dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Anthony Brown  ( Ohio State University ) for suggesting the use of Utr-CH,  William Bement  ( University of Wisconsin ) for technical advice, and  Ivonnie Shih  ( University of California, San Diego ) for help with phalloidin staining experiments. We also acknowledge the  University of California, San Diego Neuroscience Microscopy Shared Facility Grant  ( P30 NS047101 ) for confocal FRAP imaging. This work was supported by a grant from the  National Institute of Neurological Disorders and Stroke/National Institutes of Health  ( R01NS075233 ) to  S. Roy. The authors declare no competing financial interests.
EBI metagenomics (https://www. ebi. ac. uk/ metagenomics/) is a freely available hub for the analysis and archiving of metagenomic and metatranscriptomic data. Over the last 2 years, the resource has undergone rapid growth, with an increase of over five-fold in the number of processed samples and consequently represents one of the largest resources of analysed shotgun metagenomes. Here, we report the status of the resource in 2016 and give an overview of new developments. In particular, we describe updates to data content, a complete overhaul of the analysis pipeline, streamlining of data presentation via the website and the development of a new web based tool to compare functional analyses of sequence runs within a study. We also highlight two of the higher profile projects that have been analysed using the resource in the last year: the oceanographic projects Ocean Sampling Day and Tara Oceans.
As sequencing costs continue to diminish, the breadth of metagenomic research increases. The approach has now been successfully applied to a wide range of research areas, including agriculture  (7, 8) , bioenergy production  (9) (10) (11) , bioremediation  (12) , and animal and human health  (13) (14) (15) (16). Whilst the take-up of metagenomics is both wide-scale and burgeoning, the analysis of metagenomic sequence data can be particularly challenging and is an increasing bottleneck. One significant problem is the sheer volume of sequencing data generated. For example, a typical Illumina HISEQ 2000 paired-end run, with 150 million forward and reverse 150 nt-long reads, can produce over 50 Gb of data in D596 Nucleic Acids Research, 2016, Vol. 44, Database issue FASTQ format. Traditional sequence analysis approaches, such as BLAST, are unable to scale with such data volumes (for example, a BLAST search for this size of sequencing run, analysed against the UniProtKB (17) database, would require in the region of 15 billion pairwise sequence comparisons).
Substantial compute resources are required, as are specialist analysis algorithms and approaches that are both fast and sensitive, as the majority of organisms present in a metagenomics dataset are not found in typical reference databases. Furthermore, if such data is to have longevity and be reproducible, it must be archived in publicly accessible repositories and described with detailed and accurate contextual data. EBI Metagenomics (EMG) has been developed as a freeto-use, large-scale analysis platform for metagenomic sequence data. The resource is capable of processing WGS sequenced metagenomic and metatranscriptomic reads, 16S rRNA amplicon data and user-submitted sequence assemblies. Regardless of the data source (metagenomic, metatranscriptomic, amplicon or assembly), EMG provides a standardized analysis workflow, capable of producing rich taxonomic diversity and functional annotations.
As a result, analyses can be compared both within and across projects at a broad level, and across different data types (e. g. metagenomic versus metatranscriptomic). Rather than develop an entirely new repository for metagenomic data, EMG has partnered with the European Nucleotide Archive (ENA)  (18)  to provide a permanent metagenomics data archive and data sharing/publication service. Leveraging the ENA's existing infrastructure and interfaces for data submission ensures that datasets are described with standards-compliant contextual data, and are made available to the scientific community for data mining purposes and for meta-analysis. In this article, we present the current status of EMG and its data content. We describe recent improvements to the resource, including a revamp of the analysis pipeline that has improved our ability to analyse large-scale projects.
DATA CONTENT Analysed data in EMG are now structured into projects, samples and runs. The addition of the run level ensures that EMG mirrors the data object organization in ENA, providing greater consistency between the two resources and a natural relationship between sequence data and the corresponding analysis. In this arrangement, one project contains one or more samples, and each sample can have one or more experiments associated with it (e. g. metagenomic and metatranscriptomic), which can be from individual runs or pooled runs from a sequencing machine. At the time of writing (September 2015), EMG contains 6705 samples from 132 public and 83 private projects. This represents a growth of over four-fold in the number of projects and five-fold in the number of samples since the first release in 2013  (19).
