dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellite-derived burned area data The GFED3. 1 fire carbon emission dataset provides monthly burned area data at 0. 5 • resolution for 1997-2009 with global coverage  (Giglio et al. , 2010). The GFED3. 1 data were mainly generated using MODIS imagery with additional images from TRMM IRS and ERS ATSR. The fire carbon emissions data were also provided which are model simulation results by applying the CASA model  (van der Werf et al. , 2010). L3JRC dataset provides daily global burned area data at 1 km resolution for April 2000 to March 2007; these data were generated from the 1 km SPOT VEGE-TATION satellite imagery  (Tansey et al. , 2008). This dataset was assembled at 0. 5 • resolution at monthly time step for use in the present study. GLOBCARBON burned area dataset was produced from a combination of SPOT VEGETATION and ERS2-ATSR2/ENVISAT AATSR data as one of the four land products of the ESA GLOBCARBON initiative  (Plummer et al.
Historical burned area reconstruction for the 20th century To evaluate the simulated burned area for the 20th century, historical burned area data were used. These data, which cover the period 1900-2000, were compiled by Mouillot and Field (  2005 ) at 1 • resolution and monthly time step (hereafter referred to as the Mouillot data). The data were generated by first synthesizing the burned area information from published data at national or regional scale for the periods of the 1980s Figures.
We calculated the ratio of burned area by the Mouillot data to GFED3. 1 for 1997-2009 for each region (Fig. 2b ) and also the globe. This ratio was then applied to correct for each decade the burned area data in the Mouillot dataset. Note that the regional breakdowns of the globe by GFED3. 1, and Mouillot and Field (2005) are different (Fig. 2 ). For comparison of burned area for the 20th century, the regional breakdown by Mouillot and Field (  2005 ) was adopted as it is based on maximum temporal stability (error consistency), a highly important factor when comparing long-term data.
Fire patch data Alaskan and Canadian fire management agencies have maintained historical fire monitoring for a relatively long time (dating back to 1950s). Their observations provide robust datasets for model validation of fire trend and variability. The historical fire information for the US state of Alaska was retrieved from the Alaska Interagency Coordination Center (AICC, http://afsmaps. blm. gov/imf_firehistory/imf. jsp?site=firehistory). The fire information for Canada was from the Canadian Wildland Fire Information System (http://cwfis. cfs. nrcan. gc. ca/ha/nfdb). These datasets contain information on fire location, fire size (burned area), fire date and fire cause. Note, in these two datasets fires with all sizes are included. Introduction.
Back Close Full   2010 ) classified the MCD45A1 500 m MODIS fire burned area data into individual fire patches for the southern African region. This fire patch information includes location, patch size (with minimum fire size of 0. 25 km 2 ), and the start and end dates of burning. The fire patch data for boreal North America and southern Africa are used to evaluate the model simulated fire size distribution (Fig. 3 ).
2. 4. 3 Fire season length, the 95th quantile fire size and 95th quantile fire intensity Global observation datasets for fire season length, the 95th quantile fire size and 95th quantile fire intensity are provided by  Archibald et al. (2013). The fire season length was quantified as the number of months required to reach 80 % of the total average annual burned area by using GFED3. 1 data. The MCD45A1 burned area product at 500 m resolution was used to derive the individual fires by applying a flood-fill algorithm, and the 95th quantile fire size in each grid cell was extracted to represent the size of large fires. The fire intensity is approximated by fire radiative power (FRP), which is derived from the satellite middle-IR wavelength measurements sensed over actively burning fires. The MCD14ML active fire product was used to calculate the 95th quantile FRP as a proxy for the fireline intensity of large fires.
1 data. Finally, the seasonality similarity (S season ) is given by: S season = 12 i =1 min(frac_model i ,frac_GFED i ) (3) where frac_model i and frac_GFED i are the fraction of burned area for the i th month relative to the annual burned area. Generally, L3JRC and GLOBCARBON have less burned area in the Southern Hemisphere than GFED3. 1 (see also Fig. 5 ), with smaller spatial extent of burning in the (wooded) grassland systems in Africa and Australia. By contrast, in the middle to high latitudes of the Northern Hemisphere, L3JRC and GLOBCARBON have more burned area than GFED3. 1. All three datasets capture the grassland burning in central and eastern Asia. ORCHIDEE coupled with SPITFIRE is generally able to reproduce the spatial distribution and magnitude of satellite-observed burned fraction.
