dataesr/scientific-paragraphs-categorization huggingface dataset:
Species identification. Biological material from skinks of the Anguilla Bank allows morphological comparisons between St. Martin (Spondylurus martinae, known by nine old museum specimens), Tintamarre (this document), St. Barts (S. powelli, six specimens), and Anguilla (S. powelli, ten specimens)  (Hedges & Conn 2012). It also allows genetic comparisons between skinks of all these islands, with the exception of historical St. Martin speci- The DNA sequences of the three samples from Tintamarre are identical to those of Spondylurus powelli from Anguilla (the type locality), with only a single base in cytochrome b showing variation in the three genes (~3000 sites). The sequences of S. powelli from St. Barts that were obtained from GenBank (collected by other authors) are less complete across the region compared, and they include ambiguous sites.
Acknowledgements The field work was conducted under the scope of a project to study the conservation of skinks of the French Antilles, hosted since 2012 by the " Association pour l'Étude et la protection des Vertébrés et végétaux des petites Antilles " (AEVA). We thank the  "Direction de l'Environnement, de l'Aménagement et du Logement de la Guadeloupe" (DEAL) , especially  Luc Legendre , for funding, particularly the November 2014 survey trip, authorization for three of us (OL, NB, and SBH) to collect biological material of a protected species in the territory of  St. Martin , and authorization to transport that material to the  USA. We thank the conservators of the RNNSM,  Romain Renoux  and  Nicolas Maslach , for co-funding with DEAL the March 2013 survey trip, and authorization for working and sampling in the reserve.
Cette distinction en quatre groupes d'acteurs-usagers s'établit selon les classifications couramment retrouvées dans les bases de données statistiques (classifications INSEE, par exemple) et sur la base des résultats issus des groupes de discussion. Bien que très artificielle, cette classification permet tout de même d'aborder la question de la responsabilité perçue par quatre questions rapides dans le questionnaire. Ensuite, concernant plus précisément les ménages, les activités pratiquées au sein d'une habitation perçues comme source potentielle de micropolluants sont mesurées. Enfin, lesLes micropolluants d'origine domestique dans l'eau : enquête sur la représent. pertinence et la capacité de réalisation de différentes solutions pour réduire le flux de micropolluants. 3. Enfin, les répondants sont interrogés sur le rôle de l'Eurométropole de Strasbourg dans la sensibilisation aux micropolluants et sur leur propre capacité d'action pour réduire la part des micropolluants d'origine domestique.
Although large-scale atmospheric phenomena or modes of climatic variability are not studied in this paper, our results should be valuable for further studies addressing this topic, in the Sahel as well as in other regions. The methodology used to build our heat index and to study meteorological heat waves over the Sahel in spring is explained in section 2. Section 3 presents the climatological characteristics of these heat waves, including their frequency, duration, intensity, and spatial extent. Benefits and limits of the present heat wave definition are further discussed in section 4, via a comparison with other definitions. An assessment of the sensitivity of the results across different temperature datasets (including three meteorological reanalyses) is also presented. Finally, section 5 emphasizes the evolution of heat wave properties over the recent historical period and compares it to regional warming.
a. Datasets We use daily minimum and maximum temperature datasets provided by one observationally based gridded product and retrieved from three meteorological reanalyses over 08-408N, 208W-408E, hereinafter referred to as the African domain. These datasets are the following: The use of several datasets is motivated by the fact that data are sparse in the Sahel. The Berkeley Earth website provides historical values of the number of stations used in their dataset at regional, national, and local scales. For instance, in Mali (http://berkeleyearth. lbl. gov/regions/ mali), around 10 stations from inside the country and 100 stations from neighborhood regions are used. The numbers of stations used are not constant in time, with fewer stations prior to 1950. Thus the influences of distinct physical parameterizations and assimilation procedures in the three reanalyses, as well as the interpolation method used in BEST, are all likely to induce differences in surface temperatures.
The main differences are documented in the appendix and briefly summarized below. BEST and the three reanalyses were compared over 1979-2010 to two surface synoptic observation (SYNOP) stations with consistent long-time temperature series: Hombori, Mali (15. 338N, 1. 88E)  (Guichard et al. 2015 ; see Fig. A1  in the appendix), and Niamey, Niger (13. 488N, 2. 178W)  (Leauthaud et al. 2017 ; not shown). Observations from both stations are included in BEST, but the time series of Hombori used by BEST has many gaps, especially over the recent years. The dataset we used was enhanced and quality checked by Mougin and collaborators from the Mali Meteorological Agency from paper archives  (Guichard et al. 2015). In addition, BEST, ERA-Interim, NCEP-2, and MERRA were compared over 108-208N, 108W-208E, hereinafter referred to as the Sahelian domain (Fig.
A2 ). Numerous differences are found between datasets; their magnitudes fluctuate from month to month. As may be expected from its construction, BEST is the closest to these two SYNOP stations in terms of monthly and seasonal mean. BEST is constructed by kriging (interpolating) in situ data, notably including SYNOP stations. Away from the observations, BEST might be less accurate. On average, in NCEP-2 Tmax and Tmin are both too low (e. g. , by more than 38C in April for Tmin), whereas Tmax is too high in MERRA and too low in ERA-Interim (Figs. A1a  and A2a ). In NCEP-2, the seasonal cycle is shifted by about one month (Figs. A1b  and A2b ) and the Tmin variance is largely overestimated (Figs. A1c  and A2c ). The Tmax variance is generally higher in the three reanalyses than in BEST, without any clear link between the grid size of the reanalysis and the magnitude of the variance (Figs.
A1c  and A2c ). The distributions are most of the time skewed to the left, especially for Tmax in April and May (Figs. A1d  and A2d ): this negative skewness highlights the predominance of time sequences of relatively similar warmer days, interrupted by less frequent but much cooler days. It also suggests that physical and dynamical mechanisms constrain Tmax upper extrema. Following these comparisons, BEST is hereinafter set as the reference. Furthermore, its historical depth, going back to 1950, enables trend computation over longer time periods than 1979-2012. Differences between the various databases go further than a mean shift, and therefore one may expect differences between detected heat waves (see section 4b).
Sensitivity of heat wave characteristics to the definition and dataset a. Impact of the heat wave definition We assess the sensitivity of our heat wave definition by comparing our results (section 3) with those obtained with definitions that are often used in the literature (section 2c). The heat wave characteristics obtained with each definition are summarized in Fig. 4  and Table  1. The 15DW set tests the impact of taking a 15-day moving window to define a seasonally dependent temperature anomaly threshold, instead of a constant percentile over March-July. 15DW heat wave frequency of occurrence is now fairly constant throughout the season (Fig. 4a ): indeed, the use of a moving window for the determination of the 90th percentile causes a fixed number of days to be extreme for each calendar day.
Otherwise, other heat wave properties are not significantly modified, except for a small difference in the anomaly amplitudes (Figs. 4d, e ): the temperature anomalies are now higher in March-April and then lower in June-July. This is related to the stronger temperature variability in the early months, resulting in more heat waves found in these months with our own definition. The NoFilter_15DW is often used in the literature. As for 15DW, the frequency of occurrence of heat waves is almost constant throughout March-July. The NoFilter_15DW heat waves last longer, are overall larger, and their temperature anomalies are smaller, while their mean Tmax, Tmin, and Tmean are higher (Fig. 4 ). Finally, as for 15DW and the reference dataset, the duration of both HWmin and HWmax also decreases markedly from March to June.
The NoFilter heat wave set is mainly driven by the annual temperature cycle and most heat waves occur near its marked peak, in April-May for Tmax and in May-June for Tmin, with very few heat waves in March and July. Essentially, using filtered temperatures tends to increase the heat wave temperature anomalies while using raw temperatures tends to increase the heat wave temperatures (see 15DW compared to NoFilter_15DW and the reference data compared to NoFilter in Figs. 4d, e, g, h) ; this demonstrates the result that there is no direct correspondence between the strongest intraseasonal and synoptic temperature fluctuations and the highest temperatures, the latter being more controlled by longer-term variability. On average over the whole spring extended period, heat wave properties are not significantly sensitive to the chosen approach (see Table  1 ), except for temperature-related differences that are directly linked to the approach.
b. Sensitivity to the meteorological dataset For this study BEST is our reference dataset. In this section, the sensitivity of heat wave properties to the dataset is explored by applying the same methodology to three meteorological reanalyses, ERA-Interim, NCEP-2, and MERRA. As the meteorological reanalyses only cover 1979present, heat wave detection is restricted to their common period 1979-2012. BEST heat wave characteristics are very similar when detected over this shorter period (Fig. 5 ), with overall no significant difference at the 95% level for the mean morphological and temperature-related characteristics (except for a small increase of 0. 28C for HWmin Tmin anomalies). The four products have distinct temperature distributions and annual cycles (see the appendix), which induces differences between heat wave properties detected with one or the other dataset.
