dataesr/scientific-paragraphs-categorization huggingface dataset:
The data have been processed from all the previous publications of the Global Cities Database  [24, 26]  and from recent unpublished data (e. g. [23] ) and have been put on the same basis for comparison: the average speeds of transit systems are weighted carefully on the basis of passenger hours of travel by each mode. Where more than one rail system is involved in any city, the rail speed is also weighted the same way. The data used here have been collected since the 1980s for a large sample of world cities, for as far back as 1960. The methodologies and uses of the data are explained in the above mentioned references and others such as  [25]. The most significant trend to help explain the decline in car use and the increase in rail is that the world's cities all showed a plateau or an increase in the ratio of transit to traffic speeds, when tracked over the period from 1960 to 2005 (see Table  3 ).
From 1960 to 2005 the amount of data increased considerably, so there is a general increase across the table in the number of cities involved in each year. Within the regions it can also be seen that even today in American and Canadian cities, transit overall is barely half as fast as general traffic speed, whereas their rail systems are about 90% to 95% as fast, meaning that in many cases they are competitive with the car, especially into dense urban centers where clogged highways are losing out to fast rail systems time and again. Australian cities do a little better with transit overall now being only about 25% slower than cars, while the rail systems are now on average about 8% faster and have generally been improving their competitive position since the 1960s and 1970s. When major corridors into city centers are considered, the rail systems are at a clear advantage.
Such data give hope to the many emerging cities struggling with the car that rail systems can help them to develop a very different kind of overall urban transportation system that is not so overwhelmed by the automobile. In the emerging cities traffic speeds were very slow (around the low 20's km/h) and bus speeds always slower than this. Bangkok had a traffic speed of 14 km/h and a bus speed of 9 km/h. Thus as these emerging cities build rail (often above or below the traffic) the data will be reflected in dramatic transit speed improvements. The data also highlight the fact that it is bus systems (as well as tram and LRT systems that are not operating on dedicated rights-of-way), which are dragging the speed performance of transit down. However, it also must be recognized that buses operate in nearly all cities under intolerable congestion levels that are permitted to continue by not controlling congestion through economic and physical means.
Urban Economic and Cultural Change The biggest change in the economy during the period leading up to and including the period of car use decline and rail growth has been the digital transformation and the consequent knowledge/service economy. Despite this being global and enabling long distance communication, it has in fact been a concentrating force in terms of city structure and fabric. We provided new data on global cities  [37]  that showed a universal increase in density in the past decade or so after over one hundred years of decline. The knowledge economy and digital jobs are focused in city centers, as these are where the creative synergies between people occur  [21]. Old CBD's have been transformed back into functional Walking Cities and, those which have done this best, have attracted the most capital and young talent to work there  [12, 29].
JEFF-3. 1. 1 is the reference nuclear data library in CEA for the design calculations of the next nuclear power plants. The validation of the new neutronics code systems is based on this library and changes in nuclear data should be looked at closely. Some new actinides evaluation files at high energies have been proposed by CEA/Bruyères-le-Chatel in 2009 and have been integrated in JEFF3. 2T1 test release. For the new release JEFF-3. 2, CEA will build new evaluation files for the actinides, which should be a combination of the new evaluated data coming from BRC-2009 in the high energy range and improvements or new evaluations in the resolved and unresolved resonance range from CEA-Cadarache. To prepare the building of these new files, benchmarking the BRC-2009 library in comparison with the JEFF-3.
1. 1 to BRC-2009  [2]  for some U, Pu and Am actinides only (proposed in JEFF-3. 2T1 library released in 2011) is invertigated. a e-mail: yannick. peneliau@cea. fr This is an Open Access article distributed under the terms of the Creative Commons Attribution License 2. 0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To have a detailed information on the origin of the discrepancies, some reactivity effects breakdown are performed for some benchmarks of interest with the deterministic code package PARIS  [3]  and with the stochastic code TRIPOLI-4  [4]. The nuclear data processing is performed by CEA for the different codes, for both JEFF-3. 1. 1 and JEFF3. 2T1 libraries. This article first presents the selected set of benchmarks.
