dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The  SAB thanks Ambassador Ahmet Uzümcü , the OPCW Director-General from July 2010-July 2018, for his support to the  Board  and for making its work available to the States Parties of the Convention to aid decision-making. The SAB acknowledges  Dr Hugh Gregg , Head of the  OPCW Laboratory  from  2010  to 2017, for providing the initial list of RCAs as an aid to its deliberations, and the staff of the  OPCW Library  for retrieving some of the references cited. The SAB also thanks  The Royal Society of Chemistry  for supporting the OPCW.
An exception is interestingly found during summer at midand high latitudes of the Northern Hemisphere (NH; ∼ 40 to ∼ 75 • N), where the large absolute fitted trend values (∼ |0. 5| DU yr -1 on average) combined with the small model residuals (∼ 10 %) allow for detection of a band-like pattern of significant negative trends. Despite no consensus in terms of tropospheric O 3 trends having been reached from the available independent datasets (UV or IR satellites, O 3 sondes, aircrafts, ground-based measurements, etc. ) for the reasons that are discussed in the text, this finding is consistent with the reported decrease in O 3 precursor emissions in recent years, especially in Europe and USA. The influence of continental pollution on that latitudinal band is further investigated and supported by the analysis of the O 3 -CO relationship (in terms of correlation coefficient, regression slope and covariance) that we found to be the strongest at northern midlatitudes in summer.
g. ,  Stohl et al. , 2002; Parrish et al. , 2012)  and climate. Monitoring and understanding the time evolution of tropospheric O 3 at a global scale is, therefore, crucial to apprehend future climate changes. Nevertheless, a series of limitations make O 3 trends particularly challenging to retrieve and to interpret. While the O 3 precursors anthropogenic emissions have increased and shifted equatorward in the developing countries  (Zhang et al. , 2016)  since the 1980s, extensive campaigns and routine in situ and remote measurements at spe-Published by Copernicus Publications on behalf of the European Geosciences Union. C. Wespes et al. :  Decrease in tropospheric O 3 levels in the Northern Hemisphere cific urban and rural sites have provided long-term but sparse datasets of tropospheric O 3 (e.
g. ,  Cooper et al. , 2014 and references therein). Ultraviolet and visible (UV/VIS) atmospheric sounders onboard satellites provide tropospheric O 3 measurements with much wider coverage, but they result either from indirect methods (e. g. ,  Fishman et al. , 2005)  or from direct retrievals which are limited by coarse vertical resolution  (Liu et al. , 2010). All these datasets also suffer from a lack of homogeneity in terms of measurement methods (instrument and algorithm) and spatio-temporal samplings (e. g. ,  Doughty et al. , 2011; Heue et al. , 2016; Leventidou et al. , 2017). Those limitations, in addition to the large natural interannual variability (IAV) and decadal variations in tropospheric O 3 levels (due to large-scale dynamical modes of O 3 variations and to changes in stratospheric O 3 , in stratosphere-troposphere exchanges, in precursor emissions and in their geographical patterns), introduce strong biases in trends determined from independent studies and datasets (e.
g. ,  Zbinden et al. , 2006; Thouret et al. , 2006; Logan et al. , 2012; Parrish et al. , 2012 and references therein). As a consequence, determining accurate trends requires a long period of high density and homogeneous measurements (e. g. ,  Payne et al. , 2017). Such long-term datasets are now becoming obtainable with the new generation of nadir-looking and polar-orbiting instruments measuring in the thermal infrared region. In particular, about one decade of O 3 profile measurements, with good sensitivity in the troposphere independent from the layers above, is now available from the IASI (Infrared Atmospheric Sounding Interferometer) sounder aboard the European Metop platforms, allowing the monitoring of regional and global variations in tropospheric O 3 levels (e. g. ,  Dufour et al.
