Joachim Schöpfel
email: joachim.schopfel@univ-lille.fr

Dominic Farace
email: dominic.farace@textrelease.com

Hélène Prost
email: helene.prost007@gmail.com

Antonella Zane
email: antonella.zane@unipd.it

Data Papers as a New Form of Knowledge Organization in the Field of Research Data

Keywords: Data papers, research data, knowledge organization, open science, data journals, FAIR principles, academic publishing

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e. disciplines, publishers and business models, and about their structure, length, formats, metadata and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machinereadable. Data papers are essentially information, i.e. description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.

INTRODUCTION

In the context of open science, an increasing volume of research data are made available on Internet, contributing to the so-called big data of science. New tools, methods and infrastructures have been developed for the dissemination, processing, analysis and preservation of research data. Data papers are part of them.

Data papers are a young species of academic publishing. In 2006, Paä rtel stated for the field of ecology that "until now (...) very few data papers have appeared" (p.99). In fact, most of the data papers or papers about data papers have been published since 2008 and 2009 [1]. Yet, as Smith (2011) reminds, "the concept has actually been around for quite a while (even if) the older journals that date from the print era tend to be not particularly useful in the modern environment " (p.16). In fact, one (the first?) data journal (Journal of chemical and engineering data from ACS) was already launched in 1956 (see the timeline in [START_REF] Alicia | Data journals: eclosioé n de nuevas revistas especializadas en datos[END_REF].

The simplest definition is that data papers focus on "information on the what, where, why, how and who of the data" rather than original research results (Callaghan et al. 2012, p.112).

Data papers have been defined as "a searchable metadata document, describing a particular dataset or a group of datasets, published in the form of a peer-reviewed article in a scholarly journal" [START_REF]Global Biodiversity Information Facility[END_REF]. They are published in specific data journals like Data in Brief (Elsevier) and Scientific Data (Nature), or in regular academic journals with special sections for data papers, like BMC Research Notes GigaScience (Oxford University Press) and PLoS One. Most data papers are published in journal platforms; yet, some are (also or exclusively) published on data repository platforms [3]. Unlike usual 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 4 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 the paper's main function is to inform about and link to research data on data repositories, contributing to their findability and reusability. Are data papers complementary to research papers, or will they replace them, as a seamless and direct way of providing access to research results? Also, traditional knowledge organization makes a 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. This emerging category of data papers appears to challenge this clear distinction, interlinking datasets, papers and metadata, blurring boundaries, changing priorities and modifying the basic purpose of academic publishing.

Built on an overview of recently published studies, the following study produces an empirical update on the publishing of data papers: the number and development of data papers and journals, the country and language of publications, the platforms and publishers, as well as the business models. The purpose of our paper is to analyse data papers as a new tool of scientific communication and to produce insight on their contribution to the organization of scientific knowledge via questions pertaining to the production and the functions of data papers:

 How are they "written"?  Which is the link with data repositories, metadata and other papers?

 Which is the (potential and real) part of automatic or semi-automatic production  Which is the part of human added value?

Colloque international ISKO-France 2019  Which degree of standardization, which link between metadata formats and the data journals' author guidelines?

 In which way are data papers related to the so-called "FAIR Guiding Principles for scientific data management and stewardship" [START_REF] Wilkinson Mark | The FAIR Guiding Principles for scientific data management and stewardship[END_REF])?

 Do they just improve the referencing of datasets on repositories, or do they fulfil other roles?

 Are data papers "written by machines" and meant in fine to be "read by machines"?

The paper will conclude with a conceptual approach to data papers as part of the organization of knowledge based on research data, in the context of open science.

-LITERATURE OVERVIEW

Definitions and functions

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 et al. 2017, p.291).

 The French journal of information and communication sciences RFSIC invites data papers to describe the scientific process, methods and tools that result in research data in a Bruno Latour perspective, "since they never just magically appear" (Le Deuff 2018, §2).

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" [START_REF] Penev Lyubomir | Data papers as incentives for opening biodiversity data: one year of experience and perspectives for the future[END_REF].

The term remains ambiguous. For instance, [START_REF] Bordelon Dominic | Trends and developments in VLT data papers as seen through telbib[END_REF] define data papers as "papers that present, analyze, or use data obtained with the respective facilities" (i.e. observatories) (p.1). Paä rtel (2006) consider data papers as a kind of "abstracts" that aim to collect, organize, synthesise, and document data sets of value in a given field; only the abstract appears in a data journal (or the data paper section of a regular journal) while the data and metadata are available through a field-specific data repository on the Internet. For Penev et al.

(2012), their purposes are three-fold: "to provide a citable journal publication that brings scholarly credit to data publishers; to describe the data in a structured human-readable form;

(and) to bring the existence of the data to the attention of the scholarly community ". At first sight, data papers, in spite of their common general purpose, appears to belong to a rather heterogenous and dissimilar new kind of documents. Our study will reveal, nevertheless, more common features, such as the fundamental structure.

Data journals

A first survey on data journals was conducted by [START_REF] Candela Leonardo | Data Journals: A Survey[END_REF], with a sample of 116 data journals published by 15 different publishers. They distinguished 7 "pure" data journals publishing only data papers and 109 "mixed" data journals publishing any typology of paper including data papers. The most represented subjects (in terms of number of journals) were Medicine (53%), Biochemistry, Genomics and Molecular Biology (26%), and Agricultural and Biological Sciences (16%). They identified only 9 data journals in social sciences and humanities (8%). Their results show a recent and slowly developing landscape (the average number of data papers per journal is <10), with conceptual, structural and terminological diversity (they identified 10 different terms assigned for data papers). 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. 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 adopted an "open peer review". Most journals are published in open access, with an average APC [4] amount of 1,300 euros.

The Grey Journal, published by Textrelease (Amsterdam) is one of those "mixed" data journals.

Initially a regular journal with papers from international conferences and original research articles, The Grey Journal started to publish a collection of data papers in 2017. This collection was born out of an 'Enhanced Publications Project' fueled by the FAIR Data Principles (Farace et al. 2018). The main pillars for this collection are the International Conference Series on Grey Literature, the research data that is created and archived within this framework (actually 37 datasets housed in the Dutch data repository DANS), and the existence of a flagship journal for the publication of the data papers. A standardized template is provided to ensure the identity and longevity of the collection and to guide prospective authors and researchers in submitting a data paper.

The template consists of five sections each of which has a note field providing examples and/or a maximum word count. The fields are labelled as follows: overview, methods, data description, potential reuse, and references. Currently, 7 of GreyNet's 37 datasets are supported by a data paper (19%). Yet, even on a small scale this data paper collection illustrates an operational and functional ecosystem of open science constructed year after year with five main elements, i.e. an academic community, original research within this community, conferences, a journal, and a data repository. In this emerging framework, data papers gain their particular relevance, different from regular articles.

Features and metadata

Yet, other aspects appear challenging the idea of a clear distinction between data papers and regular papers. Li et al. ( 2019) conducted a content analysis with 82 data papers from 16 journals to investigate what information they describe regarding the methods 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.

Metadata are constitutive for data papers. [START_REF] Candela Leonardo | Data Journals: A Survey[END_REF] produced a conceptual map of the data paper (see figure 1). They insist not to confuse the data paper's content, its metadata, and the datasets' metadata. "The concept of data paper has at least two elements that have to be materialized into concrete and identifiable information objects in order to fully implement it: the dataset, i.e., the subject of the data paper, and the data paper itself, i.e., the artefact produced to describe the dataset" (p.1752). The link between data papers and the metadata of research data is essential because both have similar functions (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.

Chavan & Penev (2011) describe a tool that "facilitates conversion of a metadata document into a traditional manuscript for submission to a journal" (p.7) for biodiversity resource datasets. The human contribution is minimal if the metadata is standardized (with controlled vocabulary), exhaustive, and of sufficient quality: "Once the metadata are completed to the best of the author's ability, a data paper manuscript can be generated automatically from these metadata using the automated tool (...) The author checks the created manuscript and then submits it for publication in the data paper section through the online submission system of an appropriate (...) journal" (p.7).

This kind of generated data papers can be further enhanced in different ways, such as "describing fitness for use of data resources (which) will increase the usability, verifiability and credibility of those resources", persistent identifiers, an "interpretive analysis of the data (which) could include taxonomic, geospatial or temporal assessment of data and its potential of integration with other types of data resources" or the inclusion of "a taxonomic checklist and/or the data themselves". 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).

