dataesr/scientific-paragraphs-categorization huggingface dataset:
All analyses were done in R software using the netmeta package and the CINeMA web application (Box 2)  [13, 14]. Box 1. Description of 2 network meta-analyses used to illustrate the CINeMA approach to assess confidence in network meta-analysis. Diagnostic strategies for patients presenting with symptoms suggestive of acute coronary syndrome Siontis et al. performed a network meta-analysis of randomised trials to evaluate the differences between the non-invasive diagnostic modalities used to detect coronary artery disease in patients presenting with symptoms suggestive of acute coronary syndrome  [10]. Differences between the diagnostic modalities were evaluated with respect to the number of downstream referrals for invasive coronary angiography and other clinical outcomes. For the outcome referrals, 18 studies were included.
This outcome was evaluated in 101 studies. The network is presented in Fig 1B , and the outcome data are given in S2 Data. The results of the network meta-analysis are presented in S1 network plot, while study limitations in the estimates from a network meta-analysis are presented for each comparison in bar charts. Box 2. The CINeMA web application. The CINeMA framework has been implemented in a freely available, user-friendly web application aiming to facilitate the evaluation of confidence in the results from network meta-analysis  [13]. The web application is programmed in JavaScript, uses Docker, and is linked with R; in particular, packages meta and netmeta are used, and an R package to calculate the contribution of studies in network meta-analysis treatment effects  [14, 15]. Knowledge of these languages and technologies is not required to use CINeMA.
Loading the data In the 'My Projects' tab, CINeMA users are able to upload a comma-separated values (csv) file with the by-treatment outcome study data and study-level risk of bias and indirectness judgments. The CINeMA web application can handle all the formats used in network meta-analysis (long or wide format, binary or continuous, arm-level or studylevel data) and provides flexibility in labelling variables as desired by the user. A demo dataset is available under the 'My Projects' tab. Evaluating the confidence in the results from network meta-analysis A preview of the evidence (network plot and outcome data) and options concerning the analysis (fixed or random effects, effect measure, etc. ) are available under the 'Configuration' tab. The next 6 tabs guide users to make informed assessments of the quality of evidence based on within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence.
Summarising judgments The 'Report' tab includes a summary of the evaluations made in the 6 domains and gives users the possibility to either not downgrade, or downgrade by 1 or 2 levels each relative treatment effect. Users can download a report with the summary of their evaluations along with their final judgments. CINeMA is accompanied by documentation describing each step in detail ('Documentation' tab). CCTA, coronary computed tomography angiography; CMR, cardiovascular magnetic resonance; ECG, electrocardiogram; echo, echocardiography; SPECT-MPI, single photon emission computed tomography-myocardial perfusion imaging. The CINeMA software facilitates judgments, based on the data presented in the bar graphs combined with specific rules. For example, a weighted average of the risk of bias can be computed by assigning scores of -1, 0, and 1 to low, moderate, and high risk of bias, respectively.
Reporting bias Background and definitions. Reporting bias occurs when the results included in the systematic review are not a representative sample of the results generated by studies undertaken. This phenomenon can be the result of the suppression of statistically non-significant (or 'negative') findings (publication bias), their delayed publication (time-lag bias), or omission of unfavourable study results from study reports (outcome reporting bias). The presence and the impact of such biases has been well documented  [23] [24] [25] [26] [27] [28] [29] [30]. Reporting bias is a missing data problem, and hence it is difficult to conclude with certainty for or against its presence in a given dataset. Consequently, and in agreement with the GRADE system, CINeMA assumes 2 possible descriptions for reporting bias: suspected and undetected.
Researchers should not, however, naively rely on the software's programmed rules. The CINeMA application safeguards against transcription errors and thus will enhance reproducibility, but judgments should be revisited and reconsidered, taking into account the particularities of each network meta-analysis. This paper and the CINeMA tool extend the framework previously described by Salanti and colleagues  [5]. Our original framework also addressed the credibility of a treatment hierarchy, which we plan to develop further and implement in CINeMA in the future. Any evaluation of the confidence in evidence synthesis results will inevitably involve some subjectivity. Our approach is no exception. While the use of bar charts to gauge the impact of within-study bias and indirectness provides a consistent assessment across all comparisons in the network, their summary is difficult.