, 2012; Safieddine et al. , 2014; Wespes et al. , 2016). In this study, we examine the tropospheric O 3 changes behind the natural IAV as measured by IASI over January 2008-May 2017. To that end, we use the approach described in  Wespes et al. (2017) , which relies on a multi-linear regression (MLR) procedure, for accurately differentiating trends from other sources of O 3 variations, the latter being robustly identified and quantified in the companion study. In Sect. 2, we briefly review the IASI mission and the tropospheric O 3 product, and we shortly describe the multivariate models (annual or seasonal) that we use for fitting the daily O 3 time series. In Sect. 3, after verifying the performance of the MLR models over the available IASI dataset, we evaluate the feasibility of capturing and retrieving significant trend parameters, apart from natural O 3 dependencies, by performing trend sensitivity studies.
, 2017) , with the possibility of further differentiating long-term O 3 changes in the troposphere  (Wespes et al. , 2016). Note, however, that a drift in the NH middle-low troposphere (MLT) O 3 over the whole IASI dataset is reported in  Keppens et al. (2018)  and  Boy-nard et al. (2018)  from comparison with O 3 sondes. This drift (∼ 2. 8 DU decade -1 in the NH) is shown in  Boynard et al. (2018)  to result from a discontinuity (called "jump" by the author) in September 2010 in the IASI O 3 time series, for reasons that are unclear at present. Furthermore, the drift strongly decreases (< |1| DU decade -1 on average) after the jump and it becomes even non-significant for most of the stations (significant positive drift is also found for some stations) over the periods before or after the jump, separately.
, the RMSEs relative to the IASI O 3 time series are of ∼ 15 % on global average and vary between 10 % in the NH in summer and 30 % in specific tropical regions). On an annual basis (data not shown), the model explains a large fraction of the variation in the IASI O 3 dataset (from ∼ 45 to ∼ 85 %) and the RMSEs are lower than 4. 5 DU everywhere (∼ 3 DU on the global average). The relative RMSE is less than 1 % in almost all situations indicating the absence of bias. The seasonal distributions of the contribution to the total variation in the MLT from the adjusted harmonic terms and explanatory variables, which account for the "natural" variability, and from the LT term are shown in Fig. 2a  and b , respectively. The grey areas in the LT panels refer to the LT terms rejected by the stepwise backward elimination process.
3c , grey lines; the trend and the RMSE values are also indicated) and the adjustment without LT leads to larger residuals (e. g. , RMSE _JJA_w/o_LT = 3. 37 DU vs. RMSE _JJA_with_LT = 3. 21 DU in summer). This result demonstrates the possibility of capturing trend information from ∼ 9 years of IASI-MLR with only some compensation effects from the other explanatory variables, contrary to what was observed when considering a shorter period of measurements or less-frequent sampling (i. e. , monthly dataset; e. g. ,  Wespes et al. , 2016). It is also worth mentioning that the O 3 change calculated over the whole IASI dataset in summer is larger than the RMSE of the model residuals (increase of 6. 50 ± 2. 35 DU vs. RMSE of 3. 21 DU), underlying the statistical relevance of trend estimates. The robustness of the adjusted trend is verified at the global scale in Fig.
The magnitudes of the variation and the autocorrelation in the noise residuals are taken into account for reaching a better trend estimate precision. Given that large variance (σ 2 ε ) and large positive autocorrelation φ of the noise induce small signal-to-noise ratio and long trend-like segments in the dataset, respectively, these two parameters increase the number of years that would be required for detecting a specified trend. CL N * is the 95 % confidence limit, which is not symmetric around N * and depends on B, an estimated uncertainty factor calculated as 4 3 √ D 1+φ 1-φ , with D the number of days in the IASI datasets, which accounts for the uncertainty in φ (the uncertainty in σ ε being negligible given that only a few years of data are needed to estimate it; see  Weatherhead et al.
, 1998). As a result, based on the available IASI-A and proxies datasets and assuming that the MLR model used in this study is accurate, we estimate, in Fig. 6a  and b , the expected year when an O 3 trend amplitude of |5| DU decade -1 (i. e. , τ yr = |0. 5| DU yr -1 which corresponds to the averaged absolute value of the fitted negative trends in the NH summer; see Fig. 5b ) is detectable, and its associated maximal confidence limit, respectively. The results in Fig. 6  clearly demonstrate the possibility of inferring, from the available IASI dataset, such significant trends in the mid-and high latitudes of the NH in summer and fall (trend detectable from ∼ 2016 to 2017 with an uncertainty of ∼ 6 to ∼ 9 months; see Fig. 6b ). Conversely, the tropical regions and the NH in winter-spring would require additional ∼ 6 months to ∼ 6 years of measurements to detect an amplitude of |0.
