dataesr/scientific-paragraphs-categorization huggingface dataset:
05, it is nearly linear. We extracted data on the dynamics of each coating growth from the original research papers. Then we used these data to calculate coefficients of growth function. Having these coefficients, we defined the thickness of coatings having eight bilayers (N = 16). With this, we created a new target value, the thickness of the 8-bilayer coating, which was not dependent on the number of bilayers. In this configuration, the type of polyanion, its concentration and its molecular weight were found to positively influence the thickness of the coating, while the charge density of polyanion negatively correlated with the coating thickness (Fig. 4A ). Three features (on the x-axis) contributed to the coating thickness (on the y-axis) (Fig. 4B ): type of polyanion, polyanion unit molecular weight, and resulting molecular weight.
Coating thickness predictions. After the determination of the most influential parameters, the next step was the build-up of a predictive model. We constructed a Bagging Regressor model  18  to make predictions about coating thickness using ten features from the original data set: presence of HA, presence of poly(styrene sulfonate) (PSS), resulting MW of the polycation and of the polyanion, unit MW of the polycation and of the polyanion, concentration of polycation and concentration of polyanion, the concentration of NaCl, and charge density of polyanion. Two quantitative structure-property relationship (QSPR) regression models were constructed using the selected features, Bagging Regressor and support vector regression (SVR) (Table  1 ). These models have smaller root-mean-square error (RMSE) values for the validation set than the Bagging Regressor constructed using the original dataset features.
From here, we conclude that polymers as specific chemical entities are not good features by themselves, and generating numerical features that describe the chemical properties of polymers can improve the model performance. To get features of a molecule, firstly we had to get information about its structure, which is commonly represented in simplified molecular-input line-entry system (SMILES) format, available in the PubChem database. This information is further used to predict molecular features by deep learning pre-trained models available in the DeepChem library. For each polycation and polyanion, 123 molecular descriptors were generated, therefore each coating in the dataset was characterized by 246 molecular descriptors. Many molecular features have a moderate correlation with the thickness of the coating, so we assume that they can be used to predict this target value (Fig.
Descriptors related to van der Walls forces and also partial charges underline the highly intricate nature of the LbL film formation at molecular level. As the last step, in order to further test the generalization capacity of the final model, we have tested polymers which were not in the training set that are described solely by molecular descriptors. In this configuration, we observed more inaccurate predictions (Table  2 , Fig. 6 ). However, this could be expected, given the size of the training dataset. As we can see, the best results are obtained with the use of the QSPR RFE/Bagging regressor. It can predict the thickness of the coating better than two other models. We can also see that predictions of QSPR models are better than for the model RFE/Bagging constructed with original features, which predicts almost constant thickness values for all coatings (302 ± 25 nm).
Predictions of QSPR models are correlating, and there are four coatings for which both regressors failed to predict thickness correctly (relative error > 100%): PAR30/PSS0. 2, PAR30/CARiota_2, PAR30/PSS4, PAR30/CARiota_1 (Fig. 6B ). Two other coatings, that have inaccurate predictions, contain CARiota polymer which was not in the dataset during training. The fact that the model fails to make predictions for the coatings which are made of unseen polymers confirms the assumption the generalization is not complete. It is interesting that despite this, there is one coating with CARiota for which relative error is small (47%), and the model was able to make an accurate prediction in this case. Below, we discuss some of the possible reasons that may have caused large errors in the predictions made by the best of the constructed models, QSPR RFE/Bagging.
Finally, we combined molecular descriptors and numerical features of the polymers from the original data set to build new models, and these models had better performance for the validation set than the model constructed using the original dataset features, which indicates their greater potential for generalization. In conclusion, the generalization capacities of an algorithmic model predicting coating thickness can be improved by delving into the determining properties of the polymers in the context of LbL film formation dynamics. Harnessing the available data science techniques in biomaterial design and development such as multifunctional coatings will decrease lead time, empirical experimental load and also establish relationships between structure and function, which are otherwise hard to guess or estimate.
Data collection. To collect the data from the literature, we searched articles describing LbL film buildup using PubMed website and Google Scholar (keywords: LbL, polyelectrolyte, film, thickness). In total, 31 articles were found. Among them, only articles specifying the parameters of the film buildup, as well as film thickness, in the text and/or in the figures, were selected. Thus, 19 articles were used in this study  16, [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]. The second dataset consisted of experimental data produced in the laboratory using QCM (Q-Sense, Sweden). For this, a SiO 2 coated-crystal was excited at different resonance frequencies (fundamental frequency, third, fifth, and seventh overtones), and changes in frequency and dissipation were measured during the successive deposition of polymers and rinsing steps (Fig.
Statistical significance of the Pearson correlation coefficient was checked using confidence intervals for p = 0. 05 after Fisher's Z-transformation. Three regression models were built to predict coating thickness: one based on features from the original data set, and two Quantitative Structure-Property Relationships (QSPR-type) models based on the RDKit molecular descriptors. To build QSPR models, firstly the SMILES (simplified molecular-input line-entry system) representations of molecules were obtained from the PubChem database. Then, for each polymer SMILES, 123 molecular descriptors were generated using RDKit Descriptors from DeepChem library  40. Features, that are further used to build models, were selected from original feature space and from molecular descriptors space with the Recursive Feature Elimination method having Random Forest as the basis.
be caused by the too low molecular weight of the PSS polyanion compared to the one that the model has seen in the training set. The smallest polyanion in the dataset used for training was DEX with MW = 7. 2 kDa, hereas in the validation set we have coatings where PSS has lower MW values (0. 2 and 4 kDa). Moreover, in the training set, PSS MW was greater than in the validation set (60-70 kDa), so the combination of polyanion/MW of polyanion is new to the model. Because the model has made inaccurate predictions for the kind of polymers that it did not see during training, we assume that it can not generalize well and this is the reason for the prediction failures.
thickness (nm) Predicted thickness, QSPR RFE/Bagging Predicted thickness, QSPR RFE/SVR Predicted thickness, RFE/Bagging be caused by the too low molecular weight of the PSS polyanion compared to the one that the model has seen in the training set. The smallest polyanion in the dataset used for training was DEX with MW = 7. 2 kDa, hereas in the validation set we have coatings where PSS has lower MW values (0. 2 and 4 kDa). Moreover, in the training set, PSS MW was greater than in the validation set (60-70 kDa), so the combination of polyanion/MW of polyanion is new to the model. Because the model has made inaccurate predictions for the kind of polymers that it did not see during training, we assume that it can not generalize well and this is the reason for the prediction failures. PAR30/DEX40 51.
Life Cycle Inventory (LCI) data are the backbone of Life Cycle Analysis (LCA). They reflect modelling choices made by the LCA practitioner that can strongly influence the results of environmental impact calculations. The high sensitivity of these data are the reason why they must be accurately documented and tend to the highest possible quality. High quality, well documented LCI data can also be shared between LCA practitioners, allowing productivity gains and better research reproducibility, in particular between practitioners who do not work on the same life cycle inventory database. Unfortunately, SimaPro (PRé Sustainability B. V. , 2020), one of the most used LCA software solutions, has only limited features to document, review and exchange LCI data. ELDAM has been developed to fill this gap.
Statement of need ELDAM is constituted by an LCI documentation and review procedure, a visualization and editing spreadsheet format and a feature-rich utility software written in Python. Metadata used to document LCIs are extended with a focus on data quality characterization  (Biard et al. , 2015). A review procedure inspired from scientific peer review helps to improve the quality of LCIs (Figure  1 ). The use of a spreadsheet based format for LCI editing, documentation and visualization has several advantages. It is a well known interface for most users and can be easily shared with non LCA experts  (Coste et al. , 2018). It also allows easy versioning of the LCIs with a separate sheet for each version. This versioning system is at the basis of the review procedure but can also be used to track changes made by a single user.
