dataesr/scientific-paragraphs-categorization huggingface dataset:
3 % relative error. Therefore relative biases can be different from absolute biases as can be observed in Fig. 5. We provide the stability here in relative terms because the World Meteorological Organization's (WMO) Global Climate Observing System (GCOS) requirements for temporal stability of TCWV are formulated in relative term as well  (GCOS-107, 2006). Comparing time series of MWR-GNSS, the expected stability (i. e. the slope of regression line) should be zero. The observed long-term stabilities are +0. 68 % decade -1 and -0. 38 % for the entire dataset and for.
Envisat Difference in variance [cm ] 2 ERS-2 Difference in variance [cm ] 2 ERS-1 Difference in variance [cm 2 ] Latitude M M M Figure 7. Zonally averaged patterns of improvement (green) and deterioration (red) of our retrieval over the operational ESA retrieval. the reduced LWP < 200 dataset respectively. Both stability values are not statistically significantly different from zero (P values for two-sided t test are 0. 148 and 0. 572 respectively), so that at least for the comparison against GNSS the dataset can be considered stable over time. Several other tests including separation of daytime from night-time observations did not yield any additional insights into the uncertainties. 2. Table  1  further compares the validation against GNSS for between the MWR retrievals and the ERA-Interim profiles used as a background.
For the less stringent screening including all GNSS-matches, the RMSE is reduced only slightly from 4. 87 to 4. 68 kg m -2. For the more stringent screening (LWP < 200, H < 50), the RMSE for the MWR-retrieved TCWV is reduced to 3. 95 kg m -2 , compared to 4. 53 kg m -2 when directly compared to ERA-Interim. The retrieval biases show a less clear improvement. While the RMSE shows improvements, the assessment of the bias is inconclusive, with an improvement in bias on average for all cases but degradation in bias, if the more stringent screening criteria are applied. The long-term stability of the dataset of the differences also shows somewhat inconclusive results. It has to be noted though that none of the stability estimates are statistically significantly different from zero.
Conclusions We have established a new long-term (1992-2012) dataset of TCWV and WTC over the global oceans, which is readily available for users. The main difference between our dataset and earlier datasets, such as ESA's operational dataset, is that we explicitly intercalibrate the MWRs on the three different satellite platforms. Such an intercalibration is a key requirement for the generation of satellite-based climatologies. We have introduced a novel intercalibration method that allows for the adjustment of biases between the different satellites based on environmental conditions over ocean. Similar to operational data assimilation systems, we estimate calibration offsets at 23 and 36 GHz by comparing simulated and observed cloud-free brightness temperatures over the lifetime of each satellite.
We only adjust for long-term, global calibration trends and offset but do not perform any regional, monthly, or seasonal bias corrections. A potential disadvantage of our intercalibration method is that it hinges the long-term mean retrieved TCWV to the TCWV fields that were used in the simulations used in the intercalibration process (in our case ERA-Interim). While this does limit the use of our dataset to assess the global longterm mean TCWV as well as linear trends, the dataset can be used for anomaly analyses and to provide WTCs for altimeter correction. We note that limitations on absolute accuracy or linear trends are in no way unique to our dataset. For example,  Schroeder et al. (2016)  investigate trends and discontinuities in six different global TCWV climatologies and find significant artefacts that inhibit trend analyses in nearly all datasets.
Independent validation of our dataset was performed against GNSS radiosondes. This validation shows the TCWV dataset to be stable over time and generally yielding an improvement in RMSE over the background TCWV used in the retrievals. The dataset has also been submitted to the ongoing G-VAP  (Schroeder et al. , 2016)  for evaluation in the con-text of several other TCWV datasets. A crossover analysis of altimeter corrections using the here-derived WTC provided further insights into the accuracy of the dataset. We found that for ERS-1 and ERS-2 the results of our WTC retrievals are superior to ESA's operational WTC retrieval for the midlatitudes and inferior for the tropics. For Envisat, ESA's operational WTC retrieval is generally slightly better than our retrieval. ESA's retrieval does use additional collocated altimeter backscatter information to constrain sea surface emissivity.
However, as is always the case the actual retrieval will have to be tuned to some degree. In particular, one wants to minimize the risk of artifacts in the background data to affect the final TCWV time series. Such artifacts can include slight discontinuities in the ERA TCWV time series at time steps where new sensors are added to the reanalysis. With these considerations in mind we have chosen the ERA_NEW 1D-VAR set-up to be used as the basis for the full time series. The modified background error matrix allows for large deviations from the background profile; i. e. it gives stronger weight to the observations. At the same time, it provides good convergence over the entire range of variability of TCWV and allows for a high number of converged profiles, therefore providing very little sensitivity to the choice of the background profile.
2 ISECG space agencies include, in alphabetical order: ASI (Italy), CNES (France), CNSA (China), CSA (Canada), CSIRO (Australia), DLR (Germany), ESA (Europe), ISRO (India), JAXA (Japan), KARI (Republic of Korea), NASA (United States of America), NSAU (Ukraine), Roscosmos (Russia), UKSA (United Kingdom). 3 The Global Exploration Roadmap can be downloaded at www. globalspaceexploration. org 4 Spinoff materials published by the National Aeronautics and Space Administration (e. g. Spinoff database, spinoff. nasa. gov/spinoff/database; Spinoff 2012, spinoff. nasa. gov/Spinoff2012);.
Introduction The EXFOR database  [1]  is a global repository of experimental nuclear reaction data and associated information that has been systematically collected, expanded and developed as a product of the international collaboration of Nuclear Reaction Data Centres (NRDC) under the auspices of the International Atomic Energy Agency Nuclear Data Section (IAEA NDS) since 1970. The efforts of the NRDC community to provide completeness and the high quality of the compilations made the EXFOR system a valuable source of detailed information for evaluations and analysis. This paper represents a short summary of the developments carried out since our previous report in 2014.
NRDC network The current members of the network are listed in Table  1. CAJaD (Moscow) discontinued its activity in 2014, and its responsibility has been taken over by CNPD. a e-mail: n. otsuka@iaea. org Table  1. Agreed areas of responsibility of the NRDC centres (ND: neutron data; CPND: charged-particle data; PhND: photonuclear data). Compilers from Kazakhstan and Mongolia also contribute through compilation of experimental data measured in Kazakhstan and heavy-ion induced reaction data measured in West European countries, respectively. The NDS, NEA-DB and CJD peer review the preliminary EXFOR entries regularly before their inclusion in the EXFOR database.
EXFOR upgrades The scope of the EXFOR database has gradually expanded in response to the needs of new developments and applications. In this respect retroactive scanning of the scientific literature is required to provide completeness of the database for all types of data considered for compilation. Such checking of EXFOR coverage is periodically performed by NRDC members based on reference citations in review articles, nuclear data compilations, books etc. , or user feedback. Subsequently, the data are analysed and compiled as a new EXFOR entry, or publications are added as additional references if the experimental work has already been compiled as an EXFOR entry. The systematic assessments of the EXFOR coverage have been recently carried out for the following specific types of data or areas of applications.
Neutron dosimetry Availability of all measurements and the precise description of the experimental details for the monitor and dosimetry reactions are of particular importance. The dosimetry reaction cross-sections are employed for the determination and monitoring of neutron fluence and spectra at different facilities as well as for assessments of operational safety. An on-going IAEA coordinated research project (CRP) on "Testing and Improving the International Reactor Dosimetry and Fusion File (IRDFF)" is providing an updated library in the energy range up to 60 MeV  [6]. The EXFOR database has been assessed for: -Coverage of.
Thermonuclear reaction rates for charged-particle-induced reactions Nuclear reaction data for projectiles with A≤12 are compulsory for EXFOR compilation. The Nuclear Astrophysics Compilation of Reactions NACRE II  [7]  contains thermonuclear reaction rates for 34 chargedparticle induced reactions on nuclides with mass number A < 16. Contents of the EXFOR database were compared with the experimental data sets considered in the NACRE II evaluations. We concluded that more than 80% of the relevant articles are compiled in EXFOR. About 30 missing articles have been assigned for compilation by Centres according to the distribution of the compilation responsibilities.
Neutron reaction data for the CIELO project Compilations of data for 1 H, 16  (2) replacement of digitised values with the original experimental data for the 13 C(α,n) 16 O cross-section measured at ORNL by Bair and Haas 13 C(α,n) 16 O (Entry C0489. 002); (3) addition of digitised values in the old EXFOR entries compiled without data. A functionality for the conversion and plotting of the data for reversible nuclear reactions (for example 13 C(α,n 0 ) 16 O reaction cross-section into 16 O(n,α 0 ) 13 C) by employing the principle of detailed balance has been developed by Zerkin at NDS and made available on the NDS EXFOR web retrieval system.
Improvements of the quality and consistency of the EXFOR compilation Over the course of several decades of EXFOR development, the basic principles and the format have proven to be a robust and efficient way of performing data storage and retrieval. Many new types of data have been introduced and corresponding compilation rules defined. The computer environment has also evolved in parallel, which has subsequently allowed the storage of larger amount of information and various new functionalities. One important aim of the NRDC community has always been to provide sufficient description of the experimental conditions in order to allow proper data interpretation. Evaluations of the quality and consistency of the compilation of the following types of data have been recently conducted.
Neutron resonance data from TOF facilities A Consultants' Meeting on "EXFOR data in Resonance Region and Spectrometer's Response Function"  [11]  was organised by NDS to discuss the compilation of the time-of-flight (TOF) measurements in order to allow proper interpretation and evaluation of TOF data. In particular, the storage of the response functions of the measurement set-ups was discussed. A template formulated during the workshop was recommended to be used by compilers to collect and compile experimental details. Compilation guidelines ensure good consistency in the compilation of all types of data in EXFOR. Following the meeting, the collaborations between nuclear data centres and TOF facilities led to amendments of the data in resonance regions. The n TOF Collaboration has also established a procedure for data submission through the data dissemination coordinator, who has a strong background in EXFOR compilation.
Collection, submission and incorporation into EXFOR of all data measured at the n TOF facility are currently underway as a joint effort with n TOF, NDS and NEA-DB staff  [12]. A major improvement in the EXFOR availability of the energy-dependent data measured at the ORELA facility has also been achieved due to cooperation between NNDC and Guber (ORNL). Recovery efforts have led to the collection and inclusion of F, Al, Si, Cl, Ti, Fe, Mn, Cr, NI, K, Sn, Nd, and U capture, transmission and fission data in EXFOR.
Thermal neutron scattering data Renewed interest in the revision of existing thermalscattering libraries and the undertaking of further evaluations driven by new applications necessitated a thorough inspection of EXFOR for neutron-induced data at thermal energies. Considering the physics of thermal scattering and differences in the analyses of scattering at epithermal and higher energies, agreement was reached that a review of the compilation rules for thermal-scattering data was required. Furthermore, as well as the isotopic composition, the compound, structure, phase, orientation, thermodynamic and other physical properties have to be considered. A Consultants' Meeting on "Compilation of Thermal Neutron Scattering Data in EXFOR" was organised by NDS in 2015  [13]. Various experimental and analytical techniques applied at thermal energies were discussed.
The description of the thermal scattering quantities in the EXFOR compiler's manual (LEXFOR) has been revised. Recommendations to compilers and guidelines concerning the compilation of experimental information were issued. Only nuclear quantities (double-differential cross-section, differential cross-section, and total cross-section) should be compiled in EXFOR. NDS staff were advised to set up a supplementary database for the S(q, ω), S(α, β) phonon spectrum, structural parameters etc. to support and facilitate thermal neutron scattering data analyses and evaluations. About 120 datasets have been compiled and made available on the meeting website. NDS also received a collection of publications that were missing from EXFOR for assessment, assignment and compilation within the NRDC network.
Verification and validation for neutron-induced threshold reaction cross-sections A comprehensive review of the cross-section data for neutron-induced threshold reactions has been carried out by NEA-DB staff. The review combines statistical analysis of the deviations of the EXFOR-compiled data with the corresponding values in the major evaluated nuclear data libraries, and verification of the correctness of the compiled data against the original publications those cases with large deviations. About 25000 reactions have been included in the statistical analysis, from which about 10000 have been subject to a more detailed review. A description of the method is included in the report, together with the outcome of the statistical analysis for the different types of reaction within each evaluated data library and graphical comparison of the experimental data and evaluations for a number of reactions  [14].
