dataesr/scientific-paragraphs-categorization huggingface dataset:
11b ), we found that best scores were generally obtained for IB X-VOD (over 54% of the pixels in Africa vs 29%, 12% and 6% of the pixels, for, respectively, the LPDR, VODCA and LPRM algorithms). Similar results were obtained considering NDVI instead of LAI, but the score of IB decreased to the advantage of all the three other products (43%, 33%, 20%, 10% of the pixels for, respectively, the IB, LPDR, VODCA and LPRM algorithms). One of the reasons explaining the good results of IB X-VOD could be that the modelled temperature datasets from ERA5 Land rather than TB-based temperature estimates were used in this study. Evaluation studies found model temperature simulations are more accurate than the temperature estimated by TB, such as the temperature proxy used in the LPRM model  (Cui et al. , 2018; Ma et al.
Discussion In this study, the vegetation extinction parameter (VOD) is retrieved from passive microwave observations using a radiative transfer equation which also considers another important parameter, soil moisture  (Njoku and Kong, 1977; Paloscia et al. , 2001; Mladenova et al. , 2014; Pan et al. , 2014; Paloscia et al. , 2006). Soil moisture presents high spatial variability in space and time, due to the heterogeneity of the land surface and the variability of precipitation  (Owe et al. , 2001). In this study, we focused exclusively on the retrieval of VOD, and ERA5-Land SM was selected as a known input of the retrieval algorithm in consideration of its high accuracy, high frequency and long-time series of records. Therefore, the X-VOD retrievals made in this study may be sensitive to    the quality of the ERA5-Land SM dataset.
Spatial resolution enhancement of VOD is a necessity in order to provide detailed information on vegetation at Fig. 9. Density scatter plot between average IB X-VOD (1st column), LPDR X-VOD (2nd column), VODCA X-VOD (3rd column), LPRM X-VOD (4th column) and Bouvet AGB (1st row), Saatchi AGB (2nd row), CCI AGB (3rd row), LAI (4th row), NDVI (5th row) (numbers were rounded to 2 digits). local and field scales, such as for the study of agricultural crops under different natural and man-made environments. Therefore, a key step in improving the IB X-VOD product will be to evaluate downscaling methods as proposed in the literature  (Santi, 2010; Sabaghy et al. , 2020; Gevaert et al. , 2016; Abowarda et al. , 2021; Gao et al. , 2020). In addition, fine-spatial-resolution datasets for soil moisture and temperature will be needed to run the X-MEB model.
Conclusion and perspectives The general aim of the present study was to evaluate a new approach for computing X-VOD from the dual-channel X-band passive microwave observations of AMSR2. Thus, in this evaluation step, IB X-VOD was computed only over Africa during 2014-2016. Globally-constant values (ω = 0. 06 and H R = 0. 6) were found to achieve high spatial and temporal correlations with the reference vegetation parameters (Bouvet/Saatchi/ CCI AGB, LAI and NDVI). Comparison with other X-VOD products suggested IB X-VOD had competitive advantages in terms of both spatial and temporal performances. In particular, spatial correlation with three biomass datasets (R 2 ~ 0. 76-0. 83) was found to be higher with IB X-VOD than for the other X-VOD products and temporal correlation with LAI or NDVI, particularly for dense tropical forests, showed obvious improvement.
Acknowledgements This work was supported by  CNES-TOSCA  ( Centre National d'Etudes Spatiales, France)  funding and  Mengjia WANG  was sponsored by  China Scholarship Council  (CSC; 201906040124). Lei Fan  acknowledges additional support from the  National Natural Science Foundation of China  (Grant no. 41801247 ,  41830648 ) and  Natural Science Foundation of Jiangsu Province  (Grant no. BK20180806 ). We wish to thank the three reviewers for their helpful comments that strongly contributed to improve the manuscript.