Production and processing

In fact, Chavan & Penev (2011) describe an integrated workflow of data repositories and journal platforms, requiring shared standards and formats. [START_REF] Senderov Viktor | Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases[END_REF] provide an example of this data paper generation in the field of biodiversity. Their workflow relies on three key standards (RESTful API's for the web, Darwin Core and EML) and imports metadata into the ARPHA writing tool (AWT). In other words, and more generally spoken, "the boundary between a workflow tool, a data store, and a publishing platform blurs" (de Waard 2010, p.9).

But are data papers produced only for machines? No, according to [START_REF] Li Kai | Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers[END_REF] who are convinced that "as a genre built upon natural languages, data papers are primarily a humanreadable document, much less designed for reproducing data workflows in computational approaches" (p.18). Both are complimentary, rather than competitive.

In her review of data papers, [START_REF] Reymonet | Améliorer l'exposition des données de la recherche : la publication de data papers[END_REF] compares data papers and data management plans (DMP). 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 popularity of data papers in increasingly more scientific fields " (Li et al. 2019 p.2, see also [START_REF] Costello Mark | Biodiversity data should be published, cited, and peer reviewed[END_REF]. 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 (...)" [5]. As cited above, Paä rtel ( 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.

Critics and outlook

Other arguments against data papers are their price (APCs) and the slow uptake, at least initially. "To address professional recognition and data quality control, there are viable alternatives to the data paper (such as the) implementation of a joint data-publishing and -archiving policy by databases and journals (...) instead of popularizing a new kind of publication, it is more important to improve current peer-review processes and the operating policies and integration of journals and databases" (Huang et al. 2013, p.5). Huang's critic may be specific for a given field of research (here, biodiversity) but should be taken into account for a general understanding of the future development of data papers.

Nevertheless, data journals and data papers appear to be here to stay. The French national plan for open science recommends "as part of its government support for journals (...) the adoption of an open data policy associated with articles and the development of data articles and data journals" (MESRI 2018, p. 6-7). While data papers become a legitim (mainstream) part of the landscape of academic publishing, only few studies provide empirical or conceptual elements of an answer to the question of how exactly data papers contribute to the organization of scientific knowledge, compared to regular research articles.

-METHODOLOGY

In order to analyse specific features of data papers, we established a representative sample of data journals, based on lists from the European FOSTER Plus project [START_REF]FOSTER portal[END_REF], the German wiki forschungsdaten.org hosted by the University of Konstanz [7] and two French public research organizations [START_REF]Both in the field of agronomy[END_REF]. The complete list consists of 82 data journals, i.e. journals which publish data papers. They represent less than 0,5% of academic and scholarly journals. For each of these 82 data journals, we gathered information about the discipline, the global business model, the publisher, peer reviewing etc. The analysis is partly based on data from ProQuest's Ulrichsweb database, enriched and completed by information available on the journals' home pages.

Some data journals are presented as "pure" data journals stricto sensu, i.e. journals which publish exclusively or mainly data papers. We identified 28 journals of this category (34%).

For each journal, we assessed through direct search on the journals' homepages (information about the journal, author's guidelines etc.) the use of identifiers and metadata, the mode of selection and the business model, and we assessed different parameters of the data papers themselves, such as length, structure, linking etc.

The results of this analysis are compared with other research journals ("mixed" data journals) which publish data papers along with regular research articles, in order to identify possible differences between both journal categories, on the level of data papers as well as on the level of the regular research papers. Moreover, the results are discussed against concepts of knowledge organization.

-RESULTS

Four of the 28 data journals have ceased, and two have merged. All of them are published online while 9 have still a print version. One data journal is a report series.

Research disciplines

Most data journals are from STEM domains, in particular from life and medical sciences, including genetics (see figure 2). Only four journals publish data from humanities (psychology, archaeology) and social sciences. The 5 data journals with papers on data from arts, social sciences and humanities represent 18% of all "pure" data journals. In terms of articles (see below), they represent less than 4% of all data papers published in data journals, with estimated 400-450 papers, mostly in archaeology.

Publishers

Business models

Most of the data journals are "young" products, with a short history. The overall number of data papers published by these data journals is approximately 11,500, with large differences, ranging from some papers up to more than 3,500. The median number, however is rather low, with 97 (figure 4). The major business model is OA Gold, mostly with APCs (19) but also without [START_REF]Global Biodiversity Information Facility[END_REF]. 4 journals are hybrid, and only one journal is available through the traditional subscription model (figure 5). In this small sample there is no "diamond OA journal" without subscription and APCs. In other words, 25 journals (89%) are OA journals or allows OA publishing, and in 23 journals (82%) authors have to pay for OA.

All data journals covering arts, social sciences and humanities are OA journals, all with APCs.

Licensing

21 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).

Elsevier proposes (also) its own user license.

Only one journal does not propose an open license for the dissemination of the data papers but keeps the full copyright (Journal of Physical and Chemical Reference Data).

Selection

Except for one title (European Power Watch) all data journals explicitly inform about some kind of formal selection procedure. Often the information for authors just mention "peer review" but six describe the selection as a single-blind review process where the identities of the reviewers are not disclosed to the author(s). One journal applies a "quick peer review" with focus on the data value and potential re-use but does not explain who does the peer review and how long it takes (Chemical Data Collections).

5 data journals apply some kind of innovative open peer review, either as an option (if required) or for all submitted papers. Yet, this term has different meanings:

 the reviewers are suggested (and known) by authors (F1000Research);

 community peer review (Biodiversity Data Journal);

 interactive public peer review (Earth System Science Data).

The last procedure is particular interesting: all referee and editor reports, the authors' response, as well as the different manuscript versions of the peer-review completion (postdiscussion review of revised submission) will be published if the paper is accepted [9].

Structure and length

We already mentioned that it is generally assumed that data papers are short texts, up to 4 pages. In fact, this is only partly true. In this sample, only 5 journals require short papers, limited to 4-6 pages or maximal 3,000 words. Most journals do not limit the length of submitted papers or make the usual recommendations (6-10 pages, or maximal 6,000 words).

One journal only accepts short abstracts (Ecological Archives), while others publish papers well beyond the length of regular papers, up to 20 or 30 or even 100 pages, including detailed data descriptions, illustrations (figures) or data tables like Atomic Data and Nuclear Data Tables. On the other hand, data journals in the field of arts, social sciences and humanities publish generally shorter data papers.

No results, no discussion, no conclusion: usually the data journal guidelines for authors contain these or similar recommendations, like Elsevier's Data in Brief which asks authors to "avoid using words such as 'study ', 'results', and 'conclusions'" [10]. Quite different, the Atomic Data and Nuclear Data Tables guidelines leave it to the authors whether or not to include results, discussion, and conclusion to the description of the data.

Colloque international ISKO-France 2019

Nearly all journals require or suggest a particular structure, and some of them provide a template with mandatory sections. However, 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 or the DDI [11]), and a section of materials and methods, instrumentation, on the production of the data and procedures, sometimes extended to experimental designs and calculation (figure 6). 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.

As mentioned above, 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.

A last aspect: no invitation or guidelines were found concerning machine-based generation and/or automatic processing of data papers. Apparently, the publishers' platforms do not support automatic ingestion of text files (via FTP of repository metadata or similar) but require manual deposits of manuscripts and authorship. Of course, this requirement does not Colloque international ISKO-France 2019 exclude partly or complete machine-based generation of data papers upstream of the human deposit of manuscripts.

Metadata and identifiers

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: as mentioned above, some data journals requires 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 [START_REF] Michener William | Nongeospatial metadata for the ecological sciences[END_REF]; 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 [START_REF]These eight fields are: organism/cell line/tissue; sex; sequencer or array type[END_REF]. 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 [START_REF]ISA tools[END_REF] 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) [START_REF][END_REF].

26 journals publish the data papers with a DOI (93%), and 5 also include the author identifier ORCID (18%). Also, most of them recommend if not require a standard identifier (DOI) or at least a stable address for the described datasets.

Linking

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.

