dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was supported by the  "Institut National de la Recherche Agronomique" , the  "Centre National de la Recherche Scientifique" , the  University of Nice-Sophia Antipolis  and the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference # ANR-11-LABX-0028-01. Funding: This work was supported by the  "Institut National de la Recherche Agronomique" , the  "Centre National de la Recherche Scientifique" , the  University of Nice-Sophia Antipolis  and the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference # ANR-11-LABX-0028-01.
Acknowledgements We thank  Ulrich Wagner ,  Aleksandar Zaklan  and seminar participants at ZEW for helpful comments, in particular  Robert Germeshausen ,  Kathrine von Graevenitz ,  Peter Heindl ,  Benjamin Lutz  and  Philipp Massier , as well as three anonymous reviewers and the editor. Financial support by  ZEW  within the project ' Employment Effects of Environmental Regulation ' is gratefully acknowledged. All errors remain the authors' responsibility.
In all conditions, 13,000-72,000 reads per sample were analyzed. The sequencing and the processing of NGS data was done by the GeT-Biopuces platform (INSA Toulouse) using the Ion Torrent Suite software analysis. The percentage of mutagenic event was calculated at position 145 (3nt upstream of the gUMPS1 PAM) and at +/-5 bases surrounding this position. The frequency of total INDELs (including 1 base INDEL) was calculated, with a background noise level lower than 0. 4%. The frequency of INDELs larger than 1 base was also analyzed and in this condition the background noise level was extremely low <0. 06%. Cell growth experiments. For the growth experiments reported in Fig. 1e-g , cells were cultivated in F/2 medium for five days and then diluted to 1. 10 5 cells mL -1 in F/2 medium, F/2 medium supplemented with 50 µg mL -1 uracil, or F/2 medium supplemented with 50 µg mL -1 uracil and 100 µg mL -1 5-FOA.
Acknowledgements This work was funded by a  Gordon and Betty Moore Foundation  grant ( GBMF 4966 ), a  Région Midi-Pyrénées  grant ( 15058490  financial support for  Accueil d'Equipes d'Excellence) , an  ANR   JCJC grant  ( ANR-16-CE05-0006-01 ), and the 3BCAR Carnot Institute funding. We thank  Dr. Fabien Nogué  for stimulating scientific discussions,  Dr. Peter G. Kroth  for providing the PtAureo1a anti-serum,  Dr. Jean Pierre Bouly  and  Dr. Angela Faciatore  for discussion on NPQ analyses.
As mentioned before, the data used is from previous studies using ODYSSEY  [14, 15, 19]  and from project partners' knowledge. The technical hypothesis for each of the element to be simulated is presented in Table  3. The data used in this table has been collected from recent studies developed by Ely4off partners, which includes hydrogen injection to the gas grid (to be published in 2019) and benchmarking of energy storage technologies  [20]. Solar profiles are obtained from public databases developed by NREL  [21]. 1 SOC: State of Charge of the batteries. 2 Only needed when more than 20 bar are required in the hydrogen storage.
Acknowledgments This work was partially supported by the  Russian Foundation for Basic Research  and the  Ministry of Education, Science and Youth of the Republic of Crimea , in the framework of scientific project grant no. 15 42 01008  "р юг а " and by the  V. I. Vernadsky Crimean Federal Univer sity Development Program  for 2015 2024 including the participation of one of the authors (K. Seleznyova) in the European School on Magnetism 2017 (Corsica, France).
B. -C. and G. C. ; Investigation, G. C. , C. K. , F. P. , D. I. , C. B. -A. , S. M. , C. L. , D. M, B. P. ; Resources, O. E. ; Writing-Original Draft Preparation, G. C. ; Writing-Review & Editing, S. B. -C. ; Supervision, S. B. -C. ; Project Administration, S. B. -C. ; Funding Acquisition, S. B. -C. Funding: This research was funded by The Region Alsace, France, and the Labex Chimie des Systemes Complexes, University of Strasbourg, France (doctoral fellowship to Geoffrey Cotin). This research project was also co-funded by Labex CSC, Alsace contre le cancer, INCA (project PRTK14, THERAMAG 2014-225) and the INTERREG project NANOTRANSMED.
Funding: This research was funded by The  Region Alsace, France , and the  Labex Chimie des Systemes Complexes, University of Strasbourg, France (doctoral fellowship to Geoffrey Cotin). This research project was also co-funded by  Labex CSC, Alsace contre le cancer ,  INCA  (project  PRTK14 ,  THERAMAG 2014-225 ) and the  INTERREG  project  NANOTRANSMED.
