dataesr/scientific-paragraphs-categorization huggingface dataset:
The CheckYourMeal! App and the NLG service CheckYourMeal! is an iOS app 2  (developed in the Swift functional language) designed to present the result of the reasoning on food and diet in terms of both graphics and textual messages. The app is a prototype currently in a development stage. In Fig. 2  we report three screenshots of the app. The user interface is structured in three sections: the Home section, where the users are provided with general information, the Menu section, where the users can see the suggestions for the next meal and where they can input the chosen meals, and the Profile section, where the users can modify settings and user parameters. In the Menu section (central screenshot in Fig. 2 ), the user is presented with some suggestions of meals taken from a precompiled database of menus which are ordered by their distance from the ideal values of the dietary reference values.
When a user selects a specific menu, CheckYourMeal! shows both (1) a pie-chart and (2) a textual message which contains information about the macronutrients values of the chosen menu. The server (DietManager service in Fig. 1 ) is written in Java and it uses the Spring framework to communicate with the CheckYourMeal! app. The server calls the NLG service as an external Java library compacted into a single jar file. The NLG Service is composed by two submodules: (i) a monolithic rule-based documentsentence planner  (Anselma and Mazzei, 2017)  and, (ii) a bilingual English/Italian realizer defined over the SimpleNLG-it library  (Mazzei et al. , 2016). The entire NLG service has been developed by using the clojure language, that is a functional language running over the JVM. The document and sentence planner follows simple fixed schemata.
All messages will be composed by two parts: an overall evaluation of the dish and three evaluations for carbohydrates, lipids, proteins. The sentences generated for expressing the appropriateness of the specific macronutrients are positive copula sentences with a predicate expressing the deviation (i. e. rich/poor/perfect), and a PP modifier specifying the macronutrient (e. g. in lipids). Moreover, an adverb (e. g. lightly) distinguishes distinct deviations from the optimal choice. Note that the sentence plans generated are, apart from the lexicon, independent from the language used. So, the user selection of the language for the produced messages (English or Italian) corresponds to use a different realizer class of SimpleNLG-it. The actual implementation of the generator allows to select other two features concerning the lexicon (fixed or variable) and the aggregation strategy (VP and set aggregation).
Model evaluation For model evaluation, we compare the modelled annual mean climatic fields, namely surface temperature and precipitation rate, to an observation-derived dataset. For this, we use a 1970-1999 climatological mean of annual surface temperature of the ERA-Interim reanalysis  (Dee et al. , 2011)  and the long-term mean climatology of annual precipitation of CRU CL-v2  (New et al. , 2002). We use ERA-Interim on the 0. 125 • × 0. 125 • resolution for the whole Northern Hemisphere, whilst CRU CL-v2 covers whole continental areas on a 10 grid. We use bilinear interpolation to generate these data on the NH40 grid. For diagnostic purposes, we also aggregate these data on the T21 grid with the same grid correspondence already used in  Roche et al. (2014).
The model, however, presents important biases in some places. In particular, eastern Siberia, the southern part of the Rocky Mountains and eastern North America are wetter than the CRU CL-v2 dataset. Conversely, the q q q q Figure 5. Normalized Taylor diagram on the ERA-Interim annual mean surface temperature for the standard CTRL experiment (red) and a selected downscaling experiment (with z q = 2000 m, α min q = 0. 8 and f s = 0. 6) (blue). The circles depict the score when all grid points are considered, whilst the triangles stand for points with an elevation greater than 800 m. The filled symbols correspond to the Taylor diagram computed on the high-resolution grid, whilst the empty symbols are for the T21 grid. In this figure, the metrics (standard deviation, correlation and root mean square error) are computed from the annual mean climatic variables.
This is particularly visible in the very narrow band (less than 200 km) of the extremely high precipitation rate on the western part of North America and along the Norwegian coast in the CRU CL-v2 dataset. Because, we do not take into account the winds in our approach, the main effect of the downscaling is to redistribute the precipitation according to the local topography within a native T21 grid cell. In order to better resolve the fine-scale structures, a redistribution of precipitation according to the wind direction could be a significant improvement in future versions. Over Greenland, the pattern obtained with the down- scaling is much better than in the standard version with an increased south to north precipitation decrease (Fig. 8 ). Although the northern part of Greenland is still wetter than the observations, it is drier than in the standard version of the model.
