dataesr/scientific-paragraphs-categorization huggingface dataset:
This shed light on how researchers, through their voluntary evaluation work, perform the same quality peer review, whatever the reputation of the publisher. By doing that, they do help to legitimise grey publishers. Besides, Publons data analysis allowed us to highlight several related results that deserve to be mentioned here. One of the most interesting is the fact that, even though emerging countries have made signi cant progress in terms of the number of publications, such as China becoming the world's top producer or India ranking in the top 3 (according to the Scimago ranking of 2022, see : https://www. scimagojr. com/countryrank. php?order=itp&ord=desc&year=2022), peer review remains heavily concentrated in Europe and the United States. The majority of reviewers for international journals (indexed in WoS) are either American or European.
This issue of diversity among reviewers solicited by these types of journals is not yet well analyzed, to our knowledge, in the existing literature and deserves special attention (for a more inclusive and diversi ed science). Another interesting nding, is the extent to which our study con rms from the data the strong contrast in terms of the length of reviewers' reports according to discipline. Reviewers' reports are relatively short in technical disciplines, such as mathematics or computer science, while they are signi cantly longer in the humanities and social sciences (followed by life sciences). In terms of following up this research, one of the interesting questions would be which geographical areas, and more speci cally which countries, contribute most to MDPI in terms of peer review reports.
Putting open access (author-payer) at the center of its economic model, with an editorial support meeting all the eligibility criteria to be indexed in international databases such as WoS and Scopus and nally convincing some highly renowned researchers to manage special issues in several disciplines. This in turn gives it a certain progressive reputation, since indexing in international databases is in itself a guarantee of quality and legitimacy and constitutes a strong incentive for researchers to submit their research there. To top it off, MDPI offers very short deadlines, allowing for rapid publication while meeting institutional requirements to be indexed in international databases. At the same time, competitors like Taylor & Francis, well considered and seen as legitimate, are offering accelerated publication services aligning publication speed with APC rate 3.
Limitations By using Publons data, we are aware that it is not representative of all reviewer reports from MDPI or the scienti c community as a whole. This said, we must add that these data is the only data available to date. Therefore, it is possible that the reviewers who post their reviews on Publons are those who contribute the most and this should be generalised and not only apply to MDPI reviewers. Given this possible selection bias, it would be interesting to conduct further studies of this type to con rm (or not) our results. [3] https://taylorandfrancis. com/partnership/commercial/accelerated-publication/  Length-review average by discipline Declarations Figures.
Introduction The Tools Ecosystem is a centralized repository for the open and transparent exchange of metadata about software tools and services in bioinformatics and life sciences. It serves as a foundation for the sustainability and interoperability of the diverse Tools Platform services: bio. tools  (Ison et al. , 2019) , BioContainers  (Veiga Leprevost et al. , 2017) , OpenEBench  (Capella-Gutierrez et al. , 2017) , Bioconda  (Grüning et al. , 2018) , WorkflowHub  (Goble et al. , 2021) , usegalaxy. eu  (Community, 2022a). It also includes a number of related resources outside of the ELIXIR Tools Platform (e. g. Debian Med, biii. eu). Here we report the results of a project started at the BioHackathon Europe 2022. Its goals were to: 1. Cross-compare and analyze the metadata centralized in the Tools Ecosystem, including coverage of the EDAM ontology  (Ison et al.
, 2013) , and 2. Explore methods for connecting tools used in registered Galaxy workflows (i. e. Work-flowHub entries) to the annotations available in bio. tools. This report is separated into three sections. In the first, we present the results of the two analyses described above. The second section details the methods we used, and finally we discuss potential perspectives for both improved monitoring and curation of the Tools Ecosystem metadata and EDAM, as well as for improving the connectivity and integration between elements of the Ecosystem (i. e. bio. tools, WorkflowHub) and platform services that make use of this Ecosystem (e. g. Galaxy  (Sloggett et al. , 2013) ).
