dataesr/scientific-paragraphs-categorization huggingface dataset:
Disciplinary issues and citation patterns of data documents The challenges of data publishing vary greatly among disciplines (see  Beckles et al. 2018  about disciplinary data publication guides). The disciplinary distribution of data papers can be illuminated by WoS. The disciplinary scattering in WoS for the top 25 categories are shown in Table  2 : on March 21, 2020 6621 documents in WoS were assigned the document type (DT) category "data paper" (all 6621 were also assigned DT=Article). 17 Also the number of citations of the most cited data paper in the category and, for comparation, the number of citations of the most cited journal article in the same subject category are shown. Table  2  shows a concentration of papers in one WoS category, multidisciplinary sciences (containing more than 85% of the data papers in the database), which makes the subject scattering rather unclear.
One data journal covers a large range of disciplines from sciences (Scientific Data by Nature), another is open for all topics in social sciences and humanities (Research Data Journal for the Humanities and Social Sciences by Brill). All big five academic publishers (Elsevier, Springer-Nature,  Wiley-Blackwell, Taylor & Francis and SAGE)   ACS, Wageningen, etc. ). Most of the data journals are "young" products and have been launched during the last ten years, from 2008 on. At the end of 2019, the overall number of data papers published by these data journals is estimated 18 to be approximately 11,500, with large differences, ranging from some papers up to more than 3,500, with a rather low median number (ninety-seven). Following  Schöpfel et al. (2019) , some of the data journals are considered as good or high-quality journals: from 28 "pure" data journals, eleven are indexed by Clarivate Analytics, eight by Elsevier's Scopus database while sixteen journals are referenced in the international Directory of Open Access Journals (DOAJ).
The major business model is OA Gold, mostly with article processing charges (APC) 19 but some without. Some journals are hybrid, but only one journal is available through the tra- ditional subscription model. Many data journals disseminate data papers with an open license, most often a CC-BY license, sometimes together with a public domain license (CC0) or the more restrictive CC-BY-NC-ND or CC-BY-NC-SA licenses (no commercial re-use). Most of the data journals perform some kind of traditional peer review to guarantee a certain level of the papers' quality but also to assert some quality of the datasets, in terms of utility and reusability; only few journals adopt an "open peer review" (suggestion of reviewers, community or interactive public peer review). (See endnote 20 for an example of peer-review instructions.
) There have also been critical voices about this activity (e. g. ,  Huang, Hawkins and Qiao 2013). At the end of 2019, the number of data journals and papers appears to increase slowly, on a low level. Garcia-Garcia,  Borrul and Peset (2015)  identified twenty pure data journals; four years later, the  Schöpfel et al. (2019)  sample consists of twenty-eight data journals and not all are still active or even pure (see below). Twenty-eight journals represent less than 0. 01% of the academic and scholarly serials (source: Scopus). Arts, social sciences and humanities are nearly non-existent (two journals in 2015, four in 2019). The number of data papers progressed at a faster pace, from 846 in 2013  (Candela et al. 2015)  to an estimated number of 11,500 data papers in 2019. Yet, this volume represents roughly 0.
4% of the overall number of articles published in 2017 (source: Scopus). Also, the interest of data papers and journals lies not in their volume but in the fact that they clearly are a product of the emerging ecosystem of data-driven open science. Four aspects characterise this embeddedness in the new environment: -Business model: the dominant business model (gold OA with APCs) is different from the traditional and still prevailing serials landscape, and it appears already compliant with the requirements of the new plan S. 21 -Reuse rights: most data journals allow publishing with an open license, often with generous reuse and remixing rights (e. g. , CC-BY license and/or CC0 waiver). -Findability: the editorial model of data journals requires standard identifiers for the datasets, e. g. , DataCite's DOI, to guarantee (and increase) the findability of datasets; they.
Definition and developments Data papers are authored and citable articles in academic or scholarly journals. They are mostly, but not necessarily, peer reviewed. Their main content is a description of published research datasets, along with contextual information about the production and the acquisition of the datasets, with the purpose of facilitating the findability, availability and reuse of research data; they are part of the research data management and crosslinked to data repositories. In the context of open science, an increasing volume of research data is made available on the Internet, contributing to the big data of science. New tools, methods and infrastructures have been developed for the dissemination, processing, analysis, and preservation of research data. Data papers, along with the other data documents mentioned in Section 1 are part of them.
