dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Analysis of resequencing data in a large panel of synthetic and natural B. napus provided unprecedented insights into the patterns and consequences of genome Table  2. Brassica napus genes found in regions under selection that are involved in the DNA repair system. Genes were identified on the basis of their functional annotation with gene ontology terms using Blast2GO. restructuring as a consequence of polyploidization. We developed an analysis procedure based on short-read genomic resequencing datasets to detect structural genome variation in homeologous chromosome segments of allopolyploids compared with their diploid parental genomes. The pipeline delivers a robust strategy to detect structural genome variation in B. napus, in spite of the non-uniform chromosome coverage associated with short-read resequencing data.
Acknowledgments BS and RJS acknowledge funding from the  German Federal Ministry of Education and Research (BMBF) Plant Biotechnology Program , grant number  0315964 : " Precision Breeding for Yield Gain in Oilseed Rape (Pre-Breed Yield )". Public access to the raw sequencing data for the study was provided by the  PreBreed-Yield consortium partners German Seed Alliance GmbH, Norddeutsche Pflanzenzucht Hans-Georg Lembke KG ,  Deutsche Saatveredelung AG ,  KWS SAAT SE, Limagrain GmbH, Syngenta Seeds GmbH , and  Bayer Crop Science Raps GbR  and coordinated by  Thomas Schmutzer  and  Uwe Scholz  ( Leibniz Institute for Plant Genetics and Crop Plant Research Gatersleben, Germany ). BS and RJS conceived the study. BS developed analysis tools and analyzed the data. BC provided validation data. BS and RJS wrote the manuscript.
The ability to monitor changes in biodiversity, and their societal impact, is critical to conserving species and managing ecosystems. While emerging technologies increase the breadth and reach of data acquisition, monitoring efforts are still spatially and temporally fragmented, and taxonomically biased. Appropriate long-term information remains therefore limited. The Group on Earth Observations Biodiversity Observation Network (GEO BON) aims to provide a general framework for biodiversity monitoring to support decisionmakers. Here, we discuss the coordinated observing system adopted by GEO BON, and review challenges and advances in its implementation, focusing on two interconnected core components -the Essential Biodiversity Variables as a standard framework for biodiversity monitoring, and the Biodiversity Observation Networks that support harmonized observation systems -while highlighting their societal relevance.
Major progress in the production of EBVs is expected for variables enabled by satellite remote sensing observations  [24]. An example is the Global Forest Change project  [25]  which, building on freely available and consistently processed Landsat images, delivers decade-long time series of data which can be used to produce EBVs on ecosystem extent and fragmentation from sub-national to global scales. Further agreement and community support on a prioritized list of EBVs is important in order to encourage space agencies and the Committee on Earth Observation Satellites (CEOS) to invest into new products that fill critical gaps in monitoring biodiversity change  [26, 27]. For EBVs that rely on in situ observations, GEO BON faces challenges emerging from the lack of global monitoring schemes, the integration of datasets resulting from different collection methods, and technical issues related to data product structure, storage, workflow execution, and legal interoperability  [10 ,12].
Consequently, EBV production workflows are now being designed to provide the necessary steps from identification and aggregation of candidate datasets to the elaboration of consistent and reproducible EBVs  [28 ]. The development of suitable data standards is key in this process. The Darwin Core  [29]  has already catalysed the global sharing of species occurrence data. Its recent Event Core extension now connects related sampling events and the proposed Humbolt Core standard  [30]  extends this to capture inventory processes broadly -all aimed at capturing more relevant information for EBV production (e. g. absences, abundance). Further advances in the coordinated production of EBVs will require developing data standards and minimum information specifications that can be applied accross all EBV classes.
