dataesr/scientific-paragraphs-categorization huggingface dataset:
3 Database for RRM-RNA Interactions. Data integration and warehousing; Residue interaction calculation sql 500Mb Complete and comprehensive database about RRMs (InteR3Mdb) 4 Protein bound ssRNA fragments (PSRNA) Protein interaction data; Protein binding sites ; Residue interaction calculation pdb, json <5 Gb List of characteristics for protein-RNA contacts extracted from PDB complexes. Each RNA is considered as a set of overlapping trinucleotides. Protein-nucleic acid interaction Protein-RNA interaction data produced from 5 Phage-display results for novel RRM design analysis, Protein interaction experiment; Protein interaction xlsx >1Gb processing and analyzing phage display results corresponding to in vitro binding experiments data of RNA/ ssDNA to Sex-lethal protein. 6 NMR data analysis for hnRNP A1 protein Structural biology; NMR; Protein interaction data xlsx >1Gb Data produced from processing and analyzing NMR data collected on hnRNP A1 7 NMR data analysis for Musashi-1 RRM domains NMR; Protein interaction data xlsx >1Gb Data produced from processing and analyzing NMR data collected on human Musahi-1 protein domains (RRM-1, RRM-2, RRM1-2 and RRM1-2 DM).
We build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor's construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R 2 greater than 0. 9 and mean absolute errors (MAE) smaller than 0. 23 eV both for the training and prediction. The highest R 2 of 0. 95, 0. 98 and the smallest MAE of 0. 16 eV and 0. 10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly improved as compared to those of database features-based predictions.
We also find that the hybrid features slightly reduce the overfitting despite a small scale of the dataset. The relevance of the descriptor-based method was assessed by predicting and comparing the electronic properties of several 2D materials belonging to new classes (oxides, nitrides, carbides) with those of conventional computations. Our work provides a guideline to efficiently engineer descriptors by using vectorized property matrices and hybrid features for predicting 2D materials properties via ensemble models. Data is one of the core rudiments that feeds artificial intelligence (AI) algorithms for training and data-driven inference. In the field of materials science, many efforts for discovering novel functional materials and their properties with help of the power of data and AI models have been intensely dedicated, providing alternatives to classical computation techniques such as work-horse computational and quantum chemistry methods 1,2.
2D materials band gap, which is indicative of electronic structures and band offset in 2D heterostructures, is a relevant example regarding the use of ML in the characterization of intelligent data cycles  20, 21. Besides algorithms employed to predict these properties, the descriptor engineering and feature selection are pivotal factors for reducing the error loss in the training process and for improving the prediction. Universal descriptors based on a set of feature vectors, which is constructed by structural inputs, have shown a good estimation of important properties  22. However, such a method working solely with specific features could touch limit of physics description when downsizing the dataset size. Up to date, the dataset size of 2D materials is in general small since theoretical and experimental data regarding 2D materials are scarce.
A graph-based multilayer descriptor was introduced to discover new magnetic 2D materials endowing stable magnetic ordering  23. The descriptors were built based on multilayer-based crystal graph that induces a set of more than 230 features even after having used pooling technique to reduce the dimension. Although the results are promising, the approach seems to be tedious because of involvement of high number of features and expensive calculations. Here, we make use of Computational 2D Materials Database (C2DB) dataset of about 4000 entries, which is reliable data based on DFT calculations, to introduce novel descriptors for boosting the prediction of the electronic structure properties of 2D materials  24, 25. Complementary to the study conducted by Zhang et al. 20  where the models (R 2 > 90%) are strongly biased (the feature "gap-nosoc" values are too close to the predicted values), our approach consists of introducing descriptors vectorized from property matrices for ML cycles.
The descriptors are simple to compute and directly related to electronic properties. We combined these descriptors with empirical function-based descriptor (electronegativity)  26  to strengthen the efficiency and the stability of the descriptors against the change of target properties and models. Such a combination permits to considerably reduce the dimension of the input features and results in a high performance of models as compared to previous studies. Furthermore, the proposed descriptor processing is simple to implement, robust and it requires low-computation cost. The efficiency of the constructed descriptors was evaluated through the performance of the ML cycles where the accuracy (R 2 > 90%, mean absolute error (MAE) down to 0. 1 eV) is much higher than the performance obtained with database-based features.