23  Unlike standard research papers, the main purpose of data papers is to describe datasets, including the conditions and context of their acquisition and their potential utility, rather than to report and discuss results. Also, it is generally assumed that data papers are short papers with up to four pages. In the "classical" research paradigm, the focus is on articles presenting results while research data are useful for the validation of published research findings. Data papers invert the roles, insofar as the paper's main function is to inform about and link to research data on data repositories, contributing to their findability and reusability. Also, traditional knowledge organization makes a relatively clear distinction between research results (datasets), the analysis and discussion of these results (papers) and the description (cataloguing, abstracting, and indexing) of those datasets and papers.
Functions and objectives An increasing number of journal editors announce the launch of a new section with data papers. They put forward different objectives, even if the main purpose is similar: to inform about research data and to foster their accessibility and reuse. Three examples among others illustrate the diversity of goals: - The publisher Pensoft describes a data paper as "a scholarly journal publication whose primary purpose is to describe a dataset or a group of datasets, rather than to report a research investigation. As such, it contains facts about data, not hypotheses and arguments in support of the data, as found in a conventional research article"  (Penev et al. 2012). The term remains ambiguous. For instance,  Bordelon et al. (2016)  define data papers as "papers that present, analyze, or use data obtained with the respective facilities" (i.
Nevertheless, there are more common features with regular articles, such as the fundamental structure. As we saw in Section 2, data papers provide other functions than just describing data sets. For this reason, data papers do not just improve the referencing of datasets on repositories but fulfil other roles. Their profile can perhaps be described in terms of library science, as an original integration (or merging) of writing, cataloguing, and indexing, facing major challenges like standards and terminology. Perhaps data papers are a kind of new boundary object  (Star and Griesemer 1989)  on the frontline between academic publishing and data driven research. Our analysis confirms the statement that data papers are like traditional research papers in some respects but quite different in other respects  (Smith 2011).
Nearly all journals require or suggest a particular structure, and some of them provide a template with mandatory sections. Yet, there is no standard structure. Instead of a generally accepted succession of sections, data papers are made of three constitutive elements, i. e. an introduction with information about the context and the rationale, a more or less detailed description of the datasets with specifications (sometimes formalized as disciplinary or generic metadata of data, such as the DataCite Metadata Schema 24 or the Data Documentation Initiative (DDI), 25 and a section of materials and methods, instrumentation, on the production of the data and procedures, sometimes extended to experimental designs and calculation (Figure  2 ). Figure  2  presents a core structure with three central sections (in blue), with other, optional or peripheral sections, some of them similar to regular papers (in italics), others characteristic for data papers, such as: -Value and validation: information about the (potential or real) value of the datasets and the quality control (validation), like peer review, automatic procedures (technical validation) etc.
-Potential reuse: information about potential usage, about reuse and the potential interest for scientists or other users. -Access and availability: information about the address of datasets (repository, URL) and the availability, including access and reuse rights and limitations; this part may include implementation details, information about the availability of source code and requirements, and about the availability of supporting data and materials. Information about access and availability may also be part of the appendices, like acknowledgements, references, competing interests, author roles and information, rights and permissions, or even peer review comments. Some data journals allow or invite sections about results of data analysis, together with a discussion of these results and an outlook on further research, very similar to the usual structure of scientific articles and blurring the frontiers between both types of papers.
Metadata Metadata are constitutive for data papers. Two types of metadata must be distinguished regarding data papers, i. e. metadata of the described datasets, and metadata of the data papers themselves. -Metadata of datasets: some data journals require a detailed and formalized description of datasets, in a format which potentially compliant with metadata. But only few journals insist on a specific standard. Two examples: Ecological Archives expects strict adhesion to the metadata content standards derived from a set of generic metadata descriptors published by the Ecological Society of America  (Michener et al. 1997) ; the metadata set should be sent to the editor as a separate text file. Genomics Data requires compliance with an internal standard for data description with eight fields.
Both formats have in common that they are community-specific, disciplinary metadata standards. A third example is quite different, generic and limited to the datasets' identifiers: Scientific Data requires an ISA-Tab 26 metadata text file where the DOI of all datasets are mentioned. -Metadata of data papers: most journals ask for some general and usual information, compliant with the Dublin Core format, such as author, organisation, title etc. F1000Research recommends XML Schema, Xlink, MathML, or the NLM Journal publishing DTD (JATS 27 ). Nearly all data journals publish the data papers with a DOI, and some also include the author identifier ORCID. Also, most of them recommend or require a standard identifier (DOI) or at least a stable address for the described datasets. All data papers provide information about the availability of the described datasets, mostly together with an address (URL), but they do it in different ways: -usually in a special section of the paper with a statement on data access and availability, -in an appendix which contains a declaration with data availability and address, -in the abstract, -as part of the metadata.
