dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion DataSHIELD enables co-analysis of several collaborating studies or data sources as if the data from all individuals in all studies were directly accessible but, in reality, these data remain completely secure behind the firewalls of their host computers. This is of significant value in several settings: (i)where ethico-legal or governance restrictions proscribe individual-level data release, or make permission for such release excessively time-consuming to obtain; (ii) a research group is particularly vulnerable to losing intellectual property (e. g. in a developing nation) but wishes to freely share the information held in its data without physically sharing the data themselves; and (iii)the underlying data are too large to be physically shared. All components of the combined platform (Opal/ DataSHIELD) are open source and available without restriction or payment.
Both the installation and the configuration require minimal specialist IT expertise: researchers with no IT background have already installed Opal without major difficulties by following the wiki documentation available online. 42 DataSHIELD is therefore attractive for researchers with limited resources. An extensive suite of functions already exists, but development work continues and we recently started developing a Graphic User Interface that requires no prior knowledge of R to run a DataSHIELD analysis. 43 The newest software release of Opal incorporates an important enhancement. Specifically, DataSHIELD analysis is now truly parallelized: every command is sent simultaneously to all DCs-previously, each command necessarily completed on one DC before being sent to the next. This substantially speeds up analysis, particularly with many studies or time-consuming functions.
If processing speed is particularly critical, further time may be saved by distributing the data from a large study across several Opal servers. If there are actual problems with the Opal instance at a given DC, then a message is sent to the data owner to correct that problem (e. g. the version of the libraries currently installed is not up to date, or the server is down). Crucially, if one or more of the data servers are unusable, the user can temporarily exclude them from analysis while they are repaired or updated. Because in DataSHIELD potentially disclosive commands are not allowed, some analyses that are possible in standard R are not enabled. In essence, there are two classes of limitation on potential DataSHIELD functionality: (i) absolute limitations which require an analysis that can only be undertaken by enabling one of the functionalities (e.
g. visualizing individual data points) that is explicitly blocked as a fundamental element of the DataSHIELD philosophy. For example, this would be the case for a standard scatter plot. Such limitations can never be circumvented and so alternatives (e. g. contour and heatmap plots) are enabled which convey similar information but without disclosing individual data points; (ii) current limitations which are functions or models that we believe are implementable but we have not, as yet, undertaken or completed the development work required. As examples, these latter include generalized linear mixed models  44  (including multi-level modelling  45, 46  ) and Cox regression. 47 espite its potential utility, implementation of DataSHIELD involves significant challenges. First, although set-up is fundamentally straightforward, application involves a relatively steep learning curve because the command structure is complex: it demands specification of the analysis to be undertaken, the studies to use and how to combine the results.
In mitigation, most complex serverside functions are now called using simpler client-side functions and we are working on a menu-driven implementation. Second, like anyco-analysis involving several studies, data must be adequately harmonized  27, 34, 35  and the proposed work must comply with governance stipulations in every study. Third, good research governance demands that any published analysis can precisely be replicated. We are therefore developing systems to automatically identify the particular DataSHIELD release used for a given analysis. In addition, each data provider must unambiguously record the particular freeze of data they contributed. These fundamental issues apply in many settings other than DataSHIELD, but because the project could be damaged if early users were to encounter serious scientific or governance problems, application has so far been restricted to research groups with whom we are fully collaborating.
This means we can provide active advice and support relating both to implementation and application. We plan to enable independent use as early as possible. Fourth, in undertaking a standard DataSHIELD analysis it is assumed that the data truly are horizontally partitioned, i. e. contributing sources hold the same variables but on different individuals (see Figure  3b ). So far DataSHIELD has been applied in settings where individual participants in different studies are from different countries or from different regions so it is unlikely that any one person will appear in more than one source. However, going forward, that cannot always be assumed. We have therefore been considering approaches to identify and correct this problem based on probabilistic record linkage. In the genetic setting the BioPIN 48 provides an alternative solution.
Ongoing work is required. Fifth, despite the care taken to set up DataSHIELD so that it works properly and is non-disclosive, it is possible that unanticipated problems (accidental or malicious) may arise. In order to identify, describe and rectify any errors or loopholes that emerge and in order to identify deliberate miscreants, all commands issued on the client server and enacted on each data server are permanently logged. Data sharing platforms, such as care. data, that enable powerful integrative analysis of research data as well as data generated by activity in the health service, from disease or death registries or from other administrative or governmental sources, have the potential to generate great societal benefit. Most crucially, they can provide an important route for production of the raw 'evidence' needed for 'evidence-based health care'.
