dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement The authors would like to thank the three anonymous reviewers and the guest editors for providing valuable comments. This work was financially supported by the  Center for Open Intelligent  Connectivity from The  Featured Areas Research Center Program  within the framework of the Higher Education Sprout Project by the  Ministry of Education (MOE) in Taiwan. Lin was supported in part by the  Ministry of Science and Technology (MOST)  under Grant  106-2221-E-009-006  and Grant  106-2221-E-009-049-MY2 , in part by the  "Aiming for the Top University Program " of  National Chiao Tung University  and the Ministry.
Fränti et al. (2006)  (Dataset-I). The second dataset (Dataset-II) used for clustering analysis follows Yang et al. (2012). 16 The third dataset (Dataset-III) consists of a Gamma mixture model. 4. 1. Synthetic Dataset-IAs Jain (2010) and Zimek et al. (2012)  pointed out, clustering high-dimensional data is a big challenge due to the distance concentration effect, such that irrelevant features conceal relevant information when data dimension increases. Moreover, the vast majority of real world clustering datasets have the overlapping characteristic. In Dataset-I, we check the clustering accuracy of our method under the effects of (1) varying dimensions, and (2) overlapping clusters. The Gaussian distribution dimension varies from 2, 4 to 16 (see Fränti et al. , 2006). Taking the bivariate Gaussian mixture model as an example, there are16 Clustering benchmark datasets: http://cs.
Bootstrapping algorithm Input: the dataset Θ; the number of bootstraps N B ; the sampling threshold ; Output: the set of bootstrapped data Θ B 1: Initialization: Θ B ← Ø; 2: while |Θ B | < N B do 3: K 15: end for 16: K ← argmin K min ≤K≤Kmax AIC K + BIC K + HQC K 17: µ k ← µ k for 1 ≤ k ≤ K 18: C k ← C k for 1 ≤ k ≤ K 19: w k ← w k for 1 ≤ k ≤ K Algorithm 4.
In response to the demand of SRI investors for reliable data on the social performance of firms, a large amount of ESG rating agencies have emerged. These agencies independently certify firms in terms of their ESG related efforts. Even if a consolidation process can be observed by now, there are still numerous data providers with a multiplicity of different methodologies (Avetisyan & Hockerts, 2017). It seems likely that most of these scores are associated with problems, e. g. , how positive and negative assessments are offset  (Escrig-Olmedo, Muñoz-Torres, Fernández-Izquierdo, & Rivera-Lirio, 2014). Furthermore, there is also a fundamental disagreement between the ESG ratings of different rating agencies documented in the literatur  (Berg, Koelbel, & Rigobon, 2020).
In case of considerable events, which negatively influence a company's reputation, its legitimacy is threatened  (Deegan, Rankin, & Tobin, 2002). This is particularly the case if events cause strong media response as this can induce public pressure  (Kuo & Yi-Ju Chen, 2013 ). In the United States public held companies are obligated by law to publish annual reports on form 10-K. The 10-K reports are comprehensive reports on a company's business and financial condition. The US Securities and Exchange Commission (SEC) regulates this type of disclosure. Accounting researchers have been examining numerical financial data in those reports for decades  (Li, 2010). However, SRI is about introducing new criteria apart from financial criteria in investment decisions. Consequently, the problem is to convert qualitative narrative data on the contribution of companies to CSR from annual reports into quantitative results.
Context check After completing all "further strategies" (IWL, Nexis, root words), there are several word lists. These lists are merged to a list of potential ESG words. After deleting double words, it comprises 650 words. Executing a context check is still necessary to verify them. 190 of them do not appear in any report of the dataset. 95 appear in less than 5 % of the reports. In order to verify the rest, the context is reviewed in at least two reports with the highest frequency of appearance. A word is considered as ESG related when it is used in ESG context in the majority of its appearances. This step leads to the final ESG word list with 482 words provided in Table  3.
CONCLUSION The awareness of SRI among investors is rising. Literature indicates that CSR also increased in response. Measuring a company's contribution and thereby identifying sustainable investments is difficult, since CSR is based on qualitative data. Most current SRI research applies socially responsible ratings and SRI investors have therefore to rely on third party information. In contrast, this paper approaches the problem by analyzing company data such as annual reports as the most reliable sources of firm information. The aim is to convert unstructured narrative content into quantitative data using textual analysis. For this purpose, we generated an exhaustive ESG word list based on 10-K reports and proxy statements and structured this list in 10 categories and 40 subcategories. Afterwards, we manually analyzed 100 reports and found that the typical ESG vocabulary in the reports is quite similar.
