dataesr/scientific-paragraphs-categorization huggingface dataset:
3b-d , hereafter denoted as U ). Such oversampling of reference sites in the realm of U would violate the stratified-sampling approach described in  Olofsson et al. (2014)  and would lead to an erroneous accuracy assessment. To achieve a balanced stratified sampling of reference sites across burned and unburned areas of each dataset, we generated three subsamples from the initial 1298 reference sites (red areas in Fig. 3e-g ) and used these subsamples to validate each dataset. These three subsamples were generated by randomly excluding reference sites from the realm of U in Fig. 3b-d  until the density of reference sites in U equalled the density of the larger unburned area U. For instance, for the validation of the official burned-area map, the density of reference sites in U was 10. 36 sites Mha -1 , and the extent of U was 1.
Interpretation of the burned-area reference dataset We developed a series of scripts in the Google Earth Engine to streamline the visual interpretation of the reference sites. Specifically, we adapted a script written by  Olofsson et al. (2014)  to rapidly scan the time series of original Sentinel-2 images in visible and infrared bands and thus visually detect a smoke plume, a burn, or a heat source (flaming front) and determine whether and when in 2019 a reference site burned. The script enabled the interpreter to interactively track the evolution of NBR values and patterns over the 2019 time series of 5 d images. Reference sites were investigated wherever a marked drop in the NBR time series was detected, indicating a disturbance in the vegetation. For reference sites where a disturbed area was observed, we subsequently reviewed the last few images before the drop in NBR and the first few images after the drop.
If none of these three features were observed, the reference site was declared to be unburned. Three interpreters independently reviewed the time series of original Sentinel-2 images and associated NBR trends for all reference sites (N = 1298) (see Fig. S3  for a frequency distribution of burn sizes of the Sentinel-2 burned-   A, B,  and C ) against reference sites (dots). Panel (a) shows the stratified random sampling of reference sites (black points) over the combined burned area. Note that the density of samples is higher in the combined burned area than the unburned area. Panels (b-d) show, in cyan, the area U , being classified as unburned in a given dataset i but classified as burned in at least one other datasets = i. For a given validation of A, B, and C, the sample points in the corresponding area U (b-d) were randomly excluded until the sampling density in the area U equalled that of the larger unburned area U (area in grey).
Panels (e-g) show the three final adjusted, stratified subsamples of reference points derived from the initial sample of 1298 reference points. Note that the relative areas and number of sites per class in Fig. 3  do not correspond to the actual datasets being evaluated. area map, for select spatially coincident burned reference sites). To reduce uncertainties associated with the interpretation of the imagery, the results of the three interpreters were compared to each other. If all three interpreters recorded the same interpretation and timing of a burning event for a given reference site, their interpretations were retained. If one or more interpreters disagreed, all interpreters reviewed the data and resolved discrepancies by consensus. In some cases, it was difficult to reconcile disagreements because of poor image quality or because of uncertain spectral patterns.
Therefore, if possible, interpreters also explored other satellite images (e. g. Landsat) to detect the presence of fire and resolve disagreements for a given reference site. The sites in which the three interpreters disagreed were ultimately excluded (70 sites) from the reference dataset. For these ex-cluded sites, disagreement typically resulted from uncertainties over the boundary of burned or unburned areas, or because the imagery was not clear enough. The sample size of reference points explored here, N = 1298, excludes the discarded points of disagreement in question. We created a second script to generate snapshot images (see examples in Fig. 4 ) depicting infrared spectral conditions, shortly before and shortly after a fire, as well as the corresponding image dates. Interpreters recorded and geotagged a snapshot of before and after fire conditions at every reference site (for which a burned area was detected) enable third-party reviewers to check the consistency and validity of interpretations on a site-by-site basis (see Data availability).
We excluded burns < 6. 25 ha because this is the minimum observable burn size of the Landsat 8 official estimates due to the challenging nature of visual interpretations at such scales. We note that the minimum size of the MODIS data is 25 ha; hence for comparison with the MCD64A1 product we used a 25 ha threshold. In relation to Sentinel and MODIS estimates, for which burned areas were originally mapped as arrays of pixels, we defined a burn to be any array of pixels contiguous across cardinal directions but not diagonals to render the resultant burned-area map conservative with respect to patch size (Fig. S4 ). For the official estimate, burns are manually delineated via visual interpretation by interpreters from the government of Indonesia. All burns are spatially and tempo-rally discrete, such that burns of a given estimate that overlap spatially but not temporally are considered separate.
