dataesr/scientific-paragraphs-categorization huggingface dataset:
However, considerable hurdles must be overcome  [47] , including privacy and security issues  [48] , and other practical, technological, economic, political, professional and societal barriers which impede the exploitation of EHR  [45, 47]. In particular, patients expect safe and high-quality care, privacy protection, rights of access and correction, and the opportunity to give consent for research uses of their health information  [45, 48]. Nonetheless, the vast deployment of EHRs in Europe and the development EHR4CR innovative platform are providing a ground-breaking opportunity to pioneer the research use of EHR health data  [22]. As the pharmaceutical industry is remodelling its R&D platforms to enhance its value chain, reduce costs and improve efficiency  [3, [49] [50] [51] [52] [53] , EHR4CR solutions promise to transform the clinical research landscape by enhancing and speeding up existing clinical research processes.
Delivering value The primary customer segments for EHR4CR service providers were defined as clinical trial sponsors (pharmaceutical industry and their designated CROs), and CRUs (academic clinical research centers). In this emerging market, trusted and long-term relationships will be developed by delivering reliable services and meaningful value propositions, building a well-connected EHR4CR community (i. e. a European network of excellence), and establishing best practices for the research use of health data. Customer channels will include direct service offerings to the primary customer segments, and indirect reach by engaging and leveraging data providers (hospitals) and EHR/EDC industry networks. Value propositions were thus tailored to address the specific unmet needs of key stakeholders, and where relevant, substantiated with quantitative assessments.
Moreover, the proposed business model should ideally be managed and revisited periodically in response to evolving markets' needs, emerging business intelligence, competitive activity, etc. This would contribute to engaging the EHR4CR community in establishing best practices in business model innovation and value optimisation. For achieving its full business potential, the EHR4CR plaform will also have to overcome several threats such as often inadequate and inconsistent clinical documentation within EHR  [21]. This will require building awareness and clearly describing to the medical comunity, data providers, EHR/EDC vendors, patients, CROs, and clinical trials sponsors the benefits of enabling the use of EHR health data for research. This would ensure that optimal systems and processes are established in order to collectively improve the quality of health data for research.
In some cases, this may require additional investments to upgrade existing IT infrastructures, source data systems, and data collection processes, as well as dedicated efforts for establishing best practices and optimising benefits for all stakeholders. In addition, the EHR4CR platform will require achieving optimal interoperability across EHR systems in order to offer a seamless and reliable end-to-end user experience. However, the near-universal downscaling of many national eHealth programmes might limit the investments in extending the richness and interoperability of EHR systems, and of the infrastructure to enable them to communicate at a regional or national level. This may have an impact on the costs and efforts required by EHR4CR service providers to link up individual hospitals to the platform, and on the estimated profitability ratio, especially in the first years of operation.
e. post-commercialisation). Importantly, the business model simulation having been conducted in a pre-launch context, it does not take into account specific pricing strategies and future commercial offers (e. g. competitive service bundling, discounts, etc. ) that may be established by different EHR4CR service providers. Considering that a key feature of multi-sided markets is the innovative pricing strategies they employ (i. e. to attract one group of users, other group of users may be subsidised  [55] ), competitive pricing will also directly influence the EHR4CR market uptake and profitability margins. Again, it would be interesting to conduct subsequent economic analyses using real-world evidence (i. e. once the EHR4CR solutions are implemented). Such analyses would provide additional evidence to substantiate value propositions to clinical trial sponsors, data providers, and EHR industry.
Outlook for the future Amongst other emerging technologies in the health sector, the EHR4CR platform will transform the clinical research landscape by enabling the re-use of hospital-based EHR patient-level data for research. This study has described the methods used for gathering and analysing multi-source business intelligence towards developing a sustainable business model for the EHR4CR platform, and for assessing its economic sustainability. In this emerging business ecosystem, EHR4CR service providers will be uniquely positioned to deliver trustworthy innovative and value-added solutions in compliance with all audit requirements and policies. Using a scalable approach, they will be empowered to rapidly grow this market and to establish trusted relationships with clinical trial sponsors, as well as best practices with the multi-stakeholder network of excellence enabled by the i ˷ HD.
The adoption of a systematic business model innovation process will allow EHR4CR service providers to regularly update the proposed business model in response to market demands, and to generate added value in a consistent manner. By leveraging new research opportunities, including for generating evidence of value from real-world contexts, EHR4CR service providers will contribute to creating new business opportunities, and to building further sustainability. For clinical trial sponsors, the EHR4CR platform represents a breakthrough innovation to optimise R&D value chains, reduce operational costs, add substantial value, and enable the delivery of safe and effective innovative medicines to healthcare faster. For data providers, service providers and the ICT industry, the EHR4CR platform will establish new benchmarks and best practices in advancing today's clinical research, and exploiting new business opportunities and revenue streams.
