dataesr/scientific-paragraphs-categorization huggingface dataset:
csv"). We consider two authors as exhibiting a co-authorship tie when they appear together in a scientific publication. Each time the same two authors appear together in a paper, it is counted toward the "weight" of the tie. The example of an edges list can be seen in the following figure. The data was then processed and analyzed using statistical software R (v3. 3. 1). Figure  6  shows an example of how relational data is handled in the study. To illustrate, in the first row of the table on the left side, a published paper being co-authored by scientists ID s004, s076 and s079 is recorded into the database first. Then on the right side, coauthorship relations among these three scholars are recorded; and the weight is the count of how many times each pair co-authors. This dataset contains all 412 individuals in the study and their attributes.
20170729_net412_LINKS. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174930 This dataset lists the number of co-written articles between all 412 authors of the network, where relevant. Each collaboration is counted as a link (edge) in the network. Dataset 3. 20170719_comp43_NODES. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174931 This dataset contains 43 individuals in the 43-node component and their attributes. Each individual is considered a node (vertex) in the component. 20170719_comp43_LINKS. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174932 This dataset lists the number of co-written articles between the 43 authors of the 43-node component, where relevant. Each collaboration is counted as a link (edge) in the component. Dataset 5. 20170726_comp27_NODES.
csv http://dx. doi. org/10. 5256/f1000research. 12404. d174933 This dataset contains 27 individuals in the 27-node component and their attributes. Each individual is considered a node (vertex) in the component. Dataset 6. 20170729_comp27_LINKS. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174934.
Vertex degree is the number of edges incident upon a vertex. Density is the frequency of realized edges (connections) relative to potential edges (connections). Transitivity (or clustering coefficient) is the relative frequency with which connected triples of vertices form triangles. Net412 is the full 412-node network consisting of the entire dataset. Comp43 and Comp27 are the 43-node and 27-node components, respectively, which are subsets in which every vertex can be reached by every other.
Dataset 2. Dataset 4. 24 20170729_net412_LINKS. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174930 This dataset lists the number of co-written articles between all 412 authors of the network, where relevant. Each collaboration is counted as a link (edge) in the network. Dataset 3. 20170719_comp43_NODES. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174931 This dataset contains 43 individuals in the 43-node component and their attributes. Each individual is considered a node (vertex) in the component. 20170719_comp43_LINKS. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174932 This dataset lists the number of co-written articles between the 43 authors of the 43-node component, where relevant. Each collaboration is counted as a link (edge) in the component. Dataset 5.
20170726_comp27_NODES. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174933 This dataset contains 27 individuals in the 27-node component and their attributes. Each individual is considered a node (vertex) in the component. Dataset 6. 20170729_comp27_LINKS. csv http://dx. doi. org/10. 5256/f1000research. 12404. d174934.
Simulation Environment The architecture presented was developed in Java, with the Repast Simphony platform  (North et al. , 2005)  and some of its primitives (schedulers, contexts, continuous space, and graphical interface). The representation of studies and the continual overhaul of the model also apply to its three dependencies, which benefit from this approach of continuous improvement and are also the subject of methodological studies relating to the taking into account of interdisciplinary knowledge. A 'data' package is responsible for collecting externalities to the case studies. A class system is built up progressively to process data (maps, tables etc. ), constants, parameters and seeds of random number generators. An 'observation' package aims to ensure multimodal rendering (display, data storage, connection to other software) of the functioning of the system.
ACKNOWLEDGEMENTS We would like to thank  J. F. Cosson ,  J. P. Quéré ,  C. Berthier ,  B. Gauffre ,  J. M. Duplantier ,  L. Granjon ,  G. Ganem ,  J. Britton ,  O. Ninot ,  J. Lombard ,  P. Handschumacher ,  S. Piry , the scientists who kindly agreed to decipher their disciplinary expertise for the formalization of thematic case studies. This study owes much to the work done by  Q. Baduel ,  A. Realini ,  J. E. Longueville ,  A. Comte  and  M. Diakhate , as part of their student internships. The  SimMasto  project (http://simmasto. org)was supported, through the various case studies of the Chancira projects (grant  IRD-ANR-11-CEPL-0010 ),  Cerise  (grant IRD-FRB no. AAP-SCEN -20B III), and with occasional support from the  French National Research Institute for Sustainable Development (IRD)  and the  'Centre de Biologie pour la Gestion des Populations ' (CBGP, UMR no.
