dataesr/scientific-paragraphs-categorization huggingface dataset:
Search terms in Spanish included: school leadership, school directors and education reform. The review was limited to materials from 1990 to 2015. School leadership gained prominence as a scholarly and research topic in this period. Valuable analyses in the field of school leadership reforms emerge in particular from 1990 on. The review has been updated with more recent literature to the extent that has been possible. First, etymological, methodological and temporal concepts were defined. Second, a search using a social science database (Scopus) was carried out. It was complemented with a search on google scholar for books and reports from relevant international organisations. Policy reports and policy analysis on school leadership and educational policy reform is not necessarily referenced in research databases.
The selection of sources was purposive and aimed at the development of a comparative framework. A total of 340 articles were identified; the articles focused on the combination of the terms school leadership and educational change, education policy and/ or reforms. The author selected on the basis of article summaries the most relevant articles, in order to refine and further delimit the search. This was complemented with a review of sources in the Educational Research (ERIC) database. The most cited articles by author specialization were selected, in order to further narrow the review to 80 articles covering research on school leadership and educational reform. These selected 80 articles are cited throughout the review and are included in the bibliography. The articles have been grouped by field of study and are reviewed in the following section.
Discussion The results of Experiment 1 are clear-cut: First, we obtained a CL effect, indicating that participants incidentally learned the wordcolor response associations over the course of the experiment. Second, we obtained robust response retrieval effects, reflecting faster RTs in the current trial when the same response had been given during the last occurrence of the word stimulus that was also presented in the current trial, compared to trials when a different response had been executed during the last occurrence. Third and most central to our research aims, the CL effect was effectively eliminated after controlling for effects of response retrieval. This pattern of findings emerged both for ANOVA analyses with aggregated data and also in multilevel analyses in which CL and response retrieval were coded on a trial level.
Discussion In Experiment 2, we used a stronger contingency manipulation to boost CL effects, which was successful. What is more, findings from Experiment 2 fully replicate the pattern of effects that were obtained in Experiment 1. In detail, we obtained a robust CL effect that was larger (d z = 0. 70) than in Experiment 1 (d z = 0. 57). Thus, we can conclude that participants incidentally acquired word-response associations over the course of the experiment. Second, we obtained robust response retrieval effects. Third, the CL effect was again effectively eliminated when we controlled for response retrieval. Data from both experiments thus support the law of recency according to which habit formation is mediated by episodic retrieval processes, which lead to a reactivation of the response that was stored in episodic memory together with the stimulus during the most recent occurrence of the current situation.
Second, we discuss some directions in which OrgReg might contribute to research policy and evaluation studies. On the one hand, OrgReg allows for the first time for a systematic characterization of the organizational structure of public-research across European countries, therefore enriching comparative studies of research policies  (Larédo and Mustar 2001)  with the organizational level. This is highly relevant given the increasing role of research organizations in the implementation of public policies, which requires dealing with the impact of policies on the behavior and performance of research organizations as well  (Geuna and Martin 2003). On the other hand, OrgReg functions as an interlinking facility between different datasets and, therefore, is an important step towards open linked data on research organizations  (Light, Polley and Börner 2014).
Thanks to the use of the register's identifiers, it becomes possible to assign unambiguously inputs and outputs to organizations. This applies to data derived from administrative sources  (Lepori, Bonaccorsi, Daraio, et al 2015) , from international databases on publications  (Waltman, Calero-Medina, Kosten, et al 2012)  and patents (De Rassenfosse, Dernis, Guellec, Picci and de la Potterie, Bruno van Pottelsberghe 2013), therefore overcoming a major limitation of many current studies based solely on output data (Abramo and D'Angelo 2016). Further, the availability of a stable list of public research organizations allows matching with data derived from on-line sources, such as institutional websites and Wikipedia, as well as of social media, such as ResearchGate  (Lepori, Thelwall and Hoorani 2018)  and Google Scholar, therefore significantly expanding the realm of available information on research organizations.
A useful criterion was provided by patents since these are usually filed by legal entities. Indeed, data showed that, for CNRS, almost all patents are under the name of the organization, while for Helmholtz most patents are filed by individual centers. Second, OrgReg includes a table with linkages between organizations, defined as stable and structural relationships that are lasting over time. Distinguishing between structural linkages and weaker ties such a strategic alliances was a clear concern here, also for practical purposes. Currently, OrgReg, distinguishes between three linkages types: membership, where a component (for example a research institute) is directly controlled by a parent unit; affiliation, meaning a strong structural relationships between two independent units which share some essential elements; association, a structural but less strong relationships than affiliation.
In the discontinuous approach, strata can be more than one year apart and, hence, changes between two strata are not observed, reducing the burden for data collection particularly in case of rapid change as observed for firms. In the continuous approach, the register is composed of a list of entities, while demographic events are recorded in a separate table -for example, in the case of a merger the merged organizations is attributed a closure year corresponding to the merger, while a new ID is attributed to the new organization created with the merger with foundation year corresponding to the merger. A yearly panel can be reconstructed by combining the entities and demographic events table. This approach is preferred when demographic events are of interest as such, while their number is reasonably limited.
Of course, some of these practices are also adopted by database owners to standardize their organizations, but it is fair to say that the combination of different sources and the systematic checking with national experts are a unique feature of OrgReg. Lists of candidate entities were extracted from different sources: the European Tertiary Education Register (ETER;  Lepori, Bonaccorsi, Daraio, et al 2015)  for HEIs, the list of institutions classified in public-research in the WoS database at CWTS in Leiden  (Waltman, Calero-Medina, Kosten, et al 2012) , as well as in the EUPRO database of European projects (Roediger-Schluga and Barber 2008). The combined list was then manually verified by national experts. In this process, many organizations were excluded, as the list also included a number of firms, duplicates and university institutes.
The primary list of university hospitals was derived from the CWTS Web of Science database, but was enriched by national experts, particularly in France and Italy where most of clinical research is performed by hospitals not directly associated with universities. Similarly, the basic list of RFOs was derived from the Public Research Funding contract from the EU  (Lepori, Reale and Spinello 2018) , but was then revised and harmonized with OrgReg definitions. These sets of entities were then integrated in a common list for each country, plus a specific list of international organizations, such as the EU Joint Research Center or CERN. In a further step, national experts added the linkages and recollected descriptors; demographic changes were then reconstructed by checking manually all entities founded after the year 2000.
In a number of cases, adaptations in the OrgReg handbook were also needed. A further round of standardization was performed centrally, by checking systematically organizational websites and Wikipedia entries, while also correcting different ways of writing cities or imprecise geographical coordinates. Such quality control processes are central for the robustness of the register, but also for its legitimacy in terms of a trusted representation of organizational reality  (Desrosières 2016). In this respect, there is ample potential for further automation in the retrieval of information and in data quality. Currently, a simple set of consistency checks is implemented to detect mistakes, such as organizations being merged, but without a correct foundation year for the new organization. However, there is a potential for retrieving automatically information on foundation years and geographical information, as well as for early detection of changes -the experience shows for example that websites are frequent markers of demographic events.
Automated procedures will also be required to maintain consistency between OrgReg and other databases and have a potential for pre-processing and for reducing the burden for maintenance, but final decisions will be made by experts, particularly on the most complex cases. Relatedly, there is a need for a more systematic documentation of why certain choices have been made and of tracking changes over time, for example in the classification of entities and for what concerns their inclusion and exclusion. Such a documentation will be important for many purposes: to avoid repeating the same processes or changing back decisions without a clear rationale; to allow methodological consolidation in the long term through ex-post analysis of cases; finally, as a source for ethnographic analysis of the underlying principles and assumptions of the register.
OrgReg as an interlinking tool OrgReg is not meant to be an isolated facility, but a tool to interconnect different data sources. Three levels have been identified (see Figure  1 ). First, the creation of organizational subregisters that store statistical data on the OrgReg entities and share identifiers and basic information. This is currently the case with ETER that shares the same identifiers as OrgReg and provides administrative data such as finances, staff and students on Higher Education Institutions, but similar datasets could be developed in the future for PROs, research hospitals and research funding organizations. Second, a number of core datasets within the RISIS project, including those on scientific publications, European projects and patents, are connected to OrgReg by sharing the organizational identifiers.
A key feature of these datasets is the standardization of organizations that is required for analytical purposes, but also to produce robust indicators. The construction of OrgReg however displayed a number of inconsistencies in the identification of public-sector organizations, with some companies being included in the public sector or the incomplete treatment of demographic events. In this respect, OrgReg has the potential to improve organizational standardization within each dataset, but, more importantly, across datasets. Such a process will have to be maintained over time by tracking and standardizing new organizations that appear in the interlinked datasets and integrating them into OrgReg. This matching of OrgReg generates the possibility of providing within OrgReg aggregated indicators on key research outputs for all public research organizations in Europe.
While such indicators could also be retrieved from the parent datasets, similarly to the scientific publication indicators provided by the Leiden Ranking, providing for all research organizations in Europe a core set of output data, covering both science and technology, will represent a major advance in terms of data availability to end users. Third, having a closed list of organizations is a valuable tool in order to search for information from the Internet and social media, as it makes disambiguating names much easier. Besides the organizational name (in English and national language), localization information and the website, OrgReg also includes organizational acronyms as experience showed that they are frequently used when referring to the organization. The experience made in matching OrgReg with institutional profiles in ResearchGate  (Lepori, Thelwall and Hoorani 2018)  showed that this information is sufficient for a precise matching, without having to store in the register name variants, with the exception of (major) changes in the official name.
European public-sector research as seen from OrgReg Table  1  provides an overview of the current status of OrgReg 2. The figures are expected to increase slightly in the future. On the one hand, OrgReg is continuously updated based on feedback received from the project partners, for example concerning new demographic events taking place. On the other hand, the project team is still completing the coverage of Research Funding Organizations and of large 'umbrella' PROs, as well as of a few countries where coverage is not fully complete, notably in Eastern Europe. However, we have ample evidence that coverage is now very extensive for what concerns public entities with a sizeable output in terms of tertiary education students, scientific publications and patents. Student coverage in tertiary education, as compared with EUROSTAT, is well above 90% for most countries, while very few entities not included in OrgReg could be identified in publications and patents databases.
By design, the coverage of OrgReg is by and large limited to 'classical' knowledge production manifested in outputs such as graduations, publications and patents, as well as tertiary education. actors involved in social innovation, such as citizens' associations and grassroots movements (Edwards-Schachter and Wallace 2017), are not covered as these cannot be easily identified in the data sources used. A first relevant outcome of the establishment of OrgReg is that the number of public research and higher education institutions in Europe does not exceed a few thousands entities; this is a low figure as compared, for example with firms, where preliminary work on a parallel firms' register (FirmReg) showed that the number of innovative firms in patent databases might be around 100,000. The figure for the public sector would be even lower when considering research only, as among the 4,500 HEIs covered only about 1,000 have a sizeable publication and/or patent output  (Lepori, Geuna and Veglio 2017).
The most notable case is the Paris Hospital Trust that comprises 44 hospitals, of which 12 are recorded in OrgReg since they have a sizeable publication output; most of them closely cooperate with the universities in the Paris area. Particularly when complemented on data on co-publications between hospitals and universities, OrgReg has therefore an important potential to analyze the forms of organization of healthcare research and its relationships with universities. Finally, Orgreg includes only 87 entities within the public administration and 86 Private Non Profit entities. In both cases, the coverage might not fully complete, as these entities are not straightforward to identify and, for instance, to distinguish from private companies for PNP. The public administration entities are largely concentrated in Italy and France, i.
This information is important for identifying organizations over time, but is also informative of changes in the status and self-presentation of organizations. Concerning geography, OrgReg provides for a systematic localization of the seat of research organizations at the city level; this information is being extended to include also satellite campuses of HEIs, as well as multiple locations of large PROs such as CNRS. Data display a broad regional coverage, but at the same time strong concentration in metropolitan regions. More than 1,100 NUTS3 level regions out of 1,500 hosted at least a location of a public-research organization. This geographical spread is essentially due to HEIs, which were present in more than 1,000 regions, against only about 300 regions each for PROs and research hospitals.
Conclusion OrgReg represents a significant advance in the construction of a data infrastructure for European publicsector research and higher education. For the first time, users can dispose of a reference list of organizations that follows clear methodological principles and has been systematically checked against different sources. OrgReg also documents to an unprecedented level inter-organizational linkages and demographic changes in European public research, allowing therefore a deeper understanding of the organizational diversity of public systems in Europe. Finally, OrgReg represents an important tool for database interlinking and for supporting data collection from different sources, including the Internet. As highlighted in this paper, the success of OrgReg is also due to choices that limit its scope in terms of what entities are covered, but also of how much the complexity of organizational settings is represented in the register.
A key parameter in dealing with methodological issues cases was the trade-off between taking into account the complexity of systems on the one hand and the goal of providing a manageable helicopter view of European public research on the other hand. In this paper, we have also provided some illustrative examples of the analyses that could be performed by using OrgReg, particularly when combined with interlinked data on organizations. In this respect, the planned integration within OrgReg of data on the research and educational output of organizations will represent a major leap forward.
