dataesr/scientific-paragraphs-categorization huggingface dataset:
By way of example, TDQ, which is an open source profiling tool, generates statistics about data sources and creates matching analysis. The latter is dedicated to analyze duplicates. Since the elimination of duplicate records requires similarity detection, TDQ creates a matching analysis which is based on computing similarity distance using different methods (e. g. , Jaro Winkler, Soundex). In the following, we illustrate through an example how TDQ performs a partial duplicate detection. We used a data source stored in MySQL database that contains 5 records. In the latter, we have four duplicate records and one unique records. The first duplicate ones have the same city names but different employee names which are spelled differently (i. e. , "Marhta" and "Martha"). For the other duplicate records, the names are written differently (i.
On the other hand, we have presented a comparative study based on four ETL tools to show how much these tools consider DQ dimensions. We noticed that ETL tools components improve partially the DQ according to DQ dimensions. We have studied TDI and TDQ as well-known examples of open source tools to evaluate their assistance to quality. We conclude that these tools have many DQ limits that may affect the accuracy of the final result. For instance, the Talend data quality component tUniqRow, which is dedicated to remove duplicate entries, can lead to a loss of information whenever the data source is a semi-structured file, e. g. , XML and JSON files. In case of two different records having the same ID, the tUniqRow removes the second one. The study presented in this paper has shown that the problem of quality in ETL process still requires a lot of efforts, especially if the BI&A system is based on non-relational data sources, e.
g. , NoSQL databases  [1]. With the emergence of big data, data analytics is gaining increasing attention in BI&A solutions that become Data-Driven rather than Business and intuition driven. Predefined processes and analysis models may not be able to provide a clear explanation on why a given phenomenon happens. The solution requires often, extra data that is not included in the corporate systems. Consequently, here is an obvious need to be able to cope with high technology standards to face big data specific complexity (Volume, Variety, Velocity, etc. )  [8, 10]. Today, Data Management Systems based on non relational models as key-value, column-oriented, document-oriented and graph-oriented, which adopt a schemaless representation are widely accepted as efficient systems to store data. To store heterogeneous data with non predefined schemas, schemaless databases could be a promising issue.
ACKNOWLEDGEMENTS The authors wish to thank all the patients who responded to the survey, the patient and patient advocate organizations of APTED, CCF, and  Netzwerk NeT e. V. for their collaboration, and  Kay Wesley  of  Kanga Health  for her assistance in setting up and conducting the survey. Special thanks are owed to  Katharina Mellar  of  Netzwerk NeT e. V. , who was instrumental to this project and participated as an author on an earlier draft of this manuscript, but sadly died in August 2018. Funding. This survey was sponsored by  Ipsen ,  Boulogne Billancourt ,  France , which has also funded the journal's Rapid Service fee. Medical Writing Assistance. Sarah Griffiths  and  Rebecca Crosby  of  Oxford PharmaGenesis  and  Richard Watt  and  Lindsay Queen  of  Sudler Medical Communications  provided writing support, funded by  Ipsen.
In the present study, we used data from a relatively large cohort of children born with HIV, followed for more than 6 years and with frequent height measurements. We were able to develop a mathematical function that describes the height evolution of children from birth to adulthood. This allowed to analyse the effect of ART on the growth of children, taking into account other factors that could influence it such as sex, clinical, virological and immunological status at the time of treatment initiation, as well as initial ART regimen and the occurrence of AIDS-defining events during the follow-up.
Patients and follow-up We used data from all HIV-infected children who started ART within the Program for HIV Prevention and Treatment (PHPT) cohort study initiated in 1999 in 40 hospitals in Thailand (Clinicaltrial. gov: NCT00433030). Children's anthropometric parameters were measured at enrolment and at each monthly visit during the first 2 years of follow up and every 6 months thereafter. HAZ, WAZ and weight-for-height (WHZ) were computed using the reference growth curves for Thai children updated in 2000  [35]. CD4 percentage, HIV-RNA viral load and Centers for Disease Control and Prevention HIV classification (CDC stage)  [36, 37]  were assessed at enrolment and every 6 months thereafter. AIDS-defining events  [36, 37]  were reported at the time of their occurrence.
