dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Limitations A specific limitation of this initiative is that high-risk anatomy (eg, annular calcification, unfavorable coronary anatomy) and bicuspid aortic valves were excluded from PARTNER 3, 3 making the conclusions difficult to generalize to the overall aortic stenosis population. As described, there are many inherent limitations to a cost-effectiveness model, including assumptions made in the presence of "best fit" data or a paucity of data, extrapolations into time horizons that are modeled beyond the scope of existing input data, and the underestimations and overestimations potentially caused by differences in healthcare systems or by the criteria for intervention and treatment selection within that specific system. The disutility scores were based on values obtained from Lanitis et al,  24  which were originally informed by a study 1 using EQ-5D scores from the United Kingdom for a range of different conditions.
Acknowledgment: The authors acknowledge  Sandrine Baffert ,  Marc-Anthony Chechoi Mienje ,  Julien Robert , and  Cécile Fabron  from  CEMKA  for their valuable input regarding the costing and the literature review specific to France. Writing support was supplied by  Zenith Healthcare Communications Ltd  ( Chester, United Kingdom ) and  Griffin Scientific Ltd  ( Gloucestershire, United Kingdom ), and this was funded by  Edwards Lifesciences.
Limited reproducibility and validity are major sources of concern in biology and other fields of science. Their origins have been extensively described and include material variability, incomplete materials and methods report, results selection, defective experimental design, lack of power, inappropriate statistics, overinterpretation, and reluctance to publish negative results. Promoting complete and accurate communication of positive and negative results is a major objective. Multiple steps in this direction are taken, but they are not sufficient and the general construction of articles has not been questioned. I propose here a simple change with a potentially strong positive impact. First, when they complete a substantial coherent set of experiments, scientists deposit their positive or negative results in a database ["deposited results," (DRs)], including detailed materials, methods, raw data, analysis, and processed results.
The DRs are technically reviewed and validated as "validated DRs" (vDRs) or rejected until satisfactory. vDR databases are open (after an embargo period if requested by the authors) and can later be updated by them or others with replications or replication failures, providing a comprehensive active log of scientific data. Articles, in this proposal, are then built as they currently are, except they only include vDRs as strong and open building blocks. I argue that this approach would increase the transparency, reproducibility, and reliability of scientific publications and have additional advantages including accurate author credit, better material for evaluation, exhaustive scientific archiving, and increased openness of life science material.
Yet the way articles are constructed has not been questioned. I propose a radical change, which is now made possible by current information technology and which would have a profound impact on reproducibility and validity and would make science fully open. The principle is very simple: first, when scientists complete a coherent group of experiments, they deposit their full methods and results in a "validating" database, which is open (after an embargo period if requested) and updatable. Second, later to write articles scientists use as many sets of validated deposited results (DRs; already open or still under embargo) as needed. Conclusions and hypotheses in published articles are thus based on strong, open, and updatable data.
These are welcome steps, which simplify the submission process. However, such preprints are just unreviewed article manuscripts with all the limitations of current articles that are discussed above. The advantages of preprint publication and the changes in articles composition proposed here (see below) will be easily combined with the publication on open databases of full articles comprising vDRs before they are actually submitted or accepted. A different useful development is study preregistration that increases methodological and statistical rigor. It is not applicable to all types of research and does not prevent all the reporting insufficiencies described above. Although a growing number of top journals encourage authors to make available more experimental details (e. g. , a list of antibodies) and raw data, and are increasingly demanding about the quality of statistical analyses, these steps occur a posteriori, often late after experiments are completed, and rely mostly on the authors' willingness for precision and exhaustiveness (a daunting task in manuscripts that include numerous experimental results).
Improvements are also underway with respect to publication of negative results, as illustrated by an increasing number of journals encouraging the publication of negative results (including eNeuro: https://www. eneuro. org/ content/general-information#types). It is important to stress that in areas of quantitative biology such as genomics, transcriptomics, or structural biology, the availability of full datasets is already the rule, not the exception. However, as in other domains, key details about the experiments from which the datasets are generated can be missing. Finally, I would like to mention the recent and laudable initiative of "micropublication"  (Raciti et al. , 2018) , which shares some features with the current proposal, such as open publication of simple data, but with important differences including the fact that in my proposal deposited results are not a final publication but the building block of full articles.
Proposal of a two-step procedure: results deposit and validation before article submission Here, I propose to dissociate the publication of results with their detailed materials, methods, and analytical procedures, as DRs, from that of articles. The deposit and validation of results would be the first step in open scientific communication and would provide the strong material from which next-generation scientific articles will be build. Such a change was not feasible a few years ago because of obvious technical limitations, but it is now possible with current information technology and powerful databases. In this proposal, the unit of DRs corresponds to a specific experiment or group of highly related experiments (including replications if already conducted). Preliminary or first results cannot be considered as DRs, which must correspond to a full experiment or set of related experiments with clear and conclusive (positive or negative) results.
In brief, a DR contains all that is needed to assess the quality of the results and reproduce exactly the same experiments. Results can be "positive" (an effect is observed) or "negative" (no effect is observed), but they have to be convincingly supported by the experimental evidence. The DR is reviewed by two experts in the methodological approach and statistics are checked. If any information is missing or flaws are detected, the authors are requested to revise and resubmit (these exchanges are part of the DR record). The format of the material to be provided in DR is expected to be standardized to make it easy for depositing and reviewing ("validating") scientists to be sure that all the needed information is provided. When satisfactory, the DR is validated, becoming a vDR registered in the database.
This can be a significant improvement in relation to the sensitive issues of authorship, contribution credit, and tractability. Validation is factual, based only on the technical quality of the methods, data, and analyses, but unbiased by the "interest" of the question or lack of it. Moreover, the DR submission can be easily anonymized in contrast to current whole papers. A vDR is easy to retrieve later with text-mining tools and links between databases. Anyone planning an experiment can easily access all related vDRs, in contrast to the current difficulties that can be encountered in retrieving previously published results and procedures often hidden in articles. Accessibility to vDRs will be a very important objective. This will be facilitated by a standardized shell for the report structure, easy to search, and the development of a specific openly accessible archiving and mining engine, similar to what PubMed does for published articles.
Scientists who repeat the same or a similar experiment can publish their material, methods, and results in the database where the first vDR is included, following the same high-standard validation procedure. If the results are similar to the original ones, conclusions are strengthened, whereas if they differ, they raise attention to possible uncontrolled parameters or underlying variability, prompting moderation of the conclusions. Negative results and replication failures are published and are as tractable as positive results. Their availability together with the initial results is a practical advantage. Thus, the logs of vDRs (including revisions, repetitions when available, or their failure) become invaluable open and updatable documents. The vDRs are included in curricula vitae and facilitate the evaluation of expertise, know-how, and contributions, especially useful for students, early-career scientists, and technicians, even if the final full papers are not (yet) published or if the overall scientific program is stopped.
vDRs are fully open (after the optional embargo period) and can be used for new analysis or as supporting evidence, with explicit reference to the source, in a completely transparent manner. vDRs can be used by any scientist for different analyses or meta-analyses. This releases the full potential of open and interactive science. The vDR databases provide an exhaustive archiving system of scientific production. To a large extent, they can, in the long-run, replace the laboratory notebooks, dematerialized or not, which are not open and not searchable, and are hindered by archiving difficulties and, in many cases, their unavoidable disappearance or loss. As long as these databases are maintained, mirrored, and saved, they are an invaluable resource, including for those who will study the history of science.
Will scientists want to submit their results to a vDR database? Having results saved as vDRs would bring many benefits to both senior and junior scientists. vDRs are strong and tractable pieces on which publications are built, whether they are large multiauthor/laboratory studies, resulting in important "big" articles or smaller focused ones. Laboratory expertise and know-how can be appreciated based on its vDRs. For junior scientists, vDRs attest their exact contribution and expertise even when the full paper is not yet accepted or if the overall project fails and no publication is ever produced. In fact, valid results obtained in lines of research that are not pursued to the full article level can be made available and contribute to scientific knowledge. The practice of writing DRs is excellent training for experimental precision and rigor for early career scientists.
A short internship can result in a vDR, and negative results of well done experiments can be deposited and appreciated. The recent spectacular success of preprint databases demonstrates that scientists are looking forward to improving publication procedures. Will dissociation of results from the broader context of the article alter their evaluation? Currently, experiments reported in articles are introduced and their results interpreted in the context of a broader biological question. It can be argued that when the two are dissociated, it could lead to difficulties or lack of rigor in the evaluation of the value of the experiment and its results. In the current proposal, when DRs are submitted they first include "a clear exposure of the precise biological question that the experiment directly aims to answer.
Who is going to organize vDR databases and cover their cost? A way to answer this question is to ask who is currently losing time and money with the insufficient quality and lack of reproducibility in biomedical sciences  (Freedman et al. , 2015). The primary source of funding is public money, supplemented by private foundations or bodies, including charities and generous donators  (Viergever and Hendriks, 2016). To increase research quality and yield, all these funders could consider building vDR databases for the research they support and request their use. Academic institutions would clearly benefit from offering a vDR database to their members, ensuring their preeminence in high-quality and open science. The vDR databases can also replace or be combined with and improve other methods of conserving raw data and material from notebooks.
Publishers and journals have a key role to play. They would boost their quality, hence their potential impact, by requesting that submitted manuscripts only include vDRs. They could naturally consider setting up vDR databases themselves. Any academic institution or funder with ambitious goals and a vision for high-quality open science could start the practice of vDR databases. Finally, the industry, which deplores the lack of reliability of academic productions and the need to repeat published experiments  (Prinz et al. , 2011) , could be interested in supporting the initiative. Modest DR submission fees could also cover their cost. A strength of the vDR database proposal is that it can be started at a "local" level (i. e. , individual funding agency or academic institution). This would increase their respectability and visibility, hopefully encouraging others to move in the same direction.
The key of course is that all interested actors make sure that once vDRs are published they are accessible and searchable, and that high standards are agreed on. Poor quality will be plainly visible and counter-selected. It is expected that reliable vDR databases in contrast to poor or insufficiently stringent databases will be easily identifiable based on the quality of their content. An archiving and mining engine for all available vDR databases (a "vDR PubMed") will be a very valuable development to facilitate their access and boost their usefulness. Finally and importantly, the requirement for vDR to publish articles in respectable journals and the open access to vDR databases should make quality checking easier, and should facilitate the fight against predatory publishers, paper mills, and fake articles.
Conclusion I propose dissociating scientific publication in two steps: (1) first deposit and validate substantial sets of experiments and results in ultimately open, searchable, and updatable databases ; and (2) then write articles using only deposited, validated results as strong and open building material. I argue that in biomedical sciences it has the potential to significantly improve the output quality, addressing many issues about validity, reproducibility, and openness of science, as well as providing better recognition of scientists' contributions and long-term storage of the material on which science is built. I am not aware of similar proposals and hope this proposal will trigger interest and generate discussion and, hopefully, action.
Results The four major medical journals published 205 research articles on COVID until the 1st of December 2020, among which 60 (29. 3%) cited at least one preprint from medRxiv (Table  1 ). Overall, 182 preprints were cited, among which 124 (68�1%) have been published in a peerreviewed journal (Fig  1 ). None of the preprints cited were withdrawn from medRxiv server. However, among those 124 "preprints", 51 (41�1%) had already been published when the citing article was published online, at least 2 months before for 33 of them, and were, in fact, not preprints anymore at that time. Among these 51 superseded preprints, differences between the preprint version that was cited and the published version were observed in the data or the conclusions for 21 (40%) of them and in the title for 23 (45%) of them (Table  2 ).
Conclusions More than 40% of preprints cited in the major medical journals were in fact already published at the time of publication of the citing article. Therefore, authors should check the accuracy of the citation and the quotations of preprints just before submitting the manuscript, and once again when signing the proofs. They should not rely only on the information displayed on MedRxiv website to identify subsequent publication. To overcome medRxiv weaknesses, authors should manually search bibliographic databases to determine if a preprint has been subsequently peer-reviewed and published, keeping in mind the nearly half of the preprints have a different title than their published version. Publishers should also check the accuracy of the citations of preprints before online publication of citing articles.
Guaranteeing durability, provenance, accessibility, and trust in open datasets can be challenging for researchers and organizations that rely on public repositories of data critical to epidemiology and other health analytics. Not only are the required repositories sometimes difficult to locate, and nearly always require conversion into a compatible format, they may move or change unpredictably. Any single change of the rules in one repository can hinder updating of a public dashboard reliant on pulling data from external sources. These concerns are particularly challenging at the international level, because systems aimed at harmonizing health and related data are typically dictated by national governments to serve their individual needs. In this paper, we introduce a comprehensive public health data platform, the EpiGraphHub, that aims to provide a single interoperable repository for open health and related data, curated by the international research community, which allows secure local integration of sensitive databases whilst facilitating the development of data-driven applications and reports for decision-makers.
