dataesr/scientific-paragraphs-categorization huggingface dataset:
Model domain To get the desirable resolution (46 m) two embedded grids with two-way nesting are used. The HR bathymetry used to build these grids is the result of the assemblage of wellknown regional bathymetric databases: Etopo 1' and Admiralty charts, local bathymetric databases provided by the Santander Port Authority and the Topography Department of the University of Cantabria and two bathymetric surveys carried out in the framework of SPRES project. Figure  3  shows the set of grids used and Table  1  summarizes their main characteristics. Table  1. Properties of the grids used in Santander Bay In relation to the vertical distribution, 5 vertical levels in σ coordinates have been defined. ROMS has a generalized vertical, terrain-following, σ coordinate system. In this application the transformation equation chosen was the first one described in  Shchepetkin and McWilliams (2005)  and the stretching function is the one developed by  Song and Haidvogel (1994).
COAWST validation The HR-OOM has been calibrated and validated using data from a permanent tidal gauge located inside the Bay (managed by Puertos del Estado) and two field campaigns carried out in summer of 2012 and winter of 2013 (see Figure  7 ). The summer campaign lasted 19 days, from 18th July to 6th August, and the winter campaign lasted 16 days, from 7th March to 22nd March. 524 The hydrodynamic model calibration consists of quantifying the optimal hydrodynamic model parameters, comparing modelled results with real observed values by using statistical descriptors. In this case, the unknowns were the hydrodynamic model parameters (bottom drag coefficient D C and the horizontal eddy viscosity, k) and the turbulence closure model (TCM). We have used several statistical descriptors such as the root mean square error (RMSE), the correlation index (  ) and the skill index ( skill ), which was finally selected to evaluate the model performance:   2 mod 1 2 mod 1 1 n el obs n el obs obs obs xx skill x x x x        (1) where obs x are the instrumental data, mod el x are the model results and n are the length of both datasets.
Acknowledgments We are grateful to the  European Commission  for the financial support of the project through the  FP7 Design Studies LAGUNA  (Project Number  212343 ) and LAGUNA-LBNO (Project Number  284518 ). We acknowledge the financial support of the  UnivEarthS LabEx programme at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). In addition, participation of individual researchers and institutions has been further supported by funds from  ERC  ( FP7 ). Open Access. This article is distributed under the terms of the Creative Commons Attribution License ( CC-BY 4. 0 ), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
Acknowledgements. The authors would like to thank  M. Robardet  for providing detailed information for locating the outcrop, and  C. Corradini ,  S. Gouwie  and  P. Serventi  for their help in the 2006 conodont sampling. We acknowledge useful comments on the manuscript by  Guillermo Albanesi  and  Peep Männik. M. Tonelli  ( Centro Interdipartimentale Grandi Strumenti CIGS, Modena, Italy ) and  M. Barbieri  are recognized for the skilled assistance during SEM investigations and figure preparation, respectively. This paper is a contribution to IGCP Project  591  ' The Early to Middle Palaeozoic Revolution -Bridging the Gap between the Great Ordovician Biodiversification Event  and the  Devonian Terrestrial Revolution '.
Introduction This paper, one of a set associated with the 2013 release of data from the Planck 1  mission (Planck Collaboration I 2014), describes the Planck-LFI instrument systematic effects and their related uncertainties in CMB temperature maps and power spectra. Systematic effects in Planck-HFI data are discussed in Planck Collaboration  VI (2014)  and Planck Collaboration X  (2014). The LFI implements a pseudo-correlation differential design similar to WMAP  (Jarosik et al. 2003a,b)  to suppress 1/ f amplifier gain and noise fluctuations  (Seiffert et al. 2002; Mennella et al. 2003; Bersanelli et al. 2010)  as well as correlated effects from thermal and electrical variations affecting both the sky signal and reference loads. The reference signal is provided by stable 4. 5 K blackbodies thermally and mechanically connected to the external structure of the High Frequency Instrument (HFI) 4 K box  (Valenziano et al.
2). In Sect. 3 we outline and discuss the known instrumental effects, separating them into two broad categories: (i) effects that do not depend on the sky signal and impact the radiometric measurements as an additive spurious fluctuation or a gain variation, and (ii) effects that do depend on the sky signal, i. e. , on its amplitude and/or on the scanned sky region. Some of these effects are removed in the data processing pipeline according to algorithms described in Planck Collaboration II  (2014). The assessment of the residual uncertainty, discussed in Sect. 4, was performed according to two different strategies. Null tests were the primary tool to check for systematic effect residuals exceeding the white noise level. We also assessed their impact on radiometric time streams, even if below the white noise limit, by exploiting in-flight housekeeping and scientific data.
Overview of LFI systematic effects Known systematic effects in the Planck-LFI data can be divided into two broad categories: effects independent of the sky signal, which can be considered as additive or multiplicative spurious contributions to the measured timelines, and effects which are dependent on the sky and that cannot be considered independently of the observational strategy. These effects can generate correlations in the data, and should be removed from timelines before noise is assessed and maps are generated. For this release, based on temperature data only, we have removed from timelines three of these effects: diode-diode correlations (Sect. 3. 1. 1), ADC non linearity (Sections 3. 2. 2 and 4. 3. 2) and 1-Hz frequency spikes (Sections 3. 1. 4 and 4. 2. 3). The remaining effects have been treated as noise, and their effect assessed via the noise covariance matrices (Planck Collaboration II 2014) and half-ring difference maps (see Planck Collaboration II 2014, and Sect 4.
We treat the combined diode data as the raw data, and calibration, noise estimation, map-making etc. are performed on these combined data. The weights were determined from some initial estimates of the calibrated noise for each detector, and are kept fixed for the entire mission. Noise parameters were reported in  Mennella et al. (2011). A longer data set, some thermal instabilities in the instrument (particularly during survey 3), and refinements of the data analysis (map making and noise covariance matrix) all require a more detailed look at the long term evolution of the noise characteristics of the receivers. The noise power spectral density P( f ) of the receivers is generally well described by P( f ) = σ 2 1 + f f k α , (1) where σ characterizes the white noise component, the knee frequency, f k , denotes the frequency where white noise and 1/ f contribute equally in power to the total noise, and α characterizes the slope of the power spectrum for frequencies f < f k.
1-Hz spikes This effect is caused by pickup from the housekeeping electronics clock that occurs after the detector diodes and before the analogue-to-digital converter (ADC)  (Meinhold et al. 2009; Mennella et al. 2010 Mennella et al. , 2011)). This spurious signal is detected in the radiometer time-domain outputs as a 1 s rectangular wave with a rising edge near 0. 5 s and a falling edge near 0. 75 s in onboard time. In the frequency domain it appears at multiples of 1 Hz. Frequency spikes are present at some level in the output from all detectors, but affect the 44 GHz data most strongly because of the low voltage output and high post-detection gain values in that channel. For this reason spikes are removed from the 44 GHz time-ordered data via template fitting, as described in Planck Collaboration II (2014).
02% to 0. 08% of the total beam power, which is about 10 times less than the fraction in far sidelobes (ranging from 0. 18% to 0. 68%). Their effect is therefore correspondingly smaller, of the order of ∼ 1. 5 µK on the maps. Moreover, because intermediate lobes involve sky regions very close to the main beam, their effect can be controlled by masking the Galaxy and point sources. In this paper we have therefore neglected the effect from intermediate sidelobes, which will be addressed in detail in a future paper dedicated to the analysis of the full mission dataset. Straylight impacts the measured signal in two ways: (i) through direct contamination and coupling with the main beam sky signal, and (ii) in the photometric calibration of the radiometer detected signal. In this paper we concentrate on the direct detection, while the impact on calibration and the adopted mitigation strategies are described in Planck Collaboration V (2014).
Fig. 10  shows this effect as a plot of relative white noise variation versus the detector output voltage for one the the most affected radiometer channels (the 44 GHz detector LFI25M-01). The grey points represent an average over each pointing (about 40 min) while the solid line has been obtained by further binning the data in 200 bins over the plotted range in order to reduce the scatter and show more detail. The figure shows that the typical amplitude of the region where the non-linearity occurs is of the order of 1 mV, corresponding to about three bits in the ADC. The ADC effect is strongest (3 to 6%) in the 44 GHz channels, because of their lower detector voltages. The ADC non-linearity effect has been characterised from flight data and removed from the data streams according to the procedure described in Planck Collaboration II (2014).
Null tests We define a "null test" as any difference between two independent data sets which are anticipated to give nearly the same signal, on the assumption of perfect calibration, pointing reconstruction, and systematic effects removal. Null tests are a powerful means to assess the validity and self-consistency of Planck data on various timescales and across different dimensions (detector, frequency, time), and to highlight systematic effects above the white noise level. The design of Planck and its observing strategy provides a wide range of opportunities for null tests, with sensitivity to different systematic effects and implications for the scientific outputs. Although we refer to these tests as null tests, the results are generally not featureless. Some of these features are caused by beam orientation and ellipticity that cause spurious effects in odd minus even survey difference maps in correspondence to point sources and in the galactic plane.