But, to be pragmatic, many of the routinely collected healthcare and administrative databases will have to undergo substantial evolution before their quality and consistency are such that they can directly be used in high-quality research without extensive preparatory work. By its very nature, such preparation-which typically includes data cleaning and data harmonization-cannot usually be undertaken in DataSHIELD, because it involves investigating discrepancies and/or extreme results in individual data subjects: the precise functionality that DataSHIELD is designed to block. Such work must therefore be undertaken ahead of time by the data generators themselves-and this is demanding of time, resources and expertise that-at presentmany administrative data providers may well be unwilling and/or unable to provide.
That said, if the widespread usability of such data is viewed as being of high priority, the required resources could be forthcoming. Then the primary challenge will be to find effective solutions to the professional and societal challenges presented by the need to ensure that all work with individual-level data is rendered adequately secure. These solutions must respect and protect individual autonomy and confidentiality while facilitating the scientific progress from which everybody benefits. This conundrum is well recognized as demonstrated in the series of articles under the heading Dealing with Data in Science in 2011,  49  and more recently in a review article exploring the combination of multiple healthcare databases for postmarketing surveillance of drug and vaccine safety. 50 Furthermore, these challenges and potential solutions provide a crucial focus for professional organizations aimed specifically at enhancing our capacity to make effective use of the rapidly accumulating body of available data in the arenas of health and social care, governmental administration and biomedical and social research.
However, DataSHIELD provides important opportunities that neatly complement other approaches. It has already been proven to work in principle,  12, 22  and this paper now addresses the equally taxing problem: how to make it work in practice. DataSHIELD now provides a real opportunity to follow the advice of Kahn in Dealing with Data 56 to move the 'computation to the data, rather than the data to the computation'. 56.
Funding This work was supported through funds from: the  European Union 's  Seventh Framework Programme BioSHaRE-EU , grant agreement  HEALTH-F4-2010-261433 ; the Welsh and Scottish Farr Institutes funded by  MRC ; joint funding from  MRC  and  Wellcome Trust  comprising a strategic award underpinning the  ALSPAC  project and an infrastructural grant entitled The 1958 Birth Cohort Biomedical Resource -facilitating access to data and samples and enhancing future utility; and the  BBMRI-LPC  project ( EU FP7 , I3 grant).
Each contri- buting study (e. g. STUDY 1 ) identifies the requisite variables and creates any new harmonized variables needed in the com- bined analysis. These harmonized variables are then transferred from the study's data- base (Original. DB STUDY1 ) to adatabase linked to the Opal server (Analysis. DB STUDY1 ) The user logs into the collaborating servers through secured web services, using the credentials provided to them. This authen- tication ensures that only users authorized by the access body (e. g. an analysis access panel put in place by the consortium) can actually carry out an analysis (2) The user sends a command to request the specific data to analyse. This could be all the variables or specific variables stored in Analysis. DB STUDY1. (3) DataSHIELD extracts data from Analysis.
It also appears that GIPAW DFT computations of the  43  Ca EFG tensor parameters for a-calcium formate are in better agreement with the experimental data than when compared to cluster-based methods. 89 o comment upon the potential applicability of  43  Ca NMR experiments in distinguishing polymorphic forms, we also performed GIPAW DFT computations on the accepted structures of the b polymorph of calcium formate. 90 As we will highlight in this manuscript on several occasions, software using GIPAW DFT is able to accurately calculate both the calcium-43 EFG and magnetic shielding tensors for a variety of calcium-containing materials. The calculated C Q ( 43 Ca) value    3 ), which is more than double the calculated (and experimentally observed) value for the a polymorph (these values were observed and computed to be 1.
iii. Sensitivity of calcium NMR tensors to structure in Ca(OH) 2 and CaCrO 4. As the calcium CS and EFG tensors of Ca(OH) 2 and CaCrO 4 were measured with rather high precision, we chose these two systems to further comment upon the sensitivity of calcium NMR tensor parameters to minor modifications in structure. Starting from the accepted crystal structures denoted earlier, we modeled structural changes by isotropic unit cell expansions or contractions (abc crystal basis), and monitored the changes in calculated GIPAW DFT NMR tensor parameters. For Ca(OH) 2 , we observe significant changes in both the d iso and |C Q ( 43 Ca)| values (Fig. 17 ), with the other tensor parameters remaining largely unchanged (for example, O, see ESI, † Fig. S29 ). The data suggest that alterations of the cell and calcium-oxygen first coordination sphere internuclear distances by as little as 1% could be detected through changes in C Q ( 43 Ca) and d iso for Ca(OH) 2.
Conclusions Using a variety of modern experimental NMR spectroscopy techniques, including cross-polarization and population transfer,  43  Ca solid state NMR data for a broad selection of common calciumcontaining materials have been acquired and interpreted. Importantly, by acquiring data at both standard (9. 4 and 11. 75 T) and very high (21. 1 T) applied magnetic fields, and by using MAS and static data acquisition, we were able to measure 13 calcium chemical shift tensors, which roughly triples the amount of data available in the literature. For a few select cases, modest isotopic enrichment in  43  Ca (ca. 7%) was used to enable very high quality data acquisition. By coupling our experimental data with GIPAW DFT computations using accepted crystal structures, we were able to calculate, with reasonably high accuracy, the EFG and magnetic shielding tensors for nearly all of the samples considered.
Acknowledgements C. M. W. and D. L. B. are grateful to the  Natural Sciences and Engineering Research Council (NSERC)  for funding. Access to the 900 MHz NMR spectrometer was provided by the  National Ultrahigh-Field NMR Facility for Solids (Ottawa, Canada ), a national research facility funded by the  Canada Foundation for Innovation , the  Ontario Innovation Trust ,  Recherche Que  ´bec, the  National Research Council Canada , and  Bruker BioSpin  and managed by the  University of Ottawa  (www. nmr900. ca). NSERC  is acknowledged for a Major Resources Support grant. We are also grateful for the technical support provided by  Dr V. Terskikh  and  Dr E. Ye  at the high-field NMR facility and by  Dr Tara Kell  (uOttawa core X-ray facility) and  Mr Kevin M. N. Burgess  for recording the powder XRD spectra.
No data specific to minors has been defined, so the proportions observed in the general French population have been used in this study  [30]. Only direct healthcare costs that are linked to the use of emergency contraception and to termination or term delivery after unintended pregnancy are considered in this analysis. The resources are valued using a production function according to the methodological guide for Economic Evaluation published by the French National Authority for Health  [31]. Otherwise, tariffs are used for the valuation of resource use: the French government fixes the reimbursement rate for medical services based on pre-set rates that are negotiated and set annually. Doctors will be reimbursed by the social security system accordingly. As there are no differences in side effects between the two emergency contraception methods  [20] , the costs associated with side effects are not considered in this analysis.
Drug prices are taken from the official French drug pricing database  [21]. For the base case analysis, the price of Norlevo (HRA Pharma, France) is used because it is the most widely used levonorgestrel method currently available  [32]. Termination costs are weighted per the location of the procedure. 84% of voluntary terminations are managed in a public hospital and 16% in a private clinic  [27, 30]. The costs of the diagnosis-related group 14Z08Z are taken into account in both sectors. The weighted average cost of a termination is estimated to be 477 €. Costs of delivery include hospital costs and costs of pre-and postnatal services for the delivery, as well as governmental assistance during pregnancy. Hospital costs of term delivery are weighted per the method and location of delivery.
Acknowledgments The author wishes to thank several people. I would like to thank my professor,  Professor Ali McGuire  as well for his assistance and guidance with this paper. I would like to thank my colleagues,  Florian Battung  and  Henri Mathe , for their contribution to realize some of the analysis. The author received editorial support in preparation of this manuscript from  Tessa Hartog, PhD , of  Excerpta Medica. This work was submitted as dissertation for an Executive Master's  Programme at London School of Economics, Health and Social Care. The author was employed by HRA Pharma at that time. HRA Pharma provided support in the form of salary for the author and sponsored.
However, it fails in controlling and monitoring its own agents. The module for controlling the agent community of AIDA as developed and presented in this paper arises precisely in order to overcome this flaw. Thus, the AIDA administrators can verify the agent functioning or detect possible failures in their operation in real time. This new module enables the AIDA administrators to perform better management of the whole platform. They can even decide the most appropriate period to make system routine changes, updates, maintenance and other operations, improving the overall performance of the AIDA. The AIDA platform with this new module will ensure interoperability and improve the quality of services in health institutions. The methodology used in the development of this new AIDA module was design science research  [6].
The first step of this methodology is to identify the problems and motivations-in this case, the difficulty in controlling AIDA agents and their activities. The second step of design science research is to delineate the objectives; in order to overcome the drawbacks of the AIDA mentioned above, the main features of a module for controlling the agent community of AIDA was studied (presented in Section 5). The design and development step is also presented in Section 5. The demonstration and evaluation steps are evidenced in Section 6, and the last step of the design research methodology (communication) is the revelation of this paper  [6]. This paper is organized into seven sections. This first section gives a short introduction of the work, the subject in which it is embedded and the main objectives and motivations.
