dataesr/scientific-paragraphs-categorization huggingface dataset:
If no flux errors were given in the original catalogues, a nominal 10% error was assumed, which sometimes was increased to 30% for particularly unreliable passbands. There are some noticeable trends among the collected visual datasets. The SDSS data, for example, are of high quality but the survey was designed to be deep, so that background sources are sometimes confused with our intended targets. Such cases are easily identifiable and corrected. Photometric data from DE-NIS/VLTI are often saturated for rather bright sources, and in such cases data are flagged as unreliable. Data from the USNO-B1 survey suffer from rather high uncertainties, estimated between 30 and 50%, and the photometric filters of the survey are not well defined  (Monet et al. 2003 ).
Near infrared For the purposes of the present data collection, near-infrared lies between 0. 8 (i. e. the Johnson I band) and ∼2. 2 µm (K S band). In addition to the references from the Visual wavelengths that also apply here in some cases (the DENIS/ VLTI datasets, for example), near infrared data were additionally collected from the following resources: -Two Micron All Sky Survey (2MASS)  (Cutri et al. 2012 (Cutri et al. , 2003)). -The Cosmic Background Explorer (COBE) Diffuse Infrared Background Experiment (DIRBE) Point Source Catalog  (Smith et al. 2004 ). -J, H, and Ks for sources in Chameleon were retrieved from  Carpenter et al. (2002).
Mid and far-IR Mid-and far-infrared refers here to photometric and spectroscopic data data between 5 and 200 µm, observed mainly with space facilities. Data collection in this wavelength range consists of already reduced and previously published data, and quite often different reductions of the same dataset exist. The wavelength range is of particular importance for the proper modeling of the dust content in disks. Therefore special care has been taken in order to evaluate the different datasets and reductions, in order to provide high quality data of silicate features, especially the most intense one centered at ∼10 µm. The mid-and far-infrared data were collected from the following resources: -The Faint Source Catalogue  (Moshir & et al. 1990)   (Mathews et al. 2013). A&A proofs: manuscript no.
Gas lines Fluxes for gas lines along with spectral line profiles have been retrieved from a limited number of gas-line surveys of protoplanetary disks. More lines were handpicked for individual sources and from articles focusing on the modeling of gas lines with thermochemical codes (e. g. Carmona et al. 2014; Woitke et al. 2018). -CO J=1-0, 2-1 transitions from   (Pontoppidan et al. 2010; Salyk et al. 2011; Boogert et al. 2008; Öberg et al. 2008; Pontoppidan et al. 2008; Bottinelli et al. 2010 ). Space-born data was complemented by data and/or line measurements from ground-based high-spectral resolution near-and mid-IR surveys: -CO ro-vibrational data from the ESO-VLT/CRIRES large program "The planet-forming zones of disks around solarmass stars" (PI. van Dishoeck)  13.
Auxiliary data and model results. As a starting point for modeling efforts, we have collected descriptive parameters of the central protostar from ∼60 refereed articles. A detailed account of these records is given in Table  6 , along with corresponding references. Stellar parameters along with the interstellar extinction are used as starting points for dust radiative transfer and thermochemical models. Along with the observational data collection, we employ the same database infrastructure to also provide results from models that were run on a subset of sources. These results include accurate SED fits to 27 sources along with consistent 18 dust and gas models using the DiscAnalysis standards as described in  Woitke et al. (2018). Modeling is divided into three major phases. The first phase involves fitting of stellar and extinction properties, using the UV to near-IR data.
Xray-derived extinction data was partly used, but only to see which range of extinction data it supports, in the case of multiple, degenerate extinction estimations. The second phase involves modeling of the SED alone using MCFOST, while the third phase involves the DIANA-standard fitting, using either a combination of MCFOST with ProDiMo, MCMax with ProDiMo, or just ProDiMo alone. We mention that all codes employed have been benchmarked for consistency  (Woitke et al. 2016). Duiring the first modeling phase, additional photometric data are searched for, and initial values for e. g. T eff , extinction, distance and luminosity values are looked up in previous spectral analysis papers. The fitting is then made by varying T eff , L star and A V by a genetic algorithm (evolutionary strategy) until a good fit with all selected photometric and (for Herbig Ae) soft UV data is obtained.
g. see Table  6 ). The second phase involves a collection of additional photometric points, extending from the near IR to millimeter wavelengths, including far-IR lines from Herschel and ISO. If PAH features are apparent in the Spitzer/IRS spectra, we include the PAH fitting (amount and average charge of PAHs) consistently in the SED fitting. As a result from the fitting process, the dust mass, disk size and shape, the dust settling and the dust-grain size parameters are constrained and if applicable, the amount and charge of PAHs. The result for 27 SED-fitted sources are included in the database and examples are presented in Fig. 5. In the third phase, line fluxes, line profiles, with resolved images from ALMA and NICMOS or visibility data from PIO-NIER and MIDI are included. The modeller decides which data to trust and which not (for example because the data is contaminated by backgroud/foreground cloud emission), assigns fitting weight to each observation, then follows the most appropriate fitting strategy, e.
g. genetic fitting algorithm or by-hand-fitting. Fitting in phases two and three starts assuming a single-zone disk without gaps. If this fails, then a two-zone disk model is employed with a possible gap between the two zones. During the third phase, all phase 2 data refitted, where in particular the radial extension, tapering parameters and shadow-casting from the inner to the outer zones can now be fitted using line observations, while the gas/dust ratio is constrained. The methods are detailed in  Woitke et al. (2016 Woitke et al. ( , 2018)) , and the results are listed in Table  7  for 27 sources. The second step of the modelling is to determine the disc shape, dust and PAH properties by means of highly automated SED-fits. The result for 27 SED-fitted sources are included in the database and examples are presented in Fig.
Summary In this paper we presented a large sample of Class II and III, T Tauri and Herbig Ae systems with spectral types ranging from B9 to M3 which cover ages between 1 and 10 Myr. The sample of 85 sources in expected to include another 30-40 sources in the near future, rendering this one of the largest and most complete collections of its kind. The collection was assembled combining data from more than 50 observational facilities and 100 published articles in a transparent manner, so that each dataset can be back-traced to each original resources. In addition, 27 of the sources in the collection have their SEDs consistently modeled with dust radiative transfer models (MCFOST, MCMAX and ProDiMo) 14 , and a subset of 18 that have both dust and gas consistently modeled with ProDiMo 15. The user interface and the supporting DIOD database provide the user with the flexibility to compare different characteristics among the sample sources 14 All SED input files and output models available at: http://wwwstar.
In both animal facilities mice were provided water and food ad libitum and housed in 12-hour-long cycles of light and darkness. SMRT sequencing of Bola2 -/-mouse genome Genomic DNA was isolated from the buffy coat layer of the blood of a male null mouse with the lacZ and neo cassettes excised  (Kronenberg et al. 2018 ). We prepared one DNA fragment library (40-160 kbp inserts) with no additional shearing. After SMRTbell preparation, the library was size-selected with the BluePippin™ system (Sage Science) at a minimum fragment length cutoff of 40 kbp. Final library average size was 90 kbp. SMRT sequence data were generated using the PacBio Sequel instrument with Sequel Binding and Internal Ctrl Kit 3. 0, Sequel Sequencing Kit 3. 0, MagBead cleanup, diffusion loading, and acquisition times of 10-hour movies.
Acknowledgements We thank families participating to the SVIP and the 16p11. 2Consortium cohorts. GG is recipient of a  Pro-Women Scholarship  from the  Faculty of Biology and Medicine, University of Lausanne. This work was supported by grants from the  Swiss National Science Foundation  ( 31003A_160203  and  31003A_182632  to AR),  National Institutes of Health  ( R01HG002385  to EEE),  Horizon2020 Twinning  project ePerMed (692145 to AR),  Simons Foundation Autism Research Initiative  (SFARI # 303241  to EEE, # 198677  to RAB, and # 274424  to AR),  INSERM ,  CNRS, University of Strasbourg  ( ANR-10-LABX-0030-INRT  and  ANR-10-INBS-07-03 PHENOMIN  to YH),  framework program Investissements d'Avenir  ( ANR-10-IDEX-0002-02  to YH ). This research has been conducted using the UK Biobank resource (# 16389 ).
Introduction Real-time monitoring of climate variables is crucial at global, national but also local scales. The characterisation of climate events at several scales is not easy and it requires the use of diagnostics or indices able to synthesise useful information. In France, at the national scale, air temperature monitoring (https://donneespubliques. meteofrance. fr/donnees_libres/bulletins/BCM/201903. pdf, last access: 6 August 2019, p. 3) is currently performed thanks to an index (called "national thermic index") described in Sect. 3. 2. At the local scale, monitoring is done using time series at observation stations. In the meantime more and more temperature gridded products are available at kilometric resolution: from regional reanalysis  (Soci et al. , 2013)  or from observation gridded dataset  (Haylock et al.
, 2008; Lussana et al. , 2016; Frei, 2014; Hiebl and Frei, 2016). Those gridded products are useful for real time climate monitoring (https://climate. copernicus. eu/climate-bulletins, last access: 6 August 2019), and new indices can be based on the aggregation of that kind of gridded analyses. This article describes a new spatial analysis of daily minimum and maximum temperatures developed at Météo-France (called ANASTASIA for "ANAlyse Spatiale des TempérAture de Surface avec Initialisation Aurelhy") available at a 1 km resolution over France from 1947 to present days. This analysis is done using a regression-kriging method described in Sect. 2. Evaluation and assessment of the temporal homogeneity of this product is shown is Sect. 3. Two applications (real-time climate monitoring and heat/cold wave detection) using this new dataset are presented in Sect.
Scores The daily dataset has been produced from 1947 to present days. A leave-one-out cross-validation method is used in order to evaluate the quality of the spatial analysis with a subset of validation stations varying from ∼ 70 to ∼ 150 along the period. Two statistical criteria are used to quantify the performance: the bias and the Root Mean Square Error (RMSE). Results on Fig. 1  show the impact of the observation network density over the quality of the analysis: -RMSE decrease from ∼ 2 • C at the beginning of the covered period for both variables to reach ∼ 1. 1/ ∼ 1. 4 • C for maximum/minimum temperature in the last 20 years. -Before 1960 bias is around 0. 6 • C for maximum temperature and -0. 3 • C for minimum temperature. But during the 1960's it is reduced with better values for minima (∼ -0.
Results At the national scale, waves detected with ANASTASIA analysis are compared with the ones obtained with current reference indices. Waves can be ordinated in terms of duration, maximum temperature and global intensity (cf. Fig. 7 ). For heat waves, the number of detected waves is quite similar (39 vs. 41) and waves ranking for each criterion is also quite close (same results for cold waves, not shown). The slight differences are due to representativeness differences between time series used to detect waves. Using ANAS-TASIA data enables to take into account temperature over the whole country (including mountainous and coastal areas) while the current reference only depends on 30 specific stations. In particular, this explains why: -For the 2003's heat wave, the difference between the two maximum temperatures reaches 1.
Conclusion and perspectives The ANASTASIA analysis of daily minimum and maximum temperatures uses a regression-kriging method with in situ observations and monthly climatological fields as input data. The 1 km gridded analysis over France covers more than 70 years starting in 1947 (this dataset is available upon request to the author for research purposes only). Due to the evolution of the observation network density since 1947 the quality of the analysis is not constant with relatively high bias before the 1960s, and RMSE being steadily reduced from 1947 to present years. In addition, heterogeneities are detected at national scale along the period in comparison to the national thermic index known as homogeneous. Nevertheless, comparisons with current operational products used for temperature monitoring over France show good results for present and recent past periods.
cbd. int/) are in the process of being provided for each sample as a first step toward ethical and responsible use of the Tara Pacific data. The detailed registry of all samples, including their provenance and environmental and legal context, is curated manually using simple semantics that enable machine-and human-readable data discovery services. Sequencing data will be deposited at the free, openaccess European Nucleotides Archive (https://www. ebi. ac. uk/ena); environmental data are deposited at the free, open-access PANGAEA database (https://www. pangaea. de/); and both archives will be interlinked via the sample registry available online at BioSamples (https:// www. ebi. ac. uk/biosamples/). Metabolomic data (mass spectrometry [MS] and nuclear magnetic resonance [NMR]) and their annotations will also be accessible through the Metabolights portal (https://www.
Biogeographic gradients will also be investigated between the studied island systems. The sequence reads are assembled into operational taxonomic units (OTUs) for bacteria, archaea, and eukaryotes and into ITS2-type profiles (using the SymPortal framework at symportal. org [40] ) for Symbiodiniaceae and taxonomically annotated by comparison to reference databases. Classical numerical ecology methods are applied to employ OTU richness and abundance data to (1) assess and compare the total diversity of bacteria, archaea, and eukaryotes associated with various compartments of the different coral holobionts; (2) compare biodiversity patterns across coral colonies, hosts, reefs, islands (geographic and Lagrangian oceanic circulation distances), and all contextual environmental parameters measured; (3) integrate the Tara Pacific data into the Tara Oceans global plankton dataset.
acknowledged funding from the  Investissement d'avenir projects France Genomique  ( ANR-10-INBS-09 ) and  OCEANOMICS  ( ANR-11-BTBR-0008 ). RVT was funded by a  Dimensions of Biodiversity NSF  grant (# 1442306 ) for this work. SS is supported by the  ETH Zurich and Helmut Horten Foundation. FL is supported by  Sorbonne Universite, Institut Universitaire de France , and the  Fondation CA-PCA. Finally, we thank the  ANR  for funding the project  CORALGENE , which will support the work the  Tara Pacific program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data is paper employs two databases on euro area banks: nancial statements data at the bank level and loan level information on banks' activity in the syndicated loan market. First, balance sheet and income statement data on euro area banks are extracted from Fitchconnect. 5  original sample covers commercial, savings and cooperative banks from all 19 euro area member countries for the period 2007-2016. Information on bank specialisation, public listing, role in the banking group and identity of the parent institution are recovered from Orbis Bank Focus. 6  e nal bank-level sample contains 2629 banks, 85% of the original sample. Following Gambacorta (2005) and Ehrmann et al. (2002), all nonsense observations (e. g. negative entries) are removed for total assets, loans, and capital and liquidity ratios.
7  Second, the analysis is restricted to unconsolidated accounts to keep the cross-sectional dimension as large as possible. ird, loan growth, total assets, return on assets, capital and liquidity ratios are trimmed at the 1 st and 99 th percentiles to minimise the incidence of outliers. e nal bank level sample is a balanced panel counting 2544 banks adding up to a total of 25440 bank-year observations over the period of interest. Second, loan level information on syndicated loans are extracted from omson Reuters LPC Dealscan. 8 Syndicated loans are provided by banks and nan-5 Fitchconnect is a database compiled by Fitch Solutions providing credit market data. 6 Orbis Bank Focus (previously Bankscope) contains information on over 40,000 public and private banks around the world. e database is compiled by Bureau Van Dijk.
See Appendix E for an account of the matching procedure between Orbis Bank Focus and Fitchconnect. cial institutions to large corporations and sovereigns. Each syndicate constitutes a lending facility wherewith di erent lenders collectively lend to a single borrower. While traditional bank lending remains the main source of nance for most european rms, syndicated loans represent an important funding channel especially for large corporations. For instance, Acharya et al. (2018) report that between 2006 and 2009 syndicated loans made up to 20% of all loans extended to euro area rms. For the purpose of this study, balance sheet data from Fitchconnect is assigned to the corresponding banks (syndicated lenders) in Dealscan. e resulting loan level sample contains 991 euro area banks (42% of the original sample) linked to 10938 international rms for a total of 42278 unique bank-rm relationships observed at quarterly frequency between 2007:Q1 and 2016:Q4.
9 e representativeness of both datasets is documented in Table  A1. For the bank level sample, lending volumes cover 27% of the euro area loan market and, breaking down by country group, 13% and 18% of stressed and non-stressed countries' national aggregates, respectively. As for the loan level sample, syndicated lead arrangers' loan shares make up 23% of the euro area syndicated loan market and 19% and 55% of stressed and non-stressed countries' national aggregates, respectively. Finally, macroeconomic data at both annual and quarterly frequency -real GDP growth, HCIP in ation and an index of house prices -are collected from Eurostat, while data on interest rates -Euribor, Eonia and sovereign debt yields -are collected from the ECB Statistical Data Warehouse. 10.