Methodology Additional descriptor construction. To do so, we have selected relevant atom-atom interactions based on elemental parameters to generate molecule-level descriptors that closely describe their impact on target properties. We use in this study 8 descriptors in total. Four descriptors (number of atoms, cell volume, molecular mass and formation heat) are picked up from the C2DB database. Four additional descriptors were created according the following procedure. One descriptor (electronegativity) was calculated by using empirical equation  26. For instance, the electronegativity of a molecular formula A a B b C c is expressed as χ = (A a B b C c ) 1/(a+b+c). The three remaining descriptors being vectorized descriptors (covalent radius, dipole polarizability, ionization energy) were built based on atomic property data  27.
e. energy states, are unique for each property matrix; (ii) provide evidence for an efficiency of this hybrid approach, i. e. combination of the ready-to-use features and the simply-built features regarding the property-prediction performance on the limited size of the dataset (around 4000 entries for C2DB). Since the molecule lengths (number of atoms) are different, for the sake of data and input consistency, we impute the missing data of short property vectors with zero eigenvalue (zero state) to achieve the longest molecular length. Figure  1  describes the scheme of data flow that shows the DB, descriptor builder, model solvers and output inference. Model selection and machine learning. Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB) were selected to perform the inference on the band gap and the work function of 2D compounds in the C2DB dataset (see Supplementary Information for basic explanation of the models).
These algorithms are tree-based models, in particular gradient boosting algorithms which are well suitable for properties classification and regression as compared to other algorithms  28. The dataset was randomly split into training and test sets with a ratio of 4:1, respectively. For each ML model, the training was carried out by tuning hyperparameters with help of grid search (see Supplementary Information) in scikit-learn  29  and python 3. 8 environment. The search permits to select the best model based on the best score (lowest function loss) for a given training set and given ensemble of features. We also employed the cross validation for training and prediction to reduce the randomness effect due to the dataset splitting and the overfitting of the data. The cross-validation sampling is generated by k-fold cross validator for a regression estimator.
Results and discussion The outcome evaluation is based on the feature selection: 4 database-based features as reference (feature set 1) and 8 hybrid features (feature set 2, composed of the set 1 and 4 additional constructed features). The details of the features for model training are given in table S1 (Supplementary Information). The first set of features is available in C2DB DB and is intuitively relevant to the target properties. These features are basically related to chemical and crystalline structures. We note that since the dataset and the models are pre-defined, less the number of DB features is, more the generated features-based ML assessment of inference is convincing and generalizing. The comparison with DB features is to show how our descriptor engineering could be simple but as efficient as complex descriptors for a same prediction purpose.
The model could capture significant trend of the bandgap variation although this set of features is not much sensitive to the bandgap variation. The MAE score is much lower (50%) for the RF regressor compared to those of two other models (Fig. 3a ). The performance of the models on the test set is quantitatively assessed with the help of MAE metrics (Fig. 3b ). The MAE value is of 0. 42 eV for RF whereas GB  and XGB predict the unseen dataset with an error of about 0. 45 and 0. 46 eV, respectively. The slight difference in MAE values (training versus test sets) is indicative of an overfitting effect for the RF model. The overfitting phenomenon generally occurs when the amount of data is small and there exists a certain inconsistency of data. Apart from this common issue, we raise two major specific reasons regarding our ML process: tiny number of features (set 1) which comes from only 4 descriptors available in C2DB DB; inappropriate tree-related hyperparameters (depth of trees, etc.
2b ). The MAE errors calculated for the test set were also greatly improved, around 0. 35 eV for all models, indicating a decent performance of the models over the test set (Fig. 3b ). It's worth noting that the error generated by ML for C2DB dataset is below the error range of experiments versus theoretical simulations which is in general between 0. 2 and 0. 4 eV  31. These findings clearly suggest that adding constructed features is beneficial for the training process and the model could catch the data tendency. This also indicates a strong relationship between the constructed vector features and the predicted band gap of 2D materials. Indeed, the selected vector features derived from the property matrices present physico-chemical relationship with respect to the electronic structure of molecules, resulting in a certain degree of dependence of the band gap on those features.
Work function. We now make use of the concept of generated features to predict work function which is also an important property of the electronic structure. The work function is a key parameter enabling to assess electrical contacts in 2D materials with metallic electrodes and the band alignment in 2D heterostructures. These concerns are of high importance for the 2D-materials-based applications in electronics, photovoltaics and photodetectors  32, 33. A rapid, low-resource and accurate prediction of such a property would be of high benefit to speed up experimental studies. We first consider the set of DB-based features (set 1). We find that the work function was predicted with high accuracy (R 2 > 0. 8 and MAE about 0. 2-0. 3 eV) for the training dataset (Fig. 4 ). The test dataset shows an overfitting for both 3 models as observed previously (Fig.
4d ). The MAE scores of the test dataset are around 0. 62 eV which is relatively high compared to those of the training set (0. 26 eV). The overfitting is expected due to the small scale of the dataset as aforementioned. The discrepancy between the training and test MAE values are found quite high, especially for GB model which shows the highest variation of 0. 38 eV despite its R 2 value of 0. 92. This means that the models, especially GB model, overfit the training set and catch much noise of the data. The results of the additional features are shown in Fig. 5. The training models are greatly improved in terms of R 2 and MAE scores. This finding indicates a booster effect of the additional features on the training process, which evidences a correlation between the generated features and the predicted property.
1 eV which is non negligible. Note that such a report of overfitting, which is necessary for ML processes was not mentioned in the previous studies on property predictions  15, 20. For a comprehensive assessment of the predicted values, we further calculate the mean absolute percent error (MAPE) which is also an accuracy indicator of the model performance on the test set. All MAPE values generated by the models are less than 10% (9. 4%, 9. 1% and 8. 9% for RF, GB and XGB, respectively). This means that the models are stable and properly perform on the test dataset. These results unveil the fact that together with empirical function-based property, descriptor engineering in the framework of vector-based features greatly improves the model training and its performance on the unseen data. Therefore, the results allow to validate our approach in processing material data inputs/features in the ML training and data-driven predictions.
For greater values of band gap, the models could catch the variation tendency. However, GB and XGB models poorly predict the values of 2D oxides and nitride while RF performs quite well the prediction for these materials except for MoO 3. We anticipate that GB, XGB are likely sensitive to a poor distribution of the training dataset with respect to this range of bandgap values (typically 3 -4 eV) as shown in the band gap histogram of the dataset (Fig. S2 , Supplementary Information). The data concerning MoO 3 we collected are perhaps not reliable for inference. For the work-function prediction, the three models infer really well the trend of the work function in 2D oxides and nitride. Interestingly, the XGB model nicely predicts the values of all 2D oxides and nitride in the error range of 0.
Acknowledgements The authors acknowledge the financial support from  NANOFUTUR  project handled by  ANR  in the framework of  Programme d'Investissement d' Avenir  ( ANR-21-ESRE-0012 ) for initiating this study. This work benefits from a France 2030 government grant managed by the  French National Research Agency  ( ANR-22-PEEL-0011 ). MTD thanks the  Université Côte d' Azur  for supporting this work within  CSI 2021  project. ARP thanks support from  UCA-JEDI Investments  in the  Future project managed by ANR  ( ANR-15-IDEX-01 ),  Doeblin Federation  ( FR 2800 ),  INP-CNRS Tremplin 2022  and  Dialog 2022.
Policy Implications The data analysed in this chapter were collected in the waning days of the Obama presidency, and represent the perspectives of workers in different occupational categories status quo ante. That is, the data were collected during "normal" timesboth before the Trump Administration's four-year assault on American workers' health and labour rights  (Madland et al. , 2018; Woolhandler et al. , 2021)  and prior to the global COVID-19 pandemic. Clearly, policies designed to extend working life had disproportionately affected some groups of workers in this research, namely the janitors and the health care workers. The teachers in the public school system were protected from EWL by their occupational pension scheme. Teachers in private schools were possibly more directly affected by EWL, but were better paid with good working conditions, and so could more afford to contribute to private pensions, 401(k)s and better manage their preferred exit trajectory from paid work.
Context, Methods and Material The empirical material centres on men aged 50 and older, working in the metal industry in two national contexts, Portugal and Sweden. Metal industry work is often physically hard, a fact which has been related to early retirement in research (see e. g. ,  Lund & Villadsen, 2005; Pohrt & Hasselhorn, 2015). The data collected in Portugal were part of a small study that focused on the life histories of men working in the metal industry in a region hit by deindustrialisation. These men were predominantly blue-collar workers, with low educational levels and ages ranging from 55 to 63 years old, thus providing an important insight into the lives of a specific disadvantaged group of workers. The average retirement age in Portugal, at the time of the data collection (  2008 ), was 59.
7 years of age  (INE, 2020). For the Portuguese study, the data material analysed was collected in 2009 and consists of interviews with ten men made redundant by companies facing readjustments and bankruptcy at the onset of the 2008 economic crisis. When the interviews were undertaken, the industry was already facing the challenges inherent to globalisation, including competition at the international level, and a decreased demand for labour. Workers in late stages of professional career, in jobs deemed non-business critical, particularly manual jobs, were increasingly being made redundant. Many of these workers had been employed in the same company for more than a decade, some even for 20 years or more. The dataset refers to men that had been working in two different companies in a region that has been experiencing deindustrialisation.