Substantial efforts in homogenizing independent tropospheric O 3 column (TOC) datasets have been made in the TOAR-Climate assessment report  (Gaudel et al. , 2018) , but large SLR trend biases remain between the TOAR datasets, in particular, between the satellite datasets where the lack of homogeneity in terms of tropopause calculation (same tropopause definition but different temperature profiles are used), of instrument vertical sensitivities and of spatial sampling have been specifically raised as possible causes for the trend divergence. If reconciling the trend biases between the datasets by applying the vertical sensitivity of each measurement type to a common platform, as proposed in the TOAR-Climate assessment report is beyond the scope of this study and if, at this stage, there is no consensus in determining tropospheric O 3 trends, the improvement in using a MLR instead of a SLR model for determining more accurate/realistic trends is explored here by comparing the seasonal distributions of the trends estimated from MLR (see Fig.
, 2016 , for a description of the NAO-related O 3 changes). Conversely, the subtropical SH exhibits similar seasonal patterns of negative trends from both the SLR and the MLR. This results from the weak natural IAV and contributions in tropospheric O 3 above that region (see Sect. 3, Figs. 1  and 2 ), which, hence, limits the compensation effects. In summary, despite the fact that considering a long period of measurements is usually recommended in SLR studies to overcome the dynamical cycles and, hence, to help in differentiating their influences from trends, we show that, considering that some dynamics have irregular or no particular periodicity (e. g. , NAO, ENSO), it is not accurate enough. Furthermore, accurate satellite measurements of tropospheric O 3 at a global scale are quite recent, limiting the period of available and comparable datasets (e.
The MLR analysis reveals a band-like pattern of high significant negative trends in the NH mid-and high latitudes in summer (-0. 47 ± 0. 22 DU yr -1 on average in the 40-75 • N band). The statistical significance of such trend estimates is further verified by estimating, based on the autocorrelation and on the variance of the noise residuals, the minimum number of years of IASI measurements that are required to detect a trend of a | 5| DU decade -1 magnitude. The results clearly demonstrate the possibility of determining such a trend amplitude from the available IASI dataset and the used MLR model at northern mid-and high latitudes in summer, while much larger measurement periods are necessary elsewhere. In particular, the regions with the longest required period, in the tropics, highlight a series of known processes that are closely related to the El Niño dynamic, which underlies the lack of associated parameterizations in the MLR model.
The importance of using reliable MLR models in understanding large-scale O 3 variations and in determining trends is further explored by comparing the trends inferred from MLR and from SLR, the latter still being commonly used by the international community. The comparison has clearly highlighted the advantage of MLR in attributing the trend-like segments in natural variations, such as ENSO, to the right processes and, hence, in avoiding misinterpretation of "apparent" trends in the measurement datasets. Nevertheless, it is worth noting that there could be a possible impact of the sampling (because of the cloud and quality filters applied) and of the jump in September 2010 that has been identified in the IASI dataset (see Sect. 2), in both MLR and SLR estimations. Finally, by exploiting the simultaneous and vertically resolved O 3 and CO measurements from IASI, we have provided and used the O 3 -CO correlations in the troposphere to help determine the origins/characteristics of patterns of negative or positive trends.
The distributions have allowed us to identify, in particular, strong positive O 3 -CO correlations, regression slopes and covariance in the NH midlatitudes and northward during summer, which suggest a continental pollution influence in the NH band-like pattern of high significant negative trends recorded by IASI and, hence, a direct effect of the policy measures taken to reduce emissions of O 3 precursor species. Overall, this study supports the importance of using (1) high density and long-term homogenized satellite records, such as those provided by IASI and (2) complex models with predictor functions that describe the O 3 regressors dependencies for more accurate determination of trends in tropospheric O 3 -as required by the scientific community, e. g. , in the Intergovernmental Panel on Climate Change (IPCC, 2013) -and for further resolving trend biases between independent datasets  (Payne et al.
