dataesr/scientific-paragraphs-categorization huggingface dataset:
14 ). Consistently with  Boselli et al. (2015) , we use the whole HRS late-type sample but ignoring the HI-deficient galaxies. The S FR is studied considering the mean value derived using the three tracers M dyn , M bar and M star. The number of galaxies Coefficients obtained for the different main sequence relations log S FR(M yr -1 ) = (β ± σ β ) + (α ± σ α ) log M galaxy (M ) for the HRS star-forming sample. S FR are derived using a Chabrier IMF. differs for the three cases according to the data available for each tracer. OLS bisector fits to the data have been computed. Fig. 14  and Table  7  show that the similar main sequence relations are present using the three different galaxy mass estimators. The slope and the scatter of the three relations are comparable. They obviously differ in their zero points given that M dyn > M bar > M star.
i) We derive the i-and the 3. 6 µm-band Tully-Fisher relations and compare them to those obtained for larger samples in the literature. Despite the difference in the kinematic data (Fabry-Perot Hα rotation curves in this work vs. Hi line width profiles or long slit optical spectra in the literature), in the dynamical range of the sample, and in the statistics, the different TF relations are very consistent, suggesting that the HRS can be taken as a representative sample for these scaling relations in the local universe. ii) Thanks to its unique multifrequency coverage, we use this dataset to derive the baryonic TF relation and the relation between the baryonic mass and the dynamical mass of galaxies. The baryonic mass is for the first time measured using direct estimates of the stellar, atomic, molecular, dust and metal mass of galaxies.
The intrinsic scatter in the baryonic TF relation (∼0. 16 dex) is in agreement with the predictions of cosmological simulations. The baryonic component is dominated by the stellar mass in massive objects and by the total gas mass (Hi and H 2 ) in low mass systems. The dust content is just a very small fraction (∼0. 2%) of the total baryonic mass, while the contribution of metals is fairly constant at ∼0. 9%. iv) We computed the baryonic and dynamical main sequence, finding relations with a similar slope and intrinsic scatter than the stellar mass main sequence. The observations of the whole star-foming galaxy sample will be completed in forthcoming runs. Combined with those available at other frequencies, these 2D-spectroscopic data make the HRS a unique dataset for studying on strong statistical basis the role of gas kinematics on the process of star formation, a major step towards the understanding of the mechanisms that drive galaxy evolution.
Poor S/N or small spatial coverage lead easily to non-realistic kinematical fittings. f) the ratio between the Hα V max of our Fabry-Perot dataset and the V max of HI data  (Boselli et al. 2015)  is bigger than 1. 5 or lower than 0. 5. Since the rotation curve is derived from the velocity field, poor S/N or small Hα emission spatial coverage, in addition to asymmetries in the velocity field, could easily lead to an under/over estimation of the V max of the galaxy. g) the normalized RMSD between both sides of the RC is lower than 0. 6. This distribution was divided in 4 quantiles with the same number of galaxies per quantil (35), in order to obtain four different Flag quality values indicated by the three vertical lines starting from the right to the left: Flag "1", Flag "2", Flag "3", Flag "4".
Acknowledgements. Based on observations taken at the  Observatoire de Haute Provence (OHP, France ), operated by the  French CNRS. The authors warmly thank  Olivier Boissin  from  LAM  and the  OHP team  for its technical assistance before and during the observations, namely the night team:  Jean Balcaen ,  Stéphane Favard ,  Jean-Pierre Troncin ,  Didier Gravallon  and the day team led by  François Moreau  as well as  Dr. Auguste Le Van Suu , the  Head of Observatoire de Haute Provence-Institut Pythéas. The authors thank the Mexican grants  CONACYT-253085  and  DGAPA-UNAM PAPIIT-IN109919  which have extensively supported this work. This work was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. This research has made use of data from the  HRS  project.
HRS is a Herschel Key Programme utilising guaranteed time from the  SPIRE  instrument team, ESAC scientists and a mission scientist. This research has also made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. The authors have also made an extensive use of the HyperLeda Data base (http: //leda. univ-lyon1. fr). The Digitized Sky Surveys were produced at the  Space Telescope Science Institute  under  U. S. Government  grant  NAG W-2166. The images of these surveys are based on photographic data obtained using the  Oschin Schmidt Telescope on Palomar Mountain  and the  UK Schmidt Telescope. The plates were processed into the present compressed digital form with the permission of these institutions.
