dataesr/scientific-paragraphs-categorization huggingface dataset:
Website and search interface The SSI database and associated website is located at http: //ssi. lesia. obspm. fr. It provides access to the data and a brief description of the database and its content. An User Guide is also available for download. Data retrieval is possible through the 'Search interface'. It allows the users to perform queries using star ID, star properties (e. g. T eff , log g, spectral type,. ), and finally stellar indices (seismic indices and granulation parameters). A list of star ID can be provided by downloading a file containing these ID. Access to the data is also possible through a VO-compatible web service as well as through the Seismic Plus portal (http://voparis-spaceinn. obspm. fr/seismic-plus/).
Acknowledgements The Stellar Seismic Indices Data Base has been developed in the framework of the  SPACEInn project (Exploitation of Space Data for Innovative Helio-and Asteroseismology ), initiated by the  European Helio-and Asteroseismology Network (HELAS)  and financed by the  European Union  under the  Seventh Framework Programme  ( FP7  project n. 312844). We acknowledge the  Paris Data Centre  for their financial and technical support. We thank  S. Cnudde  for the conception of the SSI logo and  F. Henry  for technical supports. The CoRoT space mission has been developed and operated by  CNES , with contributions from  Austria, Belgium, Brazil ,  ESA  ( RSSD and Science Program ),  Germany , and  Spain. Funding  for the Kepler Discovery mission were provided by  NASA's Science Mission Directorate.
Shades of Darkness: A Pecking Order of Trading Venues We characterize the dynamic fragmentation of U. S. equity markets using a unique dataset that disaggregates dark transactions by venue types. The "pecking order" hypothesis of trading venues states that investors "sort" various venue types, putting low-cost-low-immediacy venues on top and highcost-high-immediacy venues at the bottom. Hence, midpoint dark pools on top, non-midpoint dark pools in the middle, and lit markets at the bottom. As predicted, following VIX shocks, macroeconomic news, and firms' earnings surprises, changes in venue market shares become progressively more positive (or less negative) down the pecking order. We further document heterogeneity across dark venue types and stock size groups.
" The top of the pecking order consists of venues with the lowest cost and lowest immediacy, and the bottom of the pecking order consists of venues with the highest cost and highest immediacy. The pecking order hypothesis predicts that, as investors' trading needs become more urgent, they move from low-cost, low-immediacy venues to high-cost, We test the pecking order hypothesis in U. S. equity market by exploring a unique dataset on dark trading. Our dataset disaggregates dark transactions into five categories by trading mechanism, including the two types of dark pools shown in Panel (b) of Fig. 2. The other three categories of dark transactions are retail investors' trades internalized by broker-dealers, average-price trades, and other (mostly institutional) trades. The detailed descriptions are provided in Section 3.
To the best of our knowledge, this dataset provides the most comprehensive and granular view of U. S. dark trading that is accessible by academics. Without a disaggregated dataset like this, testing the pecking order hypothesis-or conducting any analysis of dark venue heterogeneity-would be very difficult because all off-exchange trades in the U. S. are reported under a single consolidated category called "trade reporting facilities" (TRFs). Data aggregation at TRFs masks the very heterogeneity in trading mechanisms that we are interested in. We estimate a panel vector-autoregressive model with exogenous variables to characterize the dynamic interrelation among dark volumes, endogenous market conditions, and exogenous shocks (see Section 4). Key to our empirical strategy are three exogenous variables: VIX, macroeconomic data releases, and earnings surprise.
We use these variables as proxies for shocks to investors' demand for immediacy. Applied to this setting, the pecking order hypothesis predicts that the proportional changes in venue market shares after these shocks should become progressively more positive (or less negative) the further down in the pecking order. The data support the pecking order hypothesis. Following a 0. 01% upward shock to VIX, dark pools that cross orders at the midpoint lose 4. 6% of their market share (from 2. 4% to 2. 29%), dark pools that allow some price flexibility lose 3. 3% of their market share (from 7. 52% to 7. 27%), and lit venues gain 1. 0% of their market share (from 76. 2% to 77. 0%). The effect is much larger for macroeconomic data releases. In the minute right after the macroeconomic news, DarkMid and DarkNMid market shares are 38.
The primary contribution of this paper to the literature is the characterization of dynamic fragmentation of dark and lit venues through the pecking order hypothesis. Rather than assessing the market composition from a static view, we study how market shares evolve dynamically upon certain urgency shocks. Our approach of focusing on exogenous shocks (VIX, macroeconomic news, and surprise earnings) differs from most existing empirical studies on dark venues, which typically relate dark trading to endogenous measures of market quality (e. g. , spread, depth, and volatility). Access to unique, comprehensive, and granular data on various types of dark trading enabled us to study the pecking order hypothesis. Most existing studies of dark venues use either aggregate offexchange trades or trades in a few selected dark pools.
S. and conclusions from these studies cannot literally be applied to U. S. equity markets. The only other academic paper we are aware of that has a similar dataset for U. S. equities is  Kwan, Masulis, and McInish (2015). They examine how the minimum tick size affects competitiveness of exchanges relative to dark venues, which is a research question very different from ours. Using a regression discontinuity design around the $1. 00 price threshold, they conclude that the tick size constraint weakens the competitiveness of exchanges.
Dark volumes In the United States, off-exchange transactions in all dark venues are reported to trade-reporting facilities (TRFs). The exact venue in which the dark trade takes place is not reported in public data. More recently, FINRA started to publish weekly transaction volumes in alternative trading systems, but these volumes are not on a trade by trade basis. 6 The TRF managed by NASDAQ is the largest TRF, accounting for about 92% of all off-exchange volume in our sample. Our first dataset contains all dark transactions reported to the NASDAQ TRF. These trades are executed with limited pre-trade transparency. 7 A salient and unique feature of our data is that the dark transactions are disaggregated into five categories by trading mechanism. The exact method of such disaggregation is proprietary to NASDAQ, but we know their generic features.
For concreteness, Appendix A presents a snippet of the raw data we use. Table  1  shows the market shares of the five types of dark venues as a fraction of total trading volume in our sample. The total trading volume is obtained from TAQ by running the algorithm developed by  Holden and Jacobsen (2014). We label the complement of these five dark venues as the "lit" venues. The "lit" label is an approximation. 8 In the first column (full sample), we observe that dark venues account for more than 30% of total transaction volume in the 117 stocks in October 2010. Ranked by market shares, the five dark categories are DarkRetail (12. 9%), DarkNMid (9. 1%), DarkOther (6. 8%), DarkMid (2. 5%), and DarkPrintB (1. 0%). Columns 2-4 show the breakdown of venue market shares for large, medium, and small stocks, respectively.
DarkMid market shares are similar across the three size terciles, but DarkNMid market shares seem to decrease in market capitalization. DarkRetail market share is visibly smaller for medium stocks than for large or small ones. It is informative to compare our five-way categorization of dark trading venues to that of the SEC. SEC (2010) classifies opaque trading centers into dark pools and broker-dealer internalization. By approximation, our DarkMid and DarkNMid types roughly fall into the SEC's dark pool category, and our DarkRetail, DarkPrintB, and DarkOther types roughly fall into the SEC's broker-dealer inter-nalization category. Using one week of FINRA audit trail data in 2012,  Tuttle (2014)  reports that about 12. 0% of trading volume in U. S. equities is executed in off-exchange alternative trading systems (ATSs), the majority of which are dark pools.
She further reports that about 18. 8% of U. S. equity volume are executed off-exchange without involving ATSs, which can be viewed as a proxy for trades intermediated by broker-dealers. The dark-pool volume and internalized volume implied by our dataset are comparable to those reported by  Tuttle (2014). Variables used in the empirical analysis. For ease of reference, all variables that are used in the empirical analysis are denoted in CamelCase italics and their descriptions are tabulated in Table  2. The sample is constructed at the minute frequency. This is necessary as the sampling frequency is constrained by NASDAQ order book snapshots, which, among all raw data used, is the one with the lowest frequency. It comprises all minutes during the trading hours (9:30am to 4:00pm) except the exact moment of market opening, 9:30am.
