dataesr/scientific-paragraphs-categorization huggingface dataset:
71 for Pretoria, to a strong correlation co-efficient of 0. 88 for Durban (Table  2 ). These high correlations are due to the dominating annual course of the two datasets. The MAPE between the two datasets tended to be between 22% and 28%, 46% for De Aar. Hence, the ground-based UV index observations are in good agreement with satellite measurement, except for De Aar where ground-based observations are higher (Figure  9 ). , 1994-2015). Quantile 25 and 75 (filled blue lines) and minimum and maximum (black dash line) are also represented.
71 for Pretoria, to a strong correlation co-efficient of 0. 88 for Durban (Table  2 ). These high correlations are due to the dominating annual course of the two datasets. The MAPE between the two datasets tended to be between 22% and 28%, 46% for De Aar. Hence, the ground-based UV index observations are in good agreement with satellite measurement, except for De Aar where ground-based observations are higher (Figure  9 ). Variability is evident in the year-over-year analyses of MAPE, standard deviation, and RMSE. The yearly MAPE range from 15 to 30%, except for De Aar, where a 60% MAPE can be reached. A   Variability is evident in the year-over-year analyses of MAPE, standard deviation, and RMSE. The yearly MAPE range from 15 to 30%, except for De Aar, where a 60% MAPE can be reached. A.
Discussion The aim of this study is to compare ground-based and satellite-derived UV index measurements at six South African sites. Two objectives were identified. When comparing the solar UV index measurements made by the ground-based stations versus the satellite-derived estimates, the correlation analyses showed that the two datasets were in good agreement with each other for most sites and time periods with an annual systematic bias (noted to be low at times). It is well known that satellite-derived solar UVR data have limitations, particularly for sites that are cloudy, polluted, and/or loaded with aerosols. In our study this was evident at the De Aar station. Additional data on factors influencing solar UVR are needed to compute the true ground-based solar UV index levels, to detail the comparison for sites with high bias, or improve the comparison for others.
For example, if high resolution and quality cloud data were available at the ground-based stations, one could detect clear-sky days and compare clear-sky ground-based data with clear-sky satellite-derived data for solar UVR. However, cloud data are not routinely collected at the ground-based stations, hence, this subset of ground-based clear-sky solar UVR data cannot be determined. Otherwise, we used the TUV model as another method of comparison. Ozone from the OMI satellite and the aerosol optical depth from MODIS were used as input parameters. The comparison between the UV index from the TUV model and the UV index from the OMI shows less than 5% of MAPE. The UV index from modelling does not provide more information for our study. The UV biometers are not manufactured with an intention of use for long-term trend analysis  [29].
• Aerosols play an important part in the UVR response  [30]. Global climatological aerosol datasets are used in OMI processing, but this is likely not relevant for an isolated, relatively clear site, like De Aar station, although wind-blown dust may be a factor. We cannot prove which one, between ground-based and satellite datasets, is responsible for the large difference without additional high-grade solar UVR measurements, for example using Kipp and Zonen UVS-AB-T UV radiometers  [28] , being made alongside the UV biometers. Recently, the South African Weather Service, together with its partners, installed UVA/UVB radiometers at several sites as part of a solar radiation network. These instruments provide reliable data at high resolution and are strictly calibrated and maintained. It is anticipated that they will provide good quality data for future analyses.
We cannot prove which one, between ground-based and satellite datasets, is responsible for the large difference without additional high-grade solar UVR measurements, for example using Kipp and Zonen UVS-AB-T UV radiometers  [28] , being made alongside the UV biometers. Recently, the South African Weather Service, together with its partners, installed UVA/UVB radiometers at several sites as part of a solar radiation network. These instruments provide reliable data at high resolution and are strictly calibrated and maintained. It is anticipated that they will provide good quality data for future analyses.
Conclusions In this paper we analysed ground-based UV indices recorded during the 1994-2015 period (more than two decades) by the SAWS (South African Weather Service) at six sites at different latitudes. In fact, South Africa has been measuring solar UVR to raise awareness about the impacts of the solar UV index on human health. The SAWS UV index observations are based on the use of UV-biometers and the six datasets are not homogeneous in terms of time coverage and observations (Table  1 ). However, the present work is an exploratory study. It is based on the comparison with satellite UV index measurements from the OMI/AURA experiment. We found that relative UV index differences between ground-based and satellite-derived data range from 0 to 45%, depending on the site and year. Overall, there was a good agreement (absolute difference within ±25%) between South African ground-based station solar UV measurements and satellite-derived data from OMI/Aura, except for one station where an important bias is found.
Acknowledgments: The  National Research Foundation  and  South African Medical Research Council  provided support to  Caradee Y. Wright. The SAFE-UV/ PROTEA  Project and the  CNRS-NRF GDRI ARSAIO , for sponsoring  Hassan Bencherif  and  Jean Maurice Cadet. The Région Réunion, for Jean-Maurice Cadet. GIOVANNI online data system (developed and maintained by the  NASA GES DISC ) are thanked for the provision of ground-based and satellite-derived solar UVR data, respectively.
Results and discussion In order to confirm the presence of an ERK ortholog in corals, the human protein sequence of ERK1 (NP_001035145) was compared to the transcriptome database of Stylophora pistillata using the BLAST software  (Altschul et al. , 1990; Karako-Lampert et al. , 2014). An open reading frame was retrieved from the best hit sequence with a predicted molecular weight of 42 kDa (Spi_isotig05348). This sequence (hereafter referred to as Spi-ERK for S. pistillata ERK) is the only one that shows an homology as high as 81%, 80% and 78% with the protein sequences of the cnidarians Nematostella vectensis ERK (Nv-ERK; XP_001629498. 1), Hydra vulgaris ERK (Hv-ERK; XP_002154499. 3) and the human MAPK3/ERK1 (Hs-ERK1), respectively (Figure  1 )   (  Krishna et al. , 2013; Putnam et al. , 2007).
These sequences all contain both the conserved kinase domains  (Hanks & Hunter, 1995)  and the TEY motif of the catalytic domain, which is unique for ERK orthologs (Davis, 2000; Figure  1 ). An interesting point to note is that a unique sequence showing these features is present in N. vectensis and H. vulgaris genomes, as well as in the S. pistillata transcriptome database. This result suggests that a single ortholog of ERK is present in these cnidarians, consistently with previous work where only one ERK ortholog was found  (Castellano et al. , 2014; Russo et al. , 2004)  but as opposed to the two genes encoding ERKs in most mammalian genomes  (Ip & Davis, 1998). Furthermore, based on the high level of sequence conservation between distant species  (Hanks & Hunter, 1995) , antibodies directed against portions of the ERK human proteins may recognize ERKs from other species.
Regarding the presence of an ERK ortholog in coral, the authors show the sequence with code Spi_isotig05348 (Spi_ERK) has the best hit with the human ERK protein (NP_0011035145) and they make reference to Liew 2014. I was looking for this sequence into this paper, and I could not find it. et al. , The authors should provide in which database is the transcriptome, as well as, the Spi_ERK sequence itself.
Liew et al. , 2014. I was looking for this sequence into this paper, and I could not find it. The authors should provide in which database is the transcriptome, as well as, the Spi_ERK sequence itself. We are grateful to the reviewer for questioning this particular point as it allowed us to uncover a significant error in the citation we used. The reference for the Spi EST containing the ERK orthologue open reading frame was obtained from the database generated during the study by Karako-Lampert, Zoccola et al. (Plos One 2014) and not the one by Liew et al (p. 7). The database containing the sequence can be downloaded from this address:.
et al. paper, which should allow readers to access the database without the needing to contact corresponding authors. No competing interests were disclosed. of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Naples, Italy The paper by Courtial et al. describes the cross-reactivity of two commercial antibodies produced against the mammalian forms of ERK for the scleractinian coral Stylophora pistillata. This should open new perspective for the study of ERK signalling in response to different environmental cues.