Section 2 demonstrates important concepts for this work, specifically the importance of the interoperability in health organizations and the technologies and standards. Some agent-based solutions for interoperability are presented in Section 3 (Related Work). The AIDA platform is presented in Section 4, as well as a brief study of its advantages and disadvantages. Section 5 presents the module for controlling the agent community of AIDA in detail: its features, its architecture and the description of its components. The results obtained after the module for controlling the agent community of AIDA for implementation are presented and discussed in Section 6. Finally, Section 7 summarizes the main conclusions and presents some future work.
Through these agents, which communicate with the decision agents and the interaction and explanation agents, the users/administrators are able to: access the information that they want to analyze; obtain required explanations; make decisions; visualize the results in a web browser. These operations are made through interfaces, monitoring dashboards and reports provided by AIDA to the users/administrators. Decision agents act as mediators, accepting the tasks from the proxy agents. These agents have the ability to split these tasks into several sub-tasks, sending them to the computational agents for processing. After that, the decision agents receive the respective results from the computational agents. Decision agents also communicate with the interaction and explanation agents and the resources agents whenever it is necessary to process information based on argumentative proceedings or to access data stored in the databases, respectively.
Computational agents accept requests for specific tasks from the decision agents, returning the results after processing. These agents also communicate with the resources agents whenever specific information is required. Resources agents have the ability to access specific data stored in the databases. Interaction and explanation agents act based on argumentative proceedings that are fed with information coming from the proxy agents or the decision agents. The EHR and all the IS (medical and nursing support IS, administrative, radiology, laboratory IS and others that belong to departments and services) are connected through the AIDA. Furthermore, the data center that processes and stores the information of the AIDA is connected to these systems, as well as the interfaces where users/administrators perform several operations.
The agents are the main core of AIDA. If a failure occurs in the execution of one of the agents, several consequences could happen. One of these can directly or indirectly affect the healthcare delivery to the patients. Therefore, the administrators that manage the AIDA platform should have total control of the agents. They should know when a specific agent is executing its tasks, how long it takes to accomplish its activity and other information. In this context, the necessity to create a module for controlling the agents community of AIDA arises. This module should present potentialities that allow the administrators to detect or prevent possible failures, to find their origin and to solve the problem.
Controlling the Agents Community of Archive of Medical Information (AIDA) In order to overcome one of the drawbacks of the AIDA platform, the difficulty in controlling agents and their activities, which is common to most systems based on an MAS, the main features of a module for controlling the agents community of AIDA were studied. This module should allow the AIDA platform administrators to control and manage a community of agents, ensuring their survival in a heterogeneous environment. According to the needs described by the administrators of the AIDA platform, several features of the module were stated, which aim to: • Ensure greater control over the agents of AIDA; • Facilitate the user's work in the creation and registration of new agents, locally or remotely; • Allow the user to enable and to disable services at the health unit, through the launch or stop of a particular agent; • Facilitate the scheduling and rescheduling of the agent's activity; • Monitor dynamically and in real time the activity of the agents; • Send alerts or warnings by email; • Perform auditing procedures.
The monitoring and preventing system developed in parallel with the work presented in this paper  [37]  is based on MEWS. However, in this case, instead of patients, the agents and the machines of AIDA are monitored and risk situations are detected, preventing damage to the AIDA workflow. It verifies periodically the agents' performance (CPU, RAM and frequency with which the agents execute their activities). In this way, this system detects situations conducive to failure, and it warns the administrators (via email) to take preventive actions, avoiding damage to the AIDA workflow. This system also monitors the performance and prevents failures in the AIDA machines  [37]. This module should be based on a client-server architecture, where the server is able to communicate with multiple clients simultaneously through the use of one thread dedicated to each client.
, a n are the AIDA agents that perform their activities in the same machine; W ebController is a user interface to control the agents, which allows the user to schedule the activities of each agent and also to monitor their activities; RemoteClient 1 ,. , RemoteClient n are composed of a AM S , a 1 ,. , a n , and their purpose is to connect all the machines that constitute the module. Security and privacy are guaranteed by a set of protocols that control the access and maintain information privacy. These protocols are implemented in the whole AIDA platform, including this new module, and these do not allow unauthorized users to access information that is manipulated by the agents. In addition, the CHP has implemented security protocols among all TCP/IP connections, including the ones established in the AIDA platform.