dataesr/scientific-paragraphs-categorization huggingface dataset:
Constraints in the Ω K -w plane for Planck+CMASS+LOWZ, Planck+BAO, Planck+BAO+SN, and Planck+SN. The combination of CMB and SNe (green contours) has a substantial statistical degeneracy in this parameter space; however, combining CMB and BAO strongly constrains the curvature (grey contours for the LOWZ+CMASS results presented in this paper, and red contours when adding low and high redshift BAO measurements). This makes the combination of CMB, BAO, and SNe (blue contours) a powerful one in this parameter space, yielding a fit centered around the ΛCDM values of Ω K = 0 and w = -1. We find that these datasets can constrain the equation of state of dark energy to 6 per cent and curvature to 0. 2 per cent, although the time evolution of dark energy is still unconstrained. In the DETF cosmology, we find a Figure of Merit value (inverse square root of the minor of the covariance matrix containing the covariances of w0 and wa) of 13.
5. We find that the anisotropic BAO measurement from CMASS-DR11 is much more powerful when constraining the equation of state of dark energy (even when considering time-evolving dark energy) than its isotropic counterpart. Fig. 27  shows the constraints in the H0-w plane for different BAO datasets combined with Planck results. The degeneracy between both parameters is quite evident, showing that a more negative value for w can result in a higher estimation for the Hubble constant. This effect can also be seen in Fig. 24 ; for the wCDM model, variations in the distance to intermediate redshift produce larger variations in the local distance scale. The extent of the error contours as we vary the choice BAO data set is somewhat complicated, as was illustrated in Fig. 25. The efficacy of a given BAO distance precision to constrain w degrades as the fit shifts to more negative values of w; this is because models with w -1 have their dark energy disappear by intermediate redshift, leaving the BAO and CMB constraints degenerate.
The improvement when we change from the isotropic CMASS results to the anisotropic ones is partially due to a shift in w toward 0 and partially because of the rotation of the contours to favor a DA constraint. Overall, this figure also shows the consistency between the various BOSS results and the tight constraints on w that the BAO now provides. We turn next to the owCDM case, attempting to measure a constant dark energy equation of state in the presence of non-zero spatial curvature. These constraints are shown in Fig. 28  for several combinations of datasets. The allowed region in this parameter space by the combination CMB+SN is large, due to a substantial degeneracy between w and curvature. This degeneracy is lifted by the BAO, which in combination with the CMB sharply constrains the curvature.
(Mehta et al. 2012;Anderson et al. 2012;combination CMB+BAO+SN, in the ΛCDM (left) and ow 0 waCDM (right) cosmological models. Here we show the degeneracy direction in this plane and we compare the allowed regions in this parameter space when the CMB dataset used is WMAP9 (red) or Planck (blue). The allowed regions open up when adding more degrees of freedom to the cosmological model; however, they still exclude values of 73 km s -1 Mpc -1 and above. The BAO and SN datasets make the H 0 values from WMAP9 and Planck agree with each other. The best-fit value of Ωm is slightly different between the two, but still consistent within 1 sigma. As was discussed inMehta et al. (2012)andAnderson et al. (2012), the combination of CMB, BAO, and SNe data results produces a reverse distance ladder that results in tight constraints on H0 and Ωm despite this flexibility in the cosmological model.
ACKNOWLEDGEMENTS Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U. S. Department of Energy Office of Science. The  SDSS-III  web site is http://www. sdss3. org/. SDSS-III is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the  SDSS-III Collaboration  including the  University of Arizona , the  Brazilian Participation Group ,  Brookhaven National Laboratory, University of Cambridge, Carnegie Mellon University, University of Florida , the  French Participation Group , the  German Participation Group, Harvard University , the  Instituto de Astrofisica de Canarias , the  Michigan State/Notre Dame/JINA Participation Group ,  Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington , and  Yale University.
Introduction Adequate temporally and spatially distributed climate datasets are necessary for different kinds of atmospheric studies. For example, climate scenarios research presupposes the continued development of high-quality climate datasets for model calibration and validation  (Wilby & Wigley, 1997). Rainfall and maximum and minimum temperatures are the key variables that characterize the climate of a region. Global warming may lead changes in these variables at regional scale through the modification of large-scale circulation patterns. In turn, this may affect in a greater or lesser degree other variables, such as streamflow and radiation. These variables play an essential role in the La Plata Basin (LPB) in South America, reservoir of enormous biological wealth, where the agriculture is the main source of incomes.
