dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing data processing After removing sequences which corresponded to the sequencing adapters, and filtering by size (17-28 nt), using Cutadapt  [42]  data analyses were processed using miRDeep2 software  [43] , as described by Le Guillou et al. [44]  and performed on the bioinformatics platform Genotoul (http://bioinfo. genotoul. fr/). The cleaned sequences were clustered into unique reads and then mapped to the reference goat genome (CHIR_1. 0, [18] ) using mapper. pl module. Putative miRNA and precursors were identified using the miRDeep2 core module, miRDeep2. pl (including reads corresponding to typical products of miRNA biogenesis, stability of the putative pre-miRNA hairpin and homology to previously identified miRNA). The miRDeep2 core algorithm needs to use known miRNA from the species under analysis, held in a reference database such as miRBase  [45].
As few goat miRNA are reported in miRBase v21, three approaches were used. Thus, the use of the 2 miRNA from the virus BK Polyomavirus (Bkv) (precursor: bkv-mir-B1, mature: bkv-miR-B1-5p and bkv-miR-B1-3p), the 793 bovine miRNA (Bos taurus, bta), or the 436 goat miRNA from miRBase v21 were compared. Three independent sets of potential precursors (from BK Polyomavirus, bovine and goat) were created from the miRDeep2 prediction with a miRDeep2 score ≥0. Further, precursors from the BK Polyomavirus, bovine or goat containing known miRNA not found in potential precursors were added to the sets. The same operation was then performed to create three independent datasets containing putative miRNA and all known miRNA from miRBase v21. The quantifier. pl miRDeep2 module was then used to map unique reads and new sets of miRNA on new sets of potential precursors.
Screening for intragenic miRNA in the human, mouse, cattle and goat genomes The chromosomal positions of miRNA in the human, mouse and cattle genomes were downloaded from miRBase v21 (http://mirbase. org/). Datasets for protein-coding genes were downloaded from BioMart Ensembl release 78, using the latest version of the genome (GRch38 for human, GRCm38. p3 for mouse and UMD3. 1 for cattle (http:// www. ensembl. org/biomart)). The positions of mRNA and the Coding Protein Sequence on the goat genome were obtained from Dong et al. [18]. Comparisons of the chromosomal locations of miRNA and protein-coding genes were performed using the IntersectBed tool in BEDTools software  [49]  with options requiring the same strandedness and an overlap of 100% necessary to cross miRNA and gene coordinates.
Comparison of strategies for miRNA sequence identification Next generation sequencing (NGS) technologies coupled with bioinformatic analysis offer a powerful method to analyze miRNA gene expression which allows for both the measurement of known miRNA and the identification of novel miRNA  [50]. Unlike other technologies, they enable not only the discovery of novel miRNA but also the capacity to detect weakly expressed miRNA. Here, we performed RNA-Seq on RNA isolated from the mammary gland of lactating goats, using Illumina/ Solexa NGS technologies. Bioinformatic analyses of sequenced products were performed using miRDeep2 software which can identify known and putative miRNA with an accuracy of 98. 6-99. 9%, as reported by Friedländer et al. [43]. As in a previous study where the mammary gland miRNome of lactating cow was established  [44] , the miRDeep2 software was used to determine the miRNome of lactating goat mammary gland.
Our prediction results thus showed that the use of precursors from an external reference (i. e. bovine in this study) or miRNA for the studied species is not necessary to predict most of precursors from the genome (95%). Therefore, for a species with none or only a few miRNA in miRBase, a de novo prediction can produce results almost as complete as if precursors from an external or studied species were used. For each approach, two sets of data were thus created: i) one containing potential precursors with or not known miRNA of the species, and ii) one containing putative miRNA and all known miRNA from miRBase v21 (Figure  1 ).
miRNA identification and quantification Use of the quantifier. pl module and the custom script parse_miRDeep2_outputs. pl, as described by Le Guillou et al. [44] , allowed us to map sequencing reads as well as the set of data containing miRNA (putative and known) on the data set containing potential precursors. Only precursors carrying miRNA with at least ten counts summing all libraries were retained reducing the numbers of potential precursors to 913 in the de novo approach, 924 with bovine and goat miRNA (Table  1 , 'Filtered'). In addition, depending on the location of reads relative to the position of known miRNA from miRBase on the "potential precursors", reads were assigned to miRNA, and two miRNA (5p and 3p) were retained for each precursor. This step enabled the quantification and annotation of 1,068, 1,178 and 1,095 distinct miRNA using the de novo, bovine or goat miRNA approaches, respectively; these included both known and putative miRNA which had not previously been described in any species from miRBase v21.
The number of cluster varies according to the species, since 36%, 46% and 47% of miRNA are found clustered in zebrafish, mouse and human, respectively  [9, 58]. Most of the precursor clusters reported here were discistronic, in the sense that they comprised two precursors, as is usually observed  [12]. But larger clusters exist on the goat genome; for example, CHI 21 carries a cluster of 41 precursors, which appears to be conserved in mammals  [18] , close to a cluster on BTA 21 containing 47 precursors. Moreover, 7 clusters were only made up of putative precursors, the largest of which contained 5 putative precursors on CHI 26. Most miRNA genes within 50 kb of each other displayed highly correlated expression patterns, as they could be processed from a polycistronic primary transcript  [11].
Others host genes play a critical role in mammary metabolism, such as VLDLR (Very low density lipoprotein receptor), SREBF2 (Sterol responsive element binding factor 2), or AGPAT6 (1-Acylglycerol-3-phosphate Oacyltransferase 6) (Additional file 9: Table  S6 ). Interestingly, among the 217 host genes, the expression of 76 coding genes was confirmed in the mammary gland of the same goat using previous data from microarrays (  [64] ; GEO Series accession number GSE6380). According to these observations, only 35% of host genes were expressed in the mammary gland at peak lactation. This low percentage is likely due to a lack of completeness of the microarray. The difference observed might also be due to the difference in detection level of the techniques employed. Taken together, a clearer understanding of intragenic goat miRNA may highlight potential new regulations of miRNA and gene expression, some of which may play critical roles in the lactation function.
In addition, among these ten miRNA, mir-128-2, mir-218-2 and mir-301a were found in the ARPP21 (cAMP-regulated phosphoprotein), SLIT3 (Slit homolog 3) and SKA2 (Spindle and kinetochore associated complex subunit 2) genes in human, mouse, cow and chicken  [65]. By investigating annotated goat mRNA, transcript coding for SKA2 was found to be annotated in the goat genome, suggesting that mir-301a is not intragenic in goat. However, the transcript coding for both ARPP21 and SLIT3 could not be retrieved. Furthermore, the genomes of human, mouse, cow and chicken have been well characterized by comparison with very recently assembled goat genome. Consequently, it could be hypothesized that ARPP21 and SLIT3 have not yet been described in the goat genome, and the presence of conserved intragenic precursors may indicate the location of these genes in this genome.
Screening for miRNA in bovine quantitative trait loci Results of the QTL mapping studies that have been performed in livestock species are available in the animal QTL database (http://www. animalgenome. org/). Because no QTL mapping studies have been reported for goat in this QTL database, bovine QTL data were used in our study to investigate the localization of precursors expressed in mammary gland QTL. In cattle, some 2,307 QTL out of a total of 8,305 have been reported to be associated with milk traits (Additional file 11: Table  S8 ). Comparative mapping enables exploitation of the whole genome sequence and its annotation available for a model species, and inference of this information to other species whose genome annotation has not been well characterized. In this regard, a comparative analysis of the known goat precursors identified in the present study and the precursors expressed in bovine lactating mammary gland  [44]  was performed, and revealed 255 highly precursors conserved between these species.
De novo With bovine miRNA 2 1 miRDeep2. pl 793 Dataset creation miRDeep2 processing quantifier. pl precursor Reads mapped on the miRNA With goat goat genome filtering step miRNA use as input software Virus miRNA Bovine miRNA Potential precursors excised 40,330 Potential precursors 3,322 Prediction 1,327 1,017 1,067 Potential precursors with a miRDeep2 score 0 2,654 2,034 2,134 and associated miRNA Potential precursors containing 0 328 at least one miRNA from the reference 0 656 (with miRDeep2 score >-50) and associated miRNA Precursors of reference species 0 489 containing miRNA from the reference not found in potential precursors 35,828 35,828 All miRNA from miRBase v21 1,068 1,178 and annotation Quantification.
2009 ) which is described Based on observations collected at the Centro Astronómico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck-Institut für Astronomie (MPIA) and the Instituto de Astrofísica de Andalucía  (CSIC)  in  Walcher et al. (2014, hereafter W14). From this mother sample the 600 target galaxies are randomly selected. Combining the techniques of imaging and spectroscopy through optical IFS provides a more comprehensive view of individual galaxy properties than any traditional survey. CALIFAlike observations were collected during the feasibility studies  (Mármol-Queraltó et al. 2011; Viironen et al. 2012 ) and the PPak IFS Nearby Galaxy Survey (PINGS,  Rosales-Ortega et al. 2010) , a predecessor of this survey. First results based on those datasets already explored their information content (e.
The photometric anchoring to the SDSS images of the V1. 5 data is more accurate than those of the previous version. However, there are a few datacubes where the new registering method does not return optimal results, particularly in low surface brightness edge-on galaxies or in the presence of bright foreground field stars. This effect is more likely to occur in the V1200 setup, given its lower S/N on average compared to V500. In such cases, we apply the photometric SDSS matching of pipeline V1. 3c described in H13 (to both setups, for the sake of consistency). A new "REGISTER" keyword has been included in the header of the datacubes (see Sect. 4. 4) and a dagger symbol has been added to the quality tables (Table  6  and 7 ) in order to easily identify these galaxies. The third step in the reduction sequence is the interpolation method used to convert from RSS to cube format, aimed at improving the spatial resolution.
CALIFA data format and characteristics The CALIFA data are stored and distributed as datacubes (threedimensional data) in the standard binary FITS format and consist of several FITS Header Data Units (HDU). These datacubes contain (1) the measured flux densities, corrected for Galactic extinction as described in S12, in units of 10 -16 erg s -1 cm -2 Å -1 (primary datacube), (2) associated errors, (3) error weighting factors, (4) bad pixels flags and (5) fiber coverage (Table  2 ). The last HDU is a new added content absent in DR1, as explained in Sect. 3. 1, but the others share the same properties as the previous data release. The first two axes of the cubes correspond to the spatial dimension along right ascension and declination with a 1 × 1 sampling. The third dimension represents the wavelength and is linearly sampled.
Error and weight datacubes The 1σ noise level of each pixel as formally propagated by the pipeline can be found in the first FITS extension. Sect. 3. 2 discusses on the accuracy of the formal noise and the correlation, important when CALIFA data need to be spatially binned, and an empirical function is provided to account for the correlation effect. The second FITS extension (ERRWEIGHT) stores the error scaling factor for each pixel in the limiting case that all valid spaxels of the cube would be co-added (see also Appendix A). In the case of bad pixels, we assigned an error value that is roughly ten orders of magnitude higher than the typical value. Bad pixel datacubes are stored in the third FITS extension (BADPIX). This information, in combination with the error vector, is essential to properly account for the potential problems in each spaxel.
The flags in each category are computed based on thresholds on measured quantities, possibly combined with flags given by human classifiers based on visual inspection, as detailed below and summarized in Tables  4  and 5. Thresholds are determined from the distribution of the parameters in order to exclude outliers and also by analyzing the effects of anomalous parameters on the final quality of the datacubes. The tables of the relevant QC parameters, along with the QC flags are available on the DR2 website. Each flag can have one of the following values: • -1 = undefined • 0 = good quality -OK • 1 = minor issues that do not significantly affect the qualitywarning • 2 = significant issues affecting the qualitybad By selection, DR2 only includes galaxies with warning flags in the worst cases, with just a few minor exceptions affecting previously released DR1 galaxies: in these cases the revised QC criteria adopted here would have prevented to include such galaxies in the DR, but given the incremental nature of our data releases we keep them in the current sample.
For each of these quantities we defined two thresholds (the same for V500 and V1200, see Table  6  and 7 ) above which the warning or the bad flags, respectively, are raised. The combined flag_obs_am is the worst of the three cases. The surface brightness of the sky in V-band during the observations is another critical parameter, which mainly limits the depth of the observations and the accuracy of the sky subtraction. The quantity skymag is measured in each pointing from the sky spectrum obtained from the 36 sky fibers 4. The mean and the rms over all pointings are considered to define the corresponding flags. Note that stricter requirements are applied to V1200 data (blue setup, high resolution) with respect to the V500 ones. The transparency of the sky during each pointing (ext) is obtained from the monitored V band extinction at the time of the observation.
Quality of the calibrated data products (cal) The quality of the calibrated data products is determined by checks on the global spectrophotometry, on the stability of the wavelength calibration across the spectral range, and on the quality of the resulting 2D flux distribution (synthetic image) and its ability to match the SDSS broad-band imaging. The quality of global spectrophotometric calibration is assessed by comparing the photometric fluxes derived from spectra integrated within 30 -radius apertures with the corresponding fluxes derived from SDSS imaging, as explained below in Sec. 5. 5. For the V500 setup, in particular, it is possible to derive the flux ratio between SDSS and CALIFA in g and r-band (cal_qflux_g and cal_qflux_r, respectively, averaged over all pointings for a given galaxy): values of these ratios departing from 1 by more than the tolerances listed in Table  4  are flagged.
Seeing and spatial resolution In order to cover the complete FoV of the central bundle and to increase the final resolution of the CALIFA datacubes (PPak fibers have a diameter of 2. 7 ), a dithering scheme with three pointings has been adopted, as described in S12. In imaging, in addition to the telescope aperture, instrumental and atmospheric seeing determine the final spatial resolution. This has to be added to the IFU particular characteristics. The average atmospheric seeing conditions along the time of the observation of the CALIFA data were derived from the measurements acquired by the DIMM (DIMM, Aceituno 2004), which operates fully automatically at the Calar Alto observatory during the night. DIMM has different operational constraints (humidity lower than 80% and wind speed less than 12 m s -1 ) than the 3.
Since January 2012 standard stars were observed using the same dithering pattern adopted for the science observations for the V500 setup. We observed a total of 107 nights in this period. Only 70% of the nights had weather conditions good enough to acquire a calibration star and 2/3 were observed adopting the dithering scheme, yielding a total of 45 datacubes. We reduced the data using the same procedure described before for the science objects. As the calibration stars have a very high S/N, the PSF can be measured very precisely. From the datacubes we simulate a SDSS g-band image for each of these stars. For each of these images we fit a 2-d Moffat profile using the software IM-FIT (Erwin 2014) 6. Figure  15  shows the normalized distributions of FWHM and β M parameters of the Moffat profile, weighted by the likelihood of the fit.
Spectrophotometric accuracy As mentioned in Sect. 3. 1 the new registration scheme of the pipeline uses SDSS r-band for the V500 setup and g-band for the V1200, and field calibration images of the SDSS DR7. Each V500 datacube is rescaled in the absolute flux level to match the SDSS DR7 broad-band photometry using the photometric scale factor at the best matching position for each pointing. On the other hand, the V1200 data is matched to the V500 data (S12). This procedure, together with the new re-calibrated sensitivity curve (see  Sect. 3. 1 and Husemann et al. , in preparation) , improves the spectrophotometric calibration over DR1. This is clearly shown in Fig. 17. As part of the CALIFA pipeline V1. 5, a 30 diameter photometric aperture in r and g is measured both in the SDSS DR7 images and the equivalent synthetic CALIFA broad-band images.
The mean SDSS/CALIFA g and r band ratios in DR2 are 1. 00 ± 0. 05 and 0. 99 ± 0. 06, respectively. In the right panel of Fig. 17  the distribution in ∆(gr) color difference between the SDSS and CALIFA data shows that the spectrophotometric accuracy across the wavelength range is better that 3%, with a median value of 0. 01 ± 0. 03. Spectral fitting methods can be used to perform useful tests of the data and their calibration, and this has been done before in CALIFA. H13 used starlight fits to evaluate the accuracy of the error estimates in DR1 datacubes, while Cid Fernandes et al. (  2014 ) used such fits to map systematic features in the spectral residuals which may indicate calibration issues. We have repeated the same experiments for the DR2 datacubes. Results are shown in Fig. 18. The top panel shows in blue the mean spectrum of 170670 Voronoi bins of the 200 galaxies in DR2 7.
The CALIFA DR2 search and retrieval tool The public data is distributed through the CALIFA DR2 web page (http://califa. caha. es/DR2). A simple web form interface, already in use for the first data release, allows the user to select data of a particular target galaxy, or a subsample of objects within some constraints on observing conditions or galaxy properties. Among the selection parameters we include the instrument setup, galaxy coordinates, redshift, g-band magnitudes, observing date, Hubble type, bar strength, inclination estimated from axis ratio, V band atmospheric attenuation, airmass, and relative accuracy of the SDSS/CALIFA photometric calibration. If any CALIFA datasets are available given the search parameters, they are listed in the follwing web page and can be selected to be downloaded.
The download process requests a target directory on the local machine to store the data, after the downloading option was selected. The CALIFA data are delivered as fully reduced datacubes in FITS format separately for each of the two CALIFA spectral settings, i. e. the V500 and V1200 setup. Each DR2 dataset is uniquely identified by their file name, GALNAME. V1200. rscube. fits. gz and GALNAME. V500. rscube. fits. gz for the V1200 and V500 setup respectively, where GALNAME is the name of the CAL-IFA galaxy listed in Table  1. All the QC tables discussed along this article are also distributed in CSV and FITs-table formats in the same webpage. In addition, we distribute the more relevant tables discussed in W14 regarding the characterization of the MS, using similar formats. These tables could be useful in further science explorations of the cubes.
Virtual Observatory services CALIFA data is also available through several Virtual Observatory (VO) facilities. 1. The FITS files of the full cubes are accessible through GAVO's ObsCore (Louys et al. 2011) service, which is part of the TAP (Dowler et al. 2011) service at http://dc. g-vo. org/tap. ObsCore provides a homogeneous description of observational data products of all kinds and thus allows global data set discovery. The system already supports the upcoming IVOA DataLink standard for performing cutouts and similar server-side operations. 2. At the same TAP endpoint, the califadr2. cubes and califadr2. objects tables allow queries versus CALIFAspecific metadata, in particular the quality control parameters given in Tables  6  and 7. 3. Individual, cut-out spectra can be located and retrieved from the CALIFA SSA service 10  ; advanced SSAP clients like Splat (Draper 2014) also support server-side spectral cutouts on this service via a DataLink prototype.
4. The spaxels can also be queried in database tables via GAVO's TAP service mentioned above (the tables are called califadr2. fluxv500 and califadr2. fluxv1200). An overview of VO-accessible resources generated from CALIFA -possibly updated from what is reported here -is available at http://dc. g-vo. org/browse/califa/q2. This page also gives some usage scenarios for CALIFA VO resources.
Conclusions Along this article we have presented the main characteristics of the second public data release of the Calar Alto Legacy Integral Field Area (CALIFA) survey. This data release comprises 200 galaxies (400 datacubes) containing more than 1. 5 million spectra 11  , covering a wide range of masses, morphological types, colors, etc. This subset of randomly selected objects conforms a statistically representative sample of the galaxies in the Local Uni- verse. The CALIFA DR2 provides science-grade and qualitychecked integral-field spectroscopy publicly distributed to the community at http://califa. caha. es/DR2. We have described in detail the main quality parameters analysed in the validation process, provided to the users with complete tables to select the objects for their science cases.
The data have been reduced using a new version of the pipeline (V1. 5), which improves considerable the quality of the data in terms of: (i) the spatial resolution, (ii) the covariance between the adjacent spectra, and (iii) the spectrophotometric calibration. Compared with other on-going major surveys, CALIFA offers a better spatial resolution. The PSF of the datacubes has been improved considerably, with a mean value of ∼ 2. 5 (Sect. 5. 4. 2), similar to  SAMI (Sharp et al. 2014 ). In the case of MaNGA, the combination of an average seeing at the Sloan Telescope (∼ 1. 5 ) and the fiber size (2 ), would produce a PSF with a very similar FWHM. The redshift range of SAMI and MaNGA surveys is considerably larger than of CALIFA, reaching up to z ∼0. 1. This means that only for galaxies at the lowest redshift range SAMI and MaNGA will offer a similar physical resolution.