Modelling strategy and data analysis We developed an empirical nonlinear mixed effect model to describe each child's final height (HT) using Monolix (version 4. 1. 4) nonlinear mixed effect modelling program (http://lixoft. com/downloads/) [38]. Firstly, a height versus age scatter plot was drawn. As expected, the curve was non-linear. Thereafter, a log (Height) versus age showed that the curve could be roughly described by 3 successive straight segments defining 3 growth phases. A sum of 3 exponential functions was then used for describing growth as a function of age. Parameters were estimated separately for males and females by computing the maximum likelihood estimator of the parameters without any approximation of the model (no linearization) using the stochastic approximation expectation maximization (SAEM) algorithm combined with a Markov Chain Monte Carlo (MCMC) procedure.
The final population model was evaluated by the normalized prediction distribution errors (NPDE) metrics  [39]  and the predictioncorrected visual predictive check (VPC)  [40]. Diagnostic graphics and distribution statistics were obtained using RfN (link on http://wfn. sourceforge. net) via the R program. HT versus age time-courses were simulated from their respective final population model and compared with the observed data to evaluate the predictive performance of the model. The vector of parameters from 100 replicates of the database was simulated using the final model. Each vector parameter was drawn from a distribution with a variance corresponding to the previously estimated BSV. A simulated residual error was added to each simulated variable. The 25th, 50th and 75th percentiles of the simulated dependent variables at each time were then overlaid on the observed data and a visual inspection was performed.
Characteristics at ART initiation and last follow-up visit The data from 477 children were included in this analysis. Table  1  presents their characteristics at ART initiation. Two-hundred and six (43%) males. Median age was 6. 2 years (interquartile range (IQR), 1. 8 to 9. 6), with 65 (14%) under one year, 54% were in CDC stage B or C. Of the 477 children, 77 (16%) were underweight (WAZ < -2), 235 (49%) had stunting (HAZ < -2), and 32 (7%) experienced wasting (WHZ < -2). Forty-one children (9%) had started therapy with dual NRTI regimens (before 2003) then switched to triple combination when it became widely available in Thailand, 79%   1 ). Over a median duration of 6. 3 years (IQR, 3. 0 to 8. 3) of ART, 58 (12%) children developed at least one AIDSdefining events (ADEs), 29 (6%) children died, 92 (19%) were lost to follow-up, and 101 (21%) were referred to other hospitals.
Acknowledgements We thank all the children and families who participated in the PHPT cohort study. We thank the site principal investigators in the following hospitals in Thailand as well as their temas. Lamphun: Pornpun Wannarit; Phayao Provincial Hospital:  Pornchai Techakunakorn ;  Chiangrai Prachanukroh :  Rawiwan Hansudewechakul ; Chiang Kham:  Vanichaya Wanchaitanawong ; Phan:  Sookchai Theansavettrakul ;  Mae Sai: Sirisak Nanta ; Prapokklao: Chaiwat Ngampiyaskul; Banglamung: Siriluk Phanomcheong; Chonburi:  Suchat Hongsiriwon ; Rayong:  Warit Karnchanamayul ;  Bhuddasothorn Chacheongsao :  Ratchanee Kwanchaipanich ; Nakornping: Suparat Kanjanavanit; Somdej Prapinklao:  Nareerat Kamonpakorn ,  Maneeratn Nantarukchaikul ;  Bhumibol Adulyadej :  Prapaisri Layangool ,  Jutarat Mekmullica ; Pranangklao:  Paiboon Lucksanapisitkul ,  Sudarat Watanayothin ; Buddhachinaraj:  Narong Lertpienthum ;  Hat Yai : Boonyarat Warachit; Regional Health Promotion Center 6,  Khon Kaen: Sansanee Hanpinitsak ; Nong Khai: Sathit Potchalongsin; Samutsakhon:  Pimpraphai Thanasiri ,  Sawitree Krikajornkitti ; Phaholpolphayuhasena: Pornsawan Attavinijtrakarn; Kalasin: Sakulrat Srirojana; Nakhonpathom:  Suthunya Bunjongpak ; Samutprakarn: Achara Puangsombat; Mahasarakam:  Sathaporn Na-Rajsima ;  Roi-et: Pornchai Ananpatharachai; Sanpatong: Noppadon Akarathum ;  Vachira Phuket: Weerasak Lawtongkum ; Chiangdao:  Prapawan Kheunjan ,  Thitiporn Suriyaboon ,  Airada Saipanya.
Our study is done in the scope of project DisTA 1  2 Software component design with UCM Component based software engineering (CBSE) addresses middleware dependency of software applications  [8, 9]. It consists in isolating the business code from the middleware configuration code. The business code is encapsulated inside components. Components are connected through ports and connectors. Glue code is generated from the component declarations. This glue code provides an API to the business code; this API depends only on the component declaration, and is independent from the underlying middleware. The implementation of the glue code API is specific to a given middleware implementation; it manages the middleware configuration and control. UCM  [5]  is a component model published by the Object Management Group.
Implementation And Experiments We are currently developping a UCM code generator, Sigil-UCM  [10] , to demonstrate our approach. This tool is a code generator that implements the standard mapping from UCM to C++ and also the mapping from UCM to xLIA we outlined in the previous section. Sigil-UCM produces the necessary calls to the log4cpp  [6]  library to trace the execution of all component port methods (method entry, method exit, method call, returning from call). Traces are compared with the xLIA test oracles by Diversity. Users get a test verdict that indicates if the execution traces conforms to the call sequences specified in the UCM components or not. After generating the technical code with Sigil-UCM, users have to implement the methods for which they specified the behaviour in the UCM model.
Meta-Analysis (Pipeline 1) In the Neurosynth platform, we used "MCI" as the search term to conduct forward inference analysis on all neuroimaging studies in the database that were available until February 2018. The forward brain maps were downloaded from the platform. Potential NIBS locations were chosen based on the report, and they were visually confirmed using BrainNet view 2   (Xia et al. , 2013) , as well as MRcon_GL and MRcron_ice toolboxes. These potential locations were mapped onto a standard brain map and standard head map with the international 10-20 system in MNI space  (Cutini et al. , 2011).
Resting State Functional Connectivity Analysis fMRI Data Acquisition The structural and resting state functional MRI data of MCI patients used in the present study were obtained from large multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) studies 3. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early AD. A total of 45 MCI patients were included. The inclusion and exclusion criteria for MCI patients are available at: http://adni. loni. usc. edu/methods/documents/ (ADNI 1 Procedures Manual).
Resting State Functional Connectivity Analysis Seed-to-voxel correlational analysis were calculated in MATLAB by applying the functional connectivity (CONN) toolbox v17. C 4. Similar to our previous studies  (Tao et al. , 2016 (Tao et al. , , 2019;; Liu et al. , 2019a,b) , the images were preprocessed with slice timing, realigned, co-registered to subjects' respective structural images, normalized, and smoothed with a 6 mm full width at half maximum kernel. Segmentation of gray matter, white matter, and cerebrospinal fluid for the removal of temporal confounding factors was employed. Band-pass filtering was performed with a frequency window of 0. 01 to 0. 089 Hz. To eliminate correlations caused by head motion and artifacts, we identified outlier time points in the motion parameters and global signal intensity using ART 5.
This methodology is similar to that of a previous study conducted by  Goldstein-Piekarski et al. (2018). The ROIs were further defined and refined by WFU-Pick Atlas software  (Maldjian et al. , 2003 (Maldjian et al. , , 2004) )  to ensure that the regions were within the specific structure identified. The BOLD time course from these ROIs was then extracted, and Pearson's correlation coefficients were computed between that time course and the time courses of all other voxels in the brain. Correlation coefficients were Fisher transformed into Z scores. Before the rsFC group analysis, we created a brain cortex mask to exclude sub-cortex brain regions from the AAL template using WFU-Pick Atlas software. The mask included bilateral pre and postcentral, superior and middle frontal, superior and inferior and middle occipital, superior and inferior parietal, supramarginal, angular, superior temporal, superior temporal pole, middle temporal, middle temporal pole, inferior temporal, oper inferior frontal, oper Rolandic, tri inferior frontal, superior medial frontal, calcarine, orbital middle and superior and inferior frontal, orbital medial frontal, supplementary motor area, paracentral lobule, precuneus, and cuneus.
