dataesr/scientific-paragraphs-categorization huggingface dataset:
Visualizing the Knowledge Graph on Neo4j Based on the literature [2], the knowledge graph has basic query and reasoning functions; however, the visualization function of the knowledge graph has not been reflected. Therefore, the graph database Neo4j is used to visualize the knowledge graph  [28]. Neo4j provides good visualizations of knowledge graphs. The specific implementation process and visualization are shown below. The process of generating the knowledge graph is as follows. First, table data are converted into the format of CSV. These files are then moved to the import directory of Neo4j because Neo4j defaults to open files in the import directory. After starting the database, the Cypher language  [29]  is used to import data, and the construction mode of the node is created (k: knowledge {name: "absolute orientation"}).
Visualizing the Knowledge Graph on Neo4j Based on the literature [2], the knowledge graph has basic query and reasoning functions; however, the visualization function of the knowledge graph has not been reflected. Therefore, the graph database Neo4j is used to visualize the knowledge graph  [30]. Neo4j provides good visualizations of knowledge graphs. The specific implementation process and visualization are shown below. The process of generating the knowledge graph is as follows. First, table data are converted into the format of CSV. These files are then moved to the import directory of Neo4j because Neo4j defaults to open files in the import directory. After starting the database, the Cypher language  [31]  is used to import data, and the construction mode of the node is created (k: knowledge {name: "absolute orientation"}).
This statement creates a node with a knowledge label, and this node has a name attribute, an attribute value of "absolute orientation", and a variable name k. The Cypher sentence for importing structured data entity data into the database is Code 1. The result of Code 1 is shown in Figure  9. The name of the imported file is "knowledge. csv", and the label is "knowledge". Attributes and attribute values correspond to row and column data in the table. The imports of nodes and relationships are similar. The Cypher sentence for importing structured relational data into the database is Code 2. The result of Code 1 is shown in Figure  10. Code 2 Relational Data Imported Based on Cypher Language LOAD CSV WITH HEADERS FROM "file:///knowledge_to_genre. csv" AS line MATCH (from:knowledge{id:line. knowledge_id}),(to:genre{id:line.
genre_id}) MERGE (from)-[r:belong_to{Relation:line. knowledge_to_genre}]->(to) The name of the imported file is "knowledge. csv", and the label is "knowledge". Attributes and attribute values correspond to row and column data in the table. The imports of nodes and relationships are similar. The Cypher sentence for importing structured relational data into the database is Code 2. The result of Code 1 is shown in Figure  10. The name of the imported file is "knowledge_to_genre". The partial result of the imported visualization is shown below (Figure  11 ). In this picture, the two red nodes, "Surveying" and "Remote Sensing", represent the class of knowledge, and the blue node represents knowledge. The name of the imported file is "knowledge_to_genre". The partial result of the im ported visualization is shown below (Figure  11 ).
In this picture, the two red nodes, "Su veying" and "Remote Sensing", represent the class of knowledge, and the blue node rep resents knowledge. The name of the imported file is "knowledge_to_genre". The partial result of the im ported visualization is shown below (Figure  11 ). In this picture, the two red nodes, "Sur veying" and "Remote Sensing", represent the class of knowledge, and the blue node rep resents knowledge. The database contains 1024 nodes, which belong to the two classes of nodes "knowledge" and "genre". The nodes in the knowledge graph are the primary components of the knowledge graph, primarily the knowledge of surveying and remote sensing. The library also contains 1295 relationships, which represent the relationship between nodes in the class "knowledge" and nodes in the class "genre".
For example, "belong_to" is the belonging relationship between knowledge and type. The library also contains attribute information, such as the "genre" and "name" of the "knowledge" node. Certain basic information for creating the database is shown below (Figure  12 ). Remote Sens. 2021,  13, 2511  13 of 19 The database contains 1024 nodes, which belong to the two classes of nodes "knowledge" and "genre". The nodes in the knowledge graph are the primary components of the knowledge graph, primarily the knowledge of surveying and remote sensing. The library also contains 1295 relationships, which represent the relationship between nodes in the class "knowledge" and nodes in the class "genre". For example, "belong_to" is the belonging relationship between knowledge and type. The library also contains attribute information, such as the "genre" and "name" of the "knowledge" node.
This article uses the D2RQ tool to realize mapping from ontology to the database. The D2RQ tool converts the structured data of the relational database into data in RDF format. This mapping also provides the ability to view existing relational data that exist in the RDF data model, which is represented by mapping the structure selected by the customer and the target vocabulary. The R2RML mapping itself is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementation. The processor can provide virtual SPARQL endpoints on the mapped relational data, or generate RDF dumps, provide a link data interface. (2) Application and practice of the domain knowledge graph. This article gives an example of the application of integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform.
ddlog file. There are two text fields in the articles table, articles (id text, content text). Then, we put the compressed file of the original data into the specified input folder and run the "DeepDive compile" command. Then, we execute the "DeepDive do articles" command, which imports the original data into the articles table of the associated database. At this time, we can query the imported raw data by executing the query command. 2 Adding NLP markups. We use the CoreNLP.
