dataesr/scientific-paragraphs-categorization huggingface dataset:
Rather than trying to definitively solve this problem, we decided to choose a set of protected fields that are removed or replaced (guided by existing anonymization profiles), making sure that the network tools on either end would function with our anonymized data. Table  1  shows the protected fields that are currently removed from the data before it is sent to the archive. The INCF user name and the project, subject, and session identifiers specified by the user are stored in the Study Comments field, which is replaced or created as needed. This anonymized and annotated DICOM data is then pushed to the archive using the DICOM network transport protocol by storescu from the DCMTK package. Similar to the Python dependencies described above, this can be installed separately, but FIGURE 5 | Data labeling.
In this example, the user tool found data for two subjects, BUSS_2030 and HENA_022009. The user now selects a project and specifies public subject and session identifiers for the data on the archive. Validation is done on the fly: here, an error exists because no project is given, but the tool will also inform the user if he does not have sufficient permissions to upload to the specified project, if the session already exists, if identifiers use invalid characters, and so on. NeuroDebian handles its installation on Linux and it is bundled with the Mac OS GUI tool.
• Statistics are calculated using fslstats, using -k to mask each region, -R for the minimum and maximum intensities, -r for the robust minimum and maximum intensities, -m for the mean intensity, -s for the intensity standard deviation, -v for the number of voxels and the volume. As this is a new and custom structural image QA procedure designed as a simple proof of concept for this tool, it is imperfect and likely to evolve as it is used as we study the results obtained on large numbers of scans. Time series QA. Time series QA is performed by fmriqa_generate. pl, part of the BXH/XCEDE Tools suite 26   (Friedman et al. , 2006). This program takes XCEDE wrapped data and produces a web page reporting the results, including several plots. Examples of measures are the mean volume intensity at each time point and the center of mass (x, y, and z) at each time point.
QA reporting. Quality assessment results are parsed and stored on the archive as assessments, custom XNAT data types that allow for storage, management, and display of arbitrary data types. These assessments are accessible from the web front-end and are associated with the raw data for each scan (Figure  7 ). While the diffusion QA is mainly informational with some pass/fail results, the SNR and SFNR calculated for the structural and time series QA procedures provide quantitative values that may not have much meaning in isolation but can be compared against other scans or collections of scans. For these QA reports, histograms of SNR and SFNR for similar scans in the database as well as for data in the 1000 Functional Connectomes 28  , as a reference dataset, are generated on the fly to give context of the SNR and SFNR values for these scans.
LIMITATIONS There are various limitations to the current system, on the user side, on the repository side, and on the system as a whole. At this point, the user tools trade customizability for ease of use, but this does not have to be a strict tradeoff. Anonymization should be flexible, and the target of the upload should be customizable (allowing for multiple archives; this could mean archives with other processing on the back end, or local archives). With sensible defaults in place, adding these options does not need to stand in the way of basic usability. The tools do require user attention, 29 http://freesurfer. net/ but could be even more useful if a non-interactive mode were provided. The command line script could then be embedded in processing or other pipelines so data can be uploaded to an archive as part of the same mechanism that moves it from the scanner to a local lab for analysis, or to use an archive to do some initial analysis.
The user tools are also limited as to what platforms they will run on (for the GUI tools), and the command-line script has several dependencies that must be installed by hand if the Debian package is not used. A web-based option for the user tool would be the ideal solution here, but would require that anonymization be performed on the server side or using local JavaScript code. On the server side, we identify scans for structural QA by their declared scan types (MPRAGE). This could be extended by using a lexicon of scan types (MPRAGE, SPGR, FSPGR, etc) but this solution will not scale: much structural data will be always described in terms unfamiliar to the system, and the lexicon will be forever chasing data found in the real world. A better way of identifying scan types is likely to by inspection of scan parameters reported in DICOM fields combined with a lexicon of scan types.
Allowing the user to specify the scan type unambiguously would also solve this problem. One limitation of the system as a whole is its requirement for DICOM data. While the DICOM transfer protocol was useful for this initial prototype, other data formats (NIfTI-1, MGH, MINC, etc. ) are more prevalent in day-to-day use within individual laboratories, and there is currently no good way to convert these files back to DICOM to prepare it for upload. Most imaging data starts as DICOM at the scanner, however, so this limitation is less of a problem as investigators begin to consider centralized archival of their data immediately upon acquisition. The restriction to DICOM data also limits the system to imaging data, while other modalities (e. g. , EEG) are excluded from using the system. Finally, the utility of the structural QA technique is currently unknown.
ACKNOWLEDGMENTS The authors thank the members of the  INCF Neuroimaging Data Sharing Task Force  for their input to this project, in particular  Dan Marcus  for the initial discussion and ideas. Janis Breeze 's support and enthusiasm ensured that these good ideas got the momentum and support necessary to make them a reality. Much of the work was carried out under contracts from the  INCF , and we thank the current INCF staff, in particular  Linda Lanyon  and  Mathew Abrams  for their great help and support.
We measure the large-scale cross-correlation of quasars with the Lyα forest absorption, using over 164,000 quasars from Data Release 11 of the SDSS-III Baryon Oscillation Spectroscopic Survey. We extend the previous study of roughly 60,000 quasars from Data Release 9 to larger separations, allowing a measurement of the Baryonic Acoustic Oscillation (BAO) scale along the line of sight c/(H(z = 2. 36) r s ) = 9. 0 ± 0. 3 and across the line of sight D A (z = 2. 36) / r s = 10. 8 ± 0. 4, consistent with CMB and other BAO data. Using the best fit value of the sound horizon from Planck data (r s = 147. 49 Mpc), we can translate these results to a measurement of the Hubble parameter of H(z = 2. 36) = 226 ± 8 km s -1 Mpc -1 and of the angular diameter distance of D A (z = 2. 36) = 1590 ± 60 Mpc. The measured cross-correlation function and an update of the code to fit the BAO scale (baofit) are made publicly available.
BAO model We adapted the publicly available fitting code baofit  [19]  to work with cross-correlations. The code can be downloaded from the URL in footnote 2  , together with the measured crosscorrelation and its covariance matrix, as described in appendix A. A detailed description of the fitting code can be found in  [19] ; here we only summarize the main points and highlight the differences between fitting the Lyα auto-correlation and the quasar-Lyα cross-correlation. We model the measured cross-correlation ξ(r , r ⊥ ) as a sum of the cosmological correlation ξ cosmo and a broadband distortion term ξ bb due primarily to continuum fitting (as discussed in section 3. 4) ξ(r , r ⊥ ) = ξ cosmo (r , r ⊥ , α , α ⊥ ) + ξ bb (r , r ⊥ ). (4. 1) The quantity ξ cosmo is described as a sum of two terms ξ cosmo (r , r ⊥ ) = ξ smooth (r , r ⊥ ) + a peak • ξ peak (r α , r ⊥ α ⊥ ) , (4.
In table  4  we compare the results with other BAO measurements at the same redshift from the Lyα auto-correlation measured with DR9 (  [17] ,  [18] ). We also present our results when using only data from DR10. Assuming that the uncertainties in these BAO measurements scale with the inverse of the square root of the survey area, we can extrapolate them from DR9 to DR11, using A DR11 /A DR9 = 1. 60. We show these extrapolations in the last two rows of table ), and from this work. We show our results when using only DR10 data and when including DR11 data. In the last two rows we extrapolate the uncertainties of previous work to DR11, assuming that these scale with the inverse of the square root of the survey area. The errors on the BAO scale (α ,α ⊥ ) from our DR10 analysis are considerably smaller than those reported in  [44].
Acknowledgments We would like to thank  Ross O'Connell  for detailed comparisons with his analysis, and  Shirley Ho  for very useful comments. This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , which is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. DK would like to thank  CEA Saclay  for their hospitality and productive environment during his sabbatical. JM is supported in part by  Spanish  grant  AYA2012-33938. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U. S. Department of Energy Office of Science. The  SDSS-III  web site is http://www. sdss3. org/. SDSS-III is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the  SDSS-III Collaboration  including the  University of Arizona , the  Brazilian Participation Group ,  Brookhaven National Laboratory, University of Cambridge, Carnegie Mellon University, University of Florida , the  French Participation Group , the  German Participation Group, Harvard University , the  Instituto de Astrofisica de Canarias , the  Michigan State/Notre Dame/JINA Participation Group ,  Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington , and  Yale University.
The general architecture of the system The SIMFOR architecture combines elements from the Intelligent Tutoring System and Serious Game domains (figure  2 , see  [16]  for a detailed presentation). Our goal is to associate the playful learning of SG and the different modules of an ITS (domain model, learner model, pedagogical model) to get the optimal learning environment. The SIMFOR architecture is composed of the following components: • The SG module (SIMFOR): this module includes the 3D models, user interface (as a communication channel between the learner and the system), simulation module (for natural phenomena such as fire propagation), and data models. This module constitutes the former "perimeter" of the Simfor SG to which behaviour simulation and user assessment capabilities are added.
• The Behaviours Simulation module: which allows simulating humans behaviours to replace absent players with "artificial" actors (Game Agent). • The Evaluation module: the evaluation module provides skills assessment of players in real time to the pedagogical module. • The Pedagogical module: which plays the role of a virtual tutor accompanying the learners by providing support and help during (and after) their training. • Knowledge representation module: All knowledge used or produced by the previous modules of our proposed architecture are stored as an ontology in the domain model and the learner model. The ontology illustrated in figure  3  describe the general domain of crisis management (adapted to the SIMFOR context). The domain model and learner model are described as follow: -The Domain model: the domain model represent the general concepts of crisis management and is segmented into parts representing a role or a skill to learn.
The interview questions were designed to explore the ORBIT research questions in more depth. They covered participants' understanding, use and adaptation of OER, attitudes to sharing materials, perceived added value of ORBIT and of an Open course book, ease of use and of discovering relevant and useful OER, suggested improvements to ORBIT, criteria for assessing quality of ORBIT, and importance of peer review. Qualitative data were coded and analysed thematically through a process of constant comparison (Glaser and Strauss,  1967) to provide insights into teachers' and teacher educators' understanding and attitudes.
Importance of flexible file formats and expandability Format and compatibility issues were also identified as being vital to ensure that all downloads would be useable for all. Having found what you need, you must be able to use it easily and ideally without the need for further (specialist) software. Adaptability of OER materials was seen as essential. As also indicated by the surveys, all interviewees said they preferred to tailor materials to suit their own purposes and match the needs of their students. Therefore the resource formats should be such that adaptation and incorporation is possible. A key recommendation for ORBIT was that resources should be provided in formats that permit easy adaptation: 'The idea of sort of starting small and gradually expanding or … the way in which they're linking to other projects, which, again, offers opportunities for expansion.
As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research.
The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.
This article investigates the progress in Arabic NER research. The survey by  Nadeau and Sekine (2007)  presents background on much of the work on NER for a variety of languages and myriad machine learning (ML) techniques. To the best of our knowledge, Arabic NER and classification have not yet been surveyed extensively, which has motivated us to conduct this survey. The survey is structured as follows. Section 2 provides background information relevant for working with Arabic NER. Section 3 presents some aspects of the Arabic language that will allow the reader to appreciate the difficulties associated with Arabic NER. Section 4 briefly introduces the standard tag sets commonly used to annotate named entities. Section 5 describes the Arabic NER language-specific resources that are involved in the NER task.
The Broader Role of NER The implications of research in NER for NLP more generally are too obvious to enumerate. Examples of applications for which NER is useful are shown in this section. Information Retrieval. This is the task of identifying and retrieving relevant documents from a set of data according to an input query. A study by  Guo et al. (2009)  has indicated that about 71% of the queries in search engines contain NEs. Information Retrieval can benefit from NER in two phases  (Benajiba, Diab, and Rosso 2009a) : firstly, recognizing the NEs within the query; and secondly, recognizing the NEs within the searched documents, and then extracting the relevant documents taking into account their classified NEs and how they are related to the query. For example, the word (Aljazeera) can be recognized as an organization name or a noun corresponding to the word island; the correct classification will facilitate extracting relevant documents.
This enhances the process of analyzing the nature of each cluster and also improves the clustering approach in terms of selected features. For example, time expressions along with location NEs can be utilized as factors that will give an indication of when and where the events mentioned in a cluster of documents have occurred. Navigation Systems. These systems, which facilitate navigation using digital maps, now play significant roles in our lives. They provide directions, information about nearby places possibly linked with other on-line resources, and traffic conditions. In these systems, points of interest (also known as waypoints) are NEs that are stored in a database with their geographic coordinates  (Kim, Kim, and Cho 2012). They refer to areas of interest that are typically of significance to, among others, tourists, visitors, and rescuers, allowing the location of places such as parking areas, shops, hospitals, restaurants, universities, schools, landmarks, and so on.
Linguistic Issues and Challenges Arabic is a highly inflected language, with a rich morphology and complex syntax  (Al-Sughaiyer and Al-Kharashi 2004; Ryding 2005). Current Arabic NLP research efforts cannot cope with the massive growth of Arabic data on the Internet and the heightened need for accurate and robust processing tools (Abdul-Mageed,  Diab, and Korayem 2011). NER is considered one of the building blocks of Arabic NLP tools and applications. Though significant progress has been achieved in Arabic NER research in the last decade, the task remains challenging due to the following features of the Arabic language; opportunities for improved performance are still available.
Arabic Script The Arabic language relies on the Arabic script, which is also used in writing other languages such as Persian, Urdu, Kurdish, and Pashto (Habash 2010). Some researchers have developed Arabic computational tools and resources based on Romanized 2  or transliterated 3  Arabic text rather than genuine Arabic script (e. g. ,  Buckwalter's Arabic Morphological Analyzer [Buckwalter 2002] , the CJK lexical resources  [Halpern 2009] , and Arabic NER systems  [Bidhend, Minaei-Bidgoli, and Jouzi 2012; Zayed and El-Beltagy 2012] ), either because these formats are more familiar to non-native Arabic speakers or because of limitations in the Arabic script encoding imposed by the development environment. This approach should disappear over time with the rapidly growing quantity of Arabic script-based Web content and new technologies that support multiple encodings.
Language in Use With regard to language usage, Arabic can be classified into three types  (Elgibali 2005) : Classical Arabic (CA), Modern Standard Arabic (MSA), and Colloquial Arabic Dialects (Abdel  Monem et al. 2008; Habash 2010; Korayem, Crandall, and Abdul-Mageed 2012). As far as Arabic NE is concerned, it is important to know the difference between these various uses of the language. CA is the formal version that has been used continuously for over 1,500 years as the language of Islam, used by Muslims in their daily prayers. Most Arabic religious texts are written in CA. In this context, person name recognition is of particular interest in order to identify and verify the correctness of citations  (Zaraket and Makhlouta 2012 ) (a sequence of hadith narrators referencing each other who provide narrations related to the Prophet Mohammed based on known truthful and untruthful relaters).
The importance of verification is that the authenticity of a hadith needs to be established before his narration is used in jurisprudence, and this depends on the credibility of the narrators. Furthermore, many historical Arabic manuscripts are handwritten in CA (or Arabic calligraphy); when they are digitized and converted to text, Arabic NE will become important. MSA is the language of today's Arabic newspapers, magazines, periodicals, letters, modern writers, and education. MSA is one of the six official languages of the United Nations used in meetings and official UN documents. Most Arabic NLP, including NER research projects, is focused on MSA. The main difference between MSA and CA lies in the vocabulary, including NEs, and the orthography of conventional written Arabic  (Farber et al.
2008 ): MSA does not require the inclusion of short vowels. Moreover, MSA reflects the needs of contemporary expression, whereas CA reflects the needs of older styles. For example, the Arabic NEs in rare documents and old manuscripts that refer to places, jobs, or organizations are different from the corresponding NEs in modern documents. Colloquial Arabic is the spoken Arabic used by Arabs in their informal daily communication; it is not taught in schools due to its irregularity. Unlike the widespread use of MSA across all Arab countries, colloquial Arabic is a regional variant that differs not only among Arab countries, but also across regions in the same country. Written Colloquial Arabic is presently used mainly in social media communication. For comparison, a person name in either CA or MSA could be expressed in Arabic dialect by more than one form; for example, (Abd Al-Kader) versus (Abd Al-Gader) or (Abd Al-Aader).
Lack of Capitalization Unlike languages like English that use the Latin script, where most NEs begin with a capital letter, capitalization is not a distinguishing orthographic feature of Arabic script for recognizing NEs such as proper names, acronyms, and abbreviations  (Farber et al. 2008). The ambiguity caused by the absence of this feature is further increased by the fact that most Arabic proper nouns (NEs) are indistinguishable from forms that are common nouns and adjectives (non-NEs). Thus, an approach relying only on looking up entries in proper noun dictionaries would not be an appropriate way to tackle this problem, as ambiguous tokens/words that fall in this category are more likely to be used as non-proper nouns in text (Algahtani 2011). For example, the Arabic proper name (Ashraf ) can be used in a sentence as a given name, an inflected verb (he-supervised), and a superlative (the-most-honorable)  (Mesfar 2007 ).
Agglutination The agglutinative nature of Arabic results in many different patterns that create many lexical variations. Each word may consist of one or more prefixes, a stem or root, and one or more suffixes in different combinations, resulting in a very systematic but complicated morphology. Clitics, which in other languages such as English would be treated as separate words, agglutinate to words. Arabic has a set of clitics that are attached to an NE, including conjunctions such as (Waw, and) and (if. then) and prepositions such as (Laam, for/to), (k, as), and (baa, by/with), or a combination of both, as in  (Waw-Laam, and-for). NER relies on the words forming the NE and the context in which it appears. Both the words and the contexts may appear in different inflected forms. In order to address data sparseness issues without requiring massive training corpora, these bound morphemes should undergo morphological preprocessing.