent and were chosen according to the ECOCLIMAP database  (Faroux et al. , 2013). Three simulations were performed for each site in order to assess the impact of the new canopy and litter layers on the simulated fluxes, soil temperature and soil moisture. The models were forced with atmospheric data at half-hourly time steps. In the first simulation, the reference model (i. e. , using a single composite vegetation and surface-soil layer) was used and it is hereafter referred to as ISBA. The second simulation with the explicit canopy layer corresponds to the ISBA model using the MEB approach (referred as MEB herein for simplicity).
Local scale The pedotransfer functions of  Clapp and Hornberger (1978)  are used for the soil water flow. The soil hydraulic conductivity at saturation, the b-exponent, and the matric potential at saturation are computed with the continuous formulations of  Noilhan and Lacarrere (1995)  using the textural properties at each site. The wilting point, field capacity and the water content at saturation were computed using the observations of soil volumetric water content and/or following the recommendations of the site principal investigators (Table  2 ). An hydraulic conductivity exponential profile was used to model the packing of the soil with depth. Soil organic content, which affects the hydrological and thermal coefficients as in  Decharme et al. (2016) , is modeled using the input data from the Harmonized World Soil Database (HWSD;  Nachtergaele and Batjes, 2012).
Benchmark The ECOCLIMAP land cover and the HWSD soil databases were used to prescribe most of the needed parameters as was done for the local-scale evaluation for the three French sites (in the previous section). This is also consistent with the method used for spatially distributed offline and fully coupled (with the atmosphere) simulations with ISBA. However, note that soil texture, canopy height and vegetation type were chosen in agreement with literature values for each site where available; therefore, they superseded the ECOCLIMAP values for these parameters. The default thickness of the litter layer is set to 3 cm based on the sensitivity test results presented in Sect. 5. 2 and in overall agreement with literature values (Table  3 ). Initial conditions can have a significant impact on both the sensible and the latent heat fluxes, but they were not known; thus, a spin-up period was used for each site.
Worldwide skill score assessment The comparison of modeling results with field measurements from over 42 FLUXNET sites (Fig. 2 ) reveals that the results from the benchmark are consistent with those from the three local French sites. For improved clarity, only the MEBL and the ISBA models are compared here, since the previous evaluation showed the consistent improvement of using the litter option when using MEB for forests. MEBL and ISBA generally performed well in simulating the R n , but both tended to slightly underestimate daytime values with a mean bias of 10 W m -2 corresponding to approxi- mately 9 % of the average net radiation Note that the R n was better simulated for the three local-scale sites (Sect. 5. 1), but this results in part, because the benchmark uses values from the ECOCLIMAP database (which likely introduces some error relative to using observed values of albedo for example).
Acknowledgements. This work is a contribution to the ongoing efforts to improve the SURFEX platform. The authors would like to thank the teams of the three French eddy flux towers involved in this study:  Le Bray, Barbeau and Puéchabon , for making their data available, and to all of the contributors to the FLUXNET database. This study was fully funded by a  Météo-France  grant. Edited by: S. Valcke Reviewed by: two anonymous referees.
yeast lysate and different spiked amounts of the UPS1 mixture of 48 recombinant proteins. It can be used to objectively evaluate bioinformatic pipelines for label-free quantitative analysis, and their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies. More specifically, it can be useful for tuning software tools parameters, but also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods. The raw MS files can be downloaded from ProteomeXchange with identifier PXD001819. Starting from some raw files of this dataset, we also provide here some processed data obtained through various bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold) in different workflows, to exemplify the use of such data in the context of software benchmarking, as discussed in details in the accompanying manuscript  [1].
The experimental design used here for data processing takes advantage of the different spike levels introduced in the samples composing the dataset, and processed data are merged in a single file to facilitate the evaluation and illustration of software tools results for the detection of variant proteins with different absolute expression levels and fold change values. & 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/).
Value of the data We provide a standard proteomic dataset based on a highly complex sample (yeast lysate) spiked with different levels of a second calibrated protein mixture of medium complexity (UPS1 standard, 48 proteins), that can be used to statistically evaluate label-free approaches for detection of differentially abundant proteins. This spiked dataset can be used for benchmarking and comparing softwares for label-free quantification. It may represent a convenient tool for users who want to optimize the tuning and find the best parameters for a particular software. It can be useful for developers in order to test algorithms and improve the extraction of intensity metrics for protein quantitation. It could also be applied to the evaluation of post-processing steps (normalization, imputation of missing values) and statistical methods.
