dataesr/scientific-paragraphs-categorization huggingface dataset:
It thus bridges the gap between technical and business benchmarking of Big Data and Analytics applications. The Business Benchmarks of DataBench focus on Quantitative benchmarks for areas such as Revenue increase, Profit increase and Cost reduction and on Qualitative benchmarks for areas such as Product and Service quality, Customer satisfaction, New Products and Services launched and Business model innovation. The business benchmarks are calculated on the basis of certain business Key Performance Indicators (KPIs). The business KPIs selected by the project are valid metrics and can be used as benchmarks for comparative purposes by researchers or business users for each of the industry and company-size segments measured. These indicators have been classified in the following four features which group relevant indicators from different points of views: Business features, Big Data Application features, Platform and Architecture features, Benchmark-specific features.
For each feature, specific indicators have been defined. Actually, none of the existing Big Data benchmarks make any attempt to relate the technical measurements parameters, metrics and KPIs (like Latency, Fault tolerance, CPU utilization, Memory utilization, Price performance, Energy, bandwidth, data access patterns, data processed per second, data processed per joule, query time, execution time, number of completed jobs) with the business metrics and KPIs (like operational efficiency, increased level of transparency, optimized resource consumption, improved process quality and performance), customer experience (increased customer loyalty and retention, precise customer segmentation and targeting, optimized customer interaction and service), or new business models (expanded revenue streams from existing products, creation of new revenue streams from entirely (new) data products).
This chapter presents a Big Data and AI Pipeline Framework that supports technology analysis and benchmarking for both the horizontal and vertical technical priorities of the European Big Data Value Strategic Research and Innovation Agenda  [1] , and also for the cross-sectorial technology enablers of the AI, Data and Robotics Strategic Research, Innovation and Deployment Agenda  [2]. In the following sections, we focus on the DataBench approach for Technical Benchmarks which are using a Big Data and AI Pipeline model as an overall framework, and they are further classified depending on the various areas of the Big Data Value (BDV) Reference Model. Technical benchmarks are also related to the areas of the AI Strategic Research, Innovation and Deployment Agenda (SRIDA)  [1]  and the ISO SC42 Big Data and AI Reference models  [3].
The DataBench Framework is accompanied by a Handbook and a Toolbox, which aim to support industrial users and European technology developers who need to make informed decisions on Big Data Technologies investments by optimizing technical and business performance. The Handbook presents and explains the main reference models used for technical benchmarking analysis. The Toolbox is a software tool that provides access to benchmarking services; it helps stakeholders (1) to identify the use cases where they can achieve the highest possible business benefit and return on investment, so they can prioritize their investments; (2) to select the best technical benchmark to measure the performance of the technical solution of their choice; and (3) to assess their business performance by comparing their business impacts with those of their peers, so they can revise their choices or their organization if they find they are achieving less results than median benchmarks for their industry and company size.
Therefore, the services provided by the Toolbox and the Handbook support users in all phases of their journey (before, during and in the ex-post evaluation of their BDT investment) and from both the technical and business viewpoints. In the following section, we present the Big Data and AI Pipeline Framework, which is used for the description of pipeline steps in Big Data and AI projects, and which supports the classification of benchmarks; the framework also serves as a basis for demonstrating the similarities among Big Data projects such as those in the Big Data Value Public-Private Partnership (BDV PPP) program  [4]. We also discuss its relationship with the BDV Reference Model and the Strategic Research and Innovation Agenda (SRIA)  [1]  and the Strategic Research, Innovation and Deployment Agenda (SRIDA) for a European AI, Data and Robotics Partnership (AI PPP SRIDA)  [2].
In Sect. 3, we present Big Data and AI Pipeline examples from the DataBio project  [5]  for IoT, Graph and SpatioTemporal data. In Sect. 4, we present categorizations of architectural blueprints for realisations of the various steps of the Big Data and AI Pipeline with variations depending on the processing types (batch, real-time, interactive), the main data types involved and on the type of access/interaction (which can be API access action/interaction or a Human interaction). Specializations can also be more complex aggregations/compositions of multiple specializations/patterns. These blueprints are a basis for selecting specializations of the pipeline that will fit the needs of various projects and instantiations. Section 5 presents how existing Big Data and AI Technical Benchmarks have been classified according to the Big Data and AI Pipeline Framework that is presented in Sect.
