dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS DICE Approach In DICE, a model is specified in terms of conditions, events, and outputs. Conditions represent all information in the model, such as demographics, treatment characteristics, processes, the environment, the timing of events, model controls, random number, run settings and other inputs. They are tabulated using spreadsheet software (Excel 2016; Microsoft Corporation, Redmond, Washington). Events are points in time at which the values of some conditions change. These changes in event times, accumulating outputs, or other conditions are formulated as text expressions that respect Excel syntax but do not have the leading equals sign. These event consequences are also tabulated in an Events worksheet. Outputs are special conditions that store results and are tabulated on a corresponding worksheet.
DISCUSSION Several health authorities are currently considering the adoption of structured BRAs for facilitating regulatory decisions. 47, 48 19e21 In the present article, we examined the feasibility of implementing a qBRm by using a simpler, standardized modeling framework: DICE. The qBRm was easily implemented in Excel using DICE, requiring no special programming and specifying the entire model in a few simple tables. The consistency of the results with those from previous analyses carried out by programming in SAS software  46  is reassuring. Decision makers have shown a marked preference for models implemented in spreadsheets because reviewing them does not require programming skills. 49 A DICE model is fully implemented in spreadsheets using a standard framework and terminology. The structuring choices that define the model are straightforward and, thus, easy to communicate and to understand by physicians, modelers, reviewers, decision makers, and other stakeholders.
Therefore, the availability of DICE may encourage a broader group of modelers to use more sophisticated, realistic approaches. A limitation of DICE, when implemented in Excel, is the extended run time required to complete the probabilistic sensitivity analysis: ~8 h for 10,000 simulations. In the basic DICE version, the repeated interactions between the macro code and the spreadsheets, slow down execution. Minimizing the number of times the macro interacts with the worksheets cuts run time substantially. However, this limitation is currently obsolete since a faster version that uses a compiled macro for reducing run time by > 300-fold is now available. 28 Another limitation of using Excel for these simulations was that some statistical functions, such as the Dirichlet-multinomial distribution, were not available.
DISCLOSURES Financial support for this study was provided as part of a PhD program funded by GlaxoSmithKline Biologicals SA and the Association Nationale pour la Recherche et la Technologie, Paris, France. The funding agreement ensured the authors' independence in designing the study, interpreting the data, and writing and publishing the report. H. Arlegui was employed under a PhD program by the GlaxoSmithKline group of companies at the time of the study. G. Nachbaur and N. Praet are employed by and hold stock options in the GlaxoSmithKline group of companies, the producer of Rotarix ®. J. J. Caro is employed by a consultancy that works with GlaxoSmithKline and other companies. The authors have indicated that they have no other financial and non-financial conflicts of interest with regard to the content of this article.
Key Performance Indicators of Both Gas Hubs Another set of data that is important for this study is the wholesale gas price in each market. Figure  3  shows the time series of the natural gas prices of both the Dutch market, represented by the TTF hub, and the Austrian market, represented by the CEGH. All prices are denominated in Euros per MWh. The dataset consists of monthly values recorded between January 2013 and December 2018. As shown in the graph, both price trends are, to some extent, positively correlated, albeit the price of gas sold on the Austrian side is at most times higher than that on the Dutch market.
Key Performance Indicators of Both Gas Hubs Another set of data that is important for this study is the wholesale gas price in each market. Figure  3  shows the time series of the natural gas prices of both the Dutch market, represented by the TTF hub, and the Austrian market, represented by the CEGH. All prices are denominated in Euros per MWh. The dataset consists of monthly values recorded between January 2013 and December 2018. As shown in the graph, both price trends are, to some extent, positively correlated, albeit the price of gas sold on the Austrian side is at most times higher than that on the Dutch market. This price and volume behavior should usually be justified and interpreted by a solid understanding of the economics of both market structures and evaluated by means of a quantitative analysis, which is the subject of this research.
Forecasting 2021, 3 FOR PEER REVIEW 7 Figure 2. 372 Figure 2. Monthly gas import volumes to the Netherlands and indigenous production. Source: Gasunie. The data related to the import of gas to the Dutch market can be found on the Gasunie website (https://www. gasunietransportservices. nl/en/). 2. 1. 4. Key Performance Indicators of Both Gas Hubs Another set of data that is important for this study is the wholesale gas price in each market. Figure3shows the time series of the natural gas prices of both the Dutch market, represented by the TTF hub, and the Austrian market, represented by the CEGH. All prices are denominated in Euros per MWh. The dataset consists of monthly values rec-.