, 2014). A long record of tropospheric O 3 measurements will be also provided by the Cross-track Infrared Sounder (CrIS) onboard the Joint Polar Satellite System series of satellites. Data availability. The IASI O 3 and CO data respectively processed with FORLI-O 3 and FORLI-CO v0151001 can be downloaded from the Aeris portal (http://iasi. aeris-data. fr/O3/ and http: //iasi. aeris-data. fr/CO/, respectively; Aeris, 2017a, b). Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Quadrennial Ozone Symposium 2016 -Status and trends of atmospheric ozone (ACP/AMT inter-journal SI)". It is a result of the Quadrennial Ozone Symposium 2016, Edinburgh, United Kingdom, 4-9 September 2016. Edited by: Mark Weber Reviewed by: two anonymous referees.
Acknowledgements. IASI was developed and built under the purview of the Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMET-Cast near-realtime data distribution service. We acknowledge the financial support from the ESA O3-CCI and the Copernicus O3-C3S projects. The AC SAF project is acknowledged for supporting the IASI FORLI-O3 and FORLI-CO implementation at EUMET-SAT. The research in Belgium is also funded by the Belgian State Federal Office for Scientific, Technical and Cultural Affairs and the European Space Agency (ESA Prodex IASI Flow and AC SAF).
Cytoplasms Differ in Mitochondrial Genomes and Wolbachia Presence Differentiation between Australian and Canadian mitochondrial (mt) genomes were assessed using two measures of genetic differentiation, the fixation index (FST) and Fisher's exact test for allele frequency differences (as implemented in Popoolation2  [41] ). We combined mitochondrial sequence data from the two lines containing Australian mt genomes (AA and AC) and from the two containing Canadian mt genomes (CC and CA) into single Australian and Canadian pools, respectively. FST and allele frequency differences were calculated per SNP. Sites considered significantly differentiated between Australian and Canadian mt genomes had to differ significantly in Fisher's exact test (p < 0. 05; FDR of 5%) and fall under the TOP 1% of FST values.
Cytoplasms Differ in Mitochondrial Genomes and Wolbachia Presence Differentiation between Australian and Canadian mitochondrial (mt) genomes were assessed using two measures of genetic differentiation, the fixation index (F ST ) and Fisher's exact test for allele frequency differences (as implemented in Popoolation2  [41] ). We combined mitochondrial sequence data from the two lines containing Australian mt genomes (AA and AC) and from the two containing Canadian mt genomes (CC and CA) into single Australian and Canadian pools, respectively. F ST and allele frequency differences were calculated per SNP. Sites considered significantly differentiated between Australian and Canadian mt genomes had to differ significantly in Fisher's exact test (p < 0. 05; FDR of 5%) and fall under the TOP 1% of F ST values.
BAM files were converted into BED and 17 million aligned reads were subsampled for each replicate. In our hands, this non-parametric bootstrapping approach provides superior normalization compared to "build-in" normalization tools implemented in downstream analyses. The "Ranger" method for narrow peaks of PeakRanger v1. 16  [47]  with p-value cut off 0. 0001, FDR cut off 0. 01, Read extension length 200, Smoothing bandwidth 99 and Delta 1 was used for peak identification. Input samples were used as negative control (-c). Wiggle files were generated for visualization under Integrated Genome Viewer (IGV)  [69]. Peak region positions were saved as BED files (Ranger-BED). For comparison of chromatin profiles of each mar, the HMM-based software ChromstaR v1. 3. 1  [45]  was used. Its parameters were optimized using the PeakRanger peak call as reference.
Average H3K4 trimethylation over genes were calculated using the plotEnrichment function for 'counts' over 9076 genes of the FlyBase annotation Release 6 (http://flybase. org). The identified differentially enriched regions were analyzed for GO term enrichment using ChIP-Enrich  [46]  an application for gene set enrichment testing for ChIP-seq data. ChIP-Enrich was run on the D. melanogaster reference genome Release 6 (http://flybase. org), and genes with differentially enriched regions within ±1 kb of transcription start sites were considered. All sequence data were submitted to the public repository NCBI Sequence Read Archive (SRA) and are available through the BioProject ID: PRJNA312490.