Their average properties are summarized in Table  1. For HWmax events, the frequency of occurrence is slightly lower in ERA-Interim compared to BEST, and much lower in NCEP-2 and MERRA, by 40% and 60% respectively (Fig. 5a ). In contrast, the frequency of occurrence of HWmin events is not significantly different between BEST, ERA-Interim, and MERRA. Nevertheless, all datasets capture the annual cycle of BEST heat wave frequency of occurrence documented above (i. e. , that the further into the spring season, the smaller the probability of heat wave occurrence and the shorter these heat waves; not shown). Only ERA-Interim has a different behavior for HWmax events, which are most frequent in July. This appears to be linked to monthly differences in the temperature anomaly variances: indeed, for BEST, NCEP-2, and MERRA they slowly decrease from March to July whereas the ERA-Interim variances increase from June on (see the appendix).
5d ). Tmax anomalies of HWmax are 0. 68C higher in ERA-Interim and 1. 58C higher in NCEP-2 and MERRA. By contrast, Tmin anomalies of HWmin are rather consistent across the datasets, except for NCEP-2, which strongly overestimates their intensity (on average 6. 28C compared to roughly 38C). This shift is in direct relation with the Tmin anomaly variance of NCEP-2, which reaches more than twice the variance of the other datasets (Figs. A1e  and A2e ). The analysis of the temperature distribution discussed in the appendix indicates that NCEP-2 and ERA-Interim have a cold bias in Tmax while MERRA has a warm bias. NCEP-2 has a very strong Tmin bias (up to 238C). As a result, heat wave temperatures mostly follow the dataset mean temperature biases (Figs. 5g-i ), except for HWmin Tmin in NCEP-2 for which the cold bias is compensated by an overly strong high-frequency variability of Tmin.
In summary, heat wave characteristics are quite sensitive to the meteorological product used to detect them. The main differences seem to arise from the nature of the dataset considered (model versus interpolated local observations) and from the differently biased temperature background. The former affects heat wave size and duration while the latter drives differences in heat wave temperatures. Both the synoptic and intraseasonal variability of reanalyzed temperatures, especially for NCEP-2, affect the occurrence of the heat waves. Finally, our results show that care should be taken when choosing a dataset for heat wave studies, especially over regions where in situ observations are scarce, because differences between some basic heat waves characteristics can be quite large. Overall, ERA-Interim appears to be the best suited meteorological reanalysis to study heat waves when compared with local and gridded observations over the Sahel.
2016; Déqué et al. 2017) , which is consistent with our results. The linear trends over 1979-2012 of the four datasets (section 4b) are displayed in Table  2. The smaller number of heat waves, in addition to the reduced time period, is likely to increase the odds of a nonsignificant value, and the results must be taken with care. The main result is that, when significant, the Tmax, Tmean, and Tmin show a positive trend. Conversely, for HWmin, there is a significant diminution of the area covered when using NCEP-2 and MERRA. The spread of climatological trends estimated with these reanalyses is likely to be at play in the heat wave trends: Tmax climatological trends range from 10. 38C decade 21 in ERA-Interim to 10. 558C decade 21 in MERRA and Tmin trends from 10. 38C decade 21 in MERRA to 10.
The final step of selecting a particular domain allows this heat index to be used over any region. This heat index was applied separately to daily maximum and minimum temperatures (Tmax and Tmin) of the observational-based BEST dataset. We found an average of 3. 3 ''Tmax heat waves'' (HWmax) and 2. 9 ''Tmin heat waves'' (HWmin) per year. Both types of heat waves last on average around 5 days, cover roughly 120 3 10 4 km 2 with a temperature anomaly of 138C. In May, the mean Tmax (Tmin) of HWmax (HWmin) reaches up to 428C (288C) or more. The HWmin areas are slightly smaller than the HWmax. Concomitant HWmax and HWmin events happened only one-fifth of the time. This result supports the decision to separately analyze maximum and minimum temperatures in the Sahelian region. Heat waves also become shorter and less frequent from March to July.
