dataesr/scientific-paragraphs-categorization huggingface dataset:
c2 G&C are fainter than those in c1. Contaminating sources include saturated point sources, unresolved pairs, and sources strongly masked by CCD gaps, for which not enough photons were available to permit reliable source characterisation; class 3 (c3) class includes sources at the survey sensitivity limit, and so is likely to contain G&C at high redshift. c3 G&C are faint objects and therefore have less wellcharacterised X-ray properties. The list of c1, c2, c3 detections are hosted in the Saclay database 1 (administered by Jean Paul Le Fèvre), which contains 455 analysable extended sources: 207 (∼ 46%) of them are classified as c1 sources, 194 (∼ 43%) are c2 sources, and the remaining 51 (∼ 11%) are c3 sources. Among the 455 XXL G&C, 264 are in the XXL-N area. The spectroscopic confirmations of the nature of the candidate G&C and of their redshifts were performed using an iterative semi-automatic process, very similar to the one already used for the XMM-LSS survey (e.
g. Adami et al. 2011a ). The procedure is described in detail in XXL Paper XX, and can be summarised as follows: -Within the X-ray contours, the available spectroscopic redshifts from the XXL spectroscopic database (see Sec. 2. 3) were selected; -These redshifts were sorted by ascending order to identify significant gaps (∆z >0. 003) in their distribution; -If one or more concentrations in both physical and redshift space appeared (more than three galaxies), the aggregate of galaxies closer to the X-ray centre or that including the Brightest Cluster Galaxy (BCG) were selected as 'group population'. For the vast majority of the cases, a single concentration emerged (see XXL Paper XX for a more precise discussion on multiple systems); -If no concentration appeared, a single galaxy with measured redshift which was likely to be a BCG was selected.
Photometric and photo-z databases We resort on different photometric observations that covered the XXL-N region. The largest contribution comes from the CFHT Legacy Survey  (Veillet 2007, CFHTLS)  and we rely on the Wide1 (W1) T0007 data release. Observations were obtained with the 3. 6m Canada-France-Hawaii Telescope (CFHT), using the MegaCam wide-field optical imaging facility. The Mega-Cam camera consists of 36 CCDs of 2048 × 4612 pixels each and covers a field of view of 1 deg 2 with a resolution of 0. 186 arcsec per pixel. The data cover the observed wavelength range 3500Å< λ < 9400Å in the u * , g , r , i , z filters. We note that the MegaCam filter i was broken during the survey and a new i band filter was introduced ("y "). Both filters are considered and included separately in the catalogues.
Guglielmo et al. : XXL spectrophotometric sample and mass functions u g r i y z 0. 00978 -0. 04726 -0. 02308 -0. 00567 -0. 01864 0. 06455 Table  1. Zero-point corrections for the CFHTLS-W1 field. Offsets need to be subtracted from each band. can be downloaded from the Canadian Astronomy Data Centre (CADC). We use the version of the photo-z catalogue consisting of 4613209 sources where the overlapping regions between the observing tiles have been removed through a S/N criterion, and therefore multiple objects have already been removed. We remove from the sample all bright objects with bad photometric redshift measurements, in order to avoid high levels of contamination from spurious sources, such as saturated stars. Observed magnitudes have been corrected for zero-point offsets that have been computed using spectroscopic redshifts by comparing the observed and modelled fluxes.
Spectroscopic database The galaxy spectroscopic information is hosted in the CeSAM (Centre de donnéeS Astrophysiques de Marseille) database in the processing of very large data flows from digital sky surveys (e. g. CFHTLS, WIRDS or WUDS, NGVS, CFHQSIR, KIDS/VIKING, Ul-traVISTA) and giant panoramic visible and near-infrared cameras (e. g. MegaCam and WIRCam at CFHT, or OmegaCam on the VST and VIR-Cam on VISTA at ESO/Paranal). TERAPIX is located at IAP (Institut d'Astrophysique de Paris, website http://terapix. iap. fr). Marseille. 5 The database contains data for both the XXL G&C and the galaxies in the same area. In addition to some XXL dedicated observing runs (XXL Paper XX), many other surveys have observed the galaxies in this field and the database includes them all. In particular, all redshifts from the VIMOS Public Extragalactic redshift survey, covering the redshift range 0.
4 < z < 1. 2, were made available for this analysis prior to the recent public release  (Scodeggio et al. 2016 ). As a result, there is a wide variety of spectra of different quality and origin to deal with. The final spectroscopic data release (CeSAM-DR2) is public and can be downloaded directly from the database; the complete list of the surveys and observing programmes included is given in Table  2. The samples included in the table refer to both XXL fields; however, as we focus here only on the XXL-N, we present the results only for this region. The sample contains 134604 sources; 25421 of them refer to multiple observations of the same objects from different surveys, but the multiple measurements are not flagged in CeSAM. To remove the duplicates, two different selection criteria are defined, both based on sets of priorities on observational properties of galaxies.
The first set of priorities regards the origin of the considered spectrum (the SpecOrigin column in the database catalogue). The different surveys are divided into three classes of priority (origin flag: 1, 2, 3): the smaller the value the higher the priority. The list of the surveys with their attributed origin flag is given below: The second set of priorities is given on the basis of the reliability of the redshift measurement, as given by each survey (the z f lag column in the database catalogue). All the possible values assumed by this flag in the different surveys are grouped into five classes (quality flag: 0, 1, 2, 3, 4): the higher the value the higher the precision and reliability of the redshift estimate. We list in the following all the original flags as they are in the CeSAM XXL spectroscopic database and the corresponding quality flag (the first number in the list) as they are in the final catalogue that is released in this paper (Appendix B).
Overall, the uncertainties on the galaxy redshift in the database vary from 0. 00025 to 0. 0005, computed from multiple observations of the same object and depending on the sample used (more details on the XXL spectroscopic database are given in XXL Paper XX);  we consider the highest value in this range as the typical redshift error for all objects. The redshift distribution of the 'cleaned' catalogue of galaxies is shown in Fig. 4. As the last step, we combine the spectroscopic and the photometric catalogues. We perform a match in coordinates between the two catalogues within 1 arcsec, obtaining 113732 galaxies. We exclude targets with redshifts z ∼ 0 to avoid being contaminated by stars, and are left with 113223 galaxies. In Figure  5  the CFHTLS W1, Fields A and B photometric catalogue (4508438 sources) is shown together with the spectroscopic sample in the same region in the sky (114450 sources).
Spectroscopic completeness The spectroscopic completeness of a sample is commonly defined as the ratio between the number of reliable spectroscopic redshifts in a given region and the total number of galaxies within it (i. e. the number of galaxies in the photometric catalogue). In principle, the completeness ratio depends on the sampling of the spectroscopic surveys in different regions of the sky (being our spectroscopic catalogue an heterogeneous ensemble of data coming from different surveys), the observed magnitude, and the colour of galaxies. In order to deal with the first two factors mentioned above, we divide the XXL-N field into three stripes (arbitrarily named) according to the spatial distribution of the surveys: -C-A: Number of galaxies in the spectrophotometric database = 3784, number of galaxies in the photometric database = 5292.
Completeness =71. 5±0. 8% (Poissonian error); -C-B: Number of galaxies in the spectrophotometric database = 15494, number of galaxies in the photometric database = 19944. Completeness =77. 7±0. 4%; -C-C: Number of galaxies in the spectrophotometric database = 2497, number of galaxies in the photometric database = 8751. Completeness =28. 5±0. 6%. We then further subdivide the samples according to the position in the sky and the magnitude, creating a grid of 1. 0 deg width both in RA and in DEC (for a total of 22 cells, see Fig. 6 ), and considering intervals of 0. 5 r-band observed magnitude. We compute histograms of galaxies in each cell: the ratio of the spectroscopic to the photometric histograms gives the completeness in each region of the sky and in each magnitude bin within it. Completeness curves are obtained from the completeness ratio as a function of magnitude in each of the 22 cells.
Representative completeness curves are shown in Appendix A. Considering the magnitude limited sample including 28096 galaxies with r ≤ 20. 0 (where the completeness drops dramatically, and which corresponds to GAMA observed magnitude limit r = 19. 8, see Appendix A), the completeness values for the three regions are as follows: -C-A: Number of galaxies in the spectrophotometric database = 4160, number of galaxies in the photometric database = 7491. Completeness =55. 6±0. 8% (Poissonian error); -C-B: Number of galaxies in the spectrophotometric database = 17121, number of galaxies in the photometric database = 27923. Completeness =61. 3±0. 4%; -C-C: Number of galaxies in the spectrophotometric database = 6815, number of galaxies in the photometric database = 13741. Completeness =49. 6±0. 6%.
First, we test our determination of the GSMF by comparing it with other results from the literature, as shown in Fig. 11. We use as comparison the sample presented in  Moustakas et al. (2013) , who exploited multiwavelength imaging and spectroscopic redshifts from the PRism MUlti-object Survey (PRIMUS) over five fields totaling ∼ 5. 5 deg 2 to characterise the mass functions in the redshift interval 0. 2 < z < 1. 0. To increase the statistics, we combine their redshift bins 0. 2 < z < 0. 3 and 0. 3 < z < 0. 4 in the mass range in common between the two and contrast their GSMF to that obtained from the XXL data over the same redshift interval. For this analysis we use both field and G&C galaxies together, mimicking the analysis of  Moustakas et al. (2013). In this case, the original normalisation over the comoving volume given by  Moustakas et al.
We therefore need to fix the α parameter to reduce the degeneracy and determine M *. We choose the best value that can reproduce our data point distribution, for field and for G&C galaxies separately. We caution the reader that comparisons of the parameters while fixing one of the two have to be taken carefully. Furthermore, at 0. 4<z<0. 6 we note that M is much less constrained in G&C than in the field since there are no data points at log(M /M ) > 12. 2. At lower masses, the two GSMFs clearly overlap. Our G&C sample spans a wide range of L XXL 500 (Fig. 2 ). It is therefore possible to consider separately galaxies in lowluminosity G&C (L XXL 500 < 10 43 erg s -1 ) and high-luminosity G&C (L XXL 500 > 10 43 erg s -1 ) and investigate whether the galaxy stellar mass distribution changes with X-ray luminosity.
5-2] keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. Multi-band information and spectroscopic follow-up of the X-ray sources were obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. The Australia Telescope Compact Array is part of the Australia Telescope National Facility which is funded by the Australian Government for operation as a National Facility managed by CSIRO. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing UV to radio coverage.
GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www. gama-survey. org/. This paper uses data from the VIMOS Public Extragalactic Redshift Survey (VIPERS). VIPERS has been performed using the ESO Very Large Telescope, under the "Large Programme" 182. A-0886. The participating institutions and funding agencies are listed at http://vipers. inaf. it. V. G. acknowledges financial support from the Fondazione Ing. Aldo Gini. B. V. acknowledges the support from an Australian Research Council Discovery Early Career Researcher Award (PD0028506). We acknowledge the financial support from PICS Italy-France scheme (P. I. Angela.
This cell alone constitutes the third region. Finally, the cells in the C-C stripe make up the fourth region. The curves in the four regions highlight how the coverage of the survey is different in different parts of the sky and how our choice of computing the completeness in each cell separately is indeed appropriate. Our adopted magnitude limit (r = 20 in the CFHTLS photometry) corresponds to GAMA r = 19. 8, and GAMA data drive the curves at the magnitudes of interest. Next, we tested the dependence of the spectroscopic completeness on galaxy colour, drawing completeness ratios as a function of magnitude for blue and red galaxies separately, following the procedure we adopted for the entire sample. We divided the sample into blue and red galaxies, according to the observed (g-r) median colour and computed the spectroscopic completeness for the two populations separately.