They could also make researchers change their habits and more easily apply best practices for authorship citation. Improving discoverability of original publications of scientific names, as much as that of the visibility of taxonomic authorship, can be done by developing unique identifiers for each author. With the increase of e-publications and the issues that arose around homonyms, some different systems have been developed, such as Orcid ID (a unique identifier for scientific and other academic authors and contributors which is increasingly used in universities), or the VIAF authority (Virtual International Authority File) database which works following a similar system of unique identifiers for authors (however, VIAF is more orientated towards books than to journals), but which seems more accurate as it is made by using author files from national libraries.
ZooBank accommodates the registration of three different kinds of data objects for which a Life Science Identifier (LSID) is attributed: nomenclatural acts, publications and authors. ZooBank is the Official Register of the International Commission on Zoological Nomenclature (ICZN) that provides the central, authoritative and comprehensive resource for scientific names in zoology. Registration of e-only nomenclatorial acts using ZooBank LSID's is compulsory (see above). It seems appropriate that large taxonomic databases like Catalogue of Life (CoL) clearly identify the associated original descriptions in their list of synonyms, with a link to the full publication reference. Relevant data aggregation databases like the Global Biodiversity Information Facility (GBIF) should also provide the full reference, ready-for-use by the authors.
As the largest reservoir of publications used by scientists from all disciplines related to biodiversity, the Biodiversity Heritage Library (BHL) has a key role to play in addressing and resolving this matter, with the support and collaboration of the whole community. While discussing the development of international tools based on data exchanges, the workshop underlined that it is important to ensure that all parties correctly cite a proper license such as Creative Commons by attribution (CC-BY), which facilitates data aggregation and re-use. Furthermore, easy standardized ways of citing must be provided, along with controlled vocabularies, so that automated citation can be implemented when names are used from large aggregators like GBIF. It is crucial to enable interoperability by matching data fields from one system to another.
Online or E-publishing Journals are increasingly available online, although digital dissemination on a website or platform does not necessarily comply with OA standards (see below). The digital support associated with OA also gives the opportunity for researchers to enrich a publication with three categories of information: research data; supplementary materials (to illustrate or clarify issues); and post-publication data (commentaries, rankings). Journals can now publish their papers in HTML or XML formats, which offer the option to link articles to maps, databanks, multimedia files, and so on. Another important advantage of OA is that it allows for automatic harvesting by search engines, enabling superior visibility and more citations for the research findings. E-publishing also offers the possibility to implement a fast-track publishing process, which means a shorter timeline between submission and publication.
Clarivate Analytics manages a large database (created by the ISI-Institute for Scientific Information, previously run by Thomson Reuters) covering 11,655 journals from over 2,500 publishers (Journal Citation Reports)  (Johnson et al. 2018 ) and more than 700 million cited references. It contains the following information: -the total number of articles published in the journal during the years considered; -the total number of citations of each journal during the years considered; -the main 2-year impact factor per journal; -the 5-year impact factor: the same as above, but for 5 years instead of 2 (this is important for subject fields that evolve more slowly, although it may still be too short for taxonomy); -the immediacy index: average number of times an article is cited in the year it is published calculated by dividing the number of citations to articles published in a given year by the number of articles published in that year; -journal cited half-life: refers to the median age of the citations received by the journal during the considered year.
Certain conditions have to be met: platforms or repositories have to be Open Archiving Initiative-Protocol for Metadata Harvesting-OAI-MPH compatible; metadata have to be Dublin Core Standard and works have to be reusable without legal barriers, with licences such as CC-BY from Creative Commons (http://creativecommons. org/ licenses/by/3. 0). There are different approaches to OA. The main differentiation separates Green from Gold OA. Diamond OA is a subcategory of Gold OA. (Fig. 15 ). Green Open Access or Self-archiving. This is the act of (the author) depositing a digital copy of, typically, peer-reviewed scientific articles or monographs, but also other types of research output, in an institutional or thematic repository, also called an 'open archive'  (Suber 2012). In some institutions or universities this archiving is mandatory (see below).