Phase Number of CTs in Europe § (2012) Retrospective Annual Ranges (2007-2011) Average worldwide per- patient costs (USD) per clinical trial phase across therapeutic areas † II 1,263 1,173-1,498 36,070 III 926 817-1,148 47,609 IV 729 637-908 17,042 Source:  §EudraCT 2012 (baseline), including commercially sponsored (PRO/CRO) and non-commercially sponsored clinical trials (CTs) (universities, hospitals, public organisations) †: Cutting Edge Report 2011.
We hope that our estimates will similarly be fruitful to stimulate future theoretical work on the interplay between growth and inequality. 8 5 Another possibility would be to use the CPS as the baseline dataset and supplement it with tax data for the top decile, where the CPS suffers from small samples, poorly measured capital income, and top-coding issues. The advantage of starting from the CPS would be that it has been the most widely known and used dataset to analyze US income and wage inequality for many decades. We leave this alternative approach to future work. 6 Personal income is a concept that is specific to the U. S. National Income and Product Accounts (NIPA). It is an ambiguous concept (neither pre-tax, nor post-tax), as it does not deduct taxes but adds back cash government transfers.
This result implies that tax data under-estimate  11  In the long-run, a large fraction of capital gains arises from the fact that corporations retain part of their earning, which leads to share price appreciation. Since retained earnings are part of national income, these capital gains are in effect included in our series on an accrual basis. In the short run, however, most capital gains are pure asset price effects. These short-term capital gains are excluded from national income and from our series. 12 A number of studies have tried to reconcile totals from the national accounts and totals from household surveys or tax data; see, e. g. , Fesseau, Wolff and Mattonetti (2012) and Fesseau and Mattonetti (2013). Such comparisons have long been conducted at national levels (for example, Atkinson and Micklewright, 1983, for the UK) and there have been earlier cross country comparisons (for example in the OECD report by Atkinson,  Rainwater, and Smeeding, 1995, Section 3.
We supplement this dataset using the internal use Statistics of Income (SOI) Individual Tax Return Sample files from 1979 onward. 17 For the pre-1962 period, no micro-files are available so we rely instead on the Piketty and Saez (2003)   series of top incomes which were constructed from annual tabulations of income and its composition by size of income (U. S. Treasury Department, Internal Revenue Service, Statistics of Income, 1916-present). Tax data contain information about most of the components of pretax income, including private pension distributions-the vast majority of which are taxable-, Social Security benefits (taxable since 1984), and unemployment compensation (taxable since 1979). However, they miss a growing fraction of labor income and about two-thirds of economic capital income.
Non-filers To supplement tax data, we start by adding synthetic observations representing non-filing tax units using the Current Population Survey (CPS). We identify non-filers in the CPS based on their taxable income, and weight these observations such that the total number of adults in our final dataset matches the total number of adults living in the United States, for both the working-age population (aged 20-65) and the elderly. 18 x-exempt labor income To capture total pre-tax labor income in the economy, we proceed as follows. First, we compute employer payroll taxes by applying the statutory tax rate in each year. Second, we allocate non-taxable health and pension fringe benefits to individual workers using information reported in the CPS. 19 Fringe benefits have been reported to the 17 SOI maintains high quality individual tax sample data since 1979 and population-wide data since 1996.
All the estimates using internal data presented in this paper are gathered in Saez (2016). Saez (2016) uses internal data statistics to supplement the public use files with tabulated information on age, gender, earnings split for joint filers, and non-filers characteristics which are used in this study. 18 The IRS receives information returns that also allow to estimate the income of non-filers. Saez (2016) computes detailed statistics for non-filers using IRS data for the period 1999-2014. We have used these statistics to adjust our CPS-based non-filers. Social security benefits, the major income category for non-filers, is very similar in both CPS and IRS data and does not need adjustment. However, there are more wage earners and more wage income per wage earner in the IRS non-filers statistics (perhaps due to the facts that very small wage earners may report zero wage income in CPS).
We have checked that our imputed pension benefits are consistent with the high quality information reported on W2s. 20  They are also consistent with the results of Pierce (2001), who studies non-wage compensation using a different dataset, the employment cost index micro-data. Like Pierce (2001), we find that the changing distribution of non-wage benefits has slightly reinforced the rise of wage inequality. 21 x-exempt capital income To capture total pre-tax capital income in the economy, we first distribute the total amount of household wealth recorded in the Financial Accounts following the methodology of Saez and Zucman (2016). That is, we capitalize the interest, dividends and realized capital gains, rents, and business profits reported to the IRS to capture fixedincome claims, equities, tenant-occupied housing, and business assets.
A11. (Remember that Social security pensions, unemployment insurance, and disability benefits, which together make about 6% of national income, are already included in pre-tax income). We impute monetary transfers to their beneficiaries based on rules and CPS data. In-kind social transfers. In-kind social transfers are all transfers that are not monetary (or quasi-monetary) but are individualized, that is, go to specific beneficiaries. In-kind transfers amount to about 8% of national income today. Almost all in-kind transfers in the United States correspond to health benefits, primarily Medicare and Medicaid. Beneficiaries are again imputed based on rules (such as all persons aged 65 and above or persons receiving disability insurance for Medicare) or based on CPS data (for Medicaid). Medicare and Medicaid benefits are imputed as a fixed amount per beneficiary at cost value.
g. , Katz and Autor,  1999). The US Census bureau (2016) official statistics also show very little growth of median family income in recent decades. Our value added is to include all national income accruing to the bottom 50% adults, to contrast pre-tax and post-tax incomes, and to be able to compare the bottom to the top of the distribution in a single dataset representative of the US population. million). Concretely, the in-kind health redistribution received by bottom 50% individuals is equivalent to about one week of attention provided by an average top-decile health provider, or one day of attention provided by an average top-percentile health provider. Figure  3  also displays the average post-tax disposable income of bottom bottom 50% earnersincluding cash transfers but excluding in-kind transfers and collective consumption expenditures.
44 Equal splitting implicitly assumes that all income earned by married couples is shared equally. Individualized series by contrast assume that labor income is not shared at all. There is obviously a lot of variations across couples in the actual sharing of resources and division of monetary power. Empirical studies find that actual sharing practices are in between full and no sharing (see Chiappori and Meghir, 2015, for a recent survey). Because of the lack of comprehensive data (and especially historical data), we restrict ourselves to the two polar cases of full and no-sharing. Attempting to split incomes using empirical sharing rules is left for future research. 1999 onward. Prior to 1999, we rely on IRS tabulations of how wage income is split among couples in the top 5% that are available for some years, and on similar tabulations that we computed annually in the CPS for the bottom 95%.
First, our dataset should be seen as a prototype to be further developed and improved uponjust like the national accounts themselves, including the computation of GDP, are regularly improved. Looking forward, our assumptions and imputations could be bettered by drawing on new knowledge on the incidence of taxes and transfers and by leveraging new and better data. For example, tax data after 2013 provide direct information at the micro-level on the value of employee health insurance benefits. Like the national accounts, we see our distributional national accounts as work in constant evolution. Our hope is that our prototype distributional national accounts will ultimately be taken over, refined, published, and regularly improved upon by government statistical agencies. Second, distributional national accounts can be used to compare income across countries on a consistent basis.
2) we need to calculate the estimated matrix B. With estimated B called: U W = T X T Y' T X S (3) Through all these equations (observation) we can give the general regression equation of PLM. = + + + + (4) The methodology adopted started by determining (estimating) , , , parameters of the multiple-regression function. The result of estimation is defined by: Z, W , , \. For this, we choose the method of "mean square error" calculated through Matlab. In the second step, we calculate the dependency between PLM level (result of multipleregression) and the adoption factors (H, O, T and E) by the regression coefficient (R), especially the Determination Coefficient (D). Where: ] = ^ = ___ _H , aa^=.
In the same vein as the 2011 study (UNFCCC, 2011), this report presents further evidence relating to the benefits and impacts of the CDM in time for the end of the first commitment period of the Kyoto Protocol. Specifically the CDM's contributions to sustainable development and to technology transfer are examined as well as emerging patterns in CDM capital investment, costs, savings and revenue and the geographic distribution of projects. Throughout this study it is assumed that co-benefits associated with CDM projects are solely due to the influence of the CDM, although there may be benefits which could be derived from other project baseline scenarios. The evidence comes from six sources of information: • UNFCCC (CDM) Analytical Database as of June 2012 -a database maintained by the UNFCCC secretariat comprising individual CDM project information for all projects in the CDM pipeline 1 including among other information, project status, project specific data on the expected and issued CERs, the crediting period chosen, and time related data (e.
g. project design document submission, registration, monitoring and issuance dates These data were used to classify projects by their type (wind, hydro, etc. ) and subtype (run-of-river, dam, etc. ); • The Institute for Global Environmental Strategies (IGES) CDM Project Database as of May 2012. 4  These data were used to establish the start dates of the CDM projects; • Bloomberg new energy finance (BNEF) data as of May 2012 -a proprietary database of all major financing events related to specific renewable energy projects worldwide. The data include the asset value (capital investment) and capacity for individual projects by country by year of financial closure, the originating country of the investment, and type of finance (debt/equity). This study is structured as follows. Section 2 summarizes the claimed contributions of CDM project activities 5 to sustainable development in their host countries.