To obtain the score, we performed text analysis on 243, 659 sentences drawn from 268 national manifestos, mainly from 2002 to 2019. We downloaded 229 manifestos from the Manifesto Project Database (MPD;  Krause et al. 2018). To have a more accurate representation of the party landscapes in the electoral rounds, we integrated the MPD corpus with 39 extra manifestos taken from parties' and governments' official sources. The use of electoral manifestos for measuring populism is not uncontested. One of the standard arguments against their use is that they might show lower levels of populist rhetoric relative to other types of text, for example, party magazines  (Pauwels 2017 ). However, their use is grounded in the literature (e. g. ,  (Rooduijn and Pauwels 2011; Rooduijn and Akkerman 2017)  and meets some practical needs.
We further validated the method using the Global Populism Database (GPD;  Hawkins et al. 2019). The significant correlations that we obtained show that the score captures the main dimensions of populism and can, thus, be used as a proxy of parties' levels of populism. Finally, to further assess the robustness of the method and whether it can be applied to different textual sources (or datasets) without leading to substantially different outcomes, we repeated the analysis for Italy only, building a score using 2, 151 sentences drawn from speeches of Italian leaders in electoral campaigns, talk shows, and TV programs from 2006 to 2019, and another score using the same manifesto dataset where sentences were manually coded as populist or not. Our method addresses four main issues associated with measuring populism across parties.
Results show that the average amount of populism has significantly increased in Italy, whereas other countries show weaker growth or uneven trends. Our results suggest that textual data are a promising tool for expanding political research possibilities on measuring populism and its trajectories. The paper develops as follows: in Section 2, we introduce the literature on populism and its measurement. In Section 3, we describe the datasets used to train the Random Forest algorithm. In Section 4, we describe how we trained the algorithm, the preprocessing procedure of the data, the algorithm accuracy, and the final derivation of the score. In Section 5, we validate the score, comparing it with expert-surveys' scores and other text-based measures of populism. We discuss the possibility of using different datasets (political speeches and manually coded data) for the score derivation.
However, notwithstanding the relevant achievements of some of the methodological contributions proposed to date, some of them have structural limitations, particularly when they heavily rely on the process of human annotation for the analysis of vast corpora. Reducing biases related to evaluations of individual readers  (Ray 1999 ) and ensuring intercoder reliability requires the action of numerous coders involved in resource-intensive coding processes, and this might not always be the case. In dictionary-based approaches, choosing one dictionary or another can lead to substantially different results  (Aslanidis 2018) , and even establishing whether the dictionary is valuable or not is far from easy  (Grimmer and Stewart 2013). Furthermore, the nature of comparative analyses involves multilingual datasets, and the coding of such extensive corpora can hardly be done by one or two researchers alone.
The Dataset The dataset includes 268 electoral manifestos from 99 parties with a total of 243, 659 sentences. We downloaded 229 manifestos from the MPD  (Krause et al. 2018 ) and integrated the corpus with 39 extra manifestos taken from parties' and governments' official sources to have a more accurate representation of the party landscapes in the electoral rounds. Due to the uneven availability of data and the difficulty in integrating it with new manifestos, not all countries have the same number of electoral rounds, although a minimum of three waves is ensured to improve temporal comparison. For instance, Italian manifestos cover the period from 2006 to 2018, Dutch manifestos from 2002 to 2017, and Spanish manifestos from 2004 to 2019. We did not control for manifesto length, although it can be very heterogeneous from one party to another, and kept all the sentences included in the manifestos to improve the training.
Data Processing We prepared the dataset following standard procedures in automated text analysis (for more details on preprocessing, see  Kannan and Gurusamy (2014) ). We split each national corpus into sentences according to the structure of the electoral programs and the language. Sentences were preprocessed, turning all words to lowercase, and removing punctuation, numbers, and stop words (e. g. , and, but, or, and that). We stemmed the remaining words and removed unnecessary space between words. We then converted each sentence into a "bag-of-words," in which the words' order is irrelevant. A bag-of-words is a vector X = [X 1. X N ] assigned to each sentence, where N is the total number of words in the manifestos of a selected country. Each element X i corresponds to a specific word, being X i = 1 if the word is present in the sentence and X i = 0 otherwise.
