dataesr/scientific-paragraphs-categorization huggingface dataset:
Halogens Halogens (chlorine, bromine, iodine) influence the concentrations of ozone in the troposphere either directly, by reacting with O 3 itself, e. g. 2014 ) model analysis suggest that models may overestimate the role of bromine (and the extent of ozone destruction), because they tend to overestimate marine aerosol, especially in the Southern Hemisphere. Br + O 3 → BrO + O 2 , ( R10 Our understanding of the role of halogens in ozone chemistry ultimately depends on our understanding of the physiochemical processes that release, transform and destroy halogen species into the atmosphere. There are significant uncertainties in our knowledge of the source processes and geographical distribution of halogens and severe limitations in the databases of chemical kinetics parameters  (Abbatt et al.
Additionally, since bromine release is connected with first-year sea ice (Saiz-Lopez and von Glasow, 2012), changes in sea ice formation due to climatic change are likely to affect ozone photochemistry significantly. The importance of localised sources of halogens, such as salt lakes, salt beds, saline soils and marshes, for O 3 photochemistry is at present unclear. Large concentrations of bromine and iodine (up to 200 ppt of  BrO -Tas et al. , 2005, and up  to 10 ppt of  IO -Zingler and Platt, 2005)  have been reported over the Dead Sea (Israel) causing O 3 as low as 2 ppb; however, observations in other locations have shown much lower values (e. g. 6 ppt of BrO and 15 ppt of ClO over the Great Salt Lake;  Stutz et al. , 2002). The database of observations is very sparse and shows large variability between different locations, suggesting that the local characteristics (e.
Background: Determining the evolutionary relationships among the major lineages of extant birds has been one of the biggest challenges in systematic biology. To address this challenge, we assembled or collected the genomes of 48 avian species spanning most orders of birds, including all Neognathae and two of the five Palaeognathae orders. We used these genomes to construct a genome-scale avian phylogenetic tree and perform comparative genomic analyses. Findings: Here we present the datasets associated with the phylogenomic analyses, which include sequence alignment files consisting of nucleotides, amino acids, indels, and transposable elements, as well as tree files containing gene trees and species trees. Inferring an accurate phylogeny required generating: 1) A well annotated data set across species based on genome synteny; 2) Alignments with unaligned or incorrectly overaligned sequences filtered out; and 3) Diverse data sets, including genes and their inferred trees, indels, and transposable elements.
protein-coding exon gene set This is an exon-coding sequence data set of 8295 genes based on synteny-defined orthologs we identified and selected from the assembled genomes of chicken and zebra finch  [8, 9]. We required these loci to be present in at least 42 of the 48 avian species and outgroups, which allowed for missing data due to incomplete assemblies. To be included in the dataset, the exons in each genome assembly had to be 30% or more of the full-length sequence of the chicken or zebra finch ortholog. Annotated untranslated regions (UTRs) were trimmed off to remove non-coding sequence, in order to infer a codingonly sequence phylogeny. We note that 44 genes were identified with various problems such as gene annotation issues, and we removed them in the phylogenetic analyses. However, we provide them here in the unfiltered alignments.
UCE locus set This is the ultraconserved element (UCE) data set with 1000 bp flanking sequence at the 3′ and 5′ ends. The UCE dataset was filtered to remove overlap with the above exon and intron data sets, other exons and introns in the chicken genome assembly version 3, and overlapping sequences among the UCEs. The source UCE sequences used to search the genomes were determined from sequence capture probes  [10] [11] [12]  aligned to each avian genome assembly. Unlike the exon and intron data sets, we required that all 42 avian species and the alligator outgroup contain the UCEs. We found this requirement to be sufficient, because the central portions of UCEs are highly conserved across all species.
Supergenes These are the concatenated sets of loci from various partitions of the TENT dataset (exons, introns, and UCEs described above), brought together using the statistical binning approach. The statistical binning approach put together sets of loci that were deemed "combinable". Two genes were considered combinable if their respective gene trees had no pairs of incompatible branches that had bootstrap support above a 50% threshold. Alignments of genes in the same bin were concatenated to form supergenes, but boundaries of genes were kept so that a gene-partitioned phylogenetic analysis could be performed on each supergene.
Indel dataset 5. 7 million insertions and deletions (indels) were scored as binary characters locus by locus from the same intron, exon, and UCE alignments as used in the TENT data set on the principle of simple indel coding using 2Xread  [15, 16]  and then concatenated. Coding was verified using GapCoder  [17]  and by visual inspection of alignments for a Table  1  Genomes used in the avian phylogenomics project.