-DISCUSSION

A new ecosystem

Compared to former studies, the number of data journals and papers appears to increase slowly, on a low level. [START_REF] Alicia | Data journals: eclosioé n de nuevas revistas especializadas en datos[END_REF] identified 20 pure data journals; four years later, our sample consists of 28 data journals and not all are still active and even pure (see below). 28 journals represent less than 0.01% of the academic and scholarly serials (source: Scopus). Arts, social sciences and humanities are nearly missing (2 journals in 2015; 4 in 2019). The number of data papers progressed at a faster pace, from 846 in 2013 (Candela et Colloque international ISKO-France 2019al. 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 is not their volume but 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 [START_REF]The plan S gives preference to immediate open access in 100% OA journals[END_REF].

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 also attribute DOIs to their own data papers, creating a kind of cross-linked DOI system between data papers and datasets.

Interconnectedness: perhaps the most relevant aspect is the integration of data journals and papers in a complex structure of open access journal platforms and data repositories, academic communities, research projects, conferences etc. Interconnectedness requires interoperability between platforms and infrastructures but is more than technology, formats and standards, insofar it means new ways of doing science, including research management, research environment, workflows etc.

A fifth aspect, i.e. evaluation and selection, is already visible but still in transition and not dominant. Data journals replace the usual evaluation and selection procedure (double-blind peer review) by partly open single-blind peer review and, for already one out of five journals, by some kind of open peer review, including innovative community peer review and interactive public peer review. They can also contribute to the assessment of data value through the follow-up of citations (Belter 2014).

FAIR principles and beyond

Most data journals have never been produced as traditional serials but are a pure (and young) product of this new ecosystem of open access, open (and big) data, and new forms of selection and dissemination. This makes them particular, different from other academic journals. And this makes them also particularly interesting for the requirements of the socalled "FAIR Guiding Principles for scientific data management and stewardship" [START_REF] Wilkinson Mark | The FAIR Guiding Principles for scientific data management and stewardship[END_REF]. Their data papers contribute to these principles in different ways, in order to improve the findability, accessibility, interoperability, and reuse of research data, e.g. [START_REF]The description and numbering of the principles follow the GO FAIR list[END_REF]: Along with metadata, data papers contribute to the compliance with FAIR principles, in particular to the two principles of findability and reusability, insofar they help people (and machines [START_REF]The FAIR principles have been initially designed for automatic data processing[END_REF]) finding datasets and inform about the provenance and reuse rights.

 Findable o F2.
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: 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 [18]. 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.

Specialisation: 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.

Colloque international ISKO-France 2019

Blurred boundaries

The specific identity of data papers is mainly defined in opposition with regular research papers (see for instance [START_REF] Penev Lyubomir | Data papers as incentives for opening biodiversity data: one year of experience and perspectives for the future[END_REF]). The reality is different. The empirical data of our survey provides evidence that despite a general definition of data papers and journals, there is a lot of 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. As mentioned above, one limitation of our survey is the focus on supposedly "pure" data journals. However, 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 37 journals, including one data journal and 16 other journals accepting data papers. The French Agricultural Research Centre for International Development (CIRAD) produced a list with 54 academic journals accepting data papers relevant for agricultural science, including the mega-journal PLoS One [19]. It is quite impossible to make an estimation of the real number of such "mixed" data journals and their data papers. Pensoft's Research Ideas and Outcomes for instance is part of these new "mixed" data journals but published up to now only one data paper, in biosciences. 3. Data papers are more than simple data papers. Even a superficial analysis of data papers reveals that one part of articles labeled as "data papers" do not only describe datasets but add data analysis and discussion of results. Atomic Data and Nuclear Data Tables, Dataset Papers in Science and Open Archaeology are three "pure" data journals which explicitly accept data papers with results and discussion of results. This means that a (unknown) part of data papers in fact are more than simple data papers stricto sensu because they communicate results of data analysis. 4. There are other emerging types of articles, similar to but not identical with data papers. "Pure" and "mixed" data journals are open for other categories of articles which are neither traditional journal items (research articles, reviews, comments etc.) nor data papers. Sometimes the difference may be a question of terminology. For instance, F1000Research accepts "brief descriptions of scientific datasets that promote the potential reuse of research data and include details of why and how the data were created" called "data notes" [START_REF] Zane | [END_REF] -in other words, data papers. But there are other examples (see also the listed terms in Candela et al. 2015): a. Data services paper: "papers on data services, and papers which support and inform data publishing best practices (including) the development of systems, techniques or tools that enable data analysis, data visualisation, data collection and data sharing (and) processes and procedures used in the development of datasets" (Geoscience Data Journal). b. Meta or overlay articles: "Descriptions of online simulation, database, and other experiments, partnering with digital repositories on 'meta articles' or 'overlay articles', which link to and allow visualisation of the data, thereby adding an entirely new dimension to the communication and exchange of data research results and educational materials" (Data Science Journal) [START_REF][END_REF].

These two examples of a new kind of papers are quite different, yet they have in common that they are both linked to research datasets and above all, to the dissemination and reuse of research data which is their main purpose.

Colloque international ISKO-France 2019

The boundaries between data papers and data journals and other categories of scientific communication are partly blurred, not only due to a lack of reference definitions but 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 IMRAD format as a standard format of scientific article publishing [22].

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. contributes to the heterogeneity of data journals and papers. Data papers necessarily depend on the community-specific way of how data is produced, collected, processed, preserved, reused… and it seems quite natural that they will reflect the diversity of this environment. Perhaps, fuzziness is a core element of the data paper category.  One part of the new OA journals announces an inclusive editorial policy. Instead of a selective approach and guidelines with explicit limitations, they invite submission of all kind of papers; a strategy somewhere between predatory publishing and big data principles based on volume and variety rather than on quality and trustworthiness.

4.4 Who is writing? Who is reading? Some of the underlying questions of this study were about the production and use of data papers. How are they written, and are they really "written"? Which is the (potential and real) part of automatic or semi-automatic production, and which is the part of human added value?

In fact, are data papers written by machines and to be read by machines?

The answer to these questions is neither yes nor no. Both examples, the Pensoft workflow as well as the INRA tool, reveal the potential of automatic generation of data papers, but also its requirements and limits. Automatic generation of data papers requires a high degree of standardization and interoperability between data repositories, text processing tools and journal platforms, especially regarding metadata formats and identifiers. Our study was not about metadata formats of data repositories and about their degree of standardization. But our study reveals a lack of standardization on the other side, i.e. the journal platforms. Paradoxically, this may be an opportunity for automatic generation and ingestion of data papers; yet it will not be helpful for machine-based exploitation of data papers.

The limits of automatic generation of data papers are twofold. On the one hand, journal platforms still and always require authorship, i.e. intellectual property and institutional affiliation. They do not accept automatic submission of machine-produced data papers. On the other hand, 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. [START_REF] Candela Leonardo | Data Journals: A Survey[END_REF] mention that the metadata is usually selected by both the data journal editor (for the data paper) and the data archive manager (for the dataset) which " often results

Colloque international ISKO-France 2019 in proprietary, ad-hoc-solutions". Relevant for our question is the human contribution (selection) and the resulting diversity and specificity. [START_REF] Candela Leonardo | Data Journals: A Survey[END_REF] also insist on the distinction between metadata of datasets, metadata of data papers, and data papers themselves. Metadata [24] are made for machines, and the main purpose of FAIR principles is to improve machine readability and transfer of research data. Data papers are part of this ecosystem, and they contribute to the automatic processing of research data and related metadata. However, the state of the art and our empirical results (still) reveal human added value, i.e. enhancement of the information produced by metadata, such as potential reuse (value), related datasets and research, and other contextual information useful for the understanding of the described data. But as mentioned above, this can also include more traditional content, like results of data analysis and rich textual discussion of data and results. Another "human added value" is the intellectual responsibility and property of the data papers which are always attributed to people (authors) not to machines. Instead of machine generated data papers we should speak of "machine-(or repository-) assisted writing of data papers".

So, are data papers written for machines? Penev et al. ( 2012) insist on the "humanreadability" even of automatically generated data papers. Rich and less standardized and coded textual discussion, for instance, is probably more aimed at human readers. This of course does not exclude the potential of data papers for automatic exploitation with tools of text and data mining (artificial intelligence). Similar to the generation (writing), this potential depends on the standardization of data papers, including careful coding, and their own metadata, i.e. standardized and well controlled formats and terminology. Probably, the fast development of artificial intelligence will facilitate the automatic production as well as the automatic exploitation of data papers and their metadata. However, so far, we didn't identify any study about this potential which for the moment apparently remains theoretical.

4.5 Data? Information? Knowledge?

Finally, what is the informational status of data papers, compared with the DIKW model of information sciences [START_REF] Rowley | The wisdom hierarchy: representations of the DIKW hierarchy[END_REF])? What do they carry: data, information, knowledge, or wisdom? Following the usual definitions, the answer seems easy: insofar data papers provide description of data, and insofar information is inferred from data and contained in descriptions [START_REF] Rowley Jennifer | Organizing knowledge. An introduction to managing access to information[END_REF], data papers correspond to the second level of the DIKW pyramid, i.e. information (figure 7). They are not knowledge but contribute to the generation of knowledge. Also, the main purpose of data papers -to facilitate the findability and the reusability of research data -is similar to another general aspect of information, i.e. its immediate usefulness for decisions or actions.

This characteristic of data papers is one major difference with regular research articles which are expected to provide more than simple descriptions of facts (data), i.e. insight, understanding, interpretations, hypotheses etc. However, as mentioned above, the boundaries are partly blurred and some data papers do more than carrying information about data, in particular when they include sections with data analysis results and discussions. So at least partly, data papers also convey knowledge, even if this is not part of their core function.

Colloque international ISKO-France 2019 Downside of the pyramid, the boundary to the data level seems equally blurred. Because, as described above, data papers do not only provide information about data but can be exploited as raw data on their own, generating information about research projects, scientific cooperation etc. This means that data papers are also partly data.

For both reasons, data papers do not just improve the referencing of datasets on repositories but fulfil other roles. Their particular information profile can 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 & Griesemer 1989) on the frontline between academic publishing and research. Our analysis confirms the statement that data papers are like traditional research papers in some aspects but very different in other respects (Smith 2011). Perhaps data papers are not (only) part of academic publishing but should (also) be considered and assessed as part of research data practice.

CONCLUSION

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data (facts about data). Yet, the literature overview shows that there is a lack of a generally accepted reference definition of data papers. Likewise, few conceptual studies and empirical evidence are provided. Also, up to now, the success of data papers appears of minor importance and limited to STM disciplines, primarily in the life sciences.

Our survey provides more insights about the environment of data papers, i.e. disciplines, publishers and business models, and about their structure, length, formats, metadata and licensing. Core elements of data papers are the data description and methods and materials; depending on the data journal's policy, other sections are requested or optional, such as value and validation, potential reuse, access and availability, and even results of data analyses and discussion of results.

The discussion section of this study highlights five major aspects of data papers:

1. Data papers are a product of the emerging ecosystem of data-driven open science. 2. They contribute to the FAIR principles for research data management, in particular findability and reusability, and add in some degree to the evaluation of the quality and value of the data. 3. However, the boundaries with other categories of academic publishing are partly blurred, especially with regular research papers.

4. Data papers are (can be) generated automatically and are potentially machinereadable; yet, the human contribution (still) appears vital in terms of intellectual property and richness of content. 5. Data papers are essentially information, i.e. description of data (as defined by the DIKW model) but also partly contribute to the generation of knowledge and data on its own.

As to the two camps human generated vs. machine generated, if a data paper is created by a human -whether or not machine aided, one can speak of knowledge organization. However, if the data paper is solely machine generated it is difficult to attribute this to knowledge organization (excluding any reference to artificial intelligence). The latter is more aligned with automated indexing, cataloguing, and the like.

In relation to the DIKW pyramid, data papers appear between the levels of information and knowledge given that for some people they are not knowledge but only contribute to the generation of knowledge.

However, if one looks at the metadata fields that encompass a full blown data paper -such as the explicit roles of the researchers/authors, the research methods applied, the description of the data, its reusability as well as its limitations, then one may conclude that the data paper provides a fuller understanding of the data/dataset. In itself, the data paper provides a best practice in knowledge organization -if not an example of knowledge generation.

Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing. This means that the quality and the usefulness of data papers partly depend on external variables, e.g. the metadata standards of data repositories, their trustworthiness in terms of data quality but also long-term preservation (certification) etc. Therefore, as mentioned above, quality control of data papers (i.e. some kind of peer review) always implies some kind of quality control or evaluation of the datasets themselves and their respective repositories.

Based on our empirical results and former studies, we would suggest the following definition of data papers, keeping in mind the transitional and necessarily provisional character of each conceptual attempt: Data papers are authored, peer reviewed and citable articles in academic or scholarly journals, whose 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 to facilitate the findability, availability and reuse of research data; they are part of the research data management and crosslinked to data repositories. This definition may not cover all different variants of data papers but will be helpful for a better understanding of what we called "blurred boundaries" and for further investigation.

At this stage, a couple of questions remain open; in particular, the following topics should be addressed:

 Monitoring: how can the indexing of data papers be improved in order to facilitate their identification and follow-up (search engines, databases, data repositories, journal platforms)?  Business models: what is the risk of predatory publishing of data journals and data papers? Is it different from predatory publishing of regular research papers?  Disciplines: are data papers as relevant in arts, social sciences and humanities as in life sciences, chemistry etc.? Should their data papers be published in large and multidisciplinary data journals, together with STM, or should they have their own data journals?

Colloque international ISKO-France 2019  Ecosystem: more case studies are needed on specific links between research data management, academic publishing, and the production and dissemination of data papers, in a given environment and community (equipment, discipline, structure…).  Evaluation: our study didn't assess whether (and how) scholars get credit for publishing data papers. This, however, will be a key factor for the future development of data papers.

Figure 1 .

 1 Figure 1. Data papers concept map (from Candela et al. 2015 p.1752)

  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).

Figure 2 .

 2 Figure 2. Number of data journals per domain (N=28)

  Except for Taylor & Francis, all big five academic publishers (Elsevier, Springer-Nature, Wiley-Blackwell, Taylor & Francis and SAGE) have their own data journals. Five data journals are published by Elsevier (from which two are published by Academic Press, an imprint of Elsevier, two others merged), two by Wiley, one by Springer-Nature and one by SAGE. Other data journals are published or hosted by newcomers, especially by open access publishers such as Ubiquity Press (3 journals), BioMed Central (2 journals) Hindawi, MDPI, Copernicus Publications, Pensoft or Faculty of 1000, by smaller publishing houses like Brill or De Gruyter (Sciendo) or by learned societies or university presses (AIP, ACS, Wageningen…).

Figure 3 .

 3 Figure 3. Geographical origin of data journals Most of the data journals are published in three countries, i.e. the United Kingdom, the United States and The Netherlands. The other journals are from Bulgaria, Switzerland, Germany and Poland (figure 3). All are published in English, only one journal also publish papers in another language, Dutch (Research Data Journal for the Humanities and Social Sciences).

Figure 4 .

 4 Figure 4. Number of data papers per journal (with best estimates) In terms of volume, Elsevier's Data in Brief is by far the most important data journal, followed by Elsevier's "old" Atomic Data and Nuclear Data Tables (launched in 1979) and Scientific Data, a NatureResearch journal from Springer Nature. Together, these three journals represent more than half of the data papers published in pure data journals.

Figure 5 .

 5 Figure 5. Business models Colloque international ISKO-France 2019 10

Figure 6 .

 6 Figure 6. Sections of a data paper

Figure 7 .

 7 Figure 7. Data papers and the DIKW pyramid

  Only seven journals have been launched before 2000. The other 21 journals have been launched during the last ten years, from 2008 on, and especially in 2013 (7 journals) and 2014 (5 journals). Four journals have ceased or are suspended.At least one part of the data journals are considered as good or high quality journals. 11 data journals are indexed by Clarivate Analytics, 8 by Elsevier's Scopus database. 16 journals are referenced in the international Directory of Open Access Journals (DOAJ).

