dataesr/scientific-paragraphs-categorization huggingface dataset:
google. com), gross domestic product per capita from World Bank (GDP per capita; data. worldbank. org) and the region in which each country occurs. The categorical variable region has three levels: Central America, South America and Islands (Caribbean Islands and Falklands/ Malvinas Islands). These analyses consist of fitting vectors or factors, usually environmental variables, in an ordination and the significance between ordination and descriptive variables are tested by permutations using the "envfit" function, in the "vegan" package v. 2. 5-6  (Oksanen et al. 2019). All analyses in this study were conducted in the R environment (R Core Team 2020).
Most of the costs were associated with mixed sectors (77. 4%), agriculture (15%), public and social welfare (4. 2%) and authorities and stakeholders (2. 6%). In the InvaCost database, the authorities and stakeholders sector correspond to "governmental services and/or official organizations that allocate efforts for the management sensu lato of biological invasion"  Diagne et al. (2020b). Damage costs were the predominant type of cost for all sectors, except for the health sector where management was the largest type of cost (Fig. 3 ). Except for authorities and stakeholders, none of the impacted sectors reported spent money on prevention. Based on the NMDS ordination (Table  2 ), species occurrences and costs per species amongst countries were spatially structured across the three regions (Central America, South America and Islands; Fig.
In addition, although our study is the first regional assessment in Central and South America and was based on the most upto-date database, we highlight that the costs reported here are a conservative baseline. We did not include cost entries classified as low reliability or reporting expected-only costs in the analysis and there were no published costs for some relevant invasive alien species in the region (e. g. , Pterois volitans and Tubastraea coccinea;  Adelino et al. 2021) ; furthermore, it is difficult to disentangle costs associated with multiple practices (e. g. , restoration;  Brancalion et al. 2019 ). Hence, the economic cost of biological invasions in the region is higher and must be evaluated continuously. The differences amongst the costs found here and other country-level assessments in the region are due to different methodological choices.
Adelino et al. (2021)  found a higher accumulated cost than us for Brazil because they did not remove entries from the original InvaCost dataset (USD 105. 5 billion vs. USD 76. 8 billion). For the same reason,  Duboscq-Carra et al. (2021)  found an accumulated cost USD 5. 5 million higher than us for Argentina (USD 6,907. 6 million vs. USD 6,902. 1 million). Conversely, Ballesteros-Mejia et al. (2021)  found smaller costs for Ecuador because one of the entries with high economic impact was classified in their study as low reliability and therefore removed from the main analyses (USD 86. 2 million vs. USD 604. 9 million; see details at  Ballesteros-Mejia et al. 2021). In country-level assessments with limited data availability, it is essential to use all available data for the most comprehensive assessment possible.
Hyper-costly taxa The distribution of recorded costs amongst species was highly uneven and, in a few cases, the multiple taxa category presented the highest costs (see Fig. 1A ; Suppl. material 3: Figs  S2A, E, S3A ). However, in most rankings, few taxa were responsible for a greater portion of the economic costs for most sectors and types of costs in Central and South America. The economic impact was directly related to the damage caused by some species in essential sectors, such as agriculture and public and social welfare (Fig. 3 ). The hyper-costly taxon in the terrestrial environment, Aedes spp. , are distributed across all tropical regions of the globe and transmit the viruses that cause chikungunya, dengue, yellow fever and Zika  (WHO 2009; Bhatt et al. 2013 ). In the Central and South America region, these mosquitoes affect mainly human health and have been reported in the InvaCost database since 1977, causing expenses due to damage, management and mixed.
crassipes invasions. This suggests that actions against this species in the region have been poorly reported or the costs were not included in the database because the species is native to a large portion of South America and, therefore, was not captured by the set of terms used in the search engine (see  Diagne et al. 2020b ). The lack of publications could also explain part of the large difference between the costs caused by invasive alien species on aquatic and terrestrial environments (about 170 times smaller on aquatic environments). Furthermore, although our study reveals a conspicuous difference between the economic costs in both terrestrial and aquatic environments, we cannot determine whether such differences resulted from the fact that aquatic species cause less impact or are neglected in terms of the economic cost they cause.
2015). In addition, the salt cedars (Tamarix spp. ), the costliest taxon for the public and social welfare sector, causes a negative impact on the uses of residential, industrial and agricultural water specifically. Tamarix spp. invasions are associated with the impoverishment of forage, a decrease in irrigation water, an increase in soil salinity and the frequency of fires  (Natale et al. 2008). Of note, some potential hyper-costly taxa could have been missed here due to the inherent limitations of the database, such as the lack of precise information, the terms applied for literature searching and the availability of researchers that contributed with information (see discussion in  Diagne et al. 2020b; Angulo et al. 2021). It is important to note that many references reported the costs for multiple invasive alien species jointly (assigned as "Diverse/Unspecified" by  Diagne et al.
2020b ) and, therefore, gathered the economic impact of distinct sets of taxa. These reports prevented us from more precisely assessing the hyper-costly species in general, as well as for agriculture and mixed impacted sectors and for damage type of cost (Figs 4A, 5A, B). Thus, considering the importance of identifying priorities and that invasive alien species can present synergistic impacts  (Simberloff 2006; Ricciardi et al. 2011; Zenni et al. 2020 ), we recommend that future studies on the cost of biological invasions report costs in a more standardised way  (Diagne et al. 2021b ) and, in particular, by species separately. Such detailed input information will allow researchers to improve the quality and accuracy of the InvaCost database and, consequently, favour the application of the hyper-costly taxa concept in distinct situations with even more effective practical results.
Acknowledgements The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  Invacost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on a biodiversity scenario. We also acknowledge all researchers and environmental managers who kindly answered our request for information about the costs of invasive species and  Carla C. S. Camargos  for the help with the R code. GH thanks  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (Capes) -Finance  code  001  for supporting his postdoctoral research.
CC was supported by  Portuguese National Funds through Fundação para a Ciência e a Tecnologia  ( CEEC-IND/02037/2017 ;  UIDB/00295/2020  and  UIDP/00295/2020 ). RDZ acknowledges support from  CNPq-Brazil  (grant  304701/2019-0 ). Funds for EA and LBM contracts come from the  AXA Research Fund Chair of Invasion Biology of University Paris Saclay. CD was funded by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ).
As the mandate of the Commission Expert Group on Rare Diseases expired in 2016, an alternative means of maintaining a pan-European overview was particularly important. A logical vehicle for the collection of this data existed in the form of the Resource on the State of the Art of Rare Disease Activities in Europe, a well-established resource providing valuable, detailed information for all stakeholders in the field of rare diseases and orphan medicinal products. Under the EUCERD Joint Action (2012-2015), the report was produced annually by the Institut national de la santé et de la recherche médicale (INSERM), in 5 volumes, which were downloaded 15,000 times per year. Under  RD-ACTION (2015 -2018) , production of the State of the Art moved to Newcastle University. The resource was streamlined and moved online, with several changes.
Many of these were directly based upon the aforementioned EUCERD Recommendations on Core Indicators for Rare Disease National Plans/Strategies, and provided the options proposed by these Recommendations (see for instance Fig. 2 ) Since 2016, data has been requested at intervals from all EU MS, plus the UK (the intention is to expand the geographical reach, from 2021). The data is provided through a Lime Survey interface, on a token basis, meaning the members of each country's DCC receive the same token and can work simultaneously and collaboratively to complete the various sections of the survey. Since 1st of January 2019, the core data collection and analysis aspects of the Resource on the State of the Art of Rare Disease Activities in Europe have been supported by the Rare 2030 Foresight Study (which is co-funded by the European Union Pilot Projects and Preparatory Actions Programme 2014-2020: PP-1-2-2018-Rare 2030).
Data provided in 2019 was utilised to elaborate and enrich a series of 8 Knowledge Base Summaries (https://www. rare2030. eu/knowledgebase/) constructed as part of the project's broad consultation activities and intended to illustrate the status quo and summarise relevant initiatives and outputs (as a starting point to identify gaps and future needs). The data relating to national frameworks was extracted from the surveys and analysed to support this activity. It revealed that at Member State level, there is significant heterogeneity in the state of advancement of national policies, plans, or strategies for rare diseases  (Hedley et al. 2019). Countries opted to address the challenge of the 2009 Council Recommendation in different ways. For instance, some countries have adopted a national strategy only, as opposed to adopting/following up with a plan (the usual conclusion being that a plan is composed of more specific, measurable actions, whereas a strategy may be more broad and high level).
The view from late 2020: results of a recent 'State of the Art Resource' data collection This map (Fig. 3 ) shows the status quo relating to national plans as of October 2020, following the most recent request to the Data Contributing Committees in all EU MS and former MS to review and update their data relating to NP/NS. Note that some countries have yet to provide full data to this resource, namely Poland and Greece. Other countries may update their data further in coming months, thus the picture presented here is based upon the best data available through the State of the Art Resource. NB UK data has been included in this analysis, as a former EU MS. Twenty-five European MS/Former MS have adopted a National Plan or Strategy (NP/NS) for rare diseases at some stage, but unsurprisingly perhaps have tended to approach the mission in different ways and at different times (although most strived to meet the deadline of the end of 2013): the result is significant heterogeneity across Europe.
2017; EURORDIS 2019). The State of the Art Data Contributing Committees of the EU MS are asked, as part of their 'national plans and strategies' submission 'Is there a dedicated body (expert advisory group) to oversee drafting or implementing of the NP/NS, or to evaluate the impact of the NP/NS?' The phrasing leaves this open, to apply to the initial elaboration phase, the implementation phase, and, ideally once NP/NS are drawing to a close, an evaluation phase. The response here is multiple choice and reflects the goal of a well-functioning group, meeting regularly, with a multistakeholder composition (including patients). The October 2020 data update within the scope of the Resource on the State of the Art of Rare Disease Activities in Europe yielded the following results: Of the 18 NP/NS still 'active' (i.
Conclusions and priorities in the post 2020 era The collection of data via the Resource on the State of the Art of Rare Diseases Activities in Europe creates the possibility for cross-country analysis, illustrating the European status quo for a variety of rare disease-relevant topics, amongst them national plans and strategies. A potential downside, of course, is that the data is largely self-declared-furthermore, its accuracy relies upon DCC members dedicating significant time and energies to the completion of the survey. Reviewing this status quo from the vantage point of late 2020, the request from the Council to the EU MS in 2009 was, for the most part, fulfilled: whereas 5 MS had adopted a NP/NS for rare diseases in 2009, this figure has risen to 25. Moreover, the impact of this soft-legislation has not been limited to the political or geographical confines of the European Union alone.
Acknowledgments The first author was partially supported by the  National Science Foundation  RTG grant  DMS-1246999. The third author benefited from the support of the  FMJH Program PGMO VarPDEMFG  ( ANR-11-LABX-0056-LMH ) and from the support of  EDF ,  Thales, Orange and Criteo. The authors would also like to thank  Pierre Cardaliaguet ,  Sylvain Sorin  and  Takis Souganidis  for helpful advice.
We fitted our models to daily hospitalization count data extracted from the SI-VIC database, which stores data on COVID-19 patients hospitalized in public and private hospitals in metropolitan France and overseas French territories. The data were corrected for reporting delays as described in  2. Note that the surveillance system in French Guiana is the same as the one used in Metropolitan France both in terms of data collection strategies and indicators used to monitor the epidemic. According to official statistics however, access to care differs: for example, in 2014, 79% of individuals living in French Guiana consulted a general practitioner compared to 85% in Metropolitan France  24. The model parameters were estimated via Markov Chain Monte Carlo (MCMC) sampling assuming a Poisson observation process and using uniform, non-informative, priors.
Acknowledgements We acknowledge financial support from the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (Grant  ANR-10-LABX-62-IBEID ),  Sante Publique France , the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ) and  European Union V. E. O and RECOVER  projects.
• c is the constant and α i is the individual specific effect. • Pay iT is the payment mechanism in treatment T. This is a categorical variable with three modalities: P4P, FFS and CAP. Our reference is P4P. β is a vector of parameters that identifies the pure effect of the payment method on the outcome. • INF O it is a binary variable equal to 1 in period 2 ("free information"), and to 0 in period 1 ("no information"). Our reference is "no information" (period 1). γ is a vector that captures the effect of information on the outcome. • θ captures the interaction effect between free information and payment method. • X is the fixed set of objective time invariant control variables. • is an idiosyncratic error term. Due to the repetition of observations on the same subject (through our within-subject design), our dataset is a panel.
David Bardey  acknowledges  TSE  funding from  ANR  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ). Bruno Ventelou  acknowledges  AMSE ANR 17-EURE-0020  and  Inserm  team CRISSPOP (2012)(2013)(2014)(2015)(2016)(2017)(2018).
Starting from the analysis of a levered firm's capital structure, I show that corporate default risk becomes measurable through the leverage effect, i. e. , the negative correlation observed between stock returns and changes in stock volatility. In this model, the firm's debt-to-asset ratio governs the elasticity of default probabilities relative to equity prices. I use a large dataset of S&P 500 firms and an extended timeframe (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) to examine the model's empirical implications. First, the firm's corporate leverage enhances the transmission of pricing information from the stock to the credit default swap (CDS) market. Second, equity and credit markets are more likely to be co-integrated when firms employ a higher debt-to-asset ratio.
Each market's contribution to the price discovery process then becomes accessible. Third, as already underscored in the literature  (Kapadia and Pu, 2012; Choi and Kim, 2018) , exogenous barriers to arbitrage such as funding constraints, liquidity risks, or short-sale impediments interfere with the co-movements in the equity and credit markets. This paper hypothesizes that the non-linear impact of the leverage effect may be one of the endogenous sources for the lack of integration between the credit and equity markets. This paper uses a large dataset of S&P 500 firms and an extended timeframe  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019)  to examine the transmission of pricing information from the stock market to the credit default swap (CDS) market. By identifying the genuine price innovations arising in the stock market, I offer an empirical methodology to identify the non-linear impact of the financial leverage on the information flow transiting to the credit market.
The Data For this study, I consider daily closing CDS quotes for the most widely traded, North American reference entities. To build as much as possible a large and representative CDS universe, I impose three requirements. The first constraint is for bid-ask CDS quotes to be available in Thomson Reuters over an extended 12-year sample period running from January 01, 2008, to January 01, 2020. 11 The second constraint is for the corresponding common stocks to continuously trade on the S&P 500 stock index over the full sampling period. Finally, the historical leverage ratio has to be available in Thomson Reuters over the full sampling period. For all the reference entities satisfying the previous three requirements, all the CDS quotes, stock market data, and leverage data are then consistently retrieved from the Thomson Reuters database.
To measure the firm's financial leverage, I use the ratio of total debt book value to enterprise A conservative approach to the financial leverage of financial institutions is in order. For banks, customer deposits do not appear in total debt, while cash on hand includes due from other banks. For insurance companies, policyholders' liabilities do not appear in total debt. The sample thus comprises estimates of the debt-to-asset ratio over the period 2008-2019. Notice that the fluctuations of the firm's market capitalization on top of the changes in total debt book value entail daily variations in the leverage dataset. and CDS spreads co-move as financial theory would expect, that is, ∆CDS•∆S < 0. This proportion is similar to the one reported by  Kapadia and Pu (2012) , who document that "short-term Merton model pricing discrepancies are common across firms, frequent, and economically large.
Return S&P is the contemporaneous daily log-return on the S&P 500 stock index. t-statistics in parentheses are calculated via standard errors clustered by date to correct for heteroscedasticity and cross-sectional correlation. * * * , * * and * denote statistical significance at the 0. 1%, 1%, and 5% levels, respectively. The sample period is 2008:01 to 2020:01. Data source: Thomson Reuters. Full sample in column (A). The main finding of Table  3  is located in column (E), where the interaction term ∑ 4 k=0 b k is economically substantial and statistically significant at the 0. 1% threshold. The estimate has the awaited negative sign predicted by structural models of credit risk. Its magnitude  (-690. 6)  is more than five times that of the flow of direct, un-leveraged information (-127. 3) taken alone.
Conclusions A parsimonious structural framework is sufficient to build a theoretical model connecting the firm's financial leverage and the variance-equity elasticity. This elasticity amounts to twice the debt-to-assets ratio-a standard measure of the corporate leverage. This key feature enables putting the so-called "leverage effect" into a credit risk perspective, thus giving its full meaning to a fourdecade-old term  (Black, 1976). It provides a non-linear mechanism of information transmission between the equity and credit markets. An empirical analysis over a large dataset of S&P 500 firms and an extended timeframe  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019)  highlights the non-linear role of the corporate leverage in the transmission of price information between stock markets and credit markets.
2% of Turkey's GDP (Ministry of Family and Social Policies 2017: 136). While the sums transferred to a given beneficiary have always been modest,  Öktem (2018)  has estimated that their value in terms of purchasing power had stayed roughly constant through 2018. Two national-level instruments were developed to manage this array of programs. Launched in 2010, the Integrated Social Assistance Information System (ISAIS) is a database and online management system that aggregates data from 22 public institutions and 112 web-based services, and makes it available to all public agencies involved in administering social support policies. These data are used to assess eligibility, disburse funds, and carry out audits for an array of national programs (MFSP 2017). In 2013, the PTT card, a multipurpose smart card, was developed as a common vehicle for user to access these programs.
20 In the context of a means-tested program, this additional element of social penetration was also a source of information allowing aid to be targeted and its effects to be assessed. This social anchoring of the party and its allies facilitated its effort to integrate inhabitants into the market while providing an essential relay for the municipal authorities who were thus informed of local conditions. The neighborhood president cited above who knew his neighbors' problems "from A to Z" was not an isolated case. Information available to public authorities was not limited to official databases. Our observation left little doubt that the amount and quality of relevant information available to authorities in U was significantly superior to what we saw in R. How this information was used was a matter of some controversy.
Its institutional history goes back to 2003. With the creation of Work Permit for Foreigners Law no 4817 dated 2003, it was established under General Directorate of Labor as Department of Work Permits for Foreigners. In the context of our case, it plays two critical roles. A representative of the Directorate cochairs the ESSN steering committee. It also manages procedures for the granting of work permits. The Directorate General of Social Assistance (Sosyal Yardımlar Genel Müdürlügü) develops and coordinates poverty reduction and social assistance policies. In the context of ESSN, this unit maintains the database of participants. It is also coordinates the activities of the 1,003 local SASFs. These, in turn, play a key role as frontline agencies in contact with ESSN beneficiaries, processing approximately 65% of applications-the remainder being handled by the TRC.