dataesr/scientific-paragraphs-categorization huggingface dataset:
21 , top-right). As Table  1  shows, we gain an order of accuracy via the HiMod reduction. A comparison in terms of CPU time is not reasonable in such a case since the HiMod code is not yet optimized. By resorting to a modal adaptivity and for a comparable number of dof, we obtain a HiMod approximation more accurate with respect to the uniform one (compare the contour plots in Fig. 21 , top-right and bottomleft and the values in Table  1 ) with a similar CPU time (in s). The modal distribution yielded by the adaptive procedure is shown in Fig. 22 , left. A number of modes less than 2 All the experiments have been performed using Matlab 2011a 64-bit on a Lenovo ThinkPad T430 equipped with a Intel Core i5 3320M 2x 2. 6-3. 3 GHz processor and 4 GB of RAM. 17 is demanded on the whole domain except for the last three nodes.
Acknowledgements The authors thank  Giovanni Porta  for the advices on the validation test case. Moreover, the first author gratefully acknowledges the  NSF  project  DMS 1419060 "Hierarchical Model Reduction Techniques for Incompressible Fluid-Dynamics and Fluid-Structure Interaction Problems " ( P. I. Alessandro Veneziani ) and the  MIUR-PRIN 2010/2011  project " Innovative Methods for Water Resources under Hydro-Climatic Uncertainty Scenarios " for the financial support. The second author is partially supported by the  ERC  Advanced Grant 2013 No. 321186 , " ReaDi, Reaction-Diffusion Equations, Propagation and Modeling " and by the project  ERC  Advanced Grant 2013 No. 339958 , " Complex Patterns for Strongly Interacting Dynamical Systems, COMPAT ".
The network, which is run from a central node at the University of Cape Town, consists of more than 30 nodes across 15 African countries (see http://www. h3abionet. org/home/ consortium for complete list) (Fig. 1 ) with one partner in the United States (USA) and one in the United Kingdom (UK). The institutions range in their current capacity from full nodes with a track record in bioinformatics research, training, and support; through associate nodes with some bioinformatics activities; to development nodes with little or no bioinformatics capacity. Altogether, the network funds more than 40 staff and students and includes more than 80 additional members who contribute to H3ABioNet activities. The nodes collectively provide excellent expertise in different areas of bioinformatics including functional genomics, human population genetics, GWAS and NGS analysis, microbiome analysis, SNP linked protein structure analysis, and biomedical and clinical data storage and management.
The mandate of H3ABioNet (http://www. h3abionet. org/) is to develop and roll out a coordinated bioinformatics research infrastructure that is tightly coupled to a sophisticated pan-African bioinformatics training program. In modern genome sequence analysis, analytical tools are moving to where the high dimensional data are stored, so the expertise for genome data manipulation and analysis in Africa are being developed at the source of these data. The network seeks to exploit the development and implementation of best practices in genome bioinformatics in local centers while keeping an eye on the rapidly evolving field through collaboration with centers of expertise in the United States (Harvard University and University of Illinois) and elsewhere. H3ABioNet faces a number of high priority challenges that need to be overcome to enable genomics research and competitiveness on the continent.
These include, but are not limited to, poor internet connectivity for communication, data access, transfer and remote computing; lack of significant computing infrastructure for data storage and processing; lack of bioinformatics skills in clinical genetics and genomics teams performing genomics research; and disparate pockets of bioinformatics expertise across the continent. Some of the major objectives of H3ABioNet are thus to develop human resources through the training of bioinformaticians and researchers in computational techniques and to develop a robust continent-wide research infrastructure that provides access to bioinformatics tools, computing resources, and technical and data management expertise. The network activities are being achieved through dedicated working groups and task forces comprising representatives from multiple countries.
Building computing infrastructure Analysis and management of large data sets requires computing resources and internet bandwidth, both of which can be limiting in Africa. An infrastructure working group developed a set of specifications required for different types of genomic data analyses, and nodes purchased the equipment relevant to their needs. A total of 15 servers, 512 cores, 2384 GB RAM, and 120 TB storage have been placed across the continent in the last two years. Systems administrators at these nodes were trained on how to install and manage their equipment, as well as in high-performance and cloud computing. A systems administrator task force was set up to provide ongoing support to ensure that the equipment is used effectively. This task force is also available, through requests to our help desk, to address the information technology needs of development and associate nodes as well as H3Africa project sites with little expertise.
Internet connections in Africa are often slow and unreliable. However, there is a clear need for research groups across the continent to be able to share data sets larger than those that can be attached to a standard e-mail. The Globus Online service, based on the GridFTP data transfer protocol, provides a transparent, reliable, secure, and fault-tolerant method for transferring large data sets over the internet, regardless of the bandwidth and availability of the path between sender and recipient  (Foster 2011 ). H3ABioNet has installed and tested Globus Online endpoints at a gradually increasing number of nodes, with the goal to reach all of them by early 2016. In parallel, it has developed tools based on the iPerf toolbox (http://iperf. sourceforge. net/) to monitor the performance of network connections and is installing these at the same time.
Tool development and user support H3ABioNet is working on addressing specific training and development needs of the H3Africa Consortium for all aspects of projects, from patient databases to laboratory information management system (LIMS) interoperability to genomic data processing, analysis and interpretation. As an example, the National Institute of Hygiene node is setting up a LIMS application in the National Institute of Hygiene of Morocco. This project, cofinanced by the European Union is taking advantage of the training received by the members of the node through H3ABioNet. Some consortium-wide needs being addressed include mapping of biospecimen, phenotype and experimental data, to ontologies and development of a query interface for these data to provide a harmonized catalog of the consortium products that will be available in biorepositories and public databases.
It is also developing a tool for tracking patient recruitment from different sites across projects and an H3Africa data archive to facilitate submission to the European Genome-phenome Archive (EGA), ensuring compliance with the consortium data sharing, access, and release policy. H3ABioNet has facilitated transfer of large data sets from collaborating sites abroad using Globus Online and provided support for trivial and complex data analysis queries posed to the help desk. In order for H3ABioNet nodes to effectively contribute to the analysis of high-throughput data generated by H3Africa projects, it is essential that they first demonstrate that they have mastered the requisite analytical and computational methods for this analysis. An ad hoc task force was created to develop standard operating procedures (SOPs) for analysis of data that will be produced by H3Africa projects, i.
Human capacity development A key aim of H3ABioNet is to enable African scientists to analyze their own data. This is being achieved through a well-coordinated training and support program. The training program for bioinformatics users (usually biologists and clinicians) includes short specialized courses and internships in the nodes (Tastan  Bishop et al. 2015). This approach is also being used to train bioinformaticians, with a stronger focus on the technical aspects of the topics as well as on computational biology, statistics, and computer science. Where internet is limited, we make use of eBioKits (http://www. ebiokit. eu/), which are stand-alone devices for bioinformatics data analysis, and we also try to ensure the participants have access to comparable tools and resources when they return home.
Some nodes with a better internet connectivity work with Galaxy (https://galaxyproject. org/), which is a web-based analysis platform, where researchers can perform, reproduce, and share data and complete workflows. H3ABioNet has developed training-related resources to aid with the development and organization of new courses and is actively following up with trainees to determine the impact of courses through a trainer-trainee database. In order to reach as wide an audience as possible, we have used the Vidyo system (www. vidyo. com) to live stream courses to distant classrooms and record lectures to make them available to others in the network. To enhance and sustain scientific cooperation between trainees within H3ABioNet and H3Africa, we launched a bioinformatics webinar series involving H3ABioNet staff that is open to the whole H3Africa Consortium.
In parallel with focused training, the network is currently developing curricula for degree programs at African institutions and has established an African Bioinformatics Education Committee to achieve this. In this way, we are addressing the very source of the problem of insufficient trained human capacity in bioinformatics. The first Masters program in bioinformatics in Mali started in early 2015, using the curriculum provided by this committee. Although it reaches a smaller audience, the H3ABioNet internship program has also been successful at enabling skills transfer and analysis of existing data sets. For example, sickle cell disease GWAS data sets generated from prior projects that were not yet analyzed due to lack of skills were analyzed during an internship at an H3ABioNet node. The H3ABioNet training program is closely aligned with that of the larger H3Africa Consortium to ensure coordination of training activities and that the bioinformatics training needs of the consortium are met.
Research Although research activities were not prioritized by the NIH for the bioinformatics network, some research has been necessary, such as development of new algorithms, visualization tools, and workflows relevant to genomic analysis of African populations. Specific examples include the development of a job management system (JMS), a structural analysis pipeline for nonsynonymous coding variants, an improved recombination tool for human sequences, more robust admixture analysis tools for multiway admixed populations, and new databases integrating genotype and phenotype data. The JMS  (Brown et al. 2015)  has been developed for research groups of the H3Africa Consortium for two distinct purposes: managing and monitoring their servers and using and developing tools and workflows. Currently, a number of H3ABioNet members are working to put pipelines and workflows together with the aim of making them public via JMS.
One recently developed pipeline, for ligand docking and molecular dynamics calculations, has already been applied to identify potential hits for malaria  (Musyoka et al. 2015). In addition to the development of new algorithms or pipelines, H3ABioNet is using research projects as a capacity building exercise, promoting inter-node collaborations in which groups with complementary areas of expertise can collectively work on a data set and facilitate cross-pollination of skills. A number of collaborative research projects have been established between participating nodes, which we hope will lead to more efficient addressing of health issues in a resource-poor environment, increased research output from the continent, and promotion of skills retention to ensure long-term sustainability of the network and of genomic medicine research in Africa.
One example is a collaboration between CERMES in Niger and the H3ABioNet node at the University of Illinois, working on genetic polymorphism data of Plasmodium falciparum populations infecting patients in different parts of Niger. Their analysis has revealed extensive polymorphisms in the sizes of the parasite's msp1 and msp2 genes, indicating the presence of different strains in different parts of the country as well as the fact that most patients had been multiply infected (O Ouwe Missi Oukem, pers. comm. ). The H3ABioNet Node at the National Biotechnology Development Agency (NABDA), Abuja, Nigeria, is conducting research on reference genome sequences of the Human Microbiome Project  (Isokpehi et al. 2014). The primary goal is to understand metabolic pathways encoded in microbial genomes that could influence human health.
Additionally, in collaboration with the Institute for Genomic Biology at the University of Illinois, metagenomics data have been generated on leg ulcer specimens from individuals with sickle cell disease in Ibadan, Nigeria. The H3ABioNet NABDA node is coordinating the sickle cell leg ulcer study, in which dedicated researchers are analyzing the data sets for biological significance and developing models and workflows for integrating and evaluating data sets (including human genome variants, microbial community diversity, and socioeconomic determinants) to better understand genetic and environmental predispositions to leg ulcers in sickle cell disease. Another research project was initiated by students attending an H3ABioNet course at Covenant University, Ota, Nigeria. They started a project to study the genetic components underlying uterine fibroid disease among different African populations.
Conclusions As mentioned previously, the high priority problem H3ABioNet seeks to address is the lack of infrastructure and skills for management and analysis of genomic data. The H3Africa Consortium is preparing to deliver genomic data for more than 50,000 samples, and these data should be analyzed in Africa. The key strategy of H3ABioNet for building bioinformatics capacity for genomics in Africa is pooling of resources and exploiting the skills present to develop the weaker nodes and provide a high-quality training and support platform. This coordinated approach and support provided through mailing lists, working groups, and task forces is a theme that runs through all the H3ABioNet activities to ensure that they are correctly implemented. The network will also contribute to H3Africa and other genomics projects through its legacy of developing and providing access to computing infrastructure.
It is responding to the needs of the projects by developing new and supporting existing algorithms, workflows, data management systems, and data integration platforms. A new project on the horizon, which will include contributions from multiple nodes, is the planned development of a resource for mining African variants. It is clear that the H3Africa researchers favor a local resource for their data in addition to the required submission to public repositories. The establishment of H3ABioNet comes at a time when there has been considerable investment in information communication and technology (ICT) by many African countries. These countries have ensured that the national internet infrastructure is accessible at a subsidized rate to universities and research institutions. Furthermore, the rapid growth of mobile telephony as well as the use of information management systems is changing the way health and biomedical research is being conducted in Africa.
Although government support for fields such as bioinformatics is increasing with the increased visibility through projects such as H3ABioNet, most often they do not have funds available to commit. However, with the management and analysis of big data in the biomedical sciences becoming more crucial to research, nodes need to lobby with their governments to encourage investment in developing essential skills in data science for the future. Having said that, involvement in H3ABioNet has had a financial impact on some nodes already, where it has led to cofunding or other institutional support, in a clear demonstration of the positive knock-on effect of the network. H3ABioNet is also building strong informal scientific networks across Africa, which is a very important factor in research capacity development.
Acknowledgments H3ABioNet is supported by the  National Institutes of Health Common Fund (National Human Genome Research Institute)  under grant number  U41HG006941. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank all the  Consortium members  for their contributions to H3ABioNet activities. A full list of H3ABioNet Consortium members can be found at http://www. h3abionet. org/home/consortium.
The international GEOTRACES program is emphasizing the use of the GEOTRACES reference samples program and the importance of cross-over or baseline stations where all aspects of sample collection, filtration, processing and analytical methods can be compared. These intercomparison efforts are of crucial importance to merge or link data from different origins together. The occupation of the Bermuda Atlantic Time Series (BATS) station (31°45. 92'N, 64°04. 95'W) by the Netherlands GEOTRACES (GA02; 13 June 2010) as well as US GEOTRACES (GA03; 19-21 November 2011) Atlantic sections expeditions provided an intercomparison opportunity to confirm the compatibility of the 2 different sampling systems as well as different analytical techniques used. In order to compare the data from different sampling systems or analytical techniques, a new statistical approach was developed to include the analytical uncertainty.