Conclusions: A major trend in the 2015 publications is the analysis of observational, "nonexperimental" information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery. Yearb Med Inform 2016:219-23 http://dx. doi. org/10. 15265/IY-2016-044 Published online  November 10, 2016  data enables the analysis of observational, "nonexperimental" information, the potential biases and confounding factors hidden in the data will have to be carefully taken into account to validate new predictive models  [2]. The goal of this section is to provide an overview of relevant research published in the past year in the various areas of Clinical Research Informatics (CRI): i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives.
Databases were searched on January 10, 2016 for papers published in 2015, considering the electronic publication date. From an original set of 579 references, a first subset of 406 references was considered according to the relevancy to the CRI field and blindly reviewed by the two section editors based on title and abstract. The articles were classified into several CRI categories and their contribution to CRI was rated as low, medium or high. Then, the two lists of references were merged, yielding 191 references that were classified as high contribution to CRI by at least one reviewer or medium contribution by both reviewers. The 191 references were reviewed by the two section editors jointly to select a consensual list of 16 candidate best papers representative of all CRI categories. Following the IMIA Yearbook process, these 16 papers were peer-reviewed by editors and external reviewers (at least four reviewers per paper).
Conclusion and Outlook Methods supporting clinical research: Big data is data "whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it". From an IT point of view, Bellazzi et al, reviewed the main types of big-data oriented solutions that are now available and increasingly adopted -cloud computing, parallel programming and new database technologies -and describe the research efforts carried on in the MOSAIC project in diabetes care, funded by the European Commission  [2]. New probabilistic models for large-scale discovery of computational models of disease, or phenotypes can be developed  [14]. In the domain of pharmacovigilance, Koutkias et al propose a semantically enriched framework to facilitate seamless access and use of different data sources and computational methods in an integrated fashion, bringing a new perspective for large-scale, knowledge-intensive signal detection  [9].
Data sharing and interoperability: Data heterogeneity is one of the critical problems in analyzing, reusing, sharing or linking datasets. For clinical trials, an electronic and structured study representation format used throughout the whole study life span can improve communication and potentially lower total study costs. The first selected paper from Huser et al  [6] , demonstrates the capability of the current version of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) (1. 3. 2) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health  [6]. Several studies relate standard-based solutions providing a uniform access endpoint to patient structured and unstructured data.
An approach exploiting well-established standards (such as HL7 v3), medical vocabularies (such as SNOMED CT, LOINC and HGNC) and Semantic Web technologies has been successfully tested to enable semantic interoperability in multi-centric clinical trials on breast cancer within the INTE-GRATE and EURECA EU projects  [1]. Standardizing data structure (through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to obser-vational research across multiple, disparate observational health databases  [16]. An alternative approach for data integration consists in semi-automated transformation of heterogeneous data sources to Web Ontology Language (OWL) formatted files by using a pre-defined model to extract the metadata.
This approach was tested to link the South London Stroke Register (SLSR), the London Air Pollution toolkit (LAP) and the Clinical Practice Research Datalink (CPRD). The authors demonstrated that data can be faithfully converted to more suitable formats for further analysis  [11]. The French national minimum data set (F-MDS-RD), composed of 58 data elements based on HL7 standard and various ontologies for diagnosis or sign encoding, was defined through national consensus and aligned with other similar initiatives for rare diseases, thus facilitating potential interconnections between rare disease registries  [3]. An interesting approach and tool -BiobankConnect -was developed to significantly speed up the biobank harmonization process using ontological and lexical indexing  [11]. Semi-automatically matches between 32 desired data elements of EU-BioSHaRE biobanks and ontology terms from BioPortal were computed and human curated.
Re-use of healthcare data for research: the second selected paper from Kreuzthaler et al. describes a prototype for secondary use of electronic health records (EHR) data captured by non-standardized text, which is an important part of information representation in clinical information systems. Their prototypical information extraction system achieved an F-measure of 0. 91 (pre-cision=0. 90, recall=0. 93) for the training set and an F-measure of 0. 90 (precision=0. 89, recall=0. 92) for the test set  [10]. Focusing on EHR structured data, Mate et al. present an ontology-supported approach for data integration between clinical and research systems. Ontologies are used to organize and describe the medical concepts of both source and target system. Declarative transformation rules within ontologies are defined and used to automatically generate SQL code to extract data from source systems  [12].
From an institutional perspective, the benefits of using an electronic and structured study representation format depend on the overall volume of studies, the proportion of studies eventually submitted to FDA and on the enterprise electronic research systems used at a given site. Standardization benefits are most apparent for multi-site studies using multiple non-centralized electronic data capture systems and for studies later submitting data to the FDA or to data sharing platforms where clinical research data are stored and re-analyzed by investigators. The most relevant standard for representing clinical study data -the Operational Data Model (ODM) of the Clinical Data Interchange Standards Consortium (CDISC) -was initially developed for the exchange of case report forms data but an ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements.
Since no single and permanent patient identifier exists in the US, and very few implementations of master patient index service is used, a specific method and software is required. The authors present their real-world implementation of a Distributed Common Identity for the Integration of Regional Health Data (DCIFIRHD) across 6 healthcare institutions within the Chicago region. Each healthcare institution provided pre-exitsting data from local datawarehouses or medical record systems. Global IRB ap-proval for the study to be conducted was set in 12 months. Datasets were composed of patients aged from 18 to 89 years that have visited the hospital between 2006 and 2012. Specific sensible diagnoses were excluded (HIV, etc. ). The experimentation was done on structured data only due to potential re-identification risk linked to the use of hospital discharge records (free text).
Data was pre-processed at local level first, through the DCIFIRHD application, an external trusted party was responsible for assigning a specific project passphrase and passcode to hash outgoing data for each hospital in order to protect against dictionary attacks. To ensure full HIPAA-compliance, after linkage, each cluster of site-specific PatientIDs was replaced with nonderived StudyID for use in subsequent analyses. Up to 17 512-bit hashes were generated for each patient from personal data combining first name, last name, date of birth and/or social security number (SSN) in different combinations. Three different methods were used to hash the combined variables: normal SHA 512 hash, first three letters of first and last names and hash, Soundex. The matching method used a simple deterministic algorithm.
As a result, the reducted percentage of patient duplicates ranged from 10. 9% to 28%. Age group comparison of the study and census data showed that the proportion of patients by 5-year bins was similar. The current implementation of the solution requires a central data collector hold by a central trusted provider, authors are looking to improve this through the PCROnet research initiative. The use of extra data could also improve the matching performance. Kreuzthaler M, Schulz S, Berghold A Secondary use of electronic health records for building cohort studies through topdown information extraction J Biomed Inform 2015 Feb;53:188-95 Bridging the gap between patient-based storage systems (clinical information systems) and disease-related search systems (biobanks) to build cohort studies is a key clinical research informatics challenge.
The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the XML protocol in web-based medical imaging applications, analyzing how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applications.
Medical decision support aims to assist physicians to improve their knowledge and reduce decision conflict. On the other hand, the interest in content-based image retrieval (CBIR) has grown due to the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval. The large amount of both visual and data information along with the new on-line tools underlined the need to create thematic accesses that offered more than simple text-based queries or requests based on matching exact database fields  [3]. CBIR technology has been proposed to aid clinical care, bio-medical research and education  [4, 5]. However, although the initial widespread enthusiasm for CBIR in the engineering research community, its application has still to solve practical problems, including optimization stages in the indexing and retrieval work-flow  [6].
Consequently, web-search tools have become a new research topic with the purpose to obtain digital images according to the different attributes used when issuing a diagnosis  [7]. In this sense, one of the long standing problems is finding a way to share medical data across a variety of media. XML (eXtensible Markup Language) has emerged as a leading facilitator. Some applications are developed on the integration of frameworks and data management using queries to examine information from multiple data types and large data sets  [8]. Although the XML is provided with predefined tags, one of its advantages is its extensible use. Moreover, the inclusion of the XML databases facilitates the management of XML files by storing them in an efficient way. We present in this review an overview of the most relevant web-based applications in medical informatics that use the XML protocol.
Therefore, the main focus of the review is the management of the medical data associated with imaging studies. However, for the sake of completion when developing a web-application, we also briefly review where the data is store (medical databases) and security issues. Encrypted solutions are absolutely necessary in medical applications because of the confidentiality of the personal information. The rest of the paper is structured as follows. Next section presents different key works concerning web-based applications on which we centered our review. Subsequently, we present a classification and a discussion on the use of the XML protocol in the topics of database management, information exchanging and security, the main aspects when designing and implementing a web-based application. In section 4 the most interesting aspects of this survey and future trends are discussed.
Search strategy [Figure  1  about here. ] We searched the literature in the following databases: PubMed, Scholar, Embase and Scopus. The main search strategy combined four concepts: Web-based medical applications, XML framework, PACS and security. For each concept, the following search terms were used: web-based applications, XML protocol, computational medicine, digital databases and internet security. We wanted to identify the main research works for each area in PubMed and in Scopus. As these searches resulted in many duplicates, this dual search strategy was not repeated in Scholar or Embase. To retrieve other relevant publications, we also examined the reference lists of the selected publications and reviews, and we included those works that met the eligibility criteria.
In this work, however, we focused on the implementation of web-based applications, independently of whether the goal was as a medical decision tool or a CBIR. From a web-based tool point of view the two approaches are similar. As illustrated in Figure  2 , web-based applications require a web-server to provide the interface between the usercomputer and the medical database, consisting of data, but also of images. Web-based applications run on the computer of the user, usually through a small application called applet. From the web-server page, the applet is downloaded to the user machine and it is then executed within an environment provided by the Java Virtual Machine. Notice that once applet is running, it is a process independent of the web browser itself. [Figure  2  about here. ].
Web-based applications examples A number of web-based medical applications have been presented over the last decade. Fernàndez-Bayó et al. [11]  presented an approach involving a web server that allowed the query and retrieval of images stored in DICOM format. The images could be viewed within a web browser with use of a small Java program known as the DICOM Java Viewer, which was executed inside the browser. The system, which is still working, supported mainly magnetic resonance and computed tomographic images in a very simple graphical interface. The use of a web-browser user interface allowed access, independently of its hardware and operating system. The application was based on a clientserver intranet system and on multi-platform intranet technologies such as Java. The most significant disadvantage of the system was the response speed to obtain images within the web browser.
A similar medical web-based application was presented by Deftereos et al. [12]  to facilitate clinical work by allowing the communication of patients records between experts. The aim of the work was to share patient records of a particular disease at a national level. The advantage of using an automatic tool is not only the exchange of information but also the standardization of the records. The web-application was implemented with Java technology and was connected to a web server and also with a patients' database using the SQL Language. The use of Java makes possible scalable designs, suitable for the needs of health care units differing in size. However, currently, the application operates on stand-alone computers; therefore the database server is constraint to run on the same machine. These difficulties were encountered because of the lack of more powerful database servers and workstations.
The web-based infrastructure consisted of a server that stored imaging studies and reports, with web browsers that downloaded and installed optional client software on demand. In contrast with previous works, their approach allowed the inclusion of PHP (hypertext pre-processor) as the language to implement the web-application. Application logic consists of a set of PHP modules that were accessible using an application programming interface. The system might be adapted to any clinician-specialist communication loop, and, because it integrated radiological standards with web-based technologies, it could communicate and report data in an efficient way. On the other hand, Mata et al. [15, 16]  proposed an interactive Java applet interface designed as a web-based tool. It aimed to facilitate the diagnosis of mammographic and prostate cases by providing a set of image processing tools that allowed a better visualization of the images and a set of drawing tools used to annotate the suspicious regions.
CBIR systems have been proposed for a variety of pathologies  [6]. For instance, Hsu et al. [20]  described the implementation of a web-based CBIR for spine pathologies, consisting of four components: a client applet, a gateway, an indexing and retrieval system, and a database of images and associated text data. It allowed the exploration of a large bio-medical database of digitized spine X-ray images using a combination of textual or even visual queries. In the latter case, a query editor enabled users to pose queries by sketching a unique shape or selecting or modifying an existing shape from the database. Ortega et al. [21]  presented a CBIR for retinal image analysis consisting of the web client interface, of the web server that processes the requests and of the service module that performed the image processing tasks.