dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation of medical web-based applications [Table  1  about here. ] Table  1  presents a compact at-a-glance summary of the reviewed works and their main features. In this review we aim to describe the methodological aspects of a web-based application and how to tackle the features presented in Figure  1. We focus the review on how the database is implemented, using PACS or not, and which images it stores. We also depict how the information is stored and transmitted, using or not the XML protocol, and finally we also illustrated the main security aspects.
Databases Web applications typically run on the user's computer and they communicate via standard procedures, i. e. http, to a web server application that usually contains or communicates with another computer that contains the medical data, as is depicted in Figure  3. The web server is responsible for searching within the medical database and for returning the information to the web application. As can be seen in Table  1 , in the medical context this database is currently stored under the Picture Archiving and Communication System (PACS) architecture, that allows to integrally store the patient records with their own available digital images acquired at the hospital. Notice that PACS not only refers to a single database but also consists of image and data acquisition, storage, and display subsystems integrated by various digital networks.
[Figure  3  about here. ] The advantage of web-based applications is that they allow to easily add newer capabilities to the PACS system. Hastings et al. [32]  complemented the PACS by providing an extra management of the information using XML language, allowing a more friendly environment for large biomedical databases. Similarly, Mahmoudi et al. [45]  developed a web-application tool connected to a PACS system to allow direct access to the data. The data were uploaded to the application for processing and the processed data saved on the client machine. One of the advantages was that the application supported various DICOM formats. Rosset et al. [25]  created an image-based electronic system to print film images directly from a PACS. The benefit of using a PACS in the clinical routine allows for better tools to support teaching and training tasks.
Exchange of information [Figure  4  about here. ] Once the web application receives the data from the PACS or medical database through the web-server application, it is ready to process it according to its own goal. When the application finishes, the resulting outcomes can be stored, either locally or transmitted to an external database. Since these results usually contain the analysis of the cases, they can be stored in formats compatible with word processor or spreadsheet tools in order to present the information in a structured way. Although there are different formats for information exchange  [44, 53, 60] , the eXtensible Markup Language (XML) has emerged as a leading facilitator in exchanging structured information between different platforms and storing information in an efficient way and at low memory cost.
In the column entitled "Data Exchange" of Table  1 , it is evident that the XML language is also becoming a standard in information exchange in web-based applications. In Figure  4  the common language terms used in XML are depicted: files containing XML information are called XML documents, they are stored databases called XML servers, and used by applications or XML systems. Hastings et al. [32]  addressed the work to metadata and data management issues using a generic, distributed, XML-based data management system. According to the authors, the use of XML schemas and XML documents allowed to manage and publish data models to efficiently store, query, reference and create virtualized XML views into distributed and interconnected databases. Kurc et al. [8]  showed one of the best examples of the usability of XML schemes oriented to medical research works.
The proposed XML-based data management system supported the integration of data from disparate data sources and large data sets. The system facilitated management of XML schemes and on-demand creation and management of XML databases that conform to those schemes. Recently, Chipman et al. [56]  presented a software that can convert local structured data into XML-formatted data. Hence, while clinicians and patients benefit greatly from readily available risk predictions, a large pool of multiinstitutional family history data will be continually gathered. XML has progressively become a gold standard for accessing, representing and exchanging information, especially in the health care environment. According to Choe and Yoo  [40]  a key point to allow cooperation among heterogeneous repositories held at various hospitals is the introduction of a common healthcare-record, and XML allows to efficiently store this information.
The second one contained information about the features of the query, while the their done returned the matching images along with the similarity scores. Coiera et al. [33]  proposed a similar architecture, where XML was used to represent the queries obtained from users in a consistent way. In fact, XML schemes can be useful for much more complex query languages, like the ones related to DICOM files. For instance, Marcos et al. [38]  in their work the advantages to represent DICOM header information using XML and to store their data in a XML database. In the proposal of Arnold et al. [14] , XML documents included annotations and descriptive text related to DICOM images. Authors argued that this capability allowed a more efficient exchange of templates and would represent a step toward standardization.
[27]  proposed a security model based on multi-purpose internet mail extension message to transfer medical data between servers and clients. The browser sent the request to the server and decoded the XML formatted messages. Subsequently, it looks into its local database and returned the results including patient and study information. This strategy allowed to decode anonymized data information provided from DICOM files. Differently, Neubauer et al. [50]  proposed a methodology for the anonymization of medical data that stores health data decoupled from the corresponding patient-identifying information, thus allowing privacy-preserving secondary use of the health records in clinical studies without additional anonymization steps. This approach allowed the unlinked storage of medical data and related patient-identifying information, while still making it possible to reestablish the patient/health data relationship for authorized users.