Hence, the number of included reports in our analysis seems reasonable but it should potentially be extended in the future to accommodate newly emerging topics. This structured list allows a sophisticated analysis of ESG reports and a direct quantification of several different aspects of ESG reporting. Textual analysis in this context could be a helpful instrument to make large amounts of unstructured data comparable and to support human decision-making. Furthermore, we use this list to analyze the ESG content companies state in their annual reports and thereby provide the robustness of our word selection methodology. The results show a current 4. 0 % average share of ESG words on total words. We find that the three ESG topics are addressed to a different extent. Governance, especially its category corporate governance, dominates the topics social and environmental.
To achieve this level of security, control and decentralization of data management in this app, we used a permissioned blockchain  [2]. We have provided for physicians the possibility to use the app directly or to access it from their usual working tool. On the other hand, the evaluation iterations were conducted though functional testing with eleven patients (six for mock-ups and five for the app) and five physicians only for the app. Users testing was performed through interviews after the test of the app. We started with open questions on the interviewees' personal context concerning drug allergy and allergy card and their representation about a DAC, and then we asked them about their perceptions of the mock-ups or app that they have tested. Data analysis was performed using the grounded theory  [4] , as follows: data were first coded by a first code, closed to respondents' words; then, a more general coding was performed.
The data analysis was performed on 179 out of 482 responses, remaining were discarded. The discarded responses were due to the reasons such as incompleteness or more than one options were chosen in the given items. For organization visit, questionnaire distribution, and collection, author's colleagues have assisted. In this study, a convenient sampling technique was used to get respondents. This is because majority of the employees are on work shifts and makes it difficult for data collection. The survey was administered to employee's and responses were collected on GHRM, EP, and BP. The demographic information of respondents is presented in table 2. Independent t-test method was used to determine the non-response bias by using and comparing first 20 respondents and the last 20 respondents on all variables (i.
For each fund in our sample, we measured the average herding behaviour over the commitment period. A positive value of the herding measure indicates that a fund, on average, is changing her portfolio similar to the benchmark. Additionally, we considered the top 25 quartile PE funds in terms of size as our benchmark to compute the contemporaneous and the following herding measures. Hence, we measure herding in comparison with the top players in the market. We applied this methodology on a large sample of international buyout funds covered by the Centre of Private Equity Research (CEPRES) database. The final sample includes 878 distinct buyout funds. As the database included detailed information on each portfolio company included in these 878 funds, we were able to calculate the fund-level internal rate of return (IRR) and the volatility of returns as the measures of performance and risk.
Data The dataset used in this study came from CEPRES. The unique feature of this database is that is contains detailed information at the fund level and on all the individual portfolio company investments undertaken by a fund, which includes the exact timing, performance, and industry of each of them. This unique feature of the data enabled us to construct the herding measures developed previously, which was essential to explore the determinants of herding behaviour of the PE funds and estimating the relationship between the fund-level herding and performance. Other studies have also used the CEPRES database (e. g. ,  Buchner, Mohamed, and Schwienbacher, 2017; Cumming, Schmidt, and Walz, 2010; Cumming and Walz, 2010; Franzoni, Nowak, and Phalippou, 2012; Krohmer, Lauterbach, and Calanog, 2009).
The CEPRES database collects monthly cash flow data for a large range of PE firms at the deal and fund levels. The database has detailed information related to individual deals, funds, and management firms. Moreover, these data are anonymised and, therefore, there is no incentive for PE firms to overstate or understate their performance. This is important for database providers, but, unfortunately, other databases ignore the importance of anonymity. A lack of anonymity can encourage PE firms to only provide information on better performance and suppress poor performance. Buchner, Mohamed, and Schwienbacher (2017)  provide detailed discussions of the CEPRES data collection process, the information available on PE firms, and how the database compares with other databases on PE funds. CEPRES granted us access to the information on all the funds and their portfolio company investments as of December 2017.
Purpose -This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation. Design/methodology/approach -This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014-2019 and were investigated using an ordinary least squares regression analysis.
Case Study We illustrate the application of our value proposition prototyping framework via a deductive case study  [15]  starting from the evolution of a solution developed by a fabricant of gas boilers (the OEM) mainly addressed to social housing. The OEM also performs the maintenance of its products as part of its current service catalog. Converting the current product and service offering into a Smart PSS solution would enable the target customer and maintenance companies to monitor and operate a fleet of gas boilers. To assess the potential of this change, the proposed approach is applied by modelling suitable Smart PSS configurations using the PS3M toolkit: a focus group (design group) within the company was consulted and both primary and secondary data were collected and structured as follows.
Our theory is motivated by reduced-form evidence of the effect of foreign expenditure uncertainty on French manufacturing exports. We observe the destination countries to which firms export and the products they sell over the 2000-2009 period. We match these firm-level export data with industry-wide measures of expenditure uncertainty in the destination countries, as proxied by the observed central moments of absorption: 3 the expected value, the variance (or volatility), and the skewness. We unveil two empirical regularities regarding the role of industry-wide expenditure uncertainty in the intensive and extensive margins of firm exports. First, firm export decisions in a destination market are significantly affected by the central moments of the expenditure distribution of that destination.