Increased burned-area estimates Our Indonesia-wide burned-area estimate, based on the classification of the pair of pre-and post-fire Sentinel-2 composites, is larger than the official estimates as well as the MODIS MCD64A1 to a lesser degree. We estimate 3. 11 Mha burned in 2019 across Indonesia, of which 31 % was on peat (Fig. 5 ). The extent of peatlands was defined using a national dataset from the Ministry of Agriculture  (Ritung et al. , 2011). In contrast, official burned-area estimates, based on visual interpretation of Landsat 8 imagery, report only about half as much burned area, at 1. 64 Mha, of which 39 % was on peat. Our estimates too are larger than the MODIS MCD64A1 product, which reports 2. 04 Mha burned in 2019, or two-thirds of our estimate, with 40 % on peat. The larger burning extent and proportionally lesser extent of peatland burning according to our estimates suggest that our estimates are particularly more inclusive of burning across mineral soils.
In the seven provinces for which we assessed accuracy, our Sentinel-2 estimates and the official Landsat 8 estimates both report excellent user accuracies (UAs) for the burned class, at 97. 9 % (CI: 97. 1 %-98. 8 %) and 95. 1 % (CI: 93. 5 %-96. 7 %), respectively, indicating a mere 2. 9 %-4. 9 % commission-error rate (Tables  2  and S5 ). The producer accuracies (PAs) are comparatively lower for both datasets, but notably less so for our estimates, at 75. 6 % (CI: 68. 3 %-83. 0 %) and 49. 5 % (CI: 42. 5 %-56. 6 %) for our estimate and the official dataset, respectively. In other words, for any burned area in our reference dataset, there is a 75. 6 % chance that it will be correctly mapped as burned by our estimate, compared to only 49. 5 % for the official estimate. This is in keeping with the tendency of the Sentinel-2 estimate to capture more smaller and intermediate-size burns.
6 ). Note for instance the increasingly large divergence between the cumulative burned-area curves for the Sentinel-2 and the official estimates in Fig. 6  for burn areas > 1000 ha. For very large burns (> 5000 ha), two-way comparisons in Table  4  again report no significant statistical differences in burn-scar detection rates between the Sentinel and alternative estimates. However, given the small sample of patches > 5000 ha, it is noteworthy that the Sentinel estimate captures more very large scars compared to official estimates (n = 56 vs. n = 16) and avoids critical omissions made by both official and MCD64A1 estimates for extremely large burns (> 15 000 ha) on peatlands around Berbak National Park in Jambi Province, Sumatra (Fig. 7 ). In summary, the greater overall burned-area estimate of our Sentinel data compared to the official and MCD64A1 alternatives reflects differences in the inclusion of smaller and intermediately sized scars.
Code availability The code for generating the Sentinel-2 preand post-fire composites can be found at https://doi. org/10. 5281/zenodo. 5646758  (Salim et al. , 2021). Cumulative national total burned area vs. burned-scar area, for Sentinel-2, Landsat 8 (official), and MODIS MCD64A1 burned-area estimates. Note the logarithmic axis. For a given segment of the x axis between scar sizes X 1 and X 2 , a difference in the slopes for any two estimates is indicative of inter-estimate differences in terms of inclusivity of scars between X 1 and X 2.
Data availability All the data including pre-and post-fire composites, all three burned-area products, and reference points with screenshots can be visualised online at this application portal: https://thetreemap. users. earthengine. app/view/ burn-area-validation-simplified (last access: 5 November 2021,  Salim, 2021). The Sentinel-based burned-area map and reference dataset are freely available for download at https://doi. org/10. 5281/zenodo. 4551243  (Gaveau et al. , 2021a). The dataset 2019_burnedarea_indonesia. shp contains the 2019 burned-area estimates that we developed for Indonesia using 20 m × 20 m time-series Sentinel-2 imagery. The reference dataset Reference_dataset. shp contains 1298 reference points that we assembled and used to validate all three burned-area products described in this study.
