dataesr/scientific-paragraphs-categorization huggingface dataset:
F3 31 is hard to assess, since there is no consensus on the appropriate vocabulary nor on the associated cardinality constraints aimed at unambiguously identifying data. Regarding I2 (see Table  1 ), although recent propositions concern the FAIR assessment of ontologies  [27] [28] [29] , to our knowledge, the resulting strategies have not yet been integrated into registries such as BioPortal  [30]  or FAIRSharing  [31]. We are aware that our list of terms to be evaluated by FAIR-Checker is limited, albeit founded on common usage in the Semantic Web community as well as more focused efforts such as HCLS  [32]  or FAIR data points  [33]. We propose to capture and discuss specific community needs thanks to GitHub issues that can be directly submitted from FAIR-Checker. A more general approach would consist in leveraging crosswalk resources  [34]  which propose semantic mappings between community specific metadata terms.
We envisage as future work to consume these metadata alignments to automate the enrichment of the list of metadata terms considered in FAIR-Checker. Specific community efforts such as (e. g. Codemeta crosswalks 32 is key for an increased adoption During the last 6 monthstools. During the last 6 months 33 , our national FAIR-Checker server 34 evaluated 28,464 unique resources, and performed 374,595 automated tests. This tool is used as part of the training courses of the French Institute of Bioinformatics, targeting biologists and bioinformaticians. Its national deployment will be accelerated by the high expectations of national programs for the development of Open Sciences. In line with the Bioschemas initiative, which addresses the issues of metadata discoverability, quality and maintenance, FAIR-Checker already targets a large international community of users and developers.
Although the notion of ontologies for the classification of biological objects and their annotation with a controlled terminology is well-known in the Life Science community, the underlying Knowledge Graph standards and technologies, such as RDF, SPARQL or SHACL, are still unfamiliar and difficult to learn. In the context of more open and reproducible sciences, evaluation tools such as FAIR-Checker allow building bridges between these technologies and large communities. The genericity of our approach through template SPARQL queries and template SHACL shapes makes it possible to evaluate FAIR principles and more deeply community-specific metadata profiles beyond Life Sciences. To further promote the adoption of Semantic Web technologies by large communities, progress on identification mechanisms and identity relations must be made.
Although this represents a challenging task, an increased use of sameAs links in web page metadata could make it easier to exploit public Knowledge Graphs and, as a result, reduce the cost of annotating individual web pages. The popularity of lightweight ontologies dedicated to resource discovery on the Web (e. g. Schema. org) raises concerns about the quality of these semantic annotations, which are widely available and distributed on the Web. The work on metadata profiles and validation (SHEX, SHACL) is particularly interesting in light of the widespread adoption of FAIR principles in Science. In practice, these technologies would allow for the prioritization 33 Last statistics computed the 01/06/2023 34 https:// fair-check er. france-bioin forma tique. fr 29 https:// biosc hemas. org/ profi les/ Compu tatio nalTo ol/1.
0-RELEA SE 30 A2: Metadata should be accessible even when the data is no longer available 31 F3: Metadata includes the identifier for the data 32 https:// codem eta. github. io/ cross walk/ of the metadata required for resource annotation based on specific community needs. To this aim, we intend to make available the generated SHACL shapes so that they can be inspected by developers, shared on the web or evaluated with other frameworks. The development of FAIR-Checker, as well as initial user feedback, have highlighted important expectations to support the advancement of Open Science. FAIR-Checker is still actively developed. A RESTfull API, aimed at serving FAIR assessment for other online tools, has recently been deployed. In future works, we aim at better addressing the diversity of metadata delivery methods, through content negotiation for instance, as well as better and more efficiently leveraging public Knowledge Graphs.
Several papers have suggested machine learning (ML) approaches as an alternative to polynomial regression. The approaches that are considered in the literature include Support Vector Machines (SVM)  [1] [2] [3] [4] [5] [6] , Neural Networks (NN)  [1, 2, 4, [6] [7] [8] [9] , Random Forests (rf)  [1, 3, 4, 10] , Boosting and its extension  [1, 3] , Extra Tree regression (ET)  [3, 4]  and Classification And Regression Trees (CART)  [1]. In these papers, the authors compared some of these approaches only on real datasets, using specific settings with respect to the nature and dimensions of the data. As well as focusing on a specific dataset, the comparisons considered very few approaches at the same time and were based on very different metrics. In this work, we will mainly consider the contribution of ML with respect to the RSM; these approaches have been shown to be very efficient in various fields, such as ecology  [11]  and epidemiology  [12].
This paper is organized as follows. Section 2 gives a summary of the polynomial RSM-based approach and a brief description of the most well-known ML approaches. Section 3 gives the state of the art for the applications of these approaches in various domains where DoE is practiced. In Section 4, we compare all of the ML approaches to polynomial regression on simulated multiple output datasets, varying sample sizes and optimizing the models' hyperparameters. A real dataset is used for similar experiments. The last section includes our discussions and conclusion.
Ensemble Methods The idea of ensemble methods, in their simplest approach, is to build K bootstrap samples of the dataset at hand, adjust a model chosen from within a class of functions (e. g. , decision trees) for each bootstrap sample (denoted f k ), and then use the K models for predictions. In regression, the prediction by the ensemble is a weighted average of the K predictions given from each f k. In classification, it is a weighted majority vote over the K predictions given by the models f k. Several variants of ensemble methods exist, differing either in the way in which the bootstrap samples are generated, in the estimation of each f k , in the way in which the individual models f k are combined, or in the algorithm used to estimate the whole process. Random Forests (rf;  [19]  are among the most well-known and used approaches.
This approach combines trees built over bootstrap samples by simple averages in regression and majority vote in classification. The trees that are built in rf may be very big, because they are not optimized, and the choice of the splits at each node in the trees is made on a small subset of variables chosen at random. Because bootstrap samples are independent, random forests may be parallelized. Boosting  [20]  is quite different because the trees built over bootstrap samples are obtained sequentially; weights w i are associated to each observation in the original dataset, and at each step of the algorithm, a tree f k is built and tested over the original dataset. Observations whose prediction at step k is wrong will have their weight increased. The modified weights are used at the next step k + 1 for the next bootstrap sample.
Support Vector Machines Support vector machines  [23]  seek a linear separation of observations, like linear regression, but minimize a loss function based on the margins. In binary classification, the optimal regression is the hyper-plane, which perfectly separates the two classes and stays the farthest distance possible from its nearest points from the two classes. This hyper-plane may be easily found if the classes are linearly separable. If they are not, then the dataset is projected using nonlinear transformations in a much higher space where linear separation may be guaranteed. The linear transformations that are used in this case are expressed using kernel functions, among which the most commonly used are polynomial and radial kernels. These kernels, together with the optimization algorithm used to find the optimal hyper-plane, depend in general on several hyperparameters that must be fixed or tuned by the user.
Once the structure of the network is specified (number of layers and number of neurons per layer), all of the weights of the NN that are unknown are estimated. To achieve this, a loss function should be specified (mean squared error for regression), together with an optimization algorithm (typically gradient-based algorithms). In some situations, specific activation layers are added to the networks, mainly for the last layer (softmax for multi-class learning, sigmoid for multi-label learning). Neural networks are among the most difficult to tune because of the large number of hyperparameters that may be involved in the structure of the network and in the choice of the optimization algorithms. For small datasets, they may be unstable due to random initializations of the weights within the training.
The results obtained show that all ML models outperform the polynomial models of the RSM approach. The authors also note that for larger training set sizes, nonlinear ML models were more accurate. A simulation of the data by the MC method is proposed to improve the estimation of the response surface function. Comparisons made in this work are based on a real dataset with large samples; the true underlying relation between the response and the factors is unknown. Meanwhile, eight original responses were combined to get a one-dimensional output. In  [2] , ML models were used to predict coating thickness in a nonlinear electrostatic spray deposition process and compared with the conventional RSM model. Three continuous factors and one continuous response were considered with 30 observations. A polynomial model of degree 2 (RSM), NN (Back-Propagation algorithm  [27] ) and Support Vector Machine models were used.
This model also had the greatest R 2. Hybrid regression and ML models were tested to predict the ultimate condition of fiber-reinforced polymer-confined concrete  [5]. The authors used an open database with eight continuous factors, two continuous responses and 765 observations. The authors compared some existing physical empirical models developed by other authors with the RSM and SVR models, together with a hybrid model combining SVR and RSM models. The following metrics were used to evaluate these models: RMSE, MAE, Nash and Sutcliffe Efficiency (NSE) and agreement index d. The authors show that the hybrid model presents a lower RMSE, lower MAE, higher NSE and higher d than other models. Similar comparisons with ML approaches may also be found in other fields, such as pharmaceutical development.
The objective of the authors of  [10]  was to apply RSM and ML combined with data simulation to estimate metal recovery from freshwater sediments. In this work, three continuous factors were studied, six continuous responses were measured and 18 experiments were carried out. A polynomial model of degree 2 was estimated and used for data simulation. ML models, namely Lazy KStar [33] and rf algorithms, were also tested. A comparison of the models was made by means of RMSE and coefficient of determination. The results show that RSM models overestimate the observed responses by 19% compared to ML models. Ref. [8]  compared RSM and ML approaches (NN) to predict the efficient extraction of artemisinin (a precursor molecule of the most powerful antimalarial drugs on the market). In this work, three continuous factors were studied, one continuous response was measured and a central composite design was constructed with 20 experiments.
For the RSM approach, ANOVA was calculated and shows that the variability of the response cannot be adequately predicted by the RSM model depending on the factors studied. This result can be explained by a complex relationship between variables. Multiple Layer Perceptrons (feed forward NN) were also tested. A comparison between these two models was performed by means of Absolute Average Deviation (AAD). The authors concluded that the NN model has better prediction accuracy. Ref. [9]  analysed NN models as an alternative modeling technique for datasets showing nonlinear relationships, using data from a tablet compression study. In this work, six continuous factors were studied, two continuous responses were measured, and 102 experiments were performed. The authors calculated a polynomial model of degree 2 with only important terms (p-value < 0.
For the real case study, we conducted similar experiments as for the simulated dataset, replacing random generation with random splitting. Thus, for each of the 50 repetitions, the original dataset was randomly split using proportions 0. 7 and 0. 3 for learning and testing, respectively. The results that we obtained are given in Table  7. The best models with respect to the RMSE were xgb for Y 1 , gbm for Y 2 , multivariate NN for Y 3 , NN for Y 4 and knn for Y 5. For the overall error, multivariate NN had the smallest error. For this real dataset, and for all responses, ML models gave better results than the RSM approach.
Conclusions In this work, using extensive simulations and a real use case, we have demonstrated that ML approaches present a very interesting alternative to response surface modeling in the DoE. We have tested a large panel of ML approaches, together with direct multi-output regression models (decision trees and NNs), which had never previously been used in this context. Clearly, various ML approaches outperformed RSM in all our experiments. RSM is very simple to use compared to ML approaches, due to the fact that several hyperparameters must be tuned correctly in ML algorithms. This requires optimization procedures over grids. The caret package is a good wrapper for this task and for most of the approaches that we have used, except for multi-output regression, where we implemented the grid search algorithm directly.
Data collection and analysis The questionnaire was carried out from mid-August 2022 to mid-December 2022. The answer sheets were collected and carefully checked to exclude inappropriate sheets (e. g. , missing or inappropriate answers). The collected data was used for analysis using SEM (structural  As reported by  Sarstedt and Cheah, 2019, compared  with VisualPLS and PLS-Graph, SmartPLS is rated excellent for ease of use, user-friendly interface, safety, and high reporting quality. In quantitative research, PLS-SEM is the most recommended because of its advantage over other methods  (Chin, 1998) , particularly suitable for models that have complex relationship structures (direct, indirect, mediation, and moderation)  (Hair et al. , 2019). This technique is widely used in the scope of social science studies in which this research is relevant.
Methods Ethics. All studies received ethical approval from Newcastle University Research Ethics Committee (approval 16509/2021). Pre-registration. Although this study was exploratory, we pre-registered methods, materials, and planned analyses at: https://osf. io/8ba5m. Participants. Participants (200 UK resident adults with first language English) were recruited via online platform Prolific (www. prolific. co). Mean age was 39. 9 years (SD 13. 0), with 104 women and 96 men. Participants were mostly non-students (145 non-students, 22 students, 33 not stated), in employment (124 employed full or part-time, 32 not currently employed, 34 not stated). Participants received £2. 50 for taking part. Six participants were excluded for failure to fully complete the survey, leaving 194. Design. Participants were allocated, in a between-subjects design, to one of four different brief vignettes, and one of two different response conditions (similarity or compatibility).
7. Observed correlations for each participant were lower than anticipated (similarity condition: mean r = 0. 39; compatibility condition, mean r = 0. 43). However, the correlations between the across-participants average rating the first time and the second time were extremely high (similarity condition: r = 0. 93; compatibility: r = 0. 79). We therefore adjusted our pre-registered exclusion criterion and excluded any participant who lacked a significant positive correlation between their first set of ratings and their second. This excluded 58 participants, leaving 136. In the post-exclusion dataset, the correlation between acrossparticipants average ratings the first and second time were r = 0. 96 (similarity condition) Data Analysis. Data and code for all studies are available at: https://osf.
io/wte2c/. Similarity ratings for pairs of framings were well correlated across vignettes (inter-vignette correlations 0. 68-0. 88), as were compatibility ratings (inter-vignette correlations 0. 67-0. 84). All subsequent analyses were performed on the data pooled across vignettes. First, we used multidimensional scaling to create a map of rated similarity, using R package 'smacof' (see  Jones et al. , 2018). We then used the configuration matrix from the multidimensional scaling output to perform k-means clustering analysis to detect clusters in the similarity space. We determined the optimal number of clusters to extract using the function fviz_nbclust() from the 'factoextra' R package  (Kassambara & Mundt, 2020). We then repeated the multidimensional scaling and clustering for rated compatibility.
Six of the attention checks show the right explanation (True), and six showed a different one (False). Data Analysis. As in Study 1, we pooled results across the four vignettes. Our first set of analyses used individual participant ratings as the unit of analysis. The within-subjects scaling removed the need for random intercepts by participant (since every participant had a mean rating of 0 with a standard deviation of 1). Using MANOVA and general linear models, we examined whether ratings differed by framing. For our second set of analyses, we calculated the mean malleability, externality and simplicity per framing; hence, the framing is the unit of analysis for these models. We grouped the framings into the similarity clusters observed in Study 1. We used MANOVA and linear mixed models (with a random effect of framing) to examine whether the mean malleability, externality and simplicity differed by framing cluster.
Methods Pre-registration. We pre-registered methods, materials, and planned analyses at: https://osf. io /aqsz3. Participants. Participants were recruited via Prolific as per Studies 1 and 2. We pre-registered a sample size of 320. However, due to an error, the participants in the 'no explanation' condition were not shown the Study 3B questions (for Study 3B, see below), leading us to add 80 more participants in the 'no explanation' condition. Preliminary inspection of the data after the 320 + 80 participants suggested we were under-powered to determine what was happening at the individual explanation level. We therefore added another 320 participants as per the original design. Post-hoc increases in sample size inflate type-I error rates. We therefore recalculated the critical p-values required in the final sample to keep overall type-I error rates at 0.
05, using the p crit formula  (Sagarin et al. , 2014) , for one additional round of data collection, and assuming that any p-value less than 0. 2 would have led us to collect more data. This corrected α was 0. 04, which should therefore be considered the appropriate α for the results presented below. Design. We used vignettes from Studies 1 and 2, homicide and teenage parenthood. Each participant saw one vignette. In Study 1, there were unequal numbers of framings in the three clusters: three 'biological', four 'psychological' and five 'sociocultural'. To equalize the representation of clusters, we added an additional biological framing ('brain circuits'), and omitted the 'childhood' explanation (from 'sociocultural'), producing four framings per cluster. The probability of being assigned to the 'no explanation' condition was four times that for every other condition.
| Data The data used in our case study come from the 2017 agricultural production methods survey (SAPM) 16 in France, managed by the French Ministry of Agriculture. SAPMs are surveys to inform agricultural practices, mainly fertiliser use and pest control. The surveys are conducted every 5 years on randomly selected plots in France for various crops. In addition to inputs used and crop output, the database also registers information about crop rotations and other practices (tillage, irrigation, sowing, etc. ). 17 For homogeneity, we focus only on plots where winter wheat was produced in 2017. The single good output (   ) is the wheat produced on the plot. The inputs are measured at the plot level. The three inputs are plot area in hectares, treatment frequency index (TFI), and mineral nitrogen in kilograms.
In Switzerland,  Schmidt et al. (2021)  calculated nitrogen surpluses depending on the farm's main specialisation, ranging from 37. 5 kg (for mixed dairy and arable farms) to 157. 6 kg (for specialist dairy farms) per hectare. In Italy,  Semaan et al. (2007)  indicate figures for nitrogen application and leaching 15 The trade-offs shown in Equation (  12 ) are not considered here, as in our empirical framework we only have one polluting input. 16 'Enquêtes pratiques culturales, enquêtes PK'. 17 More details on the SAPM surveys can be found in https://www. casd. eu/en/source/field-crop-cultural-practices/. 18   Jacquet et al. (2011)  also computed nitrogen excess with data from a similar survey in 2006 but did not account for pre-crop nitrogen stock. 19 See FAOSTAT. that amount to a nitrogen balance between 89 and 53 kg per hectare of wheat, depending on the type of management.
| Marginal abatement cost (MAC) As presented in Equation (  20 ), the MAC of excess nitrogen is obtained as the minimum value of all possible shadow prices. Computing the shadow prices requires output and input prices not available in the database. Therefore, official statistics are used: we set wheat price  (  ) to €154 per tonne  (Oppel, 2017)  and land price to €7420 per hectare. 24 Due to the nature of the pesticide input, the number of treatments (TFI), and not the actual volume (and toxicity) of pesticides, prices for this input were not considered. For this reason, the shadow prices are computed using only wheat and land prices. For all the observations, the cheapest strategy to reduce excess nitrogen is to reduce wheat production rather than purchasing additional land. Table  3  summarises the MAC distribution and shows that the wheat plot sample's median MAC of excess nitrogen is €20.
Although our database did not allow this, accounting for all forms of nitrogen input applied on plots would give a comprehensive view of farmers' behaviour and help better target policy design. It should be kept in mind that our analysis was conducted at the plot level, whereas policies (including the European Union's Nitrates Directive) are at the whole farm level. The latter implies that setting a limit on the farm's total use of fertilisers does not necessarily mean that all farm plots comply with this limit. However, it makes sense from an agronomic and environmental point of view to consider nitrogen use at the plot level. Our approach shows that it is possible to design policy instruments at the plot level to reduce pollution from farming, but this would require specific data. In general, agronomic information is detailed but economic information is not available in plot-level data or for too small a sample for economic analyses.