The test was programmed in Flash-Actionscript and presented to participants on a computer in a quiet room using high-quality Sennheiser headphones (HD 449). To start, the participant listened to one whistled Silbo sentence to discover the type of stimuli he/she would have to process. This allowed the participant to check the volume comfort, approximately 70 dB. Then, the training started, and the participant listened to each stimulus one by one and categorized them as "a", "é", "i", or "o" on the interface in French by clicking on the display (written "a", "e", "i", "o" for convenience in the rest of the paper). Clicking started the next trial. The test followed directly. Only one listening was possible per stimulus, and there was no feedback. Overall, the experiment lasted 20 min. The time taken to answer was not recorded, but only the answer itself ("a", "e", "i", or "o").
RESULTS First and for reference for the present protocol, we present the whistled vowel identification pattern for a single native whistler of La Gomera, as observed previously in  Meyer (2008). Next, we present the data for Standard Chinese and Spanish participants. Moreover confusion matrices of the answers ("i", "e", "a", "o") as a function of the played vowels (/i/, /e/, /a/, /o/) were derived from the participants' answers (see Table  1B  for all Spanish participants and Table  1C  for all Standard Standard Chinese participants). Two different types of analyses were performed: first, concerning the pattern of correct answers; and next, the intervocalic confusions. Correct answers and confusions were also presented together by visualizing the answers of the participants as a function of the acoustic frequency of the whistle of each played vowel (like in Figure  2 , see Figure  3  for Spanish and Figure  4  for Standard Chinese).
The solar hourly global irradiation received at ground level estimated by the databases HelioClim-3v4, HelioClim-3v5 and CAMS Radiation Service are compared to coincident measurements made in five stations in Oman and Abu Dhabi. CAMS is an abbreviation of Copernicus Atmosphere Monitoring Service. Each database describes the hour-to-hour changes in irradiation very well with correlation coefficients greater than 0. 97 for all stations. Each database exhibits a tendency to underestimate the irradiation in this area; the bias is small and less than 5 % of the average of the measurements in absolute value. The RMSE ranges between 70 and 90 Wh m -2 (11 to 16 %). This validation of the three databases for this arid region on the edge of the Meteosat coverage reveals satisfactory results. Each database captures accurately the temporal and spatial variability of the irradiance field.
Global meteorological reanalyses exist that go several decades back in time. It has, however, been shown that the SSI data from these have significantly lower qual-ity than the SSI data from satellite-based databases  (Boilley and Wald, 2015). Accurate assessments of the SSI can be made from satellite images which may supplement radiometric measurements  (Lefèvre et al. , 2014; Wagner et al. , 2011). Several databases have been constructed from satellite images that contain 15 min SSI. Among them, are the HelioClim-3 databases (version 4 and version 5) and the CAMS Radiation Service (CAMS-Rad) detailed hereafter, where CAMS stands for Copernicus Atmosphere Monitoring Service. These databases are accessed on-line by several hundreds of users each year  (Thomas et al. , 2016a). Many validations were performed and are still being performed to supply users with the most complete knowledge of the accuracy of each database.
Such validations are made by comparing satellite-derived estimates against coincident qualified ground measurements for various climates to better understand the strengths and weaknesses of the databases. For example, joint validations of these three databases were performed for nine stations belonging to the Baseline Surface Published by Copernicus Publications. Radiation Network (BSRN) and located in various climates in Europe  (Thomas et al. , 2016b) , for nine stations in Egypt  (Eissa et al. , 2015a) , and in Brazil by  Thomas et al. (2016a). The present work contributes to this continuous effort and supports the gathering of evidence of site-specific strengths and weaknesses of the databases. It contributes to addressing the issue of the spatial consistency of errors in the retrieval of hourly global irradiation.
The family of Heliosat methods, the HelioClim databases and the CAMS Radiation Service The Heliosat family is a series of methods whose aim is to convert images acquired by satellites into fields of SSI. The original method was published by  Cano et al. (1986). It assumes that the appearance of a cloud over a pixel results in an increase of radiance in visible imagery. It examines the change between the radiance that should be observed in cloud-free conditions and that currently observed; this change is quantified by a cloud index. The extinction of the downwelling radiation by the atmosphere is related to this cloud index. Of interest here, is the follow-up Heliosat-2  (Rigollier et al. , 2004)  which yields more accurate results and is easier to implement. Espinar et al. (2009)  analysed the influences of uncertainties of inputs on the outcomes of Heliosat-2.
The Meteosat series of geostationary meteorological satellites have been initiated by the European Space Agency and are currently operated by Eumetsat, a European agency comprising the national weather offices. They provide synoptic views of Europe, Africa and Atlantic Ocean. Images are acquired in several spectral channels from which the presence of clouds and their effects on radiation may be deduced. Within the HelioClim project  (Blanc et al. , 2011) , images of the first generation of the Meteosat series were processed with the Heliosat-2 method to create and update the HelioClim-1 database containing daily SSI  (Lefèvre et al. , 2007). The HelioClim-1 database has been promoted to Data Collection of Open Resources for Everyone (Data-CORE) by the GEOSS (Global Earth Observation System of Systems)  (Lefèvre et al.
, 2014). The GEOSS Data-CORE is a distributed pool of documented data sets with full, open and unrestricted access. Heliosat-2 was further adapted to processing of images from the second generation of the Meteosat satellites  (Cros et al. , 2006). Since 2004, Meteosat images are acquired every 15 min and routinely processed to update the HelioClim-3 database containing 15 min SSI, abbreviated hereafter in HC3. Access to the HelioClim databases is given by the SoDa Service (www. soda-pro. com) (Gschwind et al. , 2006). A clear-sky model is a model that predicts the SSI that should be observed at a given site any time in cloud-free conditions. The Heliosat-2 method combines the cloud index with the ESRA clear sky model (European Solar Radiation Atlas,  Page et al. , 2001). This model is detailed in  Rigollier et al.
(2000)  with corrections in  Geiger et al. (2002). Main inputs are the reflectance of the ground that originates from the Meteosat images themselves  (Moussu et al. , 1989; Rigollier et al. , 2004 ) and the Linke turbidity factor which accounts for the optical state of the cloudless atmosphere  (Remund et al. , 2003). Post-processing layers are applied when a request for a time-series of SSI is launched to bring improvements and corrections to the original HC3 database. For example, the SSI stored in HC3 is modulated to account for the actual elevation of the required location or the shadowing effect of the horizon. In order to avoid several re-processings of the whole set of images dating back to 2004, improvements in HC3 are made by changes in the post-processing layers, leaving the original database unchanged.
HC3v4 is the most advanced version of HC3 using the ESRA model. Building on the approximation of Oumbe et al. (  2014 ) for decoupling the effects of the clouds from those of the cloudless atmosphere,  Qu et al. (2014)  have proposed to combine HC3v4 and the recent McClear clear sky model  (Lefèvre et al. , 2013)  in an attempt to overcome the fact that the Remund's database of the Linke turbidity factor is not updated and does not take into account temporal changes in the atmosphere turbidity. McClear takes as input properties of the cloud-free atmosphere updated every 3 h supplied by CAMS and provides estimates of the SSI that should be observed in cloud-free conditions for any site in the world since 2004. McClear has been validated against measurements performed in the BSRN all over the world  (Lefèvre et al.
65) are large: the atmosphere is very often clear and not turbid. In order to assess the ability of a database to depict spatial variability, the correlation coefficient between time-series of measurements was computed for each pair of stations for G (Table  5 ). The same procedure is applied to each database. Table  5  gives the correlation matrix of G between stations. Upper right part of the matrix (in bold) is for measurements, and lower left part is for databases. For a given database, the closer the coefficients of the lower part to those of the upper part, the more accurately this database depicts the variability in space. This was performed only for stations covering the same period of time and for coincident measurements: Sunainah and Adam Airport for the period 2010-2015 on the one hand, and Sur and Muscat Airport for 2004-2007 on the other hand.
Discussion The correlation coefficient in G is large for all stations and all databases: it usually exceeds 0. 97, meaning that the variability in time of the measurements expressed as a variance is well reproduced by each database. As a whole, HC3v5 exhibits the greatest correlation coefficients, then HC3v4, then CAMS-Rad. These large correlation coefficients are partly due to the fact that the daily course of the sun and seasonal effects are well reproduced by models leading to a de facto correlation between observations and estimates of SSI hiding potential weaknesses of models. K T is a strict indicator of the ability of the model to estimate the optical state of the atmosphere as its dependency with the solar zenith angle is less pronounced than the SSI. Hence, the correlation coefficient for K T is less than for G: it ranges between 0.