Comparison of model simulation to satellite observation for the spatial and temporal pattern of burning Fires in grassland-dominated systems are well captured by the model, including steppe fires in central and east Asia, savanna fires in northern Africa, northern Australia and central to east South America. Two regions could be identified where model simulation is different from all the three observation datasets. One is the woodland savanna (miombo) in southern Africa, where burned area is underestimated by the model (simulated annual burned fraction is ∼ 4 %, but 14-24 % is observed). The other is western and central continental US (dominated by C3 and C4 grass in the land-cover map used by ORCHIDEE) where fires are overestimated by the model (simulated annual fraction is ∼ 6 %, but 1-2 % is observed).
For the fires in high-latitude (> 45 • N) boreal forest and tundra, the magnitude of burned fraction by ORCHIDEE falls between that found in the GFED3. 1 and in the L3JRC/GLOBCARBON datasets (Fig. 5 ). The simulation pixels are divided into five classes according to their simulation quality, as shown in Fig. 4e. Table  1  shows the mean burned area for each category and dataset. The grid cells with burning collocated for both the model and observation data (labelled as ORC-good, ORC-max, ORC-min in Fig. 4 ) cover the majority of the global burned area (78 % to 92 % depending on different datasets), indicating that the model Figures.
Back Close Full can reproduce the major spatial extent of burning. However, discrepancy still remains, in that 50 % of the modelled burned area is classified as ORC-max (i. e. , overestimation of burned fraction by the model), whereas observation datasets have half of the burned area labelled as ORC-min (i. e. , underestimation of burned fraction by the model). Shifting from the single value of a(ND) (0. 22 over the globe in Eq. 1) to a spatial dataset did not improve the result (Supplement Table  S1 ). Figure  5  also illustrates the lower burned area found in L3JRC and GLOBCARBON in comparison with GFED3. 1 for the Southern Hemisphere and subtropical Northern Hemisphere, in contrast to the higher burned area in the middle-to-high latitude region in the Northern Hemisphere. The simulated latitudinal distribution of burned area generally falls within the minimum-maximum range of the three observation products.
Exceptions are the regions of ∼ 5-15 • S and 30-40 • N, corresponding to the underestimated burning in southern African savanna and the overestimate in western and central US, discussed above. The interannual (12-months smoothed global total burned area) time series of burned area by the GFED3. 1, model simulation and the GLOBCARBON data are quite similar, but the J3JRC data has a different temporal phase (Fig. 6 ). The model result agrees best with the GFED3. 1 variability, except that the model fails to reproduce the 1998 peak burning. Shifting from the single a(ND) value (0. 22 over the globe) to the spatial dataset (Supplement Fig. S1 ) improves the agreement of the simulated global total burned area with the GFED3. 1 data. This indicates that the model is able to reproduce the temporal trend of burning on the global scale as observed by the satellite data.
Model evaluation against GFED3. 1 burned area data The simulated burned area for 1997-2009 is evaluated against the GFED3. 1 data for each region (shown in Fig. 2a ) in terms of mean annual burned area, and similarity in the interannual variability and seasonality (see metrics in Sect. presented in Table  2. The comparison of annual burned area at regional scale is shown in the Supplement Fig. S4. The model error for the annual burned area (BA) is highest for the region of Middle East (MIDE, by a factor of 38, occupying 0. 1 % vs. 2. 9 % of global BA by GFED3. 1 vs. ORCHIDEE) and lowest for the region of Boreal Asia (BOAS, by a factor of 0. 1, occupying 1. 6 % of global BA by both data). The model underestimates the burned area in the three biggest fire regions of the globe (Northern Hemisphere Africa, Southern Hemisphere Africa and Australia, together occupying 86 % vs.