Selected set of benchmarks In order to cover the whole energy range and all actinides, some thermal, intermediate and fast spectrum benchmarks for Uranium, Plutonium or Mixed fuels are selected. The benchmarks are either taken from the ICSBEP  [5]  criticality experiments database or from the IRPhE  [6]  reactor experiments database. The ICSBEP benchmarks are taken from the HMF, IMF and HST series for uranium and from PMF and PST series for plutonium. Additional HxI intermediate spectrum benchmarks have also been calculated in order to test 235 U and 238 U in softer spectra (closer to SFR ones). The reactor benchmarks are the MAS1B uranium configuration in the french reactor MASURCA and the 7A and 7B plutonium configurations in the german reactor SNEAK.
Deterministic method The deterministic method used in the analysis of the nuclear data is based on the exact perturbation theory. Actually, the reactivity breakdown decomposition can be assessed per isotop, per nuclear reaction type and per energy group. Considering the direct flux Φ and the adjoint flux Φ + , the transport, removal and diffusion operator A and the fission operator F, the transport equation is : (A - F k e f f )Φ = 0 (1) with : AΦ(r, Ω, E) = Ω•∇Φ(r, Ω, E)+Σ t Φ(r, Ω, E)- Σ s (r, E → E, Ω → Ω)Φ(r, Ω , E ) × dΩ × dE (2) and : Fφ(r, Ω, E) = χ(E) 4π × ν f (r, E , Ω Φ(r, Ω , E ) × dΩ × dE (3) Then, the reactivity variation dρ can be written : dρ = dk e f f k 2 e f f = - < Φ + , (dA -dF k e f f )Φ > < Φ + , FΦ > (4) where the scalar product <. ,. > has the classical definition. Then, the reactivity change from state (1) to state (2) is obtained by the formula : δρ (1)→(2) = - < Φ + (1) , (δA -δF k e f f )Φ (2) >) < Φ + (1) , FΦ (2) > (5) The PARIS package performs this calculations.
Monte Carlo methods This study used three Monte Carlo methods to analyze the discrepancies between one evaluation (BRC-2009) and the other one (JEFF-3. 1. 1). The first method is very simple. It consists simply in modifying the TRIPOLI-4 isotops' general dictionary by adding one or some fictive new isotop(s) (with new nuclear data) in order to replaced the original one(s). The second method is quite particular to TRIPOLI-4. Actually, the simulation code needs, as nuclear data, the evaluation ASCII file and a pointwise cross sections ASCII or BINARY file. So, it is possible to replace a whole ENDF format block by another one (MF=1, MT=456 for the number of prompt fission neutrons for example) to perform a new simulation. This may typically be applied to independent data like number of fission neutrons, fission spectra, some anisotropy data ((ν, χ, pdfs f (µ)).
ICSBEP Uranium benchmarks Concerning the uranium fuel, it is very important to ensure that the actual validation of the CEA neutronics codes and their nuclear data libraries is not affected by the BRC-2009 changes. In this part, the fast and intermediate spectra are tested. Figure  1  shows graphically the effect of BRC-2009 library for the HMF and IMF series. The standard deviation for JEFF-3. 1. 1 or BRC-2009 calculations is close to 10 pcm. These results shows that the JEFF-3. 1. 1 tends to underestimate the experimental values, and that BRC-2009 corrects this underestimation (the mean value of the correction is +280 pcm, and the value goes from +150 to +360 pcm). Concerning intermediate enrichment, the Big Ten benchmark (IEU-MET-FAST 007) is also better estimated whereas the JEMINA (IEU-MET-FAST 001) change is not significant, compared to the experimental value.
