dataesr/scientific-paragraphs-categorization huggingface dataset:
Intriguingly, we observe that the treatment with context reduces underprovision from female sellers and especially toward male buyers (from 0. 70 to 0. 10). Regarding paid prices, even though some differences can be noticed in prices across the composition of pairs, no clear pattern is observed. 12  In what follows, we consider first the sellers' behavior (Section 4. 3. 1) and second, the consumers' behavior (Section 4. 3. 2), by testing the hypotheses presented above. Since we have 192 subjects, this gives us 96 buyer-seller pairs (i. e. 48 in each condition) interacting over 16 rounds generating an unbalanced panel dataset of 1536 observations in total (see Table  5 ). 13 In our econometrics analysis, we first used a panel data random effects linear specification to analyze the determinants of posted prices.
Acknowledgments We thank the audiences at the Winter School on Credence Goods 2018 in Innsbruck, the ASFEE 2018 in Nice, the ASFEE 2019 in Toulouse and internal seminars at  Université Côte d'Azur  for helpful comments. Special thanks are due to  Mira Toumi  and  Imen Bouhlel  for assisting in the experiment. We express our gratitude for the comments of the editor and three anonymous reviewers. Financial support from the  JSPS KAKENHI : (i) Fund for the Promotion of Joint International Research, Fostering Joint International Research  ( 15KK0123 ) and ( ii) Grant-in-aid for Scientific Research  ( 18K19954 ,  20H05631 ), as well as the  Joint Usage/Research Center at ISER, Osaka University , are gratefully acknowledged.
Metadata Necessary technologies are already in place for data and metadata publication and identification, effectively making it largely Findable, Accessible, Interoperable, Reusable (FAIR) data  [26]. On the storage and publication side, popular resources such as Zenodo, FigShare, OSF storage and various dataverses offer longterm storage and identification of data. Stored data is associated with metadata. The used metadata schemes, or the part understood by repositories, are easily converted in a myriad of formats and schemata. However, they are rather poor. These metadata are pushed and consumed by a variety of indexes such as re3data, DataCite or Google dataset search  [21]. The latter offers search among data sets described with schema. org dataset and DCAT. Along these generalist repositories and search indexes, there exist many disciplinary ones that provide more specialised metadata.
There are now indexes of these resources such as FAIRsharing. org, with proper metadata search  [23]. Many efforts are already dedicated or related to representing experiments. There exists more or less standalone software such as eLabFTW  [15]  to computerise laboratory notebooks and to make them more easily accessible. But the reproducibility requirements pushes towards more systematic efforts to describe experiments  [13]. Concerning computational experiments, of particular interest is the COMSES Computational Model Library  [22]  which tracks mostly agent-based models in life and social sciences. It aims at reproducing computational simulations. However, not all experiments are computational by nature, and experiment descriptions should go beyond data manipulation. A natural way to implement more semantic experiment descriptions is to use semantic web technologies (RDF, OWL, SPARQL).
BEYOND METADATA CATALOGUES: SCIENTIFIC KNOWLEDGE GRAPHS There are four actors in the dissemination of such experiments descriptions: experimenters who provide experiment description (metadata) and experimental data. repositories which provide storage and identification services. catalogues (or indexes) which provide description aggregation and search. users who will use data described in these catalogues. All these actors are providing a necessary service, but users have a very limited opportunity to access relevant information for fulfilling their needs. They may be scientists as well as evaluators, integrity investigators, etc. They may have very different needs (dataset look up, similar experiments, similar hypotheses) and goals (reproducibility, repurposability, enquiry). Hence, we need scientific knowledge graphs in which to search, navigate, query and manipulate experiment descriptions.
For instance, eLabFTW  [15]  is able to connect to laboratory supply databases: identifying exactly what product 'item' was used in an experiment. This contributes to accountability, but can also help providing rich data. Extracting this data already facilitate providing it to publication platforms such as Zenodo. incrementality The main point is to be able to provide as much data as one wants, not to force providing a complete description. The openness and extensibility of semantic technologies allow to overcome incomplete metadata and to make the best out of it. reuse by referring to other components (design, authors, papers, genes) it is easier to describe experiments. An FRBRinspired modular design would also facilitate component reuse. I simply claimed that precisely describing experiments semantically and connecting them in a scientific knowledge graph have many benefits.
We use several sources to collect and evaluate academic scientific publication on a country scale, and we apply it to the case of France for the years 2015-2020, while presenting a more detailed analysis focused on the reference year 2019. These sources are diverse: databases available by subscription (Scopus, Web of Science)  or open to the scientific community (Microsoft Academic Graph), the national open archive HAL, and databases serving thematic communities (ADS and PUBMED). We show the contribution of the different sources to the final corpus. These results are then compared to those obtained with another approach, that of the French Open Science Barometer (Jeangirard, 2019)  for monitoring open access at the national level. We show that both approaches provide a convergent estimate of the open access rate.