However, major gaps in the spatial, taxonomic, and temporal coverage continue to impose constraints on the global and regional production of Species Populations EBVs  [10 ,40]. Future directions include the implementation of workflows for data integration  [28 ,37]  and the development of models that link in situ observations to environmental covariates supporting EBV production  [39 ,41]. An on-going priority application of the Species Distribution EBV is monitoring invasive alien species from national to global scales  [42, 43]. The development of species trait EBVs has been slowed by the challenge of measuring traits repeatedly across time. Most available datasets (e. g. plants  [44] ) do not provide within species temporal variation of traits. Exceptions are repeated measurements of fish body size and plant phenology  [19] , and work is under way to integrate, standardize, and harmonize such measurements.
Ecosystem level Because of the interdependence between ecosystem composition, structure and function, and all other dimensions of biodiversity, EBVs at the ecosystem level provide a synoptic perspective of critical components of biodiversity change. Satellite information that can support monitoring of structural and functional aspects of ecosystems globally has been recently detailed  [24] , but agreement on EBVs per structure and function still needs to be reached. Adapted workflows for translating potentially usable datasets into EBVs, as recently done for species populations  [28 ] , now need to be considered for ecosystems. One suggested priority for monitoring ecosystems is developing metrics incorporating descriptions of properties such as canopy height, leaf area, biomass  [45] , as well as structural biochemical components.
On the other side, while nations collaborate to mobilize data to inform EBVs, GEO BON contributes to the national efforts by providing guidance and support for BON development and data standards. At the global scale, the Marine BON (MBON) is working in coordination with the Global Ocean Observing System (GOOS) and the Ocean Biogeographic Information System (OBIS) to develop Essential Ocean Variables  [22, 59]. The MBON facilitates the development of a common framework for the integration of marine biodiversity observations with environmental variables  [13]. The goal is to facilitate the sharing of regional observations through common data standards while offering access to the advanced geospatial analysis tools of OBIS, which would in turn support future World Ocean Assessments of the UN  [59] , or the needs of the Barcelona Convention for instance.
A process for BON development The general approach for BON development is guided by a framework that ensures the resulting system directly serves users' needs  [60] , while allowing for interoperability with other observation systems (Figure  4a ). This framework emphasises the establishment of conduits between data collection, management, analysis, and communication that are driven and validated by the users. Building the BONs around user needs further contributes to ensuring their sustainability beyond the lifespan of the funded projects that might have initiated the process of a BON development. In practice, GEO BON suggests a stepwise, iterative approach to establishing and implementing BONs, drawing upon existing processes, standards, and tools. An example of such sequenced process is divided into nine steps applied to build each component of an observing system (Figure  4b ) and involves four development phases: engagement, assessment, design, and implementation.
Policy relevance and indicators The policy relevance of GEO BON was acknowledged early on. Its establishment was recognised by the Conference of the Parties of the CBD (UNEP/CBD/COP/ DEC/IX/15), and it has been identified as a key partner of the IPBES  [2]. EBVs have also been proposed by the IPBES as an appropriate framework to determine common metrics for the biodiversity modelling, reporting, and observation communities  [20]. In practice, monitoring progress towards conservation and sustainable development targets and the effectiveness of policy decisions, will be facilitated by BONs that apply the EBV framework  [17 ,32]  (Figure  1 ). For instance, the linkages between the Intergovernmental Oceanographic Commission of UNESCO and GEO BON are based on the value chain between data collectors (GOOS), a community of practice that shares standards (MBON), and the data hosting and analysis services established by OBIS as a contribution to BON-in-a-Box.
Mainstreaming EBVs The value of EBVs to policy will be determined by the degree to which they enable the production of indicators and their incorporation into decision making to help countries meet their internal and international obligations. Since they were proposed in the 1990s, the ECVs are now widely accepted and used to structure national reporting to the UN Framework Convention on Climate Change, for global climate annual assessments, and to support the work of the Intergovernmental Panel on Climate Change  [14]. Similarly, EBVs need to be both accessible and usable by a variety of stakeholders regardless of their familiarity with their production process. To be useful, EBV datasets will need to adhere to scientific standards of peer-review, replicability and sensitivity to detect changes, as well as the inclusion of uncertainty metrics, all of which must be fully reported.