Half-lives adopted in decay analysis The half-lives adopted by experimentalists for their decay analyses of the reaction products should be recorded in EXFOR as information for cross-section evaluators. NDS compared half-lives coded in EXFOR in 2014 with those in Nuclear Wallet Cards  [16]. About 1180 halflives were found to deviate by more than 50% from the half-lives recommended in Nuclear Wallet Cards. All such cases were checked against the values given in the publications. The compilation mistakes and incorrect half-lives, confirmed by authors, were corrected. A better consistency in the isomeric state assignment with the Nuclear Wallet Cards data is now provided. For example the 5 hr level of 110 In is coded as a ground state in all EXFOR entries even though it is treated as a metastable state in some old articles.
Duplication As a rule an EXFOR entry represents the results of work performed at a given laboratory at a given time. If the same experimental work is partly published in different scientific publications all of them have to be included in one entry with the most representative paper considered as the main reference. However, if not stated clearly by the authors or overlooked by the compiler, two or more entries may be identified with the same experimental results. This cannot just be considered a technical problem because such an approach may eventually give a higher weighting factor to a particular experiment in an evaluation. Plots of both preliminary and final data sets by EXFOR users must be avoided. Both preliminary and final data sets are systematically compiled in EXFOR for intermediate energy charged-particle induced reaction activation crosssections measured by the group led by Michel (Leibniz Universität Hannover) and neutron induced reaction activation cross-sections measured at the OKTAVIAN facility by the group led by Kawade (Nagoya University).
Summary and conclusions The International Network of Nuclear Reaction Data Centres continues to collaborate in the collection, compilation and dissemination of experimental nuclear reaction data. New articles continue to be compiled in a timely manner. Collaboration with authors is extremely important to ensure availability of the original experimental data and compilation of the key experimental information. There have been no major amendments to either the scope of the EXFOR database or the format. Recently our efforts have focused mainly on improving the completeness and quality of the compilation for specific data types that are important for the development of nuclear physics and applications.
SNP array analyses DNA samples were analyzed with the SNP arrays Illumina Human1M-Duo v3 (;1 000 000 SNP probes, 17 samples), HumanOmniExpressExome-8 v1. 2 (;960 000 SNP probes, 1 sample), HumanOmniExpress-12 v1-BeadChip (;730 000 SNP probes, 96 samples), or InfiniumHumanCore-12 v1 (;250 000 SNP probes, 16 samples), at the Spanish National Center for Genotyping. A total of 9 samples from FA patients with CMEs were also analyzed with Illumina InfiniumHumanCore-12 v1 (;250 000 SNP probes). The detection of mosaic rearrangements was based on the assessment of allelic imbalance and copy number changes using the Mosaic Alteration Detection (MAD) algorithm implemented in R Genomic Alteration Detection Analysis software. 6  defined the breakpoint interval for each CME as the region located between 2 informative SNP probes (the first probe within and outside the event).
We restricted the search to breakpoints mapped to intervals ,200 Kb in order to compare our results with reported data generated with SNP arrays of lower probe density. Then, we analyzed putative enrichment on genomic features at these regions by calculating the percentage of total breakpoint interval length overlapping with each genomic feature (segmental duplications [SD], copy number variants [CNV], genes, and meiotic recombination hotspots [MRH]), and comparing these values with the established distribution of each genomic feature in the human genome (University of California, Santa Cruz genome browser [http://genome. ucsc. edu/ cgi-bin/hgGateway] and HapMap database [http://hapmap. ncbi. nlm. nih. gov/]). The cellular proportion with a specific rearrangement was calculated by using the b allele frequency (BAF) split in the altered region.
Control datasets In order to estimate the frequency of CMEs in the population of patients ,18 years old and between 19 and 50 years old, we analyzed SNP array data obtained from blood DNA of unaffected individuals (siblings and parents) from several datasets: (1) the Simons Simplex Collection (https://sfari. org/),  14  characterized sample consisting of 2644 families of autistic probands that includes nonaffected parents and siblings; we analyzed SNP array data (Illumina Human 1MDuo and Omni2. 5) of 2209 children (siblings) and 4354 parents; (2) the Autism Genome Project (https://www. autismspeaks. org/ science/initiatives/autism-genome-project),  15  which consists of 2611 families, each one with a child affected by an autism spectrum disorder and nonaffected parents; we analyzed SNP array data (Illumina Human 1M) of 4086 parents (between 19 and 50 years old); and (3) we added the published data also generated with the MAD algorithm as described above from the Avon Longitudinal Study of Parents and Children (http://www.