Data and experimental design We provide a dataset composed of raw MS files corresponding to the analysis of a series of yeast cell lysate samples spiked with different amounts of an equimolar mixture of 48 recombinant proteins (Sigma UPS1). The 9 different samples were analyzed in triplicate on a LTQ-Velos Orbitrap and the resulting 27 raw files can be downloaded from ProteomeXchange using the identifier PXD001819. The spiked UPS1 proteins can be easily identified after database search and constitute the "ground truth" panel of differentially abundant proteins in quantitative pairwise comparison of samples from the dataset. Conversely, the background of yeast proteins should remain invariant after quantitative comparison of these samples. As UPS1 proteins constitute a very minor proportion of the global proteome for each sample (see Fig.
1  showing the histogram of iBAQ values for yeast background an UPS1 proteins respectively in the different samples), the samples can in principle be used to simulate a biological situation where only a minor part of the protein population undergoes expression changes, and data can be normalized based on the median of intensity values for the global protein Fig. 1. illustration of the absolute abundance of spiked proteins compared to the yeast background in the 6 last samples of the dataset. Absolute abundances were estimated using the iBAQ metric calculated by MaxQuant in workflows 6 and 7 (see below for the details of the workflows). population. Nevertheless, the spiked mixture is relatively complex (48 proteins) and can be used to statistically approximate the sensitivity of the analytical and bioinformatics workflows, by calculating the proportion of UPS1 proteins truly found as showing differential signals.
As a proof of principle of the potential utility of this dataset, we also provide some processed data, in Excel format (Supplementary Table  1 ), obtained from different bioinformatics workflows combining tools for database search, protein validation, and label-free quantification. These workflows are described in details in  [1]  and in the Materials and Methods section below, and include MFPaQ  [2, 3] , Irma/Heidi, Scaffold, MaxQuant  [4] [5] [6]  and Skyline  [7, 8]  as tools to extract quantitative metrics. The quantitative value extracted for each protein is either a total spectral count obtained after the validation step, or a protein intensity value obtained from the MS signal intensity of associated peptides. The Excel file provided here (Supplementary Table  1 ) is composed of 8 different sheets containing the quantitative data from the 8 different workflows tested (4 spectral count and 4 MS intensity based workflows).
The experimental design of the data processing is illustrated in Fig. 2. Among the 9 spiked samples analyzed, we selected 5 of them in order to perform different pairwise quantitative comparison of samples, trying to mimic distinct biochemical situations (comparisons A, B and C: detection in only one condition; high fold change; moderate fold change). The quantitative outputs obtained from these pairwise comparisons were then combined, in order to reconstruct a simulated dataset containing true-positive hits with different intensities and fold change values, and to illustrate the performances of the bioinformatic and statistical methods in a more comprehensive way. Each Excel sheet in Supplementary Table  1  corresponds to this mixed dataset, for a particular bioinformatic workflow.
2 mL of each sample were loaded on a C-18 precolumn (300 mm IDx5 mm, Dionex) at 20 mL/ min in 5% acetonitrile, 0. 05% TFA. After 5 min desalting, the precolumn was switched online with the analytical C-18 column (75 mm IDx15 cm, in-house packed with C-18 Reprosil) equilibrated in 95% solvent A (5% acetonitrile, 0. 2% formic acid) and 5% solvent B (80% acetonitrile, 0. 2% formic acid). Peptides were eluted using the following gradient of solvent B at 300 nL/min flow rate: 5-25% gradient during 75 min; 25-50% during 30 min; 50-100% during 10 min. The LTQ-Orbitrap Velos was operated in data-dependent acquisition mode with the XCalibur software. Survey scan MS were acquired in the Orbitrap on the 300-2000 m/z range with the resolution set to a value of 60,000. The 20 most intense ions per survey scan were selected for CID fragmentation and the resulting fragments were analyzed in the linear trap (LTQ).
Dynamic exclusion was employed within 60 s to prevent repetitive selection of the same peptide. MS data processing. The dataset was processed according to different workflows listed in Table  1  from Ref. [1] , consisting in the following steps: peaklist generation, database search, validation of the identified proteins and extraction of quantitative metric (spectral count or MS signal). According to the different tools used for each step, 8 distinct workflows were evaluated. The same databases were used for peptide identifications: yeast database from UniprotKB (S_cerevisiae_ 20121108. fasta, 7798 sequences) and a compiled database containing the UPS1 human sequences (48 sequences). Bioinformatic workflow 1: ExtractMSn/Mascot/MFPaQ/Spectral Counting. The Mascot Daemon software (version 2.