Temperature and labour productivity The literature distinguishes three mechanisms through which heat affects the total productivity of the labour force  (Heal & Park, 2016 ): (i) labour supply, that is, the total hours that individuals choose to work, (ii) labour effort, that is, the amount of effort workers choose to expend while at work, and (iii) labour productivity, that is, the degree to which workers' effectiveness is degraded while at work. This paper is concerned with the last of these effects. We start with a brief synopsis of the relevant literature. The literature review was carried out using keyword searches in Publish and Perish, a software tool that retrieves and analyses citations of both grey and peer-reviewed publications from Google Scholar and Microsoft Academic Search. We used the Publish or Perish software, which enables rapid and broad reviews of existing literature, in tandem with author-identified literature to ensure that the targeted literature synopsis did not omit any key references academic or grey literature.
Acknowledgements This research was supported by the  UK Department for International Development  under its  Research for Development (R4D) programme. We are grateful to  Jonathan Beynon ,  Siddhartha Haria ,  Jisung Park ,  Stephanie Trinci ,  Rosalind West  and three anonymous referees. At the time of writing the paper Costa was supported by the  European Community 's  7th Framework Program  under Grant Agreement No. 308497  (RAMSES). Fankhauser acknowledges funding from the  Grantham Foundation for the Protection of the Environment  and the  UK Economic and Social Research Council (ESRC)  through the  Centre for Climate Change Economics and Policy. The views expressed do not necessarily reflect the UK government's official policies.
Funding This research was supported by the  UK Department for International Development  under its  Research for Development (R4D) programme. We are grateful to  Jonathan Beynon ,  Siddhartha Haria ,  Jisung Park ,  Stephanie Trinci ,  Rosalind West  and three anonymous referees. At the time of writing the paper Costa was supported by the  European Community 's  7th Framework Program  under Grant Agreement No. 308497  (RAMSES). Fankhauser acknowledges funding from the  Grantham Foundation for the Protection of the Environment  and the  UK Economic and Social Research Council (ESRC)  [grant number  ES/K006576/1 ] through the  Centre for Climate Change Economics and Policy. The views expressed do not necessarily reflect the UK government's official policies.
4% of GVA for London and 9. 5% for Bilbao. Some sectors are more exposed to economic lossesfor example, financial services, public administration and retail trade are most exposed in London Study Context Dunne, Stouffer, and John (2013)reduced labour productivity for 140 'regions' in the Global Trade Analysis Project database, for a 3°C temperature increase A 3°C temperature increase is projected to lead to costs of up to 8% of GDP in some countries due to impacts on labour productivity, with West African projected to be the worst affectedDunne, Stouffer, and John (2013)Assesses loss of labour productivity during the hottest months in each part of the world over the period 1975-2200 Reductions in work capacity, during the hottest months already occur at the global level, on the order of 6% to 10%.
, 2014; Wagner et al. , 2012). Several databases have been constructed from images acquired by the Meteosat series of satellites. Three of these are dealt with hereafter: the HelioClim-3 databases (version 4 and version 5) and the CAMS Radiation Service (version 2), where CAMS stands for Copernicus Atmosphere Monitoring Service. Each database is available online and has more than several hundred users each year  (Thomas et al. , 2016a). Validations of each database are routinely performed and their results are published, thus supplying users with up to date knowledge of each database. For example, validation is performed every quarter that compares ground-based measurements made at several stations in Europe, Africa and South America to the SSI estimated by the CAMS Radiation Service. A report is published and made available on-line at https://atmosphere.
copernicus. eu/validation-supplementary-products (last access: 17 April 2018). Joint validations of the three databases have also been performed for various climates in Europe  (Thomas et al. , 2016b)  and for more specific geographical regions, such as Brazil  (Thomas et al. , 2016a) , Egypt  (Eissa et al. , 2015a) , Oman  (Marchand et al. , 2017)  and the tropical Atlantic Ocean  (Trolliet et al. , 2017). Pursuing this effort is important to users of the databases and the present work is a contribution to this on-going activity. Like the previous ones dealing with specific geographical regions, this work addresses the issue of the spatial consistency of errors. In other words, it addresses the question of how much the accuracy found in the retrieval of hourly global irradiation for a given database varies within a region of similar climate.
