dataesr/scientific-paragraphs-categorization huggingface dataset:
e. drainage). A full and detailed description of the abovementioned parameterizations used by the Noah scheme can be found in  Chen and Dudhia (2001). For the definition of vegetation and land cover WRF uses the United States Geological Survey (USGS) land cover dataset, which has a resolution of 1 km with 24 categories  (Loveland et al. , 2000; Hibbard et al. , 2010; Sertel et al. , 2010) ; this land cover dataset is derived from the 1 km satellite Advanced Very High Resolution Radiometer (AVHRR) data. In addition to land cover, WRF defines 12 soil types and 4 non-soil types, including organic material, water, bedrock, and ice. Soil types are classified based on the percentage of sand, silt, and clay in the soil  (Dy and Fung, 2016) ; for each soil type, WRF has a default soil parameter table that generalizes the hydraulic and thermal properties of the soil.
The chemistry transport model CHIMERE The chemistry transport model used in this study is CHIMERE (v2014b), an Eulerian model developed to simulate gas-phase chemistry, aerosol formation, transport and deposition at a regional scale  (Menut et al. , 2014). The gas-phase chemical mechanism used by CHIMERE is MELCHIOR2  (Lattuati, 1997) , which consists of a simplified version (40 chemical species, 120 reactions) of the full chemical mechanism MELCHIOR; this latter mechanism describes more than 300 reactions of 80 species. Photolysis rates are explicitly calculated using the FastJ radiation module  (Wild et al. , 2000) , as described by  Mailler et al. (2016 Mailler et al. ( , 2017)). External meteorological forcing required by CHIMERE to calculate the atmospheric concentrations of gas-phase and aerosol species are directly provided by the WRF simulation.
In addition, to accurately reproduce the gas-phase chemistry, emissions must be provided every hour for the specific species of the chemical mechanism. For studies over Europe, the EMEP inventory  (Vestreng et al. , 2009)  is usually used for anthropogenic emissions of NO x , CO, SO 2 , PM 2. 5 and PM 10. Biogenic emissions of six species (isoprene, α-pinene, β-pinene, limonene, ocimene, and NO) are calculated through the MEGAN model  (Guenther et al. , 2006). This model parameterizes the bulk effect of changing environmental conditions using three time-dependent input variables: surface air temperature, radiation and foliage density (i. e. leaf area index, LAI). In the standard version of CHIMERE, the LAI database is given as a monthly mean product derived from MODIS observations, referred to base year 2000  (Menut et al.
Measurement data and statistical analysis In order to assess how the new parameterization of dry deposition changes the ability of CHIMERE to reproduce the spatial distribution of surface O 3 concentration, we compare the simulated data at surface level against in situ measurements. Station data were obtained from the European air quality database (AirBase) and maintained by the European Environment Agency (EEA) (http://acm. eionet. europa. eu/databases/ airbase/, last access: 20 April 2018). For the validation of O 3 bias, computed comparing hourly simulated O 3 concentrations with AirBase data, we use the root-mean-square error (RMSE), while to assess the agreement in the phase (i. e. hourly cycle) we use the correlation coefficient. Considering the soil moisture, we retrieve precipitation data over four forested eddy covariance sites belonging to the European flux network (http://www.
Changes in the model performances As discussed above, the inclusion of soil water limitation to stomatal conductance leads to increased O 3 concentration due to the reduced dry deposition rates; this clearly affects the model performances in reproducing both the phase and amplitude of hourly O 3 concentration. Therefore, here we validate the simulated O 3 against AirBase measurements. Figure  6  (upper panels) shows how the inclusion of the new parameterization leads to an increase of model-data misfit during the temporal period April-September, being the percentage change in RMSE positive in all the stations. Overall, the mean RMSE (average over all the stations) computed comparing hourly data is 17. 8 ppb for the NO_SWC simulation, 19. 5 ppb in the SWC_10cm and SWC_40cm, and 19 ppb in the SWC_1m and SWC_DYN simulations.
