dataesr/scientific-paragraphs-categorization huggingface dataset:
Using all existing sources and types of data 1. Searching, using, comparing and integrating all sources of data As previously discussed in paper 1, most systematic reviews currently rely on summary data extracted from reports published in peer-reviewed journals or reported in conference abstracts. This approach raises important concerns related to reporting bias  [1] [2] [3] [4]  and lack of transparency  [5] [6] [7] [8] [9]. In contrast, under pressure from editors, funders, patient and public initiatives, and regulatory authorities, new sources of data are now increasingly available. These new sources of data allow reviewers to obtain more information on the methodology of randomized controlled trials (RCTs) as well as their results through aggregated data.
Clinical trial registries: Clinical trial registries such as ClinicalTrials. gov, the World Health Organization's International Clinical Trials Registry Platform (WHO-ICTRP), and the European Union Clinical Trials Register (EU-CTR) have become important sources of information for systematic reviewers. Such registries allow for identifying unpublished trials if these trials were initially registered as required by many authorities. They can be used to identify outcome reporting bias (e. g. , change in the primary outcome) because information on the primary and secondary outcomes is included in the registration record before the start of the study  [10]. Furthermore, theoretically, one should be able to access summary results of trials including at least one site in the United States or European Union.
Indeed, by law, the section 801 of the 2007 US Food and Drug Administration (FDA) Amendment Act (FDAAA), requires all sponsors/investigators of clinical trials of drugs, biologics, and medical devices regulated by the FDA with at least one site in the United States to post their results at ClinicalTrials. gov within 1 year after trial completion. The US National Institutes of Health, the major US funder of academic trials, enforces this policy for all trials they fund, whether subject to FDAAA section 801 requirements or not. In 2014, the European commission on clinical trials directive required mandatory posting of results for any interventional trials registered in the European Clinical Trials Database (EudraCT). Currently, the results of 36,751 trials are available at ClinicalTrials. gov [11]  and 12,235 at EudraCT  [12].
To overcome this limitation, ClinicalTrials. gov has now made it possible to upload the protocol and statistical analysis plans. Moreover, discrepancies in the data reported in trial registrations and their publications are common  [19]. Specific journals, and appendices to journal publications: Protocols can also be available as published articlesjournals such as Trials were developed specifically to publish protocolsbut also as appendices of journal publications as now systematically requested by an increasing number of journals (e. g. , New England Journal of Medicine, Journal of Clinical Oncology or Annals of Internal Medicine).
Repositories of clinical study reports: To increase access to data from pharmaceutical companies, some repositories were created and the Wellcome Trust are now also using the CSDR platform to share their data. In addition to peer-reviewed journal articles, some funders such as the UK NIHR Health Technology Assessment Programme (https://www. journalslibrary. nihr. ac. uk/#/) also produce detailed monographs containing much more data on trial reports including all sensitivity and subgroup analyses along with information on open-access data-sharing agreements. Furthermore, trial websites also give access to detailed information such as the CSR, statistical analysis plans etc. All these new sources of data (i. e. , trial registries, CSR repositories, etc. ) represent a great opportunity because they provide access to clearly needed information related to the methods used (e.
g. , protocol, statistical analysis plan, annotated case report form) and the conduct and results of the trial (e. g. , summary results posted on a trial registry, IPD, analysis-ready dataset, CSR). However, contrary to journal publications, which are available from well-structured electronic bibliographic databases, these new sources of information are only available through various channels that are still rapidly evolving over time as new initiatives emerge  [24]. Information available for a single trial is usually scattered and not linked, so the identification of all data for a given trial is challenging. Using these data also raises specific issues related to the risk of double counting the same study  [18]  and possible discrepancies of the data reported in the various sources  [25].
Solutions based on the concept of "threaded publications," whereby all the various reports and publications related to a trial are linked (published protocol, results paper, secondary commentaries, CSR, IPD, etc. )  [26] , are being explored  [27]. Particularly, the OpenTrials database aims to host and match all existing trial publications and data in a user-friendly web interface to provide a comprehensive picture of all the data and documents available for a trial  [28]. Methods for evidence synthesis must take into account these developmentsfor example, the search strategies must cover all these new sources of data  [29]  and rules to manage discrepancies between different reports of the same trial must be pre-specified in the protocol. Furthermore, the process needs to be rethought because the large number of diverse sources needed to be continuously searched and the increased amount of data to be extracted is a huge amount of extremely time-consuming and burdensome work.
In addition, reanalysing IPD requires substantial resources and expertise. Contacting trialists (academic investigators or drug manufacturers), applying for access to IPD, negotiating confidentiality and data use agreements and contracts, technically gaining access to data and meta-data, understanding the structure of the database, querying possible data errors and undertaking analyses remains challenging and time-consuming, especially after funding for the main trial has ceased. Moreover, IPD meta-analyses are not appropriate for all clinical questions. Indeed, an essential step in research is to determine the extent and scope of evidence synthesis required to ensure that the research efforts undertaken are consistent with the relevance of the clinical question. For example, for an emergent public health issue, a rapid review ─ i.
Another example is the drug safety and effectiveness network (DSEN) which includes observational studies in network-meta-analyses of RCTs  [52]. Nevertheless, use of this kind of data raises some issues related to the quality of the data, key missing data, the risk of confounding and misclassification bias. Approaching causality in such datasets can be difficult because of the observational nature of the data and risk of confounding biases. Furthermore, including such data is challenging particularly because of the large heterogeneity in the size and quality of datasets and methods used. However, with the increased interest, new concepts, methods, and tools are being developed to support use of these data for comparative effectiveness research  [51]. For example, emulated trials aim to mimic a RCT when planning and conducting the analysis of routinely collected data or other observational data for comparative effectiveness research  [53, 54].
Automation technologies For many repetitive and labour-intensive tasks of evidence synthesis, automation can and should be preferable and more sustainable  [78]. New technologies such as textual analysis, semantic analysis, text mining and data linkage could have a major impact on the delay of systematic reviews (e. g. , EPPI-reviewer  [79] ). Automated natural language processing (text mining) may help overcome resource-intensive manual screening  [80] [81] [82] , data extraction, assessment of risk of bias and reporting the findings of a systematic review  [83] [84] [85] [86] [87]. Semiautomated web applications (AbstrackR  [88] , Rayyan  [89] , RobotAnalyst  [90] ), that employ machine-learning and text-mining technologies, are now available to support systematic reviewers during title and abstract screening.
Computing-intensive experiments in modern sciences have become increasingly datadriven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i. e. , read, write, store, and retrieve) highly-sensitive data like persons' medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists with identifying the datasets, generated by the workflows, that need to be anonymized along with setting the anonymization degree that must be met.
The availability of the data used and generated by the DWf executions is useful in checking the reproducibility of the DWf's results since it allows scientists to explore and verify that the DWf's results can be replicated, and are, therefore, trustworthy. In certain modern sciences, such as social science and medical science, DWfs incarnate experiments that manipulate sensitive data about individuals. Because of this, scientists do not publish the data used and generated by the DWf executions. This is a hurdle towards enabling reproducibility since third party scientists are unable to explore at all the data used and generated by the DWf executions. To address this problem, we investigate in this paper the problem of sanitizing the data used and generated by the DWfs that manipulate sensitive datasets, and we do so using k-anonymization.
The obtained anonymized data may not allow the scientist to repeat the experiment, i. e. , re-execute the DWf using the initial (non-anonymized) datasets and verify that the execution delivers final results that are identical (or at least similar) to those reported on. That said, the obtained anonymized data allow thirdparty scientists to explore, and ultimately verify the claims made by the authors in the scholarly article. In particular, we argue that the data used and generated by DWfs, albeit anonymized, allow the scientist to "go back and see what happened" in the data analysis and gain a better understanding of how and why the results were produced. Our objective is not to invent a new technique for anonymizing the data used and generated by DWfs. Instead, we show how an existing technique, specifically k-anonymization, can be leveraged for this purpose.
Specifically, the novelty of our solution consists of providing assistance to those who have to share datasets that have been used and generated by DWfs with peers without being sure how these datasets will be used. In doing so, we address the following two issues: 1. Not all the inputs and outputs of the operations that compose a DWf carry sensitive information about individuals at run time. The user has to manually identify the parameters that carry sensitive information. Moreover, s/he needs to specify the anonymity degree (k) that needs to be applied to the data instances of those parameters. This task can be tedious, since the user may need to examine all parameters for each operation in the DWf. 2. K-anonymization techniques expect as input a single relational table, whereas the data used and generated by the executions of a DWf is scattered into multiple tables, each representing the data instances of a given operation input or output.
Running scenario Figure  1  illustrates a DWf that consists of five operations (op i=1,5 ) connected through data dependencies. Input/Output parameters are omitted for the sake of readability. This workflow's operations are as follows. op 1 queries a dataset in order to obtain nutrition data about patients, and op 2 retrieves oncology data about patients in terms of type of cancer and age (see Table  1 ). op 3 combines the data output by op 1 and op 2. Specifically, it performs a natural join on nutrition and oncology information. The outcome is presented in Table  2. op 4 establishes correlations according to the needs of doctors. Specifically, based on a patient's nutritional details and health conditions, op 4 generates an indicator, which specifies if the nutritional habits of the patient need to be revisited.
That said, it is not suitable for our purpose since it protects individual privacy in the context of statistical queries, only. In our case, we are interested in providing users with the means to explore data produced following the executions of a workflow, as opposed to generating some statistics, which is what differential privacy is mainly targeted for. Because of differential privacy's limitation, we adopt k-anonymity, which is still widely used for relational data anonymization. To illustrate how k-anonymity operates, let us consider a dataset (d) of records referring each to an individual, e. g. , age, address, and gender that could be used to reveal his identity. Such attributes are known as quasi-identifiers. (d) is k-anonymized, where (k) is an integer, if each quasi-identifier tuple occurs in at least (k) records in (d).
Parameter anonymity degree The execution of a DWf refers to a DWf instance denoted by (insWf). The anonymity degree of a DWf's parameter ( p, op ) is defined with respect to a given DWf instance (insWf). Indeed, multiple instances of the same DWf may have as input datasets different anonymity degree requirements. For example, the owner of an input dataset used for a given workflow instance (insWf 1 ) may impose a more stringent anonymity degree than another owner of an input dataset used for a different workflow instance (insWf 2 ). As a result, the same workflow parameter may have different anonymity degrees depending on the workflow instance in question. Due to this difference in requirement, we use the following function to specify the anonymity degree of a given parameter p, op with respect to a workflow instance insWf: anonymity( p, op , insWf).
For example, anonymity( p, op 1 , insWf 1 ) = 3 specifies that p, op 1 has an anonymity degree of 3 within the workflow instance insWf 1. Consider that the dataset d is bound to p, op 1 in insWf 1. Given that anonymity( i, op 1 , insWf 1 ) = 3, (d) must be anonymized before its publication. Specifically, each record (individual) in the anonymized (d) must not be distinguished from at least (2) other individuals  [16].
This is because an operation that consumes sensitive datasets may produce non-sensitive datasets. For example, op 5 in Fig. 1  generates non-sensitive data although its outputs are sensitive inputs to a workflow. The output of such an operation is a report that is free from information about individual patients. R Identifying anonymity degree In addition to assisting the designer with identifying sensitive intermediate and final output parameters, we also identify the anonymity degree that should be applied to dataset instances of those sensitive parameters. To illustrate this, consider that p , op is a sensitive intermediate or final output parameter. The anonymity degree of such a parameter given a workflow execution insWf can be defined as the maximum degree of the sensitive datasets that are used as input to the workflow and that contribute to the dataset instances of p , op.
Anonymizing datasets We have, thus far, presented a solution for detecting parameters that may be sensitive, and for identifying the anonymity degree that should be used when anonymizing the workflow datasets. In doing so, we did not actually specify how we go about anonymizing the datasets used and generated by the executions of a given DWf. The anonymization technique that we propose is a three-step process. In the first step, we construct a schema of a global table (relation). Such a table is global in the sense that its schema union all the input and output parameters of the workflow and the steps thereof. Secondly, we populate this table using the datasets used and generated by the workflow in question over its executions. Finally, we anonymize the obtained populated global table using the anonymity degree identified using the detection technique presented in Sect.
Anonymity degree and sensitive parameter identification Our objective is to assess the overhead of the operation required for identifying sensitive parameters for computing the anonymity degree to be used for the anonymization of data obtained by DWf's executions. In particular, we computed parameters such as workflow loading times, time required for identifying parameter dependencies and sensitive parameters, and for computing anonymity degree. Number of operations, sensitive inputs, and anonymity degrees highlight the differences between these workflows. For our experiment, we used 20 different CWL workflows  6  (500 executions per workflow). We used these workflows because they are publicly available, and also because they contain workflows with different numbers of operations and data links.
Use case for anonymizing workflow data To showcase how the technique for anonymizing workflow datasets described in the previous subsections work, we will use in this section the example workflow illustrated in Fig. 1. We have used for this use case synthetic data that we generated for this purpose. Using data about real individuals with information about their health conditions cannot be published as-is. On the other hand, synthetic data that we generated for validation purposes are publicly available online. 7 We assume that the anonymity degree to apply to the data produced by the workflow is 2. In what follows, we illustrate how each step of workflow data anonymization proceeds. Step 1: Creation of the schema of the global table The specification of the workflow illustrated in Fig. 1   Notice that the attributes Patient and ID appear multiple times in the operation signatures, with the same semantics.
Such an indicator can take more specific values to indicate, for example, the intake in veg, dairy and meat needs to be lowered or increased for a given patient. The above example, albeit small, illustrated how our approach operates. We chose a small example to make it easier for the reader to grasp the solution. That said, our approach can scale to workflows with large datasets. Indeed, as pointed out earlier both the construction of the global table and its population are performed in a time that is linear to the number of operations that compose the workflow.
[1]. Contrarily, the work of Sharif et al. [15]  addresses the privacy 123 of both task and data. In the context of our work, we are concerned with the privacy of workflow data and hence, is in line with the second category of proposals. However, achieving this privacy requires that the workflow designer manually identifies sensitive workflow parameters and sets the degree to which the datasets bound to those parameters need to be anonymized. We have taken care of both aspects of our work.
Data anonymization Data anonymization is the operation by which datasets are processed so that the persons whom the data describe remain anonymous. There are five main anonymization operations  [10] , namely generalization, suppression, anatomization, permutation, and perturbation. Generalization replaces attribute values with less specific but semantically consistent values  [17]. Suppression, on the other hand, masks completely the values of given attributes  [7]. This is the technique used, for example, by Davidson et al. [6]. Anatomization is used to dissociate the correlation that may be observed between the quasi-identifier attributes and sensitive attributes by separating them into different datasets. The objective of permutation is the same as anatomization but do so by dividing a number of data records into groups and mixing their sensitive values in every group.
Algorithm 1 gives some guidelines and summarizes in an informal pseudo-code style the main steps of the primal-dual approach (the implementation of the routine "BuildQ" is left to the reader). Algorithm 1 Primal-dual algorithm: R, b = PD( M, M greedy , W, K max ) 1: Input: • training manifold M for primal dual iterations • training manifold M greedy for greedy algorithm • basis {ω i } m i=1 of measurement space W • maximum number of iteration K max 2: Generate basis {v i } N i=1 of V N // e. w = { u, ψ i } m i=1 , u = { u, ψ i } N +m i=m+1 , Q = BuildQ(w) 7: Qlist. append(Q), wlist. append(w), ulist. append(u) 8: end for 9: Estimate L // e. g. with power method 10: Set γ G and γ F such that γ G γ F < 1/ L 2 11: x = x = zeros(m(N + 1)) // starting guess for A : W → V set to A(w) = w. 12: t = 1 // starting guess for t > 0.
Major changes are now becoming more mainstream, including widespread free access to articles  (Piwowar et al. 2018 ) that have been funded not by subscriptions but by an APC. This practice increases the profit-making capacity of commercial publishers and disadvantages authors with lesser financial privileges. More equitable alternatives are required, such as returning to the earlier model by which a research paper is not regarded as a for-profit commodity but as a public-serving good. However, there is a relatively simple and cost-and risk-free option: a majority of the journals in geochemistry have a green colour according to the SHERPA/RoMEO grading system (FIG. 1 ), indicating that pre-print and post-print of articles submitted to journals can be archived in a repository. According to the Web of Science among the 885 articles published in Elements, only 56 were OA as Gold or Bronze (data accessed on 01/02/2020).
Introduction In the framework of the European project CONFI-DENCE, Work Package 1 (WP1) focused on the uncertainties in the pre-and early phase of a radiological emergency, when environmental observations are not available and the evaluation of the environmental and health impact of the accident heavily relies on atmospheric dispersion modelling. The results of these simulations, including dose calculations, are used to infer recommendations for the protection of the population. However, the model outputs are subject to uncertainties  (Gering et al. , 2016; Sørensen et al. , 2016 Sørensen et al. , , 2019)) , stemming from meteorological data (stochastic and modelling uncertainties) and lack of knowledge of the source term. The physical and numerical approximations made within the dispersion and dose models introduce additional uncertainties in the output.
REM case study For the REM case study, two release scenarios were considered: a 4-hour "short release" and a 72-hour "long release". These are described in  Korsakissok et al. (2019a Korsakissok et al. ( , 2019c)). For the short release, the overall emitted quantities of radionuclides are shown in Table  2. The starting time of release was given with an uncertainty of ±6 hours; the effective release height was 50 m ± 50 m and the released quantity varied between a third and a factor of 3 of the values given in Table  2. Each participant was free to choose how to take these uncertainties into account, depending on their computational capabilities. This resulted in ensembles with numbers of members ranging from 10 to 650 simulations. For the long release, an ensemble of 10 source terms, with complex kinetics, was extracted from a source term database built in the European project FASTNET using the severe accident code ASTEC  (Chevalier-Jabet, 2019a , 2019b).