The analysis of the effect of beam ellipticity on the CMB power spectrum is provided in Planck Collaboration IV (2014) and will not be repeated here. A part of these residuals is caused by signal pickup from beam sidelobes. Our beam model captures, at least partly, these fingerprints. Future work will be aimed at exploiting this model to remove the sidelobe signal from the data. A fraction of this largescale residual is not captured yet by our instrument model and will require further investigation to be understood and properly removed from the data. In the Planck-LFI collaboration each internal data release is accompanied by a comprehensive set of null tests as a check of our processes and ongoing improvement in terms of systematic errors. In this section we report the results from the main tests supporting the systematic effect analysis for the first Planck public data release.
In-flight LFI noise properties have been presented and discussed in  Mennella et al. (2010). -Effects at 1-Hz have been assessed from in-flight timelines by stacking data from all the mission (for each detector) in 1-second time windows. This allowed us to produce timedomain templates of the 1-Hz spurious signal that have been removed from the data at 44 GHz, which is the channel most affected by this effect. -Short timescale temperature fluctuations at the level of the radiometers and of the 4 K reference loads are not expected as the LFI and HFI focal planes act as lowpass thermal filters. Measurements from the LFI and HFI temperature sensors confirm that only the slow temperature fluctuations propagate from the cooler cold ends to the focal plane detectors and reference loads. Thermal transfer functions have been derived both before launch  (Terenzi et al.
Half-ring difference null tests Half-ring difference null tests, constructed by taking a weighted difference between the first and second halves of each pointing period, are useful to assess the data noise properties and systematic effects on time scales smaller than about 20 min. Weights are calculated as explained in Sect. 9. 2 of Planck Collaboration II (2014). In Fig. 12  we show the half-ring difference maps for the three LFI frequencies. A simple quantitative test was performed by dividing them pixel-by-pixel by the square root of the white noise covariance maps (Planck Collaboration 2013) and checking the standard deviation of the resulting maps. We found this r. m. s. value to be very close to unity: 1. 0211, 1. 0089, and 1. 0007 for 30, 44, and 70 GHz, respectively. The deviation from unity is consistent with the different level of 1/ f noise in the three frequency channels (see Tables  1  and 10  of Planck Collaboration II 2014).
Survey difference null tests Differences between single survey maps are useful to check for residual systematic effects at large angular scales. Difference maps of odd minus even surveys highlight effects arising from beam ellipticity and far sidelobes. The left-hand column in Fig. 13  shows the difference maps between surveys 1 and 2 obtained from measured data at the three LFI frequencies. The cosmic and orbital dipole signals are removed during calibration (as discussed in Planck Collaboration V 2014), so the difference in orbital dipole signal between survey 1 and survey 2 is not visible. These maps show large scale residuals above the noise floor, especially in the 30 GHz channel, and in particular far sidelobe pickup of the galactic plane in survey 2 is visible as a large blue ring. For comparison, the right-hand column of Fig.
24 ). The blue ring structure disappears, confirming both the nature of this spurious feature in the map and that our sidelobe model captures a significant part of the large-scale residuals. Further investigation is needed to fully understand and remove the remaining level of spurious large-scale structures in our maps. In Fig. 15  we show survey 1 minus survey 3 null test maps. These two surveys cover the sky in nearly identical orientations, and would be consistent with noise if calibration and other systematics were perfectly controlled. However, as seen in this figure, there are large-scale features also in these difference maps, and these are still under investigation. Because the first Planck cosmological release is based only on data from the first two surveys, though, a detailed study of effects present in data beyond survey 2 is outside the scope of this paper, and will be discussed in the second data release.
When we compare the focal plane geometry obtained from the combination of J1 and J2 with the one obtained from the combination of J3 and J4 we find a difference of about 15 ′′ in pointing, mainly along the in-scan direction. On the other hand, the comparison of the focal plane geometries determined from single Jupiter transits (J1 against J2 and J3 against J4) shows differences within the expected uncertainty. The 15 ′′ discrepancy, likely to be correlated with changes in the thermal control set-point of the data processing unit in the instrument digital electronics, is compensated using two different instrument databases in the data analysis pipeline, one for the period ranging from day 91 to day 539 after launch, and the other for the period between day 540 and day 563. Details of the focal plane reconstruction and related uncertainties can be found in Planck Collaboration II (2014).
We assess the impact of this effect using dedicated simulations constructed according to the following procedure: 1. Generate time ordered data by observing a CMB-only sky with flight detector pointing derived by applying the two focal plane database solution. 2. Reconstruct the CMB map from the time-ordered data generated in step 1, applying each of the two focal plane database solution in map reconstruction. 3. Repeat step 2 using the single focal plane database solution. 4. Compute the difference of the power spectra obtained from the two generated maps. Results. Figure  30  shows that the relative difference of power spectra is of the order of 10 -4 , which is negligible.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). We acknowledge the computer resources and technical assistance provided by the  Spanish Supercomputing Network nodes at Universidad de Cantabria  and  Universidad Politécnica de Madrid  as well as the support provided by the  Advanced Computing and e-Science team at IFCA. A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www.
