dataesr/scientific-paragraphs-categorization huggingface dataset:
First, by taking the ratio of the output distance functions any technical inefficiency is removed by definition. This means the measure is not downward biased, in contrast to most traditional CU measures. Second, capacity measures are computed at the frontier and defined relative to observed best practices. Third, it is able to accommodate a multiplicity of inputs and outputs. Input and output data describe the hospital's production technology without the use of functional forms. The advantage of using a non-parametric approach -not requiring an explicit representation of the form of the technology -is that it envelopes the observed input and output data by forming facets around the frontier of the observed data. This type of modeling has been used extensively in hospital studies as well as in health economics more generally (see Hollingsworth  25, 26  for a review of efficiency measurement and Ferrier  23  and Färe  11  for the applied nonparametric measure of CU for health facilities).
There is an abundant literature studying nanoscience publications. Methodological articles have dealt with the proper way to define nanosciences, in order to obtain relevant databases  [18] [19] [20] [21] [22]. Many papers have focused in specific subfields (ZnO nanostructures  [23] ; nano-energy  [24]. Some have addressed important features of this new field, such as its interdisciplinarity  [25] , its relation to technological innovation  [26, 27]  or its progressive institutionalization  [28]. The international structure of nanoscience research has also received considerable attention. Most articles deal with specific geographical regions: Europe  [29] ; South Africa  [30] ; Australia  [31] ; Brazil  [32] ; China  [33]. Islam and Miyazaki (2010)  have studied the worldwide landscape based on nanotechnology-related academic publications from Elsevier Engineering Index Compendex database  [34].
Each record belongs to a single subfield. This approach, detailed in the S1 Appendix, is well-known in scientometrics under the label 'bibliographic coupling' and has been shown to lead to meaningful subfields  [43, 44]. The main subfields are listed in Table  2 , and a detailed description of all of them is given in the S1 Dataset. Then, we compute the proportion of articles for each country in each cluster (S2 Dataset). This corresponds to the 'effort' or 'output' that each country devotes to each subfield of nanoscience. By normalizing by the corresponding world 'effort', one recovers the well-known "Revealed comparative advantage" (RCA) index. Finally, we perform a Principal Component Analysis (PCA) using the FactoMineR package  [45]  to find the most meaningful correlations among countries' RCAs.
The multipolar nanoscape obtained by the multidimensional landscape To go beyond this simple size analysis, we compute a partition of the nanoscience field into relevant subfields using our 'bottom-up' strategy that creates groups of articles that share many references and therefore are close in cognitive space. Table  2  shows the main nanoscience subfields found by our method (with more than 5000 articles). The next step is to map the distribution of the articles of each country over the 36 subfields (see the S1 Dataset for the whole table). Then, Principal Component Analysis (PCA, see S1 Appendix), allows to find the most significant dimensions that characterize the nanoscape, i. e. the international landscape of nanoscience research (Fig  1a  and 1c ). Intuitively, the PCA Essential size statistics for the intensity of nanoscience research among countries.
Instead, if the countries' scientific communities are young, it is easier to develop new fields through central financing agencies. We end by emphasizing the importance of building the subfields bottom-up to achieve a meaningful representation of the different scientific domains. In such a multidisciplinary field, most subfields mix various disciplines, as confirmed by their fragmented composition in terms of Journal Subjects Categories (S3 Dataset). In general, five JSCs are present at significant levels (more than 10% of the articles), and the most important JSC rarely reaches 50%. This means that JSC as "Materials Science, Multidisciplinary", "Nanoscience & Nanotechnology", "Chemistry, Multidisciplinary" or "Physics, Applied" are too wide to characterize precise subfields within nanoscience.
Instead, our bottom-up approach captures important (but subtle for the outsider) differences between subfields. Take for example the two subfields related to "Quantum dots". As can be seen through the most cited references and keywords, the first subfield (labeled "QDotsMAT") mainly deals with luminescent semiconductor quantum dots, prepared in solvents and covalently coupled to biomolecules, for use in biological imaging and detection. Instead, "QDotsPHYS" prepares quantum dots by molecular beam epitaxy, and uses them for fundamental physics problems, such as spintronics, quantum coherence and quantum computing. This scientific difference is correlated to strong contrasts in the countries' specializations. Emergent countries focus on the first subfield, while members of the OECD specialize in the second, as shown by the countries shares (S2 Dataset) and summarized by the arrows for these subfields in Fig 1b.
