dataesr/scientific-paragraphs-categorization huggingface dataset:
Are French articles more often in open access? It is possible to cross-reference the observations presented above with information on the language in which the article is written: are articles in French, for example, more often, or less often, in open access? To examine this, as this information is not systematically provided by all databases, we analyzed the title of the article as provided by Unpaywall, by applying a simple language detection software langdetect 13. Only detections assigned with a displayed probability greater than 0. 99 were retained. In the framework of our study of French national scientific production, for the year 2019, the two main languages concerned are English (83% of the detected documents) and French (15%), the rest of the detected languages not exceeding 3% in total.
Discussion of the sources used The six sources we have chosen to use actually provide three different insights: (1) Scopus and Web of Science provide extensive coverage of the literature in peer-reviewed journals and international conference proceedings; while Scopus has a slightly wider coverage, the use of the two databases together provides a 10 to 20% improvement over what would be obtained with a single database. The MAG database, which will soon be discontinued, brings, as a complement, a set of documents not indexed by WoS and Scopus, contributing to a further increase of about 10% of the corpus identified in our study. (2) The HAL open archive is filled at the initiative of the authors who deposit the bibliographic record (metadata) and, if applicable, the full text in its preprint or editor version.
Part of the archive contains grey literature  (Schöpfel, Prost, & Ndiaye, 2019)  and moreover the DOI is filled in irregularly and not systematically. The metadata and DOI do not seem to be thoroughly quality controlled: for this reason, this source should be considered with caution for bibliometric studies. However, it is a reference source for French research and a cornerstone of the national open science policy. (3) The ADS and PUBMED databases are thematic databases and are therefore only intended to cover parts of the research field. On the other hand, both databases are deep in their field and cover grey literature and sources not indexed by the large generalist databases. This study sheds new light on the coverage of French scientific production by the various databases. While the Web of Science and Scopus voluntarily restrict themselves to the perimeter of peer-reviewed publications appearing in referenced journals or books  (Birkle et al.
, 2020; Baas et al. , 2020) , the use of complementary databases, whether thematic or not, allows us to have a more complete view of the share of literature that is not or poorly referenced, and that may be less general in scope, geographically, linguistically, or thematically. We observe that the strategy adopted by the BSO allows for the systematic collection of data on a significant quantity of these publications -often neglected in bibliometric studies. Far from identifying an optimal source, our study shows the importance of diversifying the sources used to provide complementary views on a country's publication.
Validation of the open strategy used for the BSO The comparison between the result obtained with our sources and the open strategy of the BSO validates the use of the latter: this strategy, if we summarize it in a few words, consists in scanning all the DOIs available by Unpaywall, and also by HAL, to identify either the French authors, or the presence of the mention of France in the address. We observe that this strategy makes it possible to identify more than 20,000 records (if we exclude the false positives) not found by our approach, i. e. , about 17% of the total: these are mainly journals that are not indexed in the major international databases, and more particularly in the biomedical and social science fields. Our approach also identified approximately 13,000 DOIs not included in the BSO and thus estimated the false negative rate in the BSO strategy to be close to 9% (see Table  4.
Conclusions The main results of our Study are as follows: • Our study validates a strategy of determining a collection of scientific publications with an affiliation in France, for a given year. This corpus is deliberately restricted by the use of DOI. We present the details of the counts for the year 2019. We estimate that the corpus of outputs with DOI covers around 80% of the French national scholarly production in 2019, with an additional set of 40,000 articles or communications without DOI not taken into account here. • Our determination of cross-coverage by the various databases provides useful insight for users of these databases. We believe that these counts can help users of these databases to identify overlaps and complementarities, in a context comparable to that of our study. • The use of multiple sources ensures validation at a sufficiently fine level to shed light on the geographical, thematic, linguistic, etc.
Change in open access rate, observed in February 2021 for publications dated from 2015 to 2020 (The global corpus is the aggregation of the two datasets FR-2015-20 and BSO)In Table12, we give examples of observations of the open access status (Gold, Green, etc. ) as provided by Unpaywall for two distinct years. These few examples allow us to affirm the absence of significant bias between the two datasets: the two strategies lead to quite similar estimates.