Data and Formats: • a lower level of bio. tools EDAM annotation with data and formats, probably linked to the lack of data/format annotations in text-mining created entries. • a scope difference between EDAM data and formats and bio. tools, as e. g. a number of EDAM formats might not be relevant in the scientific scope of bio. tools. • the irrelevance of "legacy" formats related to life sciences technologies and tools which are now deprecated. It is important to note that such legacy file formats cannot be automatically deprecated as they are still used by older tools and can still be found in databases. This may explain the lack of usage of some valid formats in bio. tools.
Mapping between WorkflowHub and bio. tools Here, we sought to explore whether bio. tools identifiers, and by extension EDAM annotations, could be integrated with WorkflowHub entries for Galaxy workflows. WorkflowHub allows developers to register workflows, each of which are composed of one or more software tools. The connection to bio. tools is clear, and one can imagine a scenario where a workflow registered in WorkflowHub: 1. Has component tools automatically extracted (as is the case currently for Galaxy); 2. Each tool has a bio. tools identifier; 3. This identifier allows WorkflowHub to import and present bio. tools annotations in workflow entries; 4. WorkflowHub can filter workflows based on both EDAM terms and bio. tools identifiers (currently available functionality); and 5. bio. tools can perform the reverse operation and import metadata about workflows that use specific bio.
Methods To facilitate the analysis of the data extracted from the Tools Ecosystem and other resources, we integrated them in a local SPARQL endpoint, using the GraphDB commercial software, a solution that enables the querying of RDF resources. The various resources uploaded to a GraphDB-based SPARQL endpoint include: • the EDAM ontology  (Ison et al. , 2020) , available in its development version on the EDAM GitHub repository. • the bio. tools contents  (Ison et al. , 2019) , available on the Tools Platform Ecosystem git repository as a Turtle-formatted BioSchemas  (Gray et al. , 2017)  file. • the workflow data extracted from WorkflowHub and Galaxy, and formatted as well in a BioSchemas format. The analysis of the data is performed using SPARQL queries, which are performed using a number of Jupyter  (Kluyver et al.
Perspectives The results presented here represent a first approach to building a knowledge base that integrates data from the various Tools Ecosystem components. Based on these results, we plan to develop further a series of goals, which we describe below: • Publication of an open SPARQL endpoint Improving on the results presented above, we will provide a publicly available SPARQL endpoint using an opensource software. This endpoint will be automatically updated by the Tools Ecosystem CI workflows. It will allow any user to query a dataset that will include not only EDAM, bio. tools, and WorkflowHub, but many other resources, and our teams of maintainers to run queries periodically if needed. It could also be used to optimize the Caseologue EDAM CI tool. • EDAM and Bio. tools analysis With this work we created and tested a valid work environment for analysing the EDAM ontology and the Bio.
tools registry. In the same fashion as we plan on lifting up the POC level knowledge base up to an open public SPARQL endpoint, we plan on enriching the analyses to provide an exhaustive set of results that monitor the evolution and consistency of the Tools Ecosystem, for both maintainers and the Tools Ecosystem users. These will be run regularly by the Tools Ecosystem CI. • Thanks to our first query base we can easily imagine building on them to be able to evaluate and enhance metadata from the whole Ecosystem. Furthermore, we could to do so automatically to track progress using appropriate graphic visualizations, for the benefit of both maintainers and Tools Ecosystem communities and users. This first round of analysis left us with some identified curation tasks for both EDAM and bio. tools that should be investigated in the future, as discussed in the Results and Discussion.
tools interface. All operations in EDAM are linked to a data via a "has_input" and a "has_output" relation (using inferences from parent concepts). For each operation added to the tool's annotation, input and output data would be suggested to the curator/author based on EDAM relation "has_input", "has_output". The same could go for suggestion of format based on its relation with data as 533 formats (over the 619 valid formats) are related to a data with the "is_format_of" relation. For human user we could also have in the bio. tools interface a "suggested input/output" that would be displayed on the tool page but clearly identifed as an unverified annotation. The enhancement of complete EDAM annotation could lead to automatic workflow generation using the whole Ecosystem metadata.
Mapping between WorkflowHub and bio. tools In the previous section we detailed new approaches for evaluating and improving EDAM annotations in bio. tools: the other side of this coin is to ensure that other parts of the Ecosystem can share and benefit from these improvements. One of these parts is computational workflows that are registered in WorkflowHub. Each of these workflows represent a significant investment of researcher time and expertise: ideally these workflows would be able to draw directly on the wealth of metadata and EDAM ontology annotations stored by a registry like bio. tools, with minimal additional input of effort from a workflow developer. The prototype map described by this project is incomplete, with 37% of Galaxy workflow tools missing a bio. tools identifier. Likely causes include either missing annotations in the Galaxy tool wrappers or missing bio.
tools registry entries. However, the potential value is already clear: a potential next step is for the map functions between WorkflowHub and Galaxy to be productionised by the Tools Ecosystem, such that bio. tools is able to access all Galaxy workflows on WorkflowHub (N = 129 in January 2023) that make use of bio. tools entries, and WorkflowHub is able to access tool components of its workflows as well as bio. tools registry metadata. Synchronisation in this manner will give each platform the opportunity to further improve the experience of users that contribute to and maintain a FAIR software ecosystem. Many thousands of Galaxy workflows exist globally. With automated integration, users of WorkflowHub will be able to intuitively navigate the growing set of Galaxy workflows based on their tool of choice, topic, or software operation.
Participants This study encompassed 243 participants, comprising individuals from the secondary (blue-collar participants) and tertiary sector (whitecollar participants). The sample size corresponds to the total number of participants that it was possible to obtain during the period of data collection. Participant selection was based on a non-probabilistic snowball sampling method which involved recruiting initial participants (identified from the professional network of the authors) who referred additional participants, creating a chain referral process  (Naderifar et al. , 2017). This approach was employed to guarantee the inclusion of participants from diverse age groups and experiences. Specifically, the study included blue-collar and white-collar workers from different professional contexts, with participants as a novice or expert role in their workplace (inclusion criteria), as the literature shows that both novices and experts from secondary and tertiary sector can assume a relevant role in the transmission of knowledge, including safety knowledge (e.
Instruments and data collected Data were collected using a questionnaire designed as part of a larger study conducted to understand the processes of learning and knowledge transmission among workers of different ages and seniorities. Considering the objective of the present study, aimed at the perceptions of a large number of workers from two contrasted activity sectors, the questionnaire was considered the most appropriate type of instrument to use. This instrument was approved by the Ethics Committee of the university to which the principal investigator belongs (Refª 2020/ 07-07) and some example questions are presented further in this section. Prior to the release of the questionnaire for completion, a cognitive interview  (Willis and Artino, 2013)  was conducted with five participants with identical characteristics to those of the target group (e.
Data collection Data were collected in 2021 and 2022, online and in interview format, in cases where participants did not feel comfortable filling out the questionnaire digitally. The study was advertised in the professional networks of the researchers and on social media. This data collection period was preceded by a participant recruitment procedure to achieve the desired sample type. This corresponded to a non-probabilistic snowball sampling method after the first group of participants identified within the research team's professional network. The recruitment procedures were the same for all participants within the non-probabilistic snowball sampling method. The criterion for participation was to be individuals working, in Portugal, in the secondary or tertiary sector of economic activity.
As mentioned previously, the choice of the secondary and tertiary sectors was motivated by an interest in highlighting potential distinctions between sectors regarding safety knowledge at work issues. After the identification of the potential participants, these were invited to participate and previously agreed to a free and informed consent, which reflected the purpose of the study, the approximate duration of completing the questionnaire (about 15 min), how the results would be treated and reported back, and issues of anonymity and confidentiality. The confidentiality of the collected dataset was ensured, with the survey link being accessed only via a secure university service.
Data analysis Descriptive analyses of the data were carried out according to the role played by the participants in their current job (novice or expert) and to the sector of activity in which they work (secondary or tertiary). These considered the working conditions and the positioning of the participants about the three analyzed items from the questionnaire concerning safety knowledge (type of knowledge that would be most important to transmit; transmission of safety-related knowledge in the initial stage of learning the job; type of knowledge related to what was actually transmitted).
The binary logistic regression was performed for the sample of participants from the secondary and tertiary sectors, according to the "enter" method. This method was chosen because it allowed us to understand whether the data from our sample could meet the contributions from the literature, namely about the role of working conditions in transmission (e. g. ,  Cloutier et al. , 2012)  and the importance of the transmission of safety knowledge (e. g. ,  Rémery, 2022). The requirements for performing the binary logistic regression were met, namely, none of the variables exhibits multicollinearity, as none of the variance inflation factor (VIF) test values are close to or exceed the threshold of 10 (  Bowerman and O'Connell, 1990). The analyses were performed using IBM SPSS Statistics 27 for Windows software.
Funding and Acknowledgments This work was supported by the  FCT  under Grant  PD/BD/143112/ 2018  and  COVID/BD/153296/2023 , and by the  Center for Psychology of University of Porto. This work was also supported by national funding from the  Portuguese Foundation for Science and Technology  ( UIDB/ 00050/2020 ). The authors would like to thank  Márcia Bernardo  (research fellow at  FPCE-UP ;  UIDB/00050 /2020) for the support provided in the development of this article.
| Objectives for a provenance standard One of the main characteristics of present-day research in life sciences is that the research objects, such as datasets or specimens, are exchanged between organizations. Therefore, each of the organizations involved can only provide documentation for a part of the object's life cycle. Consequently, an uninterrupted chain of provenance information documenting the whole life cycle can only be formed from individual parts of provenance distributed across different sources. To enable meaningful integration and harmonized processing of the distributed provenance parts, semantic interoperability between standalone distributed provenance parts must be ensured. In addition, the processing of the resulting chain of distributed provenance must be designed to (a) deal with missing provenance components in the chain, so the chain is not interrupted or corrupted when an intermediary organization has not generated appropriate provenance information, or if the organization ceased to exist; (b) handle sensitive or confidential information contained in provenance information, keeping it opaque and disclosed only by authorization; (c) handle several versions of the same provenance information, for instance, when an error in provenance is found and is fixed; and (d) enable verification of the integrity and authenticity of provenance components, even for opaque provenance components, to ensure the trustworthiness of provenance.
The distributed provenance chain must be suited to answer essential queries independent of the research domain, such as "What are the precursors of a given dataset?" or "Which processes precede a given dataset creation?". The underlying query resolution mechanism must be able to navigate through the chain, regardless of the actual site where the corresponding part of the distributed provenance is stored, which processes or objects are documented, or what the actual source of the provenance is. The provenance standard must therefore include a general concept, providing a basis for common aspects shared between various domains which are part of the life cycle of a documented research object. In particular, these common aspects include (a) traversing distributed provenance chains; (b) implementing domain-independent properties for the provenance, such as confidentiality, authenticity, integrity, non-repudiation, and validity; and (c) locating a specific part of provenance in the distributed provenance.
W3C PROV serves as a framework that is adaptable and extensible to fit the needs of diverse domains. The W3C PROV standard has already been adopted in life science research areas,  36  for example, for computational workflows,  37  pharmacologic pipelines,  38  neuroscience,  39, 40  microscopy experiments,  41  medical sciences,  42  and health implementation care ‡ in HL7 FHIR. 43  Unfortunately, these implementations occurred without coordination and the resulting solutions are often incompatible, incomplete, expressed at different levels of granularity, and do not use a consistent approach for creating a continuous chain of provenance from the "source" to the resulting data. Instead of redefining the W3C PROV concepts, we have identified gaps that need to be filled to develop a distributed, fully technically and semantically interoperable provenance information standard that covers uninterrupted documentation of the whole life cycle of a dataset back to its "source".