With the development of platforms enabling the integration and use of phenome, genome, and exposome data in the context of international research, data management challenges are increasing, and scalable solutions for cross border and cross domain semantic interoperability need to be developed. Reusing routinely collected clinical data, especially, requires computable portable phenotype algorithms running across different electronic health record (EHR) products and healthcare systems. We propose a framework for describing and comparing mediation platforms enabling cross border phenotype identification within federated EHRs. This framework was used to describe the experience gained during the EHR4CR project and the evaluation of the platform developed for accessing semantically equivalent data elements across 11 European participating EHR systems from 5 countries.
Data management challenges are increasing with improvements in techniques and high-throughput pipelines in the 3 domains-genome (high-throughput "omics" pipelines), exposome (high-throughput pipelines of patient-centric or non-patient centric exposome data), and phenome (increasing availability of imaging and multi-channel physiologic datasets). In this context, the aim of biomedical researchers and funding bodies, such as the US National Institutes of Health, a the UK National Health Service, b or EU commission, c is to promote advances in technologies and methods for data management and analytics in order to better acquire, manage, share, model, process, and exploit big data collections and extract knowledge from them. The US National Institutes of Health Big Data to Knowledge (BD2K) Initiative is now fostering several activities to support the full exploitation of the large collection of data available in health care and funding big data excellence centers, which will be in charge of translating ideas into actionable tools and case studies.
20 ther than improving existing isolated systems, initiatives now focus on how to merge those large data collections to obtain even larger data sets, holding the promise to improve our understanding of diseases at a considerable higher pace than today. 21 rge data integration efforts based on the data warehousing approach, which extracts, transforms, and loads data from heterogeneous sources into a single view schema, have started to improve outcome research, such as PCORI in the United States. Moreover, collaborative efforts are designed not only to share data but also to disseminate large-scale analytics to bring out the value of observational health data, such as the Observational Health Data Sciences and Informatics Research Network (http://www. ohdsi. org/). An interesting area that may successfully exploit big data technologies is electronic phenotyping.
These hospital EHRs collectively contain data from over 7 000 000 patients. The EHR4CR platform is a loosely coupled service platform, which orchestrates independent services addressing semantic interoperability, data protection, privacy, security, and end-user platform services to ease and speed the conduct of clinical trials, in particular during the phases of protocol feasibility study (PFS), patient identification and recruitment services (PRS), and clinical trial execution (CTE). Both TRANSFoRm and EHR4CR projects are based on the mediation approach relying on mappings between a mediation model and the model of original sources, and transformation of queries into specialized queries to match the models of the original databases. Both projects adopted the "Local As View" approach specifying mappings from entities in the original sources to the mediation model as opposed to the "Global As View" approach where such mappings are specified from entities in the mediation model to entities in the original sources.
Our hypothesis is that semantic mediation is achievable internationally within federated database systems by implementing (i) a consistent integrative semantic abstraction on top of existing application proprietary models and (ii) a semantically enabled query engine. The mediation model provides a homogeneous view of the clinical data available in disparate systems so that data users can access these data using a library of standard queries that have been written based on the mediation model. Our goal is twofold, first to define a set of desiderata for developing a mediation platform for computing phenotype algorithms within a federated database system and second to use this evaluation framework to describe the strengths and limitations of the EHR4CR platform.
A controlled mediation model is required to support federated access to heterogeneous data sources. Mediation models must be based on the adoption and integration of multiple standards, themselves being aligned to be consistent, coherent, and crosscompatible. 25, 30 Although there is no consensus in the medical informatics community regarding a standard patient information model, HL7 FHIR specifications are gaining interest and show promise to mitigate the classic site-specific data mapping problem. In addition, as part of the Clinical Information Modeling Initiative (CIMI) launched in 2011, an international consortium of representing national bodies, Standards Development Organizations, healthcare organizations, and vendors are building collaboratively a process and tools for constructing a single curated collection of shared implementable clinical information models that are free for use at no cost.
| Mediation model Our approach is based on the realistic assumption that there will   The terminology binding is done through the definition of value sets corresponding to the data elements of each template. Figure  2  illustrates the terminology binding done for the Observable entity: "ECOG performance status. " The EHR4CR editing tool supports faceted templates. We defined a limited set of generic templates (eg, Observation) with facets, so that it is possible for each code of the template (eg, Observable entity SCT/423740007/ECOG performance status) to define its corresponding value set (eg, SCT/ 424122007/ECOG performance status finding). As much as possible, we enriched and/or merged reference terminologies in order to build multilingual terminologies and value sets (in English, French at least and when possible in the 4 languages of the EHR4CR partners: English, French, German, and Polish).
