dataesr/scientific-paragraphs-categorization huggingface dataset:
Synthetic Population Generation in Social Simulations. As mentioned in the previous section, many methods have been established to build synthetic populations. As described in the following sections, we investigated how these methods are actually used by social simulation modelers. To answer this question, we analyzed published models and identified the methods used to generate the set of simulated agents. In general, performing such analyses is time consuming because it requires the examination of the models regarding the initialization of agents and the code to identify the actual algorithm(s) used to generate agents' attributes. Following a semisystematic literature review methodology, we focused on models published solely in JASSS. This method is semisystematic because it limits the search to resources published in a single journal, while relying on the systematic framework to filter and select relevant articles and extract and analyze the content of the models.
Methodology. With the exception of the search phase, we chose to adhere to the requirements of systematic review and the PRISMA statements  (Moher et al. ). These requirements included the selection of relevant articles based on a careful reading of the titles, abstract, and/or part of the article (selection phase in Screening Subsection); definition of a content extraction framework to build a coherent set of data from the relevant articles (content extraction phase in Extraction Subsection) and systematic analysis of the recorded content using a quantitative and narrative analysis review (analysis, synthesis and review phases in Section. ). Finally, we summarized the gaps and lessons learned from the practices to identify issues and challenges in using synthetic population generation in the field of agent-based social simulation.
Nevertheless, we chose to include these papers in the analysis to identify how modelers in this context choose to obtain the value of agents' attributes and how this aspect relates to more refined synthetic population generation practices. Moreover, we analyzed articles published between the rd issue of (June ) and the nd issue of (March , the last available issue when this search was conducted). This range involved articles spread over issues, among which featured a simulation model. Despite the wide variety in the number of papers per issue, most issues were composed of a majority of papers describing a simulation model (between and ), with the exception of two special issues. Among the selected articles, focus on the synthetic population, i. e. , the main objective of the article is to present a method for generating a set of representative agents based on demographic data ).
Content extraction. In the systematic review exercise, content extraction referred to the extraction of data that served as the basis for a systematic content analysis. Explicit rules were used to format the content in a way that decreases the reader's subjectivity. The Hypothesis presented in the introduction guided the codebook used for content extraction. Specifically, we identified key aspects to answer questions regarding the procedure, input data, application scenario and adopted models. Content extraction was performed by first examining the eligibility in terms of the inclusion criteria, description of the model features and generation process. • Following the PRISMA framework  (Moher et al. ) , the first step was to establish the eligibility criteria based on the title, abstract, and sequential reading of the article to assess inclusion or exclusion in the subsequent analysis step.
The source code is o en easy to interpret and understand depending on the language / platform / toolkit used. Notably, it is considerably easier to understand a noncompiled and readable code (text file) developed with an agent-oriented language (Netlogo, GAMA, etc. ) or an object-oriented language. Within the scope of this review, the limited search for a specific journal involves certain biases. In particular, several well-known references cited in the first part of the paper come from a related field of research, i. e. , transport modeling. Such models might be under-represented in JASSS while at the same time, considerably influencing how researchers generate synthetic populations for use in ABM. It would be interesting to focus on simulation models published in journals such as Transportation Research, for instance, to gain knowledge regarding practices from a related field.
The second modeling framework is based on two-phase approaches, in which the motion equations solve both solid and fluid phases  [28] [29] [30] [31] [32] [33] [34] [35]. The common point of models based on CFD software is that the sediment bed is considered an impermeable wall (no-slip conditions). However, their modeling approaches mainly differ in treating the two-phase interaction between the fluid and the sediment bed. For example, Metzger et al. [1]  use CFD++ software to study the steady-state flow above different stationary boundary conditions (or crater geometries) but calculate the porous flow inside the sediment bed by Darcy's law and pressure values. Weidner et al. [25]  and Mercier et al. [26]  use FLUENT to dynamically adapt the computational grid into the bed's evolving geometry; however, they do not consider the sediment below the bed interface.
Numerical Technique To solve the system of Equations (  8 )-(  17 ), we use the techniques initially developed by Guillou and Barbry  [41, 48]  and later improved by Uh Zapata et al. [34, 49]. This in-house program is currently written in Fortran 90, displaying the results in Paraview software. The σ-transformation is applied to the vertical coordinates to fit the computing mesh to the free surface evolution. A finite volume formulation approximates spatial derivatives. A projection technique  [50]  is applied to calculate the pressure and velocity for each phase. We use a staggered grid to avoid the spurious oscillations induced by the projection technique  [51]. Advection terms in Equation (  9 ) are handled by a total variation diminishing (TVD) scheme. The time scheme is implicit in both vertical and horizontal directions.
Since small variations in tastant chemistry result in drastic modifications of perceived taste, ligand-based methods, merging molecular descriptors and taste information, represent powerful datadriven tools to effectively implement machine learning (ML) algorithms with the capacity to predict taste. Such methods can be applied, for example, to screen huge databases of small compounds (e. g. , ZINC15, DrugBank, and ChEMBL) to select promising tastants or to rationally drive the design of novel compounds with specific functional properties and a desired taste. Nutritious foods usually have an appetitive taste, e. g. , sweet, umami, and lower concentrations of sodium and acids, whereas toxic substances generally present an unpleasant flavor, such as bitter tastants, high concentrations of sodium, and sour taste stimuli.
Other examples linked to the importance of taste prediction include bitter masking molecules. Indeed, the bitter taste is one of the main problems for pharmaceutical industries due to its unpleasant taste, which represents one of the main barriers to taking medications, especially for children and the elderly population  [21]. Furthermore, a change in taste perception might be caused by the onset of other pathologies, such as in the case of the loss and/or impairment of taste function after COVID-19 infection  [22]. This work aims to summarize the main recent efforts in the in-silico taste prediction, starting from an overview of the major taste or food-related molecules databases and the implemented ML-based prediction tools.
Taste and food-related databases The first essential step for the implementation of ML-based tools is the definition of reliable and as comprehensible as possible databases (DBs) with information concerning the taste of each entry. In the past years, several databases of small compounds related to their specific taste sensations in foods have been developed. In this section, the authors pinpoint the major databases and their characteristics, which are summarized in Table  1. In Table  2 , other databases, which do not contain precise information regarding the taste associated with each element but are related to food ingredients and widely used in taste prediction, are reported. Most used and tested databases (DBs) are described in detail in the following paragraphs. [24]  https:// bit. ly/ 32fG9 af Sweet 316 No BitterDB  [25]  https:// bit.
ly/ 3Fins B6 Bitter 1041 Yes BTP640  [26]  https:// bit. ly/ 3pogT rj Bitter 320 Yes Non-Bitter 320 Rodgers Database  [27]  / Bitter 682 / Umami Database https:// bit. ly/ 3FheP a1 Umami 800 No UMP442  [28]  https:// bit.
SuperSweet SuperSweet (https:// bioin forma tics. chari te. de/ sweet/) contains more than 8000 artificial and natural sweet compounds  [23]. The dataset includes the number of calories, the physicochemical properties, the glycemic index, the origin, the 3D design, and other information regarding molecular receptors and targets. Sweet-tasting chemicals were taken from the literature and freely accessible data sets. The web server interface offers a very user-friendly search and a sweet tree which groups the sweet substances into three main families, (carbohydrates, peptides and small molecules).
SweetenersDB SweetenersDB (http:// sebfi orucci. free. fr/ Sweet eners DB/) is a database of 316 sugars and sweeteners from 17 chemical families. Compounds were aggregated with their 2D structure or with a 3D structure using Marvin Sketch (Che-mAxon) and the protonation state was defined at a pH of 6. 5, according to the common pH value found in the saliva. Two natural compounds of the SweetenersDB are also present in Super-Natural II (entries: 105. 620, 325. 102)  [24]. Each sweetener has also an assigned sweetness value, indicated as logS. This value is the logarithm of the ratio between the concentrations of the considered compound and sucrose, used as a reference. In this way, this value reflects the relative sweetness of a specific compound if compared to sucrose. From a physicochemical analysis of the database, an intense sweetener has low molecular weight and a hydrophobic core.