73 and 0. 86. It can be concluded that each database reproduces fairly well the variation from hour-to-hour of the measured clearness index. The hierarchy in the databases is different: CAMS-Rad performs best, then HC3v5, then HC3v4. The bias for G is often small and less than 5 % in absolute value (Tables  1  and 2 ), and 7 % in K T (Tables  3  and 4 ). It is negative in all cases but one, meaning that all databases underestimate G. Though the periods of measurements are not the same, there is sufficient overlap to conclude that there is no major change in bias from one station to another for a given database. Madinat Zayed is an exception for HC3v4 and HC3v5; the bias is very large -59 Wh m -2 (-11 %) and -50 Wh m -2 (-9 %). On the contrary, the bias in Madinat Zayed for CAMS-Rad is similar to that of the other stations.
Accordingly, the cause of the large bias in HC3v4 and HC3v5 is likely related to the specifics of the method Heliosat-2. It is known that the uncertainty on the ground albedo may have a great impact on the SSI in Heliosat-2  (Espinar et al. , 2009; Lefèvre et al. , 2007). As a whole, the surroundings of the stations in Oman are darker than the highly reflective sand dunes surrounding Madinat Zayed site. This may partly explain the special case of Madinat Zayed as an error here has a greater influence than a similar error in the other stations. In addition,  Oumbe et al. (2013)  showed an overestimation of the aerosol optical depth by CAMS which yields an under- estimation in G. This may partly explain the underestimation in CAMS-Rad. Except the case of Madinat Zayed, one may observe that for a given station, the bias varies only slightly from one database to another whether for G or for K T.
This holds also for the RMSE, with the exception of Madinat Zayed; the RMSE is the same for a given station whatever the database. Conversely, for a given database, the RMSE varies only slightly from one station to another. The RMSE ranges between 70 and 90 Wh m -2 and is 11 to 16 % of the mean of the measurements, which is very good for hourly SSI estimated from satellite imagery. Table  6  presents the relative bias, the relative RMSE and the correlation coefficient reported by  Eissa et al. (2015a)  for several stations in Egypt and for the period  2004 -2009 , and Thomas et al. (2016b) )  for the BSRN site of Sede Boqer in Israel for the period 2004-2012. For a given database, one may observe a tendency to underestimation in Oman-UAE compared to Egypt and Sede Boqer: there is a tendency for increasing absolute value of bias.
956 and 0. 945 (Table  5 ). The correlation coefficients of the three databases are close: approximately 0. 96. Hence, one may conclude that the spatial variability of the SSI within each pair of stations is well reproduced by each database. One may note a slight overestimation of the spatial correlation by each database. This can be partly explained by the coarse spatial and temporal resolutions of several inputs to Heliosat-2 and Heliosat-4 methods. CAMS products on aerosols and total content in water vapour and ozone are available every 3 h. Their spatial resolution is approximately 120 km along a longitude before 2014 and 80 km after 2014. As for the Linke turbidity factor input to the ESRA model, there is one value per calendar month and its variability in space is fairly smooth. This spatially smooth variability of the inputs increases the spatial correlation.
Conclusions and perspectives This paper presents a comparison of three satellite-derived radiation databases against the measurements of 5 stations in Oman and Abu Dhabi. A great deal of attention has been paid to the quality of the measurements prior to the comparison. It was found that the three databases reproduce the hourto-hour changes in SSI very well with correlation coefficients greater than 0. 97 for all stations. Each database exhibits a tendency to underestimate the SSI in this area. With the exception of Madinat Zayed (Abu Dhabi) for the HC3 databases, the bias is small and less than 5 % in absolute value. There is no major change in bias from one station to another for a given database. Conversely, and except Madinat Zayed, it is found that for a given station, the bias varies only slightly from one database to another whether for the SSI or the clearness index.