dataesr/scientific-paragraphs-categorization huggingface dataset:
Because the counterfactual sample consists of would-be participants in the same scientific domain, we took the number of collaborative links formed between the would-be attendees and participants of the conference. Inventive direction of output. Conditional on participation, we quantified the extent to which works of participants drew from knowledge embedded in the conference itself. We counted the number of MeSH keywords subsequent publications of attendees have in common with the conference's top 10 percent most frequent keywords (common MeSH). We defined the core knowledge space of the conference to be the aggregate knowledge of all attendees by using their five-year prior publications' MeSH terms as proxy. Attended, post, and post x attended interaction. To setup the difference-in-differences (DiD) model, we employed three indicator variables: attended equals one indicates that an individual attended a GRC conference; post equals one indicates that the data point is after the conference, irrespective of whether the individual attended a GRC conference or not; and post x attended equals one, the interaction of attended and post, indicates that the data point is after the conference and that the individual attended a GRC conference.
Agence France Entrepreneur As the objective of this study is to examine the relationship between posterity and entrepreneurial intention among third age individuals, we surveyed people between 45 and 65 years old in France. We collected data using a questionnaire that we distributed to older individuals who come from associations and third age universities in Paris that propose several courses for older individuals. This mode of data collection enabled us to get a variety of individual profiles in terms of age, gender, and occupational status. In total, 251 questionnaires were answered; of these, 27 incomplete questionnaires were excluded. The structure of all variables and scales were analysed to detect potential missing values and input errors. Then, descriptive analyses were conducted (mean and frequency analysis), which helped us to narrow the final sample down to 202 individuals with an age range from 45 to 65 years.
Experimental design and setting All models and algorithms were implemented on a computer with 2 GB RAM and 2. 00 GHz processor running on Microsoft Windows 7. All non-linear programs (NLP) were solved with LINGO 14. 0. Preliminary experiments indicated that the SLP (successive linear programming) directions strategy of LINGO was the best-performing and thus was used when solving NLP in this computational study. When solving NLPs the solver was given a time limit of three hours (10,800 seconds). If the optimal solution is not found at the end of the termination criterion, the software reports the best solutions found within the limit or the problem is considered non-solvable. The MILP presented in Section 4. 3 was solved by CPLEX. We randomly generated instances that have one of  5, 10, 20, 50, 100, 200, 500  demand points for the computational study.
We supplemented this archival data with 67 semi-structured interviews, conducted between 2008 and 2016. We interviewed informants from twelve FGECs, the Conference of Grandes Écoles, the French Foundation for Management, and l'Étudiant magazine in order to capture different perspectives and accounts. The FGECs were selected through purposive sampling, with the aim of identifying a representative sample in terms of location, age, size, and governance structure. To gain a nuanced understanding of how different schools perceived and benefited from the recruitment model, we conducted follow-up interviews in three top-tier schools located in Paris and three mid-tier schools located outside Paris. Treating these six schools as distinct cases, we followed a "replication logic" wherein each case served to confirm or disconfirm inferences drawn from the others  (Eisenhardt & Graebner, 2007; Yin, 2003).
Stage 1: Developing a multi-level event history database. In the first stage of the analysis, we mapped important events in the history of the French Grandes Écoles. Relying primarily on archival materials, we captured changes in the number and types of actors influencing the field, and changes in field-level discourse around the challenges facing the schools. We paid particular attention to public discussions on how the schools should respond to these challenges-situating pressures and responses within broader shifts in the higher education sector. As we developed a chronological narrative of events, we identified three periods, marked by intense debate and contestation. The first was in the early 1960s, when the efficacy of the model was called into question. The second was in the late 1990s, when the internationalization of higher education drew attention to a clear misfit between the model and changing competitive demands.
An optional filter process is also integrated into the decision framework. If the number of partners is too large for the optimization of the second stage, the user activates the filter process, where only the sites with a better efficiency are included to the second stage. The second stage involves the application of a mathematical model with multiple objective functions. We take into consideration the three aspects (economic, environmental and social) of sustainability, as well as the efficiency calculated in the first stage. Furthermore, in the environmental aspect, we optimize the carbon footprint and water footprint, given their significance to environmental burdens. At last, we generate a Pareto front to aid the user's decision-making. Using the described decision support framework, we developed a new decision support software system called the "Collaboration Planning Tool (CPT)".