The results are quite similar when the percentile is computed over a 15-day moving window, except for differences over the seasonal occurrences that are directly linked to methodological choices. The constant percentile led to a steady temperature anomaly distribution throughout the season while the moving-window produced a steady seasonal occurrence distribution, leading to a wide range of heat wave intensities. Over a moving window without the filtering process, we found that heat waves were longer and larger. As expected, heat waves are also becoming more frequent by one per decade when raw instead of filtered temperature are used. The heat index was also computed similarly with temperatures from three meteorological reanalyses, namely ERA-Interim, NCEP-2, and MERRA, over 1979-2012. The heat waves were smaller than for BEST; this could be related to the different nature of the datasets (observationally based versus model); the heat wave sizes in reanalyses are not smaller for the finer grid though.
Average heat wave properties obtained with our heat wave index using the BEST dataset over 1950-2012 (the reference data, first row), with other heat wave indices, and with our heat wave index using BEST, ERA-Interim,NCEP-2, and MERRA over 1979-2012. The first and second values in each cell correspond respectively to HWmax and HWmin properties. Boldface values indicate that they are significantly different (Student's t test at the 95% level) from those obtained in the reference; boldface and italic values indicate that they are significantly different from BEST over 1979-2012 and are used when comparing to various reanalyses (ERA-Interim, NCEP-2, and MERRA).
28 10. 38 15DW HWmax NS NS NS NS 10. 13 NS 10. 27 10. 35 HWmin NS NS NS NS NS NS 10. 25 10. 28 NoFilter_15DW HWmax 10. 92 10. 42 11. 37 NS NS 20. 25 NS 10. 35 HWmin 11. 12 NS NS NS 20. 16 NS 10. 30 10. 31 NoFilter HWmax 10. 90 10. 42 NS NS NS 20. 28 NS 10. 33 HWmin 11. 16 NS 12. 49 NS 20. 15 10. 15 NS NS Impact of the meteorological dataset on the detection BEST 1979-2012 HWmax NS NS NS NS NS NS 10. 40 NS HWmin NS NS NS NS NS NS 10. 55 NS ERA-Interim HWmax 20. 59 NS NS 20. 12 NS NS NS NS HWmin NS NS NS NS NS NS NS NS NCEP-2 HWmax NS 20. 89 NS NS NS NS 10. 66 NS HWmin NS NS 22. 13 NS NS NS NS 10. 78 MERRA HWmax NS NS NS NS 20. 49 NS 10. 90 NS HWmin NS NS 23. 39 NS 20. 13 10. 26 10. 86 10. 60 increase.
Isomap is the most straight way to use the geodesic distance for nonlinear projection. The goal of the Isomap  (Tenenbaum et al. , 2000)  is to preserve the geodesics rather than the Euclidian distances. It has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications  (Gámez et al. , 2004). To apply NLPCA, an initial dimensionality reduction step is required to reduce the dimensionality of the inputs so that a neural network of reasonable size can be used. Unlike some other nonlinear dimensionality reduction methods, NLPCA cannot be used directly with observed or modelled SST data sets. This contrasts with methods such as Isomap , where gridded data sets can be handled without preprocessing. The key factor that distinguishes Isomap from NLPCA is that Isomap uses a distance function that approximates geodesic distances in the data, while the latter employs Euclidean distances in the data space.
2012; Steinmann et al. 2013). Because some such measurements are routinely sampled at Opme and Lannemezan, their concentrations will be studied to develop a jet stream climatology. In this paper, after describing the instruments, datasets, and methodology, we present a case study of jet streams and stratosphere-troposphere exchange observed above the two sites. Climatology and decadal trends of wind and jet stream frequency over the two measurement sites are then presented, analyzed, and compared with stratosphere-troposphere exchanges from radionuclide measurements.
c. ERA-Interim, trajectories, and reverse domain filling In our study, we use also parameters extracted from the ERA-Interim reanalysis archive produced by ECMWF. Several horizontal resolutions have been used: 18 for backtrajectory computations, 0. 258 in latitude and longitude for synoptic fields and 0. 1258 for profiles at the grid point nearest the sites. The temporal resolution is 6 h (0000, 0600, 1200, and 1800 UTC). The parameters are extracted for 37 pressure levels between 1 and 1000 hPa. ERA-Interim is the reanalysis of the period 1989 to the present that has replaced the ERA-40 archