dataesr/scientific-paragraphs-categorization huggingface dataset:
• The 13th cell shows the crop state observed by a human. If farmers recognize a new crop state and put it in the dataset, then this state is written by the name code, and the cell is filled with yellow. Otherwise, the text entered in the cell is "null". • The 14th cell shows the value SleepingDuration inferred by the DSS. Its unit is the day. • The 15th cell shows the state of the CropWaterNeed. When CropWaterNeed reaches state Yes, the text in the cell is in uppercase, and the cell is filled with red. • The 16th cell shows the decision of farmers recorded in the dataset. The day that farmers decide to water the plot, the corresponding cell is made green, and the text inside the cell is "YES". Otherwise, there is no text. Figure  19  shows the results of the experimental period from 22/07/2013 to 31/07/2013.
Discussion This section discusses the limitations of the IRRINOV ® method, the limitations of the Arvalis dataset and the limitations of the DSS system. First, the IRRINOV ® method is not a complete method for irrigation decisions. IRRINOV ® provides guidelines for irrigation decisions based on crop variety, soil and region in metropolitan France. This method is only dedicated to climate events that have a greater chance of occurring in metropolitan France. For example, the IRRINOV ® method ignores the fact that in summer, some daily rain events with high intensity may occur for a long period, such as in tropical climates. Moreover, the IRRINOV ® method provides no guide about the amount of water that must be used for irrigation. It does not take into account that a farmer may have some water restriction usages.
For example, one farm may be allowed to water a plot only each week on Monday morning. Second, the irrigation decision in the Arvalis dataset is a human decision, and humans can make errors. For example, the first watering decision in the Arvalis dataset occurs on 08/07/2013. Following the IRRINOV ® method, farmers should not water the crop on this day since the crop is at the V7 state, and moisture sensor measurements do not reach the threshold. Maybe the farmer has made an error, or maybe he has decided to water the plot sooner because he knows that he will not be able to water the plot in subsequent days due to water restrictions. Third, the DSS provides automatic results that seem to contain no errors. Unfortunately, the DSS is limited. The IRRIG ontology and rules decision is dedicated to a maize crop on a specific type of soil.
Conclusions Precision agriculture needs CASs. A CAS that contains a WSN and a DSS can reduce the work of farmers but also improve precision in farming activities. This paper presents our experience in developing a smart irrigation CAS. This system has three advantages: (1) it automates a manual irrigation method based on the support of specialists and a real experimental dataset, (2) it uses two new ontologies called CASO and IRRIG that extend other well-known ontologies such as SSN and SAREF, and (3) it provides several rules that can work for other similar systems. This paper first describes the methodology of CAS development based on ontologies and details each step of the proposed methodology. Second, this paper also shares an example of development. Finally, an evaluation of the DSS in this system is also published.
In the case of CASO and IRRIG, reusing standard and well-known ontologies was a decision made to increase interoperability with other systems and datasets. However, in practice, this activity was actually time-consuming. When reusing ontologies, one truly needs to understand the semantics and consequences behind every axiom and definition. It becomes even more complicated when several ontologies need to be combined, such as SSN/SOSA and SAREF. Based on this experience, it can be stated that building an ad hoc model from scratch would require less time and consume fewer resources; therefore, the decision of reusing ontologies is based completely on whether interoperability is a requirement. In other words, should the sensor streams be reused by several DSSs? Third, the dataset provided by Arvalis is limited.
Thus, to test the correctness and robustness of the DSS, more datasets should be used. As soon as the sensor network can be deployed on some maize plots, our DSS will be tested on this new dataset. We plan to perform more experiments in 2021. Finally, the IRRINOV ® method itself cannot cover all situations; for example, it does not define the action when there are consecutive multiple rainy days. However, the automatic irrigation system needs a mechanism to address this issue. One solution is to use machine learning to train the system over data of different scenarios. If the training is sufficient, the system can learn the action based on the scale of the IRRINOV ® method. The DSS is a part of a smart irrigation CAS that will be deployed at the experimental INRAE farm (The experimental farm is a part of the AgrotechnoPôle, an ecosystem for agricultural machines and digital information systems developed by an alliance of several organizations and institutes in Europe.
Our tagging is not exhaustive; we are in almost all cases inferring the operators that must have been involved for each map, though indeed other unapparent processes of generalization may have taken place. We use the common conception of a thematic map as composed of a basemap (i. e. , contextual geographic information) and one or more overlaid thematic layers. For each map example, we identify which operators were employed in producing either the basemap or thematic layers, and we rank these operators as critical to supporting the map's thematic message, or incidental. Our aim here is to reintroduce generalization in thematic cartography to scholarly cartographers so that generalization's power in general, and recent advances by the map generalization research community in particular, can be consciously brought to bear on the often very large and varied datasets today's data visualization specialists engage with.
Harrison's the World Divided Richard Edes Harrison's maps for Fortune Magazine during in the mid-20th Century were famous for depicting topographic, demographic, political, and economic data around the world using unusual perspectives and map projections. One such map is The World Divided (see Figure  3 ,  [33] , and Table  4 ), set in a polar azimuthal equidistant projection, explained to the non-specialist audience as "the world centrifuged". Drawn for a US audience in 1941, during the period when the US had not yet entered WWII, the map had a strongly rhetorical and persuasive aim: to illustrate the geopolitical position of the USA and its allies, distributed around the world, as imperiled by any territorial advances by Axis powers, themselves distributed around the world (Harrison made a similar point a year earlier in  [34] ).
Basemap not applicable The Gapminder site is an outreach effort, and is designed for a non-specialist, public audience. In order to simplify their data presentation, Gapminder compiles its country-level data throughout the 19th, 20th, and 21st Centuries according to today's national borders. In their own words, "this is absolutely wrong, but it's necessary to make the animations easier to understand. If our animated bubble charts had displayed all the divisions of territories by splitting bubbles as they animated, the general movement would be much harder to follow, and we would have to manage a much more complex database"  [36]. Because Gapminder's geography is more of a concept than a particular map, it has no basemap. Critical to the national analysis units are reclassification (Cc) into continental units, as well as aggregations (Gg) of statistics from subnational units.
Interactive, Multi-Scale "Slippy" Maps Online maps offering interactive zooming and panning, or "slippy maps" as they are called in the industry, are almost certainly the most popular maps in the world today. A few commercial companies have the most notoriety for these, namely Google (e. g. , Figure  5  and Table  6 ), Bing (Microsoft), Esri, Mapbox, and MapQuest, but popular free and open-source platforms or libraries abound as well, such as OpenStreetMap, Leaflet, and OpenLayers. These are often used both as navigational aides and basemaps for thematic data (see Figure  6 ). Because these maps allow the user to zoom (i. e. , to interactively change map scale), generalization is critical to them, even if only for maintaining legibility. The generalization procedures undertaken by commercial providers are frequently not disclosed, but a few organizations do in fact communicate generalization functionality in their platforms.
Stamen Watercolor Map A particular "slippy" interactive map, the Stamen Watercolor Map (available at http://maps. sta men. com/watercolor) is especially known for its artistic style (see Figure  7  and Table  7 ). Stamen is a design studio focusing on data visualisation; here, they use computer graphics techniques to mimic watercolor painting, characterized by translucent pigments that irregularly build up on paper. This results in fuzzy borders and margins. As a consequence, the watercolor-styled map symbols need more map surface area than more traditional vector symbols, and information density is greatly decreased in comparison to other, more typical topographic maps at the same scale. Only hydrography, roads, and forests are kept in the Stamen Watercolor Map, with roads having gone through a very selective retention process.
Beccario's Earth As with "slippy" maps, virtual globes need to generalize for viewing resolution on demand, since they offer dynamic zooming and panning and, frequently, changes in perspective or viewing angle. Likely the most famous example of these is Google Earth  [47]. Another particularly compelling virtual globe is Cameron Beccario's earth (see Figure  8 ,  [48] , and Table  8 ), which continuously gathers oceanographic and atmospheric data to render scientific visualizations of these using colors, textures, and animated vector strokes. It allows users to select between views of various environmental parameters, as well as map projections beyond an orthographic one for a globe. Panning in each map projection continuously alters that projection's aspect (i. e. , its central meridian, areas shown and occluded, and its areas of relative distortion).
