dataesr/scientific-paragraphs-categorization huggingface dataset:
Training, validation and testing datasets One of the main challenges in statistical inference is that the model is set up using a samples database, but it must perform well on new, previously unseen samples. This is a complex task, and methods have been designed to perform good training, chose the suitable model, and measure the model generalisation ability realistically. For that purpose, the overall database B is divided into three datasets: B = B T rain + B V al + B T est , each one of these three datasets undertakes a specific task  (Ripley, 1996) : -The training dataset B T rain is used to calibrate the model parameters. The process of partitioning B will be called in the following as the "folding " process. For instance, the folding choice can be chosen using 50 %, 25 %, and 25 % of the whole database B for B T rain , B V al , and B T est respectively.
The need for the validation dataset is not always understood. The training dataset is used to fit the parameters; the testing dataset is often used to estimate the model quality but also to choose the best model (as in the LOO approach). However, using only this testing dataset brings a risk of choosing the model that is best suited to this particular testing dataset. This issue is a special kind of overfitting, not on the model calibration but on the model choice. If the database is big, many samples in the testing dataset will be representative enough; thus, choosing the best model based on it is acceptable. If the database is small as in crop modelling tasks, the model selection can be too specific for the particular samples of the testing dataset; thus, an overfitting problem can appear.
Measuring the quality of statistical yield models In practice, statistical yield models often face the problem of "data scarcity " (i. e. , having a limited number of crop yield data). One sample corresponds to one year of data; a 19-year database thus provides only 19 samples. This scarcity of data introduces two problems: First, it is impossible to infer a complex model from such a limited number of samples. The model complexity needs to be limited by the information provided by the samples. If samples are not enough, there is a considerable risk of overfitting the model towards the limited number of samples of the training dataset. There is no general rule determining the complexity of the model based on the number of samples. Second, it is necessary to divide the database into training, validation, and testing datasets (Sect.
2. 4). With a limited number of samples, the training process may need every possible data point to determine model parameters adequately  (Kuhn and Johnson, 2013) , and it might be impossible to choose the best model or assess its generalisation ability with the remaining samples. It is challenging to keep a significant percentage of the database for the validation and the testing datasets. To choose a model with an adequate complexity level and avoid overfitting, a robust way to measure the generalisation ability is necessary, using as few samples as possible. Cross-validation  (Allen, 1974; Stone, 1974)  was developed as an effective method for both model selection and model assessment when having a small number of samples.
Leave-One-Out The LOO method is one common type of cross-validation, in which the model trained, chosen, and evaluated using only two datasets. The main idea of LOO is that given n samp available samples in B; the model is calibrated n samp times using (n samp -1) samples in the training dataset B T rain (leaving one sample out). The resulting model is then tested (n samp -1) on the left sample (B T est ). There is n samp testing score estimations, one for each sample. In this case, B = B T rain + B T est and B V al is empty. The averaging of these n samp testing scores is expected to be a robust assessment of the model ability to generalise on new samples. However, since no validation dataset is used to select the best model, the choice of the best model is made using the testing dataset; thus, it may be biased towards this testing dataset  (Cawley and Talbot, 2010).
Leave-Two-Out LOO is very useful in many cases  (Kogan et al. , 2013; Dinh et al. , 2021) , but as described in Sect. 2. 4, the overall database needs to be divided into three datasets: B = B T rain +B V al +B T est. A LTO approach, adapted from the nested cross-validation  (Stone, 1974) , is then proposed in the following.
Folding scheme A folding process is used to generate the training, validation, and testing scores. Each fold divides the database B into a training dataset B T rain of (n samp -2) samples, a validation B V al , and a testing B T est datasets, with one sample each. Two samples are considered out of the training dataset instead of one in the LOO procedure. This folding process is presented in Fig. 2 , with the number of folds n f old = n samp × (n samp -1). This is why this approach is also called k*l-fold cross-validation when l=k-1. Each choice of the testing dataset (each id test ) corresponds to a selected best model bm i and two distributions (i. e. , probability density functions (PDFs)) for (n samp -1) validation errors and (n samp -1) testing errors, showed in Fig. 3 (a2). These 210 two distributions result in a validation score (blue dot) and a testing score (red dot).