ACKNOWLEDGMENTS This work has been co-funded by  EOSC-Life  supported by  EU   Horizon 2020 , grant agreement no. 824087 ; EJP-RD supported by  EU   Horizon 2020 , grant agreement no. 825575 ; BioExcel-2 supported by  EU Horizon   2020 , grant agreement no. 823830 ; the PAM and the  XDATA Projects , funded by the  Sardinian Regional Authority. VC and MCh are supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre  based at Guy's and  St Thomas' National Health Service (NHS) Foundation Trust  and  King's College London  ( RJ112/N027 ) and by  NIHR Application Research Collaboration South London  (ARC SL). TB, MCo acknowledges funding from  EMBL-EBI Core Funds  and the  FAIRplus  project ( H2020 No 802750 ). MCo was supported by  Wellcome Trust   GA4GH  award number  201535/Z/16/Z  and the  CINECA  project ( H2020 No 825775 ).
Literature review on the OACA OACA has been a major topic of discussion in the literature over the past twenty years, generally assuming that a better accessibility fosters research impact. In fact, this belief is not so obvious, with contradictory findings in the studies dealing with the OACA, that have often proven tough to compare, depending on many factors such as disciplines, OA status taken into account, publications types as well as the method and database used. The first paper to analyze the question of OACA is that of Steve Lawrence  (Lawrence, 2001) , published at the turn of the century in Nature. He made a correlation between the number of citations and the share of freely accessible articles. He found a positive correlation between the two indicators. Since then, dozens of papers have been published on the topic.
Overall, studies that found the existence of OACA were more common in social sciences  (Mikki et al. , 2018; Abbasi et al. , 2019; Valderrama-Zurián, Aguilar-Moya and Gorraiz, 2019) , Medical and health sciences  (Hudson et al. , 2019; O'Kelly, Fernandez and Koyle, 2019; Miller et al. , 2021) , and Natural sciences  (Lin, 2007; Wang et al. , 2015; Clements, 2017). OACA is less important (and in several disciplines non-existent) in Physical Sciences and Engineering  (Archambault et al. , 2016). In addition, there are some studies that concluded on the nonexistence of OACA in social sciences  (Zhang, 2006) , Medical and health sciences  (Tonia et al. , 2016; Mimouni et al. , 2017; Nazim and Ashar, 2018) , and Natural sciences  (Campos et al. , 2016; Peidu, 2020). On a large sample of the Web of Science database, Dorta-Gonzalez et al.
(2017)(Dorta-González, González-Betancor and Dorta-González, 2017) concluded that there is no OACA in all disciplines. Recently, a review analysis on the topic identified 134 publications dealing with OACA (Langham-Putrow, Bakker and Riegelman, 2021). Applying the EBL critical appraisal method to analyze the risks of bias based on factors like sample size, data collection or study design, the authors emphasized that most of these studies (131about 98%) present a high risk of bias. Two of the three publications with a low risk relate to medical and natural science research  (Tonia et al. , 2016; Nelson and Eggett, 2017) , and the third  (Sotudeh, Ghasempour and Yaghtin, 2015)  used relatively old data  (2007) (2008) (2009) (2010) (2011). Moreover, none of these articles used randomization techniques/control group.
Method Data We extracted the publications data from the French OST in-house database. It includes five indexes of the WoS available from Clarivate Analytics (Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), Conference Proceedings Citation Index (CPCI-SSH) and Conference Proceedings Citation Index (CPCI-S)), and corresponds to WoS content indexed through the end of March 2021. The study focuses on three types of publications: articles, reviews and conference proceedings.