It is with this in mind that the French Open Science Barometer (BSO) was carried out 5  , as described by  Eric Jeangirard (2019). For the BSO, the constitutive choice is to use only open sources. The methodology used consists in scanning all the papers references in Unpaywall and in the national open archive HAL (see below), in order to identify either the French authors or the presence of the mention of France in the affiliation. The publications thus identified were then enriched with information on their scientific discipline, using natural language processing (NLP), also based on an open source, to determine, from the title, the discipline to which a document belongs. Finally, the open access status was determined using the Unpaywall database. The corpus obtained by this strategy is available in open access from the MESRI OpenData portal 6.
In accordance with the recommendations made at the European level (Open Access Monitoring:  Philipp et al. 2021) , the French National Open Science Barometer is published on an annual basis. About 150,000 publications are thus identified each year by the BSO. The purpose of this study is to consider an alternative approach, this time based on the use of the main open or non-open bibliographic databases, and to analyse the extent to which this new corpus differs from that of the BSO. Our approach is based on the use of six complementary sources, namely WoS, Scopus, Microsoft Academic Graph, PubMed, NASA/ADS and the HAL open archive, to identify and assess academic scientific publication at the scale of a country, in this case France, for publications released during the six years 2015-2020.
As the year scale seemed to us more relevant to characterize scientific production, we chose to highlight, in the context of this article, the data related to the year 2019 7. We then compare the corpus obtained with that of the BSO, and we show to what extent the diversity of the sources used makes it possible to refine the identification and characterization of French scientific production, as well as the estimation of the open access rate. While there is an abundant literature on the comparison between Scopus, WoS and other generalist databases (see, for example, in a national production context:  Bartol et al. , 2014; Moed, Markusova & Akoev, 2018; Archambault et al. , 2009;  or for a statistical comparison of large reference databases:  Mongeon & Paul-Hus, 2016; Pranckuté, 2021; Visser et al.
DOI (Digital Object Identifier): The DOI 8  is a persistent identifier that can be assigned to any type of content, be it text, software, datasets, etc. (Simmonds, 1999). It will be used as a common metadata for the entire study. Scientific publications: We consider here scientific publications indexed in databases (private or public) and accessible in open archives. All types of documents are taken into account. This primarily concerns articles, generally published in international peer-reviewed journals, but also conference proceedings, book chapters, or any other publication, provided that it has a DOI. However, the restriction to only documents with a DOI is an important restriction, which we must explain here. In order to facilitate the aggregation of results, and to avoid duplication, we have chosen, as does the BSO (French Open Access Monitoring), to restrict the cross-referencing of data to publications identified by a DOI number.
This step is necessary to allow the efficient crossreferencing of documents identified in each database by their DOI identifier, common to all databases. In addition, the Unpaywall database, which will inform us about open access in the next step, only lists publications with a DOI. Let us note that the requirement of the presence of a DOI immediately rules out a certain number of journals that do not adhere to this very general technology of persistent identifiers  (Gorraiz et al. , 2016) ; some of these journals may be, as  Wang et al. (2020)  point out, key journals in their discipline with the example, for the field of Artificial Intelligence of the Journal of Machine Learning Research. Moreover, grey literature, under which we can group preprints, reports, theses and in some cases conference proceedings  (Schöpfel & Prost, 2019) , is often ignored by open access measurement tools, mainly for two reasons: the first corresponds to a concern to discard literature whose scientific relevance cannot be sufficiently controlled (lack of peer review); the second is rather related to technical considerations, in particular a difficulty in identifying these publications in the absence of complete and standardized metadata, and in particular persistent identifiers.
For this reason, non-academic publishing generally falls outside the scope of our study. Open access: A scientific article that is only available on payment of a subscription or a toll (price per article) is considered closed. In contrast, a scientific article that is freely available, either on a publisher's website or after the deposit of the full text (in its final layout or not) on an open archive, is deemed open. Our source of information for the open access status of an article will be the Unpaywall database  (Piwowar et al. , 2018) , specifically the data in the "is_oa" field. If the value returned for a given publication is equal to "True", the publication will be considered open. If this value is "False", the publication will be considered closed. The so-called "bronze" status is considered open.
Note that the open access status may vary over time, since a closed publication may have its embargo lifted or be subsequently deposited in an open archive. Thus, in our study, it will be the status observed in February 2021, as recorded in the Unpaywall database snapshot for that date. Let us recall that for France, the Law for a Digital Republic of October 7, 2016 9  establishes the possibility of deposit on an open archive of the postprint of any scientific article resulting from research funded at least for half by the State or public authorities, at the expiration of a period of 6 months to 12 months depending on the scientific field (respectively, STM or Humanities & Social Sciences).
Sources used to constitute the FR-2015-2020 corpus The collection of metadata related to a large set of publications is facilitated by the use of databases that systematically, if not exhaustively, collect a large part of the millions of scientific publications published each year worldwide. In this article, we have privileged the databases providing a search capability for the mention of the country in the affiliation, and we have collected the publications whose affiliation mentions the country considered in our study, France, using the corresponding query modes of six databases that, to our knowledge, effectively cover the French scientific production. We did not use the Dimensions database, as it is not considered to be a reliable source for establishing a corpus on a country scale  (Guerrero-Bote et al.
, 2021). We use the following databases in our study: § Scopus  (Baas et al. , 2020)  references more than 25,000 journals and is considered one of the most comprehensive databases for international peer-reviewed journals. Query by country is possible. Metadata extraction is limited to batches of 20,000 documents. This database is available by subscription from Elsevier. § Web of Science  (Birkle et al. , 2020)  has been the reference database for scientometrics since the pioneering work of  Garfield (1964). The query by country is provided in the advanced query mode. This database is available by subscription from Clarivate Analytics. In this study, we use all the indexes (including ESCI: Emerging Sources) except for the Book Citation Index which was not available to us. § The HAL open archive https://hal.
archives-ouvertes. fr/ (Charnay & Michau, 2007 ) is a national multidisciplinary open archive intended for the deposit and dissemination of researchlevel scientific articles (published or not), theses and other objects emanating from French or foreign teaching and research establishments, public or private laboratories. Created in 2001 with ArXiv as a model, this platform has gradually become one of the main tools for reporting French research. A partnership agreement in favour of this archive was signed in 2013 by the Conference of University Presidents (CPU) and 22 institutions. In July 2021, the MESRI also committed to supporting the development of this archive, both in terms of technical aspects and governance, as part of its second national plan for open science 2021-2024. French researchers are invited to deposit on this platform the products of their research, whether they are publications (article in a journal, communication in a conference, chapter of a book, book, poster, file, patent), unpublished documents (pre-publication, working document, report), academic works (thesis, HDR, course) or research data (image, video, software, map or sound).
The recorded documents are either in the form of a notice only, or accompanied by the full text of the article. This production can be grouped within different collections or portals relating to a theme (SHS for example), a medium (images and videos) or a research structure (university, laboratory or research team), but it remains possible to carry out queries covering all portals and collections. After 20 years of use  (Berthaud, Charnay, & Fargier, 2021) , more than 2,700,000 works are now recorded in this archive. HAL data can be queried using an advanced query or the API. The latter, which is available free of charge, allows the identification of the country of affiliation. § The NASA/ADS database  (Kurtz et al. , 2000)  is one of the most recognized examples of a bibliographic database covering a research field: astrophysics and physics.
Its query mode allows the query by country. Access is free. § The PUBMED database is one of the preferred and free access points for metadata related to biomedical science research. A query by affiliation is possible  (Ibarra et al. , 2018). § The Microsoft Academic Graph database  (Wang et al. , 2019; Herrmannova & Knoth, 2016) , one of the three products of the Microsoft Research project, is one of the largest open publication and citation data sets. It is populated automatically, using bibliographic data from web pages crawled by the Bing search engine, also a Microsoft product. The data can be accessed using the Academic Knowledge API. It should be noted that MAG does not contain structured data on affiliation country. Identification of French outputs (provided by the Curtin Open Knowledge Initiative team) was by applying a query to the affiliation string (OriginalAffiliation data element from the MAG PaperAuthorAffiliations table, linked via the PaperID to the DOI) that sought to determine whether the affiliation string finished with "France" (or one of a small set of non-English names).
Aggregation of results for publications identified by a DOI As mentioned above, in order to facilitate the aggregation of results and to avoid duplication, we have chosen, as does the BSO (French Open Access Monitoring), to restrict the crossmatching of data to publications identified by a DOI number. Table  2  shows the counts obtained for the year 2019: DOIs are available for 94% of the documents indexed in Scopus and 85% of those in the Web of Science. We can notice, in addition, that a major part of the documents without DOI corresponds to communications to conferences (for France and the year 2019: 54% of the documents without DOI in Scopus are communications; 78% in the Web of Science). For ADS the documents without DOI are mainly conference abstracts, while documents without DOI represent only 1% in PubMed.
For the HAL archive, the situation is different: the fact is that the DOI identifier is not systematically filled in because it is not a compulsory metadata during the deposit. While only 2 to 3% of the documents characterized as articles in WoS or Scopus do not have a DOI recorded, this proportion rises to 22% for the documents characterized as articles in HAL. In addition, the open archive contains many unpublished documents, preprints, reports or theses that do not have (or not yet) a DOI: with the book chapters, these documents represent half of the publications without DOI, which will not be considered for the rest of the study. However, we will return to HAL in Section 5 for a discussion of the grey literature. Note that for MAG, we had direct access to the DOI lists through the COKI team, whom we thank for their help.
Open access and external validation: using Unpaywall One of the objectives of this study is the measurement of the share of open access to publications. For this we use the Unpaywall database 11 which is the leading database in this field  (Piwowar et al. , 2018; Holly, 2018). This database offers a simplified access mode (by batches of 1,000 DOIs) which allows to easily obtain the status of a publication (open or closed access, with the publisher and/or in an open archive) at the time of the query. It is also possible to download a complete version of the database (called a Snapshot). For this study, we used the version dated February 2021. For the year 2019, this version lists more than 6 million publications. Querying the Unpaywall database also allows us to validate the DOIs identified in the previous step: we consider that DOIs not found in Unpaywall generally correspond to identifiers that have not been confirmed by Crossref, the agency that certifies their quality and continuity.
Moreover, it is not uncommon to find differences in the date of publication from one database to another (often due to the time lag between the version published online (early access) and the "final" publication). We have chosen to use the year of publication provided in the Unpaywall database as the reference year (see Table  3 ), whether or not it is consistent with the year of publication mentioned in the source database. This choice is also the one adopted by the BSO (French Open Access Monitoring). Table  3  presents the results of the cross-matching between the six sources, and their validation with Unpaywall. The first column recalls the number of DOIs obtained from each source, already presented in Table  2. The second column presents the numbers of DOIs found in Unpaywall and recorded in this database as published in 2019.
3. 1 Overlap of the two sets The corpus thus constituted (FR-2019) can now be compared with that of the French Open Science Barometer (BSO), which also aims to cover all French production, for several years including 2019 12. Since the BSO data are also restricted to publications with a DOI and have benefited from the Unpaywall query, it is easy to cross-reference the two sets of DOIs. The result is summarized in Table  4  Table  4  shows that, if we restrict ourselves to the data validated after querying Unpaywall, 8% of the total data set (i. e. , 13,707 DOIs) are not identified in the BSO, while conversely 17% of the documents (i. e. , 27,898 DOIs) had not been identified in our FR-2019 corpus 3. 2 Data from our FR-2019 corpus that are not part of the BSO corpus The data from our sources not included in the BSO corpus seem to correspond mainly to a failure to identify the France affiliation in the algorithm developed by  Jeangirard (2019).
Data from the BSO corpus absent from the FR-2019 corpus The data from the BSO corpus not included in our sources come mainly from humanities and social sciences journals (44%), biomedical journals (24%) and basic biology journals (12%). We note a significantly higher proportion of articles in French in this BSO-only subset: 31% compared to the average of 15% for the global corpus (the language analysis methodology will be presented below, in section 4. 4). These are mainly journals or resources not covered by the databases we have used, in particular documentary resources and journals with a national scope, in French or English: for example, the most represented sources in this set are: -Case Medical Research: international database of clinical trials -Faculty Opinions -Post-Publication Peer Review of the Biomedical Literature -SSRN Electronic Journal: database of social science preprints.
Search in Scopus Number  This search allows us to identify 3,616 probable false positives: the Scopus database recognizes the DOI, the year is indeed 2019, but the article does not include, according to Scopus, an affiliation in France. This corresponds to 3. 5% of the DOIs common to BSO and Scopus: this count thus seems compatible with the 4% estimated by  Jeangirard (2019). Let us note once again that the cross-referencing of the different sources highlights divergent assessments of the publication date of the articles. Table  7  presents the cross-referenced contributions of the sources to the overall corpus. It should be noted that the fact that a publication is identified in database A and is not identified in database B as being part of the corpus does not necessarily mean that it is absent from database B: it may be present in database B, but with a DOI that has not been filled in or is incorrect, or a failure to identify the country (no affiliation with France).
Estimated rate of Open Access publications The reader is referred to  Aliakbar & Stahlschmidt (2019)  for a discussion of the merits and limitations of these rate calculations. In their conclusions the authors recommend the use of multiple sources to reduce errors and gaps, and this is clearly a view we share. Cross-matching all these datasets allowed us to correct, at least in part, the problem of false negatives and to obtain a refined estimate of the open access rate.