Generalisation ability versus model complexity The process represented in Fig. 3  is used to obtain the validation (V λ ) and testing (T λ ) scores from the LTO approach for a given model complexity λ. A different complexity level (different λ) results into different V λ and T λ values. The V λ and T λ evolution curves obtained for validation and testing RMSE values of yield anomalies for an increasing model complexity are presented in Fig. 4. For simplicity, only validation and testing scores will be discussed since the training error should be consistently decreasing when increasing the model complexity. When increasing the complexity level (λ > λ), the validation error is smaller (V λ < V λ ) but the testing error is bigger (T λ > T λ ). It is also known as overfitting: the complexity level is too high; the model can highly fit the validation dataset but does not generalise well.
Yield model selection Two methods of estimating the model quality (LOO and LTO) are considered to choose the appropriate model complexity. Figure  5  shows the RMSE values of the predicted Robusta coffee yield anomalies for the LIN3 and LIN5 models, which are the linear regression models with three and five inputs, respectively. These values are computed using the LOO and LTO procedures for the training, validation, and testing datasets. The horizontal axis shows 13 models with a different number of  increasing the model complexity from five potential predictors to 38 potential predictors (Fig. 5 (a2)). Thus, models with more inputs and more potential predictors would appear adequate when using the LOO procedure. The LTO procedure is considered in Fig. 5 (b1) and (b2), the training and validation RMSE values decrease with the model complexity, in a similar way as the training and testing errors in the LOO procedure.
This similarity is because the LTO validation dataset has the same role as the LOO testing dataset: to find the best model! However, the testing errors do increase with the increase of the number of potential predictors (Fig. 5(b1) ). This behaviour is ever stronger for the LIN5 model of  Fig. 5(b2). Because the potential of overtraining is higher with a more complex model, we observe a more significant difference between the testing errors and validation/training errors in this case  (Fig. 5(b2) ) than the LIN3 model (Fig. 5 (b1)). The LTO procedure clearly indicates that a simpler model (i. e. , a lower number of potential predictors) is more suitable. This conclusion makes sense because it is inappropriate to use a very complex model (as the LOO model choice) when having only 19 samples.
The LOO procedure is actually misleading because it suffers from overfitting: it chooses the best model and assesses the generalisation ability on the same testing dataset. This overfitting issue is suppressed in the LTO procedure since we chose the model on the validation dataset and assessed its generalisation score on an independent testing dataset. Another example using the NN models (NN3 with three inputs and seven neurons in the hidden layer) in Fig. 6(a)  shows the same behaviour: the more complex the model is, the higher the testing error becomes due to the overtraining (The model is stopped at six potential predictors due to the computationally cost. More NN examples will be discussed in Sect. 5). For the same number of potential predictors, the testing errors in NN3 models (Fig. 6 (a)) are much higher than those in LIN3 models  (Fig.
5(b1) ). The significant difference between training errors and validation/testing errors in NN3 models is related to the overfitting problem (compared to the LIN3 models). Using a NN model that is too complex for a limited database is highly dangerous. We also tested the LTO procedure with an increased complexity level by keeping the same number of potential predictors (n pre =30) but increasing the number of inputs from two to seven (on the horizontal axis in Fig. 6(b) ). In this case, In short, considering the limited information in the available database-that is used to train, select the model, and evaluate its quality-it is not possible to use more than a very simple and limited model. Therefore, for this 19-samples coffee yield modelling case, using a simple LIN model is better than a complex one (NN model, for instance), and it will be illusory to think that complex plant relations can be implemented with such a limited number of samples.
Yield model selection -Focus on Bas-Rhin This section aims to define an appropriate statistical model for grain maize using 22 years of yield data. This test is done 285 over Bas-Rhin (i. e. , one major grain maize-producing department in France). As shown in Sect. 4. 1, the LOO approach is 14 https://doi. org/10. 5194/gmd-2021-218 Preprint. Discussion started: 6 August 2021 c Author(s) 2021. CC BY 4. 0 License. misleading by choosing too complex models; we focus here on the LTO results for different models with various complexity levels. Figure  8  describes the RMSE values of the predicted grain maize yield anomalies for three datasets (training, validation, and testing) of the LTO procedure for LIN3 models with various complexity levels and several architectures of NN3 models. The results of LIN3 models are presented in Fig.