CPT has a modular architecture composed of several linked modules. This architecture could facilitate the integration and connection with other management systems like ERP or LCA (Life Cycle Analysis). CPT has a modular architecture composed of several linked modules. To achieve a high quality of ICT visibility across the collaboration network, this architecture could facilitate the integration and connection with other management systems like ERP, APS, TMS (Transportation Management System), OMS (Order Management System) or LCA (Life Cycle Analysis). It could be connected with these applications through the database software as input-output exchanges. The main program needs to be implemented on a server, and then all users can access the system on their own computers by simply using an Internet connection.
Acknowledgment The work presented in this paper comes from the project titled:  Step Change in Agro-Food Logistics Ecosystems (SCALE) sponsored by INTERREG IVB North-West Europe. Project SCALE aims to increase NWE economic competitiveness and improve environmental and social sustainability of food and drink supply chain and logistics. We would to thank all partners of project  SCALE  for their contribution and help to elaborate this paper. They deserve our sincere thanks and respect for their reliable collaboration.
For international cooperation projects, public health care establishments may also sign agreements with partners from the public or private domain, in compliance with the international commitments of each of the participating partner countries. The creation of joint, anonymised databases for research purposes must be in compliance with these regulatory and legislative constraints, in order to perform modelisation and analysis of data in each of the participating partner countries in the project. For the purposes of clinical research, agreements stipulating the specific conditions for exchange of data (how, in what form, by whom, etc) have been developed.
Such cooperative approaches could help to bridge disciplines and break down cancer management differences between Caribbean countries, by evaluating patterns of care of patients via research programs and observational studies. was promoted during scientific meetings and workshops to explain the role of population-based cancer registries and what activities are ongoing and could be shared with the Caribbean. The Martinique Cancer Registry (MCR) has been participating in epidemiological surveillance and evaluation of cancer since 1981, through the analysis of incidence and mortality data. Thanks to exhaustive and continuous data collection for all cancer cases, key indicators are calculated to study the trends in different types of cancers across the region of Martinique. Incidence and mortality studies for each type of cancer, as well as prognostic studies are also initiated and performed to describe the spatial and temporal distribution of the different cancer types.
In October of 1997, the PRCCR initiated its participation in the US National Program of Cancer Registries (NPCR) coordinated by the Centers of Disease Control, of the United States' Department of Health and Human Services. In 2014, the PRCCR launched its new webpage at: www. rcpr. org. Puerto Rico' s Cancer Registry participation in international studies includes all editions of the Cancer Incidence in Five Continents and International Incidence of Childhood Cancer, Vol II and Vol. III, both published by the International Agency for Research on Cancer (IARC). The PRCCR data was approved and included in the international cancer survival study CONCORD II and CONCORD III. Several public health schools and cancer researchers are the main users of PRCCR data. The PRCCR Research and Analysis Unit work closely with all researchers in projects, presentations, and publications; however, the Registry also produces its own publications including bi-annual cancer incidence reports.
The NCR of Cuba, covers the whole population of the country, was created in 1964 and upgraded in 1986 to include mandatory reporting and improved data management systems. The information system was decentralized in 1994 to each provincial level. Since 2004 the Network of the Cancer Registries has started to work. The Cancer Registry Network of Cuba meets physically once a year, and holds virtual meetings once a month where topics of general interest are discussed and methodological and administrative guidelines are issued. This Registry is developing software for the handling and processing of data, ie, SISCAN, which should start to be used in 2018. This will lead to an update of the data collection model and the procedures manual. An increase of the quality of the data was observed using an own tool developed to check it.
However, there is a compelling need for action in the Caribbean, due to the increasing health care inequalities among elderly people. Due to the side effects of treatment, assessment of quality of life has become an important priority in cancer care management in developed countries. The EORTC QoL-Group and the Cancer in the Elderly Task-Force specific to QoL are working on questionnaires in elderly cancer patients; such initiatives could be extended to the Caribbean population for Quality of Life research projects  [10]. Accordingly, the European Organization for Research and Treatment of Cancer (EORTC) specific questionnaires on quality of life (EORTC QLQ-C30) and on prostate cancer (Prostate cancer module QLQ-PR25) will be proposed to patients. Given the high incidence of prostate cancer in the Caribbean, this project will enable us to compile, for the first time, high-quality data about quality of life in prostate cancer, based on data from population-based cancer registries.
