dataesr/scientific-paragraphs-categorization huggingface dataset:
It would be recommendable to deal intensively with the topic of data quality early on in the RIS project since depending on the amount of data or integrated data sources, a lot of effort may be required. One of the decisive reasons for the failure of many databases is poor data quality. Incorrect, redundant, incomplete, and outdated data are recorded and stored in the databases. In addition, there are errors in data transmission and data integration. At the same time, there is often insufficient data maintenance. High-quality databases are very important for achieving business goals. It is, therefore, recommended to check the data quality and improve it if necessary. If the research information is in the RIS and there is no data governance, optional cleanup actions have no effect. The quality of the RIS data is teamwork.
Conclusion Our research paper has answered the question "to what extent does the quality of data represent a critical success factor for the user acceptance of RIS?". For this purpose, a quantitative survey was carried out. With our survey, we were able to get a closer look at the data quality in RIS and the acceptance of RIS by the RIS using universities and research institutions. Our goal was to measure the dependence of acceptance success on data quality. The results showed that data quality in RIS was considered a critical success factor for user acceptance. The analysis results demonstrate a clear and unambiguous dependency of the acceptance success on the data quality, measured by the four data quality dimensions examined and 22 success criteria of the user acceptance of RIS. The result was a dependency on the acceptance of the data quality of 77.
Gradient Boosting Gradient Boosting is a machine learning meta-algorithm. It can be used in conjunction with many other types of learning algorithms to improve their performance. The output of the other learning algorithms ('weak learners') is combined into a weighted sum that represents the final output of the boosted classifier. AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. The individual learners can be weak, but as long as the performance of each one is slightly better than random guessing, the final model can be proven to converge to a strong learner. Weak classifiers can be Decision tree/Decision stump, Neural Network, Logistic regression, or even SVM. To determine the coefficient for each classifier, we initialize the weight distribution of the samples in the dataset by a uniform distribution, and update this distribution by considering the misclassified samples in each loop.
[17]do-code for Stacking[17]. Training data D = {x i , y i } m i=1 , x i ∈ X, y i ∈ y; a Stacking algorithm. Output: A meta learner H. Step1: Induce T base learners, i. e. , h 1 , h 2 ,. , h T , from the training set. for t ← 1 to T Learn a base learner h t from D. end for Step2: Construct a new dataset D , where D.
1 M EDLINE remains to date the largest publications data- base in the biomedical field. This fact, together with free access via the PubMed portal, has led to the database being widely used by researchers for literature reviews, meta-analyses, and bibliographic research. Over the past 20 years, there has been rapid development in bibliometric analyses. In 1999, ≈160 articles about bibliometrics were published and made accessible via PubMed; in 2019, this figure had risen to 850. In total, >11 000 bibliometric articles were published and made accessible via PubMed in this time period. 2] [3]  Comparisons are commonly made between countries, institutions, and disciplines, and temporal trends are frequently analyzed. ] [6]  Bibliometrics uses metrics (indicators) to evaluate research performed in countries, institutions, or disease areas.
Methods Data were extracted from the PubMed database 13 using a query based on MeSH major topics  14  and titles. PubMed does not allow analyses based on addresses or citations. Analysis was performed on articles published in the period 1999 to 2018, examining four 5-year periods within this time span (1999-2003, 2004-2008, 2009-2013, and 2014-2018). Bibliometric analysis was, therefore, performed using InCites, an analytical tool developed and marketed by Clarivate Analytics  15  and based on data indexed in Web of Science. Only documents defined as an article in Web of Science were included in the analysis; other documents such as recommendations, guidelines, reviews, letters, or editorials were excluded. The authors declare that all supporting data are available within the article and in the Data Supplement.
Search Terms We used a specific query that we had applied in our previous work on hypertension. 12 This query was reviewed to exclude heart failure. MeSH terms are available only for articles already indexed in MEDLINE; therefore, we also searched the PubMed database based on the article title and a list of journals specializing in hypertension. The manual review of each summary of the top 1% most cited articles were performed to exclude pathologies not related to hypertension. The item "heart failure", as other exclusion terms, was specifically eliminated from the search. Research was focused on original articles to look for new contributions to knowledge. A detailed analysis was performed on articles published in the 2 most recent periods (2009-2013 and 2014-2018) and classified in the top 1% (ie, the 1% most cited articles globally, adjusted by.