Each reference point includes the attribute REFERENCE to describe the values obtained by visual interpretation: either NO unburned or YES burned. Each reference point has three attributes, C_SENTINEL, C_OFFICIAL, and C_MCD64A1, to describe the values of the classification of each burned-area product: either NO unburned or YES burned. Finally, each reference point has three additional attributes SENTINEL, OFFICIAL, and MCD64A1 to describe which burned-area product this reference point validates. The values are either 0 (not validate) or 1 (validate). The  The Sentinel-2 Level 2A data used in this study are hosted and accessed in the Google Earth Engine data catalogue (the link to the data is https: //developers. google. com/earth-engine/datasets/catalog/ COPERNICUS_S2_SR, last access: 10 November 2020, European Union/ESA/Copernicus, 2020).
, 2016) , while it also captures large burn scars relatively thoroughly. The study finds similar omission and commission errors (47 % and 24 %) for the burned class of the MCD64A1 product as those presented globally (40 % and 22 %)  (Giglio et al. , 2018). The underestimation of total burned area according to the MCD64A1 product compared with our Sentinel-2 estimate is unsurprising, considering that the MODIS 500 m pixel resolution struggles to detect smaller fires  (Giglio et al. , 2018). Similar conclusions were reached by  Ramo et al. (2021)  when comparing the new "Small Fire Dataset" derived using Sentinel-2 and the MCD64A1 product over sub-Sahara Africa  (Chuvieco et al. , 2018). More surprising is the near 2 : 1 ratio by which the Sentinel-2 estimates surpass the Landsat 8 official estimate.
1 ), revealing large burned areas around Berbak National Park (areas that have transitioned from green to dark brown/red tones). These large burn scars have been detected by VIIRS hotspots and by the Sentinel-2 burned-area map, but some have been missed by the official and MCD64A1 datasets. Although both Sentinel-2 and Landsat 8 observe the infrared wavelengths required to detect charred vegetation and have similar spatial resolutions (20 m×20 m and 30 m×30 m, respectively), Sentinel-2 detects more burns because of the greater frequency of its coverage (5 vs. 16 d revisit time). Also, our method makes use of the massive computational capabilities and automation of the Google Earth Engine, allowing us to analyse more images and thus map more and smaller burn scars and associated details than even the most well-equipped team of visual interpreters could.
While further work is required to clarify and refine the optimal levels of inclusivity and reliability, we emphasise that the production of annual before-and after-fire composite images is relatively straightforward for the user community, given the availability of both the necessary imagery and our Google Earth scripts. While the accuracy assessment proved that our training dataset is valid for the classification of Sentinel-2 composites for the year 2019 in Indonesia, this training dataset might not achieve equivalent accuracy for other years and regions. The Doubts may persist concerning confident estimates of burned areas without extensive and costly ground checks. Modern high-resolution remote sensing makes such on-theground checks less essential than in the past as burned areas are readily identified with good accuracy in modern highresolution imagery such as that we used for our validation.
The protocol developed here to generate a reference dataset based on visual inspection of dense (5 d revisit time) infrared satellite imagery is better suited than ground verifications of burned and unburned locations because it allows the generation of extensive randomly distributed, well-characterised reference sites, a process too time-consuming and costly with field visits. The identification and quantification of less readily detected burned areas, such as those under a closed forest canopy, remain a challenge but will require dedicated and targeted research and would not be solved by ground checks alone. Accurate estimates of burned lands, in particular on peat, are central to addressing concerns about regional air quality and to ambitious national climate-change atmospheric carbon reduction commitments heavily reliant on improved land and fire management (DGCC, 2019).
org/10. 5194/essd-13-5353-2021-supplement. Author contributions. DLAG designed the study. DLAG, MAS, and AD designed the burn scar detection method. MAS and AD wrote the code in Google Earth Engine. DLAG, MAS, and AD carried out the validation. SS carried out the burn scar size analysis. DLAG, AD, SS, and DS interpreted the results and wrote the manuscript and produced the figures. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Financial support. This research has been partially supported by the donors of the CGIAR Fund. Review statement. This paper was edited by Alexander Gruber and reviewed by three anonymous referees.