Acknowledgements. This is a contribution to ERC project AC-CLIMATE; the research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  339108. Didier M. Roche  acknowledges financial support from the  CNRS. Christophe Dumas  and  Didier Paillard  acknowledge financial support from the  CEA. The authors gratefully thank  Mathieu Vrac  and  Kenji Izumi  for fruitful discussions, and  Brett Metcalfe  for proof-reading. We thank  Jeremy Fyke  as well as one anonymous reviewer for their insightful comments. We acknowledge the  Institut Pierre Simon Laplace  for hosting the iLOVECLIM model code under the LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus). Edited by: Gerd A.
Code availability. The iLOVECLIM source code is based on the LOVECLIM model version 1. 2 whose code is accessible at http:// www. elic. ucl. ac. be/modx/elic/index. php?id=289 (UCL, 2018). The developments on the iLOVECLIM source code are hosted at http: //forge. ipsl. jussieu. fr/ludus (IPSL, 2018), but are not publicly available due to copyright restrictions. Access can be granted on demand by request to D. M. Roche (didier. roche@lsce. ipsl. fr) to those who conduct research in collaboration with the iLOVECLIM user group. For this work we used the model at revision 706.
It is estimated that there are between 200,000 and 1,000,000 metabolites in the plant kingdom, of which about 51,000 (as listed in the KNApSAcK database 2018-02-14,  [15] ) have been found in higher plants  [15, 16]. However, many of the known compounds have been identified only in model organisms. Although metabolomics is one of many tools available in chemical ecology, its wide compound coverage sets it apart from "classic" approaches. Metabolomics allows for new strategies to discover novel compounds and their functioning in ecosystems particularly when including non-model species  [17, 18]. Thus, there is a growing interest to apply Eco-Metabolomics to various levels of biodiversity research, ranging from individuals, populations and communities to whole ecosystems. Metabolomics allows the analysis of chemical variation among non-model organisms with regard to one or more ecological factors.
Current Research The term "Eco-Metabolomics" (or "Ecometabolomics") is not yet well established in the scientific community. However, there is a growing number of publications that use cognate terms either in the abstract or as part of the keywords (Figure  1 ). To find studies related to "Eco-Metabolomics", search terms such as "metabolomics + ecology" or "metabolomics + diversity" were used in public databases such as PubMed (Figure  1a ). Tables  1  and 2  show an overview of some selected research papers in the discipline of Eco-Metabolomics. Overview of selected research studies in the field of Eco-Metabolomics in the last decade. The table was ordered by the columns "Approach", "Interaction level" and "Non-model species". Bottom-up in the column "Approach" defines an approach typically taken by biochemists who infer from spatiotemporally fine scales such as from molecular and physiological scales within plants to spatiotemporally coarser scales.
Multiple linking steps can be performed consecutively. For example, all samples from two groups or treatments can be linked separately followed by linking both groups to obtain their consensus features. Using TOPPView, these can again be visualized and evaluated using different parameter settings for RT and m/z distances  [160]. LC/MS and GC/MS feature matrices usually include redundant information in the form of adducts, isotopes and in-source fragments. These are important for metabolite identification (see below), but can also lead to collinearity of features (linear relationships of features or fragments that belong to the same feature in the peak table). Collinearity may be a problem with some subsequent statistical analyses. For instance, in the R package CAMERA  [162] , collinearity can be reduced by aggregating features that were categorized by CAMERA into the same pseudo compound group.
With GC/MS, usually very robust capillary columns and precise ionization sources are used. This allows for rather predictable retention times and reliable spectral information and has enabled the set-up and use of large libraries such as NIST, MoNA or the Golm Metabolome Database (GMD)  [5, 166, 167]. As a result, metabolite identification is more reliable when compared to LC/MS (see below). Identification of "unknown unknowns" is facilitated by using blind source separation and strategies that avoid hard chromatographic segmentation  [168, 169]. As LC/MS data are populated with different adducts, isotopes and in-source fragments, they are composed of many redundant features belonging to the same metabolite  [170]. For peak grouping and annotation, many algorithms depend on the input of known m/z distances between common adducts, fragments and multiply charged ions.
Despite the large number of bioinformatics tools and spectral libraries to annotate and identify metabolites (Table  3 ), the LC/MS-MS data processing pipeline is still very complex and time-consuming because it still involves extensive manual data inspection  [6, 11]. For metabolite identification, comparison to purified standard spectra in reference libraries is often necessary. The use of molecular networks, libraries such as the Global Natural Products Social (GNPS) Molecular Networking database and structural matching tools allow to compare the structural similarity of "unknown unknowns" with fragments of similar compounds that share a subset of the same sub-structures  [171]. Furthermore, in this context, retention time prediction has been proposed as an additional, orthogonal property for the filtering of candidate compounds  [172] [173] [174] [175] [176].