CLARIS LPB project aims at predicting the regional climate change impacts on LPB, and at designing adaptation strategies for land use, agriculture, rural development, hydropower production, river transportation, water resources, and ecological systems in wetlands (http://www. claris-eu. org/: CLARIS, 2013a). In the project there are two major lines of research: one dedicated to the past and future hydroclimate of LPB and the other related to the impacts in the context of climate change. To accomplish their main objectives, it was necessary to build a high-quality daily database, as the climate observing system exhibits serious deficiencies in the region, in spatial and temporal resolution. The baseline for this database was created during a previous CLARIS project, which belongs to the European Union Sixth Framework Programme FP6  (Boulanger et al.
Therefore, to overcome these limitations, it was necessary to go beyond the official institutions and involve non-governmental agencies that have their own climate networks and can provide good quality data. Such data sources have been already identified in CLIVAR/VAMOS/South American Low Level Jet Experiment (SALLJEX)  (Penalba et al. , 2004). The purpose of the current article is to give an overview of the contents of the CLARIS LPB database. Section 2 describes the data collection and the quality-control procedures that were applied to the daily series in the dataset. The Database features and the spatial and temporal distribution of the stations are presented in Section 3. Section 4 illustrates the potential of the CLARIS LPB database.
Sources The former FP6 CLARIS project gathered temperature and rainfall information of 107 meteorological stations, only from governmental institutions of Argentina, Brazil, Chile and Uruguay (Figure  1 ). The new FP7 CLARIS LPB project is focused in La Plata Basin, which comprehends the shaded area in Figure  1. The lack of spatial density over the basin is evident, and only 50 locations possessed long-term high-quality data. With this information as the initial database source, it was necessary to resort to other governmental and nongovernmental institutions to improve the spatial and temporal resolution of rainfall and temperature time series and to collect others variables, as streamflow and radiation. Such initiative strengthened collaborations with public and private stakeholders at national and local levels, who provided their daily station data and were interested in the CLARIS LPB results.
More than 60 institutions were gathered for the improvement of the database, whose final list is available in the CLARIS LPB website (http://eolo. cima. fcen. uba. ar/ ~sweb/collaborators. php: CLARIS, 2013b). The national institutions contributed to rainfall and temperature daily data, and most of the local institutions provided only daily rainfall data. The National Weather Services also worked in data rescue initiatives, digitalizing historical climate data in several countries of South America. This effort allowed having a greater number of official stations with longer periods. Streamflow data were obtained for the main rivers in LPB, located over Argentina and Brazil at daily and monthly basis, through interactions with the water offices of Argentina and Brazil. Daily data for radiation and heliophany were obtained only for the Argentinean territory, supplied by Universidad Nacional del Litoral and the study group of solar radiation of the Universidad de Lujan (GERSolar-INEDES, 2013, http://www.
Quality control The metadata files provided by the several sources involved in the project are available through the CLARIS LPB Data Archive Centre (CLDAC)  (Goodess et al. , 2011). These metadata files were processed and put into a unique conventional format for an easier handling. After that, we developed some basic algorithms for the quality control of the data. Within the database webpage, the user will find a list with the corrections performed in an appendix file, to let him or her decide if the correction proposed is right for their purposes or if the user thinks that another value can be given to the corrected data. Several methods were used to evaluate the consistency of the time series. First, an intuitive measure was proposed as negative values were not allowed in rainfall, radiation, and streamflow data.
In all the cases, we verified that the real values were the absolute amount of daily value, which means that the negative sign was associated with typing errors. This was the only kind of evaluation performed to radiation and streamflow data, given that the impact groups of the CLARIS LPB Project worked with these variables and submitted the data to adequate quality controls. In the case of temperature, we followed the procedures described in  Rusticucci and Barrucand (2001) , and, moreover, it was verified that the maximum temperature exceeds the minimum temperature in, at least, a value of 0. 5°C. This value was selected based on the inherent instrumental error and typical errors in the estimation and measurement of temperature. In cases that this did not happen, we analysed if the differences were physically possible.
For this propose, we used the nearest stations to perform a comparison of the temperature spatial patterns. In most of the cases, the difference was associated with typing errors. In other cases where we could not identify which were the real value of the variable, we considered the data as missing. For the rainfall data, we also analysed the length of the consecutive dry days to verify if these dry spells were natural and regionally consistent or some data missing were filled with zeroes. To identify if the dry spells were regionally consistent, we took into account the climatological study performed by  Llano and Penalba (2011). When a suspicious spell was identified, this was indicated in the correction file, but no changes were performed to the original data. For temperature and rainfall data, the values which were away from the daily average value over a range greater than four standard deviations were analysed.
In the case of rainfall values, we considered for the calculation of the mean and standard deviation only the wet daysrainfall greater than zero. The spatial analyses of these dates enable identifying whether they were outliers or extreme events. This kind of analysis allowed identifying no less than 1500 errors regarding maximum and minimum temperature and 83 errors regarding rainfall data, which belong to less than 4% of the total amount of stations of the database. There were no procedures for filling data gaps. After these checks and corrections, the data were entered in the database. In the case of rainfall and temperature variables, additional quality control procedures were performed by specific research  (Tencer et al. , 2011; Jones et al. , 2013). Related to radiation data, we digitalized and performed the quality control only for El Pozo station  (31.
64°S, 60. 67°W; period 1989-2008) , provided by Universidad Nacional del Litoral. Regarding the other 67 radiation stations and the 29 heliophany stations, the consistency was performed by the data supplier (more details in  Righini et al. , 2005; Grossi Gallegos & Righini, 2007; Raichijk, 2011 and GERSolar-INEDES, 2013). During the construction of the database we did not apply any homogeneity tests to the data. It should be noted that an algorithm for the detection of errors and inhomogeneities, called APACH  (Boulanger et al. , 2010a; Farall et al. , 2010) , is expected to run over the CLARIS LPB database in the near future. All the data collected were returned to the providers with the corrections performed during the quality control period and the recommendations for its appropriate use.
Database features The CLARIS LPB database is available at http://wp32. at. fcen. uba. ar/ (CLARIS, 2013c), which could be used for scientific purpose under the CLARIS LPB data protocol. This protocol considers that the data shall be offered free of charge, and the users should not use the data for commercial activities or exploitation nor to transfer to third parties. Therefore, the climate community working in LPB can benefit from this unique high-quality database. Up to July 2013, there are 185 registered users in the database, who performed more than 1300 successful data downloads. The features currently available in the website allow the users to easily obtain the desired data. The four main variables available in the database are rainfall, temperature (maximum and minimum), radiation (and also heliophany), and streamflow.
After the selection of the variable of interest, the user can restrict the search by country, by area, by station ID, and by date. All these options can be combined to obtain the desired data. As a result, the user will obtain a list of the selected stations according to the search options introduced before and will be able to export the data in a compressed file. Given the huge amount of information that a query can bring to the user, the exportation was limited to 500 stations. This was done to optimize the performance of the database server, although the user can export several searches containing up to 500 stations. The file containing the data will be sent as a web link to the registered e-mail of the user. Some of the institutions also provided monthly data for rainfall and streamflow.
In the case of rainfall, we received information of 93 stations located in the northern portion of Argentina, near the limit with Paraguay territory. These stations roughly cover the 1979-2008 period, although some locations started their measurements in 1967. In the case of streamflow, the water offices of Argentina and Brazil provided monthly data for the upper LPB for 39 stream gauges, some of them covering the whole 20th century. Quality control procedures were not performed upon these data. The user can obtain these monthly data from the Search/ Export Data menu of the database webpage. When the supplier provided, the metadata of the station series were collected and are available in the website and in the CLDAC website.
The number of stations in the database varied greatly as a function of time. In the case of temperature, Observatorio Central Buenos Aires station began to measure in 1908 and the other seven stations started in 1930. At least 255 stations are available between the period 1960 and 1978, with a maximum of 312 stations in 1976, dropping abruptly to 159 stations in 1980. The largest number of stations in the complete period is 348 in the year 2004,    mainly due to several Brazilian stations that began to record during the 2000s. In the case of rainfall, there are two stations located in Brazil that began in 1888, and between 1941 and 2000 there are more than 1000 series available, most of them over Brazilian territory (Figure  5 ). The maximum number of stations is in 1977 with 4606 and thereafter begins to drop down up to 292 in 2010.
Given that Figure  6  was built for the complete database, we also analysed the spatial distribution of the rainfall and temperature stations with at least 20% of data missing over periods of 10 years. We performed this analysis because decadal climate variations in LPB could impact upon agricultural and hydrological production; therefore, it is important to know the spatial distribution of stations whether for model validation or decadal variability analysis. The difference in the number of stations by changing the threshold from 20% to 10% of data missing is between 20 and 25% fewer stations (not shown). Figure  7  shows the location of the temperature stations with less than 20% of data missing over 8 periods of 10 years, from 1931 to 2010. The number of stations varies mainly between  1951-1960 and 1961-1970  (from 4 to 28 stations) and from 1991-2000 to 2001-2010 (from 50 to 16 stations).
Daily gridded data As the given figures show, for rainfall and maximum and minimum temperatures, we obtained a spatially highly dense daily database. This allowed producing gridded products that can be obtained in the database webpage and this improves the existing observed gridded datasets in terms of length of record and spatial and temporal resolution. A brief description of the gridded datasets is available in the CLARIS LPB database webpage. However, the user can refer to  Tencer et al. (2011)  for temperature gridded data and  Jones et al. (2013)  for rainfall gridded data. Both daily gridded data cover the 1961-2000 period, with a spatial resolution of 0. 5°9 0. 5°and can be downloaded in NetCDF format from http://wp32. at. fcen. uba. ar/ gridded/temp (CLARIS, 2013e) in the case of temperature and http://wp32.
Concluding Remarks During the 4 years of the CLARIS LPB Project, a highquality hydroclimate observational database was developed for extreme event and decadal-to-interdecadal variability analysis. The network of stations expanded from 107 stations in the FP6 CLARIS to more than 9000 stations in the FP7 CLARIS LPB. This achievement was accomplished due to interactions between local non-governmental institutions that provided their climate information and also with the official National Meteorological Services. Moreover, this exchange helped strengthen ties among scientists, institutions, and decision makers. A result of this interaction was the course "M etodos de homogeneizaci on -Control de calidad de los datos meteorol ogicos-APACH" that took place in the National Weather Service of Argentina in November 2011, where the participants learned different tools for quality control of meteorological data.
Temporal and spatial distribution of the available stations was studied considering the amount of data missing. This analysis, differentiated by variable, allows identify benefits and limitations of CLARIS LPB dataset. According to the characteristics analysed, this dataset provides spatially consistent climatic time series which enable a variety of empirical climate studies, such as to evaluate the changes of extremes in relation to changes in mean temperature and total rainfall. However, despite the high density of stations in the northeast of Argentina and south of Brazil, it is not enough to hold studies in the mesoscale such as the pioneer work carried out by  Velasco and Fritsch (1987)  about the mesoscale convective systems. Nevertheless, the analysis made in this article helps to identify specific locations in which new stations are needed to be located.
The database allowed an improvement in the analysis of recent past climate variability in La Plata Basin (e. g. Cavalcanti et al. , 2011; Carril et al. , 2012)  and provided a baseline for the validation and analysis of present-day regional and global climate model outputs (e. g. Men endez et al. , 2010; Solman & Pessacg, 2012; Bl azquez & Nu ñez, 2013). Moreover, the CLARIS LPB database allowed the working groups related to climate change impacts to elaborate hydrological models of the Iber a Wetlands  (Grimson et al. , 2013)  and to use observational data as inputs for these hydrological models (e. g. Popescu et al. , 2012). In particular, it helped for analysing palaeohydrological reconstructions of the past climate variability  (Piovano et al. , 2009; Troin et al. , 2010). In addition, temperature and rainfall gridded data were developed by  Tencer and Rusticucci (2012)  and  Jones et al.
(2013) , respectively, from daily data to improve the development of a high-quality hydroclimatic observational database for extreme events and decadal-to-interdecadal variability analysis. Several other works used daily surface minimum and maximum temperature and rainfall data observed at governmental and non-governmental stations from Argentina, Brazil, Paraguay, and Uruguay, also compiled in CLARIS LPB database  (Bettolli et al. , 2010a,b; D'Orgeval et al. , 2010; Detzel & Mine, 2011; Renom et al. , 2011; Urcola et al. , 2011; Bettolli & Penalba, 2012; among others). Regarding the continuity of the database, there is a commitment for keeping the database operative for further research, and it will remain available for the next years within a computer server in the Department of Atmospheric and Oceanic Sciences at the University of Buenos Aires.
Acknowledgements We thank  Brant Liebmann  and two anonymous referees for their valuable comments and critical reading of the manuscript. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 212492 :  CLARIS LPB. A Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. The authors would like to thank to all data providers, to  Natalia Bisero ,  Maite Cancelada ,  Karina Flores ,  Laura Pappalardo  and  Romina Racedo  for the quality control of the meteorological time series; and to  Eugenio Costa  for the website programming and design.
The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 212492 :  CLARIS LPB. A Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS LPB database was built within the framework of the  CLARIS LPB  project " A Europe-South America Network for climate Change Assessment and Impact Studies in La Plata Basin" of the European Community 's  Seventh Framework Programme  ( FP7 ). high-quality daily hydro-meteorological dataset for scientific purpose available at http://wp32. at. fcen. uba. ar/.
The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated data sets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models outputs, while the use of a standard data set facilitates models inter-comparison. Unlike standard stock assessment models, new state-of-the-art ecosystem models require geo-referenced fishing data with the highest possible spatial resolution. This study presents an application to the North Atlantic albacore tuna stock with a careful definition and validation of a spatially explicit fishing data set prepared from publicly available sources (ICCAT) for its use in a spatial ecosystem and population dynamics model (SEAPODYM) to provide the first spatially explicit estimate of albacore density in the North Atlantic by life stage.