dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Vegetation fires contribute to the emissions of CO 2 and other greenhouse gases to the atmosphere by both the direct biomass combustion process and the indirect post-fire decomposition of woody debris and biomass reconstruction  [1]. Including fires in the atmospheric carbon budget roughly emerged by the quantification of global biomass burning for the tropics  [2]. Global burned area (BA) from remote sensing started to provide monthly gridded global maps of burned area in the early 2000s (see  [3]  for review), used as a direct input in global biogeochemical models to calculate carbon and associated gases emitted to the atmosphere from biomass burning. The Global Fire Emission Database (GFED) now constitutes the reference information on global emissions from fires and fully relies on the accuracy of these BA products  [4].
Burned Area Dataset Among available global burned area datasets (see  [3]  for review), we selected MCD45A1  [21]  from the MODIS sensor, and ESA FIRE_CCI  [22]  from the MERIS ENVISAT sensor at 500 m and 300 m resolution, respectively, in addition to the LANDSAT-derived burned patches at 30 m resolution (Table  1 ). The fire perimeters from LANDSAT were derived from a semi-automatic algorithm (ABAMS)  [30] , following a standard protocol defined for the ESA FIRE_CCI project  [31] , based on the CEOS-Land Product Validation guidelines  [32]  and used for previous global burned area products' validation  [33]. Burned area perimeters were verified by a systematic quality control through visual inspection, where all perimeters were revised visually by an external expert.
Burned Area Dataset Among available global burned area datasets (see  [3]  for review), we selected MCD45A1  [21]  from the MODIS sensor, and ESA FIRE_CCI  [22]  from the MERIS ENVISAT sensor at 500 m and 300 m resolution, respectively, in addition to the LANDSAT-derived burned patches at 30 m resolution (Table  1 ). The fire perimeters from LANDSAT were derived from a semi-automatic algorithm (ABAMS)  [30] , following a standard protocol defined for the ESA FIRE_CCI project  [31] , based on the CEOS-Land Product Validation guidelines  [32]  and used for previous global burned area products' validation  [33]. Burned area perimeters were verified by a systematic quality control through visual inspection, where all perimeters were revised visually by an external expert. Polygons where the interpretation was uncertain were labelled and the original interpreter was assisted to solve those uncertainties.
All the datasets were re-projected at 30 m resolution in the UTM 22S datum WGS84 projection over the LANDSAT scene 223/066 of the Worldwide Referencing System 2 (WRS2) Path/Row. The description of the burned area time series, their spatial and temporal resolutions and their references are summarized in Table  1. All the data statistical analysis, and the raster maps processing for the "flood-fill" algorithm, were developed and performed with R CRAN (Comprehensive R Archive Network) with the "raster" (Geographic Data Analysis and Modeling,  [34] ) and "sp" (Classes and Methods for Spatial Data,  [35] ) packages.
Fire Patch Identification The burn date (BD) dataset from MCD45A1 and ESA FIRE_CCI was used to identify individual burned patches (ID), using the spatio-temporal flood fill algorithm  [8]. The algorithm reads pixels BD one after the other starting from the top left corner. The first pixel with BD > 0 is assigned with ID = 1. Once a burned pixel is identified (the seed), the algorithm reads the BD values in the 8-pixel neighborhood. The neighboring pixels recorded as burned, and with a BD within a defined time interval (cut-off) with the central pixel, are classified with the same fire ID. The classification process spatially spreads out until no more neighboring burned pixel can be found within cut-off. The BD used for the patch ID classification was then removed from the BD map. The algorithm then restarts from the first pixel next to the previous seed until we reach a new burned pixel identified as ID = 2.
The same flood fill process is repeated until all pixels with a BD > 0 have been assigned an individual ID code. In this work, we chose an eight day cut-off. The same algorithm was applied for ESA FIRE_CCI and MCD45A1. For LANDSAT images, ABAMS directly identified burned patches. The analysis was performed during the fire seasons for the years 2002-2009 for MCD45A1 and LANDSAT and 2006-2008 for ESA FIRE_CCI. The ID and BD maps of MCD45A1 and ESA FIRE_CCI were disaggregated to the spatial resolution of 30 m and re-projected to WGS 84 UTM 22S with the nearest neighborhood method for comparison with the LANDSAT dataset.