Conversely, the new parameterization improves the model skills in reproducing the observed hourly cycle (Fig. 6 , lower panels), being the percentage change in correlation coefficient positive in all the stations. Overall, the mean correlation computed from hourly data is 0. 6 for the NO_SWC simulation, 0. 62 in the SWC_10cm and 0. 64 in the SWC_40cm, SWC_1m and SWC_DYN simulations. This result is in agreement with a previous study which showed how, within CHIMERE, the deposition not only acts as a shifting term on the modelled concentration but also influences the variability and timing of ozone  (Solazzo et al. , 2017).
Acknowledgements. We thank the investigators and the teams managing the eddy-flux sites. We also acknowledge the entire EMEP and  AIRBASE  staff for providing ground-based O 3 data and the EMEP/MSC-W team for the anthropogenic emissions database. The computing resources and the related technical support used for this work were provided by  CRESCO/ENEA-GRID High Performance Computing infrastructure  and its staff (http://www. cresco. enea. it,last access:  20 April 2018 ). CRESCO/ENEAGRID High Performance Computing infrastructure is funded by  ENEA , by the  Italian National Agency for New Technologies, Energy and Sustainable Economic Development , and by national and European research programmes. Financial support was from the  MITIMPACT  project ( INTERREG V-A France-Italy ALCOTRA ). This work was carried out within the  IUFRO Task Force on Climate Change and Forest Health.
Endogeneity bias can lead to inconsistent estimates and incorrect inferences, which may provide misleading conclusions and inappropriate theoretical interpretations. Sometimes such bias can even lead to coefficients having the wrong sign. Although this is a long-standing issue, it is now emerging in marking and management science, with high-ranked journals increasingly exploring the issue. In this paper, we methodologically demonstrate how to detect and deal with endogeneity issues in panel data. For illustration purposes, we used a dataset consisting of observations over a 15-year period (i. e. , 2002 to 2016) from 101 UK listed companies and examined the direct effect of R&D expenditures, corporate governance, and firms' characteristics on performance. Due to endogeneity bias, the result of our analyses indicates significant differences in findings reported under the ordinary least square (OLS) approach, fixed effects and the generalized method of moments (GMM) estimations.
For example, the dynamic generalized method of moments model (GMM) is used to address panel data (i. e. , dynamic endogeneity bias) and two-stage least squares (2SLS)/threestage least squares (3SLS) are often used for survey data. Some researchers have recently provided reviews to understand the key endogeneity concepts and relevant techniques (e. g. , see  Zaefarian et al. , 2017). However, a step-by-step procedure on how to execute these techniques for a particular research problem is still missing. We therefore provide a succinct overview of the key endogeneity sources and solutions, and comprehensively demonstrate the GMM method using a case study of a panel dataset consisting of 15 years of observations. Specifically, this study explores how the dynamic nature of investment in R&D expenditures together with corporate governance affect firm performance.
To better illustrate how endogeneity bias may cause incorrect estimates, we examine our proposed model using three different approaches, namely, ordinary least square (OLS), fixed effects, and the generalized method of moments (GMM). Practically, our main aim and contribution is to provide a comprehensive procedure for researchers to produce consistent estimates and to draw valid inferences when dealing with panel data. In addition, panel data is used far less frequently in business-to-business than in the businessto-consumer marketing domain, and this article could provide a starting point as to how industrial marketing and management researchers can utilize such datasets to provide insights for business practitioners. For instance, the research and development expenditure and its relationship to firm financial performance in industrial marketing can be explored by using panel datasets that are available from different databases (e.
It is a good academic practice to acknowledge that alternative models may exist, which are equally possible on logical grounds. Researchers should use strong theoretical arguments to build the directions of hypotheses and models. Interested readers may also want to consult available studies on the topic (e. g. ,  Bentler & Chou, 1987; Martens & Haase, 2006). Cumulatively, by investigating the datasets in detail and addressing different types of endogeneity bias from research designs to statistical analysis, researchers can provide evidence that they have made efforts to follow a rigorous theoretical procedure and comprehensively investigated datasets to comply with psychometric properties and 'urban legends'. As there is no hard and fast rule or a yes or no answer for such types of bias and urban rules, that is why some of the 'urban legends' (the cut-off criteria) have recently been criticized.
