dataesr/scientific-paragraphs-categorization huggingface dataset:
https://robots. thoughtbot. com/postgresql-performance-considerations, January 2011. [8] H. Hultgren. Data vault modelling guide -Introductory guide to data vault modelling. Genesee Academy. https://hanshultgren. files. wordpress. com/2012/09/ data-vault-modeling-guide. pdf, 2012. [9] B. Inmon. Data Lake Architecture: Designing the Data Lake and avoiding the garbage dump. Technics Publications, 2016. [10] T. John and P. Misra. Data Lake for Enterprises: Lambda Architecture for building enterprise data systems. Packt Publishing, May 2017. [11] V.
The work environment has deeply evolved in recent decades with the generalisation of IT in terms of hardware, online resources and software. Librarians do not escape this movement and their working environment is becoming essentially digital (databases, online publications, Wikis, specialised software, etc. ). With the Big Data era, new tools will be available, implementing artificial intelligence, text mining, machine learning, etc. Most of these technologies already exist but they will become widespread and strongly impact our ways of working. The development of social networks that are "business" oriented will also have an increasing influence. In this context, it is interesting to reflect on how the work environment of librarians will evolve. Maintaining interest in the daily work is fundamental and over-automation is not desirable.
Many experiments are carried out all over the World; VRE4EIC, EVER-EST (H2020) in Europe, Science gateways in the USA, and Virtual Laboratories in Australia, etc. ). A VRE is not only a technical solution for a better working environment but also a way to collaborate, share, preserve, etc. The Research Data Alliance is working in this framework through an Interest Group which aim to bring people from several communities together to exchange knowledge (technical problems to solve, interoperability between VREs, common efforts to enable, etc. ). A VRE for librarians / documentalists should be experimented with. EPJ Web of Conferences 186, 09004 (2018) https://doi. org/10. 1051/epjconf/201818609004 LISA VIII 5.
Acknowledgments A preliminary version of this paper was appeared in the proceeding of the  Twenty-Ninth AAAI Conference on Arti cial Intelligence  ( AAAI-15 ) (Fujita et al. , 2015). is work was partially supported by  JSPS KAKENHI  Grant Number  JP24220003 ,  JP26730005 ,  JP17H00761 ,  JP17H04695 ,  JSPS.
III. Data We use farm-level data from the French Farm Accountancy Data Network (FADN) database during the period 2002-2015. We acknowledge that longer periods are always better for TFP analysis, but unfortunately we cannot cover a longer period. The database used is the official French FADN database and it is usable from 2002 onwards. The FADN database, managed by the French Ministry of Agriculture, includes yearly accountancy data (along with some technical and economic information) for around 7,000 commercial farms 4  in France. As the annual rotating rate of the FADN sample is about 10%, we have an unbalanced panel data sample for the analysed 2002-2015 period. Within the FADN, the data are collected using a stratification based on the region where the farms are located, their economic size and their type of farming.
We resorted to visual descriptions of farms' characteristics to detect outliers and aberrant data. We then conducted output super-efficiency estimations  (Andersen and Petersen 1993)  to detect further global extreme observations. Infeasibility issues appeared in the super-efficiency estimation when assuming VRS, and in these cases we used the correction procedure described in  Lee, Chu, and Zhu (2011). With this super-efficiency estimation, we discarded about 1% of the observations in each type of farming. 7 The final database consists of 58,365 farm-year observations over 14 years which is equivalent to about 4,160 farms per year. This represents 60% of the total FADN sample, keeping in mind that we exclude from our analysis some types of farming which cannot be included in our meta-frontier analysis since they do not use much land (pig and poultry breeding, horticulture, permanent crops).
Acknowledgments The authors acknowledge financial support from the  Centre for Studies and Strategic Foresight (CEP)  in the  French Ministry of Agriculture  in the frame of research project  COMPANI ('Competitiveness of the livestock sectors in France '). Remote access to the the French FADN data provided by the  CEP  and used in this work, is supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  'Investissements d'Avenir' program  (reference:  ANR-10-EQPX-17-Centre  d'accès sécurisé aux données-CASD).
Motivation: Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. Results: We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine.
Introduction The European Translational Information and Knowledge Management Services (eTRIKS, https://www. etriks. org/, 2017) is tasked with providing tools and services to support data management and analysis for >60 diverse biomedical research projects which have been funded by the Innovative Medicines Initiative (IMI). As Europe's largest public-private partnership, IMI funds projects ranging from molecular and systems biology to clinical trials and full translational research projects. The community translational research system under use is tranSMART  (Athey et al. , 2013; Dunn et al. , 2017) , first developed by the pharma industry, and then gifted to a global translational research development community. The tranSMART system has undergone extensive development extended by its own community and the eTRIKS project, which has focused on an implementation that serves IMI projects users based in the European Union (EU).
