dataesr/scientific-paragraphs-categorization huggingface dataset:
1. Creative programming engages the learner in the process of designing and developing an original work through coding. In this approach, learners are encouraged to use the programming tool as a knowledge co-constructing tool. For example, they can (co-)create the history of their city at a given historical period or transpose a traditional story in a visual programming tool like Scratch (http://scratch. mit. edu//). In such activities, learners must use skills and knowledge in mathematics (measurement, geometry and Cartesian plane to locate and move their characters, objects and scenery), Science and Technology (universe of hardware, transformations, etc. ), Language Arts (narrative patterns, etc. ) and Social Sciences (organization in time and space, companies and territories).
Hoover et al. (2016)  believe that automated assessment of CT can potentially encourage CT development. However, Dr. Scratch only considers the complexity of programs, not their meaning. This tool is suitable to evaluate the level of technical mastery of Scratch that a user has, but it cannot be used to evaluate every component of a CT competency as we defined it (i. e. the program does not give evidence of thought processes, and does not consider the task demanded). Finally, it would be hard for an automated process to measure or teach creativity since that behaviour is an act of intelligence  (Chomsky, 2008)  which should be analyzed considering the originality, value, and usefulness for a given problem-situation. In other terms, there is a need to evaluate the appropriateness of the creative solution according to the context and avoid over-complex solutions, which use unnecessary or inappropriate code for a given situation.
Participants A total of 120 undergraduate students at Université Laval in Canada (N = 120) were engaged in a story2code creative challenge. All of them were undergraduate students of a bachelor's degree in elementary school education. They were in the third year of a fouryear program and had no former educational technology courses. At the second week of the semester, they were asked to perform a programming task using Scratch. Scratch is a block-based programming language intended for children from 7 years of age. Participants were only presented two features of the language: creation of a new sprite (object) and the possibility to drag and drop blocks in each sprite's program. They were also advised about the use of the green flag to start the program.
Procedure The ill-defined problem proposed to the students is rooted in the narrative frame of a children' book introducing basic concepts of programming and robotics, Vibot the robot  (Romero & Loufane, 2016). The story introduces a robot, which has to be programmed for play. The Scratch Cat is the mascot of the visual programming tool Scratch and the default sprite appearing in each new project. Vibot is a fictional robot character, which waits for instructions to act in its environment. Based on these two characters, the story2code are short text-based stories, which engage learners in analyzing, modeling, and creating a Scratch project representing the story. In our study, participants were given a story2code and were asked to represent it in Scratch. The situation invited participants to create a Scratch project featuring a dialog between two characters: Scratch and Vibot.
The students were given a text-based script for creating the dialog including 9 quotations in which Scratch and Vibot the robot introduce themselves. After the dialog between the two characters, Scratch asks Vibot to draw a blue line. The scenario of this story2code could be solved with a certain degree of diversity in the Scratch visual programming software. Participants were asked to remix a Scratch project containing the two characters' sprites. Remixing is a feature of Scratch that allows users to duplicate existing projects and edit them. Participants were required to share their projects in order to develop a double assessment: the code-analyzer Dr. Scratch and the #5c21 assessment by an external evaluator.
Results All participants had to submit a Scratch program by providing its URL. They were required to share it in order to make it accessible to an evaluator. In this section we highlight the results obtained from the Dr. Scratch analytical tool and those obtained using the #5c21 CT competency model. CT assessment using Dr. scratch Dr. Scratch results are computed from seven criteria: abstraction, parallelism, logic, synchronization, flow control, user interactivity, and data representation. Each of them may be given a maximum of three points, for a possible total score of 21. Thirteen projects have not been passed through Dr. Scratch due to technical problems (i. e. URL not provided), so we have Dr. Scratch's results for 107 projects (n = 107, M = 0. 27%; ET = 0. 06%) Fig. 4. Ninety-one participants out of 107 got a total score of 6 at Dr.
Scratch. The highest score was 10 and was reached by two participants. Instead of organizing the dialogs using timers (wait instruction), those two participants used broadcasting. Broadcasting is a feature of Scratch that allows the user to trigger events and program the events' handler (or listener) or each of the sprites (objects). Event handlers or listeners are callback subroutines which are able to react to certain inputs. Using the broadcasting feature caused Dr. Scratch to attribute additional points of parallelism and synchronization. One of these two highest-score projects received points for the use of a single event's handler or listener "when backdrop switches to …" in both parallelism and synchronization. Typical projects (those who scored 6) all worked about the same way: there was only one event's handler for each sprite (when green flag is clicked), and dialogs were synchronized using timers (wait instruction).
