dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The aim of this work is to illustrate the influence of nuclear structure data on fission observables. To do that we consider a Monte Carlo simulation of the de-excitation of fission fragments by neutron/gamma/conversion electron emission. FIFRELIN  [1, 2]  is a Monte Carlo code developed at CEA which offers this capability by relying on nuclear structure data at low excitations energies (level schemes and transition intensities for instance). Section 2 is a short description of the de-excitation process performed in the FIFRELIN code while Section 3 resumes the type of nuclear structure data used in the code. Section 4. 1 highlights the role of the half-life of levels when comparing prompt gamma spectra measured in coincidence with fission fragments. The influence of a modification of the neutron separation energy is illustrated in Section 4.
Fission fragment de-excitation The simulation of the de-excitation of fission fragments starts by the sampling of the mass A, the nuclear charge Z, the kinetic energy KE, the excitation energy E, the spin J and the parity p of an initial state. The three first characteristics are usually based on pre-neutron emission experimental data while the three last characteristics require some assumptions and models because of the scarce information available for excitation and spin-parity sharing between fragments right after scission. In FIFRELIN fission fragments excitation energy and spin are estimated by using a mass-dependent temperature ratio law R T (A) (involving two free parameters) and a spin cut-off parameter (involving one to two additional free parameters depending on the spin cut-off model).
Nuclear structure data Different nuclear structure data are used for the simulation of the de-excitation of fission fragments. In FIFRELIN, these data are provided by the RIPL3 database  [5]. A new release has been delivered in August 2015  [6]. In addition, the knowledge of nuclear masses, provided by AME2012  [7, 8] , is required e. g. for the calculation of the fission fragment kinetic energy after neutron emission. These data are consistent with the neutron separation energy provided in RIPL3-2015. Differences between this release and the previous one have an impact on the estimation of fission observables. The main data accounted for in FIFRELIN are summarized hereafter: neutron separation energy S n ; number of levels; energy of the last level below which the scheme is supposed to be complete (at least the energy of the levels inside the scheme are known but their spin and parity can be unassigned); energy of the levels; spin and parity of the levels (they are sampled if not given); number of transitions; decay branching ratio; half-life T 1/2 ; gamma intensities I i→f g ; -Internal Conversion Coefficient ICC.
Influence of nuclear structure data on fission observables The prompt fission gamma spectrum (PFGS) calculated by FIFRELIN in the case of the spontaneous fission of 252 Cf is in good agreement with experimental values  [12]  but even if the shape is well reproduced, the average multiplicity is higher by roughly 10%. As shown in Figure  1  roughly half of the calculated low-energy gamma spectrum is purely calculated from level density and photon strength function models while the other half comes from gamma intensities provided in the database. Several explanations for this overestimation can be presented: bad or missing spin assignment; missing half-lives (some states could be nano-second isomers or micro-second isomers); incorrect gamma-ray intensities; incorrect conversion coefficients; bad level densities; bad photon strength functions.
Influence of the neutron separation energy S n Table  1  provides different nuclei for which the neutron separation energy has changed since the last release of the RIPL3 database. The influence of a modification of S n is exemplified on the thermal neutron induced binary fission of 235 U leading to the 102 Y + 134 I fragment pair. In the case of 102 Y, the S n value is lowered by almost 900 keV in the present release of RIPL3-2015. This leads to an increase of about 10% of the average neutron multiplicity as well as the gamma multiplicity. In the same time, the conversion electron multiplicity of the fragment pair is increased by 60%. Nevertheless the example of 102 Y is an extreme case: almost all levels of this odd-odd neutron rich nucleus have unknown spinparities. Not only S n has changed and for instance a level (T 1/2 = 360 ms) having an uncertain energy assignment in RIPL3-2009 was set to 200 keV in RIPL3-2015 (Tab.
Influence of the delayed neutron emission probability P n There were no P n values for 88 Br, 96 Rb, 86 As in RIPL3-2009 release leading to a strong underestimation of the delayed neutron fraction. It is now corrected in the new release since 2015. In FIFRELIN, we can calculate without additional cost the number of delayed neutron that will be emitted at the end of the prompt de-excitation cascade. This is allowed by the fact that the database provides the b À n probabilities. Note that this is not exactly the true delayed neutron fraction n d but a related parameter that we will write n 0 d because it takes into account the independent fission fragment yields calculated during the prompt cascade and not the cumulative yields. This parameter cannot be compared to experimental values but the influence of the P n values can be highlighted here in the case of 235 U(n th ,f) and 237 Np(n,f) reactions where an increase of 15% can be observed (see Tab.
If the lower part of the level scheme is known with better accuracy (in the new release of the database) then the model parameters that drive the initial fission fragment spin can be better inferred. Practically in this work it is clear that the overfeeding of high spin states in the current calculation scheme of FIFRELIN (initial spin cut-off models) leads to high intensities in transitions at 688 and 849. 3 keV. This was already observed for other nuclei by using the previous release of the database as reported in  [15]  related to isomeric yields.
Whole genome sequencing Bacterial DNA extraction was carried out as described previously. 11 DNA libraries were prepared with a Nextera XT Library Preparation Kit (Illumina, San Diego, CA, U. S. A. ) and quantified with an Agilent Bioanalyser (Agilent, Santa Clara, CA, U. S. A. ). Libraries were normalized, pooled and sequenced as 250-bp paired-end reads with a MiSeq sequencer (Illumina). The sequence data have been deposited in the European Nucleotide Archive under project accession PRJEB25052.
Bioinformatic analysis Multilocus sequence types were determined from sequence reads using SRST2. 12 Single-nucleotide polymorphisms (SNPs) were identified by mapping sequence reads to the S. aureus reference genome MSSA476  13  using SMALT. 14 A maximum likelihood phylogeny was constructed using core genome SNPs as described. 11 Isolate resistance profiles were predicted in silico from sequence reads with SRST2 by comparison with previously compiled resistance determinant databases for 18 antimicrobials. 15, 16 Core chromosomal SNPs conferring resistance were identified by manual inspection of the mapping data.
Closer examination of the sequence revealed that two isolates contained frameshift mutations within blaZ and two contained frameshifts in the regulatory gene blaR (which is responsible for expression of blaZ), both of which would ablate expression of blaZ. Additionally we identified genes for resistance to antibiotics not commonly used for treatment in AD, or routinely incorporated in AMS testing. Streptomycin resistance markers (AAD9 or aadE) were found in 12% of cases of AD vs. 6% of controls. The amikacin resistance gene aphA-3 was detected in 4% of S. aureus from cases compared with 2% from controls. However, overall there were no significant differences in these genes between the groups. Finally, we assessed the WGS data for determinants of resistance to disinfectants. In 16% of the S.
Background: The French Cystic Fibrosis Registry takes a census of the population of patients and records their annual data transmitted by Cystic Fibrosis Centers (CFCs). Quality of patient data has been a focus in the past years, with the implementation of automated controls before data integration. The objective was to assess, at the 14 CFCs trained in the quality improvement named Hospital Program to Improve Outcomes and Expertise in Cystic Fibrosis (PHARE-M), the quality of the 2012 and 2013 data transmitted to the French Registry with respect to the rules established to obtain forced expiratory volume in 1 second (FEV1%) and anthropometric data. Methods: The clinical researcher selected 20 patients at each CFC from age ranges corresponding to different visit frequencies and measurement procedures in order to reach saturation of error causes.