Additionally, data standards play a pivotal role in tackling the omnipresent problem of reproducibility. Begley et al. reproduced  53 experiments from landmark publications to find 47 out of 53 could not be replicated; a very worrying trend for preclinical studies that are used as the scientific basis for target identification for new drug development  (Begley and Ellis, 2012). The Data FAIRport initiative in 2014 prescribed a set of guiding principles known as FAIR: Findable, Accessible, Interoperable, Reusable which should be applied where data is deemed scientifically valuable  (Wilkinson et al. , 2016). Those principles have gained official recognition from G20, NIH and the Directorate General for Research and Innovation of the European Commission. The consistent application of common semantics and data structures, as outlined within data standards, is a key factor to ensure interoperability and reusability of data.
The eTRIKS Data Standards Work Package created a Standards Starter Pack (https://doi. org/10. 5281/zenodo. 50398/ , 2016), which outlines the FAIR principles and recommendations for the main clinical and genomic standards as well as supporting vocabularies and minimum information guidelines that should be applied in the entire translational research landscape. eTRIKS has also produced the IMI Data catalogue which centralizes metadata of ongoing and past IMI projects. It is part of the service that eTRIKS provides in its key knowledge management performance with a focus on the findability of project level study description metadata. Furthermore, this well received initiative facilitates broader sharing and accessibility of data (http://datacatalog. elixir-luxembourg. org/ckan/, 2017). For clinical research data, The Clinical Data Interchange Standards Consortium (CDISC, https://www.
cdisc. org/, 2018) data standards have been implemented in over 90 countries, and are now mandated by Food and Drug Administration of the United States (FDA, 2014) and Pharmaceuticals and Medical Devices Agenda (PMDA) in Japan (https://www. pmda. go. jp/files/000206449. pdf, 2018) in order to increase the uptake of data standards, which, when applied, contribute to higher data quality. The lack of implementing standards will render datasets from different cohorts inadequate when integrating with complementary research data for meta-analyses  (Elefsinioti et al. , 2016). A recent paper by the American College of Medical Genetics and Genomics  (Acmg, 2017)  discussed the importance of using the information from one patient cohort to benefit other patients. The ACMG's framework for data sharing will work best if standards are implemented within the framework, as within tranSMART, and datasets are gathered by utilizing those standards from the beginning of the research, as is also recommended by CDISC.
Implementation The eTRIKS Standard Master Tree is based on the standards for clinical data representation developed by CDISC, mainly the Study Data Tabulation Model (SDTM) standard. The proposal of eTRIKS was to create a hierarchical navigation tree in which the raw data, collected at the multiple cohorts, should be promptly mapped to the elements of this tree so that data are loaded automatically with the correct topology into tranSMART i2b2 (Informatics for Integrating Biology and the Bedside) framework. The requirement for this is that all the data collected from a patient will be organized and formatted using the SDTM model. SDTM modeling increases the ability to compare information among systems and/or organizations, whilst also decreasing the time to initiate a new research study. The use of these data standards improves the data quality, their interoperability and their management, which allows easier, faster and more reliable data aggregation.
The eTRIKS Standard Master Tree presents the clinical data within tranSMART i2b2. eTRIKS has radically updated the original tranSMART engine that sorts and presents the clinical data within the system. Users can choose to map their clinical data content to a favorite terminology prior to the SDTM modelling using global standards such as OMICS, NCI (https://www. cancer. gov/digitalstandards, 2017) or LOINC (https://loinc. org/, 2017), as long as the SDTM variable names as maintained. Further, the clinical data is mapped to a 'clinical mapping file', which requires a good working knowledge of the CDISC foundational standards, in order to represent the SDTM structure of the clinical data correctly in the hierarchy of the tranSMART i2b2 repository  (Abend et al. , 2009). In practice if one thinks about the outcome of a 'glucose test', this test may be named 'sugar test' or 'glucose test' in different differing cohorts, which may be well understood by experts but not a machine as the same concept.
The use of standard name 'Glucose Tolerance Test' (NCBI's, MeSH Unique ID: D005951) would avoid any confusion or wrong interpretation and enable data query across cohorts. Further to this, considering that the metabolite 'glucose' could be measured in different samples (e. g. blood, urine), the test results could be reported in different units (mg/dl or mmol/l) and/or the test could be performed at different periods of the time (screening, visit 1, visit 2, etc. ), error prone aspects during the data analysis. If the problem is proposed, 'How to standardize the manner by which this information should be organized and formatted for effective and precise cohorts comparisons?' One answer should be: 'Use a Standard Master Ontology Tree' or in this case, the eTRIKS Standard Master Tree. The application of this tool coupled with a good application of controlled vocabularies will increase greatly the Reusability and Interoperability Principles mentioned above.
g. mmol/l), respectively. The SDTM Implementation Guide provides a comprehensive description including four sessions: 1-Overview of topics for specific general observation class associated with specific domains, 2-Specification for table of variables, 3-Rules for correct implementation of standards and 4-Examples. To avoid the all too common pitfalls of redundant data eTRIKS developed the Standard Master Tree, using the comprehensive SDTM domain structure, to support and give structure and context to the data so it can be easily identified. The Standard Master Tree follows a basic and easy-to-understand logic, which was built upon the premise of tranSMART rules for data loading. This means that multiple data collected for one patient for the same domain (e. g. Laboratory Test Results-LB) should be distinguished based on Data Labels.
Features The eTRIKS Standard Master Tree model consists of a package of three main components: (i) a CDISC clinical dataset reported as define. xml metadata and converted in. txt tabulate files composing of 16 SDTM domains represented as review data as collected for 4 fictitious subjects (Fig. 1A ); (ii) the tranSMART standard master ontology tree as TM SMOT-SDTM_lite. txt definition file, where all the information concerned about the correct positioning of the SDTM variables can be found (Fig. 1B ); and the (iii) Mapper script where users can map their data files to the TM SMOT-SDTM_Lite definition, avoiding manual work. The script reads a target directory containing the input SDTM files and maps all the collected variables against the SMOT-SDMT_Lite. txt master tree file mentioned above.
etriks. org/transmart/ datasetExplorer under the eTRIKS-Master Tree branch. The strategy for clinical research data standards representation proposed above offers a readily available method to integrate multiple translational research datasets while meeting the Interoperability and Reusability aspects of the FAIR principles. Once the data is within the eTRIKS Standard Master Tree, it can then take advantage of the tranSMART environment, where it will receive a unique study and server specific identifier and the metadata can be given greater and essential specificity. With an effective tranSMART search tool where multiple datasets and/or studies can be pooled and queried, coupled with an entry within the eTRIKS data catalogue the data has undergone FAIR-ification to a satisfactory degree. Now the data is Findable and also Accessible, and it can begin its hopefully long life adding scientific value to any number of future studies or aggregated data comparisons.
Conclusion The tranSMART Standard Master Tree presented here adds to other efforts to make other software data interoperable with tranSMART. Projects such as 'ODM to i2b2' converts data stored in XML/ODM based systems such as OpenClinica and REDCap into i2b2 format (https://github. com/CTMM-TraIT/trait_odm_to_i2b2, 2018); and 'REDCap2SDTM' converts electronic data capture system data to SDTM  (Yamamoto et al. , 2017). Taken together, this software could benefit from the Master Tree concept in order to standardize the manner that SDTM studies should appear within a tranSMART navigation tree to users. If the tools and processes above are adopted in the scope of the NIH funded projects, it will contribute greatly to creating an overseas bridge for data sharing initiatives with the EU/EFPIA-funded (IMI) translational medicine research projects, of which over 60 are being supported by the eTRIKS project.
While not all of the eTRIKS supported projects have implemented the tranSMART Standards Master Tree they have all received the appropriate guidance and advice from eTRIKS experts or as laid out in the eTRIKS standards starter pack. Tremendous curation efforts were necessary to guarantee that IMI data was collected in good quality, once that, frequently, the big challenge for translational research projects lies on the quality of the data itself, not only its metadata. The adoption of the technologies and standards developed and presented in this paper will support a significant step towards a position where IMI data can be shared, and the findings reproduced to benefit the health care research community, allowing a standardized representation of SDTM data across multiple tranSMART servers.
For more than 30 years, data warehouses (DWs) have attracted particular interest both in practice and in research. This success is explained by their ability to adapt to their evolving environment. One of the last challenges for DWs is their ability to open their frontiers to external data sources in addition to internal sources. The development of linked open data (LOD) as external sources is an excellent opportunity to create added value and enrich the analytical capabilities of DWs. However, the incorporation of LOD in the DW must be accompanied by careful management. In this paper, we are interested in managing the evolution of DW systems integrating internal and external LOD datasets. The particularity of LOD is that they contribute to evolving the DW at several levels: (i) source level, (ii) DW schema level, and (iii) DW design-cycle constructs.
In this context, we have to ensure this co-evolution, as conventional evolution approaches are adapted neither to this new kind of source nor to semantic constructs underlying LOD sources. One way of tackling this co-evolution issue is to ensure the traceability of DW constructs for the whole design cycle. Our approach is tested using: the LUBM (Lehigh University BenchMark), different LOD datasets (DBepedia, YAGO, etc. ), and Oracle 12c database management system (DBMS) used for the DW deployment.
