dataesr/scientific-paragraphs-categorization huggingface dataset:
• C -refers to the type of the transaction: it corresponds here to the creation of a smart contract. • SC id -is the smart contract identifier. • σ k CSP -is the signature of all the above fields, by the SGX enclave of the storing server, approving the SC as well as the associated ACLs. Note that the transaction identifier id T is then shared with the server S and the corresponding user DO. As presented in section 4. 2, the DO may also create a vote-doc contract that defines the voting protocol associated with the corresponding data file and the process of voting and the choice of voters 4 (Remark 1). The vote-doc smart contract is mainly required to validate both the download and transfer actions, detailed in subsection 5. 2. 2. Remark 1. Blockchain-based Reputation Systems. Reputation systems are important for distributed ap-plications in which users have to be made accountable for their actions, namely e-commerce websites.
e. , verifying the fulfilment of ACLs' requirements, the compliance with legislation, the consideration of clients' given consents, • • •. For this purpose, the authorized PA identify the creation transactions, as well as associated processing and granted privileges update transactions, and compare the consistency of information with SC history logs and provenance database. On the other side, when a data owner requests a private audit for a misuse of his personal data, he shares the creation identifiers of the suspected smart contracts with the PA. As such, the auditing authority is able to lead an investigation, i. e. , interpret the content of blockchain transactions associated by the provenance database and to detect non-compliant activities, while crawling blockchain transactions corresponding to the provided smart contracts' identifiers, We note that private auditing has to be paid by the audited service provider, if non-compliant activities are reported.
Threat Model The Prov-Trust system considers two types of attackers: an external adversary and an internal adversary, defined as follows: • The External Adversary: is a user who is not authorized to the document in the system, but is able to modify the provenance data of the outsourced document, i. e. , including SC events and history logs. The external adversary does not have neither access to the location of the storing server nor the deciphering key. The attacker can only know the document identifier and exploit it to launch an attack against the blockchain based data provenance system. We assume that the adversary can not corrupt the information transmitted between SGX enclave and users. • The Internal Adversary: is an entity who is granted access to the document by the owner in the Prov-Trust system.
Recall that, on one side, ACLs refer to any access control mechanism selected by the DO, to be enforced on his data. ACLs on data contents may be encrypted -up-to the DO-, however, they have to be accessible to the CSP, the PA as well as voters, if a voting smart contract was created. ACLs on provenance data ensure the access to involved parties, with respective privileges. On the other side, multilevel ABE applied on processing transactions ensure fine-grained access to data provenance trails by authorized PAs, and enhance unlinkability of processing actions on DO's data by external adversaries. As the public ledger only registers random values or encrypted information, no significant information can be learnt from the blockchain.
Data sources To identify eligible studies, we screened the COVID-NMA database, which provides an exhaustive archive of COVID-19 studies assessing the effect of interventions. As part of the COVID-NMA project, medRxiv, and PubMed are searched daily, independently by two reviewers, to identify eligible studies. Secondary sources, such as the Living OVerview of Evidence (L•OVE) database, are searched as a quality control. The search strategy is in Additional file 1: Methods S1.
Eligibility criteria and study selection We included all preprints and journal articles indexed in the COVID-NMA database, from inception to 15 August 2020, with the following study designs: randomized controlled trials (RCTs), observational studies (i. e. , case series, case-control, cohort, interrupted time series, studies modeling the effects of population-level interventions). We excluded case series with < 5 participants, systematic reviews, prognosis and diagnosis studies, and modeling studies using simulation methods. We included reports on preventive, therapeutic, and postacute care interventions, for healthy individuals, patients with COVID-19, or recovered individuals, respectively. Because the scope of the COVID-NMA is already very broad, studies assessing Traditional Chinese Medicine and other types of Traditional, Complementary and Alternative Medicine (TM/CAM) interventions were excluded  [4].
Linking all evidence sources for each study We sought to link preprints and journal articles reporting results from the same study. For this purpose, we used a four-step approach: 1. We performed a systematic search of preprints and published articles as part of the COVID-NMA (as described above). We used the same eligibility criteria for preprints and articles. One author (T. O. ) downloaded these data in Excel format and used three ways to identify potential preprint-article pairs: (a) by using the Excel search function to identify articles for which the name of the article author matched that of the first author of an included preprint, and comparing the titles of potential matches to verify that they reported the same study; (b) by extracting the intervention type assessed in each preprint and article and comparing the titles of preprints and articles that assessed the same intervention to identify pairs that reported the same study; and (c) by comparing the trial registration number for preprints and articles reporting RCTs.