Some papers contain downloadable data; others require that the described datasets should be deposited in one or a shortlist of recommended repositories. The link between data papers and the metadata of research data is essential because both have similar functions, i. e. , to describe data, define accessibility, (re)usability, and content. Insofar data papers are about deposited datasets and insofar deposits require metadata, data papers can be (partly) derived from existing metadata. Also, data papers are particularly interesting for the requirements of the socalled "FAIR Guiding Principles for scientific data management and stewardship"  (Wilkinson et al. 2016)   Along with metadata, data papers contribute to the compliance with FAIR principles, in particular to the two principles of findability and reusability, insofar as they help people and machines finding datasets and inform about the provenance and reuse rights.
Additionally, data papers contribute to another aspect, beyond the FAIR principles, i. e. , the evaluation of the datasets' quality and value. In the context of open science, metadata has been considered fuel for economy  (Neuroth et al. 2013 ). As a new vector of communication of metadata on research data, data papers can be defined as a kind of pipeline for this fuel. Yet, as they also add value to metadata, through contextual information, evaluation, new identifiers etc. , they are not only pipelines but also refineries, more or less specialised, more or less standardized. To stay with the fuel metaphor, data papers are a new infrastructure of refinement and dissemination of the metadata fuel. Regarding knowledge organization, two aspects require attention and further investigation: standardization and specialization.
-The quality of data papers depends for much on the quality of the metadata of the underlying datasets; and this means, on controlled terminologies, on standard formats, well-defined elements etc. One example is the International Geo Sample Number (IGSN) designed to provide an unambiguous globally unique persistent identifier (PID) for physical samples (specimens) and to facilitate the location, identification, and citation of physical samples used in research. 29 The development of data papers and data journals should (will) be accompanied by further work on standards, by academic communities, publishers, information professionals and knowledge practitioners. -Also, to be relevant and useful, metadata standards should be as compliant as possible with the specific requirements and features of scientific communities, disciplines, methods, tools and equipment.
This specialisation, however, tends to limit their interoperability between different domains, infrastructures, information systems… and their interest and usefulness for interdisciplinary research, discovery tools etc. One solution to this problem could be described by "as specific as possible, as generic as necessary", an approach which would apply a kind of ad-hoc-compromise for each particular situation, resulting in many different formats more or less specific, and more or less generic. Another, perhaps more realistic approach would be to accept (and support) two (or more) different standards for each dataset and each data paper, one generic (like, for instance, the DataCite Metadata Schema), the other specific, depending on the particular domain, method, tool etc. https://doi. org/10.
Production and processing Li, Greenberg and Dunic (2020) conducted a content analysis with eighty-two data papers from sixteen journals to investigate what information they describe regarding methods used to create and manipulate the data objects (i. e. , "data events"). For Li and his colleagues, even if they have distinct features from research articles, data papers are "nevertheless created under similar conditions", and they reveal "functional overlaps" between both categories, related to the narratives of data events (natural language) and to their composition which is "inevitably situated in the specific epistemic communities". Their main function is to improve the findability of published datasets and, through enriched metadata description, to foster their reusability. Chavan and Penev (2011, 7)  describe a tool that "facilitates conversion of a metadata document into a traditional manuscript for submission to a journal" for biodiversity resource datasets.
Data papers represent a highly standardized type of publication, with a standard structure and a content which is largely defined in terms of metadata formats (such as DataCite Metadata Schema) and identifiers for datasets, persons etc. (such as DOI and ORCID). The integrated workflow of data repositories and journal platforms described by  Chavan and Penev (2011)  requires shared standards and formats. Senderov, Georgiev and Penev (2016)  provide an example of this data paper generation in the field of biodiversity. Their workflow relies on three key standards (RESTful APIs for the web, Darwin Core and EML) and imports metadata into the ARPHA writing tool (AWT). In other words, and more generally spoken, as expressed by  de Waard (2010, 9) : "the boundary between a workflow tool, a data store, and a publishing platform blurs".
They do not accept automatic submission of machine-produced data papers. Also, the format of data papers requires rich contextual information that may not be part of the datasets' metadata and must be added by the researchers or data officers. Instead of machine generated data papers we should speak of "machine-(or repository-) assisted writing of data papers". Are data papers produced only for machines? According to  Li, Greenberg and Dunic (2020, 18 ), the answer is no, as they are convinced that "as a genre built upon natural languages, data papers are primarily a human-readable document, much less designed for reproducing data workflows in computational approaches". Both are complementary, rather than competitive. While  Candela et al. (2015)  highlight the distinction between metadata of datasets, metadata of data papers, and data papers themselves,  Penev et al.