Introduction Endowing data with semantics is a crucial task for many applications. Motivated by the emergence of ontologies and linked open data (LOD) in different fields (e. g. , Cyc, DBpedia, Freebase, and YAGO), the amount of semantic data is rapidly increasing in various domains (e. g. , the New York Times, BBC, and Thomson Reuters semantic data) and their involvement in different data centric systems is growing. Business intelligence (BI) systems are no exception. Data warehouses (DWs) are data management systems at the core of BI applications, used in small, medium, and large organizations for decision support. DWs integrate operational data coming from heterogeneous sources, and organize data using a multidimensional perception that identifies central subjects of analysis (called the Facts) according to different dimensions of analysis, to be exploited by OLAP (on-line analytical processing) techniques and tools.
The DW community has undergone an important event materialized by the spectacular development of Semantic Web technologies that are intensively used in designing and exploring DWs (for more details, refer to the survey paper  [1] ). Many studies have used ontologies (an ontology is defined as a specification of a conceptualization)  [2]  in order to facilitate DW design. Unlike the traditional vision of DW construction which concentrates on internal sources, the new generation of DWs integrates both internal and external sources. Among external sources, recent research efforts have identified the interest of integrating the LOD in designing DW. As part of the Big Data landscape, LOD initiatives bring new and publicly available semantic data on the web that are valuable for data analytics inside DW systems.
The value of LOD for DWs manifests in their capabilities to answer requirements that are unsatisfied from internal sources, and to enrich the multidimensional expressiveness of the DW model. LOD keep adding value to DWs, since they are continuously changing (new concepts and knowledge). For instance, Yago has evolved from Yago to Yago3, passing by Yago2. Therefore, the added-value of DW has to be managed continuously through an evolution process. Traditional evolution approaches for DW manage the evolution of internal sources (usually conventional relational databases). The use of external semantic data poses new challenges related to their evolution management: (i) LOD sources bring a new kind of heterogeneity since they are presented using the semantic formalism, based on the Resource Description Framework (RDF) (https://www.
w3. org/RDF/) language using the triple format to organize data in the form of < subject, predicate, object >). The set of triples form a graph. (ii) The evolution events of LOD sources (e. g. , addition, deletion, renaming, etc. ) are gathered from the web using the triple format, which requires specific management tasks to identify the impact of such evolution events on the DW system. (iii) Finally, different scenarios have been proposed in order to integrate LOD sources in the DW that can be done at the schema level or at the query level. Contrary to traditional DWs that manage evolution either from a source perspective or from a requirements perspective, ignoring the interrelated artifacts composing the DW design cycle, LOD can be integrated at different steps of the DW cycle, possibly impacting different artifacts.
Motivating Example We illustrate our purpose by the following example: Example 1. Let us consider a DW used for analysing the research publications of a university. The number of publications is analysed according to each department and according to the type of publication (conference or journal). Information related to publications are collected from internal sources related to the university researchers, and the characteristics of publications (impact factor, type, editor, etc. ) are collected from LOD like Thomson Reuters datasets. Inspired by DBpedia (one of the most popular LOD portals), we assume that LOD portals provide the set of changes periodically in the form of N-Triples files containing added, modified, and deleted triples. Identifying the impact of these changes when dealing with large amounts of triples is a particularly difficult and error-prone task if it is not automated.
An ontological view is required for identifying the impact of these changes: (1) at the conceptual level (i. e. , which concepts are concerned by these changes and are they involved in any ETL process feeding the DW?); (2) at the requirements level (i. e. , which requirements are concerned by these changes and are they still satisfied after applying the changes?); and (3) at the physical level (i. e. , which ontological instances in the database management system (DBMS) are concerned with the changes?). Note that the reasoning skills of the ontology can also be used for inferring new traces of the impact of events evolution. This paper is organized as follows: Section 2 overviews the related work related to DW design (from internal and external sources) and DW evolution. Section 3 gives the background and essential concepts for the comprehension of our approach.
This schema can be represented according to a specific conceptual design model. The ETL phase includes three steps: the extraction of data from sources, their transformation, and their loading into the target DWO schema using mapping assertions defined between target and source schemas. Mapping assertions are defined using ETL operators (e. g. , union, aggregate, convert, etc. ), forming an ETL workflow. We consider ETL processes that define each concept of the DWO according to the concepts of sources (Global as View approach). For example, the concept University is fed using the ETL process formed by the Union of concept University from Source S1 and concept School from Source 2 (University DWO ≡ Union(University S1 ,School S2 )). The deployment design allows the implementation of the DW according to the storage model of the database management system (DBMS) chosen.