We set the data format of the document storage in the app. ddlog file. There are two text fields in the articles table, articles (id text, content text). Then, we put the compressed file of the original data into the specified input folder and run the "DeepDive compile" command. Then, we execute the "DeepDive do articles" command, which imports the original data into the articles table of the associated database. At this time, we can query the imported raw data by executing the query command. 2 Adding NLP markups. We use the CoreNLP.
Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Conceived and designed the paper: II and VL. Performed the analysis and produced the figures: II. Wrote the paper: II and VL. Funding I. I. acknowledges partial support from the UK RDRF -Urban Dynamics Lab under the EPSRC Grant No. EP/M023583/1 and from the ANR project DATAREDUX (ANR-19-CE46-0008). V. L. acknowledges support from the Leverhulme Trust Research Fellowship 278 "CREATE: the network components of creativity and success".
Material and methods Raw data were derived from two major indexing databases, namely Scopus and the Web of Science (WoS). Using both the databases, relevant information was extracted for 2018 and limited to affiliations from the six countries in which the authors of this paper work, namely China, France, Indonesia, South Africa, the United Kingdom, and the United States of America (Table  1 ). The total number of articles and that of OA articles covering the categories 'Geochemistry and Geophysics' and 'Geology' of the Web of Science and the 'Earth and Planetary Sciences' category from Scopus were counted. It should be noted that the same article can be counted more than once if the work was the result of international collaboration. We also selected the top ten earth-science journals (based on the number of articles published by them in the Scopus category 'Earth and Planetary Sciences') that also publish OA articles.
Results In 2018, a total of 13,436 articles were published in the WoS category 'Geochemistry and Geophysics'; 30,189 in the category 'Geology'; and 106,246 in the Scopus category 'Earth and Planetary Sciences'. Of the total of all the three, only 24%-31% were OA, and the UK had the largest share (46%-54%) of the OA articles, much greater than that for any of the other five countries, with China accounting for the lowest share (18%-20%) (Table  1 ). Between the two databases, Scopus was ahead of WoS in terms of both absolute number and the proportion of OA articles. Of the top ten journals that publish OA articles in earth sciences, six are fully OA and four are hybrid journals (Table  2 ). Fully OA journals are mostly published by either emerging publishers (MDPI 10 and Hindawi, for example) or long-time OA publishers (Copernicus Publications, which is the publishing arm of the European Geophysical Union, for example), and their APCs ranged from $1000 to $2200 and the journal IFs, from 1.
7 In Denmark, a green OA policy has existed since 2016. This policy, as described above, does not constrain researchers in their choice of a publishing channel, because virtually all journals allow such uploading or even deposit articles automatically in appropriate repositories, after an embargo period, often on behalf of authors. At the moment, about 45% of the annual research output of Denmark is being uploaded into its universities' repositories (https://www. oaindikator. dk/en/), a share comparable to that of France: 49% of the research publications in France in 2018 are available as OA (including 'green' OA, not considered in our evaluation; https://ministeresuprecherche. github. io/bso/). Furthermore, some libraries in Denmark have allocated specific funds for paying APCs, albeit requiring that the corresponding manuscripts be made available through institutional repositories as well for the publications to qualify as green OA in the sense of the national policy.
However, Indonesia has more than 1570 OA journals and ranks second, next only to the UK, in the Directory of Open Access Journals (DOAJ; https://doaj. org/). The majority, about 70%, of these journals do not levy APCs  24  and are funded by local universities and research institutions and published locally. Journals listed in the DOAJ mostly publish English-language articles but only represent one-sixth of the Indonesian journals listed in GARUDA, the country's national database (http:// garuda. ristekdikti. go. id/), which currently indexes more than 1. 1 million articles published in more than 9600 Indonesian journals maintained by more than 1600 publishers. Journals that charge only moderate APCs (Indonesian Journal on Geoscience, for example) are mostly the ones that are indexed by the indexing services and are considered to be of higher quality as a result.
These inequalities have led to inequities. Indeed, according to the DOAJ, approximately 71% of fully OA journals do not levy APCs but that seems to apply to only a few journals in earth sciences (Geochemical Perspectives Letters and Volcanica, for example). The bias inherent within the current APC-based OA publishing perpetuates the inequality through the Matthew effect, ultimately reinforcing the journal-coupled prestige economy that currently governs our global research systems. 16 e are well aware that our study has some limitations: our analysis was limited to only one year (given the dynamic nature of the data, especially on APCs); that we considered only six countries (although they contributed 64%-70% of the articles and 69%-79% of the OA articles); and that we chose only two databases (although the most important ones).
Data Set and the Test Equating Design The research data included the test-takers' responses to the AYOS tests applied in 2017 and 2018. The AYOS is an assessment to determine international students' qualifications for admission to Ankara University and other universities (those accepting the AYOS score) in Turkey. The test is simultaneously implemented in different countries (exam centers) in a single session once a year. In brief, the AYOS dataset consists of the test-takers' scores (AYOS 2017 and AYOS 2018) and two covariates, gender (with values of 1 if man and 0 if woman), and age (with values of 1 if 18 years of age or younger and 0 if age 19 years of age or older). This means that there are 2 x 2 = 4 possible combinations of covariates and that the frequency vector has a length of 81 x 4 = 324.
The data were first sorted by age followed by gender and the test scores on AYOS 2017. The AYOS BLST is a non-verbal aptitude test with two sections and a total of 100 binaryscored multiple-choice items. The first section tests letter, number, and shape relations as a measure of cognitive skills, such as analytical thinking, reasoning, and abstract and spatial thinking (with 60 items). The second section measures numerical thinking skills that require the use of mathematics and geometry knowledge (with 40 items). The scores obtained from the test are valid for two years. The test is newly developed every year following the psychometric properties of the test applied in the previous year. The KR-20 formula was applied in cases where the items varied greatly in difficulty  (Kuder & Richardson, 1937, p.
Real data from a non-verbal aptitude college admissions test were used. In a similar study, Akın-Arıkan (2020) used real data from the Monitoring and Evaluation of Academic Skills Project in Turkey to examine the NEAT and NEC designs comparatively. In this context, she equated the scores obtained from Mathematics subtests according to the kernel chained equipercentile, kernel post-stratification equipercentile, kernel chained linear, and kernel post-stratification linear methods. Furthermore, she sought to determine the affection status of the covariates (gender variable and socioeconomic index) used in the NEC design. From her research, it was determined that test forms can be equated using covariates when there are no anchor items. This is a noteworthy finding in terms of contributing valuable information for future studies to be carried out using the NEC design.
2006 ) used a STIRPAT model in diverse income countries during the period 1975-2000 to study the variables linked to CO2 emissions. The empirical ndings of this study provided that the impact of technology adoption on CO2 emissions varies across the various stages of economic growth. Later Kumar and Managi (2010) investigate the linkage between environmental degradation and ICT innovation, they used data for eighty developed and developing economies. The ndings of this study reveal that ICT adoption decreases emissions in developed economies, but increased them in most developing ones.
Following the current debate, we control for trade openness, population growth, GDP growth, education quality, and regulation quality. The rst argument for adopting a GMM estimation is the higher number of periods for each country in our sample. The crosssections are exceeding the period. The estimation was conducted for ve periods from 57 countries. We employ a balanced panel dataset of annual periodicity over the period 2012-2016. To give reassurance of the goodness of t information, the explanatory capacity of the models was veri ed. The model often has a dynamic effect. It is closely connected to its earlier value. Thus, it has been reported in the recently empirical studies that researchers should consider the dynamic effect when conducting times series estimation. This is usually established by adding a lagged dependent variable as an explanatory in the model.
PROBLEM STATEMENT AND ASSUMPTIONS This section presents the assumptions taken in this study, and the requirements on data and expertise are highlighted. To do that, let us first describe the context of the use of the proposed methodology for the SRUL prediction. In this paper, we are interested in systems composed of M components that interact between them (differently depending on the system architecture) and the environment. Each component is assumed to have a failure mode caused by its own degradation and the degradation impacts of other components that interact with it. A component fails when it reaches a supposed known threshold. The degradation of each component i is monitored by a relevant sensor that provides noisy measurements y i. The sensors are selected based on the failure mechanisms to track in time.
At the time k + 1, when new pre-processed degradation data acquired by sensors are available, the prior estimation is filtered to obtain the posterior one using particle filtering. If an anomaly has been detected or a threshold value for a component's inoperability has been exceeded, the posterior PDF is propagated (long-term prediction) to calculate the SRUL; otherwise, we continue filtering. After every short-term prediction, the prior health state estimation is evaluated with respect to the actual data. If there is a discrepancy, the long-term prediction is updated along with the estimated SRUL (if an anomaly is already detected). In this case, the parameter i, which represents the number of consecutive discrepancies observed, is incremented; otherwise, it is reinitialized. If several discrepancies appear consecutively (i exceeding a number set by the user), the gradient descent is used to update the IIM parameters.