Indeed, as the expected structure of such an article may be based on the items provided when preparing a DMP, Reymonet suggests a tool (or workflow) to export selected items of DMPs in order to prepare or generate a data paper. A general assumption is that data papers, like regular papers, are peer reviewed, implying some kind of quality control and selection. This means, too, that metadata of research data (and, indirectly, the datasets themselves) become object of scientific evaluation which "contributes to the https://doi. org/10. 5771/0943-7444-2021-4-307 Generiert durch Birger Hjorland, am 18. 12. 2021, 14:32:41. Das Erstellen und Weitergeben von Kopien dieses PDFs ist nicht zulässig. popularity of data papers in increasingly more scientific fields"  (Li, Greenberg and Dunic 2020, 172 ; see also  Costello et al.
2013). For the same reason, data papers contribute to the trustworthiness of research data. For example, Elsevier's Chemical Data Collections invites authors to submit data papers because this "ensures that your data […] is actively peer reviewed". 30   Pärtel (2006)  mentions that data papers were about "data sets of value in a given field" which implies a selection by the authors themselves, upstream of the writing of data papers and of peer reviews, even if the criteria of selection remain uncertain. Candela et al. (2015)  identified ten different terms assigned for data papers, including data descriptor, data note, dataset paper and data in brief. Also, there is no consensus about the usual content, the only section present in all data papers being the data availability (location, accessibility), followed by information about the provenance of the dataset.
Blurred boundaries The specific identity of data papers is mainly defined in opposition with regular research papers (see for instance  Penev et al. 2012 ). The reality is different. The empirical data of  Schöpfel et al. (2019)  provide evidence that despite a general definition of data papers and journals, there is much divergence and heterogeneity which can be described on four levels. 1. Data journals also accept other articles. Our survey put the focus on a limited number of academic and scholarly journals indexed by databases or directories as "pure" data journals. Yet, even in this sample some data journals publish regular research articles, reviews, short communications, or comments along with data papers, such as Data from MDPI and Earth System Science Data from Copernicus. 2. Data papers are published in other journals.
An increasing number of academic and scholarly journals accept data papers along with regular research papers, usually in a specific section. Pensoft for instance publishes thirtyseven journals, including one data journal and sixteen other journals accepting data papers. also due to a large diversity of publishing practices. This may have at least three explanations: -The publishing of data papers is still in transition. It took some decades to develop and accept the former mentioned IMRAD format as a widely used format of scientific article publishing. The heterogeneous character and blurred boundaries of data papers may reflect the emergence of a young and new, still not well-defined form of scientific communication. -The described proximity with research communities, the "embeddedness" in an ecosystem defined by disciplines, materials, methodologies, tools, etc.
It stands for "introduction, methods, results and discussion/conclusion". For more details and references, see  Sollaci and Pereira (2004)  and Wikipedia (2020b). 16. A systematic review is a systematic search for literature relevant to a certain issue also performing evaluations and organization of research in order to make highly qualified decisions based on published "evidence". There is a huge literature on systematic reviews from practical guides to theoretical and philosophical issues, including  Booth (2016)  and  Hammersley (2006). 17. 35 lower ranking WoS categories are not included in Table 2 but can be seen in WoS. 18. "The data are based on the authors counting of the data papers on each journal's website". Compared to the data in Table  7 , it suggests a rather selective coverage in the WoS databases (and then indicates that WoS may cover data journals rather selectively).
19. APC: the fee authors or their institutions have to pay (after the acceptance of their papers) to some publishers to be published immediately in open access. The amount of APC varies between publishers and journals; the average amount research institutions pay per article is about 2,000 euros (see OpenAPC https://treemaps. intact-project. org/apcdata/openapc/). 20. An example of peer-review instructions for data papers was provided by  Fabrissin (2019) : "Data papers aim at describing a dataset made available to a larger community. Data papers are a scientific valuable production and should provide all required information for a large use of the data. The data paper should be completed by a metadata file that describes the dataset, and by the dataset itself, made available in an open repository.
Reviewers will carefully consider: (a) the quality of the manuscript, (b) the quality, completeness and reusability of the dataset, and (c) the relevance of the dataset and its potential contribution to the progress of science. They will not review the whole data set, whose quality is under the responsibility of the authors (see our related blog post "Guidelines for authors: how to share your datasets?"). Quality of the manuscript Usual criteria for assessing manuscript quality including style, consistency, clarity. The review will address the following questions: -Is a DOI provided for the database and is the dataset accessible via the given identifier? -Is the metadata template filled out as recommended? -Do title and key message accurately reflect the content of the data paper? -Is the Data Paper internally consistent, suitably organized and written in proper English? -Are relevant non-textual media (e.