A large variability decreases the confidence a user may have in using this database for an entire region. The aim of this study is to validate and discuss the errors of the hourly SSI extracted from the three databases; HC3v4, HC3v5 and CAMS Radiation Service against reference data collected from five stations in northern and central parts in Morocco. The climate at the five stations is Csa in the Köppen-Geiger climate classification, i. e. a temperate climate with dry and hot summers  (Peel et al. , 2007).
The Meteosat satellites, the HelioClim databases and the CAMS Radiation Service The European Space Agency initiated the Meteosat series of geostationary satellites to provide services to the meteorological community by imaging clouds and other phenomena over Europe, Africa and Atlantic Ocean in a synoptic manner. The Meteosat programme is currently operated by EU-METSAT. The Earth observing instrument aboard the first generation of Meteosat satellites comprised three channels, while the instrument aboard the second generation offered 12 channels. The greater the number of channels, the more numerous and the more accurate the cloud properties that can be deduced from the images. A series of methods, called Heliosat, has developed since the 1980s in order to convert images acquired by satellites into fields of SSI  (Cano et al.
The European Solar Radiation Atlas, abbreviated as ESRA  (Greif et al. , 2000; Page et al. , 2001) , proposes such a model, called the ESRA clear-sky model, which is embedded within Heliosat-2  (Rigollier et al. , 2000 (Rigollier et al. , , 2004)). Heliosat-2 was first applied to images of the first generation of the Meteosat series to create and update the HelioClim-1 database of daily SSI  (Lefèvre et al. , 2007 (Lefèvre et al. , , 2014) )  within the HelioClim project of MINES ParisTech  (Blanc et al. , 2011). Cros et al. (2006)  proposed a procedure for allowing the application of Heliosat-2 to the im-ages from the second generation of Meteosat satellites. Since 2004, Meteosat images are acquired every 15 min and routinely processed to update the HelioClim-3 database of 15 min SSI, abbreviated hereafter as HC3.
In order to correct some drawbacks observed since its inception and to bring improvements and corrections to the original HC3 database, post-processing algorithms are applied on-the-fly when a request for a time-series of SSI is made. For example, the SSI stored in HC3 is modulated to account for the elevation of the required location and the shadowing effect of the horizon. This approach avoids several re-processing phases of the whole set of images dating back to 2004. HC3 version 4, abbreviated as HC3v4 is currently the most advanced version of HC3 that makes use of the ESRA clear-sky model. One drawback in HC3v4 is the use of a fixed climatology of the atmosphere turbidity as input to the ESRA clear-sky model. To overcome it,  Qu et al. (2014)  have proposed an improvement to HC3v4, called the HC3 version 5 and abbreviated as HC3v5.
, 2017)  and provides the cloud properties as input to Heliosat-4. The other inputs are the solar angles computed using the SG2 algorithm  (Blanc and Wald, 2012)  and the data set of quantities describing the bidirectional reflectance of the ground of  Blanc et al. (2014). The CAMS Radiation Service, abbreviated as CAMS-Rad, exploits the Heliosat-4 method. The three databases provide time series of 15 min SSI, from 2004 onwards, for any place located within the field of view of the Meteosat satellites. They can be accessed online on the SoDa web site (http://www. soda-pro. com, last access: 17 April 2018)  (Gschwind et al. , 2006).
Validation against in-situ measurements HC3v4, HC3v5 and CAMS-Rad time series of hourly irradiation G and B N were obtained from the SoDa web site at the five stations. These time-series also contain the irradiation at the top of atmosphere on a horizontal surface, E 0. From G and E 0 , one may compute the clearness index KT (= G/E 0 ). The validation against in-situ measurements was performed for G, B N , and KT. The validation followed the ISO (1995) (International Organization for Standardization) standard wherein the differences are obtained by subtracting measurements from the database estimates. The sets of differences are summarized by the bias (mean of the differences), the standard deviation of the differences and the root mean square error (RMSE). These quantities are computed relative to the mean of the measurements at a given site.
Correlation coefficients are also computed. 2-D histograms between measurements and estimates, also known as scatter density plots, are drawn. As an example, the 2-D histograms for G are presented for Erfoud (Figs. 2 3 4 ). The number of samples in each bin is coded in colour, increasing from dark blue (1 sample) to dark red (a few tens). The number of samples, the mean of the measurements, the bias, the standard deviation, the RMSE, and the correlation coefficient are also given. In this example, the points are fairly aligned along the 1 : 1 line for the three databases. The other sites exhibit similar trends. The correlation coefficients are large in the three cases; the changes in G are well represented by the three databases. The scattering is limited in the three graphs. The relative standard deviation is around 12-13 % of the mean of the measurements.