NASDAQ: trade and quote data with HFT label Our second dataset contains detailed trade and quote data for the NASDAQ market (an electronic limit order book that is part of the "Lit" market). The data include identification of the activity of highfrequency trading firms in this market. These firms are known to engage in high-speed computerized trading, but their identities and strategies are unknown to us. This dataset has two parts. First, for each transaction on NASDAQ, we observe the stock ticker, the transaction price, the number of shares traded, an indicator of whether the buyer or seller initiated the trade, and an indicator that for each side of the trade, tells whether it was an HFT or not. We refer to a trade as an HFT trade if at least one side of the trade is an HFT. All transactions are time-stamped to milliseconds.
Second, we observe the minute by minute snapshot of the NASDAQ limit order book. For each limit order, we observe the ticker, the quantity, the price, the direction (buy or sell), and a flag on whether the order is displayed or hidden, and a flag on whether or not the order is submitted by an HFT. An important and well-recognized caveat of the NASDAQ HFT dataset is that it excludes trades by HFTs that are routed through large, integrated brokers, nor does it distinguish various HFT strategies, be it market making or "front-running. " Given this caveat, a prudent way to interpret the HFT measures is that they are "control variables," needed to capture market conditions. For additional details about the NASDAQ HFT dataset, see  Brogaard, Hendershott, and Riordan (2014). Variable used in the empirical analysis.
We construct the following variables to characterize market conditions in the NASDAQ lit market. We use the absolute value of NASDAQ signed volume as a measure of volume imbalance, ImbVolume. Doing so with NASDAQ data has the advantage that the NASDAQ data provide the exact trade direction indicator, buy or sell. By contrast, constructing volume imbalance from TAQ transaction data would require using an imprecise trade-signing heuristic, such as the  Lee and Ready (1991)  algorithm. Ellis, Michaely, and O'Hara (2000)  find that compared to the actual buy/sell indicator in NASDAQ data, the Lee-Ready algorithm misclassifies about 19% of NASDAQ trades. We further add two commonly used measures of liquidity: spread and depth. RelSpread is the relative bid-ask spread of the NASDAQ lit market. Three depth measures are added: InHiddDepth, TopDispDepth, and AtHiddDepth (ordered according to execution priority).
Overall market conditions A third dataset is used to characterize overall market conditions (i. e. , based on including all markets, not only NASDAQ). The data used for this part are the standard TAQ data and millisecond-level NBBO data provided by NASDAQ. Variables used in the empirical analysis. For each stock and each minute, we calculate total transaction volume TAQVolume, realized return variance within the minute RealVar, and the variance ratio within the minute VarRat10S. The first variable is based on TAQ data, whereas the other two are based on millisecond-level NBBO data.
We micro-found the pecking order hypothesis with a stylized model of strategic venue choice among DarkMid, DarkNMid, and Lit. We test the pecking order hypothesis using a unique U. S. equity dataset that, for each trade, identifies in what type of venue it transpired. The dataset distinguishes five venue types: two types of dark pools, namely DarkMid and DarkNMid, and three types of broker-dealer internalization, namely retail trades, average-price trades, and other trades. We use three exogenous variables as proxies for investors' urgency to trade: VIX, macroeconomic data releases, and earnings announcements. These three variables are used in a panel VARX model that characterizes the dynamic interrelation between dark trading volumes and (endogenous) measures of market conditions such as spread, depth, and volatility.
This sample of stocks is the same as that in the NASDAQ HFT dataset used by many studies of HFT. The original sample compiled by NASDAQ contains 120 stocks, with 40 stocks in each size category. But only 117 of the 120 stocks are present in our sample period. Brogaard, Hendershott, and Riordan (2014)  state that "The top 40 stocks are composed of 40 of the largest market capitalization stocks. The medium-size category consists of stocks around the 1000 th largest stocks in the Russell 3000. , and the small-size sample category contains stocks around the 2000 th largest stock in the Russell 3000. ".