Such analyses may yield insights into the roles of different technology transfer channels including CDM, foreign direct investment and trade. Investment in CDM projects is estimated using information provided in PDDs. The accuracy of that information is not known. Comparison of the PDD figures with the actual capital investment of a sample of projects would be very useful. Some project developers probably consider the actual capital investment confidential information, so such a comparison would probably need to be done on a confidential basis. However, results could be reported by project types; for example, that the investment in hydro projects is greater than the estimates reported in the PDDs. Data on actual capital investment might be obtained from funds that invest in CDM projects or as part of the verification process for a sample of projects.
METHODOLOGY AND DATA SOURCES FOR COMPARISON OF CDM AND ANNEX I RENEWABLE ENERGY PROJECTS Bloomberg New Energy Finance (BNEF) records data on capital investment, project location and sources of finance for renewable energy projects. Data are collected for non-CDM projects in Annex I countries and CDM projects, which are drawn from UNFCCC sources via UNEP Risø and IGES. The BNEF database is compiled from public information, so coverage is not complete; however the number of missing projects in Annex I countries is believed to be low. Coverage of non-CDM non-Annex I country projects is poor, as information for these projects is generally much less available in the public domain. The number of projects with capital investment information is shown in Table  B -1. The BNEF database includes only renewable energy projects, so many CDM project types are excluded.
157 omparison of CDM with similar types of Annex I projects is possible only for geothermal, hydro, solar and wind projects. The number of tidal projects is too small to permit meaningful comparisons and landfill gas and methane avoidance projects are not recorded by BNEF. Information on project location and sources of finance in the BNEF database is used to classify projects as being domestic or foreign financed. A project is domestically financed if the only source of project finance is the host country. All other projects involve some foreign finance, but these projects usually have some domestic finance. All costs are expressed in USD, calculated using the current interbank exchange rate on the date the project started operation or was submitted for validation. The discount rate is the rate used in the PDD to demonstrate additionality; it is typically expressed as a benchmark rate or hurdle rate.
METHODOLOGY TO DETERMINE THE RELATIONSHIP BETWEEN OPERATING CDM PROJECTS IN A COUNTRY AND COUNTRY CHARACTERISTICS It is clear from this report that most of the investment in CDM projects originates in the host country, so its characteristics are crucial determinants of CDM activity. The relationship between the number operating CDM projects in a country as a measure of regional distribution and some of the more important country characteristics suggested by the literature 161 -national GHG emissions, population, GDP, gross national savings was analysed. The assessment relied on simple univariate regression analyses where the dependent variable -the number operating CDM projects in a host country is related to selected country characteristics. Although the number of operating projects is a smaller dataset than all registered projects in a host country, it is more germane as the project investment is known to have taken place 162.
Acknowledgements AUTHORS Grant  A. Kirkman ,  Stephen Seres ,  Erik Haites , and  Randall Spalding-Fecher ACKNOWLEDGEMENTS Mathew Wilkins ,  Niclas Svenningsen ,  Pete Eriksson ,  Micheal Lazarus ,  Mischa Claasen ,  Yunzhu Li ,  Stephan Bakker ,  Karen Holm Olsen ,  Jørgen Fenhann ,  Jacob Ipsen Hansen ,  Søren Lütken ,  Kazuhisa Koakutsu ,  Kentaro Takahashi ,  Nick Johnstone ,  Ivan Haščič ,  Guy Turner ,  Richard Chatterton  and those project participants who took the time to respond to the survey.
Such research, however, faces a key methodological challenge-specifically, the measurement of the cultural dimensions that relate to gender. To address this challenge, we propose an emerging approach that uses the gender distinctions in language to measure cultural attitudes toward gender roles. The idea that language may capture gender roles and even influence their formation and persistence has been the focus of emerging research in linguistics and economics. To support our proposition, we follow two steps. First, we review the accounting research by performing a systematic query on the bibliographic databases of the accounting articles that study gender and language. Second, we present data regarding the diversity of the global boards of the Big Four and the diversity of the linguistic environments in which they operate.
To support our claim, we proceed as follows. First, we perform a systematic literature review by tracing all published research papers from 1926 (the first issue of The Accounting Review) to October 2016 that address and connect gender, accounting and language issues. Second, we provide evidence for the diversity of linguistic environments in which the Big Four operate because of their worldwide presence. In this process, we also collect data on the characteristics of the global boards of the Big Four in terms of gender diversity and size. Our first finding shows the scarcity of papers that investigate gender in accountancy by using language to measure culture. We then show that the Big Four operate in highly diverse linguistic environments and that language may enhance our understanding of the cultural determinants of the rarity of a female presence in the Big Four across countries.