  Data are described with rich metadata: data papers enrich existing metadata of datasets. o F4. (Meta)data are registered or indexed in a searchable resource: the enriched metadata are registered, indexed and preserved on the data journal platform. France 2019 application of formal, standard language (vocabularies) for the description of datasets. As a minimum, they reproduce the data repositories' own formal dataset representation. o I3. (Meta)data include qualified references to other (meta)data: data papers can (and usually do) provide links to other related resources, e.g. research papers, institutional affiliations, similar or related datasets, etc.  Reusable o R1.1. (Meta)data are released with a clear and accessible data usage license: as mentioned above, most data papers are published with an open license; whenever the data paper is derived from the original metadata, this license may depend on the repository's initial licensing and reuse rights. o R1.2. (Meta)data are associated with detailed provenance: one of the main functions of data papers is to provide detailed knowledge about where the data came from, who to cite, who generated or collected it and how has it been processed (workflow).

	 Accessible
	o A2. Metadata are accessible, even when the data are no longer available: the
	accessibility of metadata published via data papers does not depend on the
	datasets' accessibility in a data repository.

 Interoperable o I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation: at least some of the data journals insist on the Colloque international ISKO-

  As mentioned above, data papers can be at least research data available in data repositories such as Dataverse or others (see the Pensoft workflow, Chavan & Penev 2011). The technology is there. Recently, the French National Institute for Agricultural Research (INRA) updated their Dataverse-based repository including an online tool that partly generated "by machines", i.e. through the exploitation and transformation of metadata on researchers can use to generate data papers from the deposits' DOI, in an open text format compliant with INRA's own data journal or with Elsevier's Data in Brief. [23]

Colloque international ISKO-France 2019

Similar to most cited authors,[START_REF] Smith Mackenzie | Data Papers in the Network Era[END_REF] states that data papers "are like traditional research papers in some aspects: they are formally accepted, they are peer-reviewed, they are citable entities" but then adds that "in other respects they are very different from traditional research articles because they are not about the research, they are about the data" (p.15). And this exactly is the main reason for some more critical voices, expressing concerns about the real demand by society and research, about the additional workload for authors and peer reviewers, and about the motivation of scientists to share their data. The underlying idea is that scientists should (and mostly do) publish about results, not about data.

ecosystem or not. Perhaps they will not. However, it seems probable that the number of data papers will continue to grow and gain importance, perhaps (probably) not via data journals but via increasing hybridization of research journals and journal platforms, and perhaps even through the merging of journal and data platforms. In any case, on the boundary between research data management and academic publishing, data papers will continue to provide a highly relevant object for library and information science, especially for the further assessment of the development of academic publishing and knowledge organization in the field of scientific research.

DATA AVAILABILITY

The CSV table of the underlying dataset is available in the Dutch repository DANS, at the following address: https://doi.org/10.17026/dans-zk3-jkyb

ACKNOWLEDGEMENT

The paper builds on the scientific and professional experience of all authors in the framework of the international Grey Literature Network Service [25] and its international workshops on data papers (Netherlands, Czech Republic, Italy, and the United States), its collection of data papers in the Dutch EASY data repository and its follow-up study of enhanced publications.


Pourquoi votre téléphone portable vous rend-il aveugle ?

Arnaud Rey CNRS -Marseille Le téléphone portable est une invention technologique remarquable qui nous permet de communiquer n'importe quand, avec pratiquement n'importe qui et, virtuellement, dans n'importe quel endroit. Cependant, il a été démontré récemment que, dans certaines situations, ce bel objet de communication nous rendrait dangereusement aveugles. Un grand nombre d'études révèlent en effet qu'en conduisant une voiture, l'usage du téléphone portable induit une cécité mentale, dont les conséquences peuvent être mortelles pour le conducteur et pour son entourage.

Pour mesurer de façon précise ce phénomène connu de tous, on simule l'effet du téléphone portable sur la conduite automobile en laboratoire. Par exemple, on peut placer des personnes devant un ordinateur et leur fournir une manette de jeu qui leur permet de déplacer une petite croix sur l'écran. Leur tâche consiste à maintenir la croix au centre d'un cercle qui se déplace de manière aléatoire (voir la Figure 1). A cette première tâche relativement simple s'ajoute une seconde tâche qui implique de faire attention aux changements de couleur du cercle qui, de temps à autre, peut devenir momentanément rouge ou vert (comme un feu de signalisation). Lorsque le cercle devient rouge, les personnes doivent appuyer rapidement sur le bouton de la manette de jeu, comme pour freiner. Lorsqu'il devient vert, ils doivent simplement continuer leur route.

-Figure 1 -Dans un premier temps, les personnes réalisent cette expérience sans téléphone portable, afin d'évaluer leur performance dans des conditions de référence. On constate alors qu'entre le moment où le cercle devient rouge et le moment où elles appuient sur le bouton du joystick (comme pour freiner), s'écoulent en moyenne 530 millièmes de seconde. Par ailleurs, dans 2.7 % des cas, les personnes « grillent » en quelque sorte le feu rouge, puisqu'elles n'appuient pas sur le bouton lorsque le cercle devient rouge.

On donne ensuite à ces mêmes personnes un téléphone portable. Elles doivent maintenant parler d'un sujet d'actualité avec une autre personne qui se trouve dans une pièce voisine tout en continuant leur exercice de conduite simulée sur ordinateur. Le nombre de freinages manqués augmente alors de 2.7 à 7% et le temps de freinage moyen augmente également de 50 millièmes de seconde. Aussi petites qu'elles puissent paraître, ces performances liées au téléphone portable sont toutes significativement différentes de celles observées dans la situation de référence.

Dans des conditions plus réalistes (avec des simulateurs de conduite automobile), les résultats se révèlent encore plus marqués. L'effet délétère du téléphone portable est multiplié par 5 (le temps de freinage augmentant de 250 millièmes de seconde avec un téléphone portable). Autre résultat très important: le port d'une oreillette n'améliore en rien la situation puisque le ralentissement du temps de freinage ainsi que le taux de freinages manqués sont les mêmes que lorsque le téléphone est tenu à la main. Le législateur distingue pourtant ces deux cas de figure et n'interdit nullement de téléphoner à l'aide d'une oreillette.

Que nous révèle ce type d'expérience ? La recherche en psychologie nous a appris depuis longtemps que nous disposons de ressources attentionnelles limitées. Notre cerveau ne peut traiter simultanément qu'un nombre limité d'informations. Or, réaliser en même temps la tâche de poursuite et la tâche de discrimination de couleurs exige de mobiliser une grande part de notre attention. En y ajoutant une troisième tâche (parler au téléphone), il semble que nous saturions nos capacités attentionnelles. Une conversation au téléphone nécessite effectivement de porter attention à ce que nous dit notre interlocuteur pour pouvoir ensuite lui répondre (ce qui n'est pas le cas d'une émission de radio dont les effets sont d'ailleurs pratiquement nuls). Nous devenons ainsi « mentalement aveugles » car notre attention étant dirigée vers la conversation téléphonique, elle n'est plus en capacité de traiter correctement la signalisation (ou tout autre information importante). Pour conclure, vous savez maintenant pourquoi il est préférable de laisser sonner votre téléphone portable lorsque vous êtes au volant de votre véhicule. Vous savez qu'en décrochant votre téléphone et en portant attention à la conversation, vous limitez vos ressources attentionnelles et vous risquez de devenir aveugles à certaines informations de l'environnement. Vous savez ainsi que vous disposez de ressources attentionnelles limitées et qu'à l'instar des magiciens, cela peut vous jouer des tours…

  Il faut noter que dans le cas d'une conversation avec un passager, la situation est assez différente. En effet, contrairement à un interlocuteur téléphonique, le passager partage les mêmes informations visuelles que le conducteur et peut ainsi interrompre la conversation s'il détecte un danger. Néanmoins, qui n'a pas déjà loupé une sortie d'autoroute alors qu'il était engagé dans une conversation passionnée, monopolisant toute notre attention? Afin d'illustrer ce phénomène d'une autre manière, réalisons l'expérience suivante. Je vous demande de compter aussi rapidement que possible le nombre de fois que vous voyez la lettre « O » parmi l'ensemble des lettres présentées dans la Figure 2. Vous devez normalement trouver six « O ». Regardez maintenant à nouveau la Figure 2 et essayez de trouver un mot de sept lettres. Alors même que vous ne l'aviez peut-être pas vu lorsque vous comptiez le nombre de « O », vous devez normalement avoir trouvé sans difficulté le mot « aveugle ». Cette petite expérience montre une fois encore que si votre attention est occupée à réaliser un certain traitement, vous devenez alors aveugles à d'autres formes d'informations alors même qu'elles se trouvent dans votre champ de vision. -Figure 2 -Dans un autre registre, mais suivant le même principe, les magiciens exploitent également nos limitations attentionnelles pour nous surprendre. Lors d'un tour de magie, l'objectif du magicien consiste souvent à attirer notre attention dans une direction afin que nous ne remarquions pas un changement qu'il est en train de réaliser à un autre endroit. Alors même que le changement peut avoir lieu exactement là où nous regardons, nous ne le voyons pas car notre attention est dirigée ailleurs.