The main feature of the utility software is the conversion between ELDAM's spreadsheet format and SimaPro import and export formats. It can be used as a Python package or through its Qt (The Qt Company, 2020) based graphical user interface (Figure  2 ). Its object-oriented design makes it very modular and easy to extend. ELDAM has been used in various research projects  ((Gentil et al. , 2020) ,  (Risch et al. , 2020) ), helping to improve and characterize LCI data quality. Its review procedure has proven very helpful for auditing the LCIs produced by LCA beginners such as interns.
Use case scenario The following use case scenario illustrates the workflow proposed by ELDAM. An LCA practitioner creates an LCI in SimaPro and exports it in the. xlsx format. Then, he converts it to an Elda file (the. xlsm based spreadsheet format used by ELDAM) either using the GUI or the Python API. Opening the Elda file with a spreadsheet software like Excel or LibreOffice, the practitioner documents the LCI dataset using the metadata fields provided by ELDAM. Once the documentation step is finished, he sends the Elda file to a reviewer that will ensure of the consistency of the dataset. The reviewer then creates a new version of the dataset that will be materialized as a new sheet in the file. She highlights all of her suggested modifications and returns the file to the original author.
The original author addresses the reviewer's remarks in a new version and returns the file to her. This review loop continues until the dataset is considered valid by the reviewer. Once the review process is over, the author can convert the Elda file to a. csv file using the GUI or the Python API and import it back to SimaPro. from eldam. core. simapro import SimaProXlsxReader, SimaProCsvWriter from eldam. core. elda import Elda # SimaPro export file -> Elda spreadsheet process = SimaProXlsxReader('path/to/xlsx/file'). to_processes()[0] elda = Elda() elda. add_version(process) elda. save('path/to/elda') # Elda spreadsheet -> SimaPro import file elda = Elda('path/to/elda') process = elda. read_last_version(). to_process() SimaProCsvWriter(process). to_csv('path/to/csv').
, 2002; Lotterhos et al. , 2018). Recently developed software such as the BayPass package  (Gautier, 2015) , have overcome some of the limitations of the GEA analyses mentioned above. On one hand, this software identifies those genetic variants with statistically different allele frequencies between populations and those associated with environmental variables, while taking into account the covariance between population allele frequencies due to, for instance, the joint demographic history of the samples analysed  (Gautier, 2015). On the other hand, this software includes different modules based on different models: a single-covariate regression model where the association is estimated for each covariate, and a multiple-covariate regression model where the association is estimated for several covariates assumed to be orthogonal  (Gautier, 2015).
| Environmental variables We downloaded environmental data from four different sources: WorldClim  (Fick & Hijmans, 2017) , Copernicus  (Hersbach et al. , 2020) , the US Naval Observatory (https://www. usno. navy. mil/ F I G U R E 1 Drosophila melanogaster samples used in this study. Samples were collected across Europe and North America east coast (see Table  S1 ) in four main climate zones and seven subclimate zones (depending on precipitation and level of heat), according to the Köppen-Geiger climate distribution  (Kottek et al. , 2006)  [Colour figure can be viewed at wileyonlinelibrary. com] USNO/astro nomic al-appli catio ns/data-servi ces/data-services) and The Astronomical Data Portal UK Hydrographic Office (http://astro. ukho. gov. uk/). From WorldClim, we used the 19 Bioclimatic variables, which are derived from the monthly temperature and rainfall values from the 1970-2000 time range.
We used the R package raster (v. 2. 6-7) for downloading this data. In addition, we also used year-specific environmental variables from the year previous to the collection date of each sample. For the year-specific variables, we used ERA5 "hourly data on single levels from 1979 to present" database from Copernicus, to obtain data on temperature (2 m temperature), rainfall (Total precipitation), evaporation (Evaporation), solar radiation (Clear-sky direct solar radiation at surface), wind (10 m u-component of wind and 10 m v-component of wind), and soil type. This data was downloaded as GRIB files and parsed using ecCodes package (v. 2. 8. 2). Finally, daylight hours were obtained from the US Naval Observatory for the European data sets, and from The Astronomical Data Portal UK Hydrographic Office for the North American data set.