History of the French cystic fibrosis registry The French National Cystic Fibrosis Observatory was established in 1992. Its initial objective has evolved into a comprehensive census of the population  [1] , allowing it to become the French Cystic Fibrosis Registry  [2]  certified by the French National Committee of Rare-Diseases Registries in 2008. It falls in the group of six countries whose Registry is classified as grade A based on criteria of comprehensiveness of the census population and precedence  [3]. It is funded and managed by the patient organization Vaincre la Mucoviscidose with the support of the Patient Registry Steering Committee (PRSC) including the organization's medical & scientific directors, clinicians, demographers and epidemiologists. The objectives of the French Registry are: -To take a comprehensive census of people with cystic fibrosis by including data on diagnosis (French Association for Screening and Prevention of Child Handicaps and CFTR-France), death (CépiDc -INSERM) and transplantation (HEGP); -To have annual data concerning the patients followed up at healthcare centers in France (mainland France, Réunion Island, and Guadeloupe); -To help improve knowledge of the medical and social characteristics of the population with cystic fibrosis and to assess the impact of therapeutic advances on the evolution of state of health and survival; -To assess the socioeconomic cost of this disease in terms of treatments and management and to anticipate changes in this cost; and -To have information to shed light for the choices of parents and patients and the strategic choices of associations and other institutional partners.
The annual Registry values for FEV1% and BMI are used as primary endpoints to determine the performance of the PHARE-M program by assessing the three-year trend  (2012) (2013) (2014) (2015)  between the two groups of patients. The FEV1% and BMI values are calculated by the Registry software from patient's FEV1 in liters (FEV1 L), height and weight values transmitted by the CFC. The Knudson reference equations are used to obtain the FEV1% value. Thus, best research practice led to assess the quality of the data (FEV1 L, height and weight) transmitted to the Registry to calculate FEV1% and BMI Z-score for the population enrolled in the study cohort. Some additional hypotheses led to clarify the audit's objectives and scope: -The recording of reliable data in the Registry is one essential aspect of quality improvement and as such, the CFCs from the PHARE-M group must be audited so that they take actions for improvement, if necessary; -The CFCs from the "Control" group will only be known by the end of 2015, since all those that engaged in the PHARE-M between 2013 and 2015 are excluded; thus it is difficult to audit data in this group during the course of the research project; -The research project is not a substitute for a national Registry data quality audit, which may be decided and framed at the national level by the PRSC, should the audits conducted within the framework of this research study point to the need for such a process; -The audits conducted on a sample of patient records at 14 CFCs should identify all the possible causes of error or, at least, all the main causes of error, in order to reach saturation of error causes.
Selection of the sample of patients for the audit The sample of patients whose data will be audited should reflect the distribution by age ranges of patients at each CFC in order to cover all the measurement procedures as defined by the international guidelines [7-9]. Thus, it must offer every opportunity to reach saturation of error causes. The audit also has to report the CFCs context in terms of amenities and local CF Patient software, including the nature (manual or automated) of the interface with the Patient Registry software, as there might be explanations regarding the quality of the data transmitted to the Registry. The patients were selected in each CFC according to the following steps: 1. Through an email sent to the Registry administrator, the head physician at the CFC authorizes the CRA to access the patient data undergoing the quality audit.
Local level: Patient records at the CFCs At the 14 CFCs, patient records were presented in the form of a hard-copy record and an electronic file. The hard-copy record contains examination documents including PFT source files. The electronic record is managed in the Hospital Information System (HIS). At 11 of the 14 CFCs, a software dedicated to cystic fibrosis is used concurrently with the HIS: Five CFCs used the MucoDoméos software, three used the Gulper software, and three used the eMuco software. Discrepancies in the patient records (see Fig. 2 ) For the 2455 consultation visits: -In 67 cases (3%), the consultation report, in which weight and height measurements are recorded, was not found; -In 45 instances (2%), including 43 instances for adult patients, weight was not recorded in the consultation report; weight anomalies of up to plus or minus 5 kg were identified in 22 cases (1%): these were linked to errors in entry or position of the decimal point; -In 62 instances (2.