Bioinformatics Software Tools and Workflows In many cases, bioinformatics software tools, data processing workflows and databases used in metabolomics were optimized for model species or were developed for clinical use cases. Thus, they are not directly applicable to Eco-Metabolomics studies. For non-bioinformaticians it is often hard to decide which software tools and which data sources are appropriate to the idiosyncratic Eco-Metabolomics experiments. Table  3  lists an overview of bioinformatics tools applicable to Eco-Metabolomics. Further information can be found in  [132, 157]. A major challenge in Eco-Metabolomics is that data repositories and libraries for both ecological and biochemical data are often fragmented. Some are not publicly accessible as they are owned by institutes or commercial parties.
Furthermore, primary data are not always shared with the scientific community, are restricted to project members or are lost after a paper has been published  [207]. Many biochemical databases such as PubChem or KEGG mainly contain chemical structures and information of model species. Databases such as KNApSAcK or NPASS can be used as a source of information regarding species-metabolite relations for non-model species  [208, 209]. Metabolic relationships and biochemical traits can be retrieved with databases such as MetaCyc  [210] , GMD  [166]  or BioCyc  [211]. However, currently they do not allow for scaling up to processes at ecological scales. Many Eco-Metabolomics studies rely on ecological data sources that are fragmented among countries or restricted to local floras (Table  1 ). The Plant Trait Database (TRY) agglomerates ecological traits of many different types of organisms globally and can also be used as a source in Eco-Metabolomics  [212].
However, there remains the need for federated trait databases that aggregate the information from the many small local databases  [213]. Even though there are no dedicated repositories for Eco-Metabolomics primary data, existing repositories such as MetaboLights, MetabolomicsWorkbench, MetabolomeExpress or GNPS are still rarely used to store raw profiles and to document meta-data  [171, [214] [215] [216]  (Table  1 ). Quality assurance and full reproducibility of the study are pivotal for good scientific practice  [150]. In biochemistry, there are strict rules on analytical reproducibility of experiments. These have been part of good scientific practice and quality assurance for a long time  [150]. For data processing and computational analyses, reproducibility is not always simple to achieve  [207, 217].
In the last years, bioinformatics workflow platforms have been set up to cover all the required steps of the data processing pipeline, beginning with data download from a public repository, data quality control  [224]  and the various biostatistical analyses (see above). It is vital to reproduce the data processing pipeline to allow the scientific community to get reliable insight at any level of the study  [225]. Currently, scientists often struggle with repeating certain steps due to the technical complexity of the software used. In recent years, the Galaxy workflow platform has become increasingly popular with many "omics" technologies  [226]. For Eco-Metabolomics, several existing dedicated metabolomics workflow platforms can be used, such as the Galaxy workflow systems, Workflow4Metabolomics  [192]  and Galaxy-M  [181]  as well as the KNIME workflow system which already has some mass spectrometry related OpenMS modules integrated  [227, 228].
The challenges we identified in this review are not limited to the plant domain. In fact, other domains such as metabolomics analyses of wild animals have similar challenges, e. g. , dealing with "unknown unknowns" and with non-model species. We ascertained that the term "Eco-Metabolomics" is not used widely by the scientific community. Publishing Eco-Metabolomics primary data in repositories once studies have been published will greatly contribute to make Eco-Metabolomics available to a broader scientific audience and will allow metadata studies that re-analyse results from the various Eco-Metabolomics studies in the future. However, there is still the need for a better integration between spatiotemporal scales, a closer collaboration between researchers to improve databases for both non-model species and spectral information and, thus, also among scientists around the world.
Acknowledgments: We would like to thank the  German Centre for Integrative Biodiversity Research (iDiv) Halle Jena Leipzig , funded by  DFG  grant  FZT-118 , for supporting the workshop that allowed 32 researchers to come together and discuss current challenges in  Eco-Metabolomics , as well as the iDiv publication fund for covering open access costs. Kristian Peters acknowledges funding from the  European Commission PhenoMeNal  Grant  EC654241  and by the  Leibniz Association. Christian Ristok  acknowledges funding by the  German Federal Environmental Foundation (DBU). Oliver Alka ,  Steffen Neumann , and  Christoph Ruttkies acknowledge BMBF  funding (grants  031A535A  and  031L0107 ) as part of the  German Network of Bioinformatics Infrastructures (de. NBI). Onno Calf  is supported by grant number  823.