• Req B. 5: Expressiveness The current limited set of FHIR-based templates allows the representation of the main textual clinical data (signs, symptoms, diseases, outcome, procedures, care plans, etc. ). We defined context-dependent value sets for representing multiple views or contextual information (eg, organ specific scores or histologic types). • Req B. 6: Scoped to the needs of the users of the research network in the context of dedicated use cases but scalable and sustainable (designed to be rapidly and efficiently scoped to cover any new requirement, extensible in terms of structure and content) The EHR4CR mediation model (EHR4CR CIM) has been developed and can be extended, through a global consensus-based development process in order to cover the scope of both (i) eligibility criteria and data items identified from a given set of specific clinical trials (bottom up approach resulting in the creation of "useful data elements") and (ii) standard reference clinical information models or data elements (eg, CDISC SHARE) (top down approach).
| The EHR4CR case study The EHR4CR mediation platform fulfills-at least partially-most of the 23 requirements of the proposed conceptual framework. The mediation model is based on multiple standards: standard models (HL7 FHIR templates, ISO 21090, ISO11179), standard value sets, and terminologies. Integrating these different multi-level standards is challenging, and terminology binding is especially a difficult issue while contextual and versioning issues need to be addressed. We developed specific data structures-faceted templates-to get a good balance between complexity (a limited set of generic templates) and expressiveness (major scalability in terms of structure and content thanks to the facets). As much as possible, we enriched and/or merged reference terminologies in order to build multilingual terminologies and define multilingual value sets (at least in the 4 languages of the EHR4CR partners: English, French, German, and Polish).
| Limits, related works, and perspectives The evaluation framework of mediation platforms computing phenotype identification algorithms within federated databases was useful for identifying the limitations of the EHR4CR mediation platform. Some of these limitations correspond to biomedical informatics challenges that are subject to active ongoing research. eMERGE,  24, 25, 41  SHARPn,  42, 43  and other initiatives. 19, 44, 45  Developing a smart user interface for searching and/or browsing within complex semantic resources remains problematic. It is also important to enhance the tooling for collaborative edition of models by medical experts (using the user interface and/or CSV files) and to support a robust distribution process of these models (with 3 modes: full, snapshots, and/or deltas).
CDISC SHARE is Another challenging issue is to extend the mediation platform in order to query federated data lakes combining unstructured and varied data and do not necessarily require a (often complex) master relational schema to structure and define all data. International collaboration as part of initiatives such CIMI, FHIR, or the European Institute for Innovation through Healthcare Data (I--HD), e launched in March 2016 is important for improving the interoperability of healthcare systems through shared implementable clinical information models.
| CONCLUSION Cross-border networking coordination and new technologies for data integration facilitates interoperability among research networks. Clinical research is on the threshold of a new era in which EHRs are gaining an important novel supporting role. The EHR4CR project developed an instance of a platform, providing communication, security, and semantic interoperability services to the 11 participating hospitals located in 5 European countries and 10 pharmaceutical companies. 17, 48 is paper proposed desiderata for mediation platforms for computing phenotype algorithms within a federated database system and described the strengths and limitations of the EHR4CR mediation platform. What was already known on the topic? • Semantic interoperability is one of the main challenges to address to enable the reuse of hospital EHR data to support research.
an important initiative in addressing the interoperability between care and research domains through maintaining common data elements built upon BRIDG DAM where they are annotated with CDISC data sets like CDASH and SDTM, and other CDISC terminologies. 46 CDISC SHARE CDEs need to be considered for enriching the EHR4CR mediation model. Several other efforts like the DebugIT 47  or SALUS projects 45  propose an ontological framework where each local system can continue to use its own local models and terminology systems, while both structural mapping and terminology mapping are handled through rulebased reasoning on formal representations of reference models and terminology systems. The rationale of these projects is that addressing syntactic and semantic interoperability should not be separated from each other, because the binding between models of use and models of meaning also has an impact on Semantic Interoperability.
The EHR4CR mediation platform fulfills most of the requirements and demonstrated the feasibility of computing phenotype identification algorithms within multilingual federated databases • Some requirements remain problematic • The scope of the mediation model needs to be continuously adapted to the user's needs. Because the update can hardly be fully automatized (eg, through automatic coding of free text clinical trial protocols), a collaborative editor needs to efficiently support the creation of new semantic resources scoped to any additional use case. • Despite recent efforts, formal representation of multimodal and multi-level data supporting data interoperability across clinical research and care domains is still challenging • Terminology mapping in hospital sites is the major bottleneck of the data standardization pipeline.
List of 23 requirements defined for the 3 main components involved in a mediation platform enabling phenotype identification in federated databases: (i) query language and model, (ii) patient data model (mediation model), and (iii) standardization pipeline for data providers The mediation model shall be based on standard domain knowledge and reference models provided by standard development organizations that are and will be used by EHR vendors, clinicians, and government mandates (eg, Meaningful Use Stage 3 in US).
4. 1 | Contribution 4. 1. 1 | 1411 The evaluation frameworkMo et al 22  proposed 10 desired characteristics for a flexible, computable phenotype representation model. We extended this list and proposed 23 requirements classified to match the 3 main components of a mediation platform for computing phenotype algorithms within a federated database system: (i) query language and model, (ii) patient data model (mediation model), and.
• The EHR4CR mediation platform fulfills most of the requirements and demonstrated the feasibility of computing phenotype identification algorithms within multilingual federated databases • Some requirements remain problematic • The scope of the mediation model needs to be continuously adapted to the user's needs. Because the update can hardly be fully automatized (eg, through automatic coding of free text clinical trial protocols), a collaborative editor needs to efficiently support the creation of new semantic resources scoped to any additional use case. • Despite recent efforts, formal representation of multimodal and multi-level data supporting data interoperability across clinical research and care domains is still challenging • Terminology mapping in hospital sites is the major bottleneck of the data standardization pipeline.
This paper presents results of the ongoing development of the CYCLONE as a platform for scientific applications in heterogeneous multi-cloud/multi-provider environment. The paper explains the general use case that provides a general motivation for the CYCLONE architecture and provides detailed analysis of the bioinformatics use cases that define specific requirements to the CYCLONE infrastructure components. Special attention is given to the federated access control and security infrastructure that must provide consistent security and data protection for distributed bioinformatics data processing infrastructure and distributed cross-organisations collaborating teams of scientists. The paper provides information about selected use cases implementation using SlipStream cloud automation and management platform with application recipe example.
II. GENERAL USE CASE FOR INTERCLOUD APPLICATIONS DEPLOYMENT PLATFORM AND CYCLONE ARCHITETCURE Multiple individual use cases for multi-cloud applications that require cloud and non-cloud resources integration into one intercloud infrastructure that executes a single or multiple enterprise or scientific workflows can be abstracted into general scenario and use case illustrated in Figure  1. It includes two interacting applications, that in general can be multi-cloud, that contain both application related and management components. Application component interacts with end users, management component is controlled by application administrator and interacts with the (inter)cloud management software. The figure also shows Cloud Applications Deployment and Management Software and Tools as an important component to support cloud applications deployment and operation during their whole lifecycle.
) Workflow The use case workflow to ensure data security includes the following steps (see Figure  2 ): (1) a biomedical user connects to the cloud through the IFB web authenticated dashboard; uses it to (2) run an instance of the appliance containing the relevant pre-configured analysis pipeline. At step (3) the VM containing genome applications is deployed on the cloud (testbed); then (4) the user signs into the web interface of the VM, (5) uploads the patient's biomedical data, and (  6 ) runs the analysis in a secure environment. Finally, (7) the user gets the results. The bioinformatics treatment generally relies on a comparison with the current release of the reference human genome hg19 (Human Genome version 19 or GRCh37). The hg19 is a database consisting of many files containing the public genomics data.
C. UC2 -Cloud pipeline for microbial genomes analysis 1) Description In the post-NGS research, sequencing bacterial genomes is very cheap (few hundreds €) what allows researchers to compare large collections of related genomes (strains). Thus, this brings requirements to increasing need for automating the annotation of bacterial genomes. The IFB-MIGALE platform (one of the bioinformatics platforms of the IFB) has developed an environment for the annotation of microbial genomes and a tool for the visualization of the synteny (local conservation of the gene order along the genomes). The platform automatically launches a set of bioinformatics tools (e. g. BLAST, INTERPROScan) to analyse the data and stores the results of the tools in a relational database (PostgreSQL). These tools use several public reference data collections.
2) Workflow As illustrated in Figure  3 , a bioinformatician (1) connects to the cloud web dashboard, uses it to (2) run and (3) deploy with one click a genomes annotation platform consisting of many VMs, comprising of a master node of the virtual cluster that provides also the visualization web-interface, associated with several computing nodes. Then the user (4) uses secure communication over SSH to connect to the master and (  5 ) uploads the raw microbial genomic data (MB) to the cloud storage. SCP/SFTP protocols are used from a command line tool or a GUI, to ensure AuthN/Z for the data transfer, and to overcome the performance issues of HTTP for large datasets. Still in command line interface, the user (6) runs the computation to annotate the new microbial genomes. The first step consists of many data-intensive jobs performing the comparisons between the new genome and the reference data The results are stored in a relational database (provided by a cloud service or a VM deployed within the platform).