This article underlines the importance of creating a consortium of researchers within the Caribbean to develop priority research goals for elderly persons suffering from cancer, based on data from population-based Cancer Registries. The Cancer Registries of Cuba, Puerto Rico and Martinique are among the oldest in the Caribbean, and contain high-quality data that can contribute to international knowledge on the epidemiology of cancer in the Caribbean region. The strategic action plans to fight cancer that are present show the perspectives for cooperation through the implementation of multicentre research projects in the future. The creation of the "Cancer Task Force Project for cooperation in the Caribbean and Aging Research" underscores the importance of innovative approaches to cooperation for datasharing and biotechnology transfer in the domain of health.
The testing data are collected from the 2013 vehicle emissions testing database published by the U. S. EPA  [32] , which includes valid data on 12 new models introduced by three different manufacturers (see Appendix A for the original data). In the two-stage DEA model, cubic inch displacement (cid), rated horsepower (rhp), engine speed versus vehicle speed at 50 mph (n/v ratio), and axle ratio (axle) are treated as the inputs; equivalent test weight (etw) and fuel economy (mpg) as the intermediates; and hydrocarbon emissions (hc), carbon monoxide emissions (CO), carbon dioxide emissions (CO 2 ), and nitrogen oxide emission (NO x ) as outputs. Note that higher levels of outputs usually indicate better performance in DEA. Thus, we treat the reciprocals of cubicinch displacement, rated horsepower, compression ratio, and equivalent test weights in our analysis to fit the DEA use.
Acknowledgements The authors wish to thank  Valeria Cirillo ,  Giovanni Dosi ,  Francesco Garibaldo , and participants to the Workshop " A new Industrial Revolution?Labour, Technology and the Automotive Industry " (2018, Pisa, Italy), for helpful comments and insightful suggestions at various stages of this work. The authors wish to thank the  Claudio Sabattini Foundation (Bologna, Italy)  and the  Institute of Economics of Scuola Superiore Sant'Anna (Pisa, Italy)  for their support during fieldwork. Maria Enrica Virgillito  acknowledges support from  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 822781   GROWINPRO -Growth Welfare Innovation Productivity.
Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc. ) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term.
The Concept of Data Quality In the field of databases and large information systems, data quality has been defined as "data that are fit for use by data consumers", i. e. , those who use data  [2]. This means that data quality is not an absolute value but a dynamic concept  [3] , correlated with the usage of the system and thus, with user satisfaction and system acceptance. Studies on data quality most often assess multiple dimensions of the concept, with attributes such as accuracy, timeliness, precision, reliability, currency, completeness, relevancy, accessibility and interpretability. Emphasizing the consumer viewpoint, some attributes appear as preliminary or necessary conditions, e. g. ,.
Current Research Information Systems A current research information system (CRIS) is a specialized database or federated information system to collect, manage and provide information on research activities and results, such as projects, third-party funds, patents, cooperation partners, prices and publications  [9] [10] [11]. The building blocks of a CRIS architecture can be described as a three-stage structure  [10]  (see Figure  1 ). consumers, in different contexts and at different times. It is "fitness for all purposes in the enterprise processes that require it", and quality information is data that "consistently meets knowledge workers and end-consumers expectations"  [5]. Data quality should therefore be an object of constant care and attention ("quality management"), in order to improve the system performance and the user satisfaction and acceptance.
Current Research Information Systems A current research information system (CRIS) is a specialized database or federated information system to collect, manage and provide information on research activities and results, such as projects, third-party funds, patents, cooperation partners, prices and publications  [9, 10, 11]. The building blocks of a CRIS architecture can be described as a three-stage structure  [10]  (see Figure  1 ). The data access layer contains the internal and external data sources, e. g. , operational databases (human resources, finance, project management. ), open repositories, identifiers (ORCID, DOI, etc. ), bibliographic data from the Web of Science, Scopus or PubMed, etc. This layer includes data models for the standardized collection, provision and exchange of research information, such as the Research Core Dataset (RCD) and the Common European Research Information Format (CERIF).