In the case of validation, the values shown represent the mean and standard deviations of the AuROC over the different split of the K-Fold cross-validation. 1  shows the fraction of sentences belonging to populist manifestos (i. e. , labeled as Y = 1). This fraction is typically around 0. 2. Finally, we divided each national dataset into two parts, one used for training and validation and the other for testing. For every country, 70% of the sentences were used for training and validation to let the model learn to make the prediction and tune its hyperparameters using K-Fold crossvalidation, and the remaining 30% was used to test its predictive power on out-of-sample data and to build the score.
In Table  1 , we show the fraction of sentences belonging to populist manifestos in each country. There is not a significant unbalance with those belonging to nonpopulist manifestos; therefore, we did not apply weights to reduce it during training. However, we can argue that weighting the data would improve the classification accuracy. Once we found the best hyperparameters' combination, we retrained the model on the whole training set. Finally, we used the whole training set to find the best threshold for the probabilities given by the Random Forest algorithm using the Receiver Operating Characteristic curve and the Youden index  (Ruopp et al. 2008). This latter procedure further increases the model's final accuracy, choosing the best combination of true positive and false positive rates.
Validation of the Score We validated the score using two different approaches. On the one hand, we relied on populismrelated dimensions drawn from expert surveys (i. e. , CHES  (Polk et al. 2017)  and POPPA (Meijers and Zaslove 2020b)) and the GPD database score  (Hawkins et al. 2019). We selected two dimensions relevantly connected to populism from the 2017 CHES and five attributes of populism from the 2018 POPPA. In the CHES, a team of experts estimate the party positioning of national parties regarding integration, ideology, and policy issues in several European countries. In the 2017 wave, Austria is not covered, and we only have data for five of the countries in our analysis. In the POPPA, 294 country experts ranked positions and attitudes of 250 parties regarding key attributes related to populism, political style, party ideology, and party organization in 28 European countries.
Rather than imposing a specific definition of populism, the POPPA aims to measure the relevant constitutive dimensions that underlie dominant conceptualizations of populism for all the political parties in each party system. Different from the 2017 CHES, this survey is explicitly focused on populist attributes and includes Austria. The GPD measures the level of populist discourse in the speeches of 215 chief executives (presidents and prime ministers) from 66 countries across all continents. The dataset includes more than 1, 000 speeches, mostly between 2000 and 2018. Each speech is coded manually by one or two coders using the "holistic grading" technique  (Hawkins 2009). The use of several validation sources allowed us to assess the results' validity and provide insights into the dimensions that the score captures.
Validation with POPPA. We repeated the validation process using the POPPA dataset  (Meijers and Zaslove 2020b). The attributes that we selected for validating our score are considered as the five components of populism according to the ideational approach by  Meijers and Zaslove (2020a). These attributes include the Manichean vision of politics, the indivisibility of the ordinary people, people's general will, people-centrism, and anti-elitism. Figure  3  presents correlations between our score and the selected dimensions for parties distributed according to the left-center-right classification. 5 Since a measurement of the ideational approach to populism is valid when it captures its five components  (Hawkins et al. 2018) , we also validated our score against a latent populism variable constructed using the five POPPA dimensions as suggested by  Meijers and Zaslove (2020a).
Validation with GPD. For further validation of the score, we used the GPD by  Hawkins et al. (2019). We only used the nine parties available both in our and their database for the same years for the validation. If we exclude the Spanish Socialist Workers' Party (PSOE), which stand out as outliers, there is a significant correlation between the two scores (ρ = 0. 69 with p < 0. 001). However, for a correct validation, we should have used the same corpus as the GPD, because manifestos and speeches can exhibit different levels of populism. Furthermore, the GPD scores measure the populism levels of leaders, whereas our scores measure the populism levels of parties. As a consequence, there can be discrepancies between the two measures. For example, according to our classification, the PSOE ranks significantly lower in its level of populism, whereas, according to the GPD, this party exhibits a more significant degree of populism.