2. We performed additional searches of the Dimensions academic search engine (https://app. dimensions. ai). We used the name of the first author of each preprint to identify articles by the same author in any authorship position. We did this by downloading the Dimensions dataset in Excel format, filtering entries that were not journal articles, and using the search function to search for the name of the preprint's first author in the column containing all article author names. When a potential preprint-article pair was identified in this manner, we compared the titles and the names of the remaining authors to decide whether the two sources reported findings from the same study. Methods S2 for a template) to ask whether the preprint had been accepted for journal publication. For preprints that had not been accepted, we asked whether they had been submitted to a journal, or if the authors intended to submit them in the future.
First preprint version versus journal article Latest preprint version versus journal article (if > 1 preprint versions were available) Second, we sought to compare evidence components within evidence sources (the first preprint version versus the latest preprint version, if > 1 versions were available). To perform these comparisons, we downloaded all abovementioned sources for each study in portable document format (PDF). We entered each pair of files in PDF Converter (Enterprise 8). This software allows users to compare documents by automatically detecting and highlighting changes in text (words added or deleted between versions). We reviewed the highlighted text to identify changes in the following evidence components, which may affect systematic reviewers' appraisal of the effect of the intervention assessed in the study: 1) Change in any study result.
Dissemination of evidence sources To describe the dissemination of the different evidence sources, we used an algorithm designed by one of the authors (G. C. ) to automatically extract the following usage data for each preprint-journal article pair: citation count, Altmetric Attention Score (extracted from the Dimensions database)  [5] , and the number of PubPeer comments (extracted from the PubPeer Application Programming Interface [API]). These metrics were selected because they represent evidence dissemination by different end-user groups. Citations reflect use of an evidence source in academic communications. The Altmetric Attention Score tracks the occurrences of a source mentioned in the media and online bibliographic reference managers  [6]. Finally, PubPeer data reflect attention by researchers in the form of crowdsourced peer review.
One reviewer (T. O. ) extracted data for all included studies by using a structured, piloted form (see Additional file 1: Methods S3). A second reviewer (O. P. ) independently extracted data from all sources for 20% of the studies for all variables except for assessment of the conclusion, which was duplicated in full. Disagreements were resolved by consensus or by consulting a senior reviewer (I. B. ). The agreement between reviewers was > 80% for all variables.
Dissemination of evidence sources Usage data for preprint-journal article pairs are presented in Additional file 1: Table  S2  and Additional file 1: Figure  S1. More preprints than articles received 0 citations (n = 10 of 65 [usage data could not be retrieved for 1 preprint], 15%, and n = 3 of 66, 5%, respectively). Of the total citations for each study, a median of 29% (IQR 14-50) was attributed to preprints. A similar proportion of preprints and articles had an Altmetric Attention score of 0 (n = 4, 6% for both preprints and articles). Furthermore, we examined the breakdown of the Altmetric Score for the 66 preprint-journal article pairs. Specifically, we examined their score in the Policy documents domain, which reflects the citations a report has received in policy documents. Of the 66 preprint-journal article pairs, 9 preprints (14%) and 10 articles (15%) had a score > 0 in the Policy documents domain (median score for preprints 0, IQR 0-0; median score for articles 0, IQR 0-0).
However, preprints constitute "living evidence" that is not stable. This flexibility allows researchers to keep up with evolving datasets (e. g. , in studies exploring the association between policy interventions and mortality rates) and disseminate Fig. 2  Survival plot depicting the probability of a preprint remaining unpublished as journal article for 338 studies initially published as preprints. This survival plot presents the probability that a study initially published as a preprint remains unpublished as a journal article. The x-axis represents the number of days elapsed since the preprint first appeared online. Of the 338 studies published as preprints from the onset of the COVID-19 pandemic to 15 August 2020, 66 had an associated journal article by 2 September 2020 findings from interim analyses.
Therefore, it is possible to observe changes in the reported evidence among sources for the same study, even though both sources were accurate at the time of redaction. For example, the first version of a preprint may accurately report the findings of an interim analysis of an RCT, before participant enrollment has been completed. The final version of the preprint, reporting the analysis of the complete dataset could have a different sample size and different results. The flexibility of preprints also allows authors to correct potential errors in the report following crowd-sourced peer review (e. g. , in the form of PubPeer comments). One consequence of this flexibility is that evidence changes can lead to misleading results of systematic reviews. Systematic reviewers need to be aware of updates in the evidence reported in preprints in order to update the review accordingly.