Analysis After extraction, data were transferred into InCites for analysis of several bibliometric indicators relating to countries. We first looked at the number of documents identified in PubMed, then the number of documents found in Web of Science after transfer, and finally, the number of documents declared as articles in InCites. We then looked at the contributions from major countries to the global output, assessed on the basis of the number of articles that had authors from each country. In addition, 2 other bibliometric indicators were computed. • The SI is the ratio of the percentage of articles from a given country relating to specific disease area to the percentage of articles about the same disease area worldwide. If the index is >1, the country is regarded as having an over-specialization in that disease area; if it is <1, the country is regarded as having an under-specialization.
Results Our literature search identified 127 786 documents in PubMed for the period 1999 to 2018. Of these, 100 789 were indexed in Web of Science and were available via the InCites platform and 90 308 were declared to be original articles (70. 7%). The percentage of PubMed documents that were indexed in Web of Science and declared to be articles remained at a similar level over the 20-year period (Table  S1  and Graph S1 in the Data Supplement). Figure  1  shows the trends in research output for the 6 countries that had >5000 articles related to hypertension over the past 20 years. The United States output remained predominant over this period and showed an increase overall in the number of articles published. There was a slight decrease in output overall from Japan, a slight increase in output from the United Kingdom and a relatively stable output from Germany and Italy.
Analysis of these articles was then performed using VOSviewer software (Figure  3 ). Only countries with at least 25 articles in the top 10% for each of these periods are presented. In the first period, 3 clusters appeared: one focused on countries in the European Union, another focused on Nordic countries, and the third centered on the United States. Germany and United Kingdom, 2 members of the European Union at the time of the study, held one of the central positions. In the last period, 2 clusters were observed: one cluster focused on the United States, United Kingdom, Canada and China, and the other on Germany, Italy, the Netherlands and France. In this last period, the United Kingdom had moved closer to the United States but also remained near of the European cluster. When a similar analysis was conducted for individual investigators in the same 2 periods, the top 10% most cited articles showed complete reorganization of collaborations with different clusters (Graph S4 and Graph S5).
Discussion Our bibliometric analysis of the worldwide output of articles in the field of hypertension revealed major changes over the period 1999 to 2018. There has been an increase in the number of articles, an increase in the number of collaborations Figure  1. Numbers of articles by year of publication for the 6 countries that had a total of >5000 articles related to hypertension over the 20-year study period. October 2020 between countries, and changes in the relative contributions of different countries. The increase in the published research in hypertension is, however, markedly less than the corresponding increase in biomedical fields overall and in cardiovascular research. Like any bibliometric analysis, the results of our study have been influenced by the database used and the query posed: limiting the analysis to original articles included in Web of Science excluded about 30% of the corpus of articles identified via PubMed.
Key messages from the platforms -CONFIDENCE contributed to many challenges identified in the SRAs of the various platforms and helped to answer some, but not all questions; -Furthermore CONFIDENCE has raised further research questions that have not been considered. Key messages from end users -How do we keep databases, in particular of radioecological models, up to date; does the IAEA compilation of data summarised in "TRS472" (https://www-pub. iaea. org/ MTCD/publications/PDF/trs472_web. pdf) require updating? -The use of methods developed within CONFIDENCE might help IAEA to support member states in stakeholder engagement and societal investigations; -Further research is needed to better understand the general public and provide better advise to them; -Stakeholder panels are needed to consider not only the technical or scientific aspects (as can be considered in the simulation models) that improve the understanding or knowledge of the situation, but the complexity of the relationships among the actors involved in the decisionmaking confronted with their different views and preferences.
Acknowledgement. The work described in this paper was conducted within the  CONFIDENCE project  which was part of the  CONCERT  project. This project has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  662287. Disclaimer (Art. 29. 5 GA). This publication reflects only the author's view. Responsibility for the information and views expressed therein lies entirely with the authors. The European Commission is not responsible for any use that may be made of the information it contains.
Numerical simulation, and in particular simulation of the earth system, relies on contributions from diverse communities, from those who develop models to those involved in devising, executing, and analysing numerical experiments. Often these people work in different institutions and may be working with significant separation in time (particularly analysts, who may be working on data produced years earlier), and they typically communicate via published information (whether journal papers, technical notes, or websites). The complexity of the models, experiments, and methodologies, along with the diversity (and sometimes inexact nature) of information sources, can easily lead to misinterpretation of what was actually intended or done. In this paper we introduce a taxonomy of terms for more clearly defining numerical experiments, put it in the context of previous work on experimental ontologies, and describe how we have used it to document the experiments of the sixth phase for the Coupled Model Intercomparison Project (CMIP6).
We describe how, through iteration with a range of CMIP6 stakeholders, we rationalized multiple sources of information and improved the clarity of experimental definitions. We demonstrate how this process has added value to CMIP6 itself by (a) helping those devising experiments to be clear about their goals and their implementation, (b) making it easier for those executing experiments to know what is intended, (c) exposing interrelationships between experiments, and (d) making it clearer for third parties (data users) to understand the CMIP6 experiments. We conclude with some lessons learnt and how these may be applied to future CMIP phases as well as other modelling campaigns.
Traditionally numerical experiment protocols have appeared in the published literature, often alongside analysis. This approach has worked for years, since mostly the same individuals designed the experiment, ran the simulations, and carried out the analysis. However, as model inter-comparison has become more germane to the science, there has been growing separation between designers, executers, and analysts. This separation has become acute with the advent of sixth Coupled Model Intercomparison Project (CMIP6, Published by Copernicus Publications on behalf of the European Geosciences Union. The process of defining an "experiment" involves multiple steps, interactions, and component descriptions. In the simplest case, ideas are iterated leading to some sort of final description (white boxes), but at scale, there is a need to control the structure used to document the experiments (blue) and their intended output (data request, magenta), and such structure needs to utilize controlled vocabularies (shaded).
(In this section we use italics to denote specific concepts in the ES-DOC taxonomy. ) Experiment definitions are adopted by modelling groups who use a model to run Simulations, with Output Data requirements ("data requests") being one of the many experimental requirements. A simulation is run with a Configured Model, using a configuration which will include details of InputData and may include Modifications required to conform to the experiment requirements. Not all of the configuration will be related to the experiment, aspects of the workflow and computing environment may also need to be configured. In practice, simulations can deviate in detail from the experiment protocol; that is, they do not conform exactly to the requirements. A key part of a simulation description, then, is the set of Conformance descriptions which indicate how the simulation conforms to the experimental requirements.
Dictionary definitions of "scientific experiment" generally emphasize the relationship between hypothesis and experiment (e. g. "An experiment is a procedure carried out to support, refute, or validate a hypothesis. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. ", Wikipedia contributors, 2018). In this context "factor" has a special meaning, a factor generally being one of a few input variables; but in numerical modelling there can be a multiplicity of such factors, leading to difficulties in formal experimental definition and consistency of results  (Zocholl et al. , 2018  in the context of big data experiments). The first formal attempt to define a generic ontology of experiments (as opposed to workflows), appears to be that of  Soldatova and King (2007)  (who also expressly identify the limitations of natural language alone for precision and disambiguation).
2. 2) and sought opportunities for synergy between MIPs. The CMIP6 team at PCMDI developed the necessary common controlled cross-experimental CMIP vocabularies (the CMIP6-CV). The data request was an integral part of the process, since some MIPs were dependent on data produced in other MIPs, and in all cases the data were the key interface between the aspirations of the MIP and the community of analysts who need to deliver the science. The semantic structure of the data request was developed in parallel to the development of the CMIP6 version of ES-DOC; each had to deal with a distinctive range of complex expectations and requirements. Hence ES-DOC has not yet fully defined or populated the OutputRequirement shown in Fig. 4. Similarly, the data request was not able to fully exploit ES-DOC experiment descriptions.
Discrepancies also arose from the parallel nature of the workflow. For example, specifications could vary between what was published in a CMIP6-endorsed MIP's GMD paper and what had been agreed by the MIP authors with the data request and/or the PCMDI team with the controlled vocabulary. On occasion ES-DOC publication exposed such issues, resulting in revisions all round. This process required the sustained attention of representatives of each of these groups and eventually resulted in a system relying on Slack (https://slack. com/, last access: 2 March 2020) to notify all involved of updates but usually requiring initiation by a human who has identified an issue. However, synchronicity was and is a problem, with quite different timescales involved in each of the processes. For example, the formal literature itself evolved and so version control has been importantall current ES-DOC documents cite the literature as it was during the design phase and will be updated as necessary.
Forcing constraints in practice Somewhat naively, the initial concepts for ForcingConstraints anticipated the description of forcing in terms of specific input boundary conditions or, perhaps, specific mod-Table  3. The experiments within the DECK, as described in ES-DOC. The content of this table, like all the ES-DOC tables in this paper, was generated directly from the online documentation using a Python script (details in the Appendix). The choice of content to display was made in the Python code; other choices could be made (e. g. see https://documentation. es-doc. org/cmip6/mips/deck, last access: 2 March 2020). ifications needed to models -this was how they were described for the CMIP5 documentation. The ES-DOC semantics introduced for CMIP6 are more inclusive and allowed a wider range of possible forcing constraints.