Previously, the resource focused mainly on the analysis of submitted WGS metagenomic data. It thus neglected much of the 16S rRNA amplicon data, which has entered ENA directly or via its INSDC partners. As 16S rRNA data analysis is increasingly used as a diagnostic tool in the human host-associated setting (and often as a precursor to WGS metagenomic analysis) this stance has been reviewed. EMG now provides 16S rRNA amplicon data analysis (even in the absence of WGS metagenomic data--a previous requirement) and existing datasets in ENA can be prioritized for analysis on request. EMG currently contains analyses for 4037 metagenomes, 2975 16S rRNA amplicon datasets, 389 metatranscriptomes and 67 assemblies. Almost 100 billion nucleotide sequences have now been processed, yielding over 50 billion predicted protein coding sequences (pCDS), ∼18 billion of which have been annotated with functional information.
ANALYSIS PIPELINE UPDATE A new version of the analysis pipeline (v2. 0) (Figure  1 ) was released in March 2015. Since the release of EMG in early 2010 and prior to this update, there had been no major changes to the reference data libraries used by the pipeline--most notably InterPro  (20) , which was 19 releases behind the most recently available version. Similarly, some of the software algorithms were outdated, with improved versions available. The tools and libraries updated in pipeline v2. 0 are summarized in Table  1. The newer version of rRNASelector uses a more up-todate version of HMMER (3. 1b compared to 3. 0), which is substantially faster (∼10-fold). Some of the member databases within InterPro also use HMMER and benefit similarly from an increase in data processing speed following the pipeline update.
For example, when processing moderately-sized runs (10 Gb Illumina sequencing data, comprising ∼20 million sequences), the clustering step typically took over 12 CPU hours, yet <1% of sequences were merged. An added advantage of removing the sequence clustering step is that pipeline v2. 0 is able to directly provide abundance counts. The pipeline code (wrapping each algorithm) was also substantially refactored as part of the update process to improve performance (e. g. better horizontal scaling), stability (e. g. better error handling) and throughput (e. g. checkpointing and recovery). Furthermore, the development work focused on pipeline modularization, ensuring that both component upgrades and extensions to the pipeline can be more readily achieved in the future. Overall, analysis throughput using the new pipeline is ∼15× faster than with v1.
0. Illustrating this increased throughput, almost 3000 runs (comprising over 34 billion nucleotide sequences, ∼ 1  3 of the content of EMG), have been analysed since the deployment of v2. 0 of the pipeline 6 months ago. Results for projects analysed with both v1. 0 and v2. 0 of the pipeline are able to co-exist within EMG and, where applicable, data files for both versions are presented on the website. The pipeline version used for analysis is clearly labelled on the project and sample web pages. Users of EMG can request a project be re-analysed with v2. 0. Rather than re-analysing all projects, our current objective is to use the increased capacity to focus on adding some of the absent public metagenomic datasets that have entered ENA via other routes, while maintaining the analysis of new projects being submitted to EMG.
LARGE-SCALE DATA ANALYSIS PROJECTS Amongst the projects analysed this past year have been two large-scale oceanographic datasets: Ocean Sampling Day (OSD) and Tara Oceans. OSD is a simultaneous sampling for marine micro-organisms in a global network across all continents  (21). Tara Oceans  (22) (23) (24) (25) (26)  is a project to map the biodiversity of a wide range of planktonic organisms and their interactions with the surrounding environment. While these projects share a common biome, they have been independently run and analysed by their respective research consortia. However, both projects are united under the EMG, having been archived with ENA (study accessions PRJEB5129 and PRJEB402), described with contextual data compliant to the M2B3 data standard  (27)  and analysed using v2. 0 of the EMG pipeline.
The use of a consistent description and analysis approach allows results from the two projects to be compared, identifying common and contrasting functional and taxonomic assignments. They may also be compared to pre-existing marine metagenomes, such as the Global Ocean Sampling Expedition datasets (EMG study accession SRP003580). EMG provided analysis for the metagenomic component of Ocean Sampling Day data collected in 2014 (EMG study accession ERP009703) and the Global Ocean microbiome subset of the Tara Oceans project, which contains metagenomic sequences, size fractionated for prokaryotes (ERP001736). Whilst roughly equivalent in sample size (150 and 135 samples, respectively), the two projects differ substantially in the amount of sequencing data, with the subset of OSD analysed comprising ∼120 Gb in total data size (∼220 million sequences) and the Tara Oceans subset representing ∼10 Tb of sequence data (∼29 billion sequences).
WEBSITE IMPROVEMENTS EMG now represents a major (and growing) wealth of taxonomic and functional data pertaining to a broad collection of metagenomics datasets from a myriad of different biomes. As a consequence, EMG no longer a site devoted to data analysis, but is increasingly a place where users come to access existing datasets and to compare their data to flagship projects. To accommodate this additional scope, the EMG website has been extended to increase the discoverability of these data and to facilitate further analysis.