Acknowledgements The authors would like to thank the reviewers for their constructive comments, which helped to improve the manuscript. Their support is highly appreciated. E. B. was supported by the  COMET-K2 Center of the Linz Center of Mechatronics (LCM)  funded by the  Austrian federal government  and the federal state of  Upper Austria , number  37920770. A. S. was supported by  MIAI@Grenoble Alpes , ( ANR-19-P3IA-0003 ). E. B. , M. T. and I. T. were supported by the  Austrian Science Fund (FWF) :  W1214-N15 , project  DK14. All authors were supported by the  Austrian Exchange Service (OeAD) , bilateral project  FR 03/2017.
We use the same set of variables and extend their analysis by considering KAM capabilities and KAM communication as additional dimensions that likely determine KAM outcomes. Moreover, we use a semiparametric approach to capture the effects of KAM cluster membership on the outcomes. Thus, we seek to update the findings from  Workman Jr et al. (2003)  with the following research question (see Fig. 1B ): Research question 2: Does this study produce the same determinants of KAM effectiveness and performance in the market as in  Workman Jr et al. (2003)  with a new data collection from a comparable population, when considering KAM capabilities and KAM communication as additional determinants, and when accounting for KAM cluster membership? To achieve these objectives and answer the research questions, we collected survey data from 411 senior managers, consisting mainly of managing directors, VPs of marketing, sales, or sales and marketing, and KA managers.
Communication in KAM Another stream of research that has developed in the KAM literature concerns KAM communication (see, for example,  Lacoste, 2016; Storbacka, 2016). KAM communication can be defined as the management of information exchange processes by the organizational actors involved in KAM (such as the KA manager or members of the KAM team) with the KA  (Ivens & Pardo, 2007). Communication constitutes a critical factor determining the successful development of relationships between a supplier firm and its KAs  (Schultz & Evans, 2002)  and has been   Homburg et al. (2002) and Workman Jr et al. (2003)  use the same dataset and the same measurement in their studies. described as "the glue that holds together" actors in a business relationship  (Mohr & Nevin, 1990, p. 36). Communication takes place through different channels, such as personal conversations, written messages, and increasingly through social media  (Cartwright, Liu, & Raddats, 2021).
The first 25% of responses were treated as early respondents, and the last 25% as late respondents. Chi-square tests did not reveal any significant difference between the two groups. As the independent and dependent variables in the model are all measured from the same key informant, we used various procedures to ex-ante reduce the risk of common method bias (i. e. , we employed different response scales for different variables, physical separation of the dependent variables from the independent variables within the questionnaire, neutral wording of all questions, assurance of respondent anonymity, and data confidentiality). We also highlighted that there are no "right" or "wrong" answers, and emphasized that the benchmark report would be meaningless for the participants in case of exaggerated answers.
Introduction Each particle of the Universe, known or unknown by what is widely accepted as Science, is information. Different datasets can be associated to each particle to convey information, as, for example: where has this particle been discovered? By whom? At what time? Is this particle a constituent element of a rock, or a plant, or…? Indeed, as living entities of the Earth planet, …we are all part of this Universe and every atom in our bodies came from a star that exploded…, therefore …we are all stardust…. 1 or databases, like the Creative Commons licenses 9 or the Open Data Commons Licenses 10. On the other hand, let us remark that Data Management Plans are nowadays required by several research funders (see for example 10,11) and that Software Management Plans are also available 12.
An additional factor that adds complexity to the concept of scientific data has to do with the potential use(s) and sharing of these data. As remarked by the OECD Glossary of Statistical Terms 30 : The context provides detailed background information about the definition, its relevance, and in the case of data element definitions, the appropriate use(s) of the element described. The importance of the context is also noted in 18: …research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. This opens the door to a series of complex issues. For example, to the need for complementary, technical information associated to a given dataset in order to facilitate its reuse. See 40 (p. 16) (and also 36) that highlights the difficulties raised by the concept of temperature related data, as explained by a CENS biologist: There are hundreds of ways to measure temperature.
Release may take many forms, from private exchange upon request to deposit in a public data collection. Posting datasets on a public website or providing them to a journal as supplementary materials also qualifies as sharing. • The importance of licenses to set the sharing and re-use conditions as highlighted in 42,28,5. • The concepts of Open Data 33  and Open access to data, see for example 21,29,43,44. • The recent and relevant introduction of the term Big Data 34  , that refers to the exploitation of larger amounts of data. They can appear in medical research, meteorology, genomics, astronomy, demographic studies … and in real life, as we live all in a digital world where we generate large amounts of data every day by the use of phones and computers to do work, traveling, e-mail, business, shopping etc.
Big data is associated mainly to four "V" characteristics: Volume, Variety, Velocity, Veracity, and others can be found for example in the mentioned Wikipedia page and in the references mentioned there. See also 45. Let us remark again that legal aspects arise quite naturally in the above list of items. Among others, some aspects are related to the fact that the datasets are usually organized in databases, where data is arranged in a systematic or methodical way and is individually accessible by electronic or other means 16,17,20,24,28. The intellectual property rights can apply to the content of a database, the disposition of its elements and to the tools that make it working (for example software). The sui generis database rights primarily protects the producer of the database and may prohibit, for instance, the extraction and/or reuse of all or a substantial part of its content 20.
Finally, let us quote here this paragraph from the OpenAIRE project report 16 (p. 19) that highlights the difficulties to set a research data definition in the context of legal studies: From a legal point of view, one of the very basic questions of this study is which kind of potentially protected data we are dealing with in the context of e-infrastructures for publications and research data such as OpenAIREplus. The term "research data" in this context does not seem to be very helpful, since there is no common definition of what research data basically is. It seems rather that every author or research study in this context uses its own definition of the term. Therefore, the term "research data" will not be strictly defined, but will include any kind of data produced in the course of scientific research, such as databases of raw data, tables, graphics, pictures or whatever else.
But their role is very important as observed in 40 (p. 6): Data creators usually have the most intimate knowledge about a given dataset, gained while designing, collecting, processing, analyzing and interpreting the data. Many individuals may participate in data creation, hence knowledge may be distributed among multiple parties over time. Indeed, as for each research output, the producer team is the guarantor of the data quality, in particular to ensure that the data are not outdated, erroneous, falsified, irrelevant, and unusable. Note that this is particularly relevant in the case of RD, as a consequence of the lack of a widely accepted publication procedure as the one existing for articles in scientific journals, where the responsibility of the quality of the publication is somehow shared by the authors, the journal editors, and the reviewers.
Each research data encloses a set (of files) that contains the dataset maybe organized as a database, and it can also include other elements as the documentation, specifications, use cases, and any other useful material as provenance information, instrument information, etc. It can include the research software that has been developed to manipulate the dataset (from short scripts to research software of larger size) or give the references to the software that is necessary to manipulate the data (developed or not in an academic context). As previously declared, we have followed closely the RS definition in Section 2. 2, translating, as a consequence, the digital nature of RS to RD. This does not mean that we do not consider physical samples as possible RD, but rather we assume that the information extracted from the physical samples has been already treated as digital information to be manipulated in a computer system, which simplifies the manipulation of physical data and its inclusion in the proposed RD definition.
35 This principle may not have the same sense in the building of a dataset for which a research team collects, processes and analyzes data with a very particular research purpose, maybe difficult to share with a large or external community in the early stages of the RD production. Note also that, in this work, we do not consider production issues like best software development practices or data curation. In here, the research outputs have reach a status in which the research team is happy enough for its dissemination. Neither we do enter in the different roles (see 18) that may appear in the RD team, taking care of actions involving: collection, cleaning, selection, documentation, analysis, curation, preservation, or the role of Data Officer proposed in. 11.
"For example, to the need for complementary, technical information associated to a given dataset in order to facilitate its reuse. " -This is part of the FAIR principles which are not mentioned/linked to this comment. Obviously, a dataset without any information about how data have been produced/obtained, etc. are not valuable. Authors write: "In here, the research outputs have reach a status in which the research team is happy enough for its dissemination. " -This seems a very naïve assertion. Because the authors "do not consider production issues like best software development practices or data curation", it seems they do not care about these important issues. It would be strange to consider any article published in a newspaper as a scientific publication. On the other hand, scientists may read the newspapers and many other documents, including tweets, and may use these documents as input information for a research work.
[Definitions given by dictionaries] In the difficulties to explain easily the concepts of data and information we have ended in the consultation of several dictionaries, including some in English. Some of the found definitions, mainly in Spanish and French have attracted our attention and we have decided to included them in Box 1. This box can be easily skipped by readers not interested in these definitions. We prefer to leave the reading of the content of this box at the choice of readers. 5. [FAIR and "For example, to the need for complementary, technical information associated to a given dataset in order to facilitate its reuse. "] Please note that FAIR principles appear in the [Reference 55] dated 2016, while [Reference 36] that we have chosen to illustrate the need for complementary, technical information is dated 2012.
They can also launch the very first steps of the dissemination protocol as proposed in [Reference 13, section 3. 2]. We will include these considerations in a new version of this article, once the ongoing open peer review is completed. Teresa Gomez-Diaz, Tomas Recio The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000. com.
