dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We greatly thank  Crystal Schaaf  and  Qingsong Sun  from  University of Massachusetts Boston  for providing us MCD43A V006 data before the official release. Data from in situ mass balance surveys and on glacier automatic weather stations come from joint projects of the National Power Company and the  Glaciology group of the Institute of Earth Science, University of Iceland. The staff members at the  Institute of Earth Sciences, University of Iceland  were funded by the  the Icelandic Research Fund  (project  SAMAR   140920-053 ) and the  National Power Company of Iceland. Simon Gascoin  and  Etienne Berthier  are supported by the  CNES  through the  TOSCA program. We thank the three anonymous reviewers and the editor of this special issue  Frank Paul  for their constructive comments during the peer-review process.
Extisting biodiversity databases contain an abundance of information. To turn such information into knowledge, it is necessary to address several information-model issues. Biodiversity data are collected for various scientific objectives, often even without clear preliminary objectives, may follow different taxonomy standards and organization logic, and be held in multiple file formats and utilising a variety of database technologies. This paper presents a graph catalogue model for the metadata management of biodiversity databases. It explores the possible operation of data mining and visualization to guide the analysis of heterogeneous biodiversity data. In particular, we would propose contributions to the problems of (1) the analysis of heterogeneous distributed data found across different databases, (2) the identification of matches and approximations between data sets, and (3) the identificaton of relationships between various databases.
INTRODUCTION Accurate and publicly available information on biodiversity observations can contribute to scientific knowledge, foster multidisciplinary studies, and provide new perspectives to environmental and societal responses including decision-making  (Lausch et al. , 2015). To this end, several biodiversity metadata projects have been established which describe and characterize the information hosted in a range of distributed databases  (David et al. , 2016; Dodge et al. , 2013). As these metadata projects grow horizontally, with more databases and types of data sets, as well as vertically, with more documents, the ecologist will need information management tools to enable the following common tasks: 1. To discover existing but heterogeneous, dispersed data sets of different origins and scales of observation; 2.
To discover relationships between documents of potential interest to the scientist; 3. To interpret the semantic meaning of relations without the need to know the meta-model; 4. To enable the scientist to understand the data context and collection methods of multiple fields and topics; 5. To determine the quality associated with the data, including the data sets inter-calibration; and 6. To be aware of the conditions of access and use. This proof of concept presents a case study of the ECOSCOPE metadata catalogue  (Taffoureau et al. , 2016; Eco, )  which provides data mining and visualization capabilities. ECOSCOPE is a metadata collection service for databases of different fields of ecology in lato sensu. We follow a behaviour-driven development (BDD)  (Solis and Wang, 2011)  of a minimal operational set and the assessment of the ecologist at each operation.
RELATED WORK AND MOTIVATION The consortium IndexMed (renamed recently "Index-Meed -Indexing for Mining Ecological and Environmental Data" to build international projects) was created by the axis " Management of biodiversity and natural spaces " of the IMBE (Mediterranean Institute of freshwater and marine Biodiversity and Ecology)  (David et al. , 2015). Its main goal is to develop awareness of databases and their effective use in the ecological research community. This consortium is particularly useful as a bridge between existing networks and initiatives at national and international levels. The aim of the consortium is to index biodiversity data (and to provide an index of qualified existing open datasets) and to make it possible to build graphs to assist in the analysis and the development of new ways to mine data.
ECOSCOPE is an infrastructure funded and managed by French research organisations through the Foundation for Research on Biodiversity (FRB) which ensures its coordination. The scientific aim is to document the state and trends of biodiversity and ecosystem services, enabling scenarios for the future to be built. In this framework, ECOSCOPE promotes the complementarity of observations and links between research observation systems that vary across spatial and temporal scales, variables, studied ecosystems and kingdoms, levels of organization and data sources. In cooperation with existing initiatives, ECOSCOPE provides an entry point for the discovery of observations and datasets for research on biodiversity across the entire data life cycle, facilitating links between data producers and users.
Note -for deletion on final version: Scholes does not refer to ECOSCOPE. hence it cannot be used in this manner as a reference. You could say "These aims are consistent with  Scholes et al. (2012). " The ECOSCOPE metadata catalogue delivers freely available online information about who, where, what, when, why and how the research observation data were collected. It is build on the EBV concept, developed by GEO BON (Group on Earth Observation Biodiversity Observation Network), which is designed to serve as the foundation for interoperable sub-national, national, regional and global monitoring initiatives. Precise and public information on biodiversity observation datasets contribute to data openess and reuse, in full conformity with data producers and owners. Metadata formats the description, characterisation and specification of data hosted in datasets, allowing the discovery of data, whether heterogeneous or dispersed and across locational and observational scales.
Metadata permits the understanding of the context of the dataset, collection methods and data quality. It gives information on access and use of data and other resource conditions and the contact persons  (Michener 2006). Michener W. M. (  2006 ) Meta-information concepts for ecological data management. Ecological Informatics 1:3-7 The ECOSCOPE metadata catalogue answers to this need thanks to providers and exchanges with other information systems. As it is based on standards in use, the metadata profile can be exported into other information systems, and metadata files (such as Ecological Metadata Language: EML) can be imported into the ECOSCOPE metadata portal. It contributes to global efforts to make research on biodiversity data more available for scientific projects, synthesis and indicators.
In this context of a prototype for collecting ecological metadata of various fields and topics, our motivation is to explore the possibilities of graph techniques for visualization and data mining supported in graph databases. The graph databases have been a proven feasible backend to provide semantic services  (Riesen and Bunke, 2008; Angles and Gutierrez, 2008). Furthermore, the indexing capabilities of graph databases ensures the scalability of the system response  (Williams et al. , 2007) , which is a critical factor in our needs to increase various databases, data sets, and document integration. In a graph catalogue the database mapping model is isomorphic with the represented structure. The resulting model enables the evolution of applications with linear complexity in the data mining operation, which is critical for scale-up in data volume and variety.
The overall architecture of our vision is shown in Figure  1. There are increasing number of database managers adopting a metadata standarization process, using the ECOSCOPE portal to deliver a meaningful metadata description into the ECOSCOPE database. Other external sources are used to complement related information about curation and publication, like GBIF  (Flemons et al. , 2007). GBIF attempts to bring together all biodiversity and collections data to make them available to researchers and the general public. To do this, the GBIF provides a search engine for databases connected to GBIF in a standardized way. Data owners can connect all or part of their resources to GBIF to make them visible and interoperable, but they keep the control of their data, which they continue to host and use in their work.
A SEMANTIC METADATA SERVICE WITH GRAPH CATALOGUE To prove the concept, we have dumped the current ECOSCOPE document database into a graph database and we test the ecologist operations. Scientists produce knowledge by analysing data into information and the goal is to elaborate theories from information. Data constitute the primary material from which hypotheses are first formulated, then refined and validated. Metadata permit the data openness and data sharing, as the way to give value to data after their primary use  (McNutt et al. , 2016).
Operation: Show Graph for Spatial and Temporal Relations Given the complete graph nodes and their direct relations can be categorized as follows: • Data set core information: Dataset→Description; Person→Address Assets: Here a clear segmentation of the graph in 5 categories is obtained (Figure  2 ). This general spatial temporal graph shows two types of node segments. On one hand, some segments are irrelevant because a single data set (in blue) is related to its core information: the three segments in the bottom right. In the other hand, node segments with several data sets are related with temporal spatial information. For example, the segment on the top shows all the data sets (nodes in blue) are related to a single geographical area (node in yellow), even when they have been tagged with different geographical names in the database (nodes in pink).
Operation: Common 6 Given the metadata catalogue, When the ecologist wants to be aware of the conditions of access and use of data sets and more documents; Then provide access policy to sets and objects Weakness: In our case study the metadata source only provides a secondary way to obtain the data, by giving the contact person and web information for a data set. So the ecologist can manually manage their access to the data, and there is no automation in this behaviour. This is a critical point to overcome the current collection scope by tools and methods to enable the access policy to the existing database objects.