25 N/A 31. 8 a (+0. 32) -70. 6 a > 25 14. 7 a (+0. 24/ -0. 15) -20. 1 b 13. 2 a (+0. 18) -28. 6 b > 100 7. 9 a (+0. 23) -16. 6 b 1. 6 d (+0. 04/ -0. 04) -0. 57 > 1000 0. 76 (+0. 06/ -0. 03) -0. 79 1. 5 e (+0. 01/ -0. 12) -3. 1 c > 5000 f 0. 72 (+0. 14/ -0. 08) -0. 77 0. 70 (+0. 13/ -0. 20) 0. 10 cess: 12 August 2021, Giglio et al. , 2015, https: //doi. org/10. 5067/MODIS/MCD64A1. 006). The offi- cial burned-area dataset from the Ministry of Envi- ronment and Forestry (MOEF, 2021) was obtained at https://geoportal. menlhk. go. id/webgis/index. php/en/ (last access: 1 April 2021). Significance.
General workflow The workflow consists of seven parts, as shown in Figure  1. First, DEL-compatible chemical building blocks (BBs) were selected from the eMolecules and Enamine in-stock BB libraries described in the Data section. It was done on the basis of the Goldberg rule of two (Ro2)  [17]  and eDesigner builtin filters for selecting DNA-compatible BBs. Using these BBs, thousands of DELs were designed and generated with the help of eDesigner. The size of each DEL varied from 1M to 1B but for easier and quicker analysis, only a representative subset of 1M compounds per DEL was enumerated using the random sampling approach. In the third step, generated compounds were standardized according to the protocol explained in the Data section. ISIDA descriptors  [18]  were used to represent molecular structures in a machine-readable form of numerical N-dimensional vectors.
They were then projected onto uMap1. Comparative landscapes were created and visualized to compare DEL compounds to biologically relevant molecules from the ChEMBL database. Then a so-called "golden" DEL that provides the highest coverage of ChEMBL chemical space was identified using responsibility patterns (RPs)  [19]. To achieve even better coverage, complementary DELs were added to the "golden" one to give a "platinum" pool of DELs.
The GTM algorithm "bends" the manifold to pass through the densest areas of the data cloud formed by the points representing molecules of the input dataset. Then, the molecules are projected from the high-dimensional space onto the 2D map by associating each molecule to the several closest grid nodes. The degrees of association of each molecule to each node of the grid are called "responsibilities". The responsibility of a node for a compound is the contribution of this node to the likelihood of this compound. Therefore responsibilities are real number vectors summing up to 1 over all nodes. Finally, the manifold is flattened out to obtain a 2D representation of the map with compounds projected onto it. Based on the responsibility vectors, different types of landscapes can be created, where each node is colored using the weighted average of the properties of the compounds projected there.
The size of the map was chosen to be 41x41 nodes and the number of RBFs -18x18. Since the ChEMBL database is the most reliable source of the compounds with experimentally measured biological activity  [13]  , the universal maps trained on the ChEMBL data series are highly oriented towards biologically relevant compounds. Apart from predicting biological activity, these maps can also be used as frameworks for analyzing large chemical libraries in medicinal chemistry and drug design context. The uMap1 has been used in this project to compare biologically relevant compounds from ChEMBL with the DNA-encoded compounds. This choice was motivated by previous results in identifying biologically relevant molecules missing from the chemical market, as well as untested commercially available compounds when comparing ChEMBL and ZINC  [15].
Commercially available BBs A set of 450K commercially available BBs was provided by eMolecules Inc. [24]  They were complemented by an "orthogonal" (i. e. containing completely different BBs) dataset of 10K Enamine  [25]  in-stock BBs. Among them, only 79 141 BBs that satisfy Ro2 and eDesigner built-in DNA-compatibility filters were selected.