Introduction Research information is often spread across different systems and tables within a research institution. The term research information includes all metadata that arise in connection with research activities, for example, information about persons, publications, project data, and patent data, etc. Since this information is often stored in different systems, research information systems (RIS) are needed to structure the information and to simplify the preparation of reports or to enable valueadded services. RIS is viewed as a holistic task to support research processes and as a central database for collecting, managing, and providing information about research activities and research results  [1]. The special feature of RIS can combine the institution's internal systems such as personnel, student administration, finance, and price management systems as well as a variety of external data sources (e.
g. , Scopus, Web of Science, PubMed, arXiv, CrossRef, Mendeley, etc. ). Researchers and administrators enter research information only once, and staff across the institution use the information in RIS for a variety of purposes. RIS provides the institution with a comprehensive overview of the activities, specialist areas, and services of its researchers and saves researchers time and effort because RIS makes it easier to create, update, and correct research profiles by automatically retrieving publication lists from relevant internal and external databases. RIS exist on the market with various solutions both proprietary (such as Pure by Elsevier, Converis by Clarivate Analytics, and Symplectics by Elements) and open source solutions (e. g. , VIVO by Connect-Share-Discover, FACTScience by QLEO Science GmbH, etc.
) and provide an important basis for the analysis of research information. They supply a timely basis for decision-making and thus exempt the decision-maker from the need for an intuitive decision. Apart from its strategic importance to a research institution, a RIS project is usually time-consuming and costly. One of the key reasons for the failure of many RIS implementations is the lack of data quality. Incomplete, incorrect, redundant, and obsolete research information is collected and stored in the RIS. In addition, errors occur in data transmission and integration. Besides, there is often a lack of data maintenance by RIS administrators. Furthermore, analyses based on poor data values are carried out, which may lead to wrong decisions in research institutions. All of this can directly or indirectly contribute to the impairment of the reputation of institutions.
Methodology and Results To increase the acceptance and use of RIS, it is important to understand how users make decisions about the selection and use of RIS. Now, in this section, the results of the empirical study are evaluated. The investigation was carried out at 240 German universities and non-university research institutions and evaluated anonymously. In total, 160 of the 240 surveyed scientific institutions in Germany took part in the survey. The empirical study achieved a response rate of 67%. In 160 participants, 51 institutions used a RIS, while 61 institutions are in the implementation phase and the remaining 48 institutions do not use RIS but use their own developments or other databases. Our addressees are the administrative staff (in the area of controlling, third-party funded projects, research funding and transfer, patents, and library), as speakers or project managers at universities and non-university research institutions who are responsible for introducing RIS or with their RIS for a maximum of six hours work per day and their facility has had the RIS for at least 24 to 100 months.
Data quality was measured by the four key indicators (completeness, correctness, consistency, and timeliness). For the consideration of their dependency relationship, a reliable and valid SEM was developed in the paper  [6]  and thus it turned out that these four dimensions have a high load. This means that they influence the improvement process in the RIS and are crucial for measuring quality in RIS  [6]. A well-managed RIS with correct and complete, consistent, and up-todate research information is a prerequisite for everything to work smoothly. If the research information is incorrect, outdated, incomplete, or duplicate and multiple, the best RIS processes are ineffective. The basis of any RIS project in institutions is, therefore, a well-designed and maintained RIS. For the measurement of user acceptance, the variables (PU1, PU2, PU3, PU4, PU5, PU6, PU7, PU8, PU9, PEOU1, PEOU2, PEOU3, PEOU4, PEOU5, PEOU6, PEOU7, PEOU8, PEOU9, PEOU10, PEOU11, PEOU12, and ATU1) are used as indicators.
The framework as a SEM, which was created with the SmartPLS software on the data results of the survey conducted to assess the dependency between data quality and user acceptance of RIS, is shown in Figure  5. In the framework, data quality is started with the first latent variable and, for this, the SEM and its calculation in paper  [6]  can be taken. The second latent variable user acceptance is described by 22 indicators. To assess the reliability and validity of the RIS user acceptance indicators, Cronbach's Alpha Analysis, Factor Analysis, and Principal Component Analysis (PCA) were calculated. Table  2  shows the analysis results. 1. 000 • Cronbach's Alpha determines the reliability of the variables. This shows the reliability of the repeated measurements of a matter with a measuring instrument which give the same results  [25].