) and the large diversity of its sources and providers (internal and external databases, directories and registries of research projects, programs, results, calls, events, budgets, human resources, etc. ), all with their own formats and standards which can vary over time. In the words of  [16] , the main problem to be solved is as follows: "users must be supplied with heterogeneous data from different sources, modalities and content analysis processes via a visual user interface without inconsistencies in content analysis, for example, seriously impairing the quality of the search results". Thus, completeness of data, i. e. , the presence of data about all entities which are subject of a given CRIS  [17] , is one of the main parameters of data quality, along with correctness, consistency and timeliness.
In addition to the internal sources, external sources, such as other publication platforms, commercial databases, research data repositories, etc. , are also used for data collection. This results in a large variety of data types that must be further processed by the CRIS. The quality of the source data has a direct influence on the quality of the CRIS. At each university, research information is entered and recorded into the CRIS, so the processing and management of these data must be of good quality so that users can get good results  [9, 11]. The problem is even more complicated when it comes to national or international research information systems. Although specific scientific, political and institutional environments may be different, the challenges of data quality remain more or less similar, as different studies have shown from the Czech Republic  [18] , Finland  [15] , Norway  [19] , Sweden  [20] , Poland  [21]  and the United Kingdom  [22].
Other approaches to reduce the heterogeneity of data and increase the systems' interoperability are standardization of metadata formats 2 and terminology 3. Another approach to further standardization has been presented in the German context, i. e. , the definition of a core dataset  [25]. This approach may be helpful in ensuring a level of "minimum interoperability" with clearly defined data requirements, but will reduce the range of purposes for which the data are fit and thus, impact the data quality. The specific situation of migrating to a new CRIS has been addressed by  [26] , who describe how the process "unearthed massive data quality issues" and admit that "even after a year and a half of  cleaning, much work remains to be done and some parts of the dataset may never be fully improved".
Methodology To obtain additional empirical evidence on data quality management in research information systems, we conducted a survey with academic institutions which are implementing or already running a CRIS. The intent of the study was not a large-scale survey with a representative sample, but to conduct a quantitative, exploratory investigation with a small international sample of recognized experts, i. e. , a preliminary study to prepare a larger and representative study with a large sample of nearly 250 German universities and research organizations. CRIS experts affiliated to universities were identified from papers and case studies presented at euroCRIS meetings and conferences during the last five years 4. Thus, contact was made by email with CRIS experts from 30 universities in 14 countries, inviting them to answer a short, anonymous online-based questionnaire with nine questions.
Results When examining the concept of data quality in CRIS, the following results are found from the survey with the institutions and CRIS experts. First of all, a distinction must be made between design quality and execution quality. Design quality concretizes in the specification of standards, data definitions and documentation. With regard to execution quality, the focus is on the data contents and the data values. Another important aspect of the data quality is the quality of the data provision, which particularly takes into account the software components of the overall system. The significance of the individual aspects is summarized in Figure  2.
Results When examining the concept of data quality in CRIS, the following results are found from the survey with the institutions and CRIS experts. First of all, a distinction must be made between design quality and execution quality. Design quality concretizes in the specification of standards, data definitions and documentation. With regard to execution quality, the focus is on the data contents and the data values. Another important aspect of the data quality is the quality of the data provision, which particularly takes into account the software components of the overall system. The significance of the individual aspects is summarized in Figure  2. The majority of the respondents considered the quality of data provision and the quality of data content (values) to be most important; more important than data models, processes or other aspects.
Which are the real problems the experts observe in their everyday work with their CRIS? Thirteen issues were mentioned, ranging from the most frequent problems, like spelling mistakes, multiple and incorrect input or missing data relationships, to less frequent problems like inconsistent data formats, different data types, or wrong rules (see Figure  3 ). The majority of the respondents considered the quality of data provision and the quality of data content (values) to be most important; more important than data models, processes or other aspects. Which are the real problems the experts observe in their everyday work with their CRIS? Thirteen issues were mentioned, ranging from the most frequent problems, like spelling mistakes, multiple and input or missing data relationships, to less frequent problems like inconsistent data formats, different data types, or wrong rules (see Figure  3 ).
Half of the respondents identified problems via data modeling and formal quality assurance during the development of the CRIS, while one third of the experts control the quality of their data through well-established, regular quality checks. 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 indicated that they discovered data problems through an analysis of the system, with the help of business intelligence tools, through reporting, etc. Half of the respondents identified problems via data modeling and formal quality assurance during the development of the CRIS, while one third of the experts control the quality of their data through well-established, regular quality checks.