dataesr/scientific-paragraphs-categorization huggingface dataset:
Ensembles of climate projection data The AP1000 NPPs are design to operate for 80 to 100 years. It is, then, reasonable to investigate how the climate change and, in particular, the change of air temperature and pressure, might affect the reliability performance and the risk of these NPPs. To this aim, the air temperature forecast T projeted from the ClimateCHIP site (www. climatechip. org) has been taken. Five different climate models, developed within the Coupled Model Intercomparison Project (CMIP5) (the most relevant global project aimed at analysing the past and predict the future climate changes  [35] ), have been considered: 1. the USA GFDL-esm2m (GFDL) model  [36] ; 2. the UK HadG-EM2-es (HadGem) model  [14] ; 3. the French IPSL-CM5a-lr (IPCM) model  [37] ; 4. the Japanese MIROC-esm-chem (MIROC) model  [38] ; 5.
3 ). The historical air temperatures are retrieved from the NOAA and NASA databases, which provide the temperatures of three weather stations nearby the NPP (shown with stars in Fig. 3 ). The historical values real i T , i=1,2,…,12, are assumed as the mean air temperatures recorded by the three weather stations for each i-th month in the time interval from 1981 to 2005. It is worth underling that since air temperature is strongly correlated for weather stations that are separated by up to 1200 km  [45] , in this case, although the weather stations are not placed closely to the NPP, the real i T approximation is reasonable. To improve projection accuracy, a procedure of aggregation of the climate change models into an ensemble is introduced. Three ensemble approaches, which differ on the definition of the considered weighting strategies, are defined in order to obtain a projection that relies on the strengths of each single climate method.
From a decision maker point of view, we expect this latter method to be preferred, because temperature is a physical variable more easy to understand than the concept and meaning of probability. Three ensemble approaches, based on the aggregation of the projections of five climate models, have been proposed to be used within two alternatives ways of investigation. It has been demonstrated that, using a database of real recorded air temperatures, the three ensemble approaches give more accurate forecasts than the individual climate models. Results have shown that, whilst each individual climate model leads to a different risk assessment, the ensemble strategies lead to very similar risk assessment results and, consequently, the evaluation of the risk is more robust than that one obtained by using an individual climate model, due to the fact that the results do not depend on the particular climate change model.
1 , from 1981 to 2005. For each AP1000 location of Fig 2, the values of the air temperature provided by each climate model in the  1 time interval can be compared with the real air temperatures recorded in the same location, during the same period of time. The historical recorded temperatures have been retrieved by two historical databases provided by the National Oceanic and Atmospheric Administration (NOAA) [https://www. ncdc. noaa. gov] and by the National Aeronautics and Space Administration (NASA).
1 , from 1981 to 2005. For each AP1000 location of Fig 2, the values of the air temperature provided by each climate model in the  1 time interval can be compared with the real air temperatures recorded in the same location, during the same period of time. The historical recorded temperatures have been retrieved by two historical databases provided by the National Oceanic and Atmospheric Administration (NOAA) [https://www. ncdc. noaa. gov] and by the National Aeronautics and Space Administration (NASA) 12 , 1 , 2 ; 12. , , 1 , 2. , 5 [43; 44]. At each month i, a weight j, is associated to each j-th climate model. The weight j of the j-th climate change model is calculated for each month i as to the Mean Absolute Error (MAE) of the jth climate model projection projected i T (where projected i T is either   projected or   projected of the j-th climate model):.
Corpora The first clustering step (represented in blue on Figure  1 ) is performed on a corpus from Agence France-Presse (AFP) news agency. Each news article comes with several metadata providing additional information about its time-space context of creation, such as its UTC time-stamp, and its content, through International Press Telecommunications Council (IPTC) NewsCodes. NewsCodes are a standard subject taxonomy created and maintained by the IPTC, with a focus on text. From the 1,400+ existing NewsCodes, we selected 72 that can be viewed as event types 1  , cov-ering as many event types as possible without overlapping with one another, and retrieved all news articles tagged with at least one of these NewsCodes. This resulted in a corpus of about 52,000 documents for the year 2015. The second clustering step (in orange on Figure  1 ) takes as input news articles crawled from a list of Web news feeds in English.
Evaluation and Results In our evaluation, we focus on assessing the quality of the clusters produced at the end of the alignment filtering step. We performed our experiments on the AFP and Web data for the whole year 2015. Considering that the AFP corpus sometimes develops more "France-" and "Europecentric" content while our Web corpus is more "Anglo-Saxon-centered", we need to ensure that we evaluate on event instances that are covered in both corpora, which is the case in the resulting outputs of the coarse-grain agglomeration phase, by construction. We therefore selected 12 of these "pre-clusters" of event instances, based on the notable events of the year 2015 as per Wikipedia 3. This selection is described in Table  1. The Web articles in these intermediary outputs are sorted by descending order of their cosine similarity to the AFP reference.