Data We combine two types of data. First, French customs provide export data by firm, product and destination over the 2000-2009 period. For each firm located on French metropolitan territory, we observe the quantity (in tons) and value (in thousands of euros) of exports for each destination-product-year triplet. To match these data with other sources, we aggregate them at the industry level (4-digit ISIC code) 5. We thus obtain the exports of each firm for each destination-industry-year triplet. Prices, proxied by unit values, are computed as the ratio of export values to export quantities. Using the official firm identifier, we merge the customs data with the BRN (Bénéfices réels normaux) dataset from the French Statistical Institute, which provides firm balance-sheet data, e. g. , value added, total sales, and employment.
Our sample contains a total of 105,777 different firms that are located in France, serve 90 destination countries and produce in 119 manufacturing industries (based on the 4-digit codes). In an average year, 43,586 firms export to 71 countries in 117 industries, amounting to 187. 8 billion euros and 71. 2 million tons. The firm turnover in industries and destinations is rather high over the 2000-2009 period. On average, a firm is present for 2. 72 years in a given destinationindustry and serves 1. 99 industries per destination-year and 3. 19 destinations per industry-year. 6  addition to firm-level data, we use annual destination country-industry (4-digit ISIC codes) information on manufacturing production, exports and imports. These data come from COM-TRADE and UNIDO and cover our 119 manufacturing industries over the period from 1995 to 2009.
Intensive Margin of Trade We estimate two intensive margins of trade: first on quantities and then on unit values. Quantities. We estimate the following specification of firm-level export quantities at the destinationyear-(4-digit-)industry triplet: ln q k f jt = δ 1 ln E(R K jt ) + δ 2 ln V(R K jt ) + δ 3 S(R K jt ) + FE + ε k f jt , ( 4 ) where q k f jt is now the export quantity of French firm f to destination j at the 4-digit manufacturing level k in year t. As previously described, E(R K jt ), V(R K jt ), and S(R K jt ) are the first three central moments of the expenditure distribution, and ε k f jt represents the usual error term. Compared with the industry-level estimations, firm-level data offer considerably more observations and mitigate concerns about the inefficiency of the panel estimator when introducing various combinations of fixed effects.
a b s t r a c t The dataset displays the pharmacokinetics data obtained from the TRACES pilot study. The nine patients included were undergoing haemorrhagic caesarean section (blood loss > 800 mL) and receiving a single i. v dose of tranexamic acid (0. 5, 1 or 2 g over 1 min). The dataset gathers the tranexamic acid blood and urinary concentrations. With these first elements, a pharmacokinetic compartment model was built as described in Gilliot et al. and the individual pharmacokinetic parameters were estimated. In parallel, the patients anthropometric, biological, and clinical characteristics were collected. The correlation between the patient data and the estimated individual pharmacokinetic parameters were tested. The correlation tests revealed that the dose, the height, the body weight, and the ideal bodyweight had and impact on the volume of distribution of tranexamic acid.
According to these results, these latter covariates were explored using a multi-regression analysis in The toxicological data were obtained from the results of tranexamic acid concentration dosage. Concentration measurement used a liquid chromatography system coupled with tandem mass spectrometry (Acquity Xevo-TQ Detector, Waters, Milford, MA, USA). Data acquisition and quantification were performed using MassLynx 4. 1 Software (Waters). These concentrations were analysed using the parametric NLMEM software program Monolix 2019R1 (Lixoft, Orsay, France). A pharmacokinetic model was built and the populational pharmacokinetic parameters were estimated in Gilliot et al. [1]. In parallel to the recruitment of the patients, anthropometric, biological, and clinical data were prospectively collected.
Description of data collection The toxicological data collected were tranexamic blood concentrations measured at T0 (time of inclusion, when bleeding ≥ 800 mL is diagnosed), T1 (at the end of the tranexamic acid injection), T15, T30, T60, T120, T180, and T360 (respectively 15, 30, 60, 120, 180 and 360 min after the injection), and the tranexamic acid urinary concentrations measured in the urinary samples collected within 6 h after treatment. The anthropometric data collected were the height and bodyweight of each patient. The body surface area, the body mass index and the ideal weight were secondarily calculated. In the dataset, the anthropometric data were divided by the administrated dose. The biological data reported were urea and the creatininemia, from which were deducted the corrected glomerular filtration rate and the renal clearance according to several formulas.