Updated height: at the 11 pediatric or mixed CFCs, height was updated at each consultation in the spirometry software; at the three adult CFCs, height was not updated at each consultation; and at eight CFCs, height and weight measurements were taken in both PFTs and consultations; Standards for calculating FEV1%: 21 spirometers apply the Zapletal pediatric benchmarks  [11]  and ECCS/ ERS adult benchmarks  [12] ; seven spirometers only use the Zapletal benchmarks, 14 only use the ECCS/ERS benchmarks, and two centers were unable to specify the benchmarks applied in their spirometer; at three pediatric CFCs, the ECCS/ERS standard was used as a benchmark; and, using the Knudson reference equations  [13] , the Registry recalculates the FEV1% based on the value in liters transmitted by the center.
It should be noted that: -Discrepancies on the weight data in the PFT file have no impact on the result of the calculation of FEV1 as a percentage; -Discrepancies on height (and gender) do have an impact on the result of the calculation of the FEV1 as a percentage. National level: results from the control of data transmitted to the registry The values appearing in the Registry and the values appearing in the patient visit record with the best FEV1 in liters for the year are compared. An anomaly is counted each time a difference existed between the values in the Registry and the values in the patient record with the best FEV1 L. When a patient (especially a small child or infant) has not done any PFTs during the year, only the anthropometric data have to be transmitted to the Registry and must be those measured at the last visit for the year.
A difference between these values is then counted as an anomaly. Various rules are applied by the CFCs to transmit the spirometry data and the associated anthropometric data to the Registry. The MucoDoméos, Gulper and e-Muco software programs semi-automatically transmitted data to the Registry by selecting the data corresponding to the best FEV1 in liters, for the years 2012 & 2013. Three CFCs did not use software specific to cystic fibrosis and thus transmitted the data selected manually from their Hospital Information System. Given the variety of procedures for transmitting data to the Registry and the ambiguity of the wording of the rule in the document accompanying the annual Registry questionnaire for the years 2012 and 2013, variability in the selection rules has been observed, causing anomalies.
The anomalies observed are often "massive" as they generally result from the procedure applied at the CFC for all their patients. The controls showed that: Nine CFCs transmitted the value in liters corresponding to the best FEV1% for the year to the Registry; The CFCs using the CF software for semi-automatic transmission generally reported the data corresponding to the best FEV1 in liters for the year; The weight and height data transmitted to the Registry by the eMuco software are those appearing in the PFT data set, while the primary data measured by the nurse are most often recorded by the healthcare providers at the CFC in the consultation data set; -Three CFCs transmitted FEV1 values measured after short-acting bronchodilators were taken, in line with their interpretation of the rule for selecting the "best spirometry of the year"; -At one CFC, in 2013, a replacement staff member entered theoretical FEV1 values by age and sex, instead of patients' measured values; -At another CFC, the data transmitted to the Registry corresponded to the last FEV1 for the year in 2012 in accordance with the rule valid up to 2010; -One CFC did not report FEV1 values in liters in 2012 (only FEV1% value).
In summary, in 110 patient records out of the 242 audited (45%), there were anomalies between the FEV1 L appearing in the Registry and the value that would have been expected according to the given selection rule (see Table  4 ). They mainly relate to the date of the venue not corresponding to the visit at which the best FEV1 value in liters for the year was measured. Other causes of anomalies derive from the conditions of FEV1 measurement (transmission of the value after bronchodilator), the absence of data (no transmission of FEV1 L) or an error in the value transmitted (theoretical value). Among those 110 patient records presenting anomalies, 33 were children aged 6 to 17 for whom further investigations were made.
Impact of various standard references for the calculation of the value FEV1% For the 33 children aged 6 to 17, Table  6  shows that: -the different selection rules applied were either the best FEV1 L or the best FEV1% or another value corresponding to an undetermined rule. Some standardization appeared when a CF software is used to transmit the data to the Registry; -the Registry applies the Knudson reference equations to the FEV1 L value transmitted by a CFC: a discrepancy then appeared between the FEV1% in the Registry and the FEV1% in the patient record when local standards used were different from the Knudson   reference values, even though the FEV1 L was identical in the 2 files. For the 33 children in the age range of 6-17 years old, the deviation between the FEV1% value appearing in the Registry and the FEV1% value appearing in the Patient Record of the visit with the "Best FEV1 L", is an average deviation of +4.
Standardization of data transmitted to the registry with the use of a CF software The example of 4 pediatric CFCs equipped with the 3 different CF software programed to select the visit at which the "Best FEV1 L" had been measured, shows (Table  7 ): -standardization of the data selection in these CFCs -deviations on FEV1% value remained when the local standard reference in the CFC was different from the Knudson reference value used in the Registry.
Discussion This first on-site quality audit of the data transmitted to the French CF Registry showed a great deal of diversity in terms of organization, information circuits, equipment, and practices concerning taking measurements as well as in the rules applied for selecting the values to be transmitted to the Registry. While discrepancies in the recording of each value for weight, height, or FEV1 in liters in the patient records are observed in less than 5% of casesexcept for the height in PTF files, discordant in 11% of the 1855 PFTs, anomalies between the data appearing in the Registry and the data from the patient records occur for a great number of patients in the sample controlled (45%). The rule of selecting the annual visit when the "best spirometry in the year" was measured, enacted from the 2011 survey, was not homogeneously applied at the 14 CFCs for the years 2012 and 2013, except when the CF Software selected semiautomatically the visit at which the "Best value L" was measured.
Organization of data and suitable use of patient information systems The quality of the organization of the data in the patient record, whatever the format (hard-copy or electronic), is a criterion of the French program of Financial Incentives for Quality Improvement (IFAQ) of patient management. The challenge of the CF electronic patient record is that of taking into account multiple interventions by various professionals in the course of the CF patient's visit while organizing the data collected in a database such that a given piece of information is recorded in a single structured field. Within the framework of an outpatient visit, the weight and height values measured must be entered just once by a qualified professional, and must be available in read-only real-time mode in the later steps of the patient's circuit.
This issue is to be addressed by the PRSC. Any change in the selection rule would need to be largely explained and implemented in the software used by the CFCs to ensure its application and avoid misinterpretations. The checks showed that standardization is achieved through automation with software programs selecting suitable data. The annual process of transmitting data to the Registry should be under the responsibility of an identified and trained person at each CFC. An audit of the practices at each site should identify the operations required to check missing or aberrant data and validate the data before transmission. Just one out of the 14 centers audited did not have any discrepancy in the data. At this center, measurements are taken only once. They are recorded in a software dedicated to cystic fibrosis patients which selects automatically the data to be transmitted to the Registry.
Conclusions In general, registry data quality, unlike clinical research study data, is rarely audited at the source. However, one intends to use these data for epidemiological studies, phase 4 clinical studies, and care quality improvement follow-up, as well as for national or international comparisons. The European CF Patient Registry Working Group recognizes the current difficulties and limitations in the \interpretation of variations in indicator values across the countries and emphasizes that their transparency may increase their reliability. This first onsite quality audit of the data transmitted to the French CF Registry pointed out variability in the measurement process at the CFCs. The rule for selecting the data for the Registry was applied differently at some CFCs, and various local References for the FEV1% calculation were used.
g. , comma-separated values, or CSV). Ideally, the script for generating these processed data from the raw data should be made available as well to ensure full transparency (see SM: Automate or thoroughly document all analyses). One critical ingredient of data sharing is often overlooked: the need for metadata. Metadata is a term describing documentation that accompanies and explains a dataset (see SM: Data documentation). In psychology, metadata typically include information on who collected the data, how and when they were collected, the number of variables and cases in each data file, and dataset-level information such as verbose variable and value labels. Although they can also be shared in standardized, highly structured, machine-readable formats, often metadata are simply a separate document (called a "codebook" or "data dictionary"; see SM: Data documentation) that gives verbal descriptions of variables in the dataset.