The second-order transformation (two-step GMM) applies 'forward orthogonal deviations', which means that instead of subtracting the previous observations of a variable from its current value, the two-step GMM model subtracts the average of all future available observations of a particular variable  (Roodman, 2009, p. 86). Using a two-step GMM model, researchers can prevent unnecessary data loss. Therefore, in the case of a balanced panel dataset, a two-step GMM model provides more efficient and consistent estimates for the involved coefficients  (Arellano and Bover 1995  Following  Arellano and Bond (1991)  and  Blundell and Bond (1998) , we use the following general dynamic generalized method of moments model (GMM): it it it it it t i it Xn G RDSALES P P           1 , (1) Where: Pit denotes firm performance, which in our example is measured in terms of return on assets (ROA), Pi,t-1 is a one period lag operator (previous year firm performance); RDSALESit represents our main explanatory variable, R&D expenditures; G it represents corporate governance variables; Xnit represents control variables over the time period; it  is firm- specific fixed effects; and it  represents the error term.
Data and Sample Selection Procedures Researchers in the Journal of Marketing, and Journal of Marketing Research have been using panel data for many years (see for example,  McAlister, Srinivasan, & Kim, 2007; Nierop Bronnenberg, Paap, Wedel, & Franses. 2010; Ma, Ailawadi, Gauri, & Grewal, 2011; Sridhar, Germann, Kang, & Grewal, 2016; Han, Mittal & Zhang, 2017) , while IMM researchers are still predominantly relying on the use of survey data. The emergence of business/financial databases (Bloomberg, Datastream, Thomson One, Compustat) provides more flexibility to marketing researchers to effectively utilize the marketing and product related data available in these databases. As panel datasets combine the characteristics of cross-sectional and time series data, we believe that the IMM community could benefit from the marketing related data available in these databases.
For example, corporate investment and financial data for US and non-US companies can be collected from Compustat. The Thompson Financial's Securities Data Corporation (SDC) database provide detailed information on different types of joint ventures. This database compiles information from different publicly available sources. Datastream includes global data on patents, brand value, number of consumer controversies directly linked to companies' products or services, product recall, eco-design products, energy footprint reduction, organicproduct initiatives, among others. If IMM community could utilize such massive global marketing datasets for listed companies, they could capture interesting insights about the ongoing marketing management issues around the world. The companies associated with these databases put enormous efforts in collecting and reporting this data that is largely unexplored, particularly in the context of industrial marketing.
Furthermore, using a panel data approach, researchers can use more observations and panel data has the capability to control for unobserved heterogeneitya potential source of endogeneity discussed in the literature. To demonstrate a step-by-step and rigorous procedure, we first collected the data of UK listed companies (FTSE ALL Share index)available on the Datastream. The data included 15-year observations, ranging from 2002 to 2016. We included corporate governance data in our analysis, although this data is only available from 2002 onwards in Datastream. Our final sample includes data from the 101 UK listed companies, after excluding companies which were subsequently delisted and whose financial data was not available in the database. Where financial data was not available for the sample, we collected such data from the alternative databases (i.
e. , Bloomberg). Table  1  shows the industrial classification of our sample firms. The chosen 101 sample firms belong to 25 different industrial sectors, with over 1500 observations. Table  2  includes the definitions of these variables and relevant codes available for downloading data from the Datastream, which is a powerful database and it allows to scrutinise patterns, generate and test research questions and develop research on market trends. -  -------------------------------- Insert Table  1  about here  ---------------------------------.
Implementing GMM in STATA This study uses STATA software to execute a generalized method of moments (GMM) model to deal with endogeneity, showing how this robust technique can control for different kinds of endogeneity issues and thus providing unbiased estimates. However, it may be complicated to implement in STATA, as  Roodman (2009)  argues that such analysis is complicated and can easily mislead researchers. "Implementing them with a Stata command stuffs them into a black box, creating the risk that users, not understanding the estimators' purpose, designs and limitations, will unwittingly misuse it"  (Roodman, 2009, p. 87). This is another reason that our step-by-step procedure can help researchers to better understand endogeneity sources and address them accordingly. Lagged values (l.
