dataesr/scientific-paragraphs-categorization huggingface dataset:
Parsing There are a few novel parsing tools for JSON data that take into account dynamic type information for improving the efficiency of the applications relying on them. In a recent work  [20] , Li et al. present streaming techniques for efficiently parsing and importing JSON data for analytics tasks; these techniques are then used in a novel C++ JSON parser, called Mison, that exploits AVX instructions to speed up data parsing and discarding unused objects. To this end, it infers structural information of data on the fly in order to detect and prune parts of the data that are not needed by a given analytics task. In  [14] , Bonetta and Brantner present Fad. js, a speculative, JIT-based JSON encoder and decoder designed for the Oracle Graal. js JavaScript runtime. It exploits data access patterns to optimize both encoding and decoding: indeed, Fad.
(Redmond, USA) , member of the W3C XML Query Working Group, member of the board of the EAPLS. He worked on database programming languages and type systems for these languages, especially in the fields of object oriented and XML data models. Carlo Sartiani  (Ph. D. ) is an assistant professor at  Università della Basilicata. He received his PhD from  Università di Pisa , and he completed his postdoctoral studies at  Università di Pisa. He worked on database programming languages and data integration systems, and his current research activities focus on semistructured and big data.
Methods The study protocol was registered with ClinicalTrials. gov (NCT02569411; October 5th, 2015). Our methods are described briefly here; additional details can be found in the protocol publication and Additional File 1: Appendix 1, 2. [34]  Our RCT conforms to the Consolidated Standards to Reporting Trials (CONSORT) guidance  [35]  (Additional File 2). We used RCTs identified through two systematic reviews and NMAs,  [36, 37]  and we followed the process as depicted in Figure  1a. Overall, we contacted both study authors and study sponsors (Additional File 1: Appendix 2) to obtain IPD. The process varied across sponsors (Additional File 1: Appendix 3). (Figure  1  here) We performed a descriptive analysis using frequencies and percentages for all characteristics we either abstracted from trial publications or collected through the author and sponsor contacting process (Additional File 1: Appendix 2).
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 7 We compared author responses for which we received complete IPD, author response type (positive vs. negative) and response rate (response vs. no response) between experimental and control groups using the OR and its corresponding 95% CI. Upon IPD receipt, we assessed data completion and time needed to share. Since only 2 IPD datasets were available across the intervention and control groups at the time these analyses were done, we could not compare the intervention group results according to the IPD characteristics. The OR and its corresponding 95% CI was used to compare author and sponsor response type and response rate in the following groups: low vs. high/unclear risk of bias, industry/mixed-sponsored vs. publicly-sponsored studies, large vs.
In some cases, even when conducting exhaustive searches (Additional File 1: Appendix 18), an NCT number (or other related study ID) was difficult to find or did not exist, and hence sponsors could not locate the study in their database solely based on the study citation details. Being unable to match study publication to the underlying studies when sponsors needed to be contacted was the most frequent reason for IPD being unavailable (see Figure  8 ). Of the 98 studies with unavailable IPD, 41 (42%) studies could not be located by the with the analysis once IPD was received was that IPD were only available through proprietary sponsor-specific platforms. This does not allow for IPD from different sponsor platforms to be combined (and could be a challenge for those who are unfamiliar with the software provided in the underlying platform).
Discussion Our results showed that offering small financial incentives to study authors does not improve IPD retrieval. In our particular example, by the end of July 2017 we were unable to obtain any IPD datasets from trial authors, and were only able to obtain two IPD datasets after contacting industry sponsors. We found that obtaining a response from authors to requests to access IPD may depend on study characteristics. Authors of publicly-sponsored studies, those that included between <50 and 150 patients, and those with a medium to large treatment effect (i. e. , an estimated treatment above 0. 2 on the SMD scale) tended to respond positively to IPD requests. Increased odds of a positive response were also found in studies at high risk of bias. This is because small to moderate studies are typically at high risk of bias in most domains and are associated with large treatment effects.
Sharing IPD may be constrained by a number of legal, ethical, and logistical factors, which may deter researchers from undertaking them and trial investigators participating in them. This may perpetuate reliance on the conduct of aggregate data meta-analysis and NMA that may reduce statistical power and accuracy of results. Significant barriers in obtaining study IPD from trial sponsors may include matching study publication to the underlying study,, issues around data ownership, and acquiring of data dictionary licenses. In general, time and cost may be a barrier to carrying out an IPD NMA. Costs include not only staff wages, including administrative, legal, library, and research staff, but also license costs (when applicable, e. g. WHO Drug Dictionary license approximate cost $8,958. 25 USD per sponsor).
meta-analyses that involve obtaining data from trial sponsors through data sharing platforms should include sufficient time for the IPD retrieval process, (probably at least twoyears). For example, the IPD retrieval process for a recently published IPD-NMA of 10 antiepileptic drugs required 4 years to obtain the 65% of the included participants and 38% of identified clinical trials. [39] Even if access to IPD is granted,, additional barriers may arise, such as having to analyze IPD datasets using different sponsor data sharing platforms and software making one-stage analysis impossible. Being able to access IPD only for a limited time (e. g. 4 to 6 weeks) is also a serious limitation and constraint as when analysing IPD from different studies provided by different sponsors, the IPD datasets can be available at different time points.
unique studies, since some studies reported multiple sponsors, where 83 studies were funded by 1 sponsor, 23 studies by 2 sponsors, and 1 study by 5 sponsors; see Additional File 1: Appendix 12for list of co-sponsored studies); 3 sponsors (18%) did not respond to any of our contact attempts. We contacted industry sponsors only, as we were not able to locate the contact information for the majority of the included public sponsors. In the following, we refer to the term 'sponsor' to indicate an industry sponsor. CSDR and YODA databases facilitated data sharing for 59 trials funded by 4 sponsors who made 18 trials available. In total, we included 25 type 1 diabetes studies, of which 20 (80%) compared NPH, 15 (60%) compared detemir, and 13 Additional File 1: Appendix 15).
Funding This work was supported by the  Canadian Institutes for Health Research Knowledge Synthesis  [No. 351143 ]. AAV was previously funded by the  Canadian Institutes of Health Research (CIHR) Banting Postdoctoral Fellowship Program  [No. 139157 ]. AAV and DM are funded from the  European Union's Horizon 2020  [No. 754936 ]. SES is funded by a Tier 1  Canada Research Chair in Knowledge Translation. ACT is funded by a Tier 2  Canada Research Chair in Knowledge Synthesis.
The response rate when the type 1 diabetes studies were categorized per treatment was 100% across all treatments. The positive response rates ranged from 15% (in glargine) to 80% (in detemir). The response rates for the Alzheimer's dementia treatments ranged between 85% (in memantine) and 100% (in galantamine), with positive responses ranging from 19% (in galantamine) to 29% (in memantine). Of the total 133 studies, 38 (29%) unique studies were deemed available by the sponsors. However, the majority (89 studies; 67%) of the IPD were unavailable and the reasons for refusal of providing IPD varied, including difficulty with studyidentification (46%), non-ownership of IPD (26%), and the age of the study (too old, 12%) (dataset complete according to the study protocol. Allocation concealment was rated as low risk of bias for both studies; however, for incomplete outcome data, one study had low risk of bias and one had a high risk of bias.
Anyway, the value is not critical. Within the feedback-loop  bandwidth, the phase-control word is equal to the phase difference between the input and the local clock. The FPGA guarantees that all the measures are simultaneous. This is necessary to cancel the fluctuation of the internal 1 GHz clock, common to all the channels. The phase error is converted into a stream of phase-time data x(t), low-passed at the cutoff frequency f H = 5 Hz, sampled at 10 S/s and transferred to the external PC by a VoCore2 Linux computer  [33]. For best stability, we care about low dissipated power and geometrical symmetry. The DDS is an Analog Devices AD9912, which has a typical dissipation of 650 mW, smaller than that of other high-frequency DDSs. The FPGA is an Altera CYCLONE III with 25000 logic elements, 30% of which are actually used.
V. THE EXPERIMENT The block diagram of the experiment follows Fig. 7. The 100 MHz output of the three CSOs is measured with the TDDS in 6-channel mode, using the covariance. The 10 GHz outputs are beaten down to HF and measured with a multichannel counter. The latter is a dedicated "Time and Frequency Monitor" made by K&K Messtechnik (now Lange-Electronic  [37] ), originally described in  [38] ,  [39]. The data averaged on τ = 1 s are directly available at the output of the the K&K. With the TDDS, the average on τ = 1 s is obtained by decimation of the 10 S/s output stream. We collected all the data measured simultaneously for a duration of 4. 7×10 5 s (5. 5 days). The cutoff frequency f H is 5 Hz for the TDDS and 0. 5 Hz for the K &,K. This difference is irrelevant because there is no white PM in our results (Sec.
ACKNOWLEDGEMENTS This work is partially funded by (1) the  ANR   Programme d'Investissement d'Avenir (PIA)  under the Oscillator  IMP  project and the  First-TF network , (2) by grants from the  Région Bourgogne Franche Comté  intended to support the PIA, (3) FEDER, (4 ) EMRP program  ( IND55   Mclocks ). A special thank to the  European Space Agency  for supporting this activity.
Processing the collected data: As for the analysis of the data, the exploitation and the interpretation of the collected data were carried out by means of SPHINX, SPSS and Smart PLS software. The first analysis carried out is the validity test in order to verify whether the purpose of the instrument is the same as that fixed for the study. In other words, it is a matter of judging the relevance and the validity of the measuring instrument used. The validity of the construct is usually used if the study is about an abstract domain that is difficult to specify. It is defined by two concepts; the validity of convergence and the validity of divergence. The validity of convergence  (Midi, 1996)  indicates that the measurement of a construct is independent from the measurement process that has been followed.
We were able to set our measurement model which represents all the explanatory variables. The figure below shows the measurement model that was designed with SMART PLS software. the goal is to check whether or not there is a relationship between psychological biases and decision-making. The purpose is to confirm or refute this relationship among investors of equity securities in Morocco. This study is known as the validation of the structural model. To do so, we analyzed the links between the measurement indicators and the constructs. This is a vital approach that will allow us to validate our hypothesis. In this sense, we proceed to confirm the latter on the basis of the importance of the significance of the structural relationships obtained. Using the PLS method, the quality of the structural model is measured by a coefficient of determination R², it informs on the contribution of each explanatory variable to predict the explained variable.
The project The CMS-IBM partnership was born with the goal to improve data quality monitoring operations in two ways: facilitate the current monitoring of the quality of each data taken and look towards the automatic detection of anomalies in data. The data collected by the CMS detector is monitored in real-time to guarantee its quality during the data taking, and to detect, as promptly as possible, deviations from normal behavior. The real-time effort uses as tool a Graphical User Interface (GUI)  [6]. This CMS typical graphical setup is composed of a set of histograms, produced for each component of the detector, in order to monitor key quantities and compare them to pre-defined reference distributions. Trained physicists monitor the produced histograms, mainly by visual inspection and through a set of statistical tests (χ2, Kolmogorov-Smirnov, etc) and report issues found.
The success of the project will be to move from a rules-based assessment toward a supervised (and later unsupervised) interpretation of the data quality. The data of two CMS sub detectors, Electromagnetic and Hadronic Calorimeter were used for this work. State of the art of application development is discussed in the following. IBM company is the industry partner of CMS experiment in this project and we believe this partnership represents an evident benefit for all the developers and the analysis results. We established a two-fold collaboration based on activities of knowledge transfer and problems solving and provision to the project of an IBM Minsky Power 8+ cluster, hardware infrastructure, described in details in Table  1. The first built the substrate for a fast development of the project, the latter is absolutely critical now we're entering the consolidation phase of the project.
A technical challenge has been to choose the data, apt for the exercise, and to format them to emulate a typical online streaming rhythm/conditions. Each of the inspected distributions are refreshed, in the online GUI application used for DQ, every 23 sec (commonly called lumisection, LS)  1. We treated the single LS images as frozen frames of a streaming video (e. g. data taking) and approached the problem as an anomaly detection problem on images. For the preliminary exercise we had access to a dataset of ∼40K samples of data, collected by the CMS experiment in 2016/17, manually pre-scrutinized by a human certifier and declared of good quality. Because the anomalies encountered, in the considered data taking period, were so rare and limited in typology, we decided to artificially manufacture some ∼8K samples of poor quality data, enriched of potential danger, as described in the following.
ECAL: semi-supervised learning Preprocessing data standardization, scale to 0 mean and standard deviation of 1 Activation LeakyRelu activation for the input-to-hidden and hidden-to-hidden layers Linear activation for the hidden to output layer Optimizer AdaDelta Loss function MSE loss Net Architecture Auto-Encoder with convolutional layers, in framework Keras library (tensorflow backend) Regularization Dropout (10-15%) Table  3 : ECAL semi-supervised approach: model details Two types of anomalies were analyzed to test the detection abilities of the proposed model: a hot tower 2  and a missing module  3  , left columns (top and bottom raws respectively) of Figure  1. The artificially manufactured samples, previously described, were constructed by inserting, at random position, either a hot tower (red spot in the original image on the left) or a missing module (blue box in original image).
Acknowledgement Authors would like to thank the  CMS collaboration  for believing in this project, for dedicating some woman/manpower to it and for allowing the use of the data analyzed. We acknowledge the support of the CERN openlab project in creating the best conditions of communications and in providing an exclusive technical support infrastructure. The participation to the CHEP 2018 conference has been possible thanks to the funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement n. 772369), authors are grateful for the endorsement.
Numerical strategy Compressible Navier-Stokes equations in axisymmetric formulation are solved with a finite volume approach. The numerical code used in this project is based on the C++ toolbox OpenFOAM. The transient compressible solver density rhoCentralFoam has been chosen for its ability to capture shocks, in particular through the use of centralupwind schemes of Kurganov and Tadmor  [11]  and  [12]. An implicit second-order backward time scheme is used for time discretization, but only a limited CFL constraint can be imposed due to the severity of flow conditions. Simulations required around 0. 02ms (6×10 7 integration steps with time steps of order 10 -10 s) to reach a relevant convergence of the solution (CD converged at less than 0. 02%). The domain is relatively long so, in order to have the whole flow, relatively long simulation times are required compared to the characteristic time of the flow.
This specificity is because the risk of bias is outcome specific for domains such as bias in measurement of the outcome, and could be specific to a particular estimate (eg, when both intention-to-treat and per protocol analyses have been conducted). We recommend that overall RoB 2 judgments of risk of bias for individual results should be the primary means of distinguishing   21  We strongly encourage stratification by overall risk-of-bias judgment as a default meta-analysis strategy, as shown in figure 2. To facilitate this, we suggest that software for systematic review preparation provides data fields for risk-of-bias assessments. We are preparing an interactive web tool for completing RoB 2 assessments, which we hope will interface well with other systematic review software. In RoB 2, judgments about risk of bias are derived by algorithms on the basis of answers to specific signalling questions.
Grey literature (eg, unpublished thesis) • Conference abstract(s) about the trial • Regulatory document (eg, clinical study report, drug approval package) • Research ethics application • Grant database summary (eg, NIH RePORTER or Research Councils UK Gateway to Research) • Personal communication with triallist • Personal communication with the sponsor on 6 September 2019 at The Librarian J B Morrell Library. Protected by copyright. Version 2 of the Cochrane risk-of-bias assessment tool for randomised trials: bias domains, signalling questions, response options, and risk-of-bias judgments.
Funding: The development of the RoB 2 tool was supported by the  UK Medical Research Council (MRC)   Network of Hubs for Trials Methodology Research  ( MR/L004933/2-N61 ), with the support of the host MRC  ConDuCT-II Hub (Collaboration and innovation for Difficult and Complex randomised controlled Trials In Invasive procedures ,  MR/K025643/1 ), by  MRC  research grant  MR/ M025209/1 , and by a grant from the  Cochrane Collaboration. JACS, SME , and  JPTH are National Institute for Health Research (NIHR)  senior investigators. JACS,  NSB ,  H-YC ,  BCR , and JPTH are supported by  NIHR Bristol Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust  and the  University of Bristol. JACS, JPTH, and KT are members of the  MRC Integrative Epidemiology Unit at the University of Bristol  (supported by  MRC  grant  MC_UU_00011/3 ) and the  University of Bristol.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www. icmje. org/coi_disclosure. pdf and declare: support from the  MRC  and  Wellcome Trust  for the submitted work; JACS reports grants from the  MRC  and  NIHR  during the conduct of the study; JS reports grants from the  MRC, Cochrane Collaboration , and  NIHR  during the conduct of the study; CJC is one of the coordinating editors of  Cochrane Airways  and has responsibility for training Cochrane authors in the UK in Cochrane methodology (including the assessment of risks of bias); JRE reports grants from the  MRC and Boehringer Ingelheim , outside the submitted work; MAH reports grants from the  National Institutes of Health (NIH)  during the conduct of the study; PJ serves as an unpaid member of the steering group of cardiovascular trials funded by  Astra Zeneca ,  Biotronik ,  Biosensors ,  St Jude Medical , and  The Medicines Company , has received research grants to the institution from  Astra Zeneca ,  Biotronik ,  Biosensors International ,  Eli Lilly , and  The Medicines Company , and honorariums to the institution for participation in advisory boards from  Amgen , but has not received personal payments by any pharmaceutical company or device manufacturer; JJK reports personal fees from BMJ outside the submitted work; TLa is an employee of Cochrane; TLi reports grants from the  NIH National Eye Institute , and the  Patient-Centered Outcomes Research Institute  during the conduct of the study; AM reports grants from  MRC  and  Cancer Research UK  during the conduct of the study; KT reports grants from  MRC  during the conduct of the study and personal fees from  CHDI  outside the submitted work; JPTH reports grants from the  MRC  and Cochrane Collaboration during the conduct of the study; the authors declare no other relationships or activities that could appear to have influenced the submitted work.
We leverage on multiple existing initiatives, some international, some national, such as Unpaywall, HAL (main open repository in France), ORCID (Open Researcher and Contributor ID) and IDRef. IDRef is a referential for Higher Education and Research, built and maintained by the ABES (Bibliographic Agency of Academic Libraries which is French public institution and supervised by the Ministry for Higher Education Research and Innovation). Our key objective is to measure and describe, on a yearly basis, the open access level for all the publications with at least a French affiliation. This objective raises 3 challenges: (1) identify the publications with a French affiliation, (2) enrich the metadata of the publications to make it as interoperable as possible and (3) detect the OA status of each publication.
In this work, we focus on a five-year period, from 2013 to 2017. For this period, we estimate the global OA rate. We also describe the OA variation between disciplines and publishers. We describe each step of the method and estimate the associated error rates. Several attempts at building a monitor for Open Science and/or Open Access already exists. The European Commission launched an Open Science Monitor initiative but chose to use "closed" data (Scopus). That makes this methodology not relevant for our use case as we want it to be fully transparent and reproducible. A recent paper from  Piwowar, Priem et al. (2018)  also analyses the OA trends using oaDOI service (previous name of Unpaywall). But this is a global approach that cannot be used directly for national level as geographical metadata are most of the time missing.