Comparison with Leader Speeches' and Manually Coded Datasets. To check whether our methodology can be applied to different textual sources and lead to substantially different outcomes when using a corpus made of manifestos rather than one comprising leaders' speeches, we repeated the analysis, building a score using 2, 151 sentences drawn from speeches of Italian leaders in electoral campaigns, talk shows, and TV programs from 2006 to 2019. The method of derivation of the score is the same as the one used in Section 4. Moreover, even the method that we proposed for labeling is resource-efficient, it could lead to less accurate classification than the one obtained with training based on a manually coded corpus. Whereas our method models the probability that a sentence is drawn from a manifesto of a prototypically populist party, a method using a manually coded dataset for training would model the probability that a sentence is populist, regardless of the party.
For assessing the effects of building the score with different datasets, we performed two separate analyses. First, we used a similar labeling procedure for sentences: we labeled Y = 1 the sentences from populist party leaders' speeches andY = 0 the others. More information about this dataset can be found in Section G in the Supplementary Material. The comparative analysis reveals similar outcomes whether the algorithm was trained with manifestos or with speeches (ρ = 0. 75 and p < 0. 001; see Figure  C  in the Supplementary Material). Second, to assess the potential of using manual coding to train the model and to check consistency between the scores obtained using the two methods, we manually coded the Italian corpus and used it to derive new party scores. More information about this dataset can be found in Section G in the Supplementary Material.
When coding, we gave label Y = 1 to sentences including content that expressed the three basic components of populism: people-centrism, anti-elitism, and the Manichean view of society as intended in the ideational approach  (Hawkins et al. 2018; Mudde and Kaltwasser 2013). Some examples of sentences labeledY = 1 can be found in Table  N  in the Supplementary Material. In addition to concerns related to biased coding and reliability, this extra analysis allowed us to determine if training the algorithm with a manually coded dataset would lead to a more precise quantification of levels of populism in parties' documents. The score obtained by training the algorithm with the manually coded dataset is significantly correlated with the one obtained without manual coding ( ρ = 0. 88 and p < 0. 001).
Fourth, unlike other methods for measuring populism, it obtains updated and fast results with a low allocation of time and resources. Furthermore, our method allows text analysis to be performed even when researchers have little or no polyglot knowledge, an element that is crucial in the perspective of spatial comparisons. We validated our populism scores by comparing them with some populism-related dimensions of the 2017 CHES  (Polk et al. 2017 ) and 2018 POPPA datasets  (Meijers and Zaslove 2020b). We also validated the scores using the GPD  (Hawkins et al. 2019 ), although we highlighted that the crossvalidation should be based on the same corpus or a speech corpus. The scores are significantly correlated with the main attributes of populism, anti-elitism, and people-centrism in particular, as well as with a latent variable of populism built upon the five dimensions of populism proposed by  Meijers and Zaslove (2020a).
Acknowledgments Special thanks go to  Manuela Caiani ,  Steven van Hauwaert , and  Stefaan Walgrave  for their helpful and constructive comments on earlier versions of this study. We are also grateful to  Theofanis Exadaktylos ,  Fabio Sabatini ,  Martin Guzi ,  Tommaso Reggiani , and  Mario Pianta  for the interesting discussions on the topics related to our work. Finally, we express our gratitude to the three anonymous reviewers as well as the journal's editor  Jeff Gill  for their insightful comments.
Thermal Preferences We quantified lizard thermal preferences for both treatment groups using standard procedures in a thermal gradient. After two hours acclimating to ambient temperatures (20 • C) in their home cages, four lizards were placed in individual lanes of a thermal preference arena (each lane 90 × 15 cm). On one side of this arena, we suspended four ceramic lamps (150 W) to create a thermal gradient ranging from 20 to 60 • C. The animals were left undisturbed for a one-hour acclimation period. Using two thermal cameras (model C3, Flir Systems, Wilsonville, OR, USA) placed on tripods above the arena at a distance of approximately 1 m, we captured images of the lizards on the gradient every 5 min for 3 h. Temperature data were extracted from the thermographs with Flir Tools (v. 6. 4, Flir Systems).