Background: The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. Results: We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role.
Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer.
Background The exponential increase of biological data (genomic, transcriptomic, proteomic)  [1]  and of biological interaction knowledge in Pathway Databases allows modeling cellular regulatory mechanisms. Modeling biological mechanisms is done, most of the time, using boolean or ordinary differential equation representations. Those approaches have shown their efficiency in cellular phenomena study  [2] , disease research  [3, 4] , and bioproduction optimization  [5]. However, those modeling approaches cannot take into account the large amount of OMIC data. This limitation requires that the researcher preselects the OMIC data and network, adding bias to the analysis  [6]. A classical way to perform OMIC data preselection is to use differentially expressed genes  [7] , this leads to select genes by imposing common fixed thresholds while their activation threshold may be specific for each gene.
As a consequence the selected pathways may not be specific for the biological problematic. A common way to perform network preselection consists on choosing specific pathways according to the type of data and the biological problematic. Moreover, several regulatory databases such as KEGG, CBN, and Reactome  [8] [9] [10]  allow to select specific (e. g. apoptosis) pathways directly. Nevertheless, this network preselection approach can hide unsuspected pathways, reducing the possibility to discover new ones. Some of the methods that identify subnetworks or network components, recognize specific pathways based on differentially expressed genes  [11]. However, this kind of approaches considers pathways independently, and does not take into account the interactions between biological compounds.
Answer Set Programming (ASP) The perfect colorations identification is implemented in Answer Set Programming (ASP)  [20]. This declarative programming approach allows us to express a problem in the form of a logic program (LP). The syntax of ASP is close to Prolog syntax because the grammatical structure of both LPs rules expresses a logical implication from the right terms of the rule towards the left terms of the rule. However, ASP semantics, which stands for the meaning of Fig. 1  Overview of the perfect colorations modeling framework. Blue boxes refer to processing steps that are detailed in the Methods section and yellow boxes refer to input/ouput data the vocabulary symbols used in each rule, allows a different type of solving mechanism. While in Prolog there is an inference process to search for an answer to a query, ASP programs allow to find all (Herbrand stable) models satisfying all the LP rules.
An ASP program consists of a set of predicates and first order logic rules of the form : 1 A0 ← A1,. , An, not An+1,. , not An+k. where Ai are atoms, i. e elements of the Herbrand base, which is composed of all the possible relations or predicates in first order logic of the LP. The Herbrand base is built by instantiating the LP predicates with the LP terms (constants or elements of the Herbrand universe). Basically, the line 1 explicits that A0 will be true if A1,. , An are true and An+1,. , An+k cannot be proven to be true (not in the Herbrand base). In ASP, a solution or answer set is a stable Herbrand model, that is, a minimal set of true atoms without variables (grounded atoms) where all the logical rules are satisfied. We give now a brief description of the ASP rules used in this study; for deeper ASP understanding, please refer to  [20, 21].
Component identification Graphs or networks built from pathway databases, such as NCI-PID  [22]  are composed of nodes that can represent proteins, complexes, genes, transcription or proteins modification events. A component is defined as a set of molecular-species nodes which are color-dependent or color-correlated. That is, by fixing the color of one molecular-species node in this component, the colors of the other molecular-species nodes can be established so that the perfect coloring constraints hold. Given a graph, it is possible to identify its entire set of components by building a correlation matrix from the perfect coloring models obtained in "Modeling perfect coloring with ASP" section for each couple of nodes. Given a couple of nodes, 3 types of correlations are possible (Table  1 ).
Maximal similarity This step computes the similarity between the components' coloring and the dataset with the experimental Table  1  Correlation matrix informing about the dependence between two nodes colorations among perfect colorations. a and b inform for each coloring combination occurrence observations present in one expression profile. Due to the perfect coloring framework and the fact that our model is based on a two-signs coloring, the nodes of a component C i will have exactly two coloring configurations, we denote them by C 1 i and C 2 i. C 1 i will be the exact reverse of C 2 i (the reverse of up is down and vice versa). We represent a dataset of experimental observations by a set of nodes in the graph with a fixed coloration obtained via a prior discretization of the experimental measurements.
Implementation To identify perfect graph colorings we used Answer Set Programming (ASP), namely clingo 4. 5. 4. The graph extraction from PID and the reduction algorithms were implemented with python 2. 7 using the package Net-workX  [23]. The components identification from perfect graph colorings were implemented in R  [24]  and python 2. 7. All the computation (graph extraction, perfect coloration identification, components identification and MS computing) were made on a standard machine.