Slow infrastructure for reporting of health data. Prior to this pandemic, sharing of disease surveillance data was often slow, taking weeks or months  (5, 6)  to reach country-level data repositories. COVID-19's Omicron variant had spread to 89 countries within approximately one month of being detected  (7). 2. Insufficient testing capacity. Since the beginning of the pandemic, production of test kits increased substantially, but the costs and the logistics for large-scale testing is still beyond reach for many countries  (8). 3. Lack of a common global data model for disease reporting. Comparability of surveillance datasets is paramount to manage risks at a global scale  (9, 10). 4. Lack of interoperability for data exchange between countries. Improving items 1 and 2 depends strictly on country funded infrastructure, but the last two items can be tackled through concerted actions by non-governmental actors and the global health research community in general  (11).
The development of an ecosystem of tools for online analysis of the surveillance data-stream creates a demand for better quality primary data, which can act as an incentive for countries to invest more in health data monitoring infrastructure. In this paper, we present an initiative co-funded by the World Health Organization to build an open source platform for continuous epidemiological data analysis. It aims at filling multiple gaps in the current public-health data analysis ecosystem whilst functioning as a simple-to-use and responsive tool for decision-makers. We call this platform EpiGraphHub. It provides for automated data integration, cleaning, and harmonization, combined with a web interface for easy data exploration and building of live, interactive dashboards. The proposed platform shares some similarities with other open data platforms as shown in Table  1 , however it differentiates itself for the additional features it provides as well as for its focus on public health data and epidemiological data analysis.
The remainder of the paper is divided in 7 sections, System Architecture -where the architecture of the platform is described along with its design goals; Data Collection -in this section the general design of the data collection module is detailed; Data Transformationwhere the set of transformations available to clean and preprocess datasets are described; Data Exploration and Visualization -The interactive web interface for visual analytics is presented in this section; Data Analyses -Here the Analytical methods currently implemented are described with examples; Application Hosting -In this section, we describe the usability of the EpiGraphHub platform as a common backend for web and mobile applications; Finally, the Discussion section talks about the platform's relevance and applicability in the context of similar initiatives.
In order to make such continuous analyses possible, data from multiple sources must be integrated into a consistent data model before it can be used for analyses. A local storage layer is also provided to guarantee the persistence of the data used in the analyses. Moreover, for dynamic datasets (that are updated regularly), snapshots can be created, so the exact version of a dataset associated with published analyses can also be persisted. EpiGraphHub is built upon the open source business intelligence platform Apache Superset(13), with added features to make it more suitable to epidemiological data analysis. As such, it is already able to scale well horizontally, to serve heavy loads on a distributed computing infrastructure. The architecture of the platform facilitates the automated collection, transformation, storage, and visualization of data on an entirely open-source software stack.
Portability and replication of the entire stack is an important aspect of the platform's design, which places each software service within Docker containers (Figure  2 ). A container is a standard unit of software that packages up code and all its dependencies, so the application can be deployed quickly and runs reliably across different computing environments. In the diagram above, two of the key components of the platform are represented as the boxes with green lids. Containers are connected into a virtual network within the host environment and can exchange data. External web apps can connect directly to the containers to request data via the exposed APIs. To maximize code re-usability and value for the open source community, all the tools for data collection, transformation and analysis are developed as EpiGraphHub software libraries available both in Python and the R languages.
These libraries are fully documented, and ready for usage independent of the platform as well. Figure  3  show how a simple data upload to the platform can be accomplished with Python code. The project provides a continuous integration (CI) workflow, based on the GitHub actions tools, CI consists in pre-configured testing scripts that run before any contribution can be merged into the main branch of our repositories. The CI routine builds all containers, checking for any misconfigurations and run unit tests on the EpigraphHub libraries. CI is key to preventing contributions from breaking the platform. All pull-requests have to pass CI tests before being merged into the main branch. A continuous deployment (CD) workflow is currently under development to enable new releases on GitHub to automatically trigger an update on specified deployment servers.