dataesr/scientific-paragraphs-categorization huggingface dataset:
15. Figure  1  shows the footprint of all of the observations. Figure  2  shows the transmission curves of all of the filters 24  (filter, atmosphere and detector). COSMOS NIR data come from several sources: WIRCam data  (Mc-Cracken et al. 2010 ) covering the entire field, and Ultra-VISTA  (McCracken et al. 2012 ) data, covering the central 1. 5 deg 2. The UltraVISTA data includes the DR2 "deep" and "ultra-deep" stripes. Note that this implies that the depth and completeness in our final catalog are not the same over the whole COSMOS field because they are derived in part from these data. The COSMOS2015 catalog also offers a match with X-ray, near ultraviolet (NUV), IR, and Far-IR data, coming, respectively, from Chandra, GALEX, MIPS/Spitzer, PACS/Herschel and SPIRE/Herschel. In this paper, we limit ourselves to the inner, deep part covered by both UltraVISTA-DR2 and the z ++ band (which is flagged accordingly in our catalog).
We denote as A UD the part of the field covered by the "ultra-deep stripes" (K s = 24. 7 at 3σ in a 3 diameter aperture) and as A UVista the full region covered by UltraVISTA-DR2 (K s = 24. 0 at 3σ in a 3 diameter aperture). A Deep is the difference between A UVista and A UD. In our analysis, we limit ourselves to the intersection of A UD and A Deep within the 2 deg 2 COSMOS area YKHKs-z ++ detection image (as described in section 3). For reference, the region covered by the COSMOS-Advanced Camera for Surveys (ACS) HST data  (Koekemoer et al. 2007 ) is shown in cyan. A COSMOS defines the 2 deg 2 COSMOS square (dark blue). A Uvista (orange area) is the region covered by the UltraVISTA-DR2 observations. We define A UD as the light green area, corresponding to the ultra-deep stripes in the UltraVISTA-DR2 observations.
A Deep is the difference between A UVista and A UD. In our analysis of the performance of the catalog, we limit ourselves to the intersection between A UD with A COSMOS and A Deep with A COSMOS , after removing the masked objects in the optical bands (A !OPT , not shown on this figure). The effective areas are given in Table  7. after removing the masked area in the optical. The effective areas corresponding to these intersections are 0. 46 deg 2 for A UD and 0. 92 deg 2 for A Deep. Details of these flagged regions can be found in Table  7  (section 7. 1) and on Figure  1. All of the input data are summarized in Table  1. The limiting magnitudes can be observed in Figure  3.
Optical-ultraviolet data The optical-ultraviolet data set used here is similar to those used in previous releases  (Capak et al. 2007; Ilbert et al. 2009). It includes near-UV (0. 23µm) observations from GALEX  (Zamojski et al. 2007) , u *band data from the Canada-France Hawaii Telescope (CFHT/MegaCam), and the COSMOS-20 survey, which is composed of 6 broad bands (B, V , g, r, i, z + ), 12 medium bands (IA427, IA464, IA484, IA505, IA527, IA574, IA624, IA679, IA709, IA738, IA767, and IA827), and two narrow bands (N B711, N B816), taken with Subaru Suprime-Cam  (Taniguchi et al. 2007 (Taniguchi et al. , 2015)). We have discarded poor seeing (∼ 1. 3 ) g-band data. Finally, the initial COSMOS z-band data were replaced by deeper z ++ band data taken with thinned upgraded CCDs and a slightly different filter.
NIR data The Y JHK s -band data used here were taken between 2009 December 2009 and 2012 May with the VIRCAM instrument on the VISTA telescope as part of the Ul-traVISTA survey program and constitute the DR2 Ul-traVISTA release 25. The UltraVISTA-DR2 processing steps are the same as those in the DR1 release  (Mc-Cracken et al. 2012). Compared to DR1, the exposure time has been increased significantly in the ultra-deep stripes, as shown in yellow in Figure  1 ; these cover an area of 0. 62 deg 2. An important consequence of this is that the signal-to-noise ratio for an object of a given magnitude is not constant across the image. To provide NIR photometry in zones not covered by UltraVISTA, we include H and K WIRCAM data  (McCracken et al. 2010)  in our photometric catalog. However, this paper does not discuss the performance of photometric redshifts and physical parameters in these WIRCAM-only areas.
Mid-Infrared data The 3. 6µm, 4. 5µm, 5. 8µm and 8. 0µm (respectively, channel 1, 2, 3, and 4) IRAC data used in this paper consist of the first two-thirds of the SPLASH COSMOS dataset together with S-COSMOS  (Sanders et al. 2007 ), the Spitzer Extended Mission Deep Survey, the Spitzer -Candels survey data, along with a several smaller programs that observed the COSMOS field. The final processing is described in a companion paper  (Capak et al. 2015 in prep). The average exposure time per pixel is 3. 8 hr, increasing to 50hr in the central S-CANDELS coverage. Before processing, a median image was created for each AOR (observing block) and subtracted from the frames to remove residual bias in the frames and persistence from previous observations. For the S-CANDELS data, a secondary median was subtracted from the observations taken with repeats to remove the "first frame effect" residual bias.
This is the most extreme case in our catalog. 1. 0 1. 5 2. 0 2. 5 F i ( <r)/F T ( <r) 0. 0 0. 5 1. 0 1. 5 2. 0 r(") 0. 5 1. 0 1. 5 2. 0 2. 5 F i ( <r)/F T ( <r) u B V r ip zpp IB427 IB464 IA484 IB505 IA527 IB574 IA624 IA679 IA738 IA767 IB709 IB827 NB711 NB816 yHSC Y J H Ks Hw Ksw 0. 52 0. 72 1. 02 1. 1. 5 r k (") 0. 0 0. 5 1. 0 1. 5 2. 0 m i (r k )-m iTotal age  (Szalay et al. 1999 ) is produced using SWarp  (Bertin et al. 2002)  starting with the non-homogenized images. Since the main objective of our new catalog is to probe the high-redshift Universe and also to provide a catalog containing UV-luminous sources at z > 2, we create a detection image by combining NIR images of UltraVISTA (Y JHK s ) with the optical z ++ -band data from Subaru. We do not use i + -band data since compact objects in the i + image saturate around i = 21.
IRAC photometry The SPLASH data exceed the confusion limits in IRAC channel 1 and channel 2; better deblending techniques are necessary to estimate fluxes in these crowded, lowresolution images. We use IRACLEAN (Hsieh et al. 27 The version of the catalog used is available at http://irsa. ipac. caltech. edu/data/COSMOS/tables/photometry/. This catalog supersedes that of  Capak et al. (2007)  with improved source detection and photometry. 2012) to derive the SPLASH photometry. This makes use of the positional and morphological characteristics of objects detected in a high-resolution image in a different waveband to deblend objects and derive more accurate fluxes in low-resolution images. Unlike other similar methods assuming that the intrinsic morphology of an object is identical in the two wavebands (i.
The astrometric zero point of this catalog was set using radio interferometric observations (Schin-  nerer et  al. 2004). Until now, at scale smaller than the size of the resampled pixels, it has been challenging to test the astrometric accuracy for our catalog given the lack of availability of sufficiently dense astrometric catalogs. However, we have compared the positions between our catalog and the catalogs extracted from the independently reduced Hyper Suprime-Cam images described here, and this has confirmed that our astrometric solutions are good at the level of one pixel. For future data releases, we intend to improve our overall astrometric precision by using densely sampled catalogs based on either Hyper Suprime-Cam or Pann-Starrs data, which are tied to 2MASS. detected objects. Faint objects may have a physically meaningful flux measurement, whereas their magnitudes and magnitude errors may be undetermined (for example, if the flux is negative).
We find that the 3 aperture photometry gives slightly better photometric redshifts than the 2 aperture at low redshift (below z ≤ 1) and we adopt this aperture over the entire redshift range of our survey. We suspect that the photometric redshift precision is lower in the 2 apertures due to small-scale residual astrometric errors. This is being investigated for the upcoming DR3 UltraVISTA release. Photometric redshift computations use colors, and, consequently, should not be sensitive to a systematic magnitude calibration offset. However, in contrast to optical and NIR data, GALEX and IRAC data provide total magnitudes or fluxes, which require an estimate of the total flux from the corrected 3 aperture fluxes to be consistent over the full wavelength range. This is also needed to derive stellar masses.
Catalog descriptionThe details of the regions flagged in the catalog are presented in Table ANR-13-BS05-0005 of the French Agence Nationale de la Recherche. H. J. M. C. C. acknowledges financial sup- port form the "Programme national cosmologie et galax- ies" (PNCG). O. I. acknowledges the funding of the French Agence Nationale de la Recherche for the project "SAGACE". J. D. S. is supported by JSPS KAKENHI Grant Number 26400221, the World Premier Interna- tional Research Center Initiative (WPI), MEXT, Japan and by CREST, JST. B. M. J. and J. P. U. F. acknowledge support from the ERC-StG grant EGGS-278202. The Dark Cosmology Centre is funded by the DNRF. S. T. and M. S. acknowledge support from the ERC Consolida- tor Grant funding scheme (project ConTExt, grant num- ber No. 648179). This research is also partly supported by the Centre National dEtudes Spatiales (CNES).
This work is based on data products from observations made with ESO Telescopes at the La Silla Paranal Observa- tory under ESO programme ID 179. A-2005 and on data products produced by TERAPIX and the Cambridge As- tronomy Survey Unit on behalf of the UltraVISTA con- sortium. VS acknowledges the European Unions Sev- enth Framework pro-gramme under grant agreement No. 337595. 7. APPENDIX 7. 1.
g. , markers differ in information content among taxa, ease of amplification with Sanger sequencing or capture with hybrid enrichment techniques, and quality of source material) and because of lack of consensus on which markers to use for inferring phylogenies. For these reasons, compiling data sets for phylogenetics usually involves some combination of automated and manual data cleaning, data selection, and data integration. For example, to clean up candidate molecular data sets from GenBank  (Clark et al. 2016)  for phylogenetic inference, simple and automated rules can be applied to filter out short DNA sequences with little reciprocal overlap, sequences with significant amounts of missing data, sequences with poor taxonomic annotations (e. g. , without full species names), and sequences that are unlikely to be true orthologs as determined by automated orthology assessment methods.
Several data cleaning and data selection pipelines have been developed to automate some of these steps for the purpose of generating suitable input data sets for phylogenetic inference. The PhyLoTA pipeline (http://phylota. net, Sanderson 2008; Sanderson et al. 2008)  pre-processes entire GenBank releases in pursuit of sufficiently overlapping reciprocal BLAST hits, which are then clustered into candidate data sets. This has the virtue that no assumptions are made about gene name annotations, but a drawback is that this "all-versus-all" approach is only computationally feasible for taxonomically constrained subsets of data from GenBank (e. g. , only all sequences within a family or order). A more targeted approach is taken by the Phylogenetic Dataset Construction toolkit (PHLAWD, http://phlawd.
net/) (Smith et al. 2009). This pipeline adds candidate sequences (identified by querying GenBank records for user-specified gene name annotations) to a user-provided set of seed sequences, provided that their reciprocal BLAST hit overlap is sufficient (this latter step is comparable to how PhyLoTA filters candidate cluster members). Although this results in data sets that are taxonomically broader than those obtained by PhyLoTA, a drawback is that under this approach only requested markers are collected-meaning that no unrequested regions are retrieved even if they contain phylogenetic information. Another drawback is that users need to specify all possible variations in the naming of gene regions. For instance, anyone who seeks to download 16S sequences from GenBank will encounter a nearendless array of orthographic and conceptual variations such as "16 S," "16S," "17S," "SSU," "ribosomal small subunit," and "ribosomal small sub-unit" (note, however, that PHLAWD only uses gene names to locate candidate sequences, subsequently validating them by homology searches).
org) and the Bio* toolkits in the Python, Ruby, Java, and Perl programming languages (http://open-bio. org), as well as stand-alone and online workflows (e. g. , http://www. arborworkflows. com and http://www. biovel. eu). Since the choice of methodology will always depend on the research question, the nature of the data, and the researcher's individual skills and knowledge to select and carry out analyses, any bioinformatic workflow to handle large amounts of data needs to be highly modular and flexible while retaining data standards to secure interoperability. Researchers should be allowed to make their own choices concerning, for instance, the inclusion/exclusion of taxa, the choice of genetic markers, which fossils and methodology to employ for molecular dating, the delimitation of areas for biogeographical and diversification analyses, and which analytical tools to use.