Long-term trends of burned area during the 20th century Over the 20th century, the historical trend of modelled global total burned area generally follows the Mouillot reconstruction data  (Mouillot and Field, 2005)  as corrected by GFED3. 1 data (see Sect. 2. 4. 1), with a gradually increasing burned area after the 1930s until the 1990s, after which global burned area began to decrease (Fig. 10 ). Regionally, simulated trends in burned area agree best with the Mouillot data in boreal Russia. The simulated burned area also agrees well with fire agency survey data for the US state of Alaska and Canada (Supplement Fig. S7 ). This reflects the model capability to capture fire trends driven by climate variation relatively well. This agreement also suggests that the CRU-NCEP climate data can be used for simulating trends in fires despite the uncertainties.
Back Close Full Above a certain threshold, the relationship between the number of fires and fire patch size conforms to the power-law distribution  (Malamud et al. , 1998 (Malamud et al. , , 2005) )  and can be described as: N = α × S -β (5) where N is the number of fires, S is the individual fire patch size, β is the slope on a log-log plot. In this section we compare the simulated fire size distribution against observational data over two regions: boreal North America and southern Africa (see map in Fig. 3 ). The simulated daily fire number and mean fire size for the same period as the observation data were used to generate the modelled fire patch data. For both simulated and observed fires, all fires within the test region were pooled together. Fires were binned according to the fire size in an equal logarithmic distance manner (with the minimummaximum size range being divided into 100 bins).
The mean annual number of fires on an area basis for each bin was calculated. The minimum fire size for southern Africa given by  Archibald et al. (2010)  is 25 ha (which is the resolution of a 500 m MODIS burned area pixel) and this value was arbitrarily used as the fire size threshold in the regression analysis for both regions. The mean value of each size range is used as the explaining variable. Both fire number and fire size data are log transformed, and OLS regression is used to estimate the parameter values (slope beta, intercept alpha). For both boreal North America and the southern Africa, the slopes obtained from the model simulation are higher than the ones derived from fire management agency data (boreal North America) or satellite-derived data (southern Africa) (Fig. 11 ). This indicates that the simulated fire distribution is skewed towards small fires and that the number of big fires is underestimated.
Back Close Full   et al. , 2013; Smith and Wooster, 2005). So the comparison here between the simulated 95th quantile fireline intensity with the 95th FRP is mainly for qualitative purposes. As shown in Fig. 14 , at the global scale, the model is able to reproduce the spatial pattern of the fire intensity revealed by the FRP data, although with rather different absolute values due to the nature of the two different datasets. Fires with high intensity occur prominently in two different ecosystems over the globe, one is the grasslanddominated system in central and eastern Asia, inland Australia, and the southern Africa and America. The other is the boreal forest system in the US state of Alaska and northern Canada, and eastern Russia. The model captures this pattern relatively well, despite the fact that the highest fire intensity is simulated to occur in Arctic tundra.
Ignition sources and fire numbers On the global scale, due to the limitation of fire by fuel load on the dry regions, the modelled annual burned area is more closely related to the total fire numbers rather than the fire danger index (Suplement Fig. S8 ). This might lead to speculation that the potential ignition source is the first identified source of error for the simulated burned area. However, the shift from a constant uniform value of a(ND) = 0. 22 in the human ignition equation to the spatial dataset (Supplement Fig. S1 ) does not significantly improve the regional model-observation agreement (Supplement Fig. S2 ). The other possible error in ignition is that potential lightning ignitions are not suppressed by humans in densely populated areas, which might cause lightning-ignited fires being overestimated.
Fire size Our simulated burned area for Canada agrees well with the fire agency survey data (Supplement Fig. S7 ), but simulated burned area for southern Africa is 60 % lower than GFED3. 1 data (Table  2 ). Given the overestimation of the number of fires, the mean fire size is underestimated by the model. The fire size distribution analysis reveals that the model mainly underestimates the number and size of the large fires in these two regions. Over the globe, despite the fact that the model correctly identifies some of the regions where large fires occur (mainly with grassland fraction higher than ∼ 70 % by the land cover used in the model), the large fire size is grossly underestimated in the model -by up to two orders of magnitude (Fig. 13 ). Within the model, the fire size is determined by the fire duration time and fire spread rate.