Defining the range of equivalence will often be subjective, and might be influenced by the data. Further limitations of the framework are associated with the fact that published articles are used to make judgments, and these reports do not necessarily reflect the way studies were undertaken. For instance, judging indirectness requires study data to be collected on pre-specified effect modifiers, and incomplete reporting will inevitably impact on the reliability of judgments. A consequence of the inherent subjectivity of the system is that interrater agreement may be modest. Studies of the reproducibility of assessments made by researchers using CINeMA will be required in this context. However, we believe that transparency is key: although in CIN-eMA judgments may differ across reviewers, they are made using explicit criteria.
However, published network meta-analyses seldom evaluate confidence in relative treatment effects  [1]. Through the free, open-source CINeMA software (see Box 2), our approach can be routinely applied to any network meta-analysis  [13, 59]. The suggested framework operationalises, simplifies, and accelerates the process of evaluation of results from large and complex networks without compromising statistical and methodological rigor. In conclusion, we believe the CINeMA framework is a transparent, rigorous, and comprehensive system for evaluating the confidence of treatment effect estimates from network meta-analysis.
V. NUMERICAL EXPERIMENT To illustrate the computing effect of the PHA, we compare the computational results obtained by the SAA. In addition, as the risk coefficient λ of the risk aversion objective function and the confidence level α of CVaR are two control parameters, at the end of this section, sensitivity analysis is performed. The commercial solver CPLEX with version 12. 8 is used. The mathematical model is implemented by MATLAB2018b and runs on a HUWEI PC with 4. 0GHz 32 RAM. Considering the complexity of the problem, we set time limit is 4800s as an acceptable time. The convergence effect of PHA is impacted by the penalty coefficient ρ. Before starting the formal numerical experiments, a pretest with the parameter ρ ∈ {1, 5, 10, 20, 50, 100, 200, 500} is performed, and ρ = 20 is selected as the optimal parameter according to the optimal results of the pretest.
The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC.
For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.
As lifescience and health research datasets rapidly grow larger, with an ever-increasing number of study participants required to detect meaningful but weak signals that may be blurred by a myriad of confounding biological, experimental, or environmental factors, the computational resources required to process and analyze this big data increasingly outgrows the capabilities of even large research institutes. The various cloud technologies and services defined in Table  1  are based on shared commercial and private computer and storage resources that can be provided on demand to users from a large number of different institutions who are conducting or participating in joint projects. They have emerged as powerful solutions to the challenges of collaborating in research on genomic, biomedical, and health data.
Biomedical and health research has yet to enter fully the big data and cloud computing era. The Health Research and Innovation Cloud (HRIC), as described in this manuscript, would help to facilitate this transition, providing access to larger datasets, cutting-edge tools, and knowledge, as envisioned by Auffray et al. [3]. For example, the HRIC should ease the incorporation of domain expert knowledge into systems disease maps in a format that can be both understood by all stakeholders (patients and clinicians, scientists, and drug developers) and processed by highperformance computers, thus supporting the development of innovative medicines and diagnostics  [4, 5]. Cloud technologies (accessed through Hadoop applications, for example) also make it possible to collaborate and to access and reuse data in situations when privacy concerns or regulation prohibits remote users from downloading data-an important benefit in Europe where national regulations can differ significantly.
Scaling and sustainably managing such solutions to support all European life scientists thus require a coordinated action from science policy makers, funders, and other actors in this complex ecosystem. Connecting Europe's health data to advance the understanding of life and disease requires that research data and analysis tools, standards, and computational services are made FAIR-that is, findable, accessible, interoperable, and reusable-for researchers across scientific disciplines and national boundaries  [9]. Truly enabling personalized and digital medicine across Europe and beyond will require a connected digital infrastructure for Europe's health data that supports systematic openness and the integration of research data with real-world datasets (e. g. , environmental monitoring data) generated inside all of the healthcare systems, government agencies, foundations, and private organizations that will adopt it.
Overall, the authors feel that the cloud described in this manuscript, which would provide the biomedical and health research community with the technical infrastructure and services necessary to support the development of innovative diagnostics methods and medical treatments, should become an integral part of the EOSC. The workshop gathered a broad range of experts from multiple biomedical research disciplines, health care, informatics, ethics, and legislation, including representatives of more than 45 collaborative projects funded by the EU through the FP7 (The European Union's seventh framework programme for Research, Technological Development and Demonstration) and Horizon 2020 (H2020) programs. The participants explored requirements and developed a set of recommendations for a European HRIC to connect researchers and health data sources in Europe  [13].
g. , links to render data sets findable) along with metadata (e. g. , type and scale of content, specifications describing in what systems the data set may be stored and processed), and indications on how to gain access (e. g. , requirements and point of contact). This could be designed as a metadata repository containing the metadata of data objects and information on how to gain access to them. The data objects as such may, or indeed should, be stored elsewhere. Beyond their metadata, however, data sets are bound to differ greatly because research projects vary largely in scope. Not only would it be cumbersome to record a large  The HPB Knowledge Graph (KG) is an online graph database that accepts submissions of anonymized human data, animal data, and models from the brain. All data that are made  [65]  number of parameters that are irrelevant to the specific question addressed, but it would also be problematic from an ethics viewpoint, considering patient personal data protection aspects  [15].
The H2020-funded project EOSC-Life  [25]  is developing policies, specifications, and tools for the management of data for biological and medical research, including aspects of eHealth data. The use of common metadata standards, developed in EOSC-Life, as a foundation for remote data discovery and access was emphasized by the workshop participants as a key enabler for the HRIC. For instance, practical and legal considerations for cloud computing of patient data, which include the responsible use of federated and hybrid clouds set up between academic and industrial partners, have been put forward by early EOSC pilot projects  [26]. The workshop participants emphasized that sustainability aspects are critical and must be considered from the start. To ensure that a HRIC could respond properly to emerging needs, innovation, and technology changes, a distributed federated storage solution offering access to FAIR data and services should be built according to modularity principles.
In particular, owing to the term nature of a HRIC, due consideration should be given to deploying generic and modular computational methods and/or data storage management systems, while the information and communications technology (ICT) infrastructure should be flexible, portable, and expandable. The million European genomes initiative  [27]  is a case in point: 18 member states have already signed the Genomics Declaration of Cooperation [28] to enable crossborder access to genomic databases and other health information. This federation of national initiatives [28] will provide secure access to such data resources in the member states to enable the discovery of personalized therapies and diagnostics for the benefit of patients. The initiative involves aligning strategies of ongoing national genomic sequencing campaigns with complementary de novo genome sequencing to obtain a total cohort of one million Europeans, accessible in a transnational framework, by 2022  [29].
Existing ethics and legal framework Unblocking the legal and administrative barriers for sharing human research data across geographical and organizational boundaries will, if the trust of research participants is preserved, pave the way for continentscale cohorts in life-science research. This will represent a significant innovation as the sharing and joint analysis of sensitive data has until now been severely limited Fig. 1  Proposed general architecture of the HRIC European (inter) national databases, with varying data formats and data types referenced in a metadata repository, following formatting rules of the federated data commons as agreed at the HRIC governance level. The different users, after access control to the cloud, use the HRIC interface to access the repository, which gathers the relevant data and performs analysis, with outputs such as mathematical models, data visualizations, statistics, and patient's profiles according to the users' needs because of the different restrictions inherent to the different classes of data.
By using a federated database model, with a metadata repository within the HRIC cloud encrypted environment, data security is maintained while innovative data analyses can be performed by bringing the algorithms to the data rather than centralizing the data  [32]. Federation, rather than full integration of all available resources, poses an important challenge for the implementation and deployment of an effective HRIC. The set-up and functioning of a HRIC requires a robust foundation of legal agreements and ethics rules and procedures, as well as security and data protection compliance protocols. Importantly, these elements must be introduced during the conception and design phase as part of HRIC governance. Indeed, in order to enable different HRIC actors to provide access to their data sources, manage these resources within the cloud, and access these resources, it is essential to incorporate policy requirements into the design of the HRIC itself and to manage the complexity involved by implementing simple and intuitive user interfaces and project portals.
In addition, any personal data provided by the user to enable the optimal use of a given health dataset should only be kept for the amount of time necessary to provide the intended product or service  [15]. Thus, successfully linking and accessing biomedical and health data across Europe will require many different disciplines and specialists working together, with a coordinated effort that should encompass controlled access mechanisms to ensure compliance with privacy and data protection regulations. Data providers need logging and monitoring functionalities to comply with the GDPR and to enable tracking of data and methods within the system, controlling instances and routines that check for the adherence to predefined standards and formats to guarantee data integrity. Access mechanisms need to be developed that support the researchers, data producers, and data analysts to request permissions and fulfill the reporting requirements for data use in national and international research projects; this is significant regulatory, political, and sustainability challenge  [34].