It is an attempt to define criteria that may be suitable to evaluate ill-defined tasks involving CT. Results from Dr. Scratch and from the #5c21 CT competency model are not to be compared on any basis as they do not evaluate the same components. Dr. Scratch evaluates the algorithmic complexity of a Scratch project based on a unique model of CT assessment which is generic and does not consider the program in relation to the situation problem. In that, it is not intended for use when the aim is to evaluate CT as a creative problem-solving competency. However, the use of Dr. Scratch is a useful tool for allowing the learners to reflect on the algorithmic implementation and could provide useful tips for improving the quality of the program. Dr. Scratch is able to identify missing names of instances, repetitions, and some coding practices that could be improved.
One of the most important selection criteria is Polyphen, "a tool that predicts the impact of an amino acid substitution on the structure and function of a human protein"  (Worthey et al. 2011). This software reduced the number very significantly so that, in the end, by way of a priori reasoning, only one mutation is left. Polyphen, developed by Harvard University (Adzhubei et al. 2010), calculates the probability that a mutation is deleterious, based on methods typical of systems biology. More generally, many mathematical and methodological innovation are increasingly investigated and resorted to in research, as well as in practice. A key paper concerning the introduction of systems biology into medical science is Ideker, Galitski, and Hood's (2001) while Barabási, Gulbahce, and Loscalzo's (2011) concerns the introduction of 'network medicine'.
2013). 2) The ability to bypass traditional disease entities and use genomic database to target the relevant biomarker, identify the involved biological network and imagine a potentially efficient treatment: this is what saved this boy's life. The National Research Council endorses the corresponding general view in a 2011 report. They played a role in the shift from 'personalized' to 'precision' in p-medicine: "as used in this report, "precision medicine" refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases they may develop, or in their response to a specific treatment.
Acknowledgements Brendan Clarke ,  Marie Darrason ,  Elodie Giroux ,  Kathryn Tabb ,  Hervé Watier  and two anonymous reviewers for precious comments. RÉFÉRENCES ABRAHAMS, Edward, GINSBURG, Geoffrey S. , SILVER, Mike. 2005. The  Personalized Medicine Coalition: Goals and Strategies. American Journal of Pharmacogenomics: Genomics-Related Research in Drug Development and Clinical Practice , 5 (6),  345-55. Article ADZHUBEI ,  Ivan A. , SCHMIDT, Steffen, PESHKIN, Leonid, RA-MENSKY, Vasily E. , GERASIMOVA, Anna, BORK, Peer, KON-DRASHOV, Alexey S. , SUNYAEV,  Shamil R. 2010. A Method  and Server for Predicting Damaging Missense Mutations. Nature Methods, 7 (4),  248-49. Article BARABÁSI ,  Albert-László ,  GULBAHCE, Natali, LOSCALZO, Joseph. 2011. Network medicine: a network-based approach to human disease.
In the traditional method for calculating EFMs, a binary distinction is made between reversible and irreversible reactions. Although this distinction is sometimes not easy to make, it is known for many biochemical reactions whether they are reversible or irreversible. This information is given in original papers, in textbooks, on pathway charts such as the Boehringer map and in databases such as KEGG  [15]. However, this binary distinction is an oversimplification because, in principle, any reaction is reversible. This can be explained by an argument based on the second law of thermodynamics. In a strictly irreversible reaction, the concentration(s) of substrate(s) would tend to zero as time proceeds. This, however, would imply the unrealistic case that entropy would diverge because it is a function of the logarithm of concentrations  [16].
However, such data in many cases are not available. In contrast, the equilibrium constants are relatively easy to obtain because they are independent of the properties of the enzymes. These constants can be found in a very useful database eQuilibrator  [31]  which used methods developed by Noor et al. [32, 33]  based on thermochemical estimation. Moreover, several approaches have been published for computing equilibrium constants (or the equivalent standard Gibbs free energy differences) from properties of the molecules involved  [34] [35] [36]. Admittedly, the concentrations of external metabolites (or equivalent information) are needed in addition to equilibrium constants for a thermodynamic description because they determine the distance from equilibrium and affect the directionality of reactions.
The model has 30 reactions including 25 irreversible reactions and 37 metabolites including 12 external metabolites (see  Fig 4). Using Metatool  [12] , we obtained 31 EFMs. They were drawn using a matlab function EFMdraw that we had implemented and connected to Metatool. All these EFMs are irreversible and all the reactions are used in only one direction. We then verified if the EFMs were consistent with thermodynamics. The model contains 27 tEFMs at T = 0 and 26 tEFMs at T = 48h, T = 72h and T = 96h. The non-thermodynamic EFMs at T = 0h are: [8 16  18 23 ] (shown in the Fig  5 ), whereas at T = 48, 72, 96h they are: [8 13 15  16 23]  (shown in  Fig 6). EFM 8 is most improbable because it is known that lactate is mostly coming from glucose and not from glutamine  [42]. In the model, we only describe one pool of pyruvate (i.
Computations and EFMs visualization We developed a software, thermoEFM, for performing the thermodynamic calculations explained above and a program, EFMdraw, allowing one to visualize all the EFMs. The implementation of both programs is performed with MATLAB 2015a, and they are freely available on our website https://www. lri. fr/*speres/EFM/ and figshare https://figshare. com/. They are both connected to METATOOL  [12]  to compute the EFMs. Celldesigner  [50]  was used to design the metabolic network and to visualize all the EFMs generated with EFMdraw.
Discussion In this work, we have compared the traditional approach in metabolic pathway analysis in which a binary distinction is made between reversible and irreversible reactions, with an approach in which more detailed thermodynamic information is used. In particular, the values of equilibrium constants and external metabolite concentrations are considered. The latter approach had been suggested even before the advent of "traditional" EFM analysis  [37]. To decide which direction is realistic for a given pair of oppositely directed reversible EFMs, we have here used a Lemma described in a previous work  [37]. In the present comparison, we have not, however, included the approaches in which also boundaries on internal metabolite concentrations were considered, such as  [24, 28]. We have implemented the method (thermoEFM) in a MATLAB function connected to Metatool  [12].
The method first computes all the EFMs and then selects those that are consistent with the formula Eq  (20). This criterion is simple to be set and efficient but has to be applied in postprocessing as filtering by Eq (19) cannot be applied during the iteration steps of the Double Description algorithm. Moreover, to analyze our results, we have implemented a matlab script (EFMdraw) using Metatool and allowing the graphical representation of all the EFMs of a metabolic network designed in cellDesigner  [50]. The number of EFMs on the basis of the binary declaration is often much lower than that based on the thermodynamic method. We show that in the case of a network composed of an arbitrary number of linear chains sharing one internal "hub" metabolite, the steady-state assumption allows a number of possible EFMs that is quadratic in the number of chains.
This paper describes the scientific and technical design of AzoFert ® for achieving this purpose. Insights into the functions of the model are provided for each process simulated by the model. To illustrate this design, we focused specifically on how the model simulates the evolution of organic products in the soil by varying the chemical composition and date of application, using normalised time and calculating the net contribution of these products to the soil mineral N pool. The outputs simulated using the estimated parameter values for organic products and various crop species were validated against independent datasets. Finally, the current dissemination of the AzoFert ® tool in France and its potential for evolution are discussed.
In AzoFert ® , Tref is 15 • C and Wref is field capacity  [20]. Consequently, the model calculates (i) the contribution of these sources to mineral N from the harvest of the previous crop until the opening date of the balance sheet using observed climatic data from the area and (ii) the N mineralised from these organic sources between the opening and closing dates of the balance sheet using mean climatic data from the area (calculated over the last 15, 20, and even 30 years according to the weather station). This means that there is no uncertainty associated to "past" climatic conditions, between the previous harvest and the opening of the balance sheet, but the model is predictive after opening the balance sheet with uncertainty related to future weather conditions. The climatic conditions are taken into account over 10-day intervals throughout the year.
Crop N Requirements The estimated crop N requirement is different between plants harvested for grain and plants harvested at a vegetative stage. The N uptake of plants harvested for grain is calculated as the product of the objective grain yield (Mg ha -1 ) and the N requirement per unit of grain production (kg N Mg -1 DM). The N requirement per unit of grain is calculated from the quantity of N accumulated by crop for a given grain yield, and these data are obtained from field experiments conducted by technical institutions. For other crops, the N requirement is considered constant per area unit (kg N ha -1 ) and according to the crop, i. e. , a potential yield is not considered when calculating the N requirements. For winter crops, whose growth cycle starts before the period in which the N balance sheet is calculated, the N that has already been taken up by the crop (which varies with the stage of crop development) is included in the term Pi and is subtracted from the total N required by the crop.
• Organic products (Mr, Ma, Mcc, Mg) The decay of crop residues (main crops, cover crops, plant residues from grassland ploughing) and organic products in the soil results in either the net mineralisation or the net immobilisation of soil N. The decay rate of these products depends on the nature of organic residues (chemical characteristics and C:N ratio) and the T and W soil conditions. The kinetics of mineralisation were determined using a large dataset of laboratory incubations conducted under controlled and optimal decomposition conditions (constant soil W and T; non-limiting N conditions)  [24] [25] [26]  and using field experiments measuring the N mineralisation of crop residues or organic products. We used a single generic formalism that describes the dynamics of either the nitrogen (Equation (  8 )) or carbon (Equation (  9 )) of these various organic sources.
While we have developed a simple module for volatilisation for this tool, we have not yet developed a module for denitrification, which remains a complex and difficult process to predict. The functions used for N immobilisation and volatilisation of ammonia were derived from 15 N experiments that demonstrated the microbial immobilisation of added fertiliser N and the incomplete recovery of the added N by different crops in the temperate climate of northwestern Europe  [17, 18]. The amount of microbially immobilised fertiliser N depends on the N immobilisation capacity and the soil N availability  [27]. In AzoFert ® , the immobilisation capacity is calculated from the availability of C in the ploughed layer using Equation  (9) , which predicts the C evolution from crop residues and organic products, and an immobilisation ratio: Y = (0.
Software Accessibility Software name: AZOFERT ® V2, Year first available: 2014 Operating system required for software: Run under MS Windows XP or higher on PC, Program language: VB. net Framework 4. 0, 260 ko Software developer and contact address: Pascal Dubrulle, pascal. dubrulle@inra. fr. Address: 2, Chaussée Brunehaut-Estrées-Mons, CS 50136, 80203 Péronne CEDEX, France.
Calculation Engine Calculations are performed using data obtained from the farmer and specific agronomic parameters included in the model equations. To increase the operational capability of AzoFert ® for extension services and farmers, data inputs provided by the farmer must be simple so that they can be collected at the field scale, and they must also be limited in number. These data consist of (i) the measured or estimated initial mineral N content and moisture in the soil profile on the opening date of the balance sheet and (ii) technical information pertaining to the plots to be fertilised. These latter data are obtained from a paper sheet filled out by the farmer. The calculation engine provides each item of the balance sheet, the N recommendations for crop fertilisation to meet the yield objectives, comments on the calculation (i.
For example, crop N requirements are calculated based on the yield objective, defined by the period and duration of the growth cycle, or correspond to a fixed value. The direct users of the software include soil-testing laboratories that provide access to farmers through their own laboratory information management system. The link between such management systems and AzoFert ® is provided by the input file and the output file, and the file format is defined by AzoFert ®. The calculation for one plot can be performed in approximately 90 milliseconds.
Experimental Data Sets To test the performance of the AzoFert ® software, several databases of field trials were used (Table  2 ). Different criteria were tested: the prediction of soil N supply under a wide range of cultural conditions (dataset 1) and organic amendments (dataset 2) and the environmental impacts of AzoFert ® N use (dataset 3). Each field trial of these datasets consisted of a response curve to fertiliser N using 4 to 6 different N doses and a control treatment with no fertilisation (0 kg N ha -1 ), with 4 replicates per N dose. Previous crops included winter and spring cereals, sugar beet, oil plants and field vegetables. The experiments were essentially located in northern France and used different soil types, i. e. , mainly silt, clay loam and chalky soils.
This calculation assumes that soil N supply is not affected by the addition of fertiliser N (or the lack thereof). The AzoFert ® N supply was calculated from the general Equation (  1 ) by considering the net N mineralisation of organic sources (Equations (  7 ) and (  8 )), mineral N in the soil at the opening and closing of the balance sheet, the N uptake by the crop on the opening date, the N added by wet deposition, irrigation and symbiotic fixation, and the N lost via leaching (Equation (  14 )). This approach was also used to predict the net N mineralisation in situations with organic amendments but without mineral fertiliser application using the field dataset 2. Finally, the environmental impact of AzoFert ® N recommendations was estimated from trials with increasing N doses (dataset 3).
Estimation of Soil N Supply The N supply estimated by AzoFert ® for control treatments (0 N) was compared to the plant N uptake measured in the same control plots (dataset 1). The experiments covered a wide range of agricultural situations; the N absorption in the control plots ranged from 21 to 259 kg N ha -1. AzoFert ® reproduced both the differences observed at different sites, with estimated values ranging from 26 to 288 kg N ha -1 , and the average soil N supply, with a significant linear correlation between the measured and estimated N supply (Y = 0. 9601X, r 2 = 0. 76; p < 0. 01) (Figure  3 ). To determine the effect of Agronomy 2017, 7, 73 11 of 21 crop type, the different crops were ranked according to the length of their growing periods (4 months for spring barley and 7 months for sugar beet) and the potential capture of annual soil mineralisation.
The RMSE and MG values were 2. 69 and -1. 00 kg N ha -1 , respectively, which are relatively low. A similar comparison was carried out in Wallonia (Belgium) on vegetables (bean, spinach and carrot) growing in loamy soil. A very high correlation (r 2 = 0. 95) was observed between the N absorbed by plants growing in a non-fertilised plot and the N supply estimated using AzoFert ®  [32]. Under conditions with organic amendments, soil mineral N availability often leads to a substantial contribution of organic sources (such as catch crops and organic products) to the N nutrition of main crops. Thus, an accurate estimate of their contribution is necessary to improve N recommendations. In sugar beet trials receiving no mineral fertiliser (T0), the differences in crop N uptake were attributed to differences in the net N mineralisation of organic products (dataset 2).