Results and discussion Table  2  reports the number of samples used for validation, the mean of the ground-based measurements, the bias and RMSE and their relative values, and the correlation coefficient for each database and each station for G. Table  3  reports the same quantities but for B N and Table  4  deals with the clearness index. The correlation coefficient for G often exceeds 0. 98 at all stations and all databases (Table  2 ). As the squared correlation coefficient, also termed the coefficient of determination, is related to the variance of the measurements, this confirms that the variability of the measurements in time is well reproduced by each database for Morocco. As a whole, HC3v4 and HC3v5 exhibit similar correlation coefficients, which are slightly greater than those for CAMS-Rad.
e. an amplitude of 50 Wh m -2. Actually, Tan-Tan exhibits a large positive bias for CAMS-Rad (31 Wh m -2 ). If this site is removed, the bias has a more limited range, from -19 to 7 Wh m -2 , and the corresponding amplitude (26 Wh m -2 ) is similar to that for HC3v4. The RMSE is similar for HC3v4 and HC3v5 and ranges between 53 and 72 Wh m -2 , and 55 and 71 Wh m -2 respectively. The RMSE is greater for CAMS-Rad and ranges from 59 to 77 Wh m -2 , with an exception of 97 Wh m -2 at Tan-Tan. The correlation coefficient for B N depends on the database (Table  3 ). The greatest correlation coefficients are attained for CAMS-Rad and range from 0. 78 to 0. 87. Those for HC3v5 are slightly less and are less variable from site to site; they range from 0. 79 to 0. 84. The correlation coefficients range from 0.
70 to 0. 79 for HC3v4. It can be concluded that each database reproduces fairly well the hourly variation in B N in Morocco. The bias in B N is negative for all databases except at Tan-Tan where it is positive. Biases are fairly similar for HC3v4 and CAMS Radiation Service; they range respectively between -102 and -36 Wh m -2 , and -113 and -29 Wh m -2 (108 and 39 Wh m -2 at Tan-Tan). The bias for HC3v5 is less than the other databases in absolute value. It ranges from -45 to -9 Wh m -2 (116 Wh m -2 at Tan-Tan). Similar observations are made for the RMSE of B N. The smallest RMSEs are observed for HC3v5; they range between 153 and 177 Wh m -2 (209 Wh m -2 at Tan-Tan because of the large bias). The relative standard deviation is 25-30 %. The RMSE for HC3v4 ranges from 170 to www. adv-sci-res.
This drawback contributes to an increased bias in absolute value and standard deviation, hence an increased RMSE. KT is less dependent than G on the solar zenithal angle and is a stricter indicator of the ability of a database in assessing the optical state of the atmosphere. The correlation coefficient for KT is always less than that of G. In Table  4 , HC3v4 and HC3v5 offer the same correlation coefficients for KT: they often exceed 0. 70 and are up to 0. 90 for Tan-Tan (0. 98 for G). The correlation coefficient is less variable for CAMS-Rad and ranges from 0. 71 to 0. 79. It can be concluded that each database reproduces the hourly variation in KT quite well. As for KT (Table  4 ), the bias ranges between -0. 06 and -0. 03 for HC3v4 and HC3v5 (0. 00 and -0. 01 at Tan-Tan) and -0. 04 and -0.
01 for CAMS-Rad (0. 03 at Tan-Tan). The RMSE is approximately 0. 12 for the three databases, except at Tan-Tan for HC3v4 and HC3v5: 0. 08 and 0. 09 respectively. 2-D histograms for KT (not shown) show that the reference KT is often greater than 0. 7, denoting frequent cloud-free skies. There is a slight underestimation of KT by HC3v4. Tan-Tan is an exception with points aligned on the 1 : 1 line and a slight underestimation for the greatest clearness indices. HC3v5 exhibits also a slight but systematic underestimation of KT but limited to KT less than 0. 7. For KT > 0. 7, the errors are small and it is believed that this is due to the use of the McClear model with CAMS estimates of atmospheric constituents as inputs. Several studies have shown the accuracy of this ensemble McClear / CAMS in similar climates  (Eissa et al.