First experimental campaign: one year simulation (8760 hours) During one year simulation, a total of N=8760 tests were observed and r=48 failures were counted. Over the whole Monte Carlo simulation duration i. e. 8760 h, the SIS was in down state during 20. 53676887 h and in well functioning state during = ⋅ - + ∑ = τ ) ( r N r i TTFi 1 27. 46323113h+ 8712h=8739. 463231 h. An estimator of experimental MTTF can be assessed by using (17) r r i r N TTFi MTTF ∑ = ⋅ - + = = 1 1 τ λ ) ( ' ' (17) Therefore, it comes for this simulation MTTF'=182. 0721506 h and = ' λ 5. 492328. 10 -  h -1. These both values agree with input data of the model for λD=5. 10 -3 h -1 and τ=1h. Moreover, Table  5  (last raw) shows an experimental PFDavg equal to 2. 344380 10 3 -⋅ and the system is of SIL2. This experimental PFDavg converges to the theoretical PFDavg according to IEC61508 which is equal to 10 3 4968776 2 2 61508 - ⋅ = ⋅ - - =.
We have launched the Open Journal of Bioresources in close collaboration with the open access publisher Ubiquity Press allowing both the resources to be cited and authors to get metrics on reuse and impact. Meanwhile, we have started developing new better adapted metrics; a first list of relevant parameters to take into account in the impact measure of bioresources has been provided. The tools proposed here foster easier access to samples and associated data as well as their optimized use, sharing and recognition for data producers. Input from the scientific information community would be highly appreciated at this stage.
Much biomedical/epidemiological research is based on using bioresources (sample collections and databases)  [2]. Sharing such resources is essential for optimizing knowledge production as their access to all relevant researchers is crucial. It enables research that builds on existing resources. It helps construct communities, optimizes fund investment and it fulfills ethical imperatives. Promoting bioresource sharing is crucial, but does not mean "just" putting files on the web! It requires work and this work is poorly recognized. De facto, although institutional incentives to sharing are there, bioresources are until now poorly shared. Since 2011, the BRIF initiative has initiated a work towards the development of bioresource standard citation and rewarding tools.
The ethical foundation of data sharing Following the Open Science movement, sharing data  1  is now an institutional request (see Table  1  for key milestones). A commitment to share the information content of bioresources within the research community is paramount to advancing translational research. In 2004, the Ministries of science and technology asked the Organisation for Economic Co-operation and Development (OECD) to define guidelines based on agreed principles in order to facilitate access to digitalized data issued from publicly funded research. OECD principles and guidelines, published in 2007  [16] , promoted and stimulated exchange of good practices in data access and sharing. In 2011 the joint statement of 17 major national health research funders sent a powerful signal that health research resources must be shared to maximize the potential of publicly funded resources  [6, 11, 12, 22].
" (See art. 17. [9] ). Table  2  The nine challenges identified by the OECD Global Science Forum The OECD Global Science Forum has recently identified a number of challenges related to data-driven and evidence-based research. Challenge 1 -Massive amounts of digital data are being generated at an unprecedented scale, thanks partly to the advent of ICTs. The reliability, statistical validity and generalisability of new forms of data are not yet fully understood. Challenge 2 -While administrative, survey and census data are widely collected by national statistical agencies and government departments, micro-data records are available to a much lesser extent. Challenge 3 -New forms of personal data, such as social networking data, are increasingly created and collected. The use of those data may generate risks to individual privacy.
What makes them being cited/acknowledged are the agreements (MTA, DTA,. ), methodology in articles, collaboration and good practices. However, no standard exists in either process. • http:// As described in 2015 OECD Making open science a reality report, an important alternative way to foster sharing could be through data citation: the possibility for researchers to be acknowledged for their work of data/samples collection and curation through mechanisms similar to the one already in place for citations of academic articles  [4, 14, 18]. This would lead to reward mechanisms that are currently under discussion and that include widespread use of dataset citation and/or proper acknowledgment of open science and data, sharing efforts as criteria to be actually taken into account in career advancement mechanisms or in grant attribution to research teams.
The BRIF initiative: An integrative approach Although the BRIF 3  initiative  [2]  was initiated much before the FAIR principles were formulated, it yet underpinned the same ideas of reusability. BRIF constitutes an enabling condition to open science in developing direct and indirect means to share data and samples by recognizing and quantifying the specific contribution of bioresources to the production of novel scientific knowledge. The basis of the BRIF concept is that making feasible to trace the use of a bioresource and to calculate a corresponding impact factor should encourage institutions, researchers, bioresource managers and other actors involved in bioresource work, to share them. Sharing would then be seen as a gain rather than a loss of control or than an additional non recognized work.
Citing bioresources: The CoBRA guideline At present, bioresources are either cited in a confusing, heterogeneous way or they are not cited at all. The use of a bioresource in a research article is not retrievable systematically via PubMed or other bibliographic databases  [13]. Traceability and visibility of bioresources in scientific literature or in other (online) sources would highlight their use. By being properly cited, bioresource use would be valued and their sharing thus encouraged. The CoBRA guideline  [1]  was hence developed to standardize citation of bioresources in scientific articles in order to trace their use on the web. This was achieved through close collaboration between the BRIF journal editors' subgroup, the European Association of Science Editors (EASE 4  ), the EQUATOR 5  (Enhancing the QUAlity and Transparency Of health Research) network and the research community managing and/or using bioresources.
Aiming to help the researcher to overtake this problem and facilitate the use of the CoBRA guideline, an e-learning tool entitled "How to use the CoBRA guideline" was conceived and presented at the ISBER 2016 annual meeting (Biobank Education Tools, BET-137, ISBER 2016 Annual Meeting & Exhibits -Berlin) in the section dedicated to the Biobank Education Tools. The video and audio tracks were recorded in digital multimedia format by using First. This e-learning video is freely available at the website: https://zenodo. org/record/55785. Implementing the CoBRA guideline further points to the necessity of integrating scientific editorial policies in the loop using several strategies. One way to enforce CoBRA use in articles is to include it in instructions to reviewers and editorial assistants as part of the checklist used to process manuscripts.
Over the last years, other initiatives throughout the world have flourished within the open access and sharing move to better identify and trace different types of resources (OpenAire, DataCite, CODATA, Force 11, ORCID and others). Among them, the Research Data Alliance (RDA) Working Group on Dynamic Data Citation  13  has provided recommendations about making subsets of data citable. Connecting to these groups would certainly facilitate CoBRA implementation and foster a better granularity by using suitable identifiers. Such identifiers of subsets or combination of subsets of bioresources must first be worked out with the idea of keeping traceable their "genealogy" (origin of parental resources). In general, coordination of all these actions has become an urge if one wishes to develop standard citation tools and improve good reporting practices.
Publishing a bioresource: A new type of journal The Open Journal of Bioresources 14  is one journal in a series of so-called 'metajournals' published by Ubiquity Press. These journals are dedicated to opening up and aiding the discoverability of all research elements involved in the research lifecycle, such as data, software, bioresources and hardware (forthcoming). The idea behind the metajournals is that researchers need to be able to discover and cite these research elements, but they also want credit for sharing them and the ability to track their impact. Given this, the metajournals offer credit -in the form of citation and altmetric data -for researchers to make their resources permanently available and discoverable in accordance with community norms. In the case of bioresources, the idea behind this journal is to provide a permanent marker paper so that users can definitively cite a bioresource they have accessed or referred to.
This could not be achieved without a standard and consistent way of citing the bioresource, which the paper itself provides. Furthermore, each paper is accompanied by a list of socalled altmetrics, highlighting article views and downloads and Tweets, Facebook posts and blogposts referring to the paper. This allows researchers to understand the impact of their bioresource with more granularity than mere citations. Finally, articles are indexed in common scientific databases so that readers can discover the bioresource in the same way as any other item in the scientific literature. Article indexes can search across titles, abstracts, keywords and full contents, meaning that bioresource papers can be easily discovered alongside other articles of interest. Bioresource papers therefore ensure that bioresources can participate in the complex networks of web-based science, harnessing the power of social media, scientific databases and the traditional scientific literature for discoverability.
Several groups of parameters were defined according to their availability and to the feasibility of their retrieving for calculating the impact using one or several specifically designed algorithm(s). The main parameters relate to indicators of research productivity and sustainability; indicators of sample/data value; indicators of workflow and efficiency and indicators of collaboration and visibility (Table  4 ). An extended study on various types of bioresources in more countries will allow refining the list and characteristics of such parameters. On the basis of the selected parameters, algorithms will be proposed for measuring the use and impact of bioresources. They will be tested in the wider context of European biobanks covered by the National Nodes of BBMRI-ERIC. As part of its perspective BBMRI-ERIC indicates that the collective adoption of an identification scheme for bioresources and the creation of a necessary bioresource ID database/registry will become an integral part of the BBMRI-ERIC Directory.
Acknowledgements 8 Standard rules should apply. * In the case of bioresources not used as a source of material for the study, but only referred to, follow the citation format:  ID/Bioresource Name  (acronym if available)/organization or network partnership, membership (optional). (a) MTA: Material Transfer Agreement. (b) DTA: Data Transfer Agreement. Associations of editors such as the EASE 4 are of great help in reaching and empowering journal editors and authors of scientific publications. EASE Guidelines for authors and translators of articles to be published in English already include the necessity to mention the origin and identity of experimental materials used in the methods section, and refer to the CoBRA guideline. The more key associations or committees of scientific journals editors will be aware of CoBRA, the more it will be applied.
Acknowledgements We wish to thank  Federica Napolitani ,  Anna Maria Rossi ,  Alessia Calzolari  from  Istituto Superiore di Sanità, Rome, Italy , as well as  Petr Holub ,  Jan Eric Litton ,  Outi Törnwall  and  Michaela Mayrhofer  from  BBMRI-ERIC, Graz, Austria  for very fruitful discussions and constructive interactions. The BRIF initiative was supported by funds from  BBMRI-LPC  project, grant agreement number  313010 , theme  FP7 -INFRA-2012-1  and is part of the actions supported by  BBMRI-ADOPT  project. The workshops held in Toulouse the 9th October 2015 and 4th December 2015 benefited from the support of  DIST-INIST CNRS  and from the help of the  Genotoul Societal platform funded by ITMO "Santé Publique ".
Collaboration is a common occurrence among Vietnamese Background: scientists; however, insights into Vietnamese scientific collaborations have been scarce. On the other hand, the application of social network analysis in studying science collaboration has gained much attention all over the world. The technique could be employed to explore Vietnam's scientific community. This paper employs network theory to explore characteristics of a Methods: network of 412 Vietnamese social scientists whose papers can be found indexed in the Scopus database. Two basic network measures, density and clustering coefficient, were taken, and the entire network was studied in comparison with two of its largest components. The networks connections are very sparse, with a density of only Results: 0. 47%, while the clustering coefficient is very high (58.
Literature review Over the years, the application of network statistical analysis on science collaboration has become pervasive; it has gleaned many insights into the dynamics of scientific activities as well as the properties of scholars' networks. By exploring a number of databases from different fields such as biomedical research, physics and computer science, Newman showed that scientific collaboration networks seem to form "small worlds", in which any randomly chosen pair of scientists would be separated only through a few intermediate collaborators. Another interesting aspect is that there are different degrees of clustering of scientists in different fields, suggesting the differences in social organizations  3. In a 2004 study of sociology collaboration networks by exploring of 30 years' worth of data in the field, from 1963 to 1999, Moody discovered that participation in the network depends on the research major, and scholars who are more inclined to quantitative work are more likely to collaborate than those in non-quantitative work  4.
Characterizing the network of Vietnamese social scientists Using R, the dataset employed in this paper counts 412 vertices in the Nodes list and 401 edges in the Edges list. Each vertex or node can be different in terms of degree. The average vertex degree is 1. 95 with standard deviation 2. 26. This means on average, one Vietnamese social scientist co-authors with about two other Vietnamese authors. Figure  1  visualizes the distribution of vertex degrees and shows the disparity between the least and most well-connected authors. (Figure  1  can be plotted using the command in Supplementary File 1 "Rcommands_fig1. doc". ) An overwhelming majority of researchers -about 280 out of 412 -possesses degree from 0 to 2; only about 50 researchers have a vertex degree of 3-4, and the number of authors with higher degree decreases dramatically from degree 4 upwards.
These individuals are few and far between in a network of high disparity in vertex degree, and present a stark contrast with their peers in terms of both connections and productivity. They have the potentials to form a group of intellectual elites. Finally, there is still much to be learned from both the dataset of 412 social scientists and the network that can be constructed from the raw data. For example, though the study has hinted at the difference in scientific output of two networks (comparison of Comp43 and Comp27 in section 2. 3), it is worth considering a more systematic examination of the relationship between a network's properties and the scientific output of the vertices it contains. Thus, finding out whether a correlation among these variables exists does merit further investigation.
Materials: Original data and the network data set The data for this study was derived from a dataset on the productivity of Vietnamese scientists in the field of social sciences collected by Vuong & Associates. The investigation, which took place within two months from March to April 2017, was conducted under the license V&A/03/2017, issued on 15 March, 2017. First, we constructed a file that contains data on all the attributes of each author, called a "Nodes list" (Dataset 1: "20170725_net412_ NODES. csv"). The data collection process was monitored regularly to ensure its reliability, including the following steps: first, the research team used sources such as personal and institutional websites of authors, websites of journals where their works were published, Google Scholar, and Scopus database to collect data.
Then, to check the accuracy of the information, we compare various online sources where each author's information can be found; for example, Google scholar versus Scopus, personal websites versus institutional websites. After this process, the research team obtained a complete dataset of 412 scholars' information, consisting of: (i) age, sex, region; (ii) affiliations; (iii) fields of study; (iv) the number of publications in Scopus, (v) the number of research years since the Master graduation; (vi) the number of researchers they collaborated with; (vii) whether or not they have the title of "Professor/Assoc. Professor". All of this essentially constitutes the node. Based on this information, we then construct our relational data, which is called an "Edges list" (Dataset 2: "20170729_ net412_LINKS.