ICSBEP Plutonium benchmarks The results for the plutonium and fast spectrum benchmarks taken from the ICSBEP database are shown in figure  2. An identical fine analysis has been performed for the PMF001 benchmark. The results are shown in table  2. The final effects are very small for the fast spectrum. The mean discrepancy is about +30 pcm, and the variation goes from -130 pcm to +100 pcm. The PMF002 benchmark (highly enriched with 240 Pu) shows a -130 pcm decrease, closer to the experimental value. For thermal spectrum benchmarks, the effects are more important, about +300 pcm. The results are shown in figure  3. A new 239 Pu evaluation based on data form BRC-2009 for the continuum energy range and from JEFF-3. 1. 1 and recent ORNL work  [7]  for the thermal and resonance energy range will be proposed for the new JEFF-3.
Except the data explicitly provided by the designer or the user (by completing a form for example), several agents are generally in charge of the data recovery. We can distinguish systems com-posed of specific agents able to capture particular kinds of data (ASK-IT) from those composed of more generic agents able to capture any kind of data (Tapia and LAICA). Once perceived by an agent, the data are made available to the system. This can be done in a centralized way, from an agent to a central server or towards a central agent (Tapia). This can be also done in a distributed way, from an agent to all other agents (LAICA and Dujardin), or towards certain specific agents (ASK-IT system). All the MAS do not explain how data are modeled. We can distinguish those that (i) use an ad-hoc format (DALICA) which simplifies processings but implies a design totally chargeable to the designer, from those that (ii) use existing technologies (such as an ontology, or cell in Satoh) that implies additional processings (deduction of information, logical reasoning) to convert the perceived data.
Advances in Distributed Computing and Artificial Intelligence Jornual Special Issue #3 http://adcaj. usal. es [LEMOUZY et al. , 2011]. An AVT is a tool based on an AMAS that enables to approximate as best as possible the real value of a changing variable. We fixed the initial wide of a range values to 5% of the possible values for a data. Each ART has a validity status: when the actual data value is included in the range of an ART, this ART is considered as valid. The Context agent has its own validity status, and when all the ART of a Context agent are valid, this Context agent becomes valid in its turn. This means it can send its proposition of action to the Controller agent. As explained in section 4. 2. 1, a proposition of actions consists of the value to affect to the effector, a forecast on the effect of this action on the user's satisfaction and the confidence level of this forecast.
Advances in Distributed Computing and Artificial Intelligence Jornual Special Issue #3 http://adcaj. usal. es proposition of action is different from the performed one, it considers that it was right to not be valid because its action would not have been relevant. So, it considers that all its invalid values ranges helped it to do not wrongly propose its action in the current situation. In this data list, it may exist useless data that are invalid in this situation, but we can be sure that at least one data of the list is useful. So, the Context agent sends a utility signal to the associated Data agent. This signal is in the form "I was invalid partially thanks to you and my invalid status was the right one; you have been useful for me". To determine the uselessness of a data, we observe a difference between useful and useless data.
Introduction A manifest proof of anthropogenic influence on climate is the recent warming of the world oceans  (Levitus et al. , 2001) , its prominent features being reproduced by models only when anthropogenic GHG (greenhouse gas) forcing is considered  (Levitus et al. , 2001; Crowley et al. , 2003; Gregory et al. , 2004; Barnett et al. , 2005; Delworth et al. , 2005; Palmer et al. , 2009; Gleckler et al. , 2012). This positive trend in upper ocean temperature and heat content (OHC) is robust to all the observational datasets and processing methods  (Lyman et al. , 2010; Trenberth, 2010). In the 20th century, two other prominent factors besides the GHG rise are known to have had a noticeable contribution to the global OHC integral: anthropogenic, and to a lesser extent, volcanic aerosols have partly offset the ocean warming resulting from increasing GHG concentrations  (Delworth et al.
, 1996)  and ocean model HOPE-G  (Wolff et al. , 1997) ). An equivalent analysis, focused on the Atlantic meridional overturning variability and making use of the same group of experiments, is performed in  Ortega et al. (2012) , hereafter referred to as OR12. In the current study a thorough comparison of the simulations with the available observations is carried out for the instrumental period  (i. e. 1955-2010). In this way, the model performance is first assessed for the observational period before the impacts of the forcings and the influence of internal variability are evaluated from interdecadal to secular timescales. In the next section the model and the experiments are presented, as well as the observational OHC dataset. Section 3 aims at determining the realism of the simulations by comparing them against the available evidence of past OHC variability (both observations and proxy reconstructions).
OHC variability in the instrumental period OHC estimates are calculated from global observations of upper ocean temperature. This computation requires reliable and globally distributed subsurface instrumental data, as well as the use of interpolation and regridding techniques. Since 1950, the spatial coverage of temperature measurements has improved as new data sources have become available (e. g. moored buoys, ARGO floats, bathythermograph measurements;  Boyer et al. , 2009). The integration of these data is subject to important uncertainties, since it is sensitive to the records considered and the different methodologies applied, such as the mapping and bias correction techniques  (Gregory et al. , 2004; Lyman et al. , 2010). This has led to the publication of somewhat different OHC estimations by different groups and institutions  (Domingues et al.
This points to a probable different role of internal variability in this period for the two datasets. Also, this calculation can be affected by the absence of anthropogenic aerosols in the simulations, which may be crucial to explain recent decadal changes in North Atlantic climate  (Booth et al. , 2012). The validity of these results will be assessed in the next section by extending the analysis to the whole FOR2 run. This will allow for providing further insight into the role of each forcing factor.
This particular pattern is not observed for the other surface heat fluxes. We now turn our attention to the temporal features associated with the influence of the different radiative forcings. A wavelet coherence (WTC) analysis  (Torrence and Compo, 1998)  is used to investigate the common spectral features between the OHC700 and the different forcings throughout the last millennium (Figs. 9 and 10). For all practical purposes, wavelet coherence can be regarded as a localised correlation coefficient but in time frequency space. All plots are generated with the software package provided in  Grinsted et al. (2004) , following their recommendations for the choice of the wavelet transform (i. e. Morlet function) and scale resolution (i. e. 10 scales per octave). Likewise, significance is assessed using a Monte Carlo approach based on an ensemble of 1000 surrogate dataset pairs with the same AR1 coefficients as the input time series.
Two plant genome size databases have been recently updated and/or extended: the Plant DNA C-values database (http://data. kew. org/cvalues), and GSAD, the Genome Size in Asteraceae database (http://www. asteraceaegenomesize. com). While the first provides information on nuclear DNA contents across land plants and some algal groups, the second is focused on one of the largest and most economically important angiosperm families, Asteraceae. Genome size data have numerous applications: they can be used in comparative studies on genome evolution, or as a tool to appraise the cost of whole-genome sequencing programs. The growing interest in genome size and increasing rate of data accumulation has necessitated the continued update of these databases. Currently, the Plant DNA C-values database (Release 6.
0, Dec. 2012) contains data for 8510 species, while GSAD has 1219 species (Release 2. 0, June 2013), representing increases of 17 and 51%, respectively, in the number of species with genome size data, compared with previous releases. Here we provide overviews of the most recent releases of each database, and outline new features of GSAD. The latter include (i) a tool to visually compare genome size data between species, (ii) the option to export data and (iii) a webpage containing information about flow cytometry protocols.
This was because values were either scattered in the literature or unpublished. Nevertheless, this impediment has now been largely overcome by the release of electronic databases for several major groups of eukaryotes  (15, 16) : animals (http://www. genomesize. com), fungi http://www. zbi. ee/fungal-genomesize) and plants (http://data. kew. org/ cvalues and http://www. asteraceaegenomesize. com). Together these databases currently contain data for >15 000 species comprising 4972 animals, 1581 fungi and 8922 plants. Interest in the field of genome size research remains high and new genome size data continue to be published in the literature. Thus, keeping the databases up to date has necessitated the continued release of new versions. This article focuses on the two open-access plant genome size databases, which have recently been updated: the Plant DNA C-values database (Release 6.
THE PLANT DNA C-VALUES DATABASE The Plant DNA C-values database (http://data. kew. org/ cvalues) was first launched in 2001 to provide a userfriendly searchable database where both published and unpublished values of plant genome size could be readily found  (15, 17). It contained data for 3864 species that had been compiled and published by Bennett and colleagues in hard copy between 1976 and 2000  (18) (19) (20) (21) (22) (23). Since 2001, the increasing volume and rate of production of new data on plant genome sizes (Figure  1 ) has led to five further updates of the database, with the most recent release (Release 6. 0, December 2012) containing data for 8510 species compiled from 808 original reference sources. The majority (89%) of estimates are for angiosperms (7542 species from 695 references), with the others comprising 365 gymnosperms (from 48 references), 128 pteridophytes (comprising monilophytes and lycophytes from 21 references), 232 bryophytes (from seven references) and 253 algae (from 37 references) (Figure  2 ).
A detailed description of the organization, search options and output fields in the Plant DNA C-values database has already been given in  (15)  and is also available from the 'Help' web page of the database (http://data. kew. org/ cvalues/searchguide. html). This outlines the diverse and flexible search options available to enable the user to interrogate the database. For example, the user can choose to (i) search the whole database, or just a subset of it (e. g. just angiosperms), (ii) restrict searches to a specific range of DNA amounts, chromosome numbers and/or ploidy levels, (iii) restrict searches to a particular family, higher order plant group and (iv) conduct wild card searches. In addition, the various options available for displaying the results of the search are given, such as the choice to output the data as 1C, 2C or 4C values in Mb or pg, and to sort the results by DNA amount, chromosome number, ploidy level or taxonomically (e.
g. alphabetically by family, genus, species). It is noted that the Plant DNA C-values database does not currently display information about which calibration standard has been used to estimate the genome size of a particular species, despite the realization that choice of standard and its assumed C-value are two of the major factors contributing to artifactual genome size variation, as outlined in Dolezˇel and Greilhuber  (24)  and Suda and Leitch  (25). Clearly there is a need to deal with these important issues and to reach a consensus on the selection of appropriate calibration standards and uniformity on the C-values assumed. However, as an interim measure, the option to display the standard species used will be included in the next release of the database. 1954 1955-1959 1960-1964 1965-1969 1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999  2000 known (i.
Angiosperms Most of the novel additions to the database have come from research in angiosperms, where data for 1255 species not previously listed have been added. Not only has this increased the percentage of angiosperm species with genome size data to approximately 2. 1% [based on an estimate of 352 000 angiosperm species in total,  (26) ], but representation at the generic and family levels has also improved. At the generic level, the new release includes estimates for 187 genera not previously listed and brings the number with at least one genome size estimate to 1635, corresponding to 12. 6% of the 12 962 genera recognized  (27). The database also includes genome sizes for 249 families, although only nine families not previously represented in the database were added in Release 6. 0. Of the 415 families currently recognized  (28) , 60% have at least one genome size estimate.
Other land plant groups and algae In other land plant groups, the most notable progress in improving genome size representation has been in the gymnosperms where the number of 'new' species has increased by 43%. This is largely due to several recent surveys by Zonneveld  (29) (30) (31) , which together have generated data for all cycad genera and 64 of the 69 conifer genera now recognized  (32). Consequently, genome size data are now available for 35% of gymnosperm species (355 out of the 1026 species recognized by 32), including representatives of all 12 gymnosperm families, and 98% of the genera (81 out of 83 genera recognized by 32). Gymnosperms are the best represented of all land plant groups in terms of genome size (Table  1 ). Progress in other land plant groups and algae remains poor, with the addition of only 46 pteridophyte species not previously included in the database and no new data for bryophyte or algal species.
Nevertheless, this will be addressed in Release 7. 0 planned for 2014 as new genome size data for the bryophyte groups liverworts [67 species from 33 families,  (33) ] and hornworts [24 species from 5 families, (34)] will be added, together with new data for algae [e. g. (35) (36) (37) (38) ] and other land plant groups collated from the literature.