Fire Patch Indices and Spatial Features From the yearly patch ID maps, where each burned pixel is referenced by its patch ID, we calculated individual patch description metrics. For each patch, we calculated its surface area (A, in m 2 ), the length of its perimeter (P, in m) and its core area (CA in m 2 ) corresponding to the surface area of pixels located at a given distance (we chose 500 m corresponding to the coarser pixel resolution of the BA products) inside the external boundary of a patch (buffer zone). Patch shape complexity was evaluated from its fractal dimension (FD) calculated as 2 × ln(P)/ln(A) and its shape index (SI) calculated as P/A 0. 5 (Table  2 , Figure  2 ). These commonly used patch metrics in landscape ecology were first developed in the Fragstats software  [36]  and we used the corresponding R cran package "SDMtools" (Species Distribution Modelling Tools,  [37] ).
We also computed the ellipse fitting the spatial distribution of burned pixels within a patch to capture the directional azimuthal angle and the elongation of the patch, two key variables describing fire spread processes. In addition, ellipses are the theoretical shapes simulated in DGVMs, and with which they can be benchmarked. Pixels belonging to the same patch ID were converted into points with geographic coordinates corresponding to the center of the pixels. We then used the "aspace" R cran package  [38]  to fit the ellipse. From this ellipse, we extracted its main features including: i) the projection of the longest axis along X and Y coordinates respectively named x and y, ii) the eccentricity (E) as an index of patch elongation calculated as the square root of 1 minus the ratio of the squared lengths of the shortest and longest axis of the ellipse so that E = 0 corresponds to the most elongated shape, and E = 1 corresponds to a perfect circle, and iii) the theta () azimuthal angle corresponding to the clockwise deviation of the longest axis of the ellipse from the northern direction (Table  2 , Figure  2 ).
These selected indices give information on the size and complexity of the fire patch, and the features of the fitted ellipse as a proxy of the theoretical fire spread simulated in DGVMs. We also computed the ellipse fitting the spatial distribution of burned pixels within a patch to capture the directional azimuthal angle and the elongation of the patch, two key variables describing fire spread processes. In addition, ellipses are the theoretical shapes simulated in DGVMs, and with which they can be benchmarked. Pixels belonging to the same patch ID were converted into points with geographic coordinates corresponding to the center of the pixels. We then used the "aspace" R cran package  [38]  to fit the ellipse. From this ellipse, we extracted its main features including: i) the projection of the longest axis along X and Y coordinates respectively named σ x and σ y , ii) the eccentricity (E) as an index of patch elongation calculated as the square root of 1 minus the ratio of the squared lengths of the shortest and longest axis of the ellipse so that E = 0 corresponds to the most elongated shape, and E = 1 corresponds to a perfect circle, and iii) the theta (θ) azimuthal angle corresponding to the clockwise deviation of the longest axis of the ellipse from the northern direction (Table  2 , Figure  2 ).
Data Analysis We compared the MCD45A1, LANDSAT, and ESA FIRE_CCI BA products to each other using accuracy indices based on their patch feature relationships. Our goal here was to compare how patch morphological features are conserved between different burned area products of different spatial resolutions. However, we considered that LANDSAT burned polygons were the most accurate as they are derived from the fine tuning of a semi-automated algorithm ABAMs with much better spatial resolution than the global products (30 m versus 300-500 m) and which were thoroughly visually validated following the standards to generate a correct reference dataset  [32]. We first cross tabulated yearly patch ID maps to identify overlapping patches between two sensors. For each patch ID and a given sensor, we obtained the list of the overlapping patch IDs in each of the other sensors, and the percentage of overlapping surface.