This stipulates that patient donors should be able to retain control over their data regardless of technological developments. A European HRIC could be important in enabling researchers to comply with these requirements. For example, once certain conditions are met between European and international partners, including those pertaining to data protection and use, federated and hybrid clouds could facilitate the deletion of data sets once a donor exercises her/his 'right to be forgotten', which could minimize the necessary transfer of large raw data sets across borders, as the deletion can be performed in the original dataset and easily propagated to the relevant federated data sources.
Like the EJP-RD, the European Strategy Forum on Research Infrastructures (ESFRI) can play a role in the development of the HRIC. Two further aspects of the EJP-RD are worth noting: the program has a strong emphasis on the importance on the diverse workforce required, with a training program that stretches beyond academia and research networks to reach a broad set of individuals in health systems and the education sector. Moreover, the HRIC should fund as broadly as the EJP-RD and should recognize that successfully addressing many of the identified challenges will require a diverse portfolio of projects that avoid any artificial boundary between biomedical and health research. Horizon Europe should allow for linkages between the HRIC, ESFRI, and other themes of Horizon Europe and, importantly, between the HRIC and other funding sources such as the European Structural and Innovation Funds (ESIF) and the cutting-edge basic research successfully supported by the European Research Council (ERC) during the past decade  [57, 58].
The HRIC should enable investments in product development for future health-care solutions and should allow health care providers to procure such solutions. People need to be an integral part of the innovation vision in which the HRIC supports a highly skilled future workforce that makes Europe attractive for locating R&D investments. The experience gained in IMI publicprivate partnership infrastructure projects such as eTRIKS and the European Medical Information Framework (EMIF) should be leveraged. Finally, the current and future EU Framework Programmes for Research and Innovation (Horizon 2020 and Horizon Europe) should consider mobilizing funds to support novel pilot actions and the pooling of data and resources across the EU, and should demonstrate the benefits in advancing data sharing, integration, and analysis across Europe, for the benefit of all citizens.
Conclusions, recommendations, and action points Clouds are increasingly becoming a key venue for enabling and hosting European and international collaborations, benefitting from the ability to hold data securely in a single location (or in few locations) and enabling collaborative research on the computational infrastructure used for analysis. In conclusion, a cloud-based federated data storage solution, with interoperable services for data-access to local repositories and modular environments that can be configured for a given use case, seems to match the data needs of the EU research and medical institutions and of all other stakeholders. The choice of cloud technology provides the ability to manage rapidly growing datasets and provides users with access to the massive computational infrastructure needed for analysis.
Acknowledgements We would like to thank the scientific policy officers at the  European Commission Health Directorate , who developed the workshop concept, organized it, and provided input to this publication:  Sasa Jenko ,  Katarina Krepelkova ,  Christina Kyriakopoulou ,  Jana Makedonska ,  Joana Namorado ,  Elsa Papadopoulou ,  Jan Van de Loo  and  Gregor Schaffrath  (National Expert in professional training). The workshop was organized by the  Health Directorate of the Directorate-General for Research and Innovation at the European Commission  with contributions of the  Innovative Medicines Initiative , the  Directorate General for Communications Networks, Content and Technology , and the  Directorate General for Health and Food Safety.
The EU should take inspiration in existing large and successful infrastructures that foster multidisciplinary teams, such as the European Organisation for Nuclear Research (CERN) -Scientists and health professionals in training should be made aware of the possibilities of the HRIC -Communication in relevant professional channels should be strengthened Fund public and private initiatives for the development of the HRIC through EU Framework Programmes (Horizon 2020 and Horizon Europe) The EU and its member states should, together with private investors, develop a coherent, ambitious, and long-term action plan supported by innovative funding mechanisms that consolidate the outcomes from the existing project portfolio into a long-term operational infrastructure -The EU should invest through calls and grants in order to build and consolidate the HRIC -The existing industrial ecosystem should be supported, to remain competitive against the other actors in the world.