, 2015a, b; Lefèvre et al. , 2013; Lefèvre and Wald, 2016; Thomas et al. , 2016b). The station at Erfoud is an exception where the McClear scheme overestimates. Tan-Tan is slightly different from the others: the points are well aligned on the 1:1 line, and a slight overestimation may be observed for the greatest clearness indices. The situation is more contrasted for CAMS-Rad. As a whole, an underestimation is observed when KT is less than 0. 7 and the greatest clearness indices are well reproduced. Similar to HC3v5, McClear overestimates the greatest clearness indices at Erfoud. The greatest clearness indices are well estimated at Tan-Tan. However, there is a large scattering when KT is less than 0. 7 and actual cloudy conditions are often reported as cloud-free by CAMS-Rad. It has been noted that Tan-Tan is often an exception for all databases.
This may relate to its particular location close to the seashore which experiences moisture-laden breezes from the ocean and where many small clouds form which may not be detectable individually in the Meteosat images. The exact performance at this site depends on each database, though HC3v4 and HC3v5 perform fairly similarly. The bias and the RMSE for G and KT are less for HC3v4 and HC3v5 than for CAMS-Rad. On the contrary, for B N , CAMS-Rad exhibits smaller bias and RMSE than HC3v4 and HC3v5.
Conclusions and perspectives This paper reports on a comparison of three satellite-derived radiation databases against the measurements of 5 stations in Morocco. 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 of around 0. 97-0. 98 for the global radiation G at all stations. For the direct irradiation B N , the correlation coefficients are around 0. 70-0. 79 for HC3v4, 0. 79-0. 84 for HC3v5 and 0. 78-0. 87 for CAMS-Rad. HC3v4 and HC3v5 have a tendency to underestimate G in Morocco. The bias relative to the average of the measurements is small and ranges between -1 and -6 % for HC3v4 and 0 and -4 % for HC3v5. It ranges between -4 and 7 % for CAMS-Rad.
The RMSE ranges between 53 and 72 Wh m -2 (11 and 13 %) for HC3v4 and HC3v5, and between 59 and 97 Wh m -2 (11 and 21 %) for CAMS-Rad. As for B N , HC3v5 offers the smallest RMSE (153 to 177 Wh m -2 ), then HC3v4 (170 to 210 Wh m -2 ), and finally CAMS-Rad (159 to 244 Wh m -2 ). It has been observed that the performance of the databases varies from site to site and is not constant. Constant performances are desirable when one wants to compare the solar irradiation between two or more places. HC3v5 does not exhibit noticeable geographical changes in performances; it captures the temporal and spatial variability of the irradiation field well. The performance is worse for HC3v4 and CAMS-Rad which exhibit more geographical variability. One may observe that CAMS-Rad is not providing much better performances than HC3v4 or HC3v5.
The contrary could have been expected as the attenuation of the SSI due to clouds in the HC3v4 and HC3v5 databases is based on the empirical concept of cloud index in the Heliosat-2 method whose design dates back to the end of the 1970s, while in CAMS-Rad it is based on the more recent and more sophisticated APOLLO processing of multispectral images from Meteosat in the Heliosat-4 method. This demonstrates that simplicity does not exclude accuracy. One may recall that Heliosat-2 has been the subject of many investigations and that successive re-processing steps of HC3 aim at correcting errors and refining estimates towards a better accuracy. This is not yet the case of Heliosat-4 which is a recent method that needs to be fully understood for its qualities and drawbacks. Assuming that the in situ measurements achieve the "moderate quality" pyranometer measurements defined by  WMO (2008 WMO ( , rev.
2012) )  for hourly global irradiation, one may ask if the estimates from the three databases are compliant with "moderate quality". Defined as the 95 % probability (P 95 ), the relative uncertainty for "moderate quality" should not exceed 20 %. The total uncertainty takes into account the uncertainty of observations and the uncertainty of the estimates. It can be expressed in a first approximation as the quadratic sum of both uncertainties. As a consequence, the total relative uncertainty should not exceed 28 % (P 95 ), or 14 % (P 66 ) if the estimates were of "moderate" quality. The relative RMSE is less than 14 % for HC3v4 and HC3v5 and for CAMS-Rad with the exception of Tan-Tan. It can be concluded that to a first approximation, the quality of the estimates meets the "moderate quality" for all databases.
Data availability. -HC3v4 and HC3v5 data may be requested by e-mail to Mathilde Marchand (mathilde. marchand@transvalor. com); -CAMS Radiation service data are freely available to anyone from the web site SoDa after registration; in-situ measurements data may be requested by e-mail to Abdellatif Ghennioui (ghennioui@iresen. org).