dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondly, for manufactured hazardous substances as such (and/or its constituents and impurities), and for mixtures, materials (virgin and recycled) or final articles made from them, the supplier should provide structured and harmonised safety datasets. Such datasets should include information on concentration of hazardous ingredients, the ingredients' function and intrinsic properties, the hazard and exposure characteristic, the extent of knowledge available on the nonhazardous ingredients and the conditions of safe and sustainable use (exposure scenarios). The legislative framework for obliging producers to provide such data exists; however, implementation and enforcement is lacking. Thirdly, the flow of safety data along supply chains (i. e. , between companies) needs to be improved via digitalisation.
Industries' readiness to invest in a harmonised electronic system for exchange of safety data in the market may need some regulatory support (e. g. , provision of electronic datasets as an obligation). Finally, more knowledge and resources are needed in industry sector organisations, to provide companies with support in organising efficient and effective communication on chemicals safety and sustainability along and across supply chains. 4. Improving the uptake of exposure science innovation into the policy cycle. Scientists and policy makers should take advantage of existing mechanisms (e. g. , stakeholder consultations and scientific input to policy evaluations and impact assessments of existing and new policies), to ensure that policy developments benefit from innovations in exposure science.
ACKNOWLEDGEMENTS Respective affiliations of all authors are being thanked for the support in terms of time and effort. The authors would like to thank  Dr Urban Boije af Gennäs  from the  European Commission Directorate General Environment ,  Dr Georg Streck  from the  European Commission Directorate General Grow ,  Dr Bob Diderich ,  Head of the Environment, Health and Safety Division of the Organisation for Economic Cooperation and Development (OECD)  and  Dr Stephanie Bopp  from the  European Commission 's  Joint Research Centre, Institute for Health and Consumer Protection , for their valuable comments and discussions.
Member States' Competent Authorities, industry and the scientific community should contribute to identify opportunities for linking and consolidating approaches with the ambition to establish internationally recognised standards and tools (e. g. , IPCHEM for monitoring data and the NORMAN network for database systems), supporting and facilitating data sharing, harmonisation and coordinated research and development globally [76, 78, 103, 104]. Implementing common standards in documenting metadata (e. g. , building on the existing IUCLID formatting for use and exposure data) stimulates data quality and acceptance criteria, streamlining data generation, storage and processing. Data owners should raise their commitments to reduce barriers to open sharing of data, information and knowledge. In line with the intention to simplify and strengthen the legal framework, EU institutions should embrace a holistic, cross- policy mindset, where risks are identified based on knowl- edge on overall exposure; and coordinated risk.
They also find that the relationship between economic 1 According to the World Religion Database, in 2015 the distribution of religious affiliation in the world was as follows: Christians 33. 2%, Muslims 24. 0%, Hindus 13. 9%, Buddhists 7. 2%, other religions 10. 1%, while agnostics and atheists represented solely 9. 7% and 1. 9% of the world population respectively. 2 There is evidence of the role of religion in building social and economic trust, either by inducing more trustworthy behavior  (Norenzayan, 2014; Esteban et al. , 2018)  or by enabling adherents to signal to others their trustworthiness  (Iannaccone, 1994; Irons, 2001; Bulbulia, 2004)  and prosociality  Levy and Razin (2012). This raises the question of what distinguishes more from less intrinsically trustworthy individuals, and why this should be correlated with religious adherence.
Acknowledgments The authors are grateful for financial support from the  European Research Council  and the  Institute for Advanced Study in Toulouse. The project has received aid from the  Agence Nationale de la Recherche  ( Programme Investissement d'avenir ), grant number  ANR-17-EURE-0010 ), and the  Swiss National Science Foundation  ( FNS CONFLICT/172814 ). The project received approval from the  TSE-IAST Ethical review board  ( 2016-11-01 ). The  TSE Review Board  had not been created at the time of the fieldwork, but the board agreed to review our experimental protocol retrospectively. The following generously provided comments on the project and on the early findings:  Jean-Pierre Albert ,  Denis Eckert ,  Nathalie Luca , and seminar audiences in Aix-en-Provence, the  University of Delaware ,  City University London, Davis, Geneva, Heriot-Watt University, Paris ,  Santa Fe  and  Toulouse.