The age, the initial blood loss volume and the volume of injected intravenous fluids were also reported. • Those data may be used as comparative data for any further study investigating the pharmacokinetic/pharmacodynamic profile of tranexamic acid in haemorrhagic post-partum, as TRACES study, for which recruitment is now complete. • The patient dataset can be useful to anyone who studies the pharmacokinetics of tranexamic acid or the evolution of biological parameters during a haemorrhagic caesarean section.
Dataset The dataset presents the pharmacokinetic data and the covariate data, displayed to be analysed on Monolix2019R1 as performed in Gilliot et al. [1]. The nine patients included in this latter study were undergoing haemorrhagic caesarean section (blood loss > 800 mL) and receiving a single i. v dose of TA (0. 5, 1 or 2 g over 1 min). The seven first columns (at the left) of the dataset describe the administration and the results of tranexamic acid concentration dosage. The blood samples were collected immediately after injection T0, and T15, T30, T60, T120, T180, and T360 (respectively 15, 30, 60, 120, 180 and 360 min after the injection). The tranexamic acid urinary concentrations measured in the urinary samples collected within 6 h after treatment. Each line of the dataset describes either the administration of tranexamic acid (administration line), the concentration measurement (on a urinary or a blood sample) or the beginning of the urinary collection (from the injection of tranexamic acid for each patient).
The anthropometric parameters were obtained as follows: -The bodymass index (BMI, in kg/m ²) was calculated as BMI = (BW in kg) / (height in m) ² -The body surface area (BSA, in m ²) was calculated as BSA = 0. 20247 * (height in m) 0. 725 * (BW in kg) 0. 425 -The ideal bodyweight (IW, in kg) was calculated using the Devine formula (1974), IW = 45. 5 + 0. 9 × (height (cm) -152) The height, the BW and the IW were divided by the dose in a second step of the correlation tests. Concerning the renal parameters, we used several formula to estimate the glomerular filtration rate with correction to the body surface area (GFRc in mL/min/1. 73m ²) or the creatinemia clearance (Clcr in mL/min), as there is no current recommended formula in parturient women. For each equation, the creatinemia used was the one collected at inclusion (T0) called SCr1 in the dataset.
[1]. Every characteristic collected for each patient and presented in the dataset was tested as a potential covariate for the PK model. The correlation tests were performed between the individual PK parameters estimated by the model validated in Gilliot et al. [1]  (rounded to two decimal places in Table  1  ) and the continuous covariates described in the dataset: dose, age, BW, IW, height, BMI, BSA, SCr at inclusion, uremia, volume of intravenous fluids, Vs1, Clcr and GFR calculated using the different formulas described above (displayed in the dataset). Table  2  summarizes the results of the correlation tests, performed preliminary to the multiple sequential regression analysis. The results presented are the coefficient of correlation r ² and the p-value.
Methodology For the research described in this paper, the data was provided from the European Road Freight Transport (ERFT) survey (anonymous micro-data), which contained information about the road freight operations in 27 EU countries and European Free Trade Association (EFTA) countries between 2011 and 2014. IBM SPSS Modeller  (Modeler, 2019) ,  Python (2019)  data analysis and programming tools were used to develop the algorithms for the three supply chain strategies of pooling, horizontal collaboration, and physical internet. The approach used in the current study was based on the steps in Knowledge Discovery in Database (KDD) process described by  Fayyad et al. (1996) , as shown in Fig. 1 , which is the process of extraction of previously unknown and useful information and knowledge from data stored in databases.
Pooling Fig. 3  compares emissions reduction and efficiency improvement by using Pooling strategy in selected EU countries. In general, it can be seen that Germany has the highest, and Ireland has the lowest carbon emissions according to dataset information. This is particularly due to higher road freight transport activities in Germany compared to other countries analysed herein. It can be seen that in all demonstrated EU countries freight transport efficiency improved substantially, and the CO 2 emissions decreased. From the simulations carried out per country, it was observed that Pooling is efficient in terms of both carbon emissions reduction and improving the total efficiency. In terms of efficiency  improvements of 35%, 27%, 24%, 36%, 21%, 32%, and 40% were obtained in Germany, France, Spain, the Netherlands, Austria, the United Kingdom, and Ireland, respectively, comparing efficiencies before and after Pooling.
Classification of journeys The journeys which were recorded as a part of the data set were broadly categorised into three distinct classes: intra-regional journeys or journeys within the same NUTS 2 regions (Nomenclature of Territorial Units for Statistics) (eurostat and eurostatc, 2019), interregional journeys or journeys between two NUTS 2 regions belonging to the same country, and international journeys between two different countries. A new feature to represent these three classes of journeys was derived based on the origin and destination fields in the original data set. Then a classification algorithm was run to find some useful characteristics for each of the classes of journeys described earlier. There were several classification algorithms available in the Modeller but CART (Classification and Regression Trees) produced the best results with the least error percentage.