FASTA files of loci datasets in alignments We provide the above loci data sets as FASTA files of both unfiltered and filtered sequence alignments. The alignments were filtered for aberrant over-and under-aligned sequences, and for the presence of the loci in 42 of the 48 avian species. All multiple sequence alignments were performed in two rounds. The first round was used to find contiguous portions of sequences that we identified as aberrant, and the second round was used to realign the filtered sequences. We used SATé  [22, 23]  combined with either MAFFT  [18]  or PRANK  [24]  alignment algorithms, depending on the limitations of working with large datasets. Alignments without and with outgroups are made available.
Filtered loci sequence alignments Exon loci alignments These are filtered alignments of exons from 8295 genes. Of these 8295, there were 42 genes that were identified to have annotation issues and we removed them from the phylogenetic analyses (the list is provided in the file FASTA_files_of_loci_datasets/Filtered_sequence_alignments/ 8295_Exons/42-exon-genes-removed. txt). Two more genes were removed because a gene tree could not be estimated for them. The first round of alignment was performed using SATé + PRANK, and the second round was performed using SATé + MAFFT. Before alignment, the nucleotide sequences were converted to amino acid sequences, and then reverted back to nucleotide sequences afterwards.
Acknowledgements The majority of genome sequencing and annotation was supported by internal funding from  BGI. Additional significant support is from the coordinators of the project:  E. D. J. from the  Howard Hughes Medical Institute (HHMI)  and  NIH Directors Pioneer  Award  DP1OD000448. S. M. from an  HHMI   International Student Fellowship. G. Z. from Marie Curie International Incoming Fellowship grant  ( 300837 ); T. W. from  NSF   DEB 0733029 ,  NSF   DBI 1062335 ,  NSF IR/D program ; and M. T. P. G. from a  Danish National Research Foundation  grant ( DNRF94 ) and a  Lundbeck Foundation  grant ( R52-A5062 ). We thank the following Centers that allowed us to conduct the computationally intensive analyses for this study:  Heidelberg Institute for Theoretical Studies (HITS) ;  San Diego Supercomputer Center (SDSC) , with support by an  NSF  grant;  SuperMUC Petascale System at the Leibniz Supercomputing Center; Technical University of Denmark (DTU) ;  Texas Advanced Computing Center (TACC) ;  Georgia Advanced Computing Resource Center (GACRC) , a partnership between the  University of Georgia's Office of the Vice President for Research and Office of the Vice President for Information Technology ;  Amazon Web Services (AWS) ; BGI; Nautilus supercomputer at the  National Institute for Computational Sciences of the University of Tennessee  and Smithsonian; and  Duke University Institute for Genome Sciences and Policy.
Acanthisitta chloris Rifleman PRJNA212877 http://dx. doi. org/10. 5524/101015 Anas platyrhynchos Pekin Duck PRJNA46621 http://dx. doi. org/10. 5524/101001 Antrostomus carolinensis Chuck-will's-widow PRJNA212888 http://dx. doi. org/10. 5524/101019 Apaloderma vittatum Bar-tailed Trogon PRJNA212878 http://dx. doi. org/10. 5524/101016 Aptenodytes forsteri Emperor Penguin PRJNA235982 http://dx. doi. org/10. 5524/100005 Balearica regulorum Grey Crowned-crane PRJNA212879 http://dx. doi. org/10. 5524/101017 Buceros rhinoceros Rhinoceros Hornbill PRJNA212887 http://dx. doi. org/10. 5524/101018 Calypte anna Anna's Hummingbird PRJNA212866 http://dx. doi. org/10. 5524/101004 Cariama cristata Red-legged Seriema PRJNA212889 http://dx. doi. org/10. 5524/101020 Cathartes aura Turkey Vulture PRJNA212890 http://dx.
doi. org/10. 5524/101021 Chaetura pelagica Chimney Swift PRJNA210808 http://dx. doi. org/10. 5524/101005 Charadrius vociferus Killdeer PRJNA212867 http://dx. doi. org/10. 5524/101007 Chlamydotis macqueenii MacQueen's Bustard PRJNA212891 http://dx. doi. org/10. 5524/101022 Colius striatus Speckled Mousebird PRJNA212892 http://dx. doi. org/10. 5524/101023 Columba livia Pigeon PRJNA167554 http://dx. doi. org/10. 5524/100007 Corvus brachyrhynchos American Crow PRJNA212869 http://dx. doi. org/10. 5524/101008 Cuculus canorus Common Cuckoo PRJNA212870 http://dx. doi. org/10. 5524/101009 Egretta garzetta Little Egret PRJNA232959 http://dx. doi. org/10. 5524/101002 Eurypyga helias Sunbittern PRJNA212893 http://dx. doi. org/10. 5524/101024 Falco peregrinus Peregrine Falcon PRJNA159791 http://dx. doi.
org/10. 5524/101006 Fulmarus glacialis Northern Fulmar PRJNA212894 http://dx. doi. org/10. 5524/101025 Gallus gallus Chicken PRJNA13342 N. A. Gavia stellata Red-throated Loon PRJNA212895 http://dx. doi. org/10. 5524/101026 Geospiza fortis Medium Ground-finch PRJNA156703 http://dx. doi. org/10. 5524/100040 Haliaeetus albicilla White-tailed Eagle PRJNA212896 http://dx. doi. org/10. 5524/101027 Haliaeetus leucocephalus Bald Eagle PRJNA237821 http://dx. doi. org/10. 5524/101040 Leptosomus discolor Cuckoo-roller PRJNA212897 http://dx. doi. org/10. 5524/101028 Manacus vitellinus Golden-collared Manakin PRJNA212872 http://dx. doi. org/10. 5524/101010 Meleagris gallopavo Turkey PRJNA42129 N. A. Melopsittacus undulatus Budgerigar PRJNA72527 http://dx. doi. org/10. 5524/100059 Merops nubicus Carmine Bee-eater PRJNA212898 http://dx.
doi. org/10. 5524/101029 Mesitornis unicolor Brown Mesite PRJNA212899 http://dx. doi. org/10. 5524/101030 Nestor notabilis Kea PRJNA212900 http://dx. doi. org/10. 5524/101031 Nipponia nippon Crested ibis PRJNA232572 http://dx. doi. org/10. 5524/101003 Opisthocomus hoazin Hoatzin PRJNA212873 http://dx. doi. org/10. 5524/101011 Pelecanus crispus Dalmatian Pelican PRJNA212901 http://dx. doi. org/10. 5524/101032 Phaethon lepturus White-tailed Tropicbird PRJNA212902 http://dx. doi. org/10. 5524/101033 Phalacrocorax carbo Great Cormorant PRJNA212903 http://dx. doi. org/10. 5524/101034 Phoenicopterus ruber American Flamingo PRJNA212904 http://dx. doi. org/10. 5524/101035 Picoides pubescens Downy Woodpecker PRJNA212874 http://dx. doi. org/10. 5524/101012 Availability and requirements Project name: Avian Phylogenomic Project scripts Project home page: https://github.
com/gigascience/ paper-jarvis2014; also see companion paper home page for related data https://github. com/gigascience/ paper-zhang2014 Operating system: Unix Availability of supporting data Other data files presented in this data note for the majority of genomes are available in the GigaScience repository, GigaDB [26] (Table 1), as well as NCBI (Table 1), ENSEMBL, UCSC, and CoGe databases. ENSEMBL: http://avianbase. narf. ac. uk/index. html UCSC: (http://genome. ucsc. edu/cgi-bin/hgGateway; under vertebrate genomes) CoGe: (https://genomevolution. org/wiki/ index. php/Bird_CoGe).
ACKNOWLEDGMENTS AND FOOTNOTES We would like to thank  T. Toda  and  P. Tran  for supplying strains and  Thomas Mangeat  for critical reading of the manuscript. H. M. was supported by the  "Fondation pour la Recherche Medicale ". JF was supported by the plan Cancer 2009-2013 " Systems Biology ". This work was funded by the  ANR-blanc120601 " Chromocatch " and the plan Cancer 2009-2013 " Systems Biology ". J. P. was supported by the  CNRS/INSERM and la "Ligue Nationale Contre le Cancer, LNCC" (ATIP/Avenir). The microscopy equipment was funded by the  CNRS , l' ANR-blan120601  and l' Association de la Recherche sur le Cancer (ARC). The authors declare no competing interests.
Introduction: Why a European Virtual Observatory project? The digital revolution is bringing a change in paradigm in the way science is done. Astronomy has long been at the forefront for widespread sharing and re-use of data, with a new step towards interoperability and data integration through the development of the astronomical Virtual Observatory (VO). The concept of a Virtual Observatory is that astronomical data worldwide should be accessible through common protocols, with interoperability standards for metadata and tools so that access and use of the data become seamless. VO is embraced as a world-wide community-based initiative that is transforming and restructuring the way astronomy research is done. The VO has now reached a significant level of maturity and provides an operational framework for interoperable and efficient access to world-wide astronomical data and services.
The construction of the VO has followed a predominantly bottom-up, incremental, pragmatic approach that has been carefully matched to the needs of the astronomical community. This has been supported by national VO initiatives and data centres, with a significant contribution from the European VO initiatives. Having reached this level of maturity, it is now timely to write its history and to describe how it was built and the lessons learnt over the years. This paper is focussed on the European Virtual Observatory, Euro-VO. The main role of Euro-VO has been to coordinate European VO activities, with also for some of the European projects support to technological activities. This paper will highlight the main VO building blocks and lessons learnt on how to coordinate them. The astronomical VO was conceived from the beginning as a global endeavour, and European participants were among the founding members together with similarly interested US-based participants.
The two colloquia which set the scene in 2000, Virtual Observatories of the Future and Mining the Sky, were organised respectively in Pasadena (USA) on June 13th-16th and in Garching near Munich (Germany) on July 31st-August 4th, both with an international Programme Committee. Then the International Virtual Observatory Alliance (IVOA) was founded during the Conference Towards an International Virtual Observatory held in 2002 in Garching, June 10th-14th. Formal participation in the VO is based on national and international collaborations gathering development teams and data providers, as stated in the "Guidelines for participation of the International Virtual Observatory Alliance"  (Hanisch et al. 2010   [1] ). VO initiatives from several European countries are IVOA members, as is the European Space Agency (ESA), which is an intergovernmental organisation.
Several of the participants of this initial meeting have since played a key role in VO initiatives that were set up later in their country or Agency. The Strasbourg meeting allowed a detailed discussion of a standard format for tabular data that was initially proposed in collaboration between the CDS and US-NVO. This was the starting point for VOTable  (Ochsenbein et al, 2004 [5] ), an XML-based standard for the exchange of tabular data, which became the first VO standard in March 2002. VOTable was derived from the astrores XML format  (Ochsenbein et al. , 2000 [6] ), itself modeled on the FITS table format  (Cotton et al. , 1995 [7] )  3. VOTable was taken up by the IVOA after it was created and is currently at Version 1. 3. It has been (with the pre-existing FITS standard) a key asset for the development of the VO, allowing data exchange and thus a first level of interoperability from the early stages, and it has been massively used since then.
The Euro-VO projects European funding is organised in cycles, the so-called Framework Programmes of the European Commission (EC). Euro-VO 4  was supported by several framework programmes, first in the Research, then in the e-Infrastructure programmes. Figure  1  displays the timeline of the successive Euro-VO projects funded by the EC, together with the EC Framework Programmes and the structuring European activities Astronet and OPTICON, also cited in the paper. Table  1  provides information about the EC-funded projects including dates and partners.
Lessons learnt for best practices for VO activities As confirmed by the Euro-VO projects, the activities required to maintain the Virtual Observatory are the following: • Development of standards and protocols, and their international agreement; • Construction of "glue" software components -registry, workflow, user authentication, virtual storage; • Uptake by data centres, who need to "publish" to the system, i. e. to write VO compliant data services connected to their holdings; • Construction of tools to effectively take advantage of seamless access to data, some of them able to act as VO portals for certain types of data; • Support for the scientific community in its uptake of the new framework. The three main components are well summarized in Figure  2 , which shows the IVOA "level zero" architecture as stated in the IVOA Architecture document (Arviset et al.
The Census is used to gather information on how data providers use the VO framework, and about their feedback and requirements. One main aim of the EuroVO-DCA project was to engage data providers, trained in the usage of data publishing tools, and were invited to apply them to their own data holdings. Several participant groups and individuals eventually posted VO-compliant data and services, and some engaged as active IVOA participants. There was initially a good response to these workshops, but at the third workshop fewer participants applied and it was felt that this was the end of a cycle. The individual VO initiatives continued to give support to data providers on request, and many potential VO contributors also attended the IVOA meetings and events organised at the national level to learn about the VO framework.