New employment policies for flexible employment, training support from the employers and their obligations to employ persons with disability that will be in place in 2020, might further improve the employment status of older workers. Active engagement of all community stakeholders such as business organizations, trade unions, and civil society is very important in the context of the EU integration process of Albania. In Albania there are no adequate research institutions covering employment policy issues such as European Employment Policy Observatory, Eurofound, Cedefop or European Training Foundation. Albanian government with the support of IPA and international donors is developing the evidence-based policy and statistical data based on the Eurostat methodology. Even with the legal improvements, the level of social partners' involvement in labour market governance, in particular that of workers' unions, need more attention.
They are shaping their different experiences in subsequent phases of life, as well as the specific nature of their cultural capital. Above mentioned differences in history and social welfare regime have been basis for the inclusion of the countries to presented below analysis. In the analysis, we return to the period of fundamental systemic changes in Europe in the analyses: in the 90s, when Central and Eastern Europe countries started to become independent from the Soviet Union in terms of their economic and political systems. As it has been stated earlier, the main analyses are based on data collected in 2012 in the frame of World Values Survey when the data concerning seniors have been part of the study. 16 https://www. worldvaluessurvey. org/ WVSDocumentationWVL. jsp (consulted 22 May 2021) The author of the chapter is taking part in the study since 1990 being responsible for its Polish part.
Data We use data from the first Austrian PUMA survey that was conducted in spring 2016 by the national statistical office of Austria  (Seymer, 2017). The survey was based on a random sample among 4,000 households that participated in the Austrian Labour Force Survey (Mikrozensus) in the second quarter of the year 2016. The persons aged 16-74 who participated in the Labour Force Survey (computerassisted telephone survey, CATI), were invited to participate also in an onlinesurvey that was designed by a group of researchers from Austrian universities. Those who agreed to participate (1,548 respondents) received an invitation (postal letter) to participate in the online-survey and the log-in details (push-to-web design), and within two weeks a reminder. The majority of potential survey participants were offered a small pre-incentive.
2  The survey consisted of about 100 items, covering respondents' sociodemographic characteristics, employment status, self-rated health, working conditions, and attitudes on a set of topics (e. g. , future plans, taxes, retirement plans). The final survey sample of those responding to the online-survey involved 1,051 respondents aged 16-75. Within this survey, we designed a module that collected data on respondents' chronological and subjective age (SA), health status and working conditions in the current or last job (see Box 8. 1 for detail). The sample of analysis was restricted to those aged 45-75, who were either already retired (26% of this age group), inactive or in (self-)employment. 3 The age restriction reduced the sample to 530 persons with valid information on basic socio-demographic characteristics (gender, age, education), financial situation, the composition of their household (i.
, 2015)  and Germany data  (Hofäcker & Naumann, 2015). More diversified findings evolve of the comparative study by  Carr et al. (2018) , analysing seven datasets from four countries (United Kingdom, France, Finland, United States). They show that occupational inequalities in work exit at older ages are far from unequivocal across gender and country. The authors report that among men, low occupational grade increased hazard rates of work exit in France, Finland and the US, but they did not find significantly higher hazard ratios of work exit in any of the four UK datasets. Among women, low occupational grade was associated with increased hazard ratios in Finland, the United Kingdom (one dataset) and France  (Carr et al. , 2018). A recent study on the Dutch population showed large educational differences in working life expectancy  (Robroek et al.
Prior Findings: How Does Work Before Retirement Moderate the Relation Between Retirement and Health? In this section, there will be a short revision of the studies that provide evidence of the differentiating effect of retirement on health depending on the quality of work performed before retirement. The studies reviewed focus on large array of objective and subjective health outcomes, i. e. hospitalization for CVD, GHQ depression, SF-12, cognitive function, physical functioning and self-rated health. Among the findings reported in the literature, a study from Italy  (Ardito et al. , 2020) , which uses a large administrative database of social security pension records matched with hospitalized data, shows that delayed retirement increases the risk of hospitalization for CVD only for specific categories of workers, mainly characterized by more disadvantaged working conditions and worse health.
Data The data used to explore retirement patterns are from the 'Gender and Generation' survey carried out in Belgium in 2008. This survey is part of the large programme of surveys developed by the UNECE (United Nations Economic Commission for Europe) and includes a number of European countries, such as France, Germany, Italy, etc. For Belgium, 7163 interviews concerning individuals living in private households and aged between 18 and 79 were conducted. When only the persons belonging to generations where all the members are no longer (or not at all) active on the labour market are considered, this represents a total of 1498 persons declaring themselves to be (pre-)retired. This sample includes only those who have left the labour market. They were either employed or registered as jobseekers, whether or not they had opted for early retirement.