dataesr/scientific-paragraphs-categorization huggingface dataset:
The increasing digitization of the biological literature and the growing availability of tools to search the literature and biorepositories are improving ways to link and contextualize sequences and biological data  (9). The ability to semantically enhance journals will also allow future taxonomic papers to be mined for valuable taxonomic information  (98). Type information is often found in a variety of formats that makes it challenging for machine reading. PubMed Central already has an initial species description extension in XML that could serve as a purpose for linking taxonomic data to additional metadata. This could include barcode data, and some shortened machine-searchable version could be placed in abstracts, so it is easily indexed in various openly accessible literature services like PubMed, PubMed Central and others without residing behind a paywall  (99).
We also advocate a newly available option to comment on papers in PubMed, PubMed Commons, by registering third-party opinions on sequences and species contained within the relevant publications (http://www. ncbi. nlm. nih. gov/pubmedcommons/). In the immediate future, we will explore ways to streamline the expansion of RTL for Fungi. We currently only rely on selection by NCBI Taxonomy and RefSeq curators in consultation with numerous taxonomists. Cooperation with the nomenclature databases, MycoBank and Index Fungorum, as well as annotation and specimen databases such as UNITE and MyCoPortal should be expanded where possible. The focus on specimen and culture designations can be extended to include reliable standardized geographical data. We have also collected a smaller set of sequence accessions from the 28S nuclear ribosomal gene and will work to expand the set of accessions to include this and several other markers, using the re-annotated bio collection data where possible.
List of collection databases with specimen pages to which links were established from records in GenBank The same accession was in UNITE's list of representative sequences (RepSs) or reference sequences (RefSs) with the same TaxID. Any synonymous taxon names used in UNITE were resolved, and the TaxID were identified with the name status tool in NCBI taxonomy (http:// www. ncbi. nlm. nih. gov/Taxonomy/TaxIdentifier/tax_identifier. cgi). • There was >99. 5% identity over >90% of the ITS region in the potential RefSeq sequence compared with RepS or RefS with the same TaxID from UNITE using megablast alignments.
ACBR  Austrian Center of Biological Resources  and Applied Mycology http://www. acbr-database. at /BioloMICS. aspxATCC American Type Culture Collection http://www. atcc. org/Products/Cells_and_Microorganisms/Fungi_and_Yeast. aspxBCRC Bioresource Collection and Research Center https://catalog. bcrc. firdi. org. tw/BSAS_cart/controller?event¼WELCOME  BPI US National Fungus Collections, Systematic Botany and Mycology Laboratory  http://nt. ars-grin. gov/fungaldatabases/specimens/Specimens. cfmCBS Centraalbureau voor Schimmelcultures, Fungal and Yeast Collection http://www. cbs. knaw. nl /Collections/Biolomics. aspx?Table¼CBS%20 strain%20database CFMR Center for Forest Mycology Research http://www. fpl. fs. fed. us/search/mycology_request. phpDSM  Deutsche Sammlung von Mikroorganismen  und Zellkulturen GmbH http://www.
dsmz. de/catalogues/catalogue-microorganisms. htmlFRR  Food Science Australia , Ryde http://www. foodscience. csiro. au/fcc/search. htmICMP International Collection of Microorganisms from Plants http://scd. landcareresearch. co. nz/Search/Search/ICMPJCM Japan Collection of Microorganisms http://www. jcm. riken. jp/JCM/catalogue. shtmlMA  Real Jardı ´n Botanico de Madrid Herbarium  http://www. rjb. csic. es/jardinbotanico/jardin/index. php?Cab¼109&len¼es  MAFF MAFF Genebank, Ministry of Agriculture, Forestry and Fisheries  http://www. gene. affrc. go. jp/databases-micro_search_en. phpMICH University of Michigan http://quod. lib. umich. edu/h/herb4ic?page¼searchMTCC Microbial Type Culture Collection and  Gene Bank  http://mtcc. imtech. res. in/catalogue. phpMUCL  Mycotheque de l'Universite Catholique de Louvain  http://bccm.
belspo. be/db/mucl_search_form. php  NBRC NITE Biological Resource Center  http://www. nbrc. nite. go. jp/NBRC2/NBRCDispSearchServlet?lang¼en NRRL Agricultural Research Service Culture Collection http://nrrl. ncaur. usda. gov/cgi-bin/usda/index. html PDD New Zealand Fungal and Plant Disease Herbarium http://nzfungi2. landcareresearch. co. nzPYCC Portuguese Yeast Culture Collection http://pycc. bio-aware. com /BioloMICS. aspx? Table¼PYCC%20strains SAG Sammlung von Algenkulturen  at Universitat Gottingen http://sagdb. uni-goettingen. de/UAMH University of Alberta Microfungus Collection and Herbarium https://secure. devonian. ualberta. ca/uamh/searchcatalogue. php Unique acronyms were taken from the GenBank collections database and, where possible, agree with labels used by  Index Herbariorum , WFCC and GRBio Acknowledgements B.