Background: Despite major advances in prevention and treatment, coronary artery disease (CAD) remains the leading cause of death worldwide. Whereas many sources of data are available on the epidemiology of acute coronary syndromes, fewer datasets reflect the contemporary management and outcomes of stable CAD patients. Hypothesis: A worldwide contemporary registry would improve our knowledge about stable CAD. The main objectives are to describe the demographics, clinical profile, contemporary management and outcomes of outpatients with stable CAD; to identify gaps between evidence and treatment; and to investigate long-term prognostic determinants. Methods: CLARIFY (ProspeCtive observational LongitudinAl RegIstry oF patients with stable coronary arterY disease) is an ongoing international observational longitudinal registry.
Moreover, despite the importance of heart rate (HR) in the prognosis of stable CAD,  [10] [11] [12] [13] [14]  HR is not a routine component of CV risk assessment, nor a tool to decide whether treatment is indicated, and most datasets have not collected detailed information on HR in stable CAD. Large datasets are available from randomized trials in stable CAD. However, although these are the gold standard to evaluate new therapies,  15  they are generally performed in highly selected populations that often do not reflect patients encountered in daily practice in terms of their clinical characteristics, comorbidities, socioeconomic status, management, and outcomes. 16 Large prospective registries often provide a more realistic description of the patients' actual characteristics, management, and outcomes, provided their recruitment is unbiased and the sample size is sufficiently large.
| Study design CLARIFY is an ongoing international, prospective, observational, longitudinal registry of outpatients with stable CAD, with yearly followup for up to 5 years. This observational registry was designed to collect the current status of outpatients with stable CAD, including their demographic characteristics, clinical profiles, therapeutic strategies, and outcomes. Data were collected prospectively at annual visits every 12 AE 3 months. Owing to substantial geographic variations in the epidemiology of stable CAD, this registry is international to generate reliable data on several regions in the world. Patients were enrolled in 45 countries in Europe, Asia, North/Central/South America, the Middle East, Australia, and South Africa (Figure  1 ). Local ethical approval was also obtained in all 45 countries before recruitment, according to national and local regulations at each site.
| Site selection To ensure that the enrolled population of outpatients with stable CAD was representative of the population of each country, sites were identified based on a predefined selection of physicians including cardiologists, general practitioners, internists, and hospital-based physicians. In each country, selection of physicians was made by national coordinators using the best available epidemiological data reflecting the burden of CAD in that country, to provide a representative distribution of physicians across regions and location types (ie, urban, suburban, and rural areas). Epidemiologic and medical care data, published and endorsed by national or international societies, either local or regional, were used to identify the distribution of coronary patients in each country, to select physician types and locations, and subsequently patients.
| Data collection and evaluation Data were collected anonymously using electronic standardized international case-report forms (translated into the local language) at baseline and annually for up to 5 years, to ascertain clinical events, hospitalization, employment status, or sick leave. Between the baseline visit and each annual visit, to maximize follow-up and retention rates, 6-month telephone calls were made to collect vital status, confirm contact details, and ensure the next annual visit was planned. To ensure data quality, every year, 1% of the centers were randomly selected to perform on-site audits. In these selected centers, 100% of the data for all patients were checked for source documentation and accuracy. Data quality control was done at face-to-face quality-control visits and involved review of source documents supporting the adequacy and accuracy of data collected on the casereport forms.
| Statistical analysis CLARIFY is an observational registry, and the size of the population was not based on a planned treatment comparison. The number of patients to be included was computed based on the aim to build a robust risk model at the completion of follow-up and depended on the CV event rate, number of subjects lost to follow-up, and study duration. Based on data from the literature, the annual rates of CV death and of major adverse CV events were expected to be approximately 2% and 4. 5%, respectively. CLARIFY had to screen ≥31 000 subjects and follow them up for 4 to 5 years (with approximately 5% per year loss to follow-up). With these assumptions, it was expected that there would be approximately 2300 CV deaths at the end of follow-up, providing ample power for risk modeling.