When a patch in a given sensor overlapped more than one patch in the other sensor, we selected the largest patch. To test for any potential biased comparisons between patches overlapping only a few pixels, we computed the coefficient of determination R 2 using a general linear model ("glm" package R cran) between overlapping patch surfaces with thresholds of overlap rates (OR%) varying from 10 to 100%. Increasing the OR threshold led to an increasing R 2 of the patch size correlation for all sensor comparisons, from R 2 = 0. 5 for OR = 10% to R 2 = 0. 95 for OR = 60% and higher. We selected an OR threshold of 30% leading to patch size correlations >0. 70 for further analysis (Figure  3 ). Eccentricity (E) Degree of divergence between a perfect circle (equal to 0) and an elliptical shape (equal to 1) -.
When a patch in a given sensor overlapped more than one patch in the other sensor, we selected the largest patch. To test for any potential biased comparisons between patches overlapping only a few pixels, we computed the coefficient of determination R 2 using a general linear model ("glm" package R cran) between overlapping patch surfaces with thresholds of overlap rates (OR%) varying from 10 to 100%. Increasing the OR threshold led to an increasing R 2 of the patch size correlation for all sensor comparisons, from R 2 = 0. 5 for OR = 10% to R 2 = 0. 95 for OR = 60% and higher. We selected an OR threshold of 30% leading to patch size correlations >0. 70 for further analysis (Figure  3 ). We then computed linear regressions between overlapping patches with OR > 30% for each patch metric and extracted their coefficients of determination R 2 , p-values and slopes.
All methodological steps are summarized in Figure  4. Flowchart of the fire data processing from (i) the raw data on burn date (BD) and Quality Assessment (QA) for the MODIS MCD45A1 and ESA FIRE_CCI pixel level fire products, the LANDSAT images and their processing into fire patches with the ABAMS software  [30]  as previously performed for this study site  [20] , (ii) the flood fill algorithm allowing pixel aggregation into patches identification (ID) according to their burn date, (iii) the crosstabulation of fire patches between fire products, the selection of patches overlapping over more than 30% of their surface, (iv) the calculation of patch morphological metrics and (v) the final correlation analysis using a linear regression.
ESA FIRE_CCI and MCD45A1 comparisons with LANDSAT performed significantly worse with a percentage of analyzed patches of about 65% for patches smaller than 900 ha, but reaching 80% and 90%, respectively, for patches larger than 900 ha (Figure  5 ). Therefore, we can conclude that ESA FIRE_CCI and MCD45A1 BA products highly overlap for patches larger than 270 ha, but that small fires (fire patches with a surface area below 270 ha) in one product do not overlap much with the others. When comparing these global BA products with the reference LANDSAT dataset, the patch size threshold is 450 ha.
The Small Fires Issue in Global BA Products Our results suggest some discrepancies in global BA products regarding fires smaller than 450 ha when compared to the reference finer resolution LANDSAT dataset. On one hand, the mean annual burned area covered by fire patches smaller than 270 ha represents 13. 5 × 10 3 ha for ESA FIRE_CCI, 22. 3 × 10 3 ha for MCD45A1 and 26. 0 × 10 3 ha for LANDSAT, out of a total burned area of 83. 5 × 10 3 ha for LANDSAT. Therefore, it is clear that missing burned area in remote sensing products comes from small fires (<270 ha). For the study region, it was identified that these areas account for an average 4%-15% of the total burned area, a little lower than the usually assumed 30% of missing burned area due to missing small fires in global BA  [7]. Beside this missing burned surface in global remote sensing BA products, our results particularly illustrate the little overlap between small fires (<270 ha) derived from different BA products.
Below 270 ha, the percentage of patches overlapping over more than 30% of their surface between both global products ESA FIRE_CCI or MCD45A1, and the reference LANDSAT dataset was less than 40%, respectively leading to commission errors of 0. 72-0. 97 and omissions errors of 0. 77-0. 96 for this fire size class. Interestingly, however, the percentage of patches with sizes varying between 270 and 450 ha and overlapping over more than 30% of their surface between ESA FIRE_CCI and MCD45A1 was high, with omission and commission of 0. 40 and 0. 42 for this fire size class, indicating that both global products tend to detect the same fire patches, considered as committed when compared to.
The Small Fires Issue in Global BA Products Our results suggest some discrepancies in global BA products regarding fires smaller than 450 ha when compared to the reference finer resolution LANDSAT dataset. On one hand, the mean annual burned area covered by fire patches smaller than 270 ha represents 13. 5 × 10 3 ha for ESA FIRE_CCI, 22. 3 × 10 3 ha for MCD45A1 and 26. 0 × 10 3 ha for LANDSAT, out of a total burned area of 83. 5 × 10 3 ha for LANDSAT. Therefore, it is clear that missing burned area in global remote sensing products comes from small fires (<270 ha). For the study region, it was identified that these areas account for an average 4%-15% of the total burned area, a little lower than the usually assumed 30% of missing burned area due to missing small fires in global BA  [7]. Beside this missing burned surface in global remote sensing BA products, our results particularly illustrate the little overlap between small fires (<270 ha) derived from different BA products.
Below 270 ha, the percentage of patches overlapping over more than 30% of their surface between both global products ESA FIRE_CCI or MCD45A1, and the reference LANDSAT dataset was less than 40%, respectively leading to commission errors of 0. 72-0. 97 and omissions errors of 0. 77-0. 96 for this fire size class. Interestingly, however, the percentage of patches with sizes varying between 270 and 450 ha and overlapping over more than 30% of their surface between ESA FIRE_CCI and MCD45A1 was high, with omission and commission of 0. 40 and 0. 42 for this fire size class, indicating that both global products tend to detect the same fire patches, considered as committed when compared to LANDSAT. We might warn, however, of some issues in small fire patch detection even with the LANDSAT sensor for patches lower than 10 ha  [39] , so it is difficult to firmly verify the overall accuracy of global BA products on the spatial location of small patches and their extent as commissions might be the result of omitted fires from the LANDSAT analysis.
74 for all fires, respectively. This result is in accordance with OE and CE observed in the region  [40] , and is in the highest range of uncertainty when compared to other biomes  [20]. These high omission and commission values are mostly related to the high proportion of fire patches below 100 ha, as it is well known that global products (commonly based on coarse resolution sensors) tend to miss those small fires  [4, 7]. These OE and CE respectively decrease to 0. 4-0. 6 and 0. 45-0. 68 for fires larger than 270 ha. Ensuring that pixel-level global fire products conserve patch morphological features at 500 m resolution despite these high OE and CE would allow for global scale investigations into local processes affecting fire spread. Only the few studies listed actually compared patch features between global products and local analysis: Patch area and perimeter features for the 2005-2007 period in Greece was compared to the MCD45A1 dataset and the European Forest Fire Information System (EFFIS)  [58] , and illustrated how patch complexity could be largely sensor-dependent.
Acknowledgments: This work was developed within the  Fire-CCI  project, in the framework of the  European Space Agency Climate Change Initiative program  and the  IRD/CNPq collaborative agreement program. The  Coordenação de Aperfeiçoamento de Pessoal Superior (CAPES, Brazil)  provided a PhD grant to  Joana Nogueira  ( BEX-1185-13-6  process). Julien Ruffault  was supported by a post-doctoral grant within the European Community's  Seventh Framework Program (CLARIS-LPB , Grant Agreement No  212492 ).
Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i. e. , number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i. e. , number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database.