Furthermore, there are countless considerations that affect the spread rate such as the density of the population, the age and general health of those exposed to the virus, and ambient air conditions. Moreover, different mutations of the virus spread at different rates and vary in terms of who they affect the most. Here, we will use RTPLS to estimate the spread rate of COVID-19 because our simulation results imply that RTPLS is better than VSGLS (which is BLUE) when (1) there is almost no random variation and the spread of COVID-19 is due to scientific forces, not random luck, and (2) RTPLS is better at handling nonlinear relationships and the spread of a virus is not linear. Weekly data on the number of new confirmed COVID-19 cases were downloaded from the Internet (European Centre for Disease Prevention and Control 2021).
Acknowledgements: The authors are indebted to two anonymous referees, whose comments led to significant improvements of the manuscript. G. B. B. acknowledges financial support from  UNSW Sydney  under a  University International Postgraduate Award , from  UNSW Business School  under a supplementary scholarship, and from the  FRQNT  ( B2 ). F. O. is supported by postdoctoral fellowships from the  NSERC (PDF)  and the  FRQNT  ( B3X supplement  and  B3XR ). This research includes computations using the computational cluster Katana supported by  Research Technology Services at UNSW Sydney.
archived but also made freely available to third parties -"as open as possible, as closed as necessary". The long-term archiving and the sharing of research data open up further perspectives for researchers to obtain the knowledge they need for their own research. If a researcher publishes the entire database accumulated during the research process, other researchers can build on it, pursue other questions and draw further conclusions without necessarily having to collect data themselves. Last but not least, the interpretation approach of the research results can be examined and a qualitative assessment can be given. In the context of open science, research institutions, funding agencies, science policy committees and publishers started to formulate "data policies" as guidelines for research data management (RDM).
The European Open Science Career Assessment Matrix OS-CAM  [16]   Other papers describe the target behavior simply as data sharing  [12] , data reuse and data sharing  [23] , or data activities related to research projects  [24]. All this remains rather general and is not operational for efficient rewarding. For the moment, we can't find a kind of classification of "data behaviors" that would be helpful for rewarding and incentives. We can't find, moreover, any differentiated approach for the rewarding of "closed" RDM. Should researchers sharing "easy data" like code or seismic data be more rewarded than those handling sensitive data with privacy issues, such as medical data? This, of course, makes no sense. What about volumes of data sets? Another issue is related to the respect of FAIR principles defined as a target behavior.
Initially, the FAIR principles have been developed not as behavioral criteria but for the assessment of systems and infrastructures  [10]. In other terms, FAIR seems less a description of specific data behavior than of the availability and the use of appropriate tools. Finally, research information management systems, at least so far, usually do not represent correctly the whole range of data-related activities. Snowball metrics, a project for standardized research assessment, simply do not include data assessment  [7]. Regarding RDM, these systems usually assess "data performance" in terms of data management plans, assignment of DOI to datasets, indexing (rich and standardized metadata), deposit in a labelled data repository (DataSeal, FAIR. ), and data sharing  [20]. Some of these indicators can be useful for rewarding and incentivizing.
Is (all) RDM research work? Should data sharing, i. e. , description, structuring and deposit of datasets, be considered as part of usual research activity? Is cataloging, indexing, formatting and shelving of scientific papers part of research work? Partly? All of it? Discussions with colleagues and staff reveal different and sometimes opposite viewpoints. At least for two reasons, one should be careful about giving an answer. First, it often depends on the work environment who does the job; when specialized staff is missing (no data librarian, no data engineer. ), RDM is on the researcher's agenda; but this does not mean that all this is genuine research work. Second, the relationship between researcher and data is conditioned by the scientific domain and equipment. Especially in social sciences and humanities, where researchers often produce "their own data" and where their personal knowledge about the context of data collection and production is required for interpretation reuse, data-related activities are intimate part of research  [13] , which is a quite different situation compared to other research disciplines, with other equipment, tools and infrastructures  [5].
Yet, research data management is not an individual problem. The OECD report puts it very clearly: the main issues are not personal but technological, institutional and financial  [17]. Following the international Confederation of Open Access Repositories (COAR), useful and efficient functionalities and tools and a supportive environment (advocacy, mandate, agreements with publishers. ) are more important for sustainable practice than rewards and incentives  [6]. Institutions and authorities should change the environment rather than the person and provide appropriate (FAIR) tools, services, infrastructures and funding rather than individual awards; enabling rather than rewarding. Researchers are not children, and research data management is worth more than some symbolic lollies or candies. In fact, we should drop research data activities from the evaluation of individual scholarship  [18].