(18,89)ult, a sequence from the most common sequence type in each SH is chosen to represent the SH and to form part of its name. It is also possible to change the chosen representative where there is a need to exercise extended control. Sequences from type material, in particular, are given priority whenever available and of satisfactory length and technical quality(18,89). functionalities for their curation and analysis (http://unite. ut. ee) (18). All sequences are clustered into SHs variously designated at 97-100% similarity (at 0. 5% intervals) to seek to reflect the species rank. The SHs are assigned unique identifiers of the accession number type-e. g. SH133781. 05FU-and are resolved with URLs such as 'http://unite. ut. ee/sh/SH158651. 06FU'. All INSDC sequen- ces that belong to an SH are hyperlinked from GenBank/ ENA directly to that SH in UNITE through a LinkOut feature.
More than 205 000 ITS accessions in the UNITE database can be accessed by using the query 'loprovunite[filter]' in GenBank. The SH concept is also implemented in the next-generation sequencing pipelines QIIME (85, 86), mothur (87), SCATA (http://scata. myko- pat. slu. se/), CREST (88) and in the recently launched EU BOLD mirror (www. eubold. org). A total of $21 000 SH or operational taxonomic units (OTUs) (excluding single- tons) at 98. 5% similarity are indexed in the current (sixth) release of UNITE. 3. Non-type/reference sequences with high LQI can be used for any purpose, other than species identification. 4. Non-type/reference sequences with low LQI until better sequences are obtained. The UNITE database for molecular identification of fungi represents another approach to improve sequence accuracy and fungal species identification.
The population depends on the agrarian economy and most of the people are from the working class with a low level of income. In order to investigate the exchange-marriage system, the Grounded Theory Method's (GTM) guidelines  (Strauss, 1987; Neumann, 1997; Glaser, 1992; Glaser and Strauss, 1965; Larossa, 2005; O'Brien, 2005)  were used to conduct and analyse the data. The GTM seems to be appropriate while it guides in developing real insight into the social world  (Zaman, 2011). The GTM guides that data collection and analysis is a simultaneous process  (Corbin & Strauss, 1990). We interviewed the couples (each spouse individually) who were married by exchange, their parents and their children who were supposed to get married in the near future after securing their formal permission. In this way, we compiled twenty-four family case histories based on forty-eight individual interviews.
In spite of an occasional failure in the detection of clouds, MODIS T s values exhibit a good performance, with a bias ranging from -1. 8 to 0. 1 • C and errors ranging from 2. 2 to 4. 8 • C root mean square at the five stations located on the plateau. These results show that MODIS T s values can be used as a precise and accurate reference to test other surface temperature data sets. Here, we evaluate the performance of surface temperature in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis known as ERA-Interim reanalysis. During conditions detected as cloud free by MODIS, ERA-Interim shows a widespread warm bias in Antarctica in every season, ranging from +3 to +6 • C on the plateau. This confirms a recent study which showed that the largest discrepancies in 2 m air temperature between ERA-Interim and the global temperature data set HadCRUT4 compiled by the Met Office Hadley Centre and the University of East Anglia's Climatic Research Unit occur in Antarctica.
To detect erroneous measurements, the authors proposed to reject any data with LW down larger than LW up. We further analyzed these observations and decided to use a more conservative filter: AWS data are rejected when LW down is larger than LW up -5 W m -2. This selection reduces the amount of data available for the winter. Nevertheless, we believe that some of the filtered data are still affected by riming. In order to assess the possible impact of such H. Fréville et al. : Antarctic surface temperature processing, we also analyzed observations from two additional pyrgeometers at Dome C which do not benefit from the standard BSRN cleaning procedure. They were available only for the year 2012. Snow surface temperature T s at the stations was derived according to the following: T s = 4 LW up -(1 -)LW down σ , (1) where is snow surface emissivity, σ is the Stefan-Boltzmann constant, and T s is snow surface temperature.
ERA-Interim surface temperature The ERA-Interim reanalysis includes a comprehensive set of variables describing the surface and the ABL. We focused on the ERA-Interim skin temperature, hereafter ERA-i T s , which forms the interface between the soil and the atmosphere in the Integrated Forecast System (IFS) (European Centre for Medium-Range Weather Forecasts (ECMWF), http://www. ecmwf. int). IFS is the meteorological model and assimilation scheme used in the ERA-Interim analysis to assimilate observations. ERA-i T s is the temperature used in the derivation of the heat budget between the atmosphere and the surface. ERA-i T s was extracted at 0. 5 • resolution in latitude and longitude and then projected at 25 km resolution on the same grid as the MODIS T s using bilinear interpolation. This step of projection aims to facilitate the comparison between ERA-Interim data set and MODIS data set.
This is illustrated with Dome C 2012 better scores (Fig. 2h ) compared to Dome C 2009 (Fig. 2g ). In spite of the possible accumulation of several error sources -MODIS cloud detection, time shift of the MODIS data to the closest full hour time step, difference in the representative scale of the different data sets (25 km for the projected MODIS T s against a few meters for the in situ observations), occasional errors in in situ observations due to riming -MODIS T s exhibits quite good performances on the Antarctic Plateau. This confirms and extends, in space and time, the results reported in  Brun et al. (2011). In this previous study, MODIS T s over Dome C was successfully compared over a 11-day period with several independent hourly time series of snow surface temperature. The MODIS T s data set clearly has great potential for the evaluation of surface temperature produced by model or analysis outputs in Antarctica.
ERA-Interim and Crocus surface temperature analysis ERA-i T s (3-hourly data described in Sect. 2. 3) and Crocus T s (hourly data described in Sect. 2. 4) were evaluated with respect to the MODIS T s data set over the period 2000-2011. It must be kept in mind that the latter includes only observations under meteorological conditions analyzed as cloud free by the MODIS cloud detection algorithm, hereinafter referred to simply as clear-sky conditions. Figure  3   The warm bias is slightly lower when all weather conditions are considered but is still quite significant (+3. 2, +3. 0, +3. 2 and +3. 0 • C, respectively). This means that ERA-i T s ex-hibits a warm bias even under cloudy conditions. In the same way Fig. 6  compares Crocus T s with in situ observations. Crocus T s exhibits a very low bias (-0.
Acknowledgements. This study is a contribution to the international  Concordiasi  project currently supported by the following agencies: Météo-France; CNES; CNRS/INSU; NSF;  NCAR ;  University of Wyoming; Purdue University; University of Colorado ; the  Alfred Wegener Institute ; the  Met Office ; and ECMWF. It was funded by the  European Commission 's  7th Framework Programme , under grant agreement no. 226520 ,  COMBINE  project. Part of the data at Dome C were acquired by the  CALVA/NIVO IPEV  programs. Installation and operation of AWS12 and 13 have been made possible with support from the  Netherlands Polar Programme  and the  Norwegian-US Scientific Traverse of East Antarctica. The installation and maintenance of AWS16 was financed by the  Belgian Science Policy Office  under grant number  EN/01/4B  supervised by  Nicole van Lipzig  and  Irina Gorodetskaya  ( KU Leuven ).
By using multiple antenna transceivers, MIMO technology not only offers multiplexing and diversity gains, but it also achieves higher conventional point-topoint link reliability in comparison with single transceiver systems  [8]. The main challenge is to design a MIMO scheme that fully exploits the presence of multiple antennas. In fact, in  [9] -  [11] , multiple copies of transmitted data symbols have been proposed to map across antennas for diversity and transmission robustness. The association of this technique with iterative receivers has shown promising performance  [12] ,  [13]. More importantly, in time domain duplex (TDD) closedloop schemes, the channel state information (CSI) is readily available at the transmitter through a feedback link, which allows us to further design a precoder that is able to adapt to the channel conditions.
A. System model Let us consider a MIMO system with n R receive, n T transmit antennas and b independent data streams to be transmitted. We assume that both the transmitter and the receiver have the full-CSI. A binary recursive-systematic convolutional (RSC) code is used to encode the information binary sequence. The FEC codeword is interleaved before being mapped onto quaternary quadrature amplitude modulation (4-QAM) symbols. The mapped symbols are converted into a b-dimensional symbol vector s. The vector s is then precoded by a precoder F and transmitted through the MIMO channel. At the receiver, after MIMO detection, an interference canceller iteratively exchanges extrinsic information with a BCJR soft decoder  [18]  by using the soft symbol-to-binary (SBC) and binary-tosymbol converter (BSC).
After an introduction to the topic, several blocks of questions regarding the changing technological environment, the diffusion of SaaS, customer perception and satisfaction, pricing variables in relation to customers' values and importance of this value realization, among others, were asked. An interview guideline was followed that nevertheless left enough room for the interviewees to set their own emphases. The interviews were then transcribed and coded using MAXQDA 11 software. 2 The literature and interview findings were then used to develop a novel conceptual software pricing model. In the second phase, this resulting model was then discussed, validated and refined with representatives of the leading five international BI&BA software solution providers. These representatives were contacted via email and phone and the discussions held with one executive at a time at the CeBIT 2014 in Hannover, Germany.
Studies comparing and evaluating the temporal (on annual, seasonal and daily basis) and spatial variability of desert dust load and deposition simulated by different models, contribute to determine the degree of uncertainty in estimates of dust emission and transport. They highlight the sources of uncertainty in these estimates, and point to the key foci for future research to constrain them (e. g. Tegen et al. , 2003; Kinne et al. , 2006; Textor et al. , 2006; Huneeus et al. , 2011)  as horizontal visibility, particulate matter concentration, aerosol optical depth (AOD), and extinction vertical profiles will likely be incorporated as added value information in future climate services databases. Long-term dust-related observations and model reanalysis may contribute to understand assessments and plan activities of health and energy communities and to other economic sectors in many regions of the world.
(1986)  and  Smith and Harrison (1998)  between 2 and 4 mm. Sources for the other aerosol types which are linked to emissions from domestic, industrial, power generation, transport and shipping activities, are taken from the SPEW (Speciated Particulate Emission Wizard), and EDGAR (Emission Database for Global Atmospheric Research) annual-or monthly-mean climatologies. More details on the sources of these aerosols are given in  Dentener et al. (2006). Emissions of organic matter (OM), black-carbon (BC) and SO 2 linked to fire emissions are obtained using the GFAS system based on MODerate resolution Imaging Spectrometer (MODIS) satellite observations of fire radiative power, as described in  Kaiser et al. (2012). The 10 year reanalysis employs GFAS-based fire emissions for 2009-2012 and the GFED3 inventory for  2003 -2008 (van der Werf et al.
The bias model for the MODIS data consists of a global constant that is adjusted variationally in the minimization based on the first-guess departures. The observation error covariance matrix is assumed to be diagonal, to simplify the problem. The errors are also chosen ad hoc and prescribed as fixed values over land and ocean for the assimilated observations. The aerosol background error covariance matrix used for aerosol analysis was derived using the Parrish and Derber method (also known as NMC method; Parrish and Derber, 1992) as detailed by  Benedetti and Fisher (2007). This method was long used for the definition of the background error statistics for the meteorological variables and is based on the assumption that the forecast differences between the 48 h and the 24 h forecasts are a good statistical proxy to estimate the model background errors.
01 for wavelengths > 0. 44 µm, and < 0. 02 for shorter wavelengths  (Holben et al. , 1998; Eck et al. , 1999). ern Mediterranean, Central Mediterranean, Eastern Mediterranean, and Middle East (Fig. 2  and Table  1 ). We are well aware that within these regions there are some subregions with certain distinguishing characteristics caused by the contribution of other aerosols, in addition to dust. These distinctive features will be identified throughout this work. Cloud-screened and calibration quality-assured AERONET Level 2. 0 data  (Holben et al. , 1998)  has been extracted for each station. Since MACC-II provides AOD at 550 nm, we used AERONET values of the AE (440 nm/870 nm) and the AOD at 440 nm to derive AERONET AODs at 550 nm, according to the Angstrom's law: AOD 550 = AOD 440 550 440 -AE 440-870 (1) Additionally, direct-sun AOD processing includes the Spectral Deconvolution Algorithm (SDA) retrievals  (O'Neill et al.
4  • N, 17. 0 • W, 13 m a. s. l. ), (2) Cinzana in Mali (13. 3 • N, 5. 9 • W, 282 m a. s. l. ), and (3) Banizoumbou in Niger (13. 5 • N, 2. 7 • E, 191 m a. s. l. ). The location of the three stations is indicated in Fig. 2. We followed two approaches. Firstly, we have used the PM 10 daily means available in the AMMA database, and secondly, daily averages were calculated from 5 min data screened by wind direction following  Marticorena et al. (2010)   measurement program, and the main spatiotemporal dust distribution is provided by  Marticorena et al. (2010).
e. , the dust optical depth (DOD). Nevertheless, the criteria should not be as restrictive as to reduce drastically the number of observations because it would preclude proper assessments of this evaluation in dust transport regions where mineral dust concentrations are significantly lower than in-near-source regions. For the present AERONET comparison, we used AE < 0. 6 as a first approach to discriminate when mineral dust is the main aerosol component in the direct-sun AERONET dataset according to previous studies  (Duvobik et al. , 2002; Wang et al. , 2004; Todd et al. , 2007; Basart et al. , 2009). A second approach was to consider the coarse mode of AOD as a proxy of DOD, utilising the AERONET 500 nm coarse mode AOD from the SDA retrievals  (O'Neill et al. , 2003). Skill scores obtained for 2007 and 2008 (Table  2 ) indicate the level of agreement between MACC-II and AERONET DOD, using both above approaches.
g. ,  Léon et al. , 2009; Cavalieri et al. , 2010; Schmechtig et al. , 2011; Mortier, 2013). In our case, the four seasons are the dry season (November-March), driven by the Harmattan winds, spring (April-May), the wet period (June-August), basically driven by the monsoon, and autumn (September-October). The averaged extinction vertical profiles of MACC-II and lidar for the different seasons show distinct characteristics in terms of mineral dust vertical distribution (Fig. 8a-d ). We have also performed a comparison with simultaneous extinction vertical profiles observed by CALIOP. Notice that in this case the number of profiles used in the averaged vertical profiles is notably lower (Fig. 8e-h ) and, therefore, the averaged vertical profiles are noisier. The lidar ratio for the whole lidar database at M'Bour is 30 ± 16 sr  (Mortier et al.
, 1996; Knippertz et al. , 2009). According to  Marticorena et al. (2010)  the summer dust events are mainly controlled by local surface wind velocity and thus most of them result from mesoscale convective systems favouring local dust emissions. The poor performance of MACC-II in capturing these mesoscale processes is largely explained by the MACC-II coarse spatial resolution and by the fact that MACC-II uses convective parameterizations where downdrafts are not simulated. We computed the skill scores of the comparison of paired daily data MACC-II-AMMA stations for the period 2007-2008, for each of the three stations, for the entire period, for the period November-March, corresponding to the dry season in which the highest PM 10 values are recorded, and for the period June-August centred in the rainy season when the lowest PM 10 values are observed (Table  4 ).
CALIOP extinction vertical profiles (532 nm) have been compared with MACC-II and ground-based lidar profiles. We find a good agreement in dust layers structures and averaged extinction vertical profiles between the lidars (at M'Bour and SCO), MACC II, and CALIOP above de MBL from 1 to 6 km altitude in all seasons, although a slight overestimation is observed in CALIOP, probably related with the higher lidar ratio used in the extinction vertical profiles determination. Furthermore, our CALIOP data correspond to a circular area of 1. 5 • radius around the ground-based lidar, and therefore dust and cloudy conditions might be totally different in some events for the lidar and CALIOP at the time of comparison. A well-defined MBL is observed from lidar extinction profiles in all seasons with a high extinction (> 0.
2 km -1 ) and a rather constant depth (∼ 1 km altitude) in both stations. Similar results are found with CALIOP vertical profiles, although CALIOP shows an even more intense extinction peak in the MBL in summer and autumn. MACC-II does not match the observed extinction within the MBL. However, we must bear in mind that lidars provide no credible data in the first hundred meters due to overlap and after pulse limitations, and that extinction overestimation might be caused by the use of a relatively low lidar ratio. Surface dust daily mean concentrations from MACC-II reanalysis has been evaluated with daily averaged PM 10 at three monitoring stations of the Sahelian Dust Transect during 2007 and 2008. MACC-II underestimates monthly means all year long and especially in winter and early spring (dry season) when Saharan dust is transported by the Harmattan winds.