Discussion This paper presents a summary of the current status and trends in web-based medical applications linked to PACS databases, to the use of XML protocols and to security systems. The main observation is the fact that no format currently exists that seeks to satisfy the needs of all potential users. Therefore, as researchers continue to find gaps in current web-based medical informatics applications, they propose their own solutions. A list of web-based medical application research works have been presented in this review. It has brought together a diverse group of works drawn from imaging, computer science, informatics and clinical disciplines. In general, the majority of web-based applications have used their own web server with a combination of Java applet tools. Note that although we focused the review in web applications implemented in Java, new web technologies are appearing such as HTML5, with its powerful and flexible image canvas, or WebGL, that are being used to redefine how users can interact with medical images through a browser.
Basically, the inclusion of web technologies in web-based medical applications has been a solution to aid in the diagnosis and visualization of medical images. Web-based applications have benefited from the inclusion of the XML protocol in the areas shown in Figure  1 : databases, information exchanging and security systems. The PACS system is the most common medical database used in medical applications (Table  1 , section Database). The inclusion of the DICOM format as the standard medical image protocol was the key to the use of PACS. The support of CBIR, CAD, and Hospital Information Systems (HIS) / Radiology Information System (RIS) architectures have demonstrated the power of PACS as a common image data model. Progress in the related areas of PACS and image processing will certainly impact strongly on imaging informatics.
The simultaneous management of several image modalities are also present in all the research works. Indeed, we can find the appropriate tools to manage these different image modalities, the standard DICOM being the most commonly used. Despite DICOM is an old standard that does not adhere to many current best-practices, XML allows integration and easy interface to represent the DICOM header. Different software tools exit for the bidirectional DICOM Header -XML document conversion  [68, 69]. However, the main issues in imaging informatics are indexing and context-specific organization of imaging data and the increment of a large DICOM repositories provided by the acquisition systems. Thus, some research works use as a solution parallel architectures (e. g. mini-pacs, GridPacs, etc. ) to improve the management of medical data in an efficient way  [34, 32, 35].
Conclusion This paper presented a summary of the most relevant research works in computational medicine applied to digital databases and digital imaging. It has dealt with the current status and trends in web-based medical applications linked to PACS and databases, the use of XML protocol and security systems in the medical field. The aim of this literature review is to address several of these issues and to have conveyed to the reader that it is a growing field which needs to benefit from future improvements on information storage, exchange and security. Web-based applications rely on the use of databases. These databases may be categorized according to their architecture and the kind of data/images they contain. 16 4  The XML protocol is used by many web-based applications to exchange information.
d x x n. d x x x x x n. d 2013 Jorritsma [55] n. d CT,MRI x x n. d x x x x x n. d Chipman [56] x MySQL n. d x HL7 x x x x x n. d Agarwal [57] x own DB n. d x x n. d x x x PHR Kuijpers [2] x own DB MRI x x n. d x x x n. d 2014 Huang [58] n. d CT,MRI x x n. d x x x n. d van de Watering [59] EMR n. d x x n. d. x x n. d Schweiger [60] x n. d n. d x x n. d. x x x x x n. d Biternas [61] x n. d n. d SpinXML x x x x x n. d Kammerer [62] MySQL CR x x n. d x XAMPP Hualei [17] n. d CT, X-Rays eXistdb x EHR Sethi [63] x n. d n. d. x x n. d HSM 2015 Mata [15] MySQL X-Ray eXistdb x n. d Web-based application PACS Database Image modality XML documents XML-server System architecture Security Authentication Data Encryption Anonimization Web-security system System architecture.
ACKNOWLEDGEMENTS I especially want to express my gratitude to  Ignacio Mastroleo ,  Anna Alexandrova , and  Joseph Millum  who helped me enormously with the conceptual elaboration of the article. Also, I want to express my thanks to the participants of the FLACSO seminar of the  Argentine Program of Bioethics , and especially  Florencia Luna , for their valuable comments. Likewise, I want to thank  Roland Mertelsmann , as well as  Jerwin Agpaoa  and  Kristi Louk  for their feedback in the NIH Early Career workshop at the IAB Conference 2016. This contribution was also made possible by financial support from  CONICET (Argentina) ,  DAAD (Germany) , and the  BioThera foundation. The views expressed in this paper are personal and do not necessarily reflect the policies of these institutions.
This paper aimed at verifying if the original assumptions of the Likert scale were or were not considered in the articles published in the EnANPAD proceedings from 2010 to 2015. This is a quantitative, exploratory and bibliometric research with data processed on Microsoft Excel and SPSS, using frequency distribution and arithmetic means. The research corpus derived from a search in the congress proceedings by using the Likert denomination when it was included in the papers abstract, methodology and/or results. From the 5342 articles published in the period, 742 (13. 89%) mentioned such denomination. To verify if the scale's original assumptions were considered, it was tried to identify the following keywords: "sum"; "summation" or "indirect scale". The results showed that in 728 articles (98.
It is noted that there are important differences between the original Likert scale and the Likert-type scale; however, it has been found that its use in the sample of analyzed articles is questionable most of times. This reality shows that, although it is very popular, it is necessary to clarify the original assumptions of the Likert scale for the Brazilian academy to avoid future misunderstandings in its use. For future research, it is suggested: 1) that the bibliometric approach regarding this topic should be extended to other periods of time, to other national and international databases, as well as other academic events, journals, repositories of thesis and dissertations, among others; 2) to carry out qualitative research to raise information among the Brazilian researchers in the CAPES area of "Management, Accountability and Tourism", about the knowledge level of the theoretical assumptions of  Likert (original)  or Likert-type scales, and also about the issues and questions regarding the use and the justifications for the misuses of such scales.
B. Comparison study: KCI vs. Ohnishi's index The two indexes (i. e. , α KCI and α Ohn ) have been implemented for comparison purposes under the MATLAB environment (version R2014b) and tested on an Intel Pentium Core i7-2677m machine with 1. 80 GHz CPU and 4GB memory. Parameters considered for this comparison study are (i) "similarity" of the scores between α KCI and α Ohn ; and (ii) "computation time" required by each index. To this end, several FPCMs from the literature have been selected from the online FAHP testbed 2 released in our recent state-of theart survey  [5]. In an effort to carry out an "heterogeneous" comparison study (i. e. , considering FPCMs whose size varies), about ten matrices per size category (from 3 × 3 to ≥ 10 × 10) have been selected, as summarized in TABLE I. First results and findings have been also reported in this table, as it is discussed in the following.
The Open Science situation For 300 years, research journals have provided a stable record of the main conclusions of scientific studies, the methods, and the contact details of the scientists from whom data and materials might be obtained upon request. As the volume and complexity of research data explode, traditional research publications are failing to pay even lip service to the task of indexing data, let alone contribute meaningfully to data stewardship and preservation. Despite widespread discussion of the importance of Open Science and a growing recognition of the need for more sophisticated data stewardship practices, such as those in the FAIR (Findable, Accessible, Interoperable and Reusable) guidelines  [1] , the research community continues to do little to use available tools to index or share our vast datasets.
Training young researchers to use the tools of Open Science can instigate a more reliable change in data stewardship. Here, we want to emphasize the importance of the implementation of Open Science principles across disciplines from a young researcher's point of view, and highlight the reasons why young researchers are the key for change. As a group of early-career researchers who convened for the 2016 LERU Doctoral Summer School on Data Stewardship, we commit to: (1) the growth of an Open Science framework within which we can explicitly receive credit for and give credit to datasets with machine-readable metadata, provenance, and reproducible workflows; (2) establish training in the principles of Open Science and the relevant software and communication tools; (3) a pledge to be the first generation that will pass on the principles and mindset of Open Science to the next generation.
Growth of an Open Science framework What does an Open Science framework look like? The European Commission on Open Science outlines a highlevel vision of a future of science that includes Open Access, collaboration on platforms such as the European Open Science cloud, and the development of alternative metrics for measuring the impact of good science  [4]. However, the roadmap to this future remains unclear to the everyday scientist working within the traditional scientific framework, and these excellent guidelines remain largely theoretical for many. The key to the growth of this Open Science framework is the cultivation of good data stewardship practices at every level of science, which conveniently is an actionable step for almost every scientist. Data stewardship, a concept that involves all those data management issues related to long-term data reusability and interoperability, requires careful planning and thought from the beginning of a research project.
Publication of data and code alongside traditional research papers, while widely done, is only the first step toward "FAIRifying" research. Perhaps more important is the creation of metadata on top of these datasets and code. It is from such a database of metadata that we will begin to drive innovation by identifying which datasets intersect well to produce results, and to create an executable data-code environment that can be peer-reviewed, built on, and reused. By changing research practices to include the creation and publication of such metadata, scientific culture will begin to change organically from the traditional focus on the static unit of the research paper to more dynamic, collaboration-based science. The growth of such a network of metadata will also provide the basic framework for the development of alternative metrics, such as precise citation to code versions, datasets, models, and the ability to quantify specific opinions and theories via the Semantic Web.
Despite its importance, data stewardship is often neglected until the end of a research project, when funding and time may be running low. The stewardship of data should be managed from the beginning of the study and included in the financial plan, as recognized by the EU recommendation for a 5% budget allocation for all funded research projects  [5]. Producing research that complies with FAIR principles is an ethical responsibility for all scientists, and a plan for reuse should be an obligatory and fundamental part of study design, especially for those working with public funding. Beyond the ethical responsibility to produce transparent and reproducible research, young scientists today should view cultivation of data stewardship skills as an opportunity to participate in the exciting, innovative research of tomorrow.
Avoid reinventing the wheel The first and most fundamental step for researchers looking to change the way research is done in their field is to investigate what has already been done by their colleagues. What are the sharing platforms, available software, and standards? Is there an already existing ontology that can be referenced, making your data findable and interoperable? Avoid creating terminology when it already exists. Similarly, are there existing datasets and analytical pipelines that can help toward answering your research question? Working within the existing scientific framework can provide an opportunity to foster collaboration and avoid duplication of research effort. Training in publishing more and better research papers, data-code objects, patents, and citable models based on open science principles should be standardized across fields and could be administered through existing infrastructures, such as Elixir, the European life science data organization.
Small steps move you forwards Practicing FAIR data principles is not a binary state, but instead a matter of setting and achieving desirable standards for data sharing. The culture of data sharing begins within your own research team. A good first step for creating an Open Science environment in your workplace is to survey your own team on their own views and hesitations about data sharing, as well as establishing where it lies on their list of priorities. This can open an important dialogue and help identify concrete actions that your team can take to begin to move toward Open Science. To facilitate the transition, we provide a summary box with practical advice for young researchers to engage in Open Science that require very little effort (Box 1). A pledge to be the first generation to pass on Open Science to our succeeding generation Publishing openly is associated with higher citation rates  [17].
Acknowledgements The  Pay It Forward project  was funded by the  Andrew W. Mellon Foundation  through a grant to the  University of California-Davis. We would also like to acknowledge our gratitude toward our survey respondents for their input. We also thank the efforts of:  Ivy Anderson  (CDL),  Sheldon Armstrong  (UBC),  Bo-Christer Bjӧrk (Hanken) ,  Laine Farley  (CDL),  Dracine Hodges  ( OSU ),  Mark McCabe (BU), Elke Piontek (Harvard) ,  Jo Anne Newyear Ramirez (UBC) ,  John P. Renaud  ( UCI ),  Karla Strieb  ( OSU ),  Greg Tananbaum  ( Scholar Next ),  Scott Wicks (Harvard) , and  Mathew Wilmott  (CDL).
G. A general principle As stated above, the proposed framework offers a common description to a large variety of existing methods. We propose to examine how state of the art methods can be expressed in the terms of our framework. 1) Active methods: Active approaches of incremental learning aim to detect change time steps t explicitly. Previous knowledge is used while no change has been detected. Adaptive sliding window approach (ADWIN)  [17]  stores all previous data points into a sliding window. At each step, the sliding window is split in two sub-windows: the method detects a drift if the difference of the errors for each subwindow is higher than a parameter. ADWIN framework can be interpreted in terms of Kolmogorov complexity as follows. The model M t at time t corresponds to the concatenation of data memorized in the sliding window and of the corresponding classifier.
D. Experimental results In order to illustrate the pertinence of our framework in practice, we have tested its performances on classical data sets with the naive prototype-based model. We considered three datasets: SEA  [30]  (50000 instances, 3 attributes, artificial), Weather  [31]  (18159 instances, 8 attributes, real) and Electricity Market  [32]  (45312 instances, 3 attributes, real). For all datasets, we tested the instance-incremental version of our algorithm (by streaming directly over the data) and the batch-incremental version (by grouping successive data into a same batch). The experiments were all done with a fix sliding window size: |∆ -1 t ({1})| = 3. We tested the two proposed algorithms. Table  I  presents the performances for different size S of batches. When S = 1, the situation corresponds to a problem of instance-incremental learning.
g. ,  Wagenmakers, 2007; Masson, 2011; Morey et al. , 2014). Given the potential confound resulting from the noncontinuous nature of the eye-tracking in Study 1, we focused on Study 2, and the authors were kind enough to share their subject-averaged results with us. Using the JASP package (JASP Team, 2016), we ran a Bayesian paired samples t-test with default priors (Cauchy prior width = 0. 707). We entered the average fixation time and number of revisits on the critical problem premises (sentence 3) on conflict and no-conflict items for the group of incorrect responders in the analyses. Results showed that the Bayes factor in favor of the null hypothesis (no effect of conflict) over the alternative hypothesis (effect of conflict) was 1. 34 for fixation time (non-directional; directional alternative hypothesis conflict > no-conflict, Bayes factor = 0.