Meta-Analysis Results (Pipeline 1) A total of 67 studies were included in the meta-analysis, and 20 clusters had a size greater than 30 continuous voxels. With the BrainNet viewing software, we visually determined that the left orbital IFG (F7) and left angular gyrus (P3) are proximal to the brain surface and can thereby serve as brain stimulation sites directly (Figures  1B, C, Table 2 , and Supplementary Figure  S1A ).
Stimulation paradigms are beyond the scope of this manuscript, and future studies are needed to explore these paradigms based on the specific NIBS modalities and, most importantly, the condition of the individual. In addition, due to the database limitation, we included patients within the age range of 55-90. Future studies with a smaller age range should be conducted to reduce the effects of standard deviation of age. Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI was funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association, Alzheimer's Drug Discovery  Foundation, Araclon Biotech, BioClinica, Inc.
The published median radial velocities provide a full sky-coverage and have a completness with respect to the astrometric data of 77. 2% (for G ≤ 12. 5 mag). The median radial velocity residuals with respect to the ground-based surveys vary from one catalogue to another, but do not exceed a few 100s m s -1. In addition, the Gaia radial velocities show a positive trend as a function of magnitude, which starts around G RVS ∼ 9 mag and reaches about +500 m s -1 at G RVS = 11. 75 mag. The overall precision, estimated from the median of the Gaia radial velocity uncertainties, is 1. 05 km s -1. The radial velocity precision is function of many parameters, in particular the magnitude and effective temperature. For bright stars, G RVS ∈ [4, 8] mag, the precision is in the range 200-350 m s -1 , which is about 3 to 5 times more precise than the pre-launch specification of 1 km s -1.
Introduction ESA's Gaia mission  (Gaia Collaboration et al. 2016b ) was launched from the Kourou space centre on 19 th December 2013. It took about 4 weeks to the satellite to reach its operational orbit around the second Lagrange point (L2) of the Sun-Earth system. Following the commissioning phase, the nominal mission began on 25 th July 2014, initially for a 5 years period, which has recently been extended a first time by 1. 5 years 1. Gaia scans continuously the celestial sphere with its 2 telescopes and its 3 instruments: the astrometric instrument, a spectro-photometer and a medium resolving power spectrograph, the Radial Velocity Spectrometer (RVS,  Cropper et al. 2018). The data collected are transmitted daily to Earth, when the satellite is in contact with one of the ground-based antennae.
Once received on the ground, the data are processed by the Gaia Data Analysis and Processing Consortium (DPAC). The consortium publishes the products of the processing in successive data releases. The first one, Gaia DR1  (Gaia Collaboration et al. 2016a ) was issued on 1 The current cold gas fuel supplies of Gaia would allow to extend the nominal mission by 5 years. 16  th September 2016 and the second one, Gaia DR2 (Gaia  Collaboration et al. 2018b) , on 25 th April 2018. Each new Gaia data release comes with new products. One of the novelties of Gaia DR2 is the publication of the median radial velocities, extracted from the RVS spectra. The Radial Velocity Spectrometer collects spectra down to G RVS = 16. 2 mag. Yet, because of the modest exposure time per CCD of 4. 42 s, the signal-to-noise ratio of the spectra of the faintest stars is very low.
3. Measuring the radial velocities per transit. For each star and for each transit one (or two, in the case of a double star) radial velocity is measured. Each radial velocity is derived by D. Katz et al. :  Gaia Data Release 2 Properties and validation of the radial velocities a series of modules which compare the 3 RVS spectra collected per transit to one (or several) template(s) shifted step by step in radial velocity, from -1000 to +1000 km s -1. This workflow is hereafter referred to as Single Transit Analysis (STA). 4. Combing the information of the successive transits. For each source, the radial velocities derived on successive transits are grouped in a time series. The radial velocities published in Gaia DR2 are the medians of these time series (see Sect. 5. 1. 1 for more details).
RVS ) is derived by the Gaia onboard software. -The External G RVS (G ext RVS ) is calculated from ground-based photometric catalogues using colour-colour transformations. When no ground-based photometry is available, the on-board G on-board RVS is adopted. G ext RVS is the magnitude used to define the limiting magnitude of Gaia DR2: G ext RVS ≤ 12 mag. -The Internal G RVS (G int RVS ) is measured by the spectroscopic pipeline from the RVS spectra. For the faint stars, the accuracy and precision of G int RVS are limited by the basic modelling of the straylight in Gaia DR2. A more elaborated calibration of the straylight is in development for Gaia DR3.
The radial velocities of these stars were removed from Gaia DR2. The spectroscopic pipeline will be upgraded to process late M-type stars and publish their radial velocities in a future Gaia data release. Hot stars. About 18% of the stars processed for Gaia DR2 had known ground-based atmospheric parameters. When available, this information was used to select the closest template in a library of several thousands synthetic spectra. When the parameters were unknown, a dedicated software module, determineAP, was in charge of choosing the template by comparison of the RVS spectrum to a set a synthetic spectra  (Sartoretti et al. 2018). For Gaia DR2, 280 million spectra were processed. The volume of data was too large to compare 85% of the RVS spectra to the full library of synthetic spectra. To keep the processing load reasonable, determineAP had to select the templates in a sub-grid restricted to 28 synthetic spectra, with in particular a single choice of surface gravity per effective temperature.
If the parameters of the star are contained in the compilation of ground-based stellar parameters used by the pipeline (see  Sartoretti et al. (2018) ), they are used to select the closest template in a library of 5256 spectra. Otherwise, the template is chosen by a dedicated module, de-termineAP, from a smaller set of 28 templates. Of the 7 224 631 stars, ∼ 18% had their templates selected using the ground-based compilation. Figures 13, 14 and 15  show respectively the distributions of the effective temperatures, surface gravities and metallicities of the templates. The alternation of tall and small peaks in the effective temperatures distribution is the consequence of certain temperatures being chosen only when the stars are contained in the ground based catalogues compilations (producing small peaks).
Gaia-DR2 shows a small 2. 4-σ level offset of -12 m s -1 with respect to CU6GB. The CU6GB stars brighter than or equal to G ext RVS = 9 mag, and showing no variability, were used in the wavelength calibration procedure to set the wavelength zero-point of each configuration: i. e. field-of-view/CCD/trending epoch  (Sartoretti et al. 2018). It is a good sanity check for the pipeline, that neither the wavelength calibration, nor the subsequent modules did modify significantly the global radial velocity zero-point defined by the CU6GB catalogue. Gaia-DR2 shows a positive global offset of about +200 to +300 m s -1 with respect to the SIM, RAVE, APOGEE and GES stars. Yet, as discussed in the following sections, the origin these offsets varies partially from one catalogue to another. low acc = π 2N V res R ( Ṽres R -Per(V res R , 15.
Accuracy versus sky coordinates Figure  25  presents the sky map, in galactic coordinates, of the median radial velocity residuals per pixel of ∼54 square degrees. To increase the number of validation stars, the 5 ground-based catalogues presented in Sect. 5. 2 have been complemented with 5820 stars from the Extended Hipparcos Compilation (XHIP;  Anderson & Francis 2012). Nonetheless, for a few pixels, the minimum number stars required to calculate the median, lowered here to 5 stars per pixel, was not reach. Those pixels appear in white on the map. To produce the map, the validation datasets have been corrected for their respective median radial velocity residuals (see Table  2 ) and then combined. This explains why the distribution of median radial velocity residuals is roughly centred on zero.
Radial velocity precision Two different datasets and statistical estimators are used to assess the precision of the Gaia DR2 radial velocities. The first dataset is made of a compilation of 6 ground-based catalogues, i. e. the five used to assess the accuracy of Gaia DR2 radial velocities 9 plus 5820 XHIP stars  (Anderson & Francis 2012). Since the different catalogues have small relative offsets, they were first corrected for their median radial velocity residuals (as listed in Table  2 ) before being combined in a single dataset, hereafter referred to as GB 10 validation dataset. With this dataset, the Gaia DR2 radial velocity precision is calculated as the robust dispersion of the radial velocity residuals: σ GB V R = Per(V res R , 84. 15) -Per(V res R , 15. 85) 2 (4) where Per(V res R , 15.