They provide robust insights into the location of psychological and pathological effects  (Kober and Wager, 2010; Nickl-Jockschat et al. , 2015; Radua and Mataix-Cols, 2012; Schilbach et al. , 2012; Yarkoni et al. , 2011). Such structured integration is enabled by community-wide standards of spatial normalization  (Amunts et al. , 2014; Evans et al. , 2012; Mazziotta et al. , 2001)  and the convention of reporting peak locations in stereotaxic reference spaces. Activation likelihood estimation (ALE) has originally been introduced more than a decade ago  (Turkeltaub et al. , 2002). It is among the most commonly used algorithms for coordinate-based meta-analyses and it is part of the BrainMap software suite  (Laird et al. , 2011a; Laird et al. , 2009)  (http://brainmap. org/ale). The key idea behind ALE is to not treat activation foci reported in neuroimaging studies as dimensionless points but as spatial probability distributions centered at given coordinates.
c) Correction for multiple comparisons using the false-discovery rate  (Laird et al. , 2005; Wager et al. , 2007)  has also been frequently employed, although this method may be less appropriate for neuroimaging data  (Chumbley and Friston, 2009). Another question that frequently comes up when planning, discussing or reviewing metaanalysis projects pertains to the necessary sample size, i. e. , number of experiments that is needed to perform robust meta-analyses. In our view, this question may be investigated along two different aspects: First, is there a lower bound such that meta-analyses involving fewer experiments may not be considered valid? Second, can we provide power-calculations for ALE meta-analyses? The present investigation addresses these questions by means of massive simulations of ALE analyses.
Importantly, this spread is distinct from the spatial uncertainty associated with each individual focus in an ALE analysis. The latter describes how much the results for a single neuroimaging experiment may vary depending on between-subject and between-template variability, while the former describes the dispersion of reported foci from different experiments assessing the same effect. In order to provide empirical estimates on this spread that will then be used to construct the simulations, we assessed two pools of meta-analyses. The first pool contained 15 hand-coded datasets  (Bzdok et al. , 2012; Cieslik et al. , 2015; Hardwick et al. , 2013; Kohn et al. , 2014; Rottschy et al. , 2012). The second pool consisted of 105 datasets that were automatically extracted from the BrainMap database (www.
brainmap. org; (Fox et al. , 2014; Laird et al. , 2011a; Laird et al. , 2009) ). This was achieved by combinations of the paradigm class and behavioral domain classifications  (Laird et al. , 2011a)  that yielded between 30 and 200 experiments. For each of the ensuing meta-analysis datasets, we performed an ALE meta-analyses and subsequently identified the peaks of the ensuing Z-map (thresholded equivalent to p<0. 001 uncorrected). In order to avoid assessment of multiple sub-peaks within a larger cluster, we ranked the Z-encoded activation peaks by significance and started selection from the top, eliminating all lower peaks within 2 cm distance of the selected peaks. Next, we identified all experiments from the respective dataset that contributed to the respective peak location. Here contribution was defined by truncating the Gaussian probability distribution of each experiment at 90% mass (i.
e. , its 3D confidence ellipsoid) and considering those experiments to contribute for which the peak was located within this Gaussian distribution with truncated tails. The distance between the coordinates for the contributing foci and the peak were then recorded and the sigma of the spatial spread (under isotropic Gaussian assumptions) computed from these. That is, we treated the peaks of convergence obtained from ALE analyses of the different datasets as the best estimators of the true effect and quantified the spatial spread around these. As illustrated in Figure  1 , the average of the standard deviation sigma reflecting the spread of contributing foci to a peak location was just over 3. 5 mm, corresponding to a FWHM of ~9. 5 mm. In spite of the differences between both pools of datasets, our assessment furthermore showed that for the vast majority of peaks from both hand-coded and automatically generated meta-analyses, the contributing experiments feature a spread between 3 and 4 mm.
Experimental details of the simulations In this study, we performed ALE analyses on simulated datasets containing between 5 and 30 total experiments. Among these, between 1 and the minimum of 10 and the total number of experiments were randomly chosen to feature activation at the target-location. This yielded 245 distinct combinations of "total number of experiments" and "experiments activating the target-location", i. e. , distinct cases of meta-analyses. For each of these cases, we then generated 500 random datasets, yielding in total 122,500 datasets that provided the basis for the systematic assessment of the behavior of the ALE algorithm and the different methods for statistical thresholding. To create artificial datasets that are as realistic as possible, we used the BrainMap database to provide a distribution of the properties, in particular, the number of subjects as well as the number and location of foci, based on the current neuroimaging literature (www.