Furthermore, modifications to an existing multi-element technique  (Biller and Bruland, 2012)  were made that increased the number of elements analysed and that allows the time consuming extractions to be done shipboard. Overall, we show excellent agreement between data generated by different sampling systems and analytical techniques. However, while both sampling systems are capable of collecting uncontaminated samples for all the GEOTRACES key elements, some apparent outliers are present. The intercomparison between the different analytical techniques also reveals subtle differences that would have gone unnoticed if only reference samples would have been used, underlining the importance and power of intercomparison stations. The results show crossover station data can be used to assess consistency between datasets if these stations have been carefully planned and analysed in combination with (internal) seawater reference samples to assure intra-dataset consistency.
Introduction The GEOTRACES program is an international study which aims to improve the understanding of the global marine biogeochemical cycles and large-scale distribution of trace elements and their isotopes. As GEOTRACES is an international effort with scientists from approximately 30 nations participating, the emerging data sets are produced using differing sampling systems and analytical methods. These datasets are merged in the Intermediate Data Product (The GEOTRACES group, in press). To assure consistency between datasets, the international program is emphasising the concurrent analysis of reference samples from the GEOTRACES reference sample program for concentrations of 9 key dissolved trace elements, as well as the planning of cross-over or baseline stations where all aspects of sample collection, filtration, processing and analytical methods can be compared.
g. Biller and Bruland, 2012; Lee et al. , 2011; Milne et al. , 2010; Sohrin et al. , 2008)  using chelating resins for off-line extraction, with subsequent detection with a high-resolution, magnetic sector, inductively coupled plasma mass spectrometer (ICP-MS). Most of the data presented in this paper has been analysed using a modified version of the  Biller and Bruland (2012)  method that will be described here. With these sampling and processing systems, datasets of unprecedented resolution are being produced that are linked together in a global database  (The GEOTRACES group, in press ). Therefore, we must be confident the sampling systems as well as different analytical techniques obtain comparable and uncontaminated samples. The occupation of the Bermuda Atlantic Time Series (BATS) station by the Netherlands GEOTRACES during a meridional section of the western Atlantic in June 2010 (GA02) and the US GEOTRACES during a zonal section of the North Atlantic in November 2011 (GA03) provided an opportunity to collect dissolved trace metal samples from two occupations of the same 'cross-over' station with different sampling systems, which were analysed for a suite of trace metals by the same analyst with the same analytical method.
The Lu and In signals were determined in both medium and low resolution, and were applied appropriately to metals measured in medium resolution ( 47 Ti, 55 Mn, 56 Fe, 59 Co, 60 Ni, 63 Cu, 66 Zn, 69 Ga) or low resolution ( 89 Y, 111 Cd, 139 La and 208 Pb). The Rh spike to the elution acid served to quickly highlight any aspiration problems or drifts in sensitivity on the ICP-MS for the original gravimetrical method. The data presented here was determined by the gravimetrical method based upon the sensitivity of the standard additions to the seawater. The Lu-In element dilution method was tested versus the gravimetrical method (see Modified Multi-element standard addition ICP-MS Method).
Comparing profiles with analytical uncertainty To compare datasets, traditionally paired two-tailed t-tests or 95% confidence intervals (CI's; expressed as 2 x standard deviation) have been used as well as the root-mean-square (RMS) deviations of the reported values from the mean values  (Bowie et al. , 2003; Landing et al. , 1995). The t-tests have the disadvantage that analytical uncertainty is ignored, for example  Bowie et al. (2003)  reported significant differences (at the 0. 05 significance level) between different analytical techniques based on paired two-tailed t-tests, whereas 95% CI's where often overlapping. In addition, a t-test assumes a normal distribution of the data which is rarely the case for an oceanographic profile. Indeed, the distribution of the data is generally skewed towards higher or lower concentrations in the shape of the vertical profile of the chemical species.
Same sample approach To compare analytical results from the same samples (or sub-samples thereof) first a standard paired two-tailed t-test (α=0. 05) was done as described by  Bowie et al. (2003). As an example, in case of an oceanographic profile of 24 samples analysed by two different analytical techniques in triplicate, the test compares the 24 analytical result pairs. If this does not result in a statistical difference, the results of the two methods are not significantly different regardless of analytical uncertainty. As mentioned above, this paired two-tailed ttest assumes a normal distribution of the data. This constitutes a potential caveat for this approach. A statistical program such as Sigmaplot 13. 0 as used in this paper, will test for normality of the data when performing a t-test and, if assumptions are violated, an alternative non parametric test such as a Wilcoxon signed-rank test can be performed instead.