Data The data used in this study derived from the Microfinance eXchange (MIX) database, 5 a web-based microfinance platform that provides data on individual MFIs. To date, the MIX platform discloses information on the viability and financial and social performance of around 2000 MFIs. Since our objective is to study the cyclical behavior of MFI lending with respect to the business cycle, we required MFI-level data over a relatively long time period. The advantage of the MIX database is that it enables to collect longitudinal data from 2001 to 2014. In addition, to date, the MIX database is, to our knowledge, the only database that allows data collection on MFIs' loan portfolio structure and composition. We were thus able to collect data on loan liability structure (individual liability loans vs.
joint liability loans). However, the data covering loan portfolio structure such as lending methodology (individual vs. joint liability contracts) are available only from 2007. For the part of the analysis involving loan portfolio structure/composition, the data period is therefore restricted to 2007-2014. Given that the MIX datasets are of unequal quality, 6 we focus only on MFIs with more reliable data (four and five diamonds), namely, those with financial statements certified by chartered accountants. Furthermore, we adjusted the database to limit the influence of outliers. We winsorized MFI lending growth to the first and 99th percentiles, as high lending growth could be a sign of merged or startup MFIs. This process removed 31 MFI-year observations from our database. Our final sample is thus comprised of 1444 worldwide MFIs over a 14-year period (from 2001 to 2014) for a total of 5996 MFI-year observations from six main regions of the world as defined by the MIX: Sub-Saharan Africa (873 observations), East Asia and the Pacific (614 observations), Eastern Europe and Central Asia (973 observations), Latin America and Caribbean (2208 observations), Middle East and North Africa (256 observations) and South Asia (1072 observations).
Descriptive evidence The descriptive statistics of the dependent and explanatory variables used in our regressions are summarized in Table 1 (Panel A). We computed MFI loan growth as the year-to-year difference in the log of the MFI gross loan portfolio. The yearly descriptive statistics of MFI loan growth given in Table  1  (Panel B) suggest a substantial drop during 5 Using the MIX database raises several issues. The first is related to the existence of sample selection bias, since not all MFIs report data to the MIX and those that report data to the MIX do so on a voluntary basis. This is not formally considered in this study. The second is the representativeness of the MIX data. Although the MIX database lacks either variables or data for some years, working with MIX data is not a serious concern because the MIX data covers a large share of the microfinance sector.
As noted by Tchakoute Tchuigoua (2014), MFIs reported in the MIX database serve a large number of microfinance clients. 6 The MIX uses a five-point ordinal scale (diamond scale) to classify MFIs according to their level of transparency and reliability of information (five diamonds being the highest). 7 The correlation between contemporaneous and lag values (a Pearson correlation of 0. 38) is significant at 1% degree of confidence. the 2007-2009 subprime crisis period with the largest effect in 2008, the year of the collapse of Lehman Brothers. The annual average growth in loans dropped from 47. 1% to 27. 7% in 2008. A similar trend is observed for asset growth as well. Comparing the loan portfolio with the other non-interest assets suggests that loan portfolio constitutes on average 77% of total assets of MFIs.
Concerning labour market structural reforms, during the years of the recent European crisis (and also before), several Union members and particularly the Mediterranean countries have introduced policies aimed at labour market flexibilization such as the Jobs Act in Italy and the reform of the Code du Travail in France. However,  Oswald (1997) ,  Baccaro and Rei (2007) ,  Avdagic and Salardi (2013) ,  Avdagic (2015)  and  Storm and Naastepad (2012) , on more recent datasets, find no compelling evidence on the revealed benefits of labour market liberalization. Regarding inequality and focusing on the US,  DiNardo et al. (1996)  and  Fortin and Lemieux (1997)  find that deunionization (for men) and the stagnant minimum wage (for women) have been the core institutional determinants leading to the increasing inequality trend in the country.
Acknowledgements We gratefully acknowledge the support by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 649186  -ISIGrowth, and by  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) , process No. 2015/24341-7. Part of the results presented here have been obtained within the project Development and piloting of a computer-based simulation model to assess impacts of  ICT-enabled social innovation initiatives promoting social investment in the EU  ( I-FRAME 2. 0 ) No. 930396-2016 A08-ES  sponsored by  DG-JRC, Seville.
[. ] By drawing in workers, tourists, and other visitors, central London areas also support jobs in accommodation, food, arts, entertainment, and retail services in the surrounding areas of inner London. In 2014, the combined Retail, and Accommodation and food services sectors for example accounted for around one in three employee jobs in Kensington and Chelsea, around one in four jobs in Newham and one in five in Haringey, with some evidence of recent growth in the number of jobs around the shopping centre developments in Stratford. "  (Girardi and Marsden, 2017, p. 2-35). In order to draw a finer picture of the London economy, at the level of workplaces across the city at different points in time, we turned to a micro dataset recording business organisations in the UK, as well as their different establishment if they are based in multiple sites.
The Business Structure Database 3  (BSD) "is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. [. ] In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK" 4. This database is provided by the Office for National Statistics (ONS) for free, although under secure access to protect anonymity and non-disclosure. In the context of the ONS non-disclosure rule, it means that no information which can allow the identification of a particular enterprise can be extracted from the secure environment. Most of the time, it means that information needs to be aggregated over at least 10 enterprises or local establishments.
However, a significant advantage of this database compared to any other free-access source which allows singling out individual enterprises (such as Companies House for example) is that this dataset is longitudinal, and hence we are able to look at changes in the distribution of firms over time. In this particular paper, we use all the active local units of Greater London in 2007 and in 2014. They represent 550,000 active units in 2014, from slightly over 400,000 in 2007. These local units were extracted from the 7. 5 million units active at one point in time in the UK, using London postcodes as a filter (table  1 ). We retrieved from the BSD information about each local units' Table  1 : Total number of local establishments from BSD, 2007-2014. Source: ONS 5-digit SIC code, which corresponds to their main economic specialisation with a precision of 5 digits, according to the 2007 standard industrial classification (SIC) in the UK.
tween LSOA, Source: ONS) is available in a FigShare repository, 10. 6084/m9. figshare. 8035961. The distance matrix between LSOA is proprietary data but can be generated again using the Google Maps API. We invited readers interested in this data to contact us or to run the queries on the API. This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.
Acknowledgments We thank the  ONS  for letting us use the  Virtual Microdata Laboratory facility. We are grateful to  Richard Milton  for providing us with the time distance matrix of London LSOA, to  Max Nathan  for insightful discussions and references as well as to the participants of the micro-networks special session at the Regional Studies Association Annual meeting 2017 for their comments and questions. We acknowledge the funding of the  EPSRC  grant  EP/M023583/1.
Office for National Statistics, 2017, Business Structure Database, 1997-2017: Secure Access, 9th edition, UK Data Service 10. 5255/UKDA-SN-6697-9 Year United Kingdom Greater London 2007 2,831,348 414,561 2008 2,833,652 416,450 2009 2,784,761 425,650 2010 2,711,446 417,451 2011 2,662,753 416,075 2012 2,749,395 446,331 2013 2,763,491 458,874 2014 2,874,224 490,011.
Access to high-quality, timely and comparable data is a prerequisite for any effective decision-making process and having such data available for the environment is absolutely fundamental to efficient and evidence-based policymaking. This article reviews the establishment of a Shared Environmental Information System (SEIS) in Europe and Central Asia to improve our understanding of how environmental data value chains are being employed to produce, share and use reliable data on the environment and whether this data is used in policymaking. Three sources of data are utilised to analyse whether relevant environmental data and information are being drawn upon by policymakers, namely, the 2019 SEIS mid-term review, SEIS Factsheets and SEIS Gap Analysis Reports. The results reveal that the pan-European region still faces significant data harmonisation problems, owing in part to differences in types and methods of data collection, data definitions and legislation.
However, while environmental agencies -both public and private -collect and process massive amounts of data, relatively little is still known about how this data is being utilised to improve policy  (Head, 2015; Rose et al. , 2017; Aggestam, 2019). Given the enormous scope of data-focused policy initiatives, this article is restricted to a review of the Shared Environmental Information System (SEIS) in Europe and Central Asia. The purpose of the paper is to contribute to a better understanding of how environmental data value chains and associated policy instruments are being employed to produce, share and use reliable data on the environment. The paper applies a definition proposed by BDVA (2017) whereby the data value chain covers data: (1) generation and acquisition, (2) analysis and processing, (3) storage and curation, as well as, (4) services and visualisation.
Approach This paper used two data sources to assess whether the quality of the environmental data and information produced by countries has improved and if it is being used by policymakers. First, we carried out additional analysis of the data collected for the mid-term review on establishing SEIS (ECE, 2019c) as well as the data from the first progress report on SEIS that was outlined above  (ECE, 2016b). The data analysis, as based on the SEIS self-assessment by countries (ECE, 2018a), provides the backbone for reviewing the added value in having shared data value chains. Subsequent to this, results from a sub-regional case study based on a number of SEIS factsheets and SEIS gap reports -as outlined belowwere reviewed and integrated to complement the overall analysis.
Data collection for the mid-term review The SEIS mid-term review was carried out against seven data flows underlying ECE environmental indicators, covering three indicators and three environmental themes (see Table  1 ). 3 Notably, this contrasts to the first SEIS progress report which covered 49 environmental indicators (based on 67 related data flows) categorised into seven environmental themes  (ECE, 2016b). The scope for comparison is thus somewhat limited. Data collection was based on the SEIS self-assessment questionnaire and data analysis for the mid-term review report, which was conducted in the second half of 2018. It should be noted that more details on the analysis can be found in the Annex to the mid-term review report  (ECE, 2019c). Self-assessments were submitted by 34 European and Central Asian countries out of the 50 ECE countries covered by the first SEIS progress report.
SEIS factsheets and gap analysis reports The analysis is complemented with a sub-regional case study on the Eastern Partnership countries (Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine), the Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and the Russian Federation. The case study is based on not only factsheets produced by the ECE 5 but also on data collected for the ENI SEIS II East project 6 in 2018 (ECE, 2019b) which aimed to continue the implementation of the SEIS principles and practices in the Eastern Partnership countries and was funded by the EU via its European neighbourhood instrument. Finally, additional data on 7 of the 12 countries are provided by the Central Asia regional synthesis report (ZOI, forthcoming) and SEIS gap analysis reports produced for Armenia, Georgia, Kazakhstan, Kyrgyzstan and Tajikistan in 2019.
The interaction between data producers, data managers and data users is another aspect of the same problem. Communication and cooperation is ultimately a key determinant in whether environmental data and information become a real tool to understand and manage environmental problems. The current state of interaction between data producers would suggest that countries would benefit substantially from improved inter-process communication on national environmental data value chains. For example, several countries highlighted that the self-assessment questionnaire facilitated communication between data producers that normally do not share or exchange information. Methodologically speaking, the development and piloting of the SEIS assessment framework is also an example of key institutional actors and countries cooperating successfully.
While there are a good number of studies on the impact of the Global Financial Crisis  (2007) (2008)  and European crisis  (2010) (2011) (2012)  on the EU based non-financial firms  (Claessens et al. , 2011; Ferreira et al. , 2016) , only limited evidence available that assessed the effects of past pandemics on the valuations. We identify this as a valid research gap and attempt to contribute to the literature and practice of valuation by investigating the effect of COVID-19 on the valuations of non-financial firms in the European Union. For this purpose, we adopt a multifold strategy. Our sample comprises firms from the ten most impacted member states. We initiate by determining the robustness of free cash flow to firm and residual income models. The input sensitivities are estimated using a random effect panel by employing the data between 2010 and 2019.
Volkov and Smith (2015)  suggested that relative valuations are specifically not suited during recessionary periods. Therefore, we employ two present value models based on free cash flows and residual income for this study. To assess the impact of COVID-19 on valuations, we consider our base case to be 2019. Using data from 2010 to 2019, we test these models' valuation accuracy by employing a within-sample approach. To evaluate the accuracy, we use two comparisons. We compare our model-based valuation with the actual year-end price and the available sell-side target price for that year. This requires us to have at least one sell-side target price (analyst forecast hereafter) for each company. This requirement results in a sample of 5342 listed nonfinancial firms across 10 EU member states that have consistent analyst coverage from 2010 to 2019.
Data description This study employs a comprehensive dataset from multiple sources. The within-sample forecast period is from January 2010 to December 2019. The financial statements related yearly data on sales, EBIT, net income, interest expense, non-cash gains and losses, current assets, current liabilities, capex, fixed assets, the book value of equity, and net borrowing is extracted from the data stream. The analyst target price and recommendations are collated from Eikon -Investment Research. Some of these recommendations are hand collected from the websites of sellside analysts if public dissemination is available. As mentioned earlier, we only include firms that have remained in business for all these years, and at least one analyst recommendation per year is available. In case there are multiple recommendations at a given point in time, we use the average value as the analyst target price.
The intrinsic values are forecasted each year as of December 31st, with a target price of one year forward. We keep this consistent for the analyst report and have included firms where investment recommendations were given at the end of the year. Based on 5342 sample firms and ten years, this results in total panel observations of 53420. This will also be the number of within-sample firm-level forecasts to establish the proposed models' accuracy for the ten years. The Euro 5 years' government benchmark bond yield is considered the risk-free rate and the S&P Europe 350 Index for the market risk premium. The European SMB, HML, and MoM factors are extracted from Kenneth R French's data library 4. The macroeconomic data, including GDP growth rate (ex-post and projections), are taken from the European Central Bank.