We performed a statistical Kolmogorov-Smirnov test (KS) on the resulting completeness curves at r≤20 and found that the two galaxy samples show no significant differences, i. e. the probability that they are drawn from the same parent sample is high, suggesting that our spectroscopic completeness estimates are not biased against any Finally, we also tested the dependence of the spectroscopic completeness on galaxy environment, to verify whether denser regions in the XXL area have the same sampling as in the field. We therefore considered separately galaxies in the 'pure' field and galaxies that fall into the projected area of G&C and computed again the spectroscopic weights, following the same method explained in the main text. A general very good agreement was found between the curves in all regions considered, suggesting that the spectroscopic data almost equally sample regions of different densities in the XXL area, as also supported by the KS test.
Robust data are the base of effective gender diversity policy. Evidence shows that gender inequality is still pervasive in science, technology, engineering and mathematics (STEM). Coastal geoscience and engineering (CGE) encompasses professionals working on coastal processes, integrating expertise across physics, geomorphology, engineering, planning and management. The article presents novel results of gender inequality and experiences of gender bias in CGE, and proposes practical steps to address it. It analyses the gender representation in 9 societies, 25 journals, and 10 conferences in CGE and establishes that women represent 30% of the international CGE community, yet there is underrepresentation in prestige roles such as journal editorial board members (15% women) and conference organisers (18% women).
, 2017). Understanding the current state of gender inequity is particularly important in fields historically dominated by men, such as in coastal geoscience and engineering (CGE), as current practices and regulations may still disadvantage women. CGE encompasses professionals working on coastal zone processes, integrating a broad range of expertise across physics, geomorphology, engineering, and sedimentology. To our knowledge, there is no published research on the gender diversity and equity in CGE. Women in Coastal Geoscience and Engineering (WICGE) is a global network launched on 8th March 2016 at the International Coastal Symposium in Sydney to inspire, support and celebrate women in CGE. Our research builds upon broader studies  (Holmes et al. , 2008 (Holmes et al. , , 2015) )  and brings unprecedented insights on the nature of gender inequality in CGE, through gathering primary data on women's representation in prestige roles, such as membership in committees of professional societies, editorial boards of journals, and organising committees of international conferences.
e. , boards/committees) in nine coastal societies (Supplementary Table  S1 ); (b) 25 journal editorial boards (Supplementary Table  S2 ); and (c) organising committees of ten recurring international conferences (Supplementary Table  S3 ); (2) assessed perceptions and experiences of gender representation in CGE using an international survey (Supplementary Table  S4 ); and, (3) used these findings to construct practical suggestions to address this inequality and inequity (Supplementary Table  S5 ). All data sets analysed are deposited in the Dataverse repository (DOI: 10. 7910/DVN/F1B2FS). Quantifying gender representation in CGE prestige roles Societal membership. We obtained primary data from nine coastal societies from a range of countries in 2016 to evaluate gender representation across the CGE discipline.
e. , the coast, which we defined here as from the landward limit of wave action, to the edge of the continental shelf. Thus, journals on general marine science were not included. This resulted in 25 journal titles in 2016 (Supplementary Table  S2 ). All of these journals were classified in Scopus as Physical Sciences at the top level, and at the next level, by at least one of: Earth and Planetary Sciences, Engineering, and Environmental Science. International conference organising committees. We assessed gender representation on the organisational committees of the largest, regular international conferences in the remit of CGE, for which we were able to obtain data on the organising committee. Each of these conferences has ''coastal'' in the name, or has it as a key theme (Supplementary Table  S3 ).
Results and discussion Our results showed underrepresentation of women in prestige roles in the CGE. The following sections present and analyse our findings, first focusing on the quantitative aspects obtained from our primary data gathering on societies, journals and conferences. We then analyse the reasons behind the underrepresentation using the responses obtained in the survey. Women represent 30% of the CGE community. There were no available sources of information to quantify women's representation in CGE prior to this study. Therefore, we used the membership in key professional societies with a coastal focus, as a proxy for women's representation in CGE. We contacted nine key coastal societies worldwide (Supplementary Table  S1 ) requesting information on membership gender representation, obtaining the required data for six of them.
