dataesr/scientific-paragraphs-categorization huggingface dataset:
RQ4: How can we build a hybrid model combining MCDM approaches and machine learning algorithms by integrating all technical and managerial-financial variables to help decision makers select the best players for a game? (3) Search process: For planning the SLR process, an appropriate mechanism must be followed to ensure the retrieval J o u r n a l P r e -p r o o f of relevant studies from the selected digital libraries. A systematic mechanism is used to collect the most pertinent articles by formulating a set of specific keywords. These data are then used to search peer-reviewed digital libraries for research articles. In the proposed SLR, several keywords are related to the analysis and applicability of multi-criteria decision models and machine learning in sports, mainly football, based on the above research questions.
However, following a search strategy that identifies the most relevant documents is essencial. To ensure the quality of the articles, the search focused on various electronic databases: IEEExplore, SpringerLink, ScienceDirect, Taylor and Francis, and Wiley Online Library. These are the most frequently used libraries and are known to publish high-quality materials. The authors use specific keywords to search for relevant articles in these libraries. The keywords are selected based on the proposed research questions. A search string S is formulated to interact with the libraries (Table  1 ). The first attempt in this search process has yielded the following results: IEEE →802, ScienceDirect →1432, Springer →398, Taylor and Francis →221, and Wiley Online Library →137. To refine the results we assigned a subset of keywords (S1, S2, S3, S4) to each research question.
Consequently, only 66 relevant documents (journal articles, conference papers, and book sections) that fit our topics and answer our research questions are identified. Figures  3  and 4  show the research trends in the selected period and the percentage of digital libraries, respectively. (5) Study quality assessment: The study quality phase aims to assess the quality of the primary studies. Each article is evaluated separately to ensure that it targets the research objective. We refer to the data analysis, figures, tables, and appendixes for analyzing and collecting data from each paper. (6) Data extraction: The data extraction process extracts items from the 66 included studies and collects all information required to answer the research questions related to our topics. An extracted data form (Table  6 ) is developed to collect data items from each primary study.
Specialized companies such as OPTA, FIFA, and the websites of the official leagues provide an exponential number of datasets for Big Data research related to football analytics, focusing on individual player characteristics  (Raheela et al. , 2016; Cacho-Elizondo, 2020). In selecting the best player,  Nikjo et al. (2015)  defines six criteria after questioning ten experts and three decision-makers. These criteria include technical and tactical skills, experience of professional play, average number of goals scored per game, ability to coordinate with the team, moral and behavioral features, and social prestige. Ozceylan (2016)  provides a set of criteria based on the position of players within a football team: Goalkeeper, Fullback, Central Defense, Central Midfield, Winger and Forward Center.
This research aims to review articles demonstrating the use of multiple criteria decision-making methods in sports, particularly in football player selection (Table  8 ). Mavi et al. (2012)  measures the sporting performance of 18 professional football teams in the German Bandesliga by collecting data for the season 1999/2000. Seungbum and Ross (2012)  ranks the criteria and sub-criteria to identify the decision making factors in sports sponsorship in the global market context. (Blanco et al. , 2018)  applies a multi-criteria outranking methodology to the Spanish ACB Basketball League, using as alternatives the potential players and as criteria different efficiency indices. According to  Ozceylan (2016) , the top-performing players in Turkish football clubs are selected for inclusion in the team.
The proposed solution was applied to a Turkish football club to demonstrate its applicability and performance. A quantitative ranking system was constructed to aggregate basketball players using the PROMETHEE methodology. A case study of 191 players who participated in the Spanish ACB basketball league during the 2014-2015 season. proposed a new methodology for personnel selection based on MCDA and integrated qualitative and quantitative data to select Mexican football teams for the 2018 W orld Cup in Russia. A system was developed to select the starting lineup of football players based on the AHP method. Three performance teams in industrial and safety critical systems were developed based on individual performance, job attributes and team attributes using the fuzzy-Ahp method. proposed an integrated approach that combines a multi-criteria decision-making analysis (FANP-PROMETHEE II-DEA) and mathematical programming to assist decision-makers in their selection process.
Compared which attributes and skills best predict the success of individual players in their positions in five top European football leagues and evaluate different machine learning algorithms in terms of their predictive performance. Developed a data-driven framework to help professional football scouts evaluate, search, rank, and recommend football players. This tool, called PlayeRank, is based on a machine learning approach. It uses a large dataset of football records consisting of millions of match events from four seasons of 18 well-known football competitions. It has been argued that when faced with a set of alternatives, multi-criteria decision making is one of the most appropriate decision-making tools. Combining deep learning algorithms with multi-criteria decision making has proven beneficial for Big Data.
Discussion and conclusion This SLR allowed us to delve deeper into the proposed study area and analyze the literature concerning four research questions. This analysis focused on the following: • First, we examined the relationship between player performance and football clubs' managerial and financial performance. • Second, the main criteria and sub-criteria related to football players' positions on the pitch were included in the selection process. In this step, we noticed a lack of publications on this topic. In addition, previous articles did not address all criteria, sub-criteria, and the correlations except for  (Tavana et al. , 2013). • Third, decision-making methods that could be available to decision-makers for solving the player selection problem were identified. • Finally, studies dealing with the construction of models, case studies, and databases for solving the selection of football players were examined.
The proposed study used the systematic literature protocol and guidelines by  Kitchenham et al. (Barbara Kitchenham and Charters, 2007). Data published between 2018 and 2023 were collected from digital libraries (IEEE, ScienceDirect, Springer, Taylor and Francis and Wiley). Other relevant articles were retrieved from the digital library of the University of Burgundy, although they were not included in the selected period of the SLR. To successfully perform the proposed SLR, all resources listed in Tables  7, 8 , and 9 were carefully analyzed: Title, Keywords, Abstract, and T ext. These findings led us to conclude that of the 66 papers identified by the SLR process, o n l y a few articles addressed our four research questions  (Blanco et al. , 2018; Flegl et al. , 2018; Nasiri et al. , 2018; Purwanto et al.
(2021) 2022 ✓ - - - ✓ National Basketball Association 59 Wieckowski and Watróbski (2021) 2022 - - ✓ ✓ ✓ Randomly generated decision matrix 60 Ribeiro et al. (2022) 2022 ✓ ✓ - - ✓ Portuguese First League from 2016 to 2018 61 Manju and Philip (2023) 2023 - - ✓ ✓ ✓ Dataset of cricket matches 2008-2022 62 Peddii and Jain (2023) 2023 - ✓ ✓ ✓ ✓ Historical data 63 Athish V et al. (2023) 2023 - - ✓ ✓ ✓ kaggle. com & Sofifa. com. 64 Joe Anand et al. (2023) 2023 - - ✓ - - - 65 McHale and Holmes (2023) 2023 ✓ ✓ ✓ - ✓ transfermarkt. com, Instat, sofifa. com 66.
Direct measures of innovation and financial constraints We apply our approach to assess the links between financing constraints and a firm's decision to innovate. Empirical evidence of the impact of financial constraints on the behaviour of firms is however not easy to obtain, essentially because the notional demand of firms for external finance is not observed directly. 15 We rely on qualitative direct information about innovation and binding financing constraints as reported by firms in surveys by the European Union (CIS, Community Innovation Surveys), as well as in a French survey about the financing of innovation (FIT, Financement de l'innovation technlogique). We merged the surveys with balance sheet dataset (Fiben, Banque de France), see Data Online Appendix for details. 16  In the survey we use, firms are asked whether some of their innovative projects were delayed, abandoned or non started because of (i) unavailability of new financing, (ii) searching and waiting for new financing, (iii) too high cost of finance.
See the Online Appendix for the detailed list of control variables. The econometric specifications we estimate below belong in three main groups. The first group contains recursive specifications following the traditional approach, which ignore the possibility that the propensity to innovate may be affected by financial constraints. The second group allows for reverse interactions, whereby a firm undertaking actively innovative activities raises significantly the probability of it encountering a binding financing constraint, possibly because potential lenders are particularly wary of granting loans to firms of such type because of the extra riskiness involved. The third group of estimated specifications, investigates state dependence in financing and innovation experiences of firms the nature of the available datasets can be exploited to study whether, ceteris paribus, past financial distress or innovation failures can affect a firm's current experiences in these two dimensions.
Other factors affecting innovative behaviour are driven by the firm environment (technological push, latent consumer demand perceived by the firm). See among others  [Crepon et al. , 1998]  or  [Raymond et al. , 2010]. 21 Though the surveys about innovation we use are not truly longitudinal "panel" sets, the information was collected in multiple biennial waves. We hence restrict our "longitudinal" dataset to two waves in order to limit the reduction of the sample size when merging the waves. Hence, we know whether a particular firm i has reported binding financing constraints in the past. See the Online Data Appendix for details about the dataset employed here and the transition tables for the 1512 firms in the sample over the two waves starting 1994 and ending 1999.
Implications of disagreement between SDG ratings for portfolio management Our analysis so far shows a low level of agreement between rating providers concerning the SDG contribution of a company. To gain further insight in the implications of the disagreement for stakeholders, we study investment outcomes of portfolios that are built based on SDG ratings. More specifically, we conduct portfolio return regressions using the 5-factor model from  Fama and French (2015). Daily developed market factor returns are obtained from Kenneth French's data library. 8  We estimate regressions for value-weighted portfolios of each SDG group (Bottom, Middle, Top) of each rating provider. To this end, we proceed as in the previous sections and use standardized SDG ratings to separate our matched sample of 1,057 companies into SDG groups for each rating provider.
France. Eligible patients were at least 18 years of age, had a confirmed diagnosis of moderate or advanced CKD with an estimated glomerular filtration rate (eGFR) < 60 mL/min per 1. 73 m 2 , were not on dialysis, and had not received a kidney transplant. From July 2013 to March 2016, CKD-REIN enrolled 3,033 patients, who gave their written, informed consent. Details of the study protocol and flow chart have been published previously. 9, 10  The study was approved by the institutional review board at the French National Institute of Health and Medical Research (INSERM; reference: IRB00003888) and was registered at ClinicalTrials. gov (NCT03381950). The results of this cohort study are reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Study Data Data were collected at baseline and then annually by trained clinical research associates (CRAs) from patient interviews and medical records. The study data included baseline sociodemographic characteristics and any history of hypertension, diabetes, heart disease (such as heart failure and coronary disease), peripheral artery disease, cerebrovascular disease, stroke or transit ischemic attack, dyslipidemia, or acute kidney injury (AKI), as defined in the Table  S1. Patients were classified with heavy alcohol use if they reported drinking at least 10 g per day (for women) or 20 g per day (for men). Medication adherence was assessed with the validated, questionnaire-based Girerd score (Table  S1 ). 12  Blood hemoglobin and serum creatinine and albumin levels were measured, as were urinary albumin-creatinine or protein-creatinine ratios (Table  S1 ).
Considering the high risk of drug-induced AKI and bleeding in patients with CKD, these events have been defined further as follows. Drug-linked AKIs were defined according to the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) AKI criteria: "an increase in serum creatinine (SCr) by ≥ 0. 3 mg/dL (≥ 26. 5 μmol/L) within 48 hours or an increase in SCr to ≥1. 5 times baseline, which is known or presumed to have occurred within the prior 7 days. "  18  A hemorrhage was considered to be serious if it was fatal, symptomatic in a critical area or organ, or caused anemia (according to the International Society on Thrombosis and Haemostasis definition of major bleeding). 19  We collected data on ADRs over a 5-year follow-up period via an electronic form designed specifically to include essential information for this study.
Statistical Analyses Baseline characteristics were described for all participants. Data were expressed as the mean ± SD, median [IQR], or number (percentage). The crude incidence rates (95% CI) for all serious ADRs, and by type of ADRs, per 100 personyears were estimated for the overall study population. Quasi-Poisson model deviance tests were used to compare incidence rates according to baseline eGFR (≥30 vs <30 mL/min/1. 73 m 2 ). We used cause-specific Cox proportional hazard models to report the association between the baseline eGFR and the first-occurring serious ADR after adjustment for patients' baseline characteristics. Data were censored at the date of death or KRT or the date of last follow-up visit, whichever came first (ie, competing events). Variables implemented in models were preselected through literature review, and variables with a P > 0.
The proportional hazard assumption was checked by testing the Schoenfeld residuals. We used restricted cubic splines in adjusted Cox models to explore the functional form of the relationship between baseline eGFR and the risks of serious ADRs, drug-linked bleeding, and drug-linked AKI (using the rms package in R software  25  ). We performed a secondary analysis of first-occurring preventable serious ADRs and first-occurring nonpreventable serious ADRs. To address missing data, we performed multivariate imputation by chained equations (20 iterations and 40 datasets) and included all patient variables used in adjusted models. 26  The data patterns suggested that the "data missing at random" assumption was plausible. In 37% of patients, at least 1 missing data occurred. The proportion of missing data by variable is presented in Table  1.
We reported high incidence rates for serious ADRs, mainly driven by AKI and bleeding events. Furthermore, our results showed that ADRs can have major consequences in a CKD population, such as hospitalization, lifethreatening events, or death. Even though the majority of serious ADRs were not fatal in the present study, the markedly elevated incidence of serious ADRs in patients with CKD compared with the general population will result in a substantial number of fatal events at the population level. This proportion is also in line with literature reports on ADRs in hospitalized patients whose CKD status was unknown. [28] [29] [30] [31]  A retrospective US study based on the Mortality Statistics Database found that anticoagulants were the drug most often responsible for ADR-related deaths. 32  In France, a recent prospective study in a sample of public hospitals, collecting causes of hospitalization over 14 days, reported that 8.
Acknowledgements: We thank the  CKD-REIN  study coordination staff for their efforts in setting up the cohort:  Elodie Speyer ,  C eline Lange ,  Reine Ketchemin , and all the clinical research associates. Disclaimer: The authors were solely responsible for interpreting the data; ANSM was not involved. Prior Presentation: Presented in part at the Annual Meeting of French  Society of Pharmacology and Therapeutics; Limoges, France ; June 12, 2023. Peer Review: Received January 20, 2023. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/ Methods Editor, an Associate Editor, and a Deputy Editor who served as Acting Editor-in-Chief. Accepted in revised form September 11, 2023. The involvement of an Acting Editor-in-Chief was to comply with AJKD's procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.
The realm of green jobs presents a fertile ground for understanding the intersecting pathways between sustainable transition and the labor market. We have crafted a bibliometric dataset centered on this concept, amassing 414 articles from the Scopus and Web of Science databases, following a laid down protocol, PRISMA, spanning the period from 1995 to 2022. This endeavor aims to depict the dynamics, themes, and conceptual approaches shaping the discourse on green jobs. The dataset, structured around 13 descriptive variables such as authors, keywords, and cited references, is made available to researchers, institutions, and decision-makers to provide insight into the academic debates on ecological transition through the lens of employment, especially in the wake of a green economy. The potential for reutilizing these data is expansive.
A comparison with media and institutional portrayals of this idea would be relevant. highlighting the evolution of the concept of green jobs. They provide an academic insight into this topic. Comparing this foundation with the media and institutional representations of this concept would be apt. • Comprising articles from scientific journals, the dataset can be expanded with books, chapters, or conference proceedings, while maintaining the same structure. Contributions in other languages or from journals not indexed on Scopus and Web of Science can also be included.
Background This bibliometric data set has been developed as part of a study on the conceptual foundations of green jobs and their academic representation. It aims to explore the resonance and compatibility of this concept in various contexts, such as those of the Global North and the Global South. To date, the concept has suffered from a lack of a consensual definition of green jobs, presenting a significant challenge and making the concept difficult to operationalize. However, the definition provided by the ILO and the United Nations in 2008 is often used as a benchmark, identifying green jobs as key positions across numerous sectors that are essential for the preservation and conservation of the environment, while also promoting the creation of decent jobs  [1]. In the face of current environmental, economic, and social challenges, green jobs fluctuate in the literature between being agents of short-term economic transformation and elements of a sustainable and inclusive green economy.
Data Description The database, named "BDGJ" as an acronym for Bibliometric Dataset on Green Jobs  [2]  , primarily consists of a dataset titled "BDGJ_dataset. csv". This file includes a single sheet, also named "BDGJ_dataset", which compiles information pertaining to 414 articles written in English, exclusively derived from the scientific databases Scopus and Web of Science, spanning from the year 1995 to 2022. The dataset encompasses 13 descriptive variables, titled in accordance with the field tags from the Web of Science Core Collection, represented as two-character tags. Table  1  provides a description of these variables within the data corpus and details their sig- nificance and the analytical possibilities each variable offers. Additionally, a separate file, named "BDGJ_variables. txt", outlines the definitions of each variable.
The selection of these variables was predicated on the availability of data from Scopus and Web of Science databases. Variables were omitted if the incidence of relevant information in the articles was too sparse to support a substantive analysis. Figs. 1-6 depict bibliometric analyses showcasing the most cited publications, journals, and authors, along with the most frequently occurring terms in abstracts and titles. These summaries provide a preliminary overview of the insights gleaned from this data.
Identification This approach merges the Scopus and Web of Science databases, distinguishing itself from many literature reviews that rely on a single source. The method of integrating multiple databases is scarcely found in existing literature  [5]. Several works have highlighted the unique strengths of each database, such as the extensive temporal coverage of Web of Science  [6]  or the broad range of publications in Scopus  [ 6 , 7 ]. Although Scopus and Web of Science show a strong correlation  [8]  , many researchers have emphasized the value of analyzing both concurrently, as their data optimally complement each other  [ 5 , 9 , 10 ]. Integrating the two datasets can pose challenges, especially due to variations in article information depending on whether the source is Scopus or Web of Science  [5].
A method to merge the Scopus and Web of Science databases has been replicated using the R 'Bibliometrix' package and Excel  [ 11 , 12 ]. With this in mind, initial searches on Scopus and Web of Science were conducted separately before merging the data during the second phase of the PRISMA protocol. Articles mentioning the terms 'Green jobs' or similar expressions in their title, abstract, or keywords were sought (see Table  2  ). Using site-specific search strings, both singular and plural forms of the expressions were considered ( Table  4  ).
Screening The literature under review is exclusively in English and isn't confined to any specific time frame. Only articles from scientific journals were considered, whether they were research papers, communications, or technical notes. Following an initial screening, 865 articles from both databases were identified. A preliminary review of titles and abstracts led to further refinement. At this stage, the aim was to address the question: "Does this article tackle the issue of green jobs as a distinct employment category addressing the challenges of sustainable transition?" The search terms used in our queries could sometimes introduce biases. For instance, some of the selected articles referred to the "green jobfish", a specific fish species. Other articles touched upon "green workplaces," focusing not on the nature of the job but on environmental aspects of the workspace, such as greenery.