4; Matrix Science, London, UK) was used to perform database searches, using the Extract_msn. exe macro provided with Xcalibur (version 2. 0 SR2; Thermo Fisher Scientific) to generate peaklists. Parameters used for creation of the peaklists were: parent ions in the mass range 400-4500, no grouping of MS/MS scans, and threshold at 1000. Peaklists were submitted to Mascot database searches (version 2. 4. 2). ESI-TRAP was chosen as the instrument, trypsin/P as the enzyme and 2 missed cleavages were allowed. Precursor and fragment mass error tolerances were set at 5 ppm and 0. 8 Da, respectively. Peptide variable modifications allowed during the search were: acetyl (Protein N-ter), oxidation (M), whereas carbamidomethyl (C) was set as fixed modification. To calculate the false discovery rate (FDR), the search was performed using the "decoy" option in Mascot.
Validation was performed with an in-house developed module associated to MFPaQ  [2]  (http:// mfpaq. sourceforge. net/), based on the target-decoy strategy, as described before  [3]. Briefly, FDR at peptide level was calculated as described in  [9]  and set at 5% by adjusting peptide p-value threshold. Validated peptides were assembled into protein groups following the principle of parsimony (Occam's razor)  [10]. Protein groups were then validated to obtain a FDR of 1% at the protein level, by adjusting the threshold on a protein group score defined as the sum of peptide score offsets (difference between each peptide Mascot score and its homology or identity threshold). The total spectral count metric was extracted for each protein group by MFPaQ in each analytical run. Workflow 2: Andromeda/MaxQuant/Spectral Counting.
Acquired MS data were processed using MaxQuant version 1. 3. 0. 5  [4]. Derived peak lists were submitted to the Andromeda search engine  [6] ) (www. maxquant. org). For database searches, the precursor mass tolerance was set to 20 ppm for first searches and 6 ppm for main Andromeda database searches. The fragment ion mass tolerance was set to 0. 5 Da. Trypsin/P was chosen as the enzyme and 2 missed cleavages were allowed. Oxidation of methionine and protein N-terminal acetylation were defined as variable modifications, and carbamidomethylation of cysteine was defined as a fixed modification. Minimum peptide length was set to six amino acids. Minimum number of unique peptides was set to one. Maximum FDRcalculated by employing a reverse database strategywere set to 1% for peptides and proteins.
Proteins identified as "reverse" and "only identified by site" were discarded from the list of identified proteins. In this particular workflow, total spectral count for each validated protein group was computed from msms. txt table. Workflow 3: Mascot Distiller/Mascot/IRMa-hEIDI/Spectral Counting. Data were processed automatically using Mascot Distiller software (version 2. 4. 3. 0, Matrix Science). ESI-TRAP was chosen as the instrument, trypsin/P as the enzyme and 2 missed cleavages were allowed. Precursor and fragment mass error tolerances were set at 5 ppm and 0. 8 Da, respectively. Peptide variable modifications allowed during the search were: acetylation (Protein N-ter), oxidation (M), whereas carbamidomethyl (C) was set as fixed modification. The IRMa software v1. 31  [11]  was used to filter the results.
Filters used were: (1) peptides whose score Zquery homology threshold (p o0. 5) and rankr1 are marked as significant; (2) Single match per query filter was: Move to ambiguous all peptides which are not assigned to best protein for this query (best is higher protein score); (3) FDR seeker filter: seek a 1% FDR based on score filtering; (4) Accession filter: Delete proteins coming from reverse database; (5) Specific peptide filter: accept only protein hits whose specific peptides count 4 ¼1. The filtered results were then compiled and structured within dedicated relational Databases and a homemade tool (hEIDI) was used for the compilation, grouping and comparison of the proteins from the different samples, analytical replicates and conditions to compare (Hesse et al. , in preparation). In such workflow, total spectral count values calculated for each protein groups are used for quantification.
Workflow 4: ExtractMSn/Mascot/Scaffold/Spectral Counting. Peaklists generation and protein identifications were made as detailed in workflow 1. Mascot results were loaded into the Scaffold software (Version 3. 6. 5, Proteome Software, Portland, USA). To minimize false positive identifications, results were subjected to very stringent filtering criteria as follows. For the identification of proteins, a Mascot ion score had to be minimum 30 and above the 95% Mascot significance threshold ("Identity score"). The target-decoy database search allowed us to control and estimate the false positive identification rate of our study, and the final catalog of proteins presented an estimated false discovery rate (FDR) below 5%. The spectral count metric used for quantitation corresponds to the Unweighted Spectrum Count values in Scaffold.