BitterDB BitterDB (http:// bitte rdb. agri. huji. ac. il/ dbbit ter. php) is a free source containing information about bitter taste molecules and their receptors  [39]. In 2019, an upgrade of the database was made with an increase in the number of compounds (from the initial 550-1041) and the insertion of new features, including for example data belonging to different species rather than humans (mouse, cat, and chicken). BitterDB contains now about 1041 molecules collected from over 100 publications. For each compound, the DB provides different information, such as molecular properties, identifiers (SMILES, IUPAC name, InChIKey, CAS number, and the primary sequence of proteins), crosslinks, qualitative bitterness category (i. e. , bittersweet, extremely bitter, slightly bitter, etc. ), origin (from a natural source or synthetic), and different file formats for download (SDF, image, smiles, etc.
). Furthermore, toxicity data were added from the Acute Oral Toxicity Database when available, reporting experimental rat LD 50 values as described in the previous literature  [40]. Most of the SMILES were taken from PubChem and the remaining ones were generated through the CycloPs server, after drawing the molecules on ChemSketch or ChemAxon. Regarding the other identifiers, the ones not available in PubChem were processed using RDKit (http:// www. rdkit. org).
BTP640 BTP640 collects 320 experimentally confirmed bitter peptides and 320 non-bitter peptides. Bitter peptides were retrieved from various literature and peptides including ambiguous residues (e. g. , B, X, Z, and U) or duplicated peptide sequences were discarded. Since few experimental data concerning non-bitter peptides are available, the negative dataset was built starting from BIOPEP dataset, which contains biologically active peptide sequences (4304) widely used in the food and nutrition field  (Minkiewicz et al. , 2019). From this dataset, 320 peptides were randomly extracted to build the negative dataset.
Rodgers database This database was collected from previous literature and patents, including studies in BIOSIS, Food Science and Technology Abstracts, databases of internal reports at Unilever and Derwent World Patents Index (WPIDS)  [27]. Structures were obtained from SciFinder, where possible, or constructed with ChemDraw. After the removal of synthetic analogs, the final database contains 649 bitter molecules. It is worth mentioning that additional 33 molecules were then considered by the authors and made public in the original paper, whereas the other 649 remain non-public. Unfortunately, no webserver or online data repository is available for this database.
Umami Database The Umami Database (https:// www. umami info. com/ umami db/) is developed by the Umami Information Center, founded with the support of the Umami Manufacturers Association of Japan in 1982. The Umami Database was created with the idea of providing information about the umami taste in foods and, currently, about 800 items are listed in the database. Amino acids in foods are mainly of two types, i. e. , ones joined together to form proteins and free amino acids, that have a more pronounced flavor. In this context, free glutamate has a remarkable umami taste. Umami Database reports also the score of free glutamate and other free amino acids which affect food taste. In addition, there are inosinate and guanylate scores, which synergistically increase umami perception. Sources for the Umami Database include public academic papers and scores analyzed by a research laboratory upon request of the Umami Information Center.
UMP442 This dataset was constructed for the development of the iUmami prediction tool  [28]. The umami set merges several experimentally validated umami peptides from the literature  [41] [42] [43] [44] [45] [46]  and the BIOPEP-UWM database  [34]. On the other hand, the non-umami peptides dataset is made by the bitter peptides from the positive set of BTP640  [47]. After removing peptides with non-standard letters and redundant sequences, the final UMP442 database collects 140 umami and 304 non-umami peptides. The dataset was made publicly available on GitHub (https:// github. com/ Shoom buato ng/ Datas et-Code/ tree/ master/ iUmami).
TastesDB TastesDB is an experimental database comprising 727 chemicals with their respective experimental taste class, retrieved from several scientific publications  [29]. Since all incorrect molecules or those with problematic molecular structures were removed, the final TastesDB contains 649 molecules, specifically 435 sweet, 81 bitter, and 133 tasteless (see also Table  S1  of the original paper for further details). For each entry of the database, the DB provides the commercial name, the SMILE, the tasting class (sweet, tasteless, and bitter), and the literature reference.