PRESENTING THE SUPERSMART APPROACH To address the challenges outlined above, we introduce a new conceptual and bioinformatic approach: SUPERSMART (http://www. supersmart-project. org). SUPERSMART provides a platform for estimating time-calibrated molecular trees for potentially all sequenced eukaryote species, offering researchers a flexible, modular, integrative, and open-source platform for hypothesis-driven research in systematics, ecology, biogeography, and evolutionary biology. The package allows users to generate custom-made sets of robustly inferred, dated trees for further analyses, or to assemble aligned DNA data sets representing optimal combinations of sequenced genes/markers and taxa (see Fig. 1  for a comparison of SUPERSMART with supertree and supermatrix approaches).
Overview SUPERSMART is implemented as a modular framework making the bridge between the data handled (sequences, taxa, fossils, and trees), the records in a relational database that contains local copies of a number of public resources, and the operations needed to assemble these records into tailored data sets and analyze them. SUPERSMART is available as a virtual machine, and can be installed in environments that support the hosting and provisioning of free operating systems of the Linux family. Because SUPERSMART is delivered as a self-contained package with all necessary dependencies including nucleotide data, and each version is long-term stored and version-tracked, it also increases reproducibility of studies. The standard implementation runs through the command line, but a graphical user interface providing simpler functionality is also available through the Biodiversity Virtual e-Laboratory platform (http://biovel.
These trees are then re-calibrated (scaled) to the posterior age obtained for the clade in the first step to yield dated species-level trees. 3. The individual dated species-level trees are then grafted onto the backbone of the first step to obtain a complete species-level chronogram of directly comparable ages. Data Selection SUPERSMART mines public databases for suitable DNA sequences by way of their globally unique taxonomic identifiers. Our present proof of concept adopts NCBI taxon IDs  (Federhen 2012) , but this could be extended to recognize other unambiguous identifiers, such as internet addresses or uniform resource identifiers. The user only needs to provide a list of taxa (species, genera, or higher taxonomic levels) to be included in the phylogeny. SUPERSMART expands any higher level taxon down to species level and maps all descendant species names onto unambiguous identifiers, by querying the TaxoSaurus service (http://taxosaurus.
org) while taking into account synonyms and misspellings. TaxoSaurus follows the same synonyms as the NCBI taxonomy and it does some amount of fuzzy matching, but may occasionally return mismatches. Also, even when names match exactly they may not reflect the intent of the user: rare taxonomic homonyms do exist (e. g. , across zoological and botanical nomenclature), and the NCBI taxonomy may recognize a different taxon concept-broader or narrower-than the user intended. The result of this name matching procedure is, therefore, written to a spreadsheet file available to the user for additional validation. SUPERSMART then compiles candidate sets of DNA sequences for alignment, orthology assessment, and subsequent phylogenetic inference by querying a local, modified version of the PhyLoTA database.
This database is the product of a workflow that crawls all taxonomically organized GenBank sequence divisions in the NCBI taxonomy, and performs all-versus-all similarity searches of the sequences subtended by each node. The sets of search hits are then grouped VOL. 66 FIGURE 2. Basic overview of the three-step approach implemented in SUPERSMART. a) A backbone tree is inferred for four hypothetical genera (A, B, C, and D), each represented by two exemplar species. The backbone is calibrated using a fossil on the node indicated with a star (which may have an own confidence interval). In this example, two genera (C and D) appear in this analysis to be polyphyletic. b) The backbone tree is decomposed into three sets of taxa (red, blue and green) containing all the intrageneric taxa for which sufficient data are available.
Multiple Sequence Alignment Following these first steps, the DNA sequences stored in the SUPERSMART database are unaligned. As merged clusters can ultimately grow to very large numbers of sequences, we designed the pipeline in such a way that multiple sequence alignment takes place as a two-step process. First, the clusters as assigned by PhyLoTA are aligned (after data reduction). Many programs for multiple sequence alignment exist and can potentially be used by SUPERSMART; wrappers for several of them are provided. By default the pipeline uses MAFFT  (Katoh and Standley 2013) , which gives good results on a variety of different markers and has the virtue of being able to auto-select its alignment strategy given the data-something that comes in handy for SUPERSMART. Second, orthology among clusters at taxonomic levels higher than PhyLoTA can manage is assessed using the all-versus-all approach described above, but applied only to the seed sequences around which the candidate clusters were built.