Influence of fire-climate-vegetation feedback In the current simulation the dynamic vegetation module of ORCHIDEE was switched off and a static land-cover map was used. Tree mortality was affected by fire-induced tree damage, but tree coverage within a given grid cell was static and not allowed to vary with fire occurrence. A test simulation has been done for Southern Hemisphere Africa following the same simulation protocol as in Sect. 2. 3 but with the dynamic vegetation module being switched on, in order to investigate the simulated fire behaviour with dynamic vegetation. Supplement Fig. S13  compares the simulated annual burned area, grass and tree coverage change and the fire danger index for 1901-2009 with the model in dynamic and static vegetation modes. In dynamic vegetation mode, the simulated burned area suddenly begins to increase around 1965, in response to increased fire danger index.
The increase in fire activity further increases the grass coverage and reduces tree coverage, causing a positive feedback to finally induce a peak of burned area around 1975, after which the burned area decreases. In static vegetation mode, the simulated burned area shows a similar peak in response to the peak in the fire danger index, however, with a much smaller peak of burned area than that simulated in dynamic vegetation mode because of the lack of fire-vegetation-climate feedback. The simulated burned area by both simulations is still lower than that given by the GFED3. 1 data for the period of 1997-2009, although Introduction.
Future model improvement directions and needed datasets Currently many efforts in global fire modelling are directed at reproducing the temporal and spatial pattern of burned areas  (Kloster et al. , 2010; Li et al. , 2012; Prentice et al. , 2011; Thonicke et al. , 2010). Total burned area is determined by ignition frequency and fire size, which itself is controlled by fire spread rate (fire intensity) and fire duration. 2008) will help to reveal gaps in this understanding. The present study is a step in this direction, bringing new in-depth model evaluation. In summary, the fire processes in the SPITFIRE model are complex enough to include many aspects of wildland and human-caused fire processes in nature. However, little is known about the parameter sensitivities and their contribution to model error.
The simulated intermediate model parameters (e. g. , fire numbers, fire size, fire spread rate, fire intensity) are poorly constrained by the observation data. As a result, error compensation could be prevalent in the model and a wider application of the model is impeded. To advance model development, global measurement datasets of the key fire parameters, including fire spread rate, fuel bulk density, wind speed, fire intensity, fire duration, etc. , should be established and used to calibrate fire models. On the other hand, the complexity of fire model parameters and the regional nature of fire processes make it unlikely that these parameters could be calibrated in a parameter-by-parameter and site-by-site way, but some more advanced techniques such as data assimilation or model-data fusion could be helpful.
-1 ) always support a relatively large fuel amount but fires are limited by the duration of the dry season when fires can occur. Burned area is maximal for regions with intermediate precipitation and productivity (Krawchuk and Moritz, 2010). Maximum burning occurs around an annual precipitation of 1000 mm according to the model simulation, compared to 1200 mm by GFED3. 1, and 1400 mm by the Discussion Paper | Discussion Paper | Discussion Paper |GLOBCARBON and L3JRC datasets. GLOBCARBON and L3JRC show the lowest burning in this tropical/subtropical belt, followed by the model simulation, with the burned area by GFED3. 1 being the highest. The fire and precipitation relationship was further divided into four sub-regions of America, Africa, Asia and Australia following van derWerf et al. (2008b)  and the results are shown in Supplement Fig.
Acknowledgments We thank the anonymous reviewers for their rich comments that prompted us to clarify our position, as well as for the indication of several references, opening up new perspectives. This research is developed in the frame of the  MAELIA  Project '' Multi-Agents for EnvironmentaL norm Impact Assessment '' (http:// maelia1a. wordpress. com/)funded by the  Thematic Network for Advanced Research ''Sciences & Technologies for Aeronautics and Space'' (RTRA STAE Foundation  http://www. fondationstae. net/) in Toulouse, France. Dr H. March  (in 2010March (in -2011) ) and  Dr R. Lardy  (in 2013Lardy (in -2014) ) have benefited from postdoctoral grants funded by the  RTRA STAE Foundation. The authors would like to thank all governmental organizations, private companies and others who supported our work by providing data and expertise necessary to feed the MAELIA model.