8 (a), and NN3 models (with seven neurons in the hidden layer) are in Fig. 8(b) , with a different number of potential predictors ranging from three to 12 in the horizontal axis. In the two cases, the LTO procedure shows a similar behaviour as for the Robusta coffee application of the previous section: the validation/training errors decrease with the number of potential predictors, while the testing errors show an opposite trend. These overtraining results suggest that a simple model (e. g. , LIN3 with three potential predictors) is more adequate: the testing RMSE value is small and close to the RMSE values over the two other datasets. More complex models are tested in Fig. 8(c ), in which NN3 models with four potential predictors are considered. The model complexity here corresponds to the number of neurons in the hidden layer (from two to 20 neurons) in the horizontal axis.
Conclusions and perspectives Crop yield modelling is very useful in agriculture as it can help increase the yield, improve the production quality, and minimise the impact of adverse conditions. Statistical models are among the most used approaches with many advantages  (Lobell and Burke, 2010; Iizumi et al. , 2013; Mathieu and Aires, 2018; Gaudio et al. , 2019). The main difficulty in this context is the limitation of the available crop databases to calibrate such statistical models. Applications typically rely on only two or three decades of data  (Prasad et al. , 2006; Ceglar et al. , 2016; Kern et al. , 2018). This small sample size issue directly impacts the complexity level that can be used in the statistical model: a model too complex cannot be fit with such limited data, and assessing the true model quality is also challenging.
In practice, statistical inference requires three datasets: one for calibrating the model, a second one for choosing the right model (or tuning the model hyper-parameters), and another for assessing the true model generalisation skills  (Ripley, 1996). Dividing a very small database into such three datasets is very difficult. The LOO method has been used as a cross-validation tool to calibrate, select, and assess the model  (Kogan et al. , 2013; Zhao et al. , 2018; Dinh et al. , 2021). It was shown in this paper that LOO could be misleading because it uses only one dataset to chose the best model and estimate its generalisation skills simultaneously. This is a true problem as LOO is one of the main statistical tools to obtain good crop yield models. This study proposes a nested cross-validation approach in the form of what we call a LTO method.
This method uses a complex folding scheme to estimate independent training, validation, and testing scores. Results show that LOO is truly misleading and can artificially request complex models that overfit the problem. In contrast, LTO shows that only very simple models can be used when the database is limited in size. The LTO implementation proposed here is very general and can be applied to any statistical crop modelling application. Two applications have been considered here. The first one concerns the coffee yield modelling over a district in Vietnam's major Robusta coffee-producing region. It was shown that monthly mean precipitation and temperature could explain more than 30 % of the coffee yield anomaly variability. The 70 % remaining variability is due to non-climatic factors (agricultural practices, diseases, or political and social context).
Acknowledgements. This work is a part of Lan Anh's Ph. D. , which benefited from the  French  state aid managed by the  ANR  under the " Investissements d'avenir " program  with the reference  ANR-16-CONV-0003 , and the  Australian Centre for International Agricultural Research, Small Research Activity  ( SLAM/2018/209 ) conducted by the  International Center for Tropical Agriculture (CIAT).
Code availability. The Matlab code used to run an example of the leave-two-out method is available at the following Zenodo link for the revision process of GMD: https://zenodo. org/record/5159363  (Anh and Filipe, 2021). Data availability. The coffee data were provided by the General Statistics Office (GSO) of Vietnam for the 2000-2018 period. These data are available from GSO on reasonable request. For any inquiries, please send an email to banbientap@gso. gov. vn. The data on French grain maize (and other French crops) are available at http://agreste. agriculture. gouv. fr from 1989 on. In addition, the weather data, i. e. , ERA5-Land data, can be downloaded from https://cds. climate. copernicus. eu (last access: 22 Apr 2021).