SERVICE ARCHITECTURE These tests have demonstrated the feasibility of graph techniques to provide semantic features in visualization and data mining of ecological metadata. However, to facilitate the ecologist's discovery and visualization, it also is necessary to provide high level applications alongside the existing graph database. The weakness analysis on the common expected operations, points to the need for more generic operations and systematic approaches, adapted to the characteristic multidisciplinary database of the ecologist. The required behaviour on several of the common operations needs the inclusion of the metadata of the documents, not only as generic information of the data set. • Common 2 and Common 4 need the integration metadata of the documents in the catalogue. • Common 6 is a critical operation to provide automated access policies to the documents.
For these reasons the present paper proposes a model-view-controller (MVC) service architecture  (Deacon, 2009)  as show in Figure  8  Figure  8 : Proposed MVC Service Architecture. • View-Controller -Web Frontend/Backend a common web framework for all the specific webs of the multidisciplinary studies, including basic frontend forms, backend handlers and driver connectivity to the common API. It supports the identity management and it is the user entry point to the data. • Model -Semantic Data API Including all the high level methods for visualization, data mining and the gateway for data access policies to various storages. -Graph Metadata Catalogue With the existing meta model for data sets, but also including the metadata of the documents, as well as the access policies between identities and documents.
CONCLUSIONS This paper presents preliminary studies on metadata semantics. Indeed, it demonstrates improved metadata qualification needs using tools, standards and recommendations at both national (SINP [National Information System on Biodiversity], RBDD [Network of Research Databases]) and international levels (Me-dOBIS [Mediterranean Ocean Biogeographic Information System], OBIS, GBIF  (Cryer et al. , 2009)  The proof of concept demonstrates the pontential of graph databases to enable metadata visualization and common operations in a scalable way through the graph database capabilities in the horizontal relations. Furthermore, it has identified the commonalities for a high level semantic data API, into a service architecture for several specific web front-ends, contributing to economies of scale in the development and exploitation of the information system.
The promising results encourage future work following the proposed service architecture, to facilitate ecological studies in heterogeneous fields and topics with their increasingly complex requirements. In transdisciplinary Global Ecology, Economy and Sociology data-Kernel)", CNRS INEE through the "CHARLIEE" project in 2015 and CNRS "Mission pour l'Interdisciplinarité" in 2016. Data used for this article were obtained through ECOSCOPE metadata tools. The authors acknowledge the support of France Grilles for providing computing resources on the French National Grid Infrastructure. Supplementary acknowledgement to organisers of the EGI workshop "design your e-infrastructure" which started this work.
r Assets: Displays the hold graph of the metadata catalogue without any previous knowledge of the meta-model. It can be a good starting point to get the number of nodes (300) and relations (1137). The nodes are: Address(5), Attribute(48), Dataset(30), Description(17), GPolygonOu-terRing(14), GeographicCoverage(19), Keyword(79), Person(8), TaxonomicClassification(45), Taxonomic-Coverage(31), TemporalCoverage(4).
Figure3shows a clear segmentation of data sets (nodes in blue) by the organization logic metadata of Keyword (in red), TaxonomicClassification (in green) and TaxonomicCoverage (in pink), but with all the nodes connected, which is of high interest to enable multidisciplinary relationship discovery. Our results show some metadata fields which are relationhubs between data sets of different databases, particularly a few generalist TaxonomicClassification values and Keywords.
)<-[]-(d)-[r]->(keyword) WHERE keyword. word = "AGROVOC" AND NOT n:Attribute AND NOT n:Keyword AND NOT n:TaxonomicClassification AND NOT n:TaxonomicCoverage RETURN n,d,r,keyword Assets: In Figure 4 a zoom view of all the data sets related to the hub metadata. In red the hub node (Keyword=AGROVOC). The cluster on the top is a segment of DIMPIE data sets, of the same database, with a temporal coverage in green (1975) and to the left there is a blue data set (Collection moisissures IFV), whith temporal coverage of 2008 in green. So both databases and datasets are related by the hub node.
ACKNOWLEDGEMENTS The authors would like to thanks  Alison Specht , director of CESAB (FRB) and to  Robin Goffaux  from  FRB  for they advisory and support to this paper. This work is co-funded by the  EGI-Engage  project ( Horizon 2020 ) under Grant number  654142  and by the  Spanish MICINN  project number  TIN2014-53234-C2-1-R. IndexMed consortium is funded by the  CNRS défi "VIGI-GEEK (VIsualisation of Graph.
Among these associations, those of rs710446 and five low-frequency variant sets in KNG1 with FXI level variation were significant after multiple testing correction and permutation. Also, two putative pathogenic mutations related to high and low FXI levels were identified by data filtering and in silico predictions. This study of KNG1 and F11 loci should help to understand the connection between genotypic variation and variation in FXI levels. The functional genetic variants should be useful as markers of thromboembolic risk.
Library preparation, sequencing and data analysis The sequencing libraries were prepared from PCR pools using the Nextera XT DNA Sample Preparation kit (Illumina, San Diego, CA, USA) with double indexing, according to the manufacturer's protocol. Paired-end sequencing was used to improve the mapping quality  [24]. We obtained 110 paired-end libraries that were pooled and simultaneously run on an Illumina Miseq sequencing system (Illumina) by the Miseq sequencing reagent kit v2 of 300 cycles (2x150 bp paired end) (Illumina). Indexed sequences were de-multiplexed and analyzed individually. Paired sequence files in fastq format were analysed with CLC Genomic Workbench version 6. 5 software (CLC Bio-Qiagen, Aarhus, Denmark). The raw data were trimmed with length (minimum 25 bp; maximum 500 bp), ambiguous nucleotide (maximum 2) and quality score (0.
05) filters. This software aligns the trimmed reads against a human genome reference (hg19). The read mapping was performed with specific parameter setting (mismatch count, 2; indel count, 3; length fraction, 0. 7; similarity fraction, 0. 9). Parameters were used for quality-based variant detection (minimum coverage, 30x; minimum variant frequency, 25%). Results in variant call format (VCF) file were used as input for the Illumina VariantStudio Data Analysis version 2. 1 Software (Illumina) to annotate the genetic variants. The amino acid numbering and the nomenclature used to describe the sequence variations followed the international recommendations of the Human Genome Variation Society (HGVS; http://www. HGVS. org).
Association analysis for common and low-frequency variants We treated our data (FXI levels) as normally distributed over the 110 samples. The normality of the distribution was tested using the Shapiro-Wilk normality test (W = 0. 97623; p-value = 0. 052) First, MAFs in the 110 individuals were calculated using the PLINK package version 1. 07  [25]. For this association analysis, the common variants were defined as genetic variants with MAF !10%. Thus, low-frequency variants were defined as genetic variants with MAF <10%  [26]. Then, LD based variant pruning was applied for each variant group (common and low-frequency) using the PLINK package to find informative variants. For this, we used the variance inflation factor (VIF) criterion to check for multi-collinearity of variants and recursively remove variants within a sliding window.
Sanger sequencing validations Putative pathogenic mutations were validated by conventional Sanger sequencing. Briefly, enzymatic purification was performed using ExoSAP-IT treatment (USB Corporation, Cleveland, OH, USA) and Sanger sequencing of both strands was carried out using the BigDye Terminator version 3. 1 Cycle Sequencing Kit (Applied Biosystems, Thermo Fisher Scientific Inc. , MA, USA). The ABI Prism 3130 Genetic Analyzer (Applied Biosystems, Thermo Fisher Scientific Inc. ) was used for capillary electrophoresis. The sequences were mapped against KNG1 (GRCh37/hg19 chr3:186,435,098-186,460,678; NM_001102416. 2) and F11 (GRCh37/hg19 chr4:187,187,118-187,210,835; NM_000128. 3) loci using CLC Genomic Workbench version 6. 5 software. We performed also a co-segregation analysis between validated putative pathogenic mutations and plasma FXI levels in available family members by Sanger sequencing.