Data Storage/Preparation This step includes the use of appropriate storage systems and data preparation and curation for further data use and processing. Data storage includes the use of data storage and retrieval in different databases systems-both SQL and NoSQL, like key-value, column-based storage, document storage and graph storage, as well as storage structures such as file systems. This is an area where there historically exist many benchmarks to test and compare various data storage alternatives. Tasks performed in this step also include further data preparation and curation as well as data annotation, publication and presentation of the data in order to be available for discovery, reuse and preservation. Further in this step, there is also interaction with various data platforms and data spaces for broader data management and governance.
This action has been realized in the DataBio project through the implementation of the instantiations of a "Pipeline for the Publication and Integration of Linked Data", which has been applied in different use cases related to the bioeconomy sectors. The main goal of these pipeline instances is to define and deploy (semi-)automatic processes to carry out the necessary steps to transform and publish different input datasets for various heterogeneous sources as Linked Data. Hence, they connect different data-processing components to carry out the transformation of data into RDF format  [9]  or the translation of queries to/from SPARQL  [10]  and the native data access interface, plus their linking, as well as the mapping specifications to process the input datasets. Each pipeline instance used in DataBio is configured to support specific input dataset types (same format, model and delivery form).
A high-level view of the end-to-end flow of the generic pipeline and its mapping to the steps of the Generic Big Data and AI Pipeline is depicted in Fig. 6. In general, following the best practices and guidelines of Linked Data Publication  [11, 12] , the pipeline takes as input selected datasets that are collected from heterogeneous sources (shapefiles, GeoJSON, CSV, relational databases, RESTful APIs), curates and/or pre-processes the datasets when needed, selects and/or creates/extends the vocabularies (e. g. , ontologies) for the representation of data in semantic format, processes and transforms the datasets into RDF triples according to underlying ontologies, performs any necessary post-processing operations on the RDF data, identifies links with other datasets and publishes the generated datasets as Linked Data, as well as applies required access control mechanisms.
The transformation process depends on different aspects of the data like the format of the available input data, the purpose (target use case) of the transformation and the volatility of the data (how dynamic is the data). Accordingly, the tools and the methods used to carry out the transformation were determined firstly by the format of the input data. Tools like D2RQ [13] were normally used in the case of data coming from relational databases; tools like GeoTriples [14] was chosen mainly for geospatial data in the form of shapefiles; tools like RML Processor [15] for CSV, JSON, XML data formats; and services like Ephedra [16] (within Metaphactory platform) for Restful APIs.
DataBench Pipeline Framework and Blueprints The top-level Generic Big Data and AI Pipeline pattern discussed in the previous sections has been used as a reference to build architectural blueprints specifying the technical systems/components needed at different stages in a pipeline. For example, in the data acquisition phase of a pipeline, a software broker synchronizing data source and destination is needed. A data acquisition broker will then send data to a lambda function that transforms data in a format that can be stored in a database. In DataBench, we have identified and classified all these technical components with an empirical bottom-up approach as follows: we started from Big Data Analytics (BDA) use cases and then we recognized the commonalities among the technical requirements of the different use cases.
DataBench Toolbox The DataBench Toolbox aims to be a one-stop shop for big data/AI benchmarking; as its name implies, it is not a single tool, but rather a "box of tools". It serves as an entry point of access to tools and resources on big data and AI benchmarking. The Toolbox is based on existing efforts in the community of big data benchmarking and insights gained about technical and business benchmarks in the scope of the DataBench project. From the technical perspective, the Toolbox provides a web-based interface to search, browse and, in specific cases, deploy big data benchmarking tools, or direct to the appropriate documentation and source code to do so. Moreover, it allows to browse information related to big data and AI use cases, lessons learned, business KPIs in different sectors of application, architectural blueprints of reference and many other aspects related to benchmarking big data and AI from a business perspective.
R-wave time instants were located on lead V1 by applying the Pan-Tompkins algorithm  [16] , and Q-wave onsets were obtained by subtracting 40 to 80 ms, depending on the QRS width. From the lead where the most prominent T-waves could be visually identified, T-wave offsets were estimated by an adapted Woody's method  [17] ; the segmented intervals were then mean centered and concatenated. In the concatenated TQ segments, the local maxima were detected, and an upper envelope was estimated by interpolation; the lower envelope was estimated in an analogous manner from the local minima. Finally, the average difference between both envelopes along time was computed as an estimate of the f-wave mean amplitude in the lead examined. Amplitude computation was performed using MATLAB, version 2020b (MathWorks, Natick, MA, USA).
Details on the technique have been given previously  [20, 21]. We favored DF measurement over sequential LAA cycle assessment because regional radiofrequency ablation in the vicinity of LAA can affect the LAA cycle, and as such, it might not reliably reflect AF complexity evolution during the ablation procedure. The atrial activity source is automatically selected as the extracted component with a dominant peak in the interval  [3] [4] [5] [6] [7] [8] [9]  Hz, the typical AF frequency band. The percentage of signal power around the dominant peak, or spectral concentration (SC), has been shown to correlate with atrial activity extraction quality  [22]  and is hence used as a measure of performance. PCA and RobustICA were implemented using MATLAB, version 2020b (MathWorks). Figure  2  illustrates the signal processing for DF measurement using RobustICA.
Again, this surface cooling is also observed by OSTIA and COPERNICUS analysis but with warmer values, although OSTIA also produces a strong cooling during mid-July with minimum SST reaching ∼ 27. 5 • C. For both summers 2014 and 2018, surface cooling is stronger in SYMPHONIE than in analysis data but occurs over similar areas  (Figs. 4, 6). This analysis confirms that summer upwelling really occurs over BoxMK. It is captured by ALIS-TSG in situ data and JAXA high-resolution satellite data and simulated accordingly by SYMPHONIE. It is also captured by OSTIA and COPERNICUS but is strongly attenuated. These results therefore highlight the added value of a high-resolution (∼ 1 km at the coast) model to study the upwelling in the SVU area, related to its capability to simulate the fine spatiotemporal-scale structures of SST and surface dynamics better than coarser-resolution gridded satellite data.
22 in winter to -0. 36 in summer, -0. 21 over SVU) and an associated NRMSE of 0. 76 over VNC and 0. 40 over SVU. This salinity difference could be due to the coarse resolution of SMOS (28 km) but also to an overestimation of atmospheric freshwater flux in the atmospheric forcing. Spatial patterns are well reproduced, with highly significant spatial correlations between observed and simulated SSS fields (0. 84 in winter and 0. 88 in summer, p<0. 01, Fig. 2e-h ). Both simulation and data show low SSS over the Gulf of Thailand and the northwestern Gulf of Tonkin, associated with the strong river and atmospheric freshwater fluxes. In the northeastern SCS, they show high SSS round the year, due to the influence of water entering through the Luzon Strait from the western Pacific Ocean  (Qu, 2000; Zeng et al.
SYMPHONIE is therefore able to capture the variability of water masses over the winter to summer transition period, from the coastal areas to the open-sea region, without producing a significant drift after more than 8 years of simulation. IO-18 and ALIS data were collected during the period and over the region of upwelling, respectively, in 2014 and 2018. Figure  9  shows the values of observed and co-localized simulated SST and SSS along the ALIS trajectory and their bias. The high-frequency variability of SST and SSS recorded by the TSG along the ALIS trajectory during summer 2014 is very well simulated, with a 0. 89 and 0. 93 correlation, respectively, between observed and simulated time series. In particular both observed and simulated outputs clearly reveal the surface cooling (by more than 1 • C, with water colder than 28 • C) and salting (with salinity reaching 34) observed at the end of June-beginning of July 2014 in the SVU area.
Acknowledgements. This work is a part of LOTUS international joint laboratory (http://lotus. usth. edu. vn, last access:  25 July 2022 ). PhD studies of Thai To Duy were funded through an  IRD   ARTS grant  and a " Bourse d'Excellence " from the  French Embassy in Vietnam. Numerical simulations were performed using CALMIP HPC facilities (project  P13120 ) and the cluster OCCIGEN from the  CINES group  (project  DARI   A0080110098 ). The IO-18 campaign belongs to the " Study on processes of air-sea-land interactions and environmental variations in Bien Dong in the context of climate change within the framework of IOC\WESTPAC " project; code: DTDL. CN-28/17. This paper is a contribution to celebrate the 100-year anniversary of the  Institute of Oceanography, Vietnam Academy of Science and Technology.
Introduction The MetaCore database (MetaCore) provides a large size network of Protein-Protein Interactions (PPIs). It has been shown to be useful for analysis of specific biological problems (see e. g. Ekins et al. 2006; Bessarabova et al. 2012 ) and finds various medical applications as e. g. the improvement of cancer prognosis  (Winter et al. 2012). At present, the network has N = 40,079 nodes with N ℓ = 292,904 links and an average of 2n l = 2N l /N ≈ 14. 6 links per node. The nodes are composed mainly by proteins but in addition there are also other molecules and molecular clusters catalyzing the interactions with proteins. This PPI network is directed and non-weighted. Its interesting feature is the bi-functional nature of the links leading to either the activation or the.
Funding This research has been partially supported through the grant  NANOX N° ANR-17-EURE-0009  (project  MTDINA ) in the frame of the  Programme Investissements d' Avenir, France. This research has been also supported by the  Programme Investissements d' Avenir   ANR-15-IDEX-0003 ,  ISITE-BFC  ( GNETWORKS  project) and the  council of Bourgogne Franche-Comté region  ( APEX  project and  REpTILs  project).
Image Analyses To create the ground truth segmentation of MS lesions, 2 senior neuroradiologists (with 14 and 12 years of experience) manually delineated MS lesions in collaboration on the standard FLAIR sequence using 3D Slicer software (www. slicer. org). 18 Each segmentation was first performed by 1 of these 2 experienced readers and carefully and independently checked by the second, who could correct any missing lesions. We considered this segmentation the criterion standard against which the other segmentations were evaluated. To evaluate the impact of combining "image acceleration 1 dDLR" on the radiologist, the 2 neuroradiologists and another reader (a radiology resident with 5 years of experience and expertise in neuroimaging) blindly evaluated the overall image quality of the 224 FLAIR sequences.
The sequences were presented in random order to limit possible bias, especially between images processed with or without dDLR. Qualitative aspects of the sequences were assessed using a 5-point Likert-type scale: 11, 1, 0, À, ÀÀ, where 0 stands for the image quality that can be used for diagnostic purpose in clinical settings, while 1 and À stand for higher and lower quality, respectively. Then, MS lesions of the 224 FLAIR sequences were manually and section-by-section delineated by the third reader during random presentation under 3D Slicer. Automatic segmentation was also conducted independently using the lesionBrain module,  19  which is freely available on the volBrain platform  20  (https://www. volbrain. upv. es).
Dist refers to Euclidean distance. Finally, to understand whether all the MS lesions were detected, independent of the delineating precision, we computed the connected components of GS and A (lesion-wise metrics). We adopted the F1 score (F1) as follows: Se L ¼ count TP GS ð Þ count GS ð Þ P L ¼ count TP A ð Þ count A ð Þ F 1 ¼ 2 Â Se L Â P L Se L þ P L ; where TP GS is lesions of A among the lesions in GS that are correctly detected by A, TP A is-vice versa-overlapped lesions of GS among the lesions of A, Se L is lesion sensitivity, and P L is the lesion positive predictive value for individual lesions. Lesion counting and labeling were performed using the multidimensional image processing (ndimage) submodule in the sciPy library (Version 1. 7. 0, www. scipy. org). Then, the overlapping labels between GS and A were counted to compute TP GS and TP A.
Statistical Analyses All statistical analyses were performed with R software (Version 4. 1. 0; www. r-project. org). First, the interreader agreement among the 3 readers for image quality was analyzed by the Kendall Concordance Coefficient W. Then, we considered 2 factors: the type of FLAIR sequence (standard, fast, ultrafast, shortest) and the type of reconstruction (without or with dDLR). For the Likert scale, which is a categoric metric, the effects of the type of reconstruction were tested with the Wilcoxon signed-rank test. For AVD, TPR, PPV, DSC, HD, and F1 scores, we used nonparametric repeat 2-way ANOVA with the aligned rank transform procedure from the package ARTool (Version 2. 1. 0; https://depts. washington. edu/acelab/proj/art/). 22 Post hoc tests were conducted with a Wilcoxon signed-rank test, especially to evaluate the impact of dDLR.
The details of Se L and P L are shown in the Online Supplemental Data. The resulting F1 score progressively decreased with shorter FLAIR sequences, but most interesting, dDLR improved this metric, especially when used on the We used equivalence tests to understand whether 1 accelerated strategy could replace the current standard FLAIR. Fast FLAIR with dDLR showed equivalency to standard FLAIR without dDLR in regard to DSC and AVD, and ultrafast FLAIR with dDLR also showed equivalency to standard FLAIR without dDLR in regard to DSC and TPR. To evaluate whether dDLR would still be beneficial for automatic detection of MS lesions by software, we computed the same metrics after running the lesionBrain module. We observed the same profiles as those for the effects on the manual delineations even though the impact of dDLR was less pronounced (Figs 2 and 3 and Online Supplemental Data).
Similarly, generalization to other sequences and other magnets has not been tested in this single-center and single-scanner study. Third, only 1 reader manually segmented lesions, and a few segmentation errors cannot be excluded even though they should not have favored 1 type of sequence. Fourth, Standard FLAIR without dDLR is also shown for reference. The red mask represents the criterion standard that comes from the delineation of standard FLAIR by 2 expert readers; the blue mask is the manual delineation from a third reader; and the green mask is the automatic segmentation from volBrain software. After we applied dDLR, some lesions showed contours closer to the criterion standard (black arrows). dDLR also retrieved lesions that were missed on the original image (white arrows and arrowhead).
T. Yamamoto  and  C. Lacheret  are co-first authors and have contributed equally to this study. This study was supported by  Roche Laboratory  and the public grant from the  French Agence Nationale de la Recherche  within the context of the investments for the future program referenced  ANR-10-LABX-57  and named TRAIL. The work was also supported by the fondation pour l'aide à la recherche sur la sclérose en plaques foundation. The sponsors did not participate in any aspect of the design or performance of the study, including data collection, management, analysis and the interpretation or preparation, review, and approval of the manuscript.
In addition to provide MDS coordinates for visualising genomic affinities (Fig. S1 ), this matrix is also used to define the starting values for the second MCMC2 chain, which aims at estimating the ancestry profile for each individual (i. e. the second functionality of Struct-f4). Leveraging the hclust function of the R programming language, all specimens are first clustered into K groups. The frequency shift between two groups is then averaged over all the respective pairs of specimens, providing a guess for the dij parameters of MCMC2. The Q matrix is similarly initialized, so that all individuals clustering within the same group fully descend from the same ancestry component. These initial dij and Q guesses assume no gene flow and provide an excellent starting hypothesis for the parameters of MCMC2.
Citizen science, the public participation in scientific research and knowledge production, is an increasingly acknowledged approach applied in a wide range of scientific domains, and particularly within the environmental and ecological sciences. We introduce contributory citizen science as a method to scientists and practitioners in this domain focusing on the full life cycle of the citizen science practice, from design to implementation, evaluation, and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution.