dataesr/scientific-paragraphs-categorization huggingface dataset:
Ducroquet JPHJ and Dalbo MA, 2007. SCS 409 Camila e SCS 410 Piunanew plum cultivars with resistance to leaf scald. Agropecuaria Catarinense, 20(1), 67-70. Elbeaino T, Valentini F, Abou Kubaa R, Moubarak P, Yaseen T and Digiaro M, 2014. Multilocus sequence typing of Xylella fastidiosa isolated from olive affected by "olive quick decline syndrome" in Italy. Phytopathologia Mediterranea, 53(3), 533-542. Ellis EA, McEachern GR, Clark S and Cobb BG, 2010. Ultrastructure of pit membrane dissolution and movement of Xylella fastidiosa through pit membranes in petioles of Vitis vinifera. Botany-Botanique, 88(6), 596-600. https://doi. org/10. 1139/B10-025 EPPO (European and Mediterranean Plant Protection Organization), online. EPPO Global Database. Available online: https://gd. eppo. int [Accessed: May 2018] Esau K, 1948.
List of contradictive findings Xylella spp. host plant database Xylella spp. host plant database Xylella spp. host plant database Xylella spp. host plant database fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies X. fastidiosa subspecies Plant family Plant family Appendix D -Plant family Adoxaceae Anacardiaceae Plant species Plant species Plant species Sambucus sp. Rhus sp. Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Natural infection Detection methods Detection methods Detection methods ELISA ELISA POS/NEG POS/NEG POS/NEG POS POS Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Artificial infection Reference Reference Reference Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand Not specified Grand ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species ST Plant species Asteraceae Anacardiaceae Asteraceae Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Argentina Brazil Baccharis pilularis Schinus molle Solidago fistulosa Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total Ecuador France Italy Mexico Spain USA unknown Total ELISA POS PCR-based methods NEG Immunological detection method NEG Costa et al.
Lines mark 8% differences in absolute origin efficiency. (C) Clustering of de-regulated origins in late-firing and inefficient chromosomal domains. Top: Red bars indicate positions of CIOs. Middle: Comparison of origin de-regulation in rad3Δ with the program of replication in WT. The percentages of CIOs in 1000-probe (∼250-kb) windows were determined across the genome (blue line, scale on left y-axis). The averages of origin efficiencies in WT were assessed for the same continuous windows (black line, scale on right y-axis). Bottom: The profiles of replication timing and efficiency in a wild-type background show identical regional characteristics. The average timings of origin firing were assessed for the same continuous windows as above (purple, left y-axis) (timing data from  Heichinger et al.
Flow cytometry Cells were fixed in 70% cold ethanol, washed in 50 mM sodium citrate, and treated with RNase A (0. 1 mg/mL) at 37°C. Samples were then stained using 2 mg/mL propidium iodide, sonicated with a Branson digital sonifier, and run on a BD Accuri C6 flow cytometer (BD Biosciences). Data were analyzed using the FlowJo software. Details for the interpretation of flow cytometry profiles the fission yeast are provided in the Supplemental Materials.
For nda3-km311, 6 mM HU was added 5 min after cells were released from mitotic arrest; samples were taken just before the release and 80 min after release. Genomic DNA was extracted (Hoffman and Winston 1987) and purified using the Qiagen Genomic DNA kit. Samples were labeled using the BioPrime Plus Array CGH Indirect Genome Labeling kit (Invitrogen) according to the manufacturer's instructions with either Alexa 555/647 (Agilent Technologies) or Cy3/ Cy5 (GE Healthcare) dyes. One to two micrograms of labeled DNA from the unreplicated and S phase samples were hybridized onto the microarrays. Two independent hybridizations of the same samples were systematically performed in a dye-swap experiment, the ratios of replicating DNA to unreplicated DNA were assessed, and these data sets were averaged.
Lines mark 8% differences in absolute origin efficiency. (C) Clustering of de-regulated origins in late-firing and inefficient chromosomal domains. Top: Red bars indicate positions of CIOs. Middle: Comparison of origin de-regulation in rad3Δ with the program of replication in WT. The percentages of CIOs in 1000-probe (∼250-kb) windows were determined across the genome (blue line, scale on left y-axis). The averages of origin efficiencies in WT were assessed for the same continuous windows (black line, scale on right y-axis). Bottom: The profiles of replication timing and efficiency in a wild-type background show identical regional characteristics. The average timings of origin firing were assessed for the same continuous windows as above (purple, left y-axis) (timing data from Heichinger et al.
Acknowledgments We thank members of the  Genome Duplication and Maintenance team  for helpful suggestions, in particular,  Anthony Perrot  for experimental as well as bioinformatics assistance and  Diane Schausi-Tiffoche  for technical aid. We thank  Biogenouest Genomics  and the  Human Environmental Genomics core facility of Rennes (Biosit/OSUR)  for technical support. We also thank  Damien Coudreuse  for valuable discussions and critical reading of the manuscript. This study was supported by funding from  Worldwide Cancer Research  ( 11-0685 ), the  CNRS/INSERM  ( ATIP-Avenir program ), the  Fondation pour la Recherche Médicale  (" Amorcage de jeunes equipes" program ), the  Fondation ARC pour la Recherche sur le Cancer (Projet ARC) , the Ligue contre le cancer (Comités 22, 29, and  35 ), and the  Institut National du Cancer (INCA ,  PLBIO 15-043 ).
We present the version of the point source catalogue of the XXL Survey that was used, in part, in the first series of XXL papers. In this paper we release, in our database in Milan and at CDS: (i) the X-ray source catalogue with 26 056 objects in two areas of 25 deg 2 with a flux limit (at 3σ) of ∼ 10 -15 erg s -1 cm -2 in [0. 5-2] keV, and ∼ 3 10 -15 erg s -1 cm -2 in [2-10] keV, yielding a 90% completeness limit of 5. 8 10 -15 and 3. 8 10 -14 respectively ; (ii) the associated multiwavelength catalogues with candidate counterparts of the X-ray sources in the infrared, near-infrared, optical, and ultraviolet (plus spectroscopic redshift when available); and (iii) a catalogue of spectroscopic redshifts recently obtained in the southern XXL area. We also present the basic properties of the X-ray point sources and their counterparts.
1)  are only available at CDS. ⋆⋆ Based on observations obtained with XMM-Newton, an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. (2. 2). Section 3 presents the multiwavelength data, describes the counterpart association (3. 2), provides some statistics (3. 4), presents the spectroscopic redshifts (3. 5), and describes the additional spectra obtained with the AAOmega spectrograph in 2016 (3. 6), supplementing those published in  Lidman et al. (2016, hereafter XXL Paper XIV). Section 4 presents the Master Catalogue database site, the X-ray catalogue tables (4. 1) with associated data products  (4. 2), and the multi-λ catalogue tables  (4. 3) with associated data products  (4. 5). Section 5 summarises the work. Appendix A compares the present catalogue with the previous XMM-LSS catalogue  (Chiappetti et al.
X-ray material The X-ray (XMM-Newton) observations of the XXL survey were obtained over several years in two sky regions as a collection of uniformly spaced contiguous pointings with an exposure time of at least 10 ks (complemented by a few GO pointings from the archives, and by pointings from earlier surveys, sometimes deeper). The list of the 622 XMM pointings (294 in the northern area at δ ∼ -4. 5 • , hereafter XXL-N; and 328 in the southern area at δ ∼ -55 • , hereafter XXL-S) is given in Appendix B of XXL Paper I (which also provides details on the observing strategy), and is available at the Centre de Données astronomiques de Strasbourg (CDS) and in our database (see section 4 and Appendix B).
Xamin characterises the sources with a maximum likelihood fit with both a pointlike and an extended (β-profile) model, and provides a fits file for each pointing with basic parameters separately for the soft [0. 5-2] keV, or B, band and for the hard [2-10] keV, or CD, band. The parameters produced by Xamin (which can be summarised as exposure times, statistics, raw source positions, source and background counts, and source count rates) are listed in detail in Table  1  of XXL Paper XXIV and Table  2  of  Pacaud et al. (2006) , and are flagged in Col. 4 ('X') of Tables 9 and 10 of this paper. Other parameters are computed a posteriori during database ingestion on individual bands, as in  Chiappetti et al. (2013). These parameters include the following: -The application of the C1/C2 recipe to classify extended sources  (Pacaud et al.
A positional error is computed as a function of count rate and off-axis angle (e. g. Table  4  of  Chiappetti et al. 2013). Flux errors are computed by calculating the Poissonian error on gross counts 2  according to the formula of Gehrels (1986), assuming that the fractional error on rates is the same as that on counts, and propagating them through the CF formula. In Fig. 1  we compare the S/N using the computed flux errors to the Xamin detection statistic. The customary 3σ and 4σ levels seem to occur for likelihood levels ∼ 65 and 115, higher than those found in the comparison of Xamin 3. 2 with the XMDS pipeline given in  Chiappetti et al. (2013) , which only deals with some of the longest exposures in XXL, and uses a different analysis software. Additional steps are performed after the ingestion of the band tables in the database for the preparation of the catalogues.
The respective numbers for XXL-S are 27 173 and 18 145. A band-merged detection in two bands that is spurious in one band (flagged by the boolean flags Bspurious=1 or CDspurious=1) still provides some usable information (rate, flux, etc. ) and is retained in the catalogue. For detections in two bands, the band in which the detection likelihood of the source is the highest is the band from which the coordinates are taken. If we compare the separation between B and CD positions (column Xmaxdist in the database) with the positional uncertainties in the two bands added in quadrature (σ) we find that Xmaxdist< σ in 33% of the cases, within 2σ in 71% of the cases and within 3σ in 77%, which looks reasonable. It is possible to have an ambiguous band merging case when a detection in a band happens to be associated with two different objects in the other band (i.
(2013)  is applied, i. e. if one detection is in a pointing with a better bad-field flag (column Xbadfield in the database), then it is kept in preference to any other. Otherwise if Xbadfield is the same, the source closest to the XMM pointing centre (columns Boffaxis and CDoffaxis in the database) is kept. There is, however, a difference with the XMM-LSS case: Xbadfield is not a boolean flag, but can have a range of values: -0 : pointing belongs to XXL, has good quality, and is the single or best pointing of a sequence of repeats; -1 : pointing does not belong to XXL (archival or deep followup pointing) and has good quality; -2 : another XXL pointing in a sequence of repeated pointings (see below) that has good quality; -3 : quality is bad (which may occur if the exposure is too shallow, or background is too high, or both).
For XXL-S the reference catalogue was from the Blanco Cosmology Survey (BCS,  Menanteau et al. 2009) , except for 11 pointings for which no correction could be computed for the same reasons noted above. The offsets per pointing, available in web pages reachable from the 'table help pages' in the database, have been added as new columns in the pointing list table, already published in XXL Paper I; the updated table is presented in Appendix B.
Bright star clean-up We have associated our counterparts with sources in the SIM-BAD and NED databases, and are providing an identifier for the relevant cases. We used this information as one way of partially screening out stars, which are not of interest for AGN studies. We also used the USNO A2. 0 catalogue to screen stars, as well as our own spectroscopic redshifts, described in section 3. 5. We assign a star flag of 1 to the counterparts with z spec < 0. 003, i. e. stars confirmed spectroscopically, a flag of 2 to objects that look like a star upon visual inspection, and a flag of 3 when both conditions occur. Visual inspection of thumbnail optical images (see section 4. 5) was done systematically for all counterparts with a match to a source in a known star catalogue in SIMBAD or NED.
The XXL database The catalogues and selected datasets of the XXL survey are publicly available in two ways: -Basic static catalogues are deposited at the CDS; -Complete queryable catalogues are available via the XXL Master Catalogue browser 7  , in a database, which allows one to select a subset of sources and to retrieve the associated data products, and provides online documentation and help files. The list of database tables associated with the earlier DR1 release or released with the present paper or in other DR2 papers is given in Table  8.
X-ray tables In an analogous manner to the XMM-LSS releases  (Pierre et al. 2007; Chiappetti et al. 2013) , we are providing a band-merged catalogue, called 3XLSS, and single-band tables for the B [0. 5-2] keV and CD [2-10] keV bands (called 3XLSSB and 3XLSSCD). Redundant sources detected in overlapping regions of different pointings are removed, as explained in section 2. 1. Only sources above a detection statistic of  15 (i. e. non-spurious)  are made available in the single-band tables, while the band-merged tables may include data with a likelihood below 15 in the other (non-best) band. For the table layout and column naming, we tried to be as consistent as possible with earlier XMM-LSS catalogues. The list of database columns with brief descriptions is given in Tables  9  and 10. Additional details about some columns are provided here: -The official source catalogue name Xcatname is in the form 3XLSS Jhhmmss.
X-ray data products The per-pointing XMM photon and wavelet-smoothed images, and the camera exposure maps were already released in XXL Paper I as data products associated with the pointing table xxlpointings. The same data products are also associated Table  9. List of parameters provided in the public X-ray catalogues. All are available at the XXL Milan database as separate tables: 3XLSSB for the soft band and 3XLSSCD for the hard band. The column name has a prefix when there are two column names given (one with the prefix B and one with the prefix CD). Only the column applicable to the given band appears in the relevant There is a limited number of sources (9 in XXL-N and 9 in XXL-S) band-merged as EP, i. e. extended in the soft and pointlike in the hard band, for which the P1 recipe is successful in the hard band.
with the 3XLSS source catalogue table. With any query returning some X-ray sources (in some pointings), it will be possible to retrieve the data products related to these pointings. We note that the WCS of X-ray images is the one generated by sas and does not take into account astrometric corrections. Strictly speaking one should use uncorrected X-ray positions to overlay on X-ray images and corrected positions to overlay on optical thumbnails (see section 4. 5). However, for X-ray images, the difference is unimportant, since the X-ray pixel size is 2. 5 ′′.
Acknowledgements. XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ~5 10 -15 erg s -1 cm -2 in [0. 5-2] keV. The XXL website is http://irfu. cea. fr/xxl. Multiband information and spectroscopic followup of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. Based in part on data acquired through the  Australian Astronomical Observatory , under programmes  A/2016B/107. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France , and 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 Saclay team acknowledges long-term financial support from the  Centre National d'Etudes Spatiales. This paper uses data from the  Galaxy And Mass Assembly (GAMA)  survey and from the  VIMOS Public Extragalactic Redshift Survey (VIPERS). 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. 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/. 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.
e. there are not enough pho- tons to unambiguously characterise them). The undefined sources are flagged as pointlike in the database. Those with pnt_det_stat < 15 are considered spurious in the band (i. e. not considered for inclusion in the catalogue). -Fluxconversion. A conventional mean flux (FLUX MOS + FLUX pn )/2 in both bands is computed from count rates via the usual conversion factors CF listed in Table2with a fixed spectral model (Γ = 1. 7, N H = 2. 6 × 10 20 cm -2.
Statistics and Representation All data was imported into Matlab (Mathworks), for representation and statistical comparisons. Statistics in the MWM task utilized 2-way ANOVA's, looking at effects of session number (1-8) and group type (SHAM; BEF; DUR). Probe-trial differences used a 1-way ANOVA (looking at all groups). Student's T-tests were utilized for post-hoc comparisons between sham and stimulus groups. For MWM swim position representation, the paths of each group were combined for a particular session, and converted to a normalized, 2D histogram. Each tracking point was converted to a 10 cm diameter circle prior to this, for better highlighting the group position preference.
DATA AVAILABILITY The design files and detailed assembly instructions for the portable devices can be found in the Figshare repository at https://figshare. com/s/31122e0263c47fa5dabd. The datasets generated can also be found at https://figshare. com/s/ 7ee8888fb8f5cb75ec14. Technical data: LINK, https://figshare. com/s/31122e0263c47fa5dabd; doi: 10. 6084/m9. figshare. 5975356. Experimental data: LINK, https://figshare. com/s/ 7ee8888fb8f5cb75ec14; doi: 10. 6084/m9. figshare. 5280679.
Before selecting the texts to be used - in other words, before starting to build the four corpora - it was necessary to decide which data should be retrieved and which data were relevant. The retained data categories were the following: -term + part of speech + gender + language + source(s) -equivalent term(s) FR - EN -subject field + subfield -variant(s) + source(s) -synonym(s)/quasi-synonym(s) + source(s) -symbol(s)/illustration(s) + source(s) -contexts + source(s) -definition(s) + source(s) -information as to how the concept fits into a concept system (subordinate, superordinate and coordinate concepts) -notes: conceptual and linguistic Each data item encountered in the corpora will be registered into the database, and only once the data processing is completed will some items be put aside if considered superfluous or repetitive.
Also, manual term (and context) extraction reduces noise significantly and offers hope for exhaustive term extraction. Although the terms (together with all the conceptual and definitional information) are collected manually, an electronic tool is, however, being used when collecting data into the database. The choice was made early on to use an already-existing database system specialised for the collection and treatment of terminological data: i-Term, a terminological database system created by DanTerm, which offers many interesting features, one of them being the possibility to attach multimedia files to each terminological record.
Conclusion The purpose of this paper was to give an overview of my research, to explain briefly some of its methodological aspects, and to offer a glimpse at some cases of interest. These are only a few examples out of hundreds contained in my database. This research is still going on and should be completed within the next couple of years. By the end of this project, I hope to be able to provide as complete a database as is possible of French and English music terminology from the second half of the 17 th century, together with advice on the correct use of this terminology according to different communication contexts. Another purpose of this work is to make a methodological contribution on how to approach similar complex historical terminological works, not only in music, but in all subject fields linked with the Arts and Humanities.
Doing so, one tends to disregard a large share of material: non-textual elements. And, when one thinks of non-textual elements, one automatically thinks of symbols and illustrations. And, of course, in what concerns sources from the second half of the 17 th century, this is correct: the only non-textual information that is at one's disposal is pictorial. But what about musical notation: aren't some examples in music musical explanations of concepts? The figure below shows examples of such musical explanations by Chambonnières (1670) and d'Anglebert (1689) of non-textual contexts related to the different graces. Hence the importance of being able to include multimedia files in the database.