In Silico prediction analysis Functional effects of putative pathogenic mutations that co-segregated with plasma FXI levels were evaluated using the in silico prediction software Alamut Visual version 2. 6. 1 (Interactive Biosoftware, Rouen, France). Potential splicing alterations in intron variants included SpliceSi-teFinder, MaxEntScan, NNSplice, GeneSplicer and Human Splicing Finder algorithms. The threshold employed was a variation between the native and the mutation score of more than 10% in at least two different algorithms  [30]. Pathogenic impact of the missense variants was analysed using the programs SIFT, PolyPhen-2, Align GVGD and Mutation Taster. Conservation phyloP scores were obtained also. Interpretation of predictive structural effects of the missense mutations was evaluated by using the Project HOPE software  [31].
We identified 504 genetic variants in the KNG1 locus and 258 genetic variants in the F11 locus. The percentage of indels in the KNG1 locus was 7. 9% (n = 40) and 8. 1% (n = 21) in the F11 locus. The percentage of exonic variants in the KNG1 locus was 2. 2% (n = 11) and 6. 2% (n = 16) in the F11 locus. We found 30. 2% (n = 152) of the variants in the KNG1 locus that had an allele frequency >1% from all populations of 1000 Genomes data (April 2012 version 3) and from four populations of 1000 Genomes (American, East Asian, African and European). The 20. 5% (n = 53) of the variants in the F11 locus had an allele frequency >1% from all populations of 1000 Genomes data (April 2012 version 3) and from four populations of 1000 Genomes (American, East Asian, African and European). Moreover, the 57.
The relatively small number of patients included in our replication study limits the confidence of our conclusions and larger studies need to be performed. Interestingly, the in silico prediction of the missense variant NM_000128. 3: c. 943G>A in the F11 locus was not conclusive. According to previously published association data  [40] , this mutation in heterozygous state is associated with low plasma FXI levels. Thus, it is possible that the in silico predictions could be underestimating its pathogenicity. We think it is important to emphasize that the 2 putative pathogenic mutations that we detected were located within 2 low-frequency variant sets (chr3:186,446,561-186,450,528 in KNG1 and chr4:187,200,524-187,204,276 in F11) that were nominally significantly associated with plasma FXI levels.
Interestingly, the intronic variation NM_001102416. 2: c. 758-12T>C in KNG1 was one of the variants included in the low-frequency variant sets that were significantly associated with plasma FXI levels after permutation testing. Most of the genetic variants associated with plasma FXI levels that we have described would not be detected by examining only the coding regions of the genes. Therefore, it is clear that the non-coding regions can contribute to the regulation of complex phenotypes. We applied in silico tools to clarify this issue and provide evidence that support the pathogenic role. However, because prediction programs have serious limitations, further analyses are needed to determine the functional characteristics of these putative mutations. In conclusion, the continuous DNA sequence data reported in our study using the targeted gene NGS strategy represent one of the largest bodies of sequence data on individuals for the KNG1 and F11 loci.
The special set of papers entitled "DNA Library of Life" constitutes an outcome of the project "Bibliothèque du vivant" (BdV), which aims to promote the molecular taxonomy of eukaryotes by offering research teams the possibility to produce and manage a molecular library linked with specimens deposited in natural history museums. The project was funded by three French institutions (the CNRS, INRA and MNHN), and provided access to the sequencing power offered by the Genoscope for 105 teams between 2011 and 2013. It was subsequently supported by the CNRS through the "Groupement de Recherche Génomique Environnementale". The scientific objectives of this programme were threefold: 1) species delimitation among species complexes; 2) phylogenetic reconstruction (including phylogenomics); and 3) metabarcoding and improving NGS methods for systematic purposes.