The good level of dissemination of knowledge about the VO in the European data provider community was demonstrated by the Data Centre Forum organised by the CoSADIE project, which gathered 40 participants in Heidelberg in June 2013. They were invited to explain how they use the VO framework, and many talks were presented, showing a high level of awareness and take-up. Data providers would welcome a regular forum to share their experience of using the VO, provide feedback and get support from the VO teams. It was emphasised that making data centre holdings available in the VO increases visibility and impact. Another important motivation for data providers to engage in VO take-up is that they are interested in having their data made useable in VO tools such as Aladin for visualization or TOPCAT for tabular data: they also use the VO because it saves time and effort and provides valuable tools.
This has been identified as a key factor to ensure VO relevance and thus its sustainability in the medium term, and establishing links with these projects has been a target for the European VO projects. The IVOA has been organising Focus sessions with large current and future projects since its meeting in Heidelberg in 2013, and has set IVOA priorities with the large recent and future projects in mind -these are currently multi-dimensional data (ALMA, VLT and E-ELT, LOFAR, SKA) and the time domain (LSST). European members of the IVOA Committee on Science Priorities have been among the driving forces of this evolution.
2012  [21] ). As explained in the preamble of SAMP "IVOA members have recognised that building a monolithic tool that attempts to fulfil all the requirements of all users is impractical, and it is a better use of our limited resources to enable individual tools to work together better". Euro-VO fully endorsed this philosophy from the beginning and its partners put emphasis in the development of tools allowing access to, visualisation of and usage of VO-enabled data such as Aladin (VO portal for images), TOPCAT (Management tool for tabular data), VOSpec (visualisation and management of spectra), VOSA (Spectral energy distribution building and analysis). Technical cooperation and coordination has been organised since the VOTECH project through bi-yearly meeting of the European technical teams involved in the VO development, the Euro-VO Technology Forums.
Euro-VO in a wider European context Euro-VO paid particular attention to liaisons with "nearby" disciplines. Several national initiatives, for instance the French and Italian ones include representatives of other disciplines, in particular planetary or heliospheric studies, or astroparticle physics, as well as representatives of the atomic and molecular physics (since atomic and molecular data are heavily used to interpret observations in several fields of astronomy). Several of these disciplines also set up European projects dealing with data aspects, for instance among others Euro-Planet, HELIO (and other projects assessing the sharing of heliospheric physics data) and VAMDC. The other disciplines assessed solutions developed for the astronomical Virtual Observatory with respect to their own needs.
In some cases it did not fit but some of the VO standards and tools were re-used and Liaison with projects which deal with the generic aspects of the data infrastructure was also sought by Euro-VO. Astronomy was one of the thematic topics of the first European project 15 devoted to the European support for the emerging Research Data Alliance RDA. The aims were both to share lessons learnt in the building of the IVOA, and to explore possible liaison with generic interoperability projects, in particular EUDAT 16 (European project which builds generic building blocks for the scientific data infrastructure). These were also among CoSADIE goals. The Virtual Observatory framework has never been an isolated island, and generic building blocks were used whenever possible. In particular, the IVOA Registry of Resources  (Hanisch et al.
Acknowledgements The  Euro-VO  project has received funding from the  European Union Fifth ,  Sixth and Seventh Framework Programmes  for research, technological development and demonstration under grant agreements no  HPRI-CT-1999-4002 ,  HPRI-CT-2001-50030  ( FP5 ),  011892 ,  RI031675  ( FP6 ),  212104 ,  261541 ,  312559 (FP7 ).
Methods Strain construction. The E. coli MG1655 His 6 ::rpoS strain (from now on MG1655-rpoS His6 ), carrying an rpoS gene in which a 6-histidine tag is added to an otherwise wild type allele, was constructed following the genetic procedures described for allele replacement  41, 42. Linear DNA fragments containing a kanamycin resistance gene and the ccdB gene under the control of a rhamnose inducible promoter were amplified by PCR from the pKD45 plasmid. The first 45 nucleotides of either primer used for amplification (primers rpoS_OF and rpoS_OR, Supplementary Table  S1 ) correspond to the DNA regions immediately upstream and downstream of rpoS, targeting the gene for mutagenesis. After PCR amplification, the resulting DNA fragment including the kanR-ccdB cassette was used to transform the DY330 strain  42  ; the rpoS knockout was then P1-transduced into MG1655, selecting for kanamycin resistance.