Data preparation and model construction The curated dataset of sweet compounds resulted in the creation of the SweetenersDB, which is constituted of 316 compounds with known sweetness values relative to sucrose (see Taste and Food-Related Databases chapter for further details). The compounds' SMILES were first collected in a 2D database, and subsequently, 3D representations were created using Marvin, ChemAxon (https:// www. chema xon. com), choos-ing the three with the lowest energy. The protonation state was set at the physiological salivary pH value  (6. 5). Dragon descriptors (http:// www. talete. mi. it/ produ cts/ dragon_ descr iption. htm) were calculated for both the 2D and 3D databases. All features with a correlation greater than 0. 9 were removed, obtaining 244 descriptors for the 2D molecules and 265 descriptors for the 3D structures.
Finally, all descriptors were normalized. The dataset was randomly divided with a 70:30 ratio and the leave-one-out method was used for the cross-validation. Support Vector Regression (SVR) and Random Forest (RF) were optimized on the training set, and the test set was used for the model performance evaluation. Model performance Performance evaluation was obtained using the squared of the correlation coefficient (R 2 ). Notably, the SVR reached a slightly better performance than RF on the test set. It is worth mentioning that the models on 2D and 3D datasets reached similar performances, suggesting the 2D approach as the best option for fast screening, since it is much less time-consuming. More in detail, the RF 2D, SVR 2D, RF 3D, and SVR 3D models obtained correlation coefficients on test sets of 0.
74, 0. 83, 0. 76, and 0. 85, respectively. To evaluate the model applicability domain, Sweeten-ersDB was compared with SuperSweet and SuperNatural II. Interestingly, 99. 5% of the molecules from SuperSweet are similar to structures in SweetenersDB, whereas only about 34% of the SuperNatural II database belong to the chemical space defined by SweetenersDB. This analysis confirmed the importance of associating an applicability domain to a prediction model to measure the reliability of the prediction.
Data preparation and model construction The starting dataset is TastesDB (see also Taste and Food-Related Databases chapter for further details). The dataset includes 649 molecules: 435 sweet, 81 bitter, and 133 tasteless; the latter two classes were combined into a non-sweet class. Extended-connectivity fingerprints (ECFPs)  [61]  and classical molecular descriptors, i. e. , Dragon 7 (3763 total descriptors) (https:// chm. kode-solut ions. net/ pf/ dragon-7-0/), were used to describe the molecules of the dataset. In all cases, the 2D representation was preferred to the 3D one, to get a conformation-independent molecular representation. Exploration of the data and similarity analysis was performed using the Multidimensional Scaling (MDS), whereas the Partial Least Squares Discriminant Analysis (PLSDA) and N-Nearest Neighbors (N3) were employed as classifiers.
Finally, the V-WSP unsupervised variable reduction method and the Genetic Algorithms-Variable Subset Selection (GA-VSS) technique were used as dimensionality reduction techniques to retrieve the most informative molecular descriptors. The dataset was divided into three parts, maintaining the proportion of the classes: the training set consisting of 488 compounds (161 non-sweet and 327 sweet molecules), the test set consisting of 161 molecules (53 non-sweet and 108 sweet molecules), and finally, the last part was used as an external dataset. Moreover, a fivefold CV was employed for the GA-VSS and the Monte Carlo (leave-many-out) random sub-sampling validation of the system. These methods iteratively and randomly divided the molecules into training (80%) and evaluation (20%) sets. Model performance Specificity (SP), sensitivity (SN), and non-error rate (NER), which is more efficient in the case of unbalanced datasets, were used as performance metrics.
Data preparation and model construction The dataset was collected from several publications  [63] [64] [65] [66] [67] , resulting in 487 unique molecules with relative sweetness compared to sucrose (ranging from -0. 699 to 5. 334) calculated as described for the SweetenersDB (see also the Taste and Food-Related Databases chapter). Compounds SMILE were converted to 3D structures and Material Studio v6. 0 was used to calculate 564 molecular descriptors. After performing a correlation analysis, only 61 descriptors were maintained and, after removing outliers, the remaining 455 molecules were randomly divided between the training and test sets (70:30 ratio). Two QSAR models were developed using Artificial Neural Network (ANN) and Genetic Function Approximation (GFA) algorithms. = 0. 832) were comparable, and both QSAR models demonstrated a reasonable prediction accuracy.
Data preparation and model construction The entire dataset includes 1380 compounds, divided into sweet and non-sweet. Sweet compounds are 530 sugars curated from SuperSweet, SweetenersDB and previous literature  [29, 58] , while 850 non-sweet comprise 718 entries from BitterDB and 132 recovered from the literature  [29]. The 80:20 data splitting scheme was adopted, resulting in 883 compounds for training and internal fivefold CV and 221 compounds for the test. Features were built using Extended-Connectivity Fingerprints (ECFP)  [61]  and subsequently selected by their importance in a trained RF model. Implemented algorithms comprised KNN, SVM, GBM, RF, and DNN with different splitting procedures (19 different splits for the former four models and 3 different splits for DNN) to reduce the bias yielded by specific splits.
Data preparation and model construction The tool is based on the SweetenersDB, collecting 316 compounds with their relative sweetness (see also Taste and Food-Related Databases chapter)  [24]. Two other databases, namely Super-Natural II database  [31]  and the phyproof catalog from PhytoLab (https:// www. phyto lab. com/ en/), were considered as an external dataset (4796 natural compounds). Each compound was collected as SMILE and sanitized using RDKit (https:// www. rdkit. org/). The protonation state was predicted using ChemAxon (http:// www. chema xon. com/) at the physiological pH of saliva (pH = 6. 5). Molecules were standardized using the flatkinson standardiser (https:// github. com/ flatk inson/ stand ardis er) and further processed with a Python package, removing salts and applying specific rules to normalize the structures.
Data preparation and model construction The interactions between the TAS2Rs and bitter compounds were curated from PubMed and BitterDB. A total of 539 bitter compounds were obtained to constitute the positive set, and their molecular structures were achieved from Pubchem. The negative set included 20 true non-bitterants (in-house experimental validation) and 519 molecules from the Available Chemicals Directory (ACD, http:// www. accel rys. com). The final dataset contained 1078 compounds, equally divided into positive and negative bitter molecules. Molecular structures were obtained from PubChem and processed with in-hose program Checker and ChemAxon's Standardizer (http:// www. chema xon. com). On the other hand, the initial dataset for the TAS2R prediction model was taken from the literature and includes 2379 negative and 260 positive bitterant-TAS2R interactions.
Due to the huge difference between the two dataset sizes, all the 260 non-redundant experimentally verified bitterant-TAS2R interactions were considered, while just 260 bitterant-TAS2R couples were selected as negatives to balance the dataset. Physiochemical descriptors for compounds were chosen based on the Handbook of Molecular Descriptors  [52] , resulting in 46 and 20 descriptors for the bitterant verification and TAS2R recognition models, respectively. Moreover, 15 descriptors were used for the TAS2R representation in the TAS2R recognition model. The descriptors were chosen using a Feature Selection (FS) method based on a Genetic Algorithm (GA). BitterX employs the support vector machine (SVM) classifier: the training was divided into two categories (+ 1 and -1) that represent the classification between bitter and non-bitter (and the bitterant-TAS2R interaction or not).
The SVM + sigmoid method was implemented to value the probability that a molecule is bitter in the bitterant authentication and the probability of a bitterant binding to TAS2Rs in the TAS2R recognition model. Data for algorithm training and test were used by adopting an 80:20 splitting strategy. For the bitterant verification model, this results in 862 and 216 compounds for training and test, respectively. To avoid any error derived from a particular data splitting, the other two partitions were made randomly from the general database, always following the equipartition between bitter and non-bitter molecules. Similarly, the TAS2R recognition dataset was also divided with a 4:1 ratio. Finally, a fivefold CV ensured the robustness of the classification algorithm.