In this paper, we compare the most popular Atomto-Atom Mapping (AAM) tools: ChemAxon  [1]  , Indigo  [2]  , RDTool  [3]  , NameRXN (NextMove)  [4]  , and RXNMapper  [5]  which implement different AAM algorithms. An open-source RDTool program was optimized, and its modified version ("new RDTool") was considered together with several consensus mapping strategies. The Condensed Graph of Reaction approach was used to calculate chemical distances and develop the "AAM fixer" algorithm for an automatized correction of erroneous mapping. The benchmarking calculations were performed on a Golden dataset containing 1851 manually mapped and curated reactions. The best performing RXNMapper program together with the AMM Fixer was applied to map the USPTO database. The Golden dataset, mapped USPTO and optimized RDTool are available in the GitHub repository https://github.
The NameRxn software  [4]  from NextMove performs AAM using large ensemble of rules describing known reaction mechanisms/transformations. The algorithm classifies a given reaction according to the implemented heuristics, followed by identification of the reaction centre described by an ensemble of atoms at chemical bonds formed, broken or transformed in course of chemical RXNMapper tool  [5]  implements a deep learning algorithm that is much faster than the abovementioned ones because it does not need to perform time-consuming combinatorial-seeking operations. RXNMapper uses a model based on the transformer architecture  [21] [22] [23]  which relates SMILES strings  [24]  of reactants (input) and products (output). An attention mechanism is used to assign a correspondence between the atoms of reactants and products.
RXNMapper was trained in unsupervised settings on a subset of the USPTO database  [25]  for which the AAM accuracy >99% was reported. Reaction Decoder Tool (or RDTool) uses a consensus approach combining four distinct MCS-based algorithms: Mixture-MCS model, Min-sub model, Max-sub model, and Assimilation model  [26]. All of them are based on pairwise MCS identification between reactant and product molecules. In the Min-sub model (further mentioned as MIN), the smallest MCS between the reactants and products are first mapped. On the contrary, in the Maxsub model (further mentioned as MAX), the largest MCS between the reactants and products are first mapped. Then, the mapped part is removed from the reaction followed by the repetition of pairwise MCS identification until no more atoms can be mapped.
Notice that the number of dynamic bonds is counted using the Dugundji-Ugi matrix  [30]  , which requires a fully balanced reaction equation. For this reason, RDTool performs some data pre-processing including reaction balancing. This, unfortunately, cancels previously performed reaction standardization. It should be noted that any mapper assigns to the atoms the numbers (hereafter, called "AAM numbers") further used in the reaction SMILES or RXN file to identify atom-to-atom mapping. Among the benchmarked mappers, only ChemAxon mapper uses the initial ordering of atoms as AAM numbers. All benchmarking calculations were performed on a reference Golden dataset containing 1851 manually mapped and curated reactions.
Optimized RDTool and consensus solutions Since RDTool is an open-source software, we decided to refactor the code in order to use independently three out of four constituent algorithms: Min-sub (MIN), Max-sub (MAX) and Mixture-MCS (MIXTURE) models. The fourth algorithm was omitted due to the code fuzziness. The code was also revised in order to accelerate the functioning of the program. This new version of the program was named new RDTool. In complement to individual AAM tools, six consensus scenarios were considered: 1 ).
Data The data set used in this study as a reference resulted from the merging of curated dataset used in the benchmarking study by Jaworski et al. [34]  and a subset of reactions extracted from USPTO database. The first set contains reactions with full stoichiometry and taken from the Organic Syntheses collection  [35]  , randomly selected and typically mostly stoichiometrically unbalanced reactions from Reaxys  [36]  , mechanistically complex reactions taken from various literature sources (e. g. , Kurti's "Strategic Application of Named Reactions in Organic Synthesis"  [37]  or Grossman's "The Art of Writing Reasonable Organic Reaction Mechanisms"  [38]  ), relatively simple reactions with full stoichiometry taken from total syntheses published in Org. Lett. , J. Am. Chem. Soc. , and J. Org.