| RESULTS A total of 33 032 patients were enrolled in the CLARIFY Registry. Of these, 329 withdrew their consent or did not meet the inclusion criteria. The baseline study population was therefore 32 703 patients (Figure  2 ). Baseline demographics are detailed in  Data are presented as n (%), mean AE SD, or median (IQR). a Inclusion criteria are not mutually exclusive; some patients may be included in >1 group. b 1 drink = 1 standard measure of spirits, 1 glass of wine, 1 bottle of beer. HDL-C, mmol/L 1. 1 (1. 0-1. 4) 1. 1 (1. 0-1. 3) 1. 1 (1. 0-1. 4) 1. 2 (1. 0-1. 4) 1. 1 (1. 0-1. 4) LDL-C, mmol/L 2. 4 (1. 9-2. 9) 2. 4 (1. 9-3. 0) 2. 3 (1. 9-2. 9) 2. 4 (1. 9-3. 0) 2. 3 (1. 9-2. 9) Fasting TG, mmol/L 1. 4 (1. 0-1. 9) 1. 4 (1. 0-1. 9) 1. 4 (1. 0-1. 9) 1. 4 (1. 0-2. 0) At baseline, the CLARIFY population had high rates of use of evidence-based drugs for prevention in CAD (Table  3 ).
a Inclusion criteria are not mutually exclusive; some patients may be included in >1 group. ischemia does not address the risks that these patients face. 33 A particular focus will be given to these populations (ie, with or without anginal symptoms and with or without proven ischemia on noninvasive testing) to try to explain the differences in CV outcomes. Despite the size and scope of CLARIFY, the registry is not without important limitations. First, as with any observational database, it is difficult to rule out selection biases and confounding. We attempted to improve the representativeness of the cohort: minimizing the risk of selection bias by drastically limiting the enrollment period and attempting to balance representation of each country by targeting a fixed proportion of patients in relation to each country's population.
The Global Fire Assimilation System (GFAS) assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily estimates of biomass burning emissions. It has been extended to include information about injection heights derived from fire observations and meteorological information from the operational weather forecasts of ECMWF. Injection heights are provided by two distinct methods: the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) parameterisation and the one-dimensional plume rise model (PRM). A global database of daily biomass burning emissions and injection heights at 0. 1 • resolution has been produced for 2003-2015 and is continuously extended in near-real time with the operational GFAS service of the Copernicus Atmospheric Monitoring Service (CAMS).
In this study, the two injection height data sets were compared with the new MPHP2 (MISR Plume Height Project 2) satellite-based plume height retrievals. The IS4FIRES parameterisation showed a better overall agreement than the observations, while the PRM was better at capturing the variability of injection heights. The performance of both parameterisations is also dependent on the type of vegetation. Furthermore, the use of biomass burning emission heights from GFAS in atmospheric composition forecasts was assessed in two case studies: the South AMerican Biomass Burning Analysis (SAMBBA) campaign which took place in September 2012 in Brazil, and a series of large fire events in the western USA in August 2013. For these case studies, forecasts of biomass burning aerosol species by the Composition Integrated Forecasting System (C-IFS) of CAMS were found to better reproduce the observed vertical distribution when using PRM injection heights from GFAS compared to aerosols emissions being prescribed at the surface.
, 2013, and references therein). This can result in acute air quality problems, such as in Singapore in June 2013, for example, when the air quality index reached record values because of smoke from fires in nearby Sumatra. Several global fire emission databases have been developed in recent years: FLAMBE (Fire Locating and Modeling of Burning Emissions), GFED (Global Fire Emissions Database), FINN (Fire INventory from NCAR), QFED (Quick Fire Emission Dataset), IS4FIRES and GFAS  (Reid et al. , 2009; Van der Werf et al. , 2010 and Giglio et al. , 2013; Wiedinmyer et al. , 2011; Darmenov and da Silva, 2013; Sofiev et al. , 2009; Kaiser et al. , 2012, respectively). All of these products rely on satellite observations of fire radiative power (FRP), hot spots or burnt areas because they alone provide sufficient spatial coverage and temporal sampling frequency  (Giglio et al.