Arnaud Rey

Arnaud Rey Regard

Regard psychanalytique sur le couplage individu-environnement dans la maladie d'Alzheimer

  Qui aurait pu imaginer trouver un lien entre psychanalyse et maladie d'Alzheimer ? Si l'on entend par psychanalyse, l'une des théories les plus complètes du fonctionnement mental qui place l'inconscient, la sexualité (au sens large) et les mécanismes de défense au coeur de notre dynamique psychique, mais aussi une méthode qui, par la parole, la libre Ce qui se manifeste est parfois révélateur d'un contenu latent qui n'ose se dire et qui néanmoins transparaît à la première occasion, à condition de savoir le voir ou l'écouter.Evidemment, avec la maladie d'Alzheimer, on pourrait considérer que tout ce que dit la personne malade est insensé et correspond au simple produit des dégénérescences neuronales. Perdant la mémoire, son discours et ses comportements deviennent incohérents.Le diagnostique de démence est alors posé. Circulons, il n'y a plus rien à voir, ni à entendre.Or, précisément, là où toute personne normalement constituée renonce et circule sans ne plus dire ni bonjour, ni merci, ni au revoir, à la personne malade de son effritement mental, la psychanalyse -elle -tend l'oreille, observe et écoute si à l'origine de chaque parole manifestement insensée ne subsiste pas encore des bribes de sens, écho lointain de ce qui animait cette personne avant qu'elle ne rentre dans les turbulences de la démence. Aussi, Dans un deuxième temps, c'est sur l'environnement lui-même que le clinicien peut agir afin de le rendre moins étrange et moins angoissant pour les patients. C'est à cet endroit qu'André Quaderi opère une sorte de retournement darwinien en demandant à l'environnement de s'ajuster au patient atteint de la maladie d'Alzheimer et non l'inverse.Car tout comme on tente de créer les conditions environnementales qui favoriseront au mieux l'éveil à la vie d'un enfant nouveau-né, il semble qu'il nous faille également penser autrement les conditions environnementales qui favoriseront au mieux la période qui conclura nos vies. Nul doute que les propositions contenues dans ce bel ouvrage nous aideront à mieux penser ces nouvelles formes de couplage individu-environnement.Plus généralement, ce livre paraît à une période où la psychanalyse est bousculée par des environnements académiques et institutionnels des plus critiques à son égard. Et cela est juste et bon, pourrait-on ajouter. Car pour qu'une critique touche, encore faut-il qu'elle appuie là où ça fait mal. Et si des points d'appui existent bel et bien pour qu'une critique s'installe, c'est qu'il est sans doute temps de remettre en chantier le couplage entre la psychanalyse et les environnements où elle se pense utile. En traçant de nouvelles voies où l'approche psychanalytique prouve une fois encore sa pertinence et la justesse de son analyse, André Quaderi creuse finalement le sillon d'une psychanalyse pleine de souffle, soucieuse de son inventivité et de sa capacité à se déplacer pour toujours mieux penser nos vies et leurs couplages environnementaux.

	Regard psychanalytique sur le couplage individu-environnement
	dans la maladie d'Alzheimer
	Postface à l'ouvrage d'André Quaderi
	« Approche non médicamenteuse dans la maladie d'Alzheimer »
	notre environnement et de notre entourage jusqu'à nos capacités de raisonnement, en
	passant -bien entendu -par le langage. Or, avec un langage et une mémoire escamotés,
	c'est évident, pas de talking cure.
	Et pourtant, après Freud et avant André Quaderi, la psychanalyse a su s'émanciper
	de la situation princeps de la cure sur le divan, initialement dédiée à l'individu qualifié de
	névrosé, pour essaimer vers d'autres domaines de la psychologie et de la psychiatrie, comme
	la clinique de l'enfant nouveau-né (où le langage verbal ne s'est pas encore enraciné dans C'est ce couplage individu-environnement qui est à comprendre et à décortiquer afin de
	l'humain) ou la clinique de la psychose (où c'est la conscience de l'individu qui s'enracine guider au mieux l'accompagnement clinique et d'ouvrir la voie vers un meilleur
	rééquilibrage.
	C'est ainsi que, dans le cadre de la maladie d'Alzheimer, l'intervention
	médicamenteuse peut (ou pourrait) agir sur le déclin mnésique et cognitif en limitant ou
	retardant l'évolution de la dégénérescence neuronale et qu'une intervention clinique non-
	médicamenteuse peut accompagner la personne pour l'aider à stabiliser ou harmoniser ce
	couplage individu-environnement à la dérive. Cette intervention clinique, d'inspiration
	psychanalytique, agit dans un premier temps en maintenant -autant que faire se peut -un
	lien entre la personne malade et son environnement humain, en s'agrippant à chaque
	morceau de sens et de vie mentale qui subsiste.

association et le transfert, permet de revisiter les contenus mnésiques inconscients (traces plus ou moins enfouies de nos expériences passées, plaisantes et déplaisantes) en prenant soin de respecter la temporalité des résistances individuelles, alors on ne voit pas très bien le lien qu'il serait possible d'établir entre psychanalyse et maladie d'Alzheimer. En effet, par définition, la maladie d'Alzheimer touche l'une des composantes majeures de notre activité mentale: la mémoire. Avec une mémoire dégradée ou diminuée, c'est toute notre vie consciente qui patine, depuis la simple perception/reconnaissance de autrement dans le réel). Dans ces autres contextes cliniques où le langage fait défaut, on parle alors d'une approche ou d'un regard psychanalytique pour dire que l'on va puiser des éléments de la théorie et de la méthode afin de tenter de comprendre et d'accompagner ces autres facettes de l'humain. Le lien entre psychanalyse et maladie d'Alzheimer que dessine André Quaderi se situe dans la lignée de ces propositions innovantes. Quel est donc ce regard (ou cette écoute) psychanalytique sur la maladie d'Alzheimer auquel cet ouvrage nous invite ? Il s'agit dans un premier temps de reprendre l'une des pierres angulaires de la psychanalyse qui porte sur la dichotomie entre manifeste et latent. Ce qui est manifeste, comme par exemple un symptôme ou un lapsus, n'est pas toujours à prendre et à interpréter au premier degré. face à l'émiettement provoqué par la maladie, le regard psychanalytique propose de continuer de tisser des liens, toujours et encore, et de ne pas abandonner la personne, seule dans cette zone de présent perpétuel dans laquelle elle surnage quotidiennement entre incompréhension et angoisse.

Et c'est ici qu'André Quaderi opère un renversement inédit et précieux. En nous proposant de garder à l'esprit que derrière les signes manifestes de la démence vit encore une personne pour laquelle c'est le monde qui l'entoure qui est devenu insensé, il nous permet de mieux comprendre les nouvelles coordonnées de la vie mentale de ces personnes et notamment, de mieux palper l'angoisse immense associée à cet environnement devenu dément. On retrouve ainsi l'une des caractéristiques majeures de l'approche psychanalytique depuis ses origines qui consiste à analyser les déséquilibres individuels en considérant avant tout le couplage et les interactions de l'individu avec ses environnements présents et passés.


Patrick Kfoury

Yann Battie
email: yann.battie@univ-lorraine.fr

E N Aotmane

Naciri

Michel Voue

Nouari Chaoui

Rapid ellipsometric imaging characterization of nanocomposite films with an artificial neural network

Imaging ellipsometry is an optical characterization tool that is widely used to investigate the spatial variations of the opto-geometrical properties of thin films. As ellipsometry is an indirect method, ellipsometric map analysis requires a modeling step. Classical methods such as the Levenberg-Marquardt algorithm (LM) are generally too time-consuming to be applied on a large data set. In this way, an artificial neural network (ANN) approach was introduced for the analysis of ellipsometric map. As a proof of concept this method was applied for the characterization of silver nanoparticles embedded in poly-(vinyl alcohol) film. We demonstrate that the LM and ANN gives similar results. However, the time requires for the ellipsometric map analysis decreases from 15 days for the LM to 1s for the ANN. This suggests that ANN is a powerful tool for fast spectroscopic ellipsometric imaging analysis.