Two weeks after the introductory week, students were invited to our experiment at the Frankfurt Laboratory for Experimental Economics (FLEX). In total, 128 (213) students of the first (second) cohort participated in the experiment  4  The online questionnaire used the survey system of the lab at Goethe University (FLEX -the Frankfurt Laboratory for Experimental Economic Research). Unique access codes were sent by e-mail and we incentivized participation by announcing that 10 people would be remunerated for their responses. We elicited their risk aversion using the Eckel and Grossman (2008a) lotteries and paid the 10 randomly drawn subjects according to their answer in that test. We obtain questionnaire data for 195 out of the 341 experimental subjects. Much of the paper focuses on these 195 subjects.
Blockchain for increased trust in observational studies A blockchain is a decentralised database allowing different stakeholders to obtain local copies of data and to be involved in how each data item is integrated into the database. Stakeholders can manipulate data in a privacy-preserving environment and the database should be immutable. Within this environment, data are time-stamped and added sequentially (by block), thus potentially guaranteeing their traceability and chronology (time-ordering). Furthermore, executable code that runs on top of the blockchain (called a smart contract) enables automation of some routine processes and allows comparison of timestamps that constitutes time-ordering. Blockchain technology could enable the consolidation of data traceability in clinical research and might have enabled earlier discovery of the scientific misconduct related to the COVID-19 hydroxychloroquine controversy.
1, 2 In Mehra and colleagues' retracted study  1  in The Lancet, the authors analysed data from 96 000 reports of patients with COVID-19, allegedly collected from an international network of hospitals and provided by the company Surgisphere. Readers observed disparities between the study data and data from the Johns Hopkins University clinical database. Most of the authors (except the author associated with Surgisphere) and the reviewers did not have reliable means of verifying the quality or authenticity of the primary data. Blockchain could have been used to trace the data from original collection through to analysis. 3 lockchain could also have enabled a secure environment for data sharing to consolidate and verify results. At the same time, the study by Lagier and colleagues 2 that has been widely commented upon,  4  which compared hydroxychloroquine with standard of care in patients with COVID-19 and claimed to have shown hydroxychloroquine's superiority, also raised numerous issues.
Ioannidis  6  underlined that observational studies can provide straightforward, low-cost clinical evidence, as long as essential information (the protocol, data collection, and analysis plan) can be registered. No equivalent of ClinicalTrials. gov exists for observational studies, unfortunately. This absence of a strong methodological guarantee for the evidence derived from these studies could impair trust in their reliability. This is where blockchain could strengthen results from observational studies, by proving the protocol's existence, tracking its updates, confirming that the analysis plan preceded its execution, and tracing the data-sharing policy. Observational studies done with blockchain data recording would benefit by enhanced reliability of their results, leveraged potential for big data retrospective analysis through consolidating their designs and, in a pandemic context, quickly deployable designs with sharable, consistent results ensured.
Description and preprocessing Orbis is a database composed of about one hundred million entities, developed by a specialized group based in Bureau Van Dijk's Brussels office, aggregating several sources of data. A detailed description is given in Dijk (2018). The location of an entity is usually a country (or else a region such as Hawaï (U. S), La Réunion (Fr. )) that defines where the legal entity has its headquarters. Therefore, a subsidiary and a parent company can have different locations. See  Beddi and Mayrhofer (2010)  for in-depth insights into the role of location in headquarters-subsidiaries relationships. The sector of a legal entity is a description of its activity. The database had some inconsistencies and missing values. In some cases entities (and edges) appear multiple times.
Degree distribution and connected components We used the igraph software implementation  (Csardi and Nepusz 2006)  to manage the graph structure emanating from this database. After pre-processing, the graph of non isolated entities (i. e. entities with at least one edge) is composed of 39, 398, 321 nodes and 80, 874, 728 edges. Following our discussion in the first section, we consider the subgraph of juridical entities (organizations) for our entity influence analysis. The subgraph of non-isolated organizations is composed of 6, 516, 332 nodes and 6, 670, 813 edges, with 1, 429, 853 components; additional numbers are in Table  1. An important metric in graph mining is the density, representing the average connectivity (i. e. abundance of edges) of the graph. The density of a directed graph is a real number in [0, 1], maximized for cliques and minimized for a graph of isolated nodes.
For the graph at hand the density is: which means that the graph is very sparse. The degree distribution is in Fig. 1. We can see that the degrees vary importantly in the graph. More specifically there are few entities with up to more that a million capitalistic relations whereas the vast majority have less that one hundred ones. The distributions of entities by country is displayed in Table  2. We see that the distribution is not uniform and is not correlated with the sizes of each country. There are relatively few entities in the United States (US) compared to some European or South American countries. This is because the data source has little knowledge of US entities. One possibility for refining the study in relation to this country would be to supplement Orbis with data from other sources (outside the scope of this work).
). We also reported top-5 neighbors sorted by weight in descending order (Fig. 4d, c ). Top in weights are much lower that the top out weights, this means that French entities have a tendency to own capital of entities in other countries rather to have capital owned at the international level. Figure  5  depicts the densest k-core ( k = 44 ) on the meta-graph of countries, and represents the most connected subgraph of countries of capitalistic ownership. We  • Some "tax havens" are present (Cayman Islands, Malta, Cyprus,. ) despite their relative low number of entities. • The United States of America are in the densest k-core of the meta-graph of countries, even though under-represented in the dataset (less than 850000 of entities, cf. Table  3 ).
This is also in accordance with experiments of  Giatsidis et al. (2019)  suggesting that coreness can yield excellent influence measurement in our context of interest of this work. We suggest that for large and relatively sparse network, influence measurement with coreness should be preferred. We outlined results that should be treated with caution, due to the sparsity of data, particularly respective of some countries, for example-in this dataset-the United States. Nevertheless, the overall results provide important insight and information on the influence of the entities and are for the most part consistent with the current global economic situation. Of course, further enrichment of the capital ownership dataset with additional entities and capital information will likely provide an even more precise quantitative measure of influence.
This work demonstrates the efficiency of using iterative ensemble smoothers to estimate the parameters of an SEIR model. We have extended a standard SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible to condition the model on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning 2020.
leads to a posterior ensemble of estimated parameters yielding model predictions in close agreement with the observations. The updated ensemble of model simulations has predictive capabilities and include uncertainty estimates. In particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development assuming knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries, i. e. , the four European countries Norway, England, The Netherlands, and France; the province of Quebec in Canada; the South American countries Argentina and Brazil; and the four US states Alabama, North Carolina, California, and New York.
Moreover, the implemented counter-measures have been mixed in rigor, timing concerning the epidemic status, and effectiveness. Matters complicate further by the very diverse data collection protocols, data quality, and degree of accessibility. Accomplishing a comprehensive analysis of such a complicated situation to infer particular aspects of the SARS-CoV-2 pandemic and predicting its course requires a joint modeling and data analysis approach and a unified protocol. The scope of this work regards the development of such a shared system. It uses a state-of-art nonlinear ensemble data-assimilation method, the ensemble smoother with multiple data assimilation (ESMDA,  [21] ), typically used in geosciences or in petroleum reservoir modeling, to study the SARS-CoV-2 pandemic. Our system combines existing data with a modular SEIR (Susceptible, Exposed, Infectious, Recovered) model with age classes and additional compartments for sick but quarantined, hospitalized, and dead.
We have integrated the SEIR model into an ensemble-based data-assimilation framework where the setup resembles methods commonly used for parameter estimation in petroleum reservoir models. The system updates the model state and calibrates its parameters to fit a time series of indirect and noisy observations of deaths, hospitalization, and infected people. The current setup constitutes an efficient tool for real-time monitoring and prediction of the SARS-CoV-2 epidemic. As opposed to off-line parameter estimation, our system "corrects" the model as soon as new data become available. A key feature of data assimilation is that the model will always track the data to an extent proportional to their assumed accuracy, allowing for a straightforward treatment of incomplete and noisy data, such as those for SARS-CoV-2.
[22] , used the EnKF to update parameters in a stochastic SEIR model. In contrast,  [45]  used the EAKF combined with a network of SEIR models, simulating different connected "cities" but without age stratification. In the context of variational data assimilation,  [62]  performed parameter estimation and predictions using a SIR model. [4]  proposed a modified SEIR model that distinguishes between symptomatic and asymptomatic and utilized data assimilation to identify appropriate observing strategies. We believe that critical characteristics of COVID-19, such as its approximately two-week timescale from infection to death and the nonlinear observation-stateparameter relation, motivate the use of smoothers instead of filters. In a smoother, state and parameter estimation is done by constraining the parameters on all the data in a time window, not only on current data.
The model-system code and input files for the countries under consideration are available from Github: https://github. com/geirev/EnKF_seir. The outline of the paper is as follows: Section 2 describes the SEIR model used, and in Section 3, we give a brief introduction to the use of ensemble methods for model calibration. In Section 4, we discuss general aspects of the data assimilation experiments. Sections 5-12 present individual results from the different countries. In contrast, in Section 13, we present an overall comparative assessment of the obtained results across the modeled countries and states.