Automated ribosomal intergenic spacer analysis (ARISA) of the archaeal and bacterial communities and real-time (q)PCR experiments targeting various sedimentary microbial lineages (Archaea, ANME-1, ANME-2a, ANME-2c, ANME-3, Methanosarcinales, Methanomicrobiales, Methanococcales, Methanobacteriales, Methanopyrales, MCG, MBGB, MBGD, Bacteria, Chloroflexi, Candidate division JS1, Desulfosarcina/Desulfococcus, Desulfobulbus, SEEP SRB2; Table  S1 ) were carried out on purified DNA samples every 50 cm from 1 mbsf to 9 mbsf as presented in Methods S1. Statistical tests were carried out using the software PAST  [28]. Nucleic acid sequences are available in the EMBL database under the following accession numbers: HF543837-HF543861 for archaeal, HF545450-HF545524 for bacterial 16S rRNA sequences and HF935025-HF935037 for mcrA gene sequences.
GeoChip analysis The GeoChip 4. 0 microarray, containing 83992 oligonucleotide probes and targeting 152414 gene variants in 401 categories for different microbial functional and biogeochemical processes was monitored as previously detailed  [29]. Although the GeoChip was initially based on the genome of cultured microorganisms, the new generation of GeoChip has been extensively enriched with metagenome data from various environments and contains now an important number of relevant probes targeting genes from cultured and uncultured microorganisms involved in key biogeo-chemical cycles. Total purified DNA samples were labeled then hybridized on GeoChip slides. Signal intensities were scanned and spots with signal-to-noise ratios lower than 2 were removed before analyses  [29]. The phylogenetic design of the data acquisition enabled confident assignment of metabolic capabilities to bacterial and archaeal phyla  [30, 31] , thus dataset were sorted according to the taxonomic affiliation of the genes (Bacteria, Euryarchaeota and Crenarchaeota).
Output was analyzed using the GeoChip 4. 0 data analysis pipeline  [32]  and tested using the statistical software PAST  [28]. Relative signal intensity was normalized by the number of the probes for each indicated metabolic pathway. List of targeted genes for each category are provided in Table  S2. Visualization of the bacterial and archaeal functional potential was achieved using spider dendrograms, where each arm of the plot corresponded to a metabolic pathway. The raw GeoChip dataset is available at http://ieg. ou. edu/4download/.
Microbial community structure and composition Microbial community structure variations with depth were compared from the Sonora Margin cold seep surface sediments using ARISA. The archaeal and bacterial community structures of the BCK1 were significantly different from the surface sediments of both cold seep (WM14 and EWM14 samples  [20] ) and outside active seepage areas (REF samples  [20] ) as shown by clustering and ANOSIM (p,0. 0008) on ARISA dataset (Figure  2 ) Dendrogram and Nonmetric Multidimensional Scaling (NMDS) analysis, based on Bray-Curtis similarity measure also indicated that BCK1 samples clustered according to sediment depths (1-4 mbsf, 4. 5-6 mbsf and 6. 5-9 mbsf). However this observation was not statistically supported by ANOSIM and seemed to rather reflect a difference in signal intensity more than in community composition.
The authors confirm that all data underlying the findings are fully available without restriction. Nucleic acid sequences are available in the EMBL database under the following accession numbers: HF543837-HF543861 for archaeal, HF545450-HF545524 for bacterial 16S rRNA sequences and HF935025-HF935037 for mcrA gene sequences. The raw GeoChip dataset is available at http://ieg. ou. edu/4download/.
Leader-driven community detection algorithms (LdCD hereafter) constitute a new trend in devising algorithms for community detection in large-scale complex networks. The basic idea is to identify some particular nodes in the target network, called leader nodes, around which local communities can be computed. Being based on local computations, they are particularly attractive to handle large-scale networks. In this paper, we describe a framework for implementing LdCD algorithms, called LICOD. We propose also a new way for evaluating performances of community detection algorithms. This consists on transforming data clustering problems into a community detection problems. External criteria for evaluating obtained clusters can then be used for comparing performances of different community detection approaches.