As discussed in Section 2. 2. 2, a key identi cation task is separating the effect of monetary policy on credit supply from that on credit demand. To this end, syndicated loan data are used to e ectively disentangle credit demand from drives them both. To orthogonalise monetary policy, Taylor residuals are obtained by regressing the nominal interest rate (Euribor rate) on present and future realisations of credit growth, real GDP growth and in ation rate at the euro area level (De Santis and Surico, 2013; Peydro et al. ,  2017). Details of the estimation are reported in appendix C). 20  Building a nonlinear term structure model to analyse an economy operating near the zero lower bound (ZLB) for interest rates, Wu and Xia (2016) construct a shadow rate that incorporates the macroeconomic e ects of unconventional monetary policy at the ZLB.
As in Peydro et al. (2017), this paper uses the shadow rates implied by Wu and Xia (2016)'s model for the euro area. Shadow rates data for Europe were downloaded from Jing Cynthia Wu's website. 21 Refer to Table  A1  for the de nition and list of stressed and non-stressed countries. 22 Bank size (ln total assets) is normalized not only with respect to the average over the whole sample period but also with respect to each single period. is aims at removing undesired trends in size, due to the fact that size is measured in nominal terms (Gambacorta, 2005). 23 A widely used measure of sovereign risk, sovereign spread is de ned as the di erence between the German and the country's 10-year government bond yield (Albertazzi et al. , 2014). credit supply. Unfortunately, lending growth from a bank to a rm is not readily available in Dealscan.
Indeed, the database contains information only at the time when the loan is originated and one cannot observe changes over time for any particular syndicated loan. Furthermore, as noted by Acharya et al. (2018), syndicated loans typically have long maturities, so that a large number of observations display no signi cant quarter-to-quarter (or year-to-year) variation. As a result, rst-di erencing to obtain lending ows from lender to rm drastically reduces the number of available observations. 24 erefore, in contrast to Model 1 but consistent with the analysis of monetary transmission of Heider et al. (2019), in Model 2 lending volumes are regressed on interest rate levels. Speci cally, the estimating model is as follows: ln Loan share ijt = MP t (β 1 + β 2 BLC banks i + β 3 BLC banks i × Stressed i ) + γ 1 X it-1 + γ 2 Y it + α i + δ t + η jt + ijt (2) where the observational unit is bank (lead arranger) i lending to rm j in quarter t.
A third model is estimated: ∆ ln Loans it = ∆MP t (β 1 + β 2 BLC banks i + β 3 BLC banks i × Stressed i + β 4 BLC banks i × Stressed i × Sovereign exposure it-1 ) + γ 1 X it-1 + γ 2 Y it + α i + δ t + it (3) where, as before, i indicates banks and t years. Model 3 augments Model 1 with one interaction term designed to capture how heterogeneity in monetary transmission to lending varies with banks' sovereign exposure, de ned as the ratio of government debt securities to total assets as in Altavilla et al. (2017). e logic is the following: if sovereign stress really constrains the response of credit supply to monetary policy in stressed countries, this response should be weaker for banks with larger (ex-ante) sovereign exposures. Unfortunately, the breakdown between domestic and non-domestic government securities holdings is not available in the Fitchconnect database.
Notes: For each country the table reports the number of banks, the volume of loans or the share of syndicated loans made by lead arrangers and their share of national aggregates as reported in the Balance Sheet Indicator statistics of the ECB Statistical Data Warehouse. Loan values are as of 2014. Stressed countries -i. e. subject to high sovereign stress -are countries whose 10-year sovereign yield exceeded 6% for at least one quarter for the period 2007-2016 (Altavilla et al. ,  2017). Notes: For each country, the table lists the number of banks, bank level gross loans averaged around 2007-2016 and then collapsed at the country level, and their relative shares of the BLC sub-sample as de ned in section 2. 2 (columns 2 and 4) and the full sample (columns 6 and 8). Dummy variables indicating whether a bank is: Orbis Bank Focus -branch location (a secondary location over which headquarters have legal responsibility); -controlled subsidiary (controlled by another entity); -global ultimate owner (GUO, the ultimate owner of a corporate group); -independent (a bank which is not a GUO but which could be GUO) -single location (a bank that is, is neither a shareholder nor a subsidiary of another bank).