Time Calibration Using Fossils SUPERSMART maps all suitable user-provided fossil records belonging to the focal clade onto the backbone trees inferred in the previous step. The trees are then dated using the relaxed clock algorithm Penalized Likelihood  (Sanderson 2002) , as further developed and implemented in treePL  (Smith and O'Meara 2012) , which can handle very large numbers of terminals. This step inputs samples of trees to produce confidence intervals of node ages rather than point estimates. In the future, SUPERSMART will take advantage of the Fossil Calibration Database  (Ksepka et al. 2015)  for automated fossil retrieval to calibrate nodes on backbone trees.
2016b ). We also use the Primates to explore what effect different combinations of parameter settings have on supermatrix assembly, and on the robustness of subsequent phylogenetic inferences. In contrast, palms comprise a much larger number of extant species (2561 according to Catalogue of Life) and several economically important genera such as the coconut (Cocos), date (Phoenix), and oil palm (Elaeis), but have received less attention and are, therefore, the subject of much lower taxonomic and genetic coverage in public sequence databases. The palms are also used here to highlight the use of SUPERSMART in historical biogeography. Table  1  summarizes the statistics on both primates and palms included in the SUPERSMART analyses. Primates To illustrate the trade-offs imposed by various parameters that influence the data selection process in primates, we explored their parameter space.
We tested the effect of imposing different thresholds of the minimum number of markers necessary for a species to be included in the analysis (Supplemetary Fig. S1  and Supplementary Material available on Dryad at http://dx. doi. org/10. 5061/dryad. sk81k). As expected, as more markers are required per taxon, the number of taxa decreases rapidly. While here we show what happens when all taxa must meet the same threshold (Supplemetary Fig. S1  available on Dryad), a more pragmatic approach is to set the minimum number of markers to a low value, but allow the maximum number to be higher, so that at least some-if not all-included taxa will have greater marker coverage. We have found that this approach increases the density of the "marker graph" (a network in which taxa are connected if they share a certain marker), reducing both its modularity and the average path length between taxa.
We also explored how the minimum number of required markers and their maximum amount of divergence affect the posterior probability of nodes (Supplementary Fig. S2  available on Dryad). The average posteriors in this example are largely a function of the number of markers. Even at high levels of allowed divergence (up to and beyond 20%) we find no evidence of deteriorating results (e. g. , due to saturation), whereas these settings allow for the acceptance of more candidate markers, which may be important in poorly sequenced groups. Figure  4a  shows the simplified results from the analysis of primates, whereas Supplementary Figure  S3  available on Dryad presents the fully annotated species tree. The species tree of the Primates comprise 251 species, calibrated using the same fossils as in  Vos and Mooers (2004).
Palms The palm phylogeny was calibrated with the macrofossils †Hyphaene kapelmanii, Mauritiidites crassibaculatus, Sabalites carolinensis, and Tripylocarpa aestuaria  (Harley 2006 ) using topological placements based on morphological synapomorphies (Supplementary Table  S1  available on Dryad). In addition, we included a well-preserved flower in Dominican amber ( †Socratea brownii)  (Poinar 2002) , which has been used to constrain the crown node of extant Socratea due to its sessile, staminate flowers with high stamen number (20-100), which are diagnostic floral characters found among extant species in the genus  (Bacon et al. 2016 ). An exponential prior was applied, with mean = 22. 5 Ma and standard deviation = 1. 17. The phylogenetic analysis recovered highly supported subfamilial relationships that are consistent with previous studies  (Baker et al.
2009 ) and the current understanding of the morphological evolution in the group  (Dransfield et al. 2008 ) (Fig. 4b  and Supplementary Fig. S4  available on Dryad). Most of the major tribes and genera are also resolved as monophyletic with strong support, except for subfamily Coryphoideae and its members that are inferred as a polytomy. The mean crown age of the family is younger (85. 8 Ma; Supplementary Table  S2  available on Dryad) compared to one previous analysis estimating it to ca. 100 Ma  (Couvreur et al. 2011). The crown nodes of each of the subfamilies differ between the SUPERSMART and earlier results, some being older and others younger than previous estimates (Supplementary Table  S2  available on Dryad). We also compared our tree topology with a recently published palm phylogeny  (Faurby et al.