However, our data and existing publications (e. g. ,  Huyer, 2015; Nielsen et al. , 2017)  suggest that more effort is needed in retaining and promoting women to prevent pipeline loss in CGE. We believe that CGE can achieve these practical steps by ensuring the following three key objectives: (1) unbiased education at all levels; (2) transparency in hiring, promotion processes and pathways to prestige roles; and, (3) establishing mentoring programs for females in CGE. We acknowledge that some of the seven steps listed above can be successfully implemented straight away (steps 1-4); steps 5 and 6 need institutional engagement; and, step 7 is a decadal endeavour. However, all the proposed steps require action of the CGE community and can start immediately. The Women in Coastal Geoscience and Engineering (WICGE) network is already successfully implementing some of these steps by influencing conference organisers to include keynote talks from female researchers and societies to improve gender balance.
Gross land-use area transition datasets including shifting cultivation practice  (Hurtt et al. , 2011)  and reconstructions using empirical ratios between gross and net transitions  (Fuchs et al. , 2015)  are now available and have been implemented in a bookkeeping model  (Hansis et al. , 2015)  as well as in some land carbon models to improve the estimate of ELUC  (Fuchs et al. , 2016; Shevliakova et al. , 2009; Stocker et al. , 2014; Wilkenskjeld et al. , 2014; Yue et al. , 2017). Bookkeeping models use response curves for biomass and soil carbon stocks consecutive to LUC disturbance and time-series of LUC areas to estimate ELUC  (Hansis et al. , 2015; Houghton, 1999). Response curves can be linear  (Houghton, 1999 (Houghton, , 2003)) , exponential  (Hansis et al. , 2015)  or of other types.
As a conceptual analysis, the assumptions we made raise uncertainties. First, the logarithmic biomass recovery curve adopted in  Poorter et al. (2016)  does not seem to be appropriate for LUC emission modelling because it does not reach an equilibrium state. We thus fitted the data from  Poorter et al. (2016)  with an exponential saturating curve to avoid this issue. Second, we used a median biomass recovery rate for the whole tropical moist forest region in Latin America. In reality, however, due to the different climate, soils and other ecosystem conditions, recovery rates vary, and thus spatially explicit recovery rates should better depict regional patterns of secondary forest regrowth and net LUC emissions. In the dry tropics, the critical ratio values may be smaller because of the slower biomass recovery rates.
, 2012) , while the new planted forest in rotation practice and even old-growth forests can act as carbon sinks  (Luyssaert et al. , 2008). In theory, likewise, a critical ratio value should exist to balance the bi-directional carbon fluxes in forest management practices. An advantage of this concept of critical ratio is that it can be directly measured with satellite observations, which provides a quick guide for local land use management practice through near-real-time forest cover change data (e. g. Global Forest Watch http://www. globalforestwatch. org/). Accurate estimates of LUC carbon fluxes in the Neotropical forests are increasingly important for climate mitigation policy with the progressive implementation of Reducing Emissions from Deforestation and forest Degradation (REDD+) programs under the United Nations Framework Convention on Climate Change (UNCCC)  (Angelsen et al.
4c , KnitGypsy-Dnmt3) and show conserved key functional amino acids (Supplementary Fig. 13a ). Moreover, the DNMT domains across distinct copies of KnitGypsy-Dnmt3 show evidence for purifying selection (Ka/Ks < 0. 2, Supplementary Fig. 13b ), suggesting that they are actively conserved. LTR Gypsy retrotransposons are distantly related to DIRS or LINE retrotransposons (Supplementary Fig. 14a ), thus there is no relationship with the Symbiodinium retrotransposons. These data nitens. b Methylation induction in pYES2-DNMT-expressing yeast lines measured in duplicates (r1 and r2) as a percentage of mC calls against all Cs in each of the four dinucleotide sequence contexts. Independent measures of methylation levels reported for the episomal pYES2 construct and the native yeast genome. Data for the non-conversion for each experiment (spiked-in lambda genome) is in Supplementary Fig.