A transparent process needs to be developed for the endorsement of EBV datasets by the GEO BON community to ensure appropriate data and metadata for measuring biodiversity change. EBV data products need to be made freely available according to Open Data principles, i. e. be accessible without restrictions on use, modification and sharing  [28 ]. Moreover, EBV data products and indicators should be resourced in a way that maximizes discoverability. One such mechanism is a GEO BON Portal that enhances the accessibility of endorsed EBV datasets. This online clearinghouse will serve as the biodiversity equivalent of the Global Observing Systems Information Centre (GOSIC) for climate variables  [14] , and will feed into the Global Earth Observation System of Systems (GEOSS).
Conclusion The biodiversity crisis  [3]  calls for both the adoption of a common framework for biodiversity monitoring, and the establishment of a system of harmonised biodiversity observation systems that supports it. In ten years of existence, GEO BON, largely as a volunteer effort, designed a monitoring framework around Essential Biodiversity Variables which supports the development of biodiversity change indicators. The next decade will be critical for the development of those EBVs and will require their refinement across all levels of biodiversity, the widespread use of common data and metadata standards, and the design of workflows. GEO BON has also facilitated the establishment of several national, regional, and thematic BONs, and developed a capacity building and knowledge transfer platform to further improve the design of biodiversity observation systems.
Future advances in the development of EBVs and generation of the corresponding data are expected given the current trend in technological improvement for in situ data acquisition, better availability of satellite remote sensing data, widespread use of emerging genetic techniques and genomic libraries, and the use of models to produce spatially and temporally comprehensive EBV data products. These developments further benefit from the establishment of national and sub-national biodiversity observation systems and the involvement of endusers in the process so as to produce policy relevant indicators (Figures  1  and 2 ). Ten years from now, GEO BON envisions a wide and robust network of national and regional BONs, with multiple EBV products openly available that cover the different dimensions of biodiversity and components of ecosystem services, all of which contributing to well informed local to global assessments of the status and trends of biodiversity and its contribution to society.
Acknowledgements LMN, NF, CG, HJK, and HMP are supported by the  German Centre for integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ). GES, IG, and CG are also supported by ECOPOTENTIAL (http://www. ecopotential-project. eu), a project funded by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement no. 641762. WDK acknoweldges financial support from the  European Commission  ( GLOBIS-B  project, grant  654003 ). WJ acknowledges support by  NASA  grant  AIST-16-0092 ,  NSF  grant  DBI-1262600 , and the  Yale Centre for Biodiverstiy and Global Change. The contribution of MES is supported by the  University of Zurich Research Priority Programme on 'Global Change and Biodiversity' (URPP GCB ).
CM and GES are supported by  ODYSSEA  (http:// odysseaplatform. eu/),a project funded by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement no  727277. PV acknowledges Ministry of the Environment , the  Finnish MAES  project and The  Strategic Research Council (SRC)  at the  Academy of Finland  (grant no:  312559 ). FMK was supported in part by the  National Aeronautics and Space Administration  ( NASA grants   NNX16AQ34G  and  NNX14AP62A ), the  NOAA US Integrated Ocean Observing System/IOOS Programme Office , the  NOAA Ocean Exploration Programme , and the  NOAA National Marine Fisheries Service  through the  US National Ocean Partnership Programme. This manuscript is a contribution to the  Marine Biodiversity Observation Network. Finally , the work developed within  GEO BON  is largely supported by the volunteer dedication of its members without whom this work, and many more, would not have been possible.
Four association tests (GRM and additive, recessive and dominant CAT tests) were applied to all replicates. Results: We showed that GRM power to detect association was similar or greater than the additive CAT test, in particular in case of recessive inheritance, with up to 67% gain in power. GRM analysis of genome-wide bipolar disorder Welcome Trust Consortium data (1998 cases/3004 controls) showed significant association in the 16p12 region (rs420259; P = 3. 4E-7) which has not been identified using the additive CAT test. As expected, rs42025 fitted a non-additive (recessive) model. Conclusions: GRM provides increased power compared to the additive CAT test for association studies and is easily applicable.
Application to the WTCCC Bipolar data Sample description We obtained approval for using the raw genotype and phenotypic data for the original WTCCC bipolar disorder (BD) data set. The dataset consisted of 1998 BD cases and 3004 controls genotyped using the Affymetrix 500K array (see WTCCC 2007  [8]  for details). We applied similar quality control (QC) filtering as the original WTCCC 2007 study, i. e. 1) individual samples excluded in case of missing data across all SNPs >3% or genomewide heterozygosity greater than 30% or lower than 23%, 2) SNPs excluded in case of MAF < 5% or significant deviation from HW equilibrium in controls (P <5. 7E-7) or between the two controls groups (P <5. 7E-7). A total of 371 137 SNPs were retained for analysis.
This shows once again that GRM can not only replicate results of CAT_ADD test but also allows detecting additional SNPs. Association of BD with the rs420259 SNP, as found here using GRM test, has been initially reported by the Welcome Trust Consortium by applying the genotypic test  [8] , which represents a general modeling framework as GRM and genotypic tests has similar power. Interestingly, association of the same SNP with BD was also reported by applying either the MAX test  [12]  or a score-based nonparametric test  [13]  to the same WTCCC case-control BD data. Moreover, a meta-analysis (including WTCCC, STEP-BD, Iceland and Scandinavia samples; n = 5547 BD cases and 20241 controls)  [14]  suggested association between rs420259 and BD (P =1. 2E-5). However, such association was not further reported by GWAS in extended datasets (  [15] , see Craddock and Sklar for review  [16] ), which were based on the CAT_ADD test.
Acknowledgements This study makes use of data generated by the  Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www. wtccc. org. uk. We thank  Cécile Julier  from  U958  for critical review and for helpful discussions. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources.
DFT simulations used the Quantum Espresso 46 (QE) suite of plane-wave codes for total energy and band structure calculations and for postprocessing to obtain electronic wavefunctions projected onto atomic bases. XCrysden  47  was used for real and reciprocal space visualisation, including the generation of Figure  1. Fully relativistic pseudopotentials and projector augmented wave (PAW) datasets were generated using QE and PSLibrary  48  for both PBESOL (generalized gradient approximation, GGA) and PZ (local density approximation, LDA) exchange-correlation functionals; the valence of Re was taken as 15 (5s 2 5p 6 5d 5 6s 2 ). Atomic coordinates were taken from Lamfers et al. 8  and were relaxed to obtain forces less than 10 -3 eV Å -1. Kinetic energy cutoffs were typically 60 Ry (816 eV) and Monkhorst-Pack 49 k-point meshes of 6×6×6 were used; meshes up to 10×10×10 produced no significant changes in the band structures obtained.
Acknowledgements Work was supported by the  Engineering and Physical Sciences Research Council of the UK  under grant  EP/P004830/1  and LSH received a studentship under grant  EP/L014544/1. JLW was supported under grant  EP/M022188. ARPES measurements were provided at the ANTARES beamline of the SOLEIL synchrotron under project  20151237. Computational work was performed on the  University of Bath's High Performance Computing Facility.
Bioinformatic Processing Sequence processing was performed in  Mothur (v. 1. 34. 4 )  (Schloss et al. , 2009)  as described in the Mothur wiki 1 , with some minor modifications  (Bell et al. , 2015). Briefly, for both soil and root 16S rRNA gene sequences, the ". sff " files of the different samples were first merged in one ". sff " file, then ". qual, " and ". fasta" files were obtained from the ". sff " file using "merge. files" and "sffinfo" commands, respectively. Low quality and short sequences were removed using the "trim. seqs" command, with the following parameters: maxambig = 0, maxhomop = 8, bdiffs = 1, pdiffs = 2, qwindowaverage = 30, qwindowsize = 50, minlength = 300 and then, we reduced the dataset to only unique sequences using the "unique. seqs" command. After sequence alignments against the Mothur interpreted Silva bacterial database using "align.
seqs, " the non-aligned sequences were removed using the "screen. seqs" command with the following criteria: start = 1044, optimize = end, criteria = 95. Prior to sequence classifications, the datasets were first subjected to a second simplification using the "unique. seq" command, followed by the commands "pre. cluster" and "chimera. uchime" to reduce the sequencing errors. Sequence classifications were carried out with the Mothur implementation of the RDP database using the "classify. seqs" command. Sequences identified as "Mitochondria, " "Chloroplast, " "Archaea, " "Eukaryota, " or "unknown" were removed using the "remove. lineage" command. Distance matrices were generated with "dist. seqs" command and OTUs were obtained using the "cluster. split" command (method = average, processors = 2, splitmethod = classify, large = T).
Removal of the singleton reads was carried out using the "split. abund" command and then, to have an equal number of reads per sample, datasets were standardized by a 1 http://www. mothur. org/ random sub-sampling using the "sub. sample" command. OTU tables at 97% similarity were generated following the steps for "create. database. " Overall, the steps for soil and root ITS datasets processing were similar to those for 16S rRNA gene datasets processing. Most of the modifications were introduced owing to the absence of reference database for ITS sequence alignments. After elimination of low quality and short sequences by the "trim. seqs" command (maxambig = 0, maxhomop = 8, bdiffs = 1, pdiffs = 2, qwindowaverage = 30, qwindowsize = 50, minlength = 250), we ran "chop. seqs" and "chimera. uchime" commands to standardize sequence length (numbases = 249) and reduce the sequencing errors, respectively.
Then, the ". fasta" files were clustered into OTUs at 97% identity using the CD-HIT software  (Li and Godzik, 2006)  and reformatted to ". list" file in order to continue Mothur processing. The remaining steps of the ITS datasets processing were the same as 16S rRNA gene datasets processing, except sequence classification was performed using the UNITE_97_v6 reference database, and the "remove. lineage" command was not used in ITS datasets processing.
Statistical Analysis All statistical analyses were performed in R (version 3. 1. 1). To compare the microbial diversity and OTU richness between soil and root datasets, Student's t-tests or Wilcoxon tests (depending on the normality of the residuals) were carried out on Shannon diversity indices and the Chao richness estimator using the "Rcmdr" package. Student's t-tests were performed on the fungal Chao values (normally distributed), while Wilcoxon tests were performed on the bacterial Chao and Shannon values, and on the fungal Shannon values (non-normally distributed residuals). Student's t-tests or Wilcoxon tests were also performed to compare the most abundant fungal and bacterial classes relative abundances between soil and roots. To verify the efficiency of our sampling efforts and sequencing depth, rarefaction curves were drawn for each individual sample using the "rarefy" function in the "vegan" package.
Depending on the normality of the residuals, the effect of contamination levels and plant species identity on Shannon diversity indices of fungi and bacteria were tested by ANOVA or Kruskal-Wallis tests using the "Rcmdr" package in R (ANOVA tests were carried out on soil fungi, root fungi and soil bacteria data, while Kruskal-Wallis tests were performed on root bacteria data. To test the effect of contamination levels, plant species and habitats (soil, roots, and AMF spores) on the bacterial and fungal community structures, PERMANOVA analyses were performed using the "adonis" function in the "vegan" package on Bray-Curtis values obtained from the community structure matrices previously normalized by the Hellinger transformation  (Legendre and Legendre, 2012). For the PERMANOVA carried out across habitats, we did two PERMANOVA tests.
The first was performed to compare between the microbial (bacterial and fungal) communities present in soil and roots, while the second was performed to compare between the microbial communities present in the three habitats (soil, roots, and AMF spores). As the AMF spore-associated fungi dataset did not contain OTU matching with Glomeromycota, all the taxa matching with Glomeromycota in soil and root matrices were removed, prior to the PERMANOVA test preformed to compare the fungal communities between the three habitats. Since the microbial datasets of soil, roots and AMF spores were obtained from different sequencing pools with different sequencing depth, the matrices were first summed at the genera level and then expressed as percentages, prior to the merging of matrices, rarefaction of data and PERMANOVA analyses.