Exacerbated clonal mosaicism in FA blood samples We evaluated the presence of CMEs in peripheral blood DNA of 130 FA patients (mean age: 14. 5 years old, range: 0-50 years old), previously collected for the purpose of genetic diagnosis. SNP arrays were used to build molecular karyotypes that were analyzed with the MAD software. 6 In total, 16 out of 130 FA patients (12. 3%) had detectable CMEs that were 4. 4 to 159 Mb in size (Figure  1 , Table  1 ; supplemental Table  1 ). Five of the CME carriers (5/91, 5. 5%) were children or adolescents ,18 years old, whereas 11 (11/39, 28. 2%) were in the 19-to 50-years-old range. The mean age at the time of sample collection was 23. 2 years for the group with CMEs and 13. 3 years for the group without CMEs. Although there is information from multiple case-control studies of cancer,  7  we analyzed an age-matched, healthy population of 15 743 controls with SNP arrays of similar density and used the same methods to establish unbiased risks in FA.
We detected a statistically significant enrichment of genes in FA BPis. BPi, breakpoint interval; GF, genomic feature; DGV, Database of Genomic Variants (http://dgv. tcag. ca/dgv/app/home). FA106 , and an interstitial gain in FA013]), suggesting that this region is a hotspot for somatic chromosomal rearrangements. Dashed lines delimitate the CIs for the specific breakpoints, spanning as much as 11 Mb in 6p UPD in FA013. (B) A total of 6 CMEs (11. 7%) involved 3q in 5 FA individuals (EGF058, FA072, FA178, FA351, and FA647), all of them genomic gains. (C) Five additional gain-type CMEs (9. 8%) in 5 individuals (FA072, 041869_b; FA648; EGF058; FA360) were located in 1q as shown. The plots, generated with the University of California, Santa Cruz genomic browser (http://genome. ucsc. edu/), show the distribution of rearrangements in FA patients along with those previously reported in the population, based on multiple case-control studies of cancer.
Acknowledgments The authors thank the FA families and FA parent organizations, namely  Asociaci ón Española de Anemia de Fanconi  and  Ralf Dietrich , for their collaboration and continuous support, as well as the  Spanish FA Clinical Network  for their contributions in taking care of these patients (supplemental Table 4). The authors would also like to thank  Francisco X. Real  for his critical review of the manuscript and  Tania Lobato  for technical support in DNA extraction of tumor samples. J. R. -C. was supported by a fellowship from the  Spanish Ministry of Education  ( FPU13/00782 ) and M. A. -C. was supported by a  Juan de la Cierva postdoctoral fellowship  ( JCI-2011-10660 ). J. Surrall  és' team is funded by the  Generalitat de Catalunya  ( SGR0489-2009 ,  SGR317-2014 ), the  ICREA-Academia program ,  Marat ó de TV3  (project  464/C/2012 ), the  Spanish Ministry of Health  (projects FANCOSTEM and FANCOLEN), the  Spanish Ministry of Science and Innovation  (projects  CB06/07/0023 ,  SAF2012-31881 , and  SAF2015-64152-R ), the  European Commission  ( EUROFANCOLEN  project  HEALTH-F5-2012-305421  and  FANCODRUG  project  H2020-703521 ), the  Fanconi Anemia Research Fund, Inc , and the  European Regional Development (FEDER).
L. A. P. -J. 's team is funded by the  Spanish Ministry of Health  ( FIS-PI1302481  cofunded by  FEDER) , the  Generalitat de Catalunya  ( SRG1468-2014 ), and the  ICREA-Academia program , and also acknowledges support from the  Spanish Ministry of Economy  and  Competiveness Programa de Excelencia Maria de Maeztu  ( MDM-2014-0370 ). CIBERER is an initiative of the  Instituto de Salud Carlos III (Madrid, Spain ). D. S. was supported by the  Schroeder-Kurth Fund.