Spectroscopic ellipsometry (SE) is one of the most powerful techniques for investigating the opto-geometrical properties of materials. SE is based on the measurement of the change of polarization state of light reflected from sample. This technique, was previously used to determine the complex refractive index of materials, the optical anisotropy, the stoichiometry of alloys, the thickness and the roughness of thin films [START_REF] Zhou | Investigation on the optical properties of annealed Mn-Co-Ni-O thin films by spectroscopic ellipsometry[END_REF][START_REF] Jayaswal | Measurement of the surface susceptibility and the surface conductivity of atomically thin MoS2 by spectroscopic ellipsometry[END_REF][START_REF] Schmidt | Generalized ellipsometry for monoclinic absorbing materials: determination of optical constants of Cr columnar thin films[END_REF][START_REF] Battie | Rapid ellipsometric determination and mapping of alloy stoichiometry with a neural network[END_REF][START_REF] Petrik | Comparative study of surface roughness measured on polysilicon using spectroscopic ellipsometry and atomic force microscopy[END_REF], the profile of diffraction grating [START_REF] Gereige | Recognition of diffraction-grating profile using a neural network classifier in optical scatterometry[END_REF][START_REF] Tchéré | Experimental identification of a grating profile using neural network classifiers in optical scatterometry[END_REF][START_REF] Novikova | Metrology of replicated diffractive optics with Mueller polarimetry in conical diffraction[END_REF] or the volume fraction and shape distribution of nanoparticles (NP) contained in nanocomposite films [START_REF] Battie | Plasmonic properties of implanted Ag nanoparticles in SiO2 thin layer by spectroscopic ellipsometry[END_REF][START_REF] Battie | Determination of gold nanoparticle shape from absorption spectroscopy and ellipsometry[END_REF]. The beam size of standard ellipsometer which limits the lateral resolution, is about few millimeters. However, the trends of devices miniaturization require the development of ellipsometric set up with microscopic scaled resolution. Cohn et al. have addressed this issue by introducing an imaging ellipsometer [START_REF] Cohn | Dynamic imaging microellipsometry: theory, system design, and feasibility demonstration[END_REF]. This kind of set-up allows recording the spatial variation of ellipsometric spectra with a lateral resolution as small as 1 μm. This technique was previously used to investigate the homogeneity and the optical properties of 2D nanomaterials [START_REF] Funke | Imaging spectroscopic ellipsometry of MoS2[END_REF][START_REF] Matković | Spectroscopic imaging ellipsometry and Fano resonance modeling of graphene[END_REF], optical waveguides [START_REF] Moreno-Zarate | Imaging ellipsometry determination of the refractive index contrast and dispersion of channel waveguides inscribed by fs-laser induced ion-migration[END_REF], patterned biosensors [START_REF] Qi | Detection of avian influenza virus subtype H5 using a biosensor based on imaging ellipsometry[END_REF], diffraction gratings [START_REF] Liu | Mueller matrix imaging ellipsometry for nanostructure metrology[END_REF] or plasmonic materials [START_REF] Guyot | Gold nanoparticles growing in a polymer matrix: What can we learn from spectroscopic imaging ellipsometry?[END_REF]. Two images representing the ellipsometric angles  and  are recorded for each wavelength. As SE is an indirect characterization tool, the extraction of physical parameters of the sample from the recorded spectra requires a modeling step. However, the analysis of this huge data cube remains challenging. To reduce the data size, pixels can be gathered into region of interest (ROI) by using the binning process. However, this approach assumes that opto-geometrical properties are homogeneous inside a ROI. On the other end, the fitting of the full ellipsometric map by using classical optimization algorithm such the Levenberg-Marquard algorithm (LM) is often time consuming. Even the interpolation of the  and  values limits to power of the analysis to 1-parameter optical models. Recent works have suggested that ellipsometric spectra, recorded without spatial resolution, can be exploited by using an artificial neural network (ANN) [START_REF] Battie | Rapid ellipsometric determination and mapping of alloy stoichiometry with a neural network[END_REF][START_REF] Gereige | Recognition of diffraction-grating profile using a neural network classifier in optical scatterometry[END_REF][START_REF] Tchéré | Experimental identification of a grating profile using neural network classifiers in optical scatterometry[END_REF][START_REF] Novikova | Metrology of replicated diffractive optics with Mueller polarimetry in conical diffraction[END_REF][START_REF] Fried | Backpropagation (neural) networks for fast pre-evaluation of spectroscopic ellipsometric measurements[END_REF][START_REF] Fried | Non-destructive optical depth pro®ling and real-time evaluation of spectroscopic data[END_REF][START_REF] Rédei | A modified learning strategy for neural networks to support spectroscopic ellipsometric data evaluation[END_REF][START_REF] Urban | Extremely fast ellipsometry solutions using cascaded neural networks alone[END_REF][START_REF] Li | Deep learning for rapid analysis of spectroscopic ellipsometry data[END_REF][START_REF] Kim | Optical characterization and prediction with neural network modeling of various stoichiometries of perovskite materials using a hyperregression method[END_REF]. Fried et al. have used an ANN analysis of ellipsometric spectra to determine the damage profile induced by ion implantation in silicon-on-insulator structure [START_REF] Fried | Backpropagation (neural) networks for fast pre-evaluation of spectroscopic ellipsometric measurements[END_REF]. This neural analysis procedure was also used to analyze ellipsometric map [START_REF] Fried | Non-destructive optical depth pro®ling and real-time evaluation of spectroscopic data[END_REF]. They also modified the sample selection strategy to improve the convergence of the learning algorithm toward global minima [START_REF] Rédei | A modified learning strategy for neural networks to support spectroscopic ellipsometric data evaluation[END_REF]. Urban III et al. have implemented a cascade of several ANN to improve the accuracy of the ANN analysis [START_REF] Urban | Extremely fast ellipsometry solutions using cascaded neural networks alone[END_REF]. Recently, some authors. have shown that ANN could be a fast, accurate, and useful approach to exploit ellipsometric measurements performed on Ge-Sb-Te alloys [START_REF] Li | Deep learning for rapid analysis of spectroscopic ellipsometry data[END_REF] or perovskite materials [START_REF] Kim | Optical characterization and prediction with neural network modeling of various stoichiometries of perovskite materials using a hyperregression method[END_REF]. In our previous work, we have also demonstrated that ANN is less sensitive to local minima and solves the inverse problem in a shorter time than the LM algorithm [START_REF] Battie | Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network[END_REF]. Despite these development in ellipsometric characterization, this approach has never been used to exploit ellipsometric images. Indeed, it was only applied to punctual ellipsometric measurements or ellipsometric map with a small number of pixels [START_REF] Battie | Rapid ellipsometric determination and mapping of alloy stoichiometry with a neural network[END_REF][START_REF] Fried | Non-destructive optical depth pro®ling and real-time evaluation of spectroscopic data[END_REF].