55 m telescope with 3 deg diameter FoV, and the Javalambre Auxiliary Survey Telescope (JAST/T80), an 83 cm telescope with a FoV diameter of 2 deg. JAST/T80 is the telescope dedicated to the development of J-PLUS , whereas J-PAS will be carried out at the JST/T250. The definition, design, construction, exploitation and management of the observatory and the data produced at the OAJ are responsibility of the Centro de Estudios de Física del Cosmos de Aragón (CEFCA 4  ). The OAJ project started in March 2010, mostly funded by the Fondo de Inversiones de Teruel, a programme supported by the local Government of Aragón and the Government of Spain, and is essentially completed since 2015. In October 2014, the OAJ was awarded with the recognition of Spanish ICTS (Infraestructura Científico Técnica Singular) by the Spanish Ministry of Economy and Competitiveness.
This first processing uses a lightweight version of the pipeline, for real time visual inspection and first order diagnosis. Also, right after image acquisition, raw data are automatically transferred to the UPAD at CEFCA for archival and a complete processing. This is done via a dedicated radio-link that connects the OAJ with CEFCA headquarters. Once the raw data reach the main UPAD archive, they are automatically uploaded to a management database which stores all the metadata of the collected images. This database also stores all the processes done on the data, and is used to control whether the inputs for each stage are ready, and to automatically prepare the jobs to be executed. On a daily basis, the Data Management Software automatically processes the data collected during the last night in order to check their quality.
It next updates the survey databases and feeds the Scheduler to compute the telescope targets of the following nights. Eventually, in order to optimize the scientific quality of the data, the pipelines reprocess all the images with the optimal master Calibration Frames (CFs) once they are available. The image processing pipelines have three main stages. The first one is related to the generation of master CFs, which are used to correct for the instrumental imprint on the individual images at a later stage. These CFs consist of a master bias, the master dome or sky flats, a fringing pattern, and the illumination correction maps. To compute any of those CFs the pipeline requires as input the time interval and the instrumental configuration (telescope, camera and detector parameters, read-out configuration, etc).
With this information in hand, the pipeline queries the database for the needed images and processes the master CFs. Once the CFs are generated, they are made available, through a web service, together with relevant information that allows to assess their quality. Finally, the master CFs require human validation to be used in further processing of science images. The second stage is related to the processing of the different individual images. The pipeline corrects the instrumental signature using the validated CFs. The corrections (bias, flat fielding, fringing, etc. ) that need to be applied to each image type are stored in the configuration files. Since the proper CFs for each image depend upon the required corrections, the observing epoch and the instrumental configuration, the pipeline must query the management database in order to assign the correct CFs.
The pipeline makes use of the packages Scamp  (Bertin 2006)  and Swarp  (Bertin et al. 2002)  to perform the astrometric calibration and image coadding. The combined images for a given tile in the whole set of filters constitute a datacube. SExtractor source catalogues are extracted from the final tiles and datacubes and are stored in an internal database system. Two kind of catalogues are produced. In the first one, the source detection and segmentation are done independently for the combined images in each filter. In the second one, the detection and aperture definition is done in the r band, which is used as a reference image. Prior to computing the final catalogues, the PSF of each filter image is homogenized to match the one of the r filter, defined as reference filter in J-PLUS. This technique was already used to produce the photometric catalogues for the ALHAMBRA survey  (Molino et al.
j-plus. es/datareleases/early_data_ release The J-PLUS EDR comprises 18 J-PLUS pointings amounting to 31. 7 deg 2 after masking. The tiles belonging to the J-PLUS EDR were selected to i) have a limiting magnitude as close as possible to the final J-PLUS goal in all the filters simultaneously, ii) have no evident issues neither in the photometry nor in the source catalogues, and iii) define a reasonably compact region on the sky, as presented in Fig. 4. The FWHM and ellipticity distributions of the J-PLUS EDR tiles in the r band are shown in Fig. 5. These values are consistent with those of the parent J-PLUS data, and show average FWHM lower than 1. 5 arcsec. The completeness for stars (point-like sources) and galaxies (extended sources) was computed by comparing with the SDSS dataset in the common areas (see  López-Sanjuan et al.
0079, We stress that the J-PLUS SVD are not strictly considered J-PLUS data due to the following reasons: i) the photometric depths and observing conditions could be slightly different to the ones set for J-PLUS ; ii) the observed fields can be either outside the J-PLUS footprint or have different coordinates than the predefined J-PLUS tiles; iii) a versioning of the SVD is not performed, i. e. , the data of the different projects could have been processed with different versions of the pipeline; iv) the source catalogs are not necessarily the official catalogs created by the UPAD inside the J-PLUS collaboration, but could have been produced by different J-PLUS members; and v) the database backend is neither available nor designed for these data. Still, the J-PLUS SVD provides very interesting data that deserve to be presented and made public to illustrate some science cases that can be developed with J-PLUS.
We have made use of these pattern recognition tools for stellar parameter determination using the J-PLUS photometric system. The training dataset for the ANNs is taken from the SEGUE  (Yanny et al. 2009 ) database, in which stellar parameters were derived from the observed (flux calibrated) spectra using the SEGUE Stellar Parameter Pipeline (SSPP,  Lee et al. 2008 ). Synthetic magnitudes are first generated for these spectra by convolution with J-PLUS filter response functions. Upon verification of the proper network inputs, functional forms for these synthetic magnitudes are then calibrated to the J-PLUS photometric system using ∼ 1 000 overlapping observations from the J-PLUS EDR. After training and for the final validation of the ANN methodology, the temperature and metallicity networks are next applied to J-PLUS photometry using the EDR.
Thus, the characterization of the ELG population with J-PLUS data is expected to be highly complementary to that coming from MOS facilities. Assuming a minimum observed equivalent width EW= 100 Å, we estimate that we can reach the [O ii] line emission down to the luminosity of ∼ 1 × 10 42 erg s -1. These objects will be used to characterize the abundance and clustering properties of star-forming galaxies at z = 0. 77  (Orsi et al. , in prep. ) 4. 3. 2. Lyα emission at z = 2. 2 and 4. 4 At higher redshifts, detections are strongly limited by the survey depth. Star-forming Lyα emitting galaxies (Lyα emitters, or LAEs in short) at z ∼ 2 are expected to be detected down to Lyα luminosities of ∼ 5 × 10 43 erg s -1 assuming a minimum observed EW of 100 Å. At z = 4. 4, we expect to detect LAEs down to ∼ 9 × 10 43 erg s -1.
Sobral et al. (2015)  have confirmed spectroscopically an extremely luminous LAE at z = 6. 604, named CR7, with Lyα luminosity of 8. 3 × 10 43 erg s -1. A thorough census of these extreme objects has not been yet possible due to the prohibitively large volume required. The J-PLUS dataset can thus perform an unprecedented search for these extremely bright emitters at high redshifts (Fig. 24 ). Furthermore, J-PLUS is also expected to detect Lyα blobs (LABs) among the usual high-z, star-forming, Lyα emitting, compact galaxies. LABs are an extremely rare class of Lyα emitters (comoving number density n c < 10 -6 Mpc -4 ) showing spatially extended Lyα emission (∼ 30 -200 kpc in size, see e. g. ,s  Smailagić et al. 2016). One straightforward approach to select LAE candidates is the three-filter method of  Vilella-Rojo et al.
2010) , the low spectral resolution tends to render the detection of the 0. 7 µm band unreliable using SDSS data  (Rivkin 2012). In this regard, the larger spectral resolution provided by the J-PLUS filter system can provide a better definition of this band, since with J-PLUS the spectral interval covered by the band is sampled also by 2 narrow band filters in addition to the r, i, and z filters. This can be seen on the reflectance spectrum of the asteroid (1024) Hale, obtained from observations taken during normal survey conditions, see Fig. 27. One way to compare and quantify the sensitivity of the SDSS and J-PLUS filter systems to the 0. 7 µm band is to use low resolution spectra of asteroids with this feature as templates to produce synthetic reflectances for each filter. We use 5 spectra of asteroids from the hydrated asteroid family of Erigone to produce a sample of 1000 synthetic spectra with Gaussian random uncertainties in reflectance driven around the nominal reflectance values and with nominal RMS.
In particular, the results obtained with the J-PLUS filter set present a considerable smaller number of false positive in comparison with data obtained with the SDSS filters, with the difference as high as ∼ 29 % less false detections for 1 % uncertainties in the reflectances. Using data from the observations obtained so far it is possible to estimate the depth at which asteroids can be expected to be observed during the survey. We find that the detection efficiency at the bluer filters starts to fall around V = 17. 5 mag, but for the filters involved in the detection of the 0. 7 µm band the detection efficiency remains high up to V = 19 mag. Simulations indicate that during the survey execution time over ten thousands known asteroids brighter than V = 19 mag will be observed. Therefore, once completed the J-PLUS data set of minor bodies will provide a valuable tool to probe the distribution of hydration in the Main Asteroid Belt.
6 for mean halo metallicity), so they are easily detectable up to a few hundred kpc from us; iii) their pulsation periods obey a period-luminositymetallicity relation that makes them standard candles, becoming very useful to constraint distances; iv) they are relatively old stars, so they are fair tracers of the MW old component. For all these reasons RR Lyrae stars are excellent tracers of the structure of the Galactic halo (e. g. Sarajedini 2011 ). On the observational side, their pulsation amplitude (from 0. 2 to 1. 5 magnitudes) and period (from 5 h to 1. 2 days) make their variability relatively easy to be detected using both, J-PLUS and external data archives. Additionally, J-PLUS will provide the SED of a unprecedented amount of RR Lyrae stars. Two examples are shown in Fig. 30 , taken from the J-PLUS EDR.
The J-PLUS photo-spectra and the SDSS spectra were acquired at different pulsation phases φ, as labeled in the panels, with φ = 0, 1 corresponding to the maximum flux of the source (i. e. , the entire period is covered by the φ range of values φ ∈ [0, 1). Both SDSS spectra were taken not far from their respective maximum brightnesses. unprecedented low resolution photo-spectrum of 12 points for every pixel of the sky in the footprint of the survey. The paper is also aimed to release the first 36 deg 2 of J-PLUS data, amounting to ∼ 1. 5 × 10 5 stars and 10 5 galaxies at r < 21 mag observed in 12 broad, intermediate and narrow optical bands. This dataset is used to illustrate some of the science cases that will be addressed by J-PLUS. As of early 2018, J-PLUS has already mapped more than 1 000 deg 2 , providing unprecedented information of the SED for millions of stars and galaxies.
A forthcoming data release with a significantly larger area to the one presented in this paper is expected to be published along 2018. With a choice of narrow and medium width filters strategically placed to render accurate stellar type information (the 3 700-4 000 Å Balmer break region, Hδ, Ca H+K, the G-band, the Mgb and Ca triplets), the addition of narrow band filters centred upon the Hα/λ6563 and [OII]/λ3727 lines constitutes a powerful window to the star formation activity in the local universe, and also to bright emission lines at higher redshifts. Consequently, J-PLUS is providing a unique data set with direct applications in the characterization of the stellar populations of our Galaxy, including metal poor stars, white dwarfs, ultracool dwarfs, planetary nebulae, symbiotic stars, cataclismic variables, and globular clusters.
The J-PLUS data will be made public progressively in subsequent data releases, an expression of its commitment to become a major legacy project for the astronomy and astrophysics of the next decades. ders program, Young Talent Fellowship, BJT) through grants  A062/2013 and CAPES-PNPD 2940 /2011. The FAPESP grants no. 2015 /12745-6, 2014 /11338-5, 2014 /07684-5, 2013 /04582-4 and 2009/54202-8  are also acknowledged. Finally, the authors acknowledge partial support from grant PHY 14-30152; Physics Frontier Center/JINA Center for the Evolution of the Elements (JINA-CEE), awarded by the US National Science Foundation.
Acknowledgements. Funding for the J- PLUS  Project has been provided by the  Governments of Spain and Aragón  through the  Fondo de Inversiones de Teruel , the  Spanish Ministry of Economy and Competitiveness  (MINECO; under grants  AYA2017-86274-P ,  AYA2016-77846-P ,  AYA2016-77237-C3-1-P ,  AYA2015-66211-C2-1-P ,  AYA2015-66211-C2-2 ,  AYA2012-30789 ,  AGAUR  grant  SGR-661/2017 , and  ICTS-2009-14 ), and  European FEDER  funding ( FCDD10-4E-867 ,  FCDD13-4E-2685 ). Further support has been provided by the  Ramón y Cajal  programmes  RYC-2016-20254 ,  RYC-2011-08262  and  RYC-2011-08529. This research has made use of the  Spanish Virtual Observatory  (http://svo. cab. inta-csic. es)supported from the  Spanish MINECO  through grant  AYA2014-55216. We also acknowledge  Spanish CSIC  ( I-COOP+ 2016 program ) through grant  COOPB20263.
The Brazilian agencies  FAPESP  and the  National Observatory of Brazil  have also contributed to this project. We acknowledge financial support from the  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro -FAPERJ  (fellowship Nota  10 ,, from  CNPq  through  BP  grant  312307/2015-2  and  Universal  Grants  459553/2014-3 ,  PQ 302037/2015-2 , and  PDE 200289/2017-9 ),  FINEP  grants  REF. 1217/13 -01. 13. 0279. 00  and  REF 0859/10 -01. 10. 0663. 00 ,from  FAPERJ  grant  E-26/202. 835/2016 ,and  CAPES (Science without Bor-.
The quality of RNA sequence reads was evaluated using FastQC (version 0. 11. 2)  (Andrews, 2010). Low quality reads and contaminants (e. g. sequence adapters) were removed using Trimmomatic (version 0. 32)  (Bolger et al. , 2014). Sequences that passed the quality assessment were aligned to the mm10 genome using tophat2 (version 2. 0. 14) (Kim et al. , 2013) , with bowtie2 (version 2. 1. 0) (Langmead and Salzberg, 2012)  or for the quiescent NSC RNAseq data set, Cufflinks  (Trapnell et al. , 2010). Transcript abundance level (transcript count) was generated using HTSeq (version 0. 5. 3p9) (Anders et al. , 2015). The transcript counts were further processed using R software environment for statistical computing and graphics (version 3. 4. 0). Data normalisation, removal of batch effect and other variant was performed using EDASeq R package  (Risso et al.
, 2011)  and RUVseq package (Remove Unwanted Variation from RNA-Seq package)  (Risso et al. , 2014). Differential expression was performed using edgeR R package  (Robinson et al. , 2010) , using the negative binomial GLM approach, or for the quiescent NSC RNAseq data set, Cuffdiff (version 7)  (Trapnell et al. , 2013). Differentially expressed genes with false discovery rate (FDR <= 0. 05, Benjamini-Hochberg multiple testing correction), expression level in control samples > 1 CPM (counts per million) or > 1 FPKM (fragments per kilobase of transcript per million mapped reads) for the quiescent NSC RNAseq data set, and log fold change > 1 were retained and used for further processing, gene ontology and pathway analysis.
Quantification and statistical analysis To measure immunofluorescence intensity, the nucleus of each identified RGL was manually outlined based on DAPI staining, and the average pixel value of the channel of interest was measured using FIJI software. Every value was normalized to the background level measured in a negative nucleus in the same z-plane as each RGL. At least 200 RGLs in each of at least three mice were quantified for each protein. For in vitro ICC quantification, average pixel intensity for each channel was measured for the area of each nuclei, using FIJI software. For each experiment, at least 100 cells were quantified across at least three biological replicates. To generate the arbitrary units (A. U. ) for both in vivo and in vitro IHC, all the values within a sample were made relative to the average of the control, and expressed as a %, with the average of control being 100%.
For quantification of RNAscope staining, the number of 'dots' in each identified RGL nucleus were counted for each probe. In addition, the average pixel intensity in and around each RGL nucleus was measured for each probe, using FIJI. 100 RGLs were quantified across five mice. For analysis of Id4 and E47 nucleofected cells, Id4+ or GFP+(E47) cells were identified by immunostaining for Id4 or GFP respectively, and positive cells compared to negative, non-transfected cells within the same coverslip. Cell counts were done from at least 3 coverslips from three biological replicates. For quantifying RGL density, the DG volume was calculated by multiplying the length, height and depth of the SGZ imaged in mm, and the number of stem cells counted expressed per mm 3. All data were analyzed with masking of genotype/ group to avoid bias.