Chem. , typical reactions without full stoichiometry extracted from patents, and mechanistically complex reactions which include rearrangements and multicomponent reactions taken from recent literature (in most cases, after 2010 and from Org. Lett. , J. Am. Chem. Soc. , and J. Org. Chem. ). A detailed description of reactions types included into the dataset can be found in ref. [34]  The second set extracted from USPTO gathers examples of the most popular reaction types used in medicinal chemistry (e. g. SN2, Buchwald-Hartwig, Suzuki-Miyaura coupling, etc. )  [39]. A manual check of 1405 organic reactions from the Jaworski's set  [34]  revealed several serious problems with structures preparation or atom-to-atom mapping. Thus, 10 reactions were identified as duplicated (see an example in Figure  S2  in Supporting Information), 30 reactions were discarded since they contained molecules with invalid valences (Figure  S3 ) or they could not be interpreted (Figure  S4 ).
The discarded reactions were analyzed manually and curated, if possible. Namely, 17 reactions with chemical structure representation errors were corrected. We've also discovered some 350 reactions containing radicals that were manually curated (see an example of such reaction in Figure  S5 ). Manual inspection of atom-to-atom also revealed several problems. Eight reactions containing obvious errors (e. g. , one carbon atom in a reactant corresponded to both oxygen and carbon atoms in a product simultaneously) were manually curated (see an example in Figure  S6 ). On the other hand, 22 reactions with nonobvious mapping which may depend on experimental conditions (see Figure  S7 ), and 3 reactions with unclear reaction mechanisms or lost reactants (see Figure  S8 ) were removed. Curated Jaworski's dataset containing 1382 reactions was extended with 469 manually mapped USPTO reactions.
The final reference ("Golden") dataset comprises 1851 reactions. Its composition has been analysed with the help of the NameRxn (NextMove) tool. The tool failed to classify 628 reactions. Occurrences of particular reaction types for remaining 1164 reactions are given in Supporting Information (see Figure  S9  in SI). In addition, the entire USPTO database  [25]  containing some 1. 3M unique and standardized reactions  [40]  (functional groups standardization, dearomatization, valence checking, selection of major tautomers, removal of explicit hydrogens, cleaning radicals and isotopes, clear stereo, split of ions, aromatization, duplicates removal) was used in order to demonstrate the functioning of AAM Fixer.
Benchmarking results 15 mapping strategies described in the Method section were applied to the Golden dataset. As it follows from Figure  4 , ChemAxon is the fastest tool: it takes approximately 0. 003 second to map one reaction. NameRXN (NextMove) and RXNMapper follow ChemAxon with 0. 02 and 0. 05 seconds per reaction, respectively. RDTool is the slowest one: it takes around 2. 46 seconds to map one reaction. This is not surprizing since RDTool executes four mapping algorithms followed by calculation of seven metrics used to perform consensus prediction. Revision of its code significantly accelerated the mapping procedure: the new RDTool including three AAM algorithms (MIN, MAX, and MIXTURE) is about 40% faster than the original tool. Figure  5  and Table  1  report an information about AAM performance of different tools and algorithms assesed for the Golden set.
However, this tool has a serious drawback: it resets any previously performed standardization operation and reorders the reactants and the products (Figure  S10 ). The original RDTool succeeded to map 76. 23% of reactions in the reference data set, which is very similar to the new RDTool (76. 18%). The code revision performed in this work allowed us to significantly reduce the time of computations (see Figure  4 ) and to apply MIN, MAX, and MIXTURE algorithms separately (see Method section). Comparing performances of the new RDTool and its constituting individual mapping algorithms, we found that the MAX algorithm already provides a 73. 96% accuracy, which corresponds to 97. 1% of the total number of reactions correctly mapped by the new RDTool. Thus, the MAX algorithm is almost as accurate as the entire RDTool but is 5 times faster.
Among all examined consensus solutions, only Consensus 3 combining RXNMapper, MAX, MIXTURE, MIN and ChemAxon methods performs slightly better than RXNMapper individually (84. 06% vs 83. 74%). This negligible accuracy improvement does not compensate a 30 folds slowdown of the consensus compared to the individual solution. Besides that, Table  1  demonstrates that the lowest Chemical Distance (CD) does not always guarantee correct AAM assignment. Thus, for a large portion of reactions, erroneous and correct mapping correspond to equal chemical distances whatever the mapping algorithm is. Moreover, in some Diels-Alder reactions, the lowest CD corresponds to erroneous AAM (see Figure  7 ). Another question concerns the difference in the AAM algorithms implemented in the benchmarked tools. Table  1  shows that the number of reactions correctly mapped with only one selected algorithm is negligible except of that for RXNMapper which is the only tool performing correct mapping for esterification and esters hydrolysis reactions on the Golden dataset.
The latter, however, wasn't reproduced for the entire set of USPTO reactions. It is also surprising that according to our tests, the tool perfectly maps reactions of some particular types in the Golden dataset, but it fails on those from USPTO  [25]. In particular, this concerns reduction of carboxyl groups to alcohols (89% reactions of this type in USPTO were erroneously mapped), methyl ester formation in methanol (31%), and esterification reactions (10-30%). c Chemical Distance for the erroneously mapped reaction is equal to that with correct AAM. d See description of the consensus strategies in Section 2. 1. e not relevant for consensus strategies.
Correction of erroneous mapping with the AAM Fixer In order to investigate the efficiency of the AAM Fixer tool, it has been applied to the mapped Golden dataset aiming (i) to extract the fixing rules and (ii) to correct erroneously mapped reactions. For 547 (out of 1851) reactions incorrectly mapped by ChemAxon, 535 unique fixing rules were extracted. Out of those, only 442 rules have been validated (see section 2. 3) which allowed us to correct 452 reactions with erroneous mappings. Interestingly, that the rules derived for ChemAxon mapping allowed us to correct 62 reactions from the Golden set erroneously mapped by RXNMapper. This proves the transferability of the fixing rules. Unfortunately, it is impossible for the moment to extract fixing rules for reactions mapped by RXNMapper since all considered here AAM algorithms except ChemAxon mapper change the atoms' order in the RXN file (see Section 2.
3). Finally, AAM Fixer powered by the rules, extracted for the Golden dataset, was applied to the USPTO dataset mapped by RXNmapper. This led to AAM corrections for 2021 reactions. Such a small portion of fixed reactions is caused by the fact that rules are quite selective due to the RC environment of radius 1 (RCclose). The advantage of the AAM Fixer is its ability to fix AAM in cases when the chemical distance for a wrong AAM is the same or even lower than for a correct one, like Diels-Alder or esterification reactions (Figure  S13  in Supporting Materials). Notice that the fixing rules can be prepared manually and then implemented in the AAM fixer. Here, the reaction itself is on the left side, and the corresponding CGR is on the right side. The symbol "n>m" corresponds to the transformation of bond order n to m  [33].
Conclusions Benchmarking study comparing the performance of Atom-to-Atom Mapping of 5 popular tools (RXNMapper, ChemAxon, NameRXN, Indigo, and RDTools) and several algorithms and consensus strategies applied to the "Golden" dataset revealed that no one provides with an ideal AAM assignment. RXNMapper is ranked the best with the accuracy 83. 74% and a reasonable speed of calculations (0. 05 seconds). However, the deep learning model integrated into this tool looks overfitted because it fails to map certain types of USPTO reactions although very similar reactions from the Golden set were perfectly mapped. It has been demonstrated that the lowest Chemical Distance doesn't always guarantee correct AAM assignment. Thus, for some Diels-Alder reactions, erroneous mapping corresponds to smaller CDs compared to correct AAM.
Data availability. The manually mapped "Golden" dataset containing 1851 reactions, the USPTO database mapped with RXNMapper, standardization workflow and the optimized RDTool (new RDTool) are available on GitHub: https://github. com/Laboratoire-de-Chemoinformatique/. The AAM Fixer tool with embedded rules is available on GitHub: https://github. com/cimmkzn/AAMFixer.