Strengths and weaknesses of the study This is the first study to assess changes between evidence sources for studies evaluating interventions for COVID-19. First, we used a large database to capture all relevant evidence sources published during the first semester of the pandemic. Second, we assessed changes in important evidence components that may affect the results of systematic reviews and thereby affect decisionmaking. Third, we elicited information about the publication status of preprints by contacting authors. Finally, we designed an algorithm that links preprints to their associated journal articles and flags the appearance of new preprint versions. Our study has limitations. First, we extracted usage data soon after publication, and a different amount of time had elapsed from the publication of each study to retrieval of usage data.
Acknowledgements The authors thank  Laura Smales  for editing the manuscript, the study authors who responded to the survey, and the doctoral network of the  École des Hautes Études en Santé Publique (EHESP). Theodora Oikonomidi  received a fellowship from the doctoral network of the  EHESP (Rennes, France). We thank  Brandon Stell  for providing access to the PubPeer API and  Digital Science  for providing access to the Dimensions API.
Acknowledgements The authors thank the anaesthesia residents who validated the reading grid:  R. Alardin ,  Y. Essaber ,  C. Jeanclerc ,  M. Ambs ,  E. Dupont ,  T. Grelet ,  C. Louyot ,  T. Vieille ,  P. A. Poinsot. Groupe M ethodes en  Recherche Clinique  (GMRC),  Public Health Service, Strasbourg University Hospital: EA Sauleau ,  F Lef  evre,  F Severac ,  M Schaeffer ,  J Muller. They also thank  Laura Smales  ( BioMedEditing, Toronto, Canada ) for English editing.
Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation.
Paper Selection Method We conducted the selection of KRM papers based on the set of queries established in the 2019 edition of the IMIA Yearbook of Medical Informatics  [1]. As compared with the previous editions of the IMIA Yearbook in 2017 and 2018  [2, 3] , both PubMed/ MELDINE and Web of Knowledge were used to search for KRM articles published in 2019. We followed a generic method to select the best papers, commonly used in all sections of the Yearbook since 2013 []. As for last year, the search was performed on MEDLINE by querying PubMed and also on the Institute for Scientific Information (ISI) Web of Knowledge database (WoL). Additionally, the articles of the Journal of Biomedical Semantics (JBS) and of the Journal of Biomedical Informatics (JBI) were manually analyzed. Our query includes Medical Subject Headings (MeSH) descriptors related to KRM in the context of medical informatics with a restriction to international peer-reviewed journals, including conference proceedings indexed in PubMed.
The result is a community-driven open-source semantic resource that follows the Open Biological and Biomedical Ontology (OBO) Foundry development principles (e. g. , openness and collaboration). The use of ODAE is demonstrated with SPARQL Protocol and RDF Query Language (SPAR-QL) queries, on a knowledge base built under ODAE for 224 neuropathy-inducing drugs, allowing deep investigations of mechanisms of action and clinical contexts. Other articles deal with ontology visualization and development tools  [12, 13]. Jackson et al. ,  [12]  propose ROBOT, a tool for automating OWL/OBO ontology workflows. This framework is an opensource library with command-line tool for various ontology development and curation tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM).
This helps ontology developers to efficiently create, maintain, and release high-quality ontologies in OWL and OBO formats, so that they can spend more time focusing on task development. Kuznetsova et al. ,  [13]  present an open source software, CirGO (Circular Gene Ontology), that provides the visualization of non-redundant two-level hierarchically structured ontology terms from gene expression data in a 2D space. This software displays the most enriched gene ontology terms in an informative, comprehensive, and intuitive format that is achieved by organizing data from the most relevant to the least, as well as the appropriate use of colors and supporting information. CirGO is freely available at https://github. com/ IrinaVKuznetsova/CirGO. git. The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation impractical and unsustainable.
Hoyt et al. ,  [11]  have developed two workflows to address this issue: the first for re-curating KGs to control syntactic and semantic quality, and the second for rationally enriching KGs through the manually revision of automatically extracted relations for the nodes with low information density. They applied these approaches to the KGs of the NeuroMMSig inventory. This KG curation workflow is freely available at https://github. com/bel-enrichment/bel-enrichment.
Semantic Resources Applications: Annotations, Mining, and Enrichment In their paper, Babbi et al. ,  [8]  introduce PhenPath, a new set of resources: Phen-PathDB and PhenPathTOOL. PhenPathDB is a database of human genes associated with phenotypes described in the Human Phenotype Ontology (HPO) and in OMIM Clinical Synopses. Phenotypes are then associated to biological functions and pathways by means of NET-GE, a network-based method for functional annotation enrichment of sets of genes. PhenPathTOOL enables the identification of molecular features relevant for investigating diseases characterized by multiple phenotypes. This framework provides a support for the characterization of molecular mechanisms and biological functions underlying the concomitant manifestation of phenotypes. The resource is freely available at http://phenpath.
They use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology metadata to evaluate the prediction of protein-protein interactions and gene-disease associations. They find that the background knowledge provided by the Gene Ontology (and other ontologies) significantly improves the performance of prediction models through the provision of domain-specific background knowledge. Their results have implications on the further development of knowledge bases and ontologies, especially since machine learning methods are more frequently applied across the life sciences. In a short paper, Siegele et al. ,  [16]  illustrate the task of phenotype annotation with the ontology of microbial phenotypes (OMP). They describe an OMP-based annotation framework that supports the representation of a wide range of phenotypes and provides flexibility for different levels of detail.
This framework can support research by the capture of phenotypes from the experimental literature for a variety of microbes, with pre and post-coordination methods. Sahoo et al. ,  [15]  introduce the ProvCare platform for mining semantic provenance information in the biomedical literature, with the aim of evaluating scientific reproducibility. This platform relies on a S3 model and a formal ontology. A provenance-focused text processing workflow generates provenance triples consisting of subject, predicate, and object, using metadata extracted from articles. The resulting ProvCaRe knowledge repository (available at https://provcare. case. edu/) supports "provenance-aware" hypothesis-driven search queries. This repository is one of the largest provenance resources for biomedical research studies that combines intuitive search functionality with a new provenance-based ranking feature to sort the retrieved articles.
Open Access (OA) publishing, with ambitious movements such as Plan S, is engendering radical changes among academic publishers. Emerging countries need to keep publishing as well as adopt open access to catch up with the changes. Using exclusive data from the Social Sciences & Humanities Peer Awards (SSHPA) database, the study employed both descriptive statistics and a Bayesian linear regression model to examine the journals and publishers in which Vietnamese social scientists published during the period 2008-2019, and the potential of pursuing the OA movement in Vietnam. We found an increasing diversification in the publishing sources of Vietnamese social science researchers with growth rates of 9. 8% and 14. 1% per annum in the number of publishers and journals, respectively. Given that the proportion of Gold OA articles had a fourfold increase over the examined period, it seems that the Vietnamese academic community is adopting OA.
, 2017; Nguyen and Pham, 2011). Vietnam was among countries of low research output, with weaknesses such as heavy reliance on foreign co-authorship and limited research capacity at higher education institutions  (Harman and Ngoc, 2010; Hien, 2010). Manh (2015)  investigated Vietnam's scientific publications 1996-2013 using the Scopus database. Total output increased by 20% annually between 2002 and 2013. However, 77% of the output was from international collaboration. The list of top 20 journals in collaboration research reflected the dominance of medicine, biological and agricultural science, while the list of top 20 journals by domestic authors reflected the dominance of mathematics. Similarly, using the Web of Science database 2001-2015 of 18044 papers,  Nguyen et al. (2017)  found the rate of growth in scientific output annually 17%, ¾ of which was attributed to international collaboration.
Nguyen and Pham (2011)  found a strong relationship between scientific output and the extent of a knowledge economy, urging more investment from government and academic institutions by examining 165,020 articles in ISI-indexed journals of 10 Southeast Asia countries. Overall, these papers are few and far between, lack updated data as well as in-depth analysis about the performance of Vietnamese researchers, particularly those in SS&H fields. As a result, the SSHPA database was built in 2017 to address these problems. The database is a comprehensive system from which accurate, updated, and focused information about the demographic characteristics and productivity of Vietnamese researchers with international publications in SSH fields can be produced  (Vuong et al. , 2018a). None of the publications specifically focus on the quality of Vietnamese SS&H studies.
Manh (2015)  found that the quality of most popular journals based on international collaboration was higher than those of domestic authors, but the list is dominated by research in natural sciences. Only a few of them discuss the potential impact of the OA movement. Vuong (2019b)  suggests the adoption of open science as a way to promote transparency. Currently, there is only one SS&H journal in the Directory of Open Access Journals (DOAJ) that belongs to the Vietnamese publisher, which is Da Lat University Journal of Science. However, this journal is not included in the SSHPA database because it is not in the official list of prestigious SS&H journals recognized by the Vietnam National Foundation for Science & Technology Development  (Nafosted, 2018). Collaboration in academic publishing has become the norm.