Conclusions Endogeneity bias is an emerging issue in marketing and management science. Different sources of endogeneity in panel data could generate bias and inconsistent estimates. We used panel data for R&D expenditures, governance and financial performance of 101 UK listed companies over a period of 15 years from 2002 to 2016 as an example to empirically demonstrate how endogeneity can be addressed in panel data using generalized method of moments. We explained step-by-step procedures that can be used to deal with endogeneity bias in panel data. Succinctly, OLS regression provides inconsistent results in the presence of any source of endogeneity bias. This is confirmed using Durbin-Wu Hausman test. To overcome unobserved heterogeneity and to capture firm-specific effects, we employed the fixed effects estimation approach, which removes unobserved heterogeneity by internally transforming the data.
This means that a variable (e. g. , RDSALES) value in previous year is subtracted from its value in current year: RDSALESit = RDSALESit -RDSALESit-1. However, fixed-effects model is also known as a 'static panel data model', which implies that implementing this model does not allow to use lagged values of the dependent variable. The dynamic panel data GMM model further extends the fixed-effects model, and, in addition to the internal transformation process, the lagged values of the dependent variable are also included as instruments to control for dynamic endogeneity. Thereby, we systematically move from basic OLS estimation to more sophisticated econometric techniques to control the different sources of endogeneity bias. We also provided user-friendly generic STATA commands that can be used by marketing researchers when they deal with marketing related panel datasets.
Finally, we briefly introduced a number of potential databases (Bloomberg, Datastream, Thomson One and Compustat) that can be utilized by IMM researchers in extracting recent marketing related global datasets on advertising expenditures, patents, brand values, number of consumer controversies directly linked to companies' products or services, product recall, eco-design products, energy-footprint reduction and organic-product initiatives. While the quantitative side of the IMM community has predominantly relied on surveys, we believe that these databases would potentially overcome some of the data collection challenges faced by marketing researchers.
implementing the GMM operations in STATA, users can apply a number of inbuilt commands e. g. , xtabond, xtdpd, and xtdpdsys commands. The STATA syntax can be used to apply the Arellano-Bover/Blundell-Bond's dynamic panel data estimator. For a sample dataset with a dependent variable y and explanatory variables x1, x2, x3, and x4, Roodman (2009) developed the following xtabond2 3 STATA codes for dynamic panel data model xtabond2 y l. y x1 x2, x3, x4 gmm(y x1 x2, x3, x4 lag(a b)) noconstant twostep (5).
For implementing the GMM operations in STATA, users can apply a number of inbuilt commands e. g. , xtabond, xtdpd, and xtdpdsys commands. The STATA syntax can be used to apply the Arellano-Bover/Blundell-Bond's dynamic panel data estimator. For a sample dataset with a dependent variable y and explanatory variables x1, x2, x3, and x4, Roodman (2009) developed the following xtabond2 3 STATA codes for dynamic panel data model xtabond2 y l. y x1 x2, x3, x4 gmm(y x1 x2, x3, x4 lag(a b)) noconstant twostep (5).
4-6f ). In the case of relative phases between Pa / Th or ln(Ti / Ca) and δ 13 C Cw , we also accounted for the additional error due to the combining of the MD09-3257 and GeoB3910-2 δ 13 C Cw records. Finally, we applied the aforementioned cross-wavelet method to the subset of Pa / Th data points not affected by large particle fluxes. For the periodicities between ∼ 1000 and 4000 yr, the results obtained using this subset (Fig. S6 ) are virtually unchanged with respect to the results obtained using the entire dataset. For longer periodicities, coherence decreases as expected because the suppressed data points are all located in the main precipitation events (i. e. , the YD and Heinrich stadials).
Background: Growing concern about the emergence of antibiotic resistance is compelling the pharmaceutical industry to search for new antimicrobial agents. The availability of genome sequences has enabled the development of computational mining as an important tool in the discovery of natural products with antibiotic effect. Results: NRPPUR (Non-Ribosomal Peptide and Polyketide Urmite) is a new bioinformatic tool that was created to detect polyketides and non-ribosomal peptide gene clusters (PKS and NRPS) in bacterial genomes using the rpsBlast program. The NRPPUR database was constructed locally by assembling all 3505 available sequences of NRPS-PKS that have been identified by in silico approaches to date, with 164 Biosynthetic Gene Clusters (BGCs) derived from the published literature that have demonstrated antimicrobial activity in vitro.