Background: Identification of MS registries and databases that are currently in use in Europe as well as a detailed knowledge of their content and structure is important in order to facilitate comprehensive analysis and comparison of data. Methods: National MS registries or databases were identified by literature search, from the results of the MS Barometer 2011 and by asking 33 national MS societies. A standardized questionnaire was developed and sent to the registries' leaders, followed by telephone interviews with them. Results: Twenty registries were identified, with 13 completing the questionnaire and seven being interviewed by telephone. These registries differed widely for objectives, structure, collected data, and for patients and centres included. Despite this heterogeneity, common objectives of the registries were epidemiology (n=10), long-term therapy outcome (n=8), healthcare research (n=9) and support/basis for clinical trials (n=8).
Introduction Multiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system that has a high impact on both the health-related quality of life (HRQoL) of patients with MS and their families, and on society. In Europe, more than 500,000 people are affected by the disease. However, healthcare for MS patients as well as their socioeconomic conditions differs greatly across the European Union. 1  Although the European Commission intends to tackle those disparities more effectively, its support to the member states is impaired due to the lack of valid data at the European level. MS registries are essential tools for providing such information, and different registries and databases do already exist in various countries, but these systems differ in terms of objectives, time, and resources spent for registration and analytical preferences.
3 e of EUReMS' principles is that it should build on already existing national or regional MS registries and databases. For this purpose, the identification of MS registries that are currently in use in Europe as well as acquiring a detailed knowledge of their content and structure is of utmost importance in order to enable merging of data and their comparison at a European level. In this paper, we report the results of a survey on MS registries in Europe that was performed between January and April 2012 as part of the EUReMS project.
Identification of MS registries and databases The existing registries and databases in Europe were identified from recent reviews,  2, 4, 5  , from records of the European Multiple Sclerosis Platform (EMSP) derived by the MS Barometer in 2011,  1  and from a PubMed search. In addition, the references of relevant publications and abstracts were checked, and the 33 national European MS societies were contacted and asked whether they were aware of systematic data collection in their countries.
Development of the questionnaire Based on a previous survey on MS registries,  5  and on the experiences with data management in the registry context, a questionnaire was developed by EUReMS Steering Committee (SC) members (Appendix 1, available to view online) using a peer focus group approach (Appendix 2, available to view online) and sent to the registries' leaders. Information on the following domains was collected (see supplementary data online for full list of SC members in Appendix 1): Along with the questionnaire, a cover letter was enclosed wherein the registry's scientific and technical staff were invited to participate in a onehour standardized interview with SC members. • • Organizational structure • • Background and purposes • • Inclusion criteria and patients • • Documentation process • • Data collected • • Quality control • • Governance • • Current.
Results Literature search and information from the MS Barometer yielded 17 national MS registries. After contacting the European MS societies, three additional registries were identified (Table  1 ). The survey was thus sent to 20 MS registry contact persons; of these, five did not respond after at least one reminder. In Austria, only registries for MS patients treated with natalizumab or fingolimod exist, and in Iceland, nearly every MS patient has been documented by one neurologist, but these data could not be retrieved. Thus, detailed information was available from 13 (65%) registries. Seven of these agreed to participate in the standardized telephone interview.
Results of the standardized questionnaire The main purposes of the registries were (multiple answers possible) epidemiological research (n=10; 77%; 95% CI: 46-95%), healthcare research (n=9; 69%; 95% CI: 39-91%), long-term therapy research (n=8; 62%; 95% CI: 32-86%), and support/basis for clinical trials (n=8; 62%; 95% CI: 32-86%). Less frequent registry aims were cost and cost-effectiveness of treatment (n=5), quality management of healthcare (n=5), and gathering data (n=1). Seven to 10 registries met at least one of the EUReMS missions, whereas Greece, Norway and Sweden met all four (Table  2 ). In Russia, there is a united registry aimed at integrating the registries of 83 regional ministries of health of the Russian Federation, which is run by the Ministry of Health with the purpose of making purchases on their own and providing MS patients with disease-modifying drugs (DMD).
All registries included patients with MS according to the McDonald criteria. In addition, six registries applied Poser criteria, and eight registries also collected data on patients with clinically isolated syndromes (CIS). The Catalan registry was specifically designed for CIS patients. The majority of respondents stated that the registry was hospitalbased (n=10, 77%), and five (38%), that it was population-based, with three registries being hospital-and population-based together. In three registries, patient lists from MS societies were used either exclusively (n=1) or in combination with the above-mentioned sources of data collection. Most registries (n=9, 69%) intended to capture all MS patients in the country, whereas four registries (31%) attempted to collect data on representative subsets (regions) of patients (Croatia, Italy, Switzerland and United Kingdom).