Teresa Gomez-Diaz and Tomas Recio Competing Interests: No competing interests were disclosed. Reviewer Report 23 February 2022 https://doi. org/10. 5256/f1000research. 82187. r121511 © 2022 Melero R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Remedios Melero Instituto de Agroquímica y Tecnología de Alimentos, CSIC, Valencia, Spain The authors proposed a Research Data (RD) definition "based in three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind.
European Parliament and the Council: Directive 96/9/EC of 11 March 1996 on the 1248 legal protection of databases. Reference Source 21. European Parliament and the Council: Directive (EU) 2019/1024 of 20 June 2019 on open data and the re-use of public sector information. Reference Source 22. European Parliament and the Council: Directive 2009/24/EC of 23 April 2009 on the legal protection of computer programs. 23. Boletín Oficial del Estado: lunes 22 de abril de 1996, Número 97. Reference Source Code de la propriété intellectuelle, Version en vigueur au 23 juin 2021. Reference Source l'informatique, Numéro complémentaire n. 0014 du 17 janvier 1982. 26. Journal officiel de la République française, Lois et décrets: Code de l'environnement, Version en vigueur au 21 juillet 2021. Reference Source Reference Source of Research Data Management (Extended Edition).
Reference Source 39. Tolone E: Analyse syntaxiquea l'aide des tables du Lexique-Grammaire du français. Linguistique: Université Paris-Est; 2011. Reference Source 40. Pasquetto IV, Borgman CL, Wofford MF: Uses and Reuses of Scientific Data: The Data Creators Advantage. Harvard Data https://doi. org/10. 5256/f1000research. 82187. r121514 © 2022 Schopfel J.
Quality management guarantees reliability and usability of the product served, it is a key element to build trust between consumers and suppliers. Untrustworthy data could lead to a negative economic impact at best and a safety hazard at worst. In a progressive commitment to establish this relation of trust, as well as providing sufficient guidance for users, the Copernicus Climate Change Service (C3S) has made significant investments in the development of an Evaluation and Quality Control (EQC) function. This function offers a homogeneous user-driven service for the quality of the C3S Climate Data Store (CDS). Here we focus on the EQC component targeting the assessment of the 2 Lacagnina et al.
As a result, an increasing amount of environmental data about past, present, and future climate is becoming available. Unfortunately, these data often come with inconsistent or missing metadata, inhomogeneous documentation, and sometimes sparse evidence concerning their uncertainty and validation. A variety of data streams is generated independently and from multiple sources, adhering to different definitions and assumptions, often not standardised across communities, and, at times, with overlapping but disconnected objectives. As a consequence, the users can feel disoriented when it comes to identifying the most appropriate dataset for an intended application  (Nightingale et al. 2019). Given the ever more prominent role that climate products are assuming in decision making, it is unavoidable that the quality of these data will come under increasing scrutiny in the future.
The GFCS offers an umbrella for the development of climate services and has identified the quality assessment, along with its use in user guidance, as a key aspect of the service. The services, and the quality assessments in particular, need to be provided to users in a seamless manner and need to respond to user requirements. 1 The ultimate goal is building trust between data providers and users, as well as maximising usage uptake  (Callahan et al. 2017; Rfll 2020). Thus, the questions of what type of quality information to provide and how to present it to users are receiving sustained attention. CDS datasets, which include satellite and in-situ observations, reanalysis, climate projections, and seasonal forecasts. The EQC function is characterised by a two-tier review system designed to guarantee the quality of the dataset information.
While the need of assessing the quality of climate data is well recognised, the methodologies, the metrics, the evaluation framework, and how to present all this information to the users have never been developed before in an operational service, encompassing all the main climate dataset categories. Building the underlying technical solutions poses unprecedented challenges and makes the C3S EQC approach unique. This paper describes the development and the implementation of the operational EQC function providing an overarching quality management service for the whole CDS data. The establishment of an EQC function has three main advantages: i) it guides the users into the documentation to understand properly the dataset, it simplifies the comparison across datasets and builds trust in the products available, ii) it helps the data providers to understand which information they need to deliver to be compliant with standards, increases data uptake from the users, and clarifies how to provide standardised dataset quality information, and iii) it triggers actions for the service improvement leading to the provision of the most relevant dataset information and ensuring that published datasets are mature enough to support the authoritative character of C3S.