These modules contain multiple functional domains that are necessary for catalyzing each condensation and chain elongation or modification reaction  [10] [11] [12]. Genes encoding biosynthetic enzymes for the synthesis of these secondary metabolites are typically co-localized on the chromosome and are referred to as "biosynthetic gene clusters" (BGCs). Since the first elucidation of the PKS gene cluster for erythromycin in the early 1990s  [13, 14] , many gene clusters responsible for the biosynthesis of NRPS and PKS have been reported and deposited in International Nucleotide Sequence Database Collection (INSDC) entries (DDBJ/GenBank/EMBL)  [15]. In addition, the community-driven website developed many specialized pieces of software such as Antismash  [16] [17] [18]  and Streptome DB  [19]  that enabled the detection of NRPS and PKS  [20] [21] [22] [23] [24]  in a wide range of microorganisms such as Bacteria, Fungi, Archaea and Eukarya.
The general principle behind in silico mining consists of using a library of enzymes/protein domains commonly observed in secondary metabolite biosynthetic pathways to identify homologues in the genome sequences of the organisms of interest. For this task, sequence based comparison software, such as BLAST  [25]  or DIAMOND  [26] , or profile-based tools such as HMMer  [27]  are usually used. Together, the stunning advances in genome sequencing and informatics tools are creating the conditions necessary to support the discovery of narrow-spectrum potent antibiotics. However, large-scale gene dispensability studies using microbial gene cloning, protein expression and high-throughput screening revealed that these databases contain numerous targets that were not always bioactive when tested in vitro  [28, 29].
In this paper, we present an in silico/in vitro combined strategy for identifying NRPS and PKS in the human gut microbiota. With this aim, we built an updated database, named NRPPUR (Non-Ribosomal Peptide and Polyketide Urmite), containing gene sequences for NRPS/PKS clusters, which products and corresponding extracts have demonstrated an interesting activity using antimicrobial testing methods during in vitro investigation. NRPPUR was queried to make the functional annotation using RPS-BLAST (Reverse Position-Specific Blast) in order to decipher NRPS-PKS BGCs on 49 bacterial genomes first isolated from human gut microbiota using the "culturomics" approach  [30]. The antimicrobial activity of the identified producers has been tested in vitro using the cross-streak method. The combined strategy using the "culturomicsgenomic-bioinformatic-antibiogram" platform has significant potential to discover new candidate antibiotic producers.
NRPPUR database construction Data collection began with a comprehensive review of the literature that reported discoveries of biosynthetic clusters encoding for secondary metabolites that have showed an antimicrobial activity in vitro. A literature search was conducted on PubMed, using keywords such as "NRPS", "PKS", "natural product biosynthesis", "biosynthetic gene clusters" and "antimicrobial activity". Further literature analysis was carried out using a paper recommendation system, PubMedScan (http://medals. jp/ pubmedscan/), which automatically reports articles highly related to a collection of literature. The INSCD accession numbers corresponding to the BGCs were extracted from these articles and used to retrieve the corresponding nucleotide sequences from GenBank  [15]. BGC sequences were annotated with the Rapid Annotation using Subsystems Technology (RAST)  [31].
Protein sequences of these experimentally characterized NRPS and PKS clusters were added to the largest NRPS-PKS currently available database, Atlas database  [32]  to constitute the Non-Ribosomal Peptides and Polyketides URmite DataBase (NRPPUR DB). Duplicate sequences were removed with the help of BLASTp program. The non-redundant dataset of NRPS/PKS sequences was submitted to RPS-BLAST (Reverse Position-Specific Blast) search against the Conserved Domain Database (CDD)  [33]  in order to determine the catalytic domains of NRPS and PKS. NRPS/PKS protein domains consist of obligatory core domains for addition of each peptide and optional domains responsible for modification of the peptide backbone. The present version of NRPPUR DB was curated to contain only the main domains of the minimal module for NRPS and PKS biosynthetic systems.