Discovery by biome EMG contains data from a wide range of different environmental biomes. A 'search by biome' feature has been developed, to aid data exploration and allow identification of projects from the same environmental category (Figure  3 ). The source biome for projects is now indicated by a specific icon on the web pages. The biome data is manually curated in EMG, using the classification scheme developed by the Genomes OnLine Database (https://gold. jgi. doe. gov/) (28). The biome classification is arranged as a hierarchy--for example, the soil biome can be subclassified into grassland or forest soil.
Analysis results comparisons EMG has also deployed a web tool that allows the direct comparison of Gene Ontology (GO) (  29 ) terms (summarized using a GO slim developed for metagenomic data (http://www. geneontology. org/ontology/subsets/ goslim metagenomics. obo)) that have been assigned to runs within a project. The tool (which is accessed by clicking on the 'Comparison tool' tab on the main web page) presents users with a list of projects with data suitable for comparison (i. e. amplicon datasets are excluded). Selecting a project brings up a set of runs from within the project, which can be compared using the tool. The comparison results are displayed as bar charts, stacked columns, heatmaps and principle component analyses (examples of three of these views are shown in Figure  4 ). The visualizations can be exported in PDF, PNG and SVG formats.
Summary files In order to provide users with a summary of analysis results for a particular study, we now aggregate the functional and taxonomic annotations across all runs for each project and make these summary files available (in TSV format) via the EMG website. These files can be found on the project pages under the 'Analysis summary' tab (Figure  5 ). The study summary data allows users to explore datasets at a high level of detail, providing a first step for in-depth metagenome exploration.
EXISTING PUBLICLY AVAILABLE METAGENOMICS DATASETS As mentioned above, there are many publicly available metagenomics datasets that are yet to be analysed by the EMG: we recently surveyed the status of ENA and revealed that there are over 900 metagenomics projects that have been deposited in the ENA since 2006, yet do not appear in EMG. With the increased capacity of the EMG pipeline, we D600 Nucleic Acids Research, 2016, Vol. 44, Database issue  have started the process of analysing some of these datasets. Nevertheless, one of the limiting factors in promoting these projects for EMG analysis is often the low quality of contextual information provided by the submitter. To investigate whether this situation could be improved by involving the scientific research community, metagenomic projects were selected as use cases for a 'Sample Record Annotation Workshop', organized jointly between the ENA and EMG teams, and external scientists.
This intensive annotation jamboree, held in December 2014, aimed at enriching the contextual information of selected metagenomic samples, thereby bringing them up to EMG's standard for processing. Over 1900 samples from 13 publicly available metagenomic projects were annotated as part of the workshop. Primary sequence data from almost 1700 samples from eight of these projects passed the EMG read data QC, and their metagenomic analysis has been published on the website so that results can be discovered, downloaded and compared.
DISCUSSION The data content of EMG has expanded considerably over the last 2 years, with 4148 new WGS metagenomic datasets and pipeline developments helping to ensure the resource is well-placed to cope with the growth in demand. In terms of publicly available datasets, EMG now contains comparable numbers of WGS metagenomic samples to portals such as MG-RAST (30) (6231 publicly available metagenomic samples), IMG/MG (31) (3193 metagenomic samples) and iMicrobe (http://imicrobe. us) (2629 metagenomic and amplicon samples). Whilst analysis of some projects may overlap between these portals, they each contain unique datasets and features, and thus offer complementary analyses of metagenomic data. Ensuring a more systematic organization of metaegnomics datasets and their analysis across all platforms is a key challenge for the community in the future.
The quality of associated contextual data is particularly important to the data discovery process, as it is directly proportional to the discoverability and usability of analysed projects. International efforts, such as the INSDC, Genomic Standards Consortium (GSC, http:// gensc. org/) and Global Microbial Identifier (GMI, http: //www. globalmicrobialidentifier. org/), focus intensively on development of contextual data standards that allow consistent description of sequence data. ENA and EMG are actively involved, especially in development of contextual information checklists for capturing provenance of sequenced and analysed samples. The growing list of ENA sample contextual data checklists currently contains 15 GSC MIxS environments, two marine environments developed for support of the marine enterprises Tara Oceans and OSD, and four microbial pathogen-related checklists.
These checklists are developed as standards evolve over time. Other metagenomics resources, such as MG-RAST and IMG/M, have also recognized the fundamental importance of contextual information and actively support efforts of contextual data standardization. The recent improvements to EMG's analysis pipeline have increased the analysis capacity of the resource. As a result, there is now an opportunity to extend its remit. Nevertheless, even with EMG's increase in analysis capacity, it is important that we continue to evaluate and balance its analysis priorities. With this in mind, we have chosen to prioritize both new submissions and publicly available D602 Nucleic Acids Research, 2016, Vol. 44, Database issue datasets that have not yet received analysis. As described above, there are a large number of such projects deposited with ENA.
We will continue the interface improvement process, as projects become larger and in response to feedback from our users (for example, providing APIs for data discovery and download). As metagenomics research becomes less correlative and more predictive, it will become increasingly important that EMG allows users to slice across datasets to compare and contrast analysis results. One particularly important area for EMG is the taxonomic analysis of non-prokaryotic organisms. As part of our collaborative analysis work performed on the OSD project (which was not size fractionated prior to sequencing), we performed a series of preliminary taxonomic investigations beyond the scope of the standard analysis pipeline. These revealed a rich array of eukaryotes and viruses, in addition to prokaryotes, within the samples.
III. JET AND EVENT SELECTION Jets are reconstructed from contiguous groups of calorimeter cells that contain significant energy above noise (topological clusters)  [39]. The anti-k t jet algorithm  [40, 41]  is applied to these clusters using a distance parameter of R = 0. 6. Effects from additional pp interactions in the same and neighboring bunch crossings are corrected for using the calibration procedure described in Ref. [42]. Simulated QCD multijet events are used for the derivation of the jet calibration to the hadronic scale. They are produced with the event generator Pythia  [43]  8. 160, using the CT10 PDF  [44]  and the AU2 set of parameters to describe the nonperturbative effects tuned to ATLAS data (the AU2 tune)  [45]. Detector effects are simulated using Geant4 within the ATLAS software infrastructure  [46, 47].
The dijet mass resolution is approximately 8% at 200 GeV, and improves to less than 4% above 2 TeV. The measured dijet mass distribution is not corrected for detector resolution effects. The data were collected using single-jet triggers  [50]. These triggers are designed to select events that have at least one large transverse energy deposition in the calorimeter. To match the data rate to the processing and storage capacity available to ATLAS, the triggers with low-p T thresholds are prescaled; only a preselected fraction of all events passing the threshold is recorded. Combinations of prescaled single-jet triggers are used to reach lower dijet invariant masses. The highest prescale for the trigger combinations used in this anal-ysis is 1/460000. This trigger combination is used for jets with p T between 59 and 99 GeV.
In this model, the Planck scale is set equal to the threshold mass for the quantum black hole production m th. These QBH models are used as benchmarks to represent any quantum gravitational effect that produces events containing dijets. The PDF used for the generation and parton shower of BlackMax is CT10, while the Qbh samples employ the MSTW2008LO PDF. In the mass range considered, the branching ratio of QBH to dijets is above 85% and the acceptance is between 52% and 55% for BlackMax and 54%-56% for Qbh. nonperturbative effects for events coming from both event generators are simulated with Pythia 8. 170. Further information on cross sections, branching ratio to dijets and acceptances for the benchmark models under consideration can be found in HepData  [71]. All MC signal samples except for the excited quark signals are passed through a fast detector simulation  [72]  with the jet calibration appropriately corrected to full simulation.
ACKNOWLEDGMENTS We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  RGC, Hong Kong SAR, China ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
A. Noise Effect on Calibration Accuracy Usually digital calibration requires averaging certain number of samples to achieve certain calibration accuracy. A behavioral 13-bit split pipeline ADC is developed in MATLAB to examine the number of averaging required in the proposed calibration. The behavioral ADC is using low gain amplifiers in the first two stages, and the random comparator offsets are modelled in the ADSC. Thermal noise equivalent to 74 dB is inserted in the ADC. The SNDR before calibration is 48. 3 dB. The SNDR after calibration is plotted in Fig. 8  with the number of samples averaged. the ADC noise-level, six averages are enough to restore the ADC to be almost as the SNR of ADC, limited by the thermal noise. The low number of averaging attributes to the fully deterministic approach of the proposed calibration, where the ADC linearity errors, represented by missing codes at the comparator decision levels, are being detected and corrected without any estimation or approximate methods.
10 , no calibration is needed as the input  is in the linear region of the ADC and doesn't experience the places of errors. To ensure a very fast calibration cycle to compensate the errors for all the decision levels independently of the input amplitude, the unit sampling capacitors can be rotated exchangeably in a non-correlated sequence with the input signal, as suggested in  [7]  (not implemented in this prototype). The split ADC calibration for the first two pipelined stages is done in the behavioral MATLAB model with different input characteristics, and the calibration parameters update for the secondandfirststagesareshowninFig. 11andFig. 12,respectively. In each case, averaging is done 10 times, and the THD of the ADC is preserved above 80 dB calibration. While the 16 QAM signal shows a significantly longer 2nd stage calibration cycle than the sine wave and uniformly distributed random input counterpart, the input statistics do not affect the calibration robustness and accuracy.