Completion of the suite of DECK experiments is intended to serve as an entry card for model participation in the CMIP exercise. The CMIP panel are responsible for the DECK design and definition, which should evolve only slowly over future phases of CMIP and will enable cross-generational model comparisons. CMIP is also responsible for the "historical" experiments, but the definition of these will change as better forcing data become available and as the historical period extends forward in time. Table  4  provides a summary of most of the CMIP6 endorsed MIPs as of December 2018, with the DECK incorwww. geosci-model-dev. net/13/2149/2020/ Geosci. Model Dev. , 13, 2149-2167, 2020 porated in CMIP as discussed above. This   reck et al. , 2018) , which could also be documented with the ES-DOC system at some future time.
It is fair to say that there was no wide community acceptance of the burden of documentation for CMIP5, but this was in part because of the tooling available then. With the advent of CMIP6, the tooling is much enhanced and available much earlier in the cycle, but both the underlying semantic structure and tooling can and will be improved. There is clearly opportunity of convergence between the data request and ES-DOC, and there will undoubtedly be much community feedback to take on board! ES-DOC is not intended to apply only to CMIP exercises. We believe the preciseness and self-consistency ES-DOC imposes on experiment design documentation should be of use even when only one or a few models generate related simulations. One such target will be the sharing of national resources to deliver extraordinarily large and expensive simulations (in time, resource, and energy) where individuals and small communities could not justify the expense without sharing goals and outputs.
Code availability. All the underlying ES-DOC code is publicly available at https://github. com/es-doc (last access: 21 March 2020; ES-DOC, 2016). The full CMIP6 documentation is available online at https://search. es-doc. org/ (last access: 21 March 2020; ES-DOC, 2015). The ES-DOC documentation of the CMIP6 experiments can be found in the ES-DOC GitHub repository at https://github. com/ES-DOC/esdoc-docs/blob/master/cmip6/ experiments/spreadsheet/experiments. xlsx (last access: 24 March 2020; ES-DOC , 2017). The code to extract and produce the ES-DOC tables in this paper is available online at https://github. com/bnlawrence/esdoc4scientists (last access: 2 March 2020)  (Lawrence, 2019). Figures  5  and 6  were produced using content (in the form of triples) generated from ES-DOC and imported into gephi (https://gephi.
[10]  reduce the number of iterations with a two-step ad hoc combinatorial B&B programming method. They first identify an optimal solution, allowing them to get the optimal objective function value. They then use this value as input when building the branch-and-bound trees corresponding to the remaining solutions. However, these are built from scratch, without leveraging the first tree. Moreover, the process is repeated for each considered number of modules. Danna et al. [13]  propose a more efficient two-step method, as they not only enumerate all the optimal solutions based on the search tree of the first step, but also take advantage of mathematical modeling to apply efficient search techniques (e. g. dual tightening). This method is incorporated in the industrial optimization solver CPLEX  [26].
Performing the Clustering Next, we apply the k-medoids clustering method  [31]  to our dissimilarity matrix. It is similar to the wellknown k-means algorithm, in the sense that it partitions the dataset into k clusters, while minimizing the dissimilarity between the members of each cluster and some center of the cluster. The difference is that in k-means, this center is an average value, whereas in k-medoids it is one of the actual data points from the dataset. The k-medoids method is generally used in place of k-means when one cannot perform the required average operation, which is true in our case (we cannot straightforwardly process an average partition, i. e. an average optimal solution). This method requires us to specify k, which we do not know in advance. In this situation, the standard approach is to use all possible values of k, from 2 to p (the number of optimal solutions), and assess the quality of the p -1 resulting clusterings through some internal criterion.
This is for example the case of certain networks representing voting behaviors  [5, 32]. More generally, authors tend to derive complete signed graphs when they work with similarity (e. g. [1] ) and correlation (e. g. [25] ) matrices, in order to use CC to perform a form of cluster analysis. As showed by our examples from Section 3. 2, their simplicity does not prevent complete unweighted signed graphs to exhibit cases of multiple and structurally very different solutions. Moreover, the literature shows that this can also be the case in sparse graphs  [14, 16, 17] , so this simplicity is not the cause of this property either. Their simplicity makes complete unweighted graphs particularly suitable in the context of this article, which is the first step of longer-term work. Indeed, it allows us to focus on the most essential parameters when randomly generating graphs to constitute our dataset, and later when studying their effect on the solution space of CC.