We extracted data from the image that had the best angle or clearest image of the lizard at a given timepoint. We used the area box tool to select pixels in the centre of each lizard dorsum. We used an emissivity value of 0. 97, appropriate for reptile skin  [80, 81] , and accounted for ambient temperature and the distance between camera and animal. Using the 36 values for each individual, we quantified the preferred temperature as the mean of the middle 50% (mean of interquartile range  [82] ). Data for one individual were excluded from analysis because this lizard wedged himself partially under the partition and did not move for the entire trial period.
Lizards were prompted to sprint from 2 to 6 lengths of the track (i. e. , until the visually fastest running speed was obtained). Data were extracted from videos using Tracker software  [84]. For each length performed by a lizard, we calculated the maximum speed as the longest distance a lizard traversed in a time step of 0. 2 s. We sprinted lizards from both treatments in the same order of test temperatures, from coolest to warmest, so that lizards in both groups would experience the exact same treatment order and to avoid any potentially detrimental effects of incubation at the highest temperatures, especially in the high-elevation treatment where animals may be more sensitive to high temperatures  [65]. To account for the potential effects of acclimation on lizard performance, after the trials at the highest temperature, we again sprinted all lizards at 15 • C to test for changes in performance over time in captivity and to estimate repeatability of sprint performance over the duration of the experiment.
We implemented models with the lme4 package  [88]  in R. We confirmed normal distribution of residuals with a Shapiro-Wilk test and determined the relative importance of fixed effects using type III sums of squares, correcting denominator degrees of freedom for F-tests  [89]. All data figures were created with the ggplot2 package  [90]. Additionally, we assessed whether sprint speed changed over time in captivity by comparing sprint performance at 15 • C between measures made after 3 days in captivity and after 6 days in captivity with a linear mixed model of log 10 -transformed sprint speed (as above), including the fixed effect of time of measurement and the random effect of individual. Further, we calculated repeatability of sprint performance at this temperature with the rptR package in R  [91] , using 1000 bootstraps and 1000 permutations to estimate 95% confidence intervals and a p-value, respectively.
A C T Thaumatin-like proteins (TLPs, osmotins) form a protein family which shares a significant sequence homology to the sweet-tasting thaumatin from the plant Thaumatococcus daniellii. TLPs are not sweet-tasting and are involved in response to biotic stresses and developmental processes. Recently it has been shown using a proteomic approach that the tuber extract from Corydalis cava (Papaveraceae) contains a TLP protein. The aim of this work was to characterize the structure and expression of TLP from C. cava tubers. The results obtained using a PCR approach with degenerate primers demonstrated a coding sequence of a novel protein, named CcTLP1. It consists of 225 aa, has a predicted molecular weight of 24. 2 kDa (NCBI GenBank accession no. KJ513303) and has 16 strictly conserved cysteine residues, which form 8 disulfide bridges and stabilize the 3D structure.
Mass spectrometry analysis Stained protein bands were excised from the polyacrylamide gel and analyzed by liquid chromatography coupled to a mass spectrometer. Samples were concentrated and desalted on a RP-C18 precolumn (LC Packings, UK) and further peptide separation was achieved on a nano-HPLC RP-C18 column (LC Packings, 75 μM i. d. ) of an UltiMate nano-HPLC system, using a 50 min linear acetonitrile gradient. Column outlet was directly coupled to the Finnigan Nanospray ion source of LTQ-FT (Thermo, USA) mass spectrometer working in the regime of data dependent MS to MS/MS switch. An electrospray voltage of 1500 V and a cone voltage of 30 V were used.
Protein identification algorithm The spectral data was analyzed by MASCOT 2. 2 (Matrix science, London, UK; www. matrixscience. com) search against the NCBInr protein database (NCBI, Bethesda, USA) with a Viridiplantae filter (Green Plants). Mascot search criteria were as follows: protein scores >52 indicated identity or extensive homology (p < 0. 05); with carbamidomethyl (C) and oxidation (M) variable modifications; peptide mass tolerance ±50 ppm and fragment mass tolerance ±0. 8 Da. Protein identifications were accepted when at least two peptide fragments per protein were identified.