For the year-specific variables, similar variables as the ones in WorldClim were constructed with the data from the year previous to the collection date using different python scripts (v. 2. 7. 12). In total, we analysed 59 environmental variables related to temperature (11), rainfall (8), evaporation (11), solar radiation (7), wind (14), daylight hours (7) and soil type (1) (Table  S3 ). We tested whether those variables that are common between WorldClim and Copernicus (i. e. , temperature and rainfall) but that were obtained from different time ranges (average data from 1970 to 2000 and year-specific data from the year before the collection date) were significantly correlated using a Spearman correlation test (ρ > 0. 8) (Table  S3A ). Most of the eight rainfall related variables were not correlated (six in Europe, six in Europe Summer, five in Europe Fall, and eight in North America), while most of the 11 variables related to temperature in Europe and NA data sets were correlated (seven in Europe, eight in Europe Summer, nine in Europe Fall, and seven in NA; Tables  S3A-B ).
For variables that were not correlated, we included the two values of the same environmental variable in the analysis. When the correlation was significant, we performed the analysis using the variable corresponding to the WorldClim database. In order to study the correlation among environmental variables, we performed Spearman rank correlation test for each pair of variables in each data set using the R function cor. test() and R package corrplot (v. 0. 84) (Table  S3C ). We considered and reported as strong correlations those with Spearman's ρ > 0. 8. We found that solar radiation and daylight hours in Europe and in North America were highly correlated. In North America, but not in the European data sets, we also found that temperature, solar radiation, and daylight hours variables were highly correlated (Figure  S1A-D ).
We also performed a PCA of the environmental variables for each of the four data sets analysed using stats (v. 3. 5. 2) and plotnScree function in nFactors (v. 2. 4. 1) R packages (Table  S3D1 -4). We found that temperature and solar radiation explained most of the variation of the PC1 both in Europe and Europe Summer data sets. In the Europe Fall data set solar radiation and daylight hours explain most of the PC1 variation while temperature and rainfall explain most of the PC1 variation in North America (Table  S3D1 -4).
The genotyping input files contain the read count data (reference and alternative) per site and per sample. For SNPs, this information was obtained from the VCF files, while for the transposable elements (TEs) the information was obtained using T-lex: the absent read count information was used as the alternative read count, and the present read count information as the reference read count  (Bogaerts-Márquez et al. , 2019; Fiston-Lavier et al. , 2015). To generate the input files for the North America samples, VCFs were parsed using the poolfstat (v. 1. 1. 1) R package  (Hivert et al. , 2018). For the three European data sets, VCFs were parsed using python scripts, and bash and awk command lines. TE frequencies were added to the data using python scripts. Invariant and nonbiallelic positions were removed from each data set.
We ran BayPass for autosomes (2L, 2R, 3L and 3R) and X chromosome separately because the autosome and X-linked variants have different haploid sample sizes as samples were obtained from male flies, and more importantly autosomes and X-chromosome have different demographic histories (e. g. ,  Clemente et al. , 2018). We ran BayPass under the core model for the computation of the XtX genetic differentiation statistics for each data set separately (Europe, North America, Europe Summer and Europe Fall). As the number of SNPs in the autosomes in the four data sets was large (Table  1 ), we used a subsampling strategy as in  Gautier et al. (2018) , dividing each data set into 50 subdata sets containing only one SNP every 50 SNPs. We run the 50 subdata sets in parallel, thus all the SNPs available were used for the analysis.
