dataesr/scientific-paragraphs-categorization huggingface dataset:
Other methods seem less important, such as manual quality checks and formalized data curation processes. After the "how" of quality control, a complementary question was asked about the "when", i. e. , the point of data processing when the quality control generally takes place. Figure  5  presents the results. Again, we can observe a main yet not unique response. Most experts indicate that their quality check takes place at the interface of the CRIS with other, internal or external systems, when ingesting data into the CRIS. Half of the experts mention three other "check points", the data storage, the data presentation (frontend) and the ETL process. Other respondents said that they control the quality of export to the open access repository, that they specifically check the control of publication records (input), and that all records are regularly validated by trained librarians, without specifying which records.
The methods of handling the data quality issues in CRIS are varied and depend on the importance of the problem, the context of the application, and the cause of the problem. The scientific literature discusses above all four dimensions which can contribute to high data quality in CRIS, i. e. , completeness, correctness, consistency and timeliness, which have proven to be easy to measure, represent a particularly representative illustration of the reporting for the CRIS users and lead to an improved basis for decision-making. The sample of experts were asked which one they would use to test and measure quality in their system (see Figure  6 ). The respondents considered correctness, completeness and consistency as the most useful criteria to control data quality, more than timeliness. Two other questions assessed the way in which the institutions deal with quality issues in their CRIS.
The first question was about the measures and methods they carry out for quality assurance and quality improvement of CRIS data (see Figure  7 ). Most experts indicate that their quality check takes place at the interface of the CRIS with other, internal or external systems, when ingesting data into the CRIS. Half of the experts mention three other "check points", the data storage, the data presentation (frontend) and the ETL process. Other respondents said that they control the quality of export to the open access repository, that they specifically check the control of publication records (input), and that all records are regularly validated by trained librarians, without specifying which records. The methods of handling the data quality issues in CRIS are varied and depend on the importance of the problem, the context of the application, and the cause of the problem.
The scientific literature discusses above all four dimensions which can contribute to high data quality in CRIS, i. e. , completeness, correctness, consistency and timeliness, which have proven to be easy to measure, represent a particularly representative illustration of the reporting for the CRIS users and lead to an improved basis for decision-making. The sample of experts were asked which one they would use to test and measure quality in their system (see Figure  6 ). Most experts indicate that their quality check takes place at the interface of the CRIS with other, internal or external systems, when ingesting data into the CRIS. Half of the experts mention three other "check points", the data storage, the data presentation (frontend) and the ETL process. Other respondents said that they control the quality of export to the open access repository, that they specifically check the control of publication records (input), and that all records are regularly validated by trained librarians, without specifying which records.
The first question was about the measures and methods they carry out for quality assurance and quality improvement of CRIS data (see Figure  7 ). The respondents considered correctness, completeness and consistency as the most useful criteria to control data quality, more than timeliness. Two other questions assessed the way in which the institutions deal with quality issues in their CRIS. The first question was about the measures and methods they carry out for quality assurance and quality improvement of CRIS data (see Figure  7 ). According to the experts' answers, the preferred approach to quality assurance are various data cleansing processes, such as parsing, enrichment or consolidation, followed by pro-active and reactive methods. Other methods seem less important; in particular, ETL processes (filtering, etc.
Discussion The results from the last question may be surprising-less than half of the sample of CRIS administrators and experts consider data quality to be an important problem. This is quite different compared to former studies, where quality issues were considered as a crucial topic for CRIS development and management (  [14] ; see above, Section 2. 3), and it probably reflects the users' satisfaction with their CRIS, their quality in terms of reliable functioning and trustworthy results, as the CRIS technology has reached a certain level of maturity. However, we must keep in mind that this may be the particular opinion of a small group of experts deeply committed to this technology, as project leaders, system administrators, etc. According to the experts' answers, the preferred approach to quality assurance are various data cleansing processes, such as parsing, enrichment or consolidation, followed by pro-active and re-active methods.
Discussion The results from the last question may be surprising-less than half of the sample of CRIS administrators and experts consider data quality to be an important problem. This is quite different compared to former studies, where quality issues were considered as a crucial topic for CRIS development and management (  [14] ; see above, Section 2. 3), and it probably reflects the users' satisfaction with their CRIS, their quality in terms of reliable functioning and trustworthy results, as the CRIS technology has reached a certain level of maturity. However, we must keep in mind that this may be the particular opinion of a small group of experts deeply committed to this technology, as project leaders, system administrators, etc.
Discussion The results from the last question may be surprising-less than half of the sample of CRIS administrators and experts consider data quality to be an important problem. This is quite different compared to former studies, where quality issues were considered as a crucial topic for CRIS development and management (  [14] ; see above, Section 2. 3), and it probably reflects the users' satisfaction with their CRIS, their quality in terms of reliable functioning and trustworthy results, as the CRIS technology has reached a certain level of maturity. However, we must keep in mind that this may be the particular opinion of a small group of experts deeply committed to this technology, as project leaders, system administrators, etc. Even if the small sample is not some aspects can be compared to other studies, including the unpublished results of our own representative survey with German universities and research organizations  [29].
Data Cleansing Due to the collection, transmission and integration of different internal and external data sources of the universities and research institutes in current research information systems, data quality issues, as mentioned above, have to be overcome. An important step can be data cleansing or data scrubbing, which corrects the existing data to provide a good overview of the CRIS data. Data cleansing involves various methods or procedures for removing or correcting data errors in current research information systems. For example, the errors may include incorrect, redundant, incomplete, inconsistent or incorrectly formatted data. Data cleansing is done in five consecutive processes (see Figure  9 ) and is critical for data quality in current research information systems  [9, 11]. Even if the small sample is not representative, some aspects can be compared to other studies, including the unpublished results of our own representative survey with German universities and research organizations  [29].
Data Cleansing Due to the collection, transmission and integration of different internal and external data sources of the universities and research institutes in current research information systems, data quality issues, as mentioned above, have to be overcome. An important step can be data cleansing or data scrubbing, which corrects the existing data to provide a good overview of the CRIS data. Data cleansing involves various methods or procedures for removing or correcting data errors in current research information systems. For example, the errors may include incorrect, redundant, incomplete, inconsistent or incorrectly formatted data. Data cleansing is done in five consecutive processes (see Figure  9 ) and is critical for data quality in current research information systems  [9, 11]. All these mentioned steps are essential for achieving and maintaining maximum data quality in current research information systems  [9, 11].
Data cleansing creates optimal data quality in terms of completeness, correctness, consistency and timeliness. With this, the data cleansing process lays the foundation for content of higher information value and better-quality analysis of research information in an organization. Figure  10 , below, illustrates an example identifying records with bad names in a publication list to show how the data cleansing process can improve the quality of CRIS data. One special question is raised by quality standards. So far, the current quality standards of CRIS appear to be shaped by communities and conditioned by the expected outcomes. Do CRIS report research activities and outcomes in a reliable and consistent way? Are these reports compliant with laws (intellectual property, public research, ethics, etc.
) and with funding bodies' criteria, such as the distinction between public and private research, different types of communication (open access, cf. plan S), etc. ? In particular, research management expects 100% reliable information, and errors have to be fixed "manually", because of their potential negative effect on evaluation. Regarding the CERIF data model, this means the data monitoring must focus especially on the fundamental entities of the research environment (project, person, organization), the entities representing research results (publication, patent, product) and their relationships, more than on other entities like infrastructures, etc.
The cycle of data quality monitoring in scientific institutions can be described in three steps (see Figure  11 ). One special question is raised by quality standards. So far, the current quality standards of CRIS appear to be shaped by communities and conditioned by the expected outcomes. Do CRIS report research activities and outcomes in a reliable and consistent way? Are these reports compliant with laws (intellectual property, public research, ethics, etc. ) and with funding bodies' criteria, such as the distinction between public and private research, different types of communication (open access, cf. plan S), etc. ? In particular, research management expects 100% reliable information, and errors have to be fixed "manually", because of their potential negative effect on evaluation. Regarding the CERIF data model, this means the data monitoring must focus especially on the fundamental entities of the research environment (project, person, organization), the entities representing research results (publication, patent, product) and their relationships, more than on other entities like infrastructures, etc.
Importantly, data stewards measure and monitor ongoing data quality improvements. In parallel, institutions should develop a culture in which high data quality plays a significant role. Not only the data steward and the IT department, but also employees from the specialist departments need to be aware of the importance of reliable information and how to maintain and optimize the data. We mentioned above the question as to data quality standards in the field of CRIS. Speaking about data cleansing and monitoring necessarily raises another, fundamental question about data quality. Following the usual definitions and models, data quality means "fitness for use by data consumers", a dynamic and multidimensional concept, correlated with user satisfaction and system acceptance, and conditioned by accessibility, interpretability and relevance for the user or "data consumer".
Yet, the problem with CRIS is that they mobilize different user groups, with different standards, expectations, needs, skills and practices. The present investigation was conducted with project leaders and system administrators, committed to CRIS and involved in the (mainly European) euroCRIS network, with papers, communications, working groups etc. However, a CRIS community in a given institution is more heterogeneous, made up of research managers, project leaders, data officers, administrative staff, librarians, people from the IT department, etc. , and, last but not least, by the scientists themselves, with their own divisions (disciplines, infrastructures, early career scientists vs. tenured professors, etc. ). Figures 12 and as iterative processes, provide a permanent assurance of data quality in current research information systems, as erroneous data in a collection are a fundamental challenge for managers and IT, quality assurance and many other fields (see also  [9, 12, 13] ).
Yet, the problem with CRIS is that they mobilize different user groups, with different standards, expectations, needs, skills and practices. The present investigation was conducted with project leaders and system administrators, committed to CRIS and involved in the (mainly European) euroCRIS network, with papers, communications, working groups etc. However, a CRIS community in a given institution is more heterogeneous, made up of research managers, project leaders, data officers, administrative staff, librarians, people from the IT department, and, last but not least, by the scientists themselves, with their own divisions (disciplines, infrastructures, early career scientists vs. tenured professors, etc. ). It seems difficult, therefore, to define simple data quality standards or criteria.
A system administrator may be satisfied with "her/his" CRIS performance and willing to recommend it to other institutions, while other users, e. g. , the technical staff or the involved librarians may be more critical regarding the system's ease of use; the focus of research managers will be on the output (reporting) quality, compliant (or not) with the requirements of authorities and funding bodies while scientists may pay more attention to the quality of the information about their output, projects, awards, affiliations, etc. , including their identity and the spelling of their name. Research on CRIS often lacks a differential approach to needs, usage and information literacy of specific user groups; at least, the link is missing with other surveys and studies on the data behavior and literacy of different communities.
This situation excludes simple and single approaches data quality management. The multiple user groups define the data "fitness for use" in their own way and in relation to their own and specific practice and needs. Also, a global model of data quality management is required, covering the whole process (and even the data models and sources outside of the CRIS) and including the whole range of methods, techniques and tools of data quality management. To raise awareness on this situation and to contribute to an appropriate, global approach is the main objective of this paper.
Conclusions The ultimate goal of research information systems is "providing the best evidence base on which to support high quality decision making"  [32]. High data quality is one condition necessary to attaining this goal. The larger the data sets become by electronic data processing, the more important good data quality and information quality becomes. The quality of the data is becoming increasingly important in a wide range of institutions. Data that have been modeled for a specific operational use and have a high level of heterogeneity needs to be adjusted and controlled in order to be used as the basis for such analyzes in the CRIS context. Basically, the data errors in CRIS should be resolved through quality assurance as early as possible in the data flow. These typical quality errors are caused by spelling errors or incorrect and duplicate entry in the data.
The new techniques and methods of data cleansing and data monitoring presented in this paper can eliminate these data quality errors within the CRIS and optimize the entire business process in CRIS. Since the data are constantly changing and a one-time cleanup is not sufficient, data must be maintained permanently, and their quality continuously monitored. Such continuous improvement and enhancement of data quality in CRIS requires specific measures such as "pro-active approach"  [9, 11]. When it comes to implementing data cleansing and data monitoring, open source tools (such as Quadient DataCleaner 5  ) help to identify, improve and monitor data errors (like spelling mistakes, inconsistencies and other noticeable problems, etc. ) from large amounts of data that can be adjusted together with the responsible CRIS employee in the specialist departments.
What is the impact (feedback loop) on the data models and quality standards of internal and external data sources providing input for the CRIS? Can the merging of CRIS and other systems (e. g. , institutional repositories) offer solutions for the data quality management? What about the outsourcing of CRIS and the partial or total transfer of data processing and quality management in the cloud? What is the impact on problems with quality issues, acceptance and satisfaction? The CRIS landscape is rapidly evolving, with new technologies and requirements and with a steadily increasing number of CRIS projects. More research and empirical evidence are needed for better solutions, improved project management and appropriate user training and assistance. Author Contributions: Both O. A and J. S authors jointly contributed to the design and implementation of the study, to the analysis of the results and to the writing of the manuscript.
Funding: The research was funded by the German Center for Higher Education Research and Science Studies (DZHW) and by the German Federal Ministry of Education and Research (BMBF) and the 16 Länder governments in the context of the project "Helpdesk to facilitate implementation of the Research Core Dataset"  6. One part of the work received funding from the European Institute of Social Sciences and Humanities (MESHS Lille) and from the Regional Council (Conseil Régional Hauts-de-France), as part of the research project "D4Humanities" 7.
Data analysis Quantitative descriptive and correlational statistics are used to analyze the collected data to understand whether the variables are correlated and if yes what is the strength of their correlation  (Bryman, 2012). Therefore, SPSS for Mac Version 25 was used to describe the characteristics of the sample at one point in time and to identify relationships between the different variables collected in the survey. The Pearson's correlation, which is considered as the best measure of correlation  (Bryman, 2012) , was set to 95% CI where values ≤ 0. 05 were considered significant.
Acknowledgements None About the authors Michael Neubert is a Professor at the  International School of Management in Paris, France , where he obtained his PhD and is now also Chair of the Strategic Management Committee. Michael is also a Visiting Professor at  Universidad Paraguayo Alemana in San Lorenzo, Paraguay. He teaches international business, intercultural communication, doing business in foreign markets, and international finance. His research interests concern the internationalization of hightech startups. Michael is a member of the  Academy of International Business , and he is a partner of a private equity firm that invests in high-tech startups and supports them in the development of new foreign markets. Michael is also the  CEO  of  C2NM  (www. c2nm. com), a Swiss consulting firm specializing in the field of international and intercultural management.
One important omission from this list is the effect of bank efficiency on the cost of credit. This omission is rather surprising, because economic theory naturally predicts that a greater ability of banks to operate at a lower marginal cost should be associated with lower "prices" -lower loan rates -and thus lower cost of credit. If correct, this argument would imply that a key instrument for facilitating firms' access to credit is to improve bank efficiency. This paper aims to fill a gap in the literature by investigating how bank efficiency affects the cost of credit in nine Western European countries. A major challenge in analyzing this impact is obtaining firm-level information on lending banks so that efficiency at the bank level and cost of credit at the firm level can be linked. The last wave of the Amadeus database provides such information, allowing us to identify which banks lend to each borrowing firm.
Thus, we can combine firm-level data from the Amadeus database with bank-level data from the Bankscope database to build a large sample of 240,000 firms from nine European countries. To perform our investigation, we model cost of credit as a function of bank efficiency and a set of firm-and country-specific control variables. We measure bank efficiency with a stochastic frontier approach commonly used in works on the banking industry (such as  Lensink, Meesters and Naaborg, 2008; Berger, Hasan and Zhou, 2009). We use different specifications of the stochastic frontier approach designed for a crosscountry analysis, by testing the influence of country-specific variables and controlling for the environmental conditions in the frontier. We also investigate whether the effect of bank efficiency on the cost of credit is contingent on firm size, bank competition, and bank size, and perform several checks to examine the robustness of our results to different specifications of key variables and sub-samples.
We face two key challenges in our investigation. First, while we can identify the lending banks for each firm, we do not have information on the share of each bank in the loans to the company. Therefore, we must assign each bank an equal weight in its importance for a firm. This is a limitation, but we address it by conducting additional estimations on the sub-sample of firms that use only one bank, for which therefore this problem does not exist. One-bank firms represent about 40% of the sample and thus provide a large enough sample to perform alternative estimations. Second, measuring the cost of credit at firm level is difficult. Information on individual loans can be found in credit registries but is limited to single-country datasets, or it can be obtained for large loans in a cross-country framework.
These data sources are problematic if one wishes, as we do, to analyze the cost of credit in a number of countries for a wide spectrum of firm size. To overcome these challenges, we therefore measure the cost of credit by calculating the ratio of interest expenses to total bank debt using firmlevel data. This indicator, measuring the implicit interest rate charged by banks, has also been used by  Carbo-Valverde, Rodriguez-Fernandez and Udell (2009)  and  Fungacova, Shamshur and Weill (2017). Our paper contributes to two debates in the literature. Firstly, we augment the vast literature on bank efficiency by investigating the impact of the cost efficiency of banks on the cost of credit for firms. Secondly, we improve our understanding of how bank behavior shapes access to credit. Several studies have investigated how bank competition and concentration influence access to credit (for example,  Beck, Demirgüc-Kunt and Maksimovic, 2004) ; our work departs significantly from the existing empirical literature by focusing on the role of bank efficiency.