An alternative could be to use machine learning and autoregressive models. Still, with the initial lack of observations to train networks or to compute statistics, the availability of well-tested dynamical SEIR models made our approach attractive. Moreover, machine-learning methods would not estimate the uncertainty that is an essential aspect of data assimilation. The additional compartments of hospitalized and deaths made it easy to condition on the observations. The starting point for our model was the online calculator: http://gabgoh. github. io/COVID/index. html. In this study, we are modeling populations with vastly regional inequalities and different access to hospitals and proper care among various countries and population groups. Still, we have used the same model for diverse populations.
2. 2. The effective reproductive number. The most critical parameter in the model is the value of the effective reproductive number R(t). When R(t) is higher than one, this leads to an exponential growth of the epidemic, while the epidemic will die out when R(t) is less than one. As long as R(t) > 1, the model simulates the development towards the population's immunity. If at some time, R(t) becomes less than one and stays so for all future times, the epidemic dies out. In the experiments below, we will estimate R(t) for the past using data assimilation. When running predictions, we set the mean and variance of R(t) to reflect the anticipated effect of planned or expected policies and interventions. It is convenient to use a square matrix R(t) with a size equal to the number of age groups, which allows for using different reproductive numbers in between the different age groups.
4. 3. Different countries' focus. When running cases for different countries, we have examined several topics and properties of the system. While in all cases, we have run hindcast experiments where we calibrate the model system to the current observations followed by scenario predictions, there are also several additional topics studied that differ between countries and states. In Norway, we model scenarios of reopening schools and evaluate the impact of enhanced infections among children. For England, we assess the effect of conditioning on different data types in hindcast experiments. In the Québec case, we study the reliability of potential probabilistic short-range forecasts issued with the system. In The Netherlands, we evaluate the impact of conditioning on different data types (including ICU patients), and we examine the effect of using different priors for R(t).
Thus, there are frequent changes made to the database. Accounting for the significant reporting delays is essential for the Québec case, as in Section 7. 3, we focus on the quality of short-term forecasts. In addition to reported hospitalizations and positive tests, we condition on reported deaths, d R , to estimate the model parameters. To reflect the approximately four-day delay between an actual or modeled death, d(t i ), at day t i , and the reported death, we use: d R (t i ) =      d(t i = 0), t i < 4 d(t i -4), 4 ≤ t i < 8 0. 37d(t i -8) + 0. 32d(t i -4) + 0. 31d(t i ), 8 ≤ t i. (17) Here, both d(t) and d R (t) refer to cumulative total values at time t. The values of d(t) only are updated as reports on deaths at time t come in with a delay. We used stabilized data to fit the coefficients in Eq.
However, ISQ points out that the estimate of the most recent weekly total deaths only covers about 80% of the real number. Thus some evidence of additional excess mortality related to COVID-19 might appear in the future. Note also that for April 25th, we extracted the data from both sources on May 29th. The ISQ data indicated an excess of 2019 deaths, and the INSPQ total was 2001, which was still in good agreement with the value provided by the ISQ. The number of hospitalizations is possibly accurate. But, we didn't know the hospitalized fraction of the fatally ill. Many deaths occurred at care homes for the elderly, so we used a fraction, p h = 0. 5, in the experiments. On May 20th, the number of hospitalizations reduced by approximately 200 due to a different counting method. From May 20th onward, one removed the recovered patients still in the hospital, awaiting a transfer, from the number of hospitalized patients.
The SEIR model does not capture such events. Instead, the model is starting with relatively high estimates of the initially exposed and infectious. Even with the restrictive measures being introduced gradually over several days, we assume that they all became effective on March 23rd. For the initial period of free exponential growth, we use a prior estimate of R 1 = 3. 0 ± 0. 6 for the effective reproductive number. To not bias our posterior estimate of the effective reproduction number to values above or below unity, the prior estimate is R 2 = 1. 0 ± 0. 5 for the second (and final) period. We estimate the effective reproductive number until May 28th and keep it constant after that. Note that the available data only weakly constrain the effective reproductive number in the final week. beginning of the epidemic.
This issue reflects the difficulty of having coherence between the SEIR model and all observed variables over the entire experimental period. Note that experiment D, which did not use the information on hospitalizations, features a broad ensemble spread for this variable. The increase of the mean-predicted number of hospital admissions in the last weeks of June results from having a few members with rapid exponential growth. For the three experiments, the first wave of the epidemic likely peaked between mid-April and early May and gradually declined. In June, due to the exponential nature of the outbreak, the uncertainty multiplies during the forecast. Recent data on the epidemic's evolution would ideally support the gradual reopening up of society in June. It is thus essential that these data become available more quickly.