Prior to further analysis, we removed SNPs and TEs with very low allele frequency (MAF <0. 01) based on the mean of the posterior distribution of the frequency of the reference allele across populations for each site (included in column M_P of the BayPass output summary_pi_xtx. out). To obtain a calibrated estimator of the XtX statistic, we relied on the XtX* estimator recently described in  (Olazcuaga et al. , 2020). We further derived bilateral p-values assuming that XtX* follows a Chi-square distribution with npop degrees of freedom under the null hypothesis of neutral differentiation  (Olazcuaga et al. , 2020). To control for multiple testing, we estimated the associated q-value with the R package qvalue (v 3. 9)  (Storey & Tibshirani, 2003). For further analysis, we focused on the SNPs and TEs with the most highly significant XtX* scores (top 0.
| Genome-environment association analysis The so-called BayPass STD model extends the previous analysis to evaluate association of the genetic variant allele frequencies with population-specific covariates. We ran this model with the environmental variables previously described for each data set, and default options except for the -scalecov option that was used to scale each covariate. As we did with the previous model, we run the four data sets independently, as well as autosomes and X chromosome separately. For the autosome data sets, we also used the subsampling strategy mentioned above. Three independent runs were performed for each data set (using the option -seed). The support for association between the genetic variants and the environmental variables was assessed using a Bayes factor (BF) measured in deciban (dB) units and estimated with an importance sampling algorithm from the MCMC samples  (Coop et al.
However, there are studies reporting similar responses to cold and desiccation stress  (Sinclair et al. , 2007). Among the top five genes with the highest BF scores in the four data sets, we found several associations with Annual mean temperature and Annual mean solar radiation (Table  5 ). We also tested whether candidate genes with SNPs associated with environmental variables were enriched inside cosmopolitan inversions (Figure  2 ; Corbett-Detig & Hartl, 2012). We found an enrichment of significant SNPs in the In(2L)t inversion in the Europe Fall data set and in the In(3R)P inversion in the North America data set (Fisher's exact test p-value <0. 001; Table  S6B ). The In(2L)t inversion in the Europe Fall data set was enriched for SNPs associated with temperature variables while the In(3R)P in the North America data set was enriched for SNPs associated with rainfall, solar radiation, and wind variables (Table  S6C -D).
melanogaster  (Matsuo & Kamikouchi, 2013; Yorozu et al. , 2009). For each data set, top 5 genes with SNPs located in the gene body or upstream region (< 1kb) with the highest significant XtX* values and their associated phenotype (see Table  S12 ). Abbreviations: Eu, Europe; EuF, Europe Fall; EuS, Europe Summer; NA, North America. TA B L E 2 Candidate genes showing the most significant population differentiation patterns.
Out of these 32 genes, 14 were associated with a different environmental variable group in each continent, and 12 were associated with different variables in the same group. This suggests that although there is a pattern of parallel adaptation, there may be different environmental pressures which may drive adaptation for the same genes. We also assessed whether samples collected in European populations in summer and fall differed in their association with environmental variables. Recent studies have shown the role of temperature in seasonal variation  (Machado et al. , 2019). We found that in the summer and in the fall data sets the majority of genes were associated with temperature and rainfall (Figure  3c , d, Table  4 ). However, there was a substantial proportion of candidate genes associated with evaporation in Europe Summer but not in Europe Fall (33% vs.
11%; Table  4 ). On the other hand, there were more candidate genes associated with wind variables in the Europe Fall than in the Europe Summer data set (31% vs. 11%; Table  4 ). These results suggest that different environmental variables, evaporation and wind, might play a role across seasons. However, temporal data series from several years should be analysed to confirm these results. Finally, we identified four TE insertions showing significant population differentiation patterns, five TE insertions associated with an environmental variable, and five insertions showing both. We described as candidates for the first time three of these TE insertions, FBti0019862, FBti0020306 and FBti0061428, which are associated with environmental variables and showed significant population differentiation patterns, as well as FBti0019164 only reported in  González et al.
DATA AVA I L A B I L I T Y S TAT E M E N T SNP genotyping data for European Samples are available at the public repository DIGITAL. CSIC (http://hdl. handle. net/10261/ 180630) and North American samples are available in the Dryad database https://datad r yad. org/stash/ share/ rHMqJ SiXuG X12eB YyPvKE_.