ChEMBL (biologically tested compounds) ChEMBL is a database containing >2M diverse and biologically relevant compounds against >14K biological targets  [13]. The major goal of this project was to find structurally diverse DELs suitable for primary screening. Since similar structures tend to have similar properties, finding a DEL containing compounds structurally similar to ChEMBL means finding a DEL that contains biologically relevant molecules. Such DEL will have a high potential to contain hit compounds. Hence, ChEMBL (version 28) was used as a reference library that guides our choice of the best DEL for primary screening. First, 2 086 898 molecules were downloaded from ChEMBL. After standardization, 1 853 565 unique compounds with known biological activities remained. The standardization of chemical structures was done using ChemAxon Standardizer  [26]  according to the procedure implemented on the Virtual Screening Web Server of the Laboratory of Chemoinformatics in the University of Strasbourg  [27].
Prevalent monofunctional BB classes in the resulting dataset are secondary and primary amines, aryl halides, and carboxylic acids (Figure  4 ). Due to their participation in common DNA-compatible combinatorial reactions (such as condensation of carboxylic acids with amines, aldehyde reductive amination, bromo-Sonogashira coupling, etc. ), there is an active development of such BBs, making these four classes more structurally rich and widely available commercially. Note that in this work, all structures were stereochemistry-depleted (a unique skeleton graph is used to represent all stereoisomers). Therefore, the number of different BBs is higher. In the case of bifunctional BBs (Figure  5 ), protected amino acids (AA) (such as amino esters, N-Boc-AA, N-Fmoc-AA, etc. ) represent the most abundant class (3 796).
In order to verify that such a library core is indeed representative, the whole library of 88M was enumerated for one of the DELs, and density landscapes were built for the whole library and 1M dataset on the same density scale. As one can see in Figure  9 , each region of the map, occupied by the members of the whole library, also has representatives in the 1M randomly generated dataset -colored regions coincide on both maps, and only the density of residents differs. Therefore, 1M randomly enumerated compounds will be considered in this work as a sufficient representation of the whole DEL for GTM-based analysis.
Conclusions In this work, for the first time, the ultra-large chemical space of DNA-encoded libraries (DELs) containing 2,5B compounds in total (2. 5K libraries 1M each) was designed and generated using eDesigner and analyzed with the help of GTM. Owing to the probabilistic nature of GTM and efficiency of the libraries analysis and comparison based on the responsibility patterns, it was possible to develop a GTM-based approach for quick selection of DELs occupying the same areas of the chemical space as the reference library. In this work, the goal was to detect the "golden" DEL or "platinum" pool of DELs for primary screening -the libraries containing the highest portion of biologically relevant chemotypes. Therefore, ChEMBL, as the largest database of dose-response activity tests and thus an optimal representation of biologically relevant space, was used as a reference.
In this work, a proof-of-concept digital twin has been designed using the software agent system NetLogo (Wilensky and Rand 2015), as also shown in Fig. 5. The NetLogo simulator is initiated with data from sensors localizing people in the train, data from the environment (geo-localization of the train, infrastructure map), and from the train itself (health-status). The fire propagation models and human behavior models that were used are very simple, as we are not concerned that much with the fire propagation accuracy of the model, but with the main goal of illustrating the utilization of the ethical controller. For rule #1, the digital twin simulates from the current time, and at the same time, the propagation of the fire, the immediate opening of the doors, and the movement of passengers in the tunnel trying to reach safe spots.
Surferlab, a joint research lab with Bombardier and Prosyst, partially funded by the  European Regional Development Fund (ERDF) ,  Hauts-de-France ; the  HUMANISM  No  ANR-17-CE10-0009  research program; the project " Droit des robots et autres avatars de l'humain", IDEX "Université et Cité " of  Strasbourg University. The authors would also thank  Amr Dalal ,  INSA Hauts-de-France  who designed the NetLogo proof-of-concept simulator of the digital twin.
Objective: Efficacy of pharmacological treatments for acromegaly has been assessed in many clinical or real-world studies but no study was interested in economics evaluation of these treatments in France. Therefore, the objective of this study was to estimate the costutility of second-line pharmacological treatments in acromegaly patients. Methods: A Markov model was developed to follow a cohort of 1,000 patients for a lifetime horizon. First-generation somatostatin analogues (FGSA), pegvisomant, pasireotide and pegvisomant combined with FGSA (off label) were compared. Efficacy was defined as the normalization of insulin-like growth factor-1 (IGF-1) concentration and was obtained from pivotal trials and adjusted by a network meta-analysis. Costs data were obtained from French databases and literature.