In this context, the aim of this letter is to demonstrate the feasibility of the complete analysis of ellipsometric images by using a neural network. Compare to punctual ellipsometric measurements or standard ellipsometric map, these images are composed of a large number of pixels. The fine analysis of this kinds of images generally constitutes a challenge. For this proof of concept, spectroscopic imaging ellipsometry measurements were performed on thin poly-(vinyl alcohol) (PVA) film containing silver nanoparticles (NPs) and deposited on silicon wafer. This sample was elaborated according to the Porel method [START_REF] Porel | In situ synthesis of metal nanoparticles in polymer matrix and their optical limiting applications[END_REF]. Briefly, a (0.12g/ml) aqueous solution of PVA (MW: 13000 -23000) is first prepared and stirred vigorously at 90°C for 1 hour, and at room temperature for 23h. Then, silver nitrate (AgNO3) is added to the solution in order to obtain an AgNO3 concentration of 0.0475 g/ml. After that, the solution was deposited by spin coating at 5000 rpm on a silicon substrate. Finally, the central part of the obtained sample was irradiated with a 150W Xenon lamp for 2h at an irradiance value of 2000 W/m². Twenty maps of ellipsometric angles  and  were recorded at 10 wavelengths in the 380-812 nm spectral range by means of an EP3-SE spectroscopic nulling imaging ellipsometer (Accurion GmbH). This corresponds to a data-cube of 8424600 elements. The objective was a Nikon 10X (NA=0.21). The angle of incidence was set to 60°. The spatial resolution was estimated to 0.52x0.52 μm² per pixel, without binning of the images. Ellipsometric spectra are also recorded by using a classical ellipsometer (UVISEL, Horiba), without spatial resolution. The measurements are performed at an angle of incidence of 70° in the 270nm-900nm spectral range and a spot size of about 800 µm. As ellipsometry is an indirect technique, each spectrum must be analyzed with a model which reflects the physical properties of the sample. In our case, the model consists in Ag NPs embedded in a PVA film on a silicon substrate. As previously shown [START_REF] Battie | Plasmonic properties of implanted Ag nanoparticles in SiO2 thin layer by spectroscopic ellipsometry[END_REF][START_REF] Battie | Determination of gold nanoparticle shape from absorption spectroscopy and ellipsometry[END_REF], the effective dielectric function (

) of the film can be described by using the shape distributed effective medium theory (SDEMT):

. [START_REF] Zhou | Investigation on the optical properties of annealed Mn-Co-Ni-O thin films by spectroscopic ellipsometry[END_REF] f is the volume fraction of Ag NPs. and are the dielectric function of PVA matrix and silver, respectively. The coefficient is given by: . ( 2)

is the normalized distribution of the depolarization factors of NP which is directly related to the distribution of NP shape. These parameters must respect the following sum rule:

. (3)

In the following, we assume that the distribution is described by a Gaussian distribution centred on the locus of spherical NP:

. ( 4) and C are the standard deviation and the normalization factor of the distribution, respectively. Three parameters must be determined for each pixel positions: f, and the film thickness h. The spatial variations of f, and h deduced from ellipsometric measurements by using the LM algorithm are depicted in Figure 1 a-c. These maps highlight the inhomogeneities of nanocomposite film at the microscale. This spatial inhomogeneities are probably related to the NPs growth mechanism and especially the diffusion and aggregation of NPs in the film. Some statistical parameters can be extracted from ellipsometric maps. The mean value of f, h and are 4.75%, 253 nm and 0.099, respectively. These results are close to the f, h and values of 4.14%, 253 nm and 0.99 by using a classical ellipsometer, without spatial resolution. The standard deviation of f, h and are 0.72%, 15 nm and 0.017, respectively. The root mean square error between the measured and modeled spectra is estimated to 0.013 0.019. This value is in the same order of magnitude as the accuracy of the ellipsometer confirming the validity of the model. Despite local information on the sample can be deduced from imaging ellipsometry, the analysis of the ellipsometric maps with the LM algorithm requires 15 days computing time. Thus, this procedure is too time consuming to be used for routine characterizations. Other optimization algorithm should be found to overcome this issue. Since ANN is a universal and parsimonious approximator [START_REF] Cybenko | Approximation by superpositions of a sigmoidal function[END_REF], it can be considered as a promising candidate to solve these kinds of problem with a shorter computing time. As a proof of concept, we implement in Matlab multilayer perceptron (Figure 2(a)) composed of a single hidden layer and an output layer of neurons. The calculations are performed with a multi-core personal computer (Intel Core i7 7700k, 4 cores, 3.6 GHz). The layers are linked together by weighted synaptic connections. Each neuron applies a specific transfer function to its weighted inputs:

. ( 5)

is the weight of the synapse which links the input and the neuron m. The transfer functions of the hidden neurons and output neurons are sigmoid and linear functions, respectively. The input vector consists in the ellipsometric parameters Is and Ic. The number of hidden neurons is arbitrary set to 30. The output layer is composed of 3 neurons. The outputs of the ANN are f, and h. The relationship between the input and output of the ANN is learned during the training step. This step requires a large number of input/output data. 400000 samples are randomly generated from a uniform distribution by taking the parameters f, and h in the following ranges. , , .

The generation of these data takes almost 6h. Then, the ellipsometric spectra of these samples were simulated by using our model. These couples of input/output data were then split into three corpuses: the training data (280000 couples), the validation data (80000 couples), and the test data (40000 couples). The ANN was then trained with the training data by using the back propagation Levenberg-Marquardt algorithm [START_REF] Rojas | Neural networks: a systematic introduction[END_REF]. During the training step, the synaptic weights were adjusted to minimize the error between the training data and the ANN output. To avoid the overfitting, the training was stopped by using the early stopping technique, i.e. until the error evaluated from the validation data increases. The training of the ANN takes almost 3h. Once trained, the ANN can be reused to analyze several ellipsometric images without any further training step. The performances of the ANN were evaluated on the test data. Figure 2(b)(c)(d) shows the value of f, and h determined by the ANN as a function of the corresponding value of the test data. Linear variation is found between the f, and h values predicted by the ANN and the nominal ones. The slope and intercept value are close to 1 and 0, respectively. The root mean square error on f, and h are estimated to 0.1%, 2 nm and 0.003, respectively. These results obtained on test data, confirm the high level of operating performance of the ANN. Nevertheless, this performance was evaluated on virtual samples. It is therefore important to evaluate the performance of our ANN on an experimental ellipsometric map. Figure 1(d)(e)(f) shows the f, and h maps deduced from the analysis of the ellipsometric map with the ANN. These maps are similar to those obtained by the LM algorithm. To give a more quantitative comparison, the results determined by the ANN for each pixel are against those deduced from the ANN (Figure 3). Each point of Figure 3 is located along the identity line. The root mean square error between the f, h and , as estimated by ANN and LM are 0.3%, 4.8 nm and 0.008, respectively. In addition, both methods give similar f, h and distributions. Thus, it can be concluded that the ANN globally reproduces the results obtained by the LM. The mean value of the root mean square error between the measured and the modeled spectra from the ANN is estimated to 0.014 0.024. This value is close to that obtained by the LM, suggesting that the LM and ANN have the same accuracy. We can also remark that for 9 pixels, the LM falls into a local minimum located at 0.3. However, the ANN gives other solutions with a smaller root mean square error for these pixels. It probably converges toward the global minima. Indeed, as shown by several works ANN is more robust than LM to local minima [START_REF] Urban | Extremely fast ellipsometry solutions using cascaded neural networks alone[END_REF][START_REF] Battie | Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network[END_REF]. In summary, a new method based on an ANN is introduced to analyze spectroscopic ellipsometric maps. This method, applied to silver NPs in PVA film, allows investigating the spatial variation of film thickness, volume fraction and the shape distribution of NPs. This neural and LM data processing gives similar results with the same accuracy. However, the computing time required for the analysis of the 842460 spectra of  and  which composed the ellipsometric map decreases from 15 days for the LM to 1s for the ANN. This can be considered as real drastic improvement for inline SE imaging characterization. In addition, once trained, the ANN can be reused without any further training step. As also reported by several works [START_REF] Urban | Extremely fast ellipsometry solutions using cascaded neural networks alone[END_REF][START_REF] Battie | Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network[END_REF], ANN is less sensitive to local minima of the error function than LM. Finally, the ANN ellipsometric map processing is fairly flexible and can be easily applied to other kind of samples., although requiring a specific training for each class of sample.

Figure 1 :

 1 Figure 1: Specially-resolved parameters of the optical model : (a)(b) Ag NP volume fraction ; (c)(d) Film thickness ; (e) and (f) Width of the normal distribution of the depolarization factors. (a)(c)(e) images: are obtained from a LM inversion. (b)(d)(f) images are obtained from ANN.

Figure 2 :

 2 Figure 2: (a) Schematic representation of the ANN used in this work. Comparison between the (b) f, (c) h, and (d)  values estimated by the ANN and the theoretical values of the test data. The linear regressions are represented by the red dashed line.

Figure 3 :

 3 Figure 3: Comparison between the (a) f, (b) h, and (c)  values and histogram estimated by the ANN and LM. Each dot represents 1 pixel of the ellipsometric map. The red dashed line represents the identity line.

Funding. This work was supported by the PHC Tournesol Program (46237UL).

Disclosure. Authors declare no conflict of interest.

Authors' contributions. These authors contributed equally to the work

Availability of data and material. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.