- The validation dataset B V al is a sample of data held back from the training dataset, which is used to find the best model. For instance, it helps tune the model hyper-parameters: choose the more adequate inputs (i. e. , feature selection), determine the number of predictors, find the best model type (LIN, random forest, NN), determine some training choices, etc. https://doi. org/10. 5194/gmd-2021-218 Preprint. Discussion started: 6 August 2021 c Author(s) 2021. CC BY 4. 0 License. -The testing dataset B T est is held back from the training and the validation datasets to estimate the true model ability to generalise.
Processed inverse toeprints were amplified by 12-16 cycles of PCR, using 0. 02 μM long NGS adapter oligonucleotides (forward and reverse) and 0. 2 μM short amplification oligonucleotides (NGS_f and NGS_r). The resulting NGS libraries were purified using a Qiagen PCR purification kit. The size and concentration of the fragments thus obtained were determined using a 2100 Agilent Bioanalyzer with the DNA 1000 kit. Next generation sequencing was performed by the BGI Facility in Hong-Kong, on an Illumina HiseqXten system in rapid run mode with 150 PE reads. Analysis of ErmDL deep mutational scanning data. Unless it is indicated otherwise, data analysis was carried out using a series of custom scripts written in-house in Python, which relied on the use of the Biopython package  39. Read pairs were assembled using PEAR v0.
9. 10 40 on a Mac Book Pro with a 2. 7 GHz Intel Core i7 processor and 16 GB 1600 MHz DDR3 memory with the maximal proportion of uncalled bases in a read set to 0 (-u option), and the upper bound for the resulting quality score set to 126 (-c option). Regions immediately upstream of the start codon and downstream of the point of cleavage by RNase R were removed using a modified version of the adaptor_trim. py script written by Brad Chapman (https:// github. com/chapmanb/bcbb/blob/master/align/adaptor_trim. py). The 5′ flanking region was defined as GTATAAGGAGGAAAAAAT, whereas the 3′ flanking region was GGTATCTCGGTGTGACTG. A maximum of two mismatches within each of these flanking regions was tolerated, whereas all other reads were discarded. Trimming of the retained reads resulted in sequences with a start codon directly at the 5′ end and the site of RNase R cleavage at the 3′ end.
The reaction was layered on 10-40% sucrose gradient in buffer A and the monosomes were fractionated by centrifugation at 34,380 × g (SW-40 Ti, Beckman Coulter), directly followed by a pelleting centrifugation step at 109,760 × g (Ti70. 1, Beckman Coulter) overnight at 4 °C. The stalled monosomes were re-suspended in buffer B to a final concentration of 150 A 260nm /mL ErmDL-TEL-SRC and 100 A 260nm /mL ErmDL-ERY-SRC. For grid preparation, 4 μL (5 A 260 /mL) of the freshly prepared ErmDL-TEL-SRC or ErmDL-ERY-SRC complex was applied to 2 nm precoated Quantifoil R3/3 holey carbon supported grids and vitrified using a Vitrobot Mark IV (FEI, Netherlands). Cryo-electron microscopy and single-particle reconstruction. Data collection of both ErmDL-TEL-SRC and ErmDL-ERY-SRC was performed on a FEI Titan Krios transmission electron microscope (TEM) (Thermo Fisher) equipped with a Falcon II direct electron detector (FEI).
Data were collected at 300 kV with a total dose of 25 e -/Å 2 fractionated over 10 frames with a pixel size of 1. 084 Å/pixel and a target defocus range of -0. 7 to -3 μm using the EPU software (Thermo Fisher). The raw movie frames were summed and corrected for drift and beam-induced motion at the micrograph level using RELION-3. 0  42. The resolution range of each micrograph and the contrast transfer function (CTF) were estimated with Gctf  43. For ErmDL-TEL-SRC, a total of 5850 micrographs were collected. After manual inspection, 5430 micrographs were used for automated particle picking with Gautomatch (http://www. mrc-lmb. cam. ac. uk/kzhang/) resulting in 433,827 initial particles, of which 384,895 were selected for further processing upon 2D classification in RELION-3. 0  42. After initial alignment with a vacant 70S reference, the 384,895 particles (defined as 100%) were 3D classified into eight classes (Supplementary Fig.
9 Å (Supplementary Fig. 5b ). Local-resolution estimation and local filtering of the final volumes were done using RELION-3. 0 42 (Supplementary Fig. 5c-e ). Molecular modeling. The model of the E. coli 70S ribosome was derived from PDB ID 6TBV  45. Domain-wise the proteins of the 30S and 50S and the rRNA were fitted separately into locally-filtered electron density maps using UCSF Chimera  46  and Coot  47. Afterwards manual adjustments were applied to all fitted molecular models using Coot. The ErmDL nascent chain was de novo modeled into the corresponding density. The final combined molecular models were then refined using the real space refinement procedure in Phenix version 1. 14 together with restraints  48. Statistics for the models were obtained using MolProbity  49  and are represented in Supplementary Table  2.
For ion placement, Mg 2+ ions with 5 Å of each simulation system were extracted from an aligned cryo-EM structure of the ribosome  52. Histidine protonation states were determined using WHAT IF  53. Next, each simulation system was first placed in a dodecahedron box with a minimum distance of 15 Å between the atoms and the box boundaries. Water molecules were added using the program solvate  54. The charge of the system was neutralized by iteratively replacing the water molecule with lowest Coulomb potential by a K + ion. Subsequently, 7 mM MgCl 2 and 150 mM KCl were added using the program GENION  54. All simulations were carried out with GROMACS 2018  54  , the amber12 forcefield  55  , the SPC/E water model 56 , as well as K + and Cl -parameters from Joung and Cheatham  57. Forcefield parameters for ERY were taken from Arenz et al.
Acknowledgements We thank  Dorota Klepacki  for her help with toeprinting experiments and  Haripriya Ramu  for the construction of the inducible ermD reporter. This work has been supported by grants of the  National Institutes of Health  ( R35 GM127134  to A. S. M. ), the  Deutsche Forschungsgemeinschaft  ( WI3285/3-1  to D. N. W. and  EXC 2067/1-390729940  to L. V. B. and H. G. ), the  Deutsche Zentrum fur Luft-und Raumfahrt  ( DLR01Kl1820  to D. N. W. ) and the  ANR  ( ANR-18-JAM2-0005  to C. A. I. ) within the "RIBOTARGET" consortium under the frame of JPIAMR (to D. N. W. and C. A. I. ). E. L. , B. S. , and C. A. I. have received funding for this project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No.
Data availability The data that support this study are available from the corresponding authors upon reasonable request. The cryo-EM maps of the ErmDL-ERY-P-tRNA-SRC, ErmDL-ERY-A-P-tRNA-SRC and ErmDL-TEL-A-P-tRNA-SRC and the associated molecular models have been deposited in the Protein Data Bank and Electron Microscopy Data Bank with the accession codes EMD-12573, EMD-12574, EMD-12575, and PDB 7NSO, 7NSP, 7NSQ respectively. Sequencing data from the inverse toeprinting analysis have been deposited in the National Center for Biotechnology Information Short Read Archive with the accession code PRJNA623725. Source data are provided with this paper.
We contend that the exclusive focus on the English language in scientific research might hinder effective communication between scientists and practitioners or policy makers whose mother tongue is non-English. This barrier in scientific knowledge and data transfer likely leads to significant knowledge gaps and may create biases when providing global patterns in many fields of science. To demonstrate this, we compiled data on the global economic costs of invasive alien species reported in 15 non-English languages. We compared it with equivalent data from English documents (i. e. , the InvaCost database, the most up-to-date repository of invasion costs globally). The comparison of both databases (~7500 entries in total) revealed that non-English sources: (i) capture a greater amount of data than English sources alone (2500 vs.
2396 cost entries respectively); (ii) add 249 invasive species and 15 countries to those reported by English literature, and (iii) increase the global cost estimate of invasions by 16. 6% (i. e. , US$ 214 billion added to 1. 288 trillion estimated from the English database). Additionally, 2712 cost entriesnot directly comparable to the English databasewere directly obtained from practitioners, revealing the value of communication between scientists and practitioners. Moreover, we demonstrated how gaps caused by overlooking non-English data resulted in significant biases in the distribution of costs across space, taxonomic groups, types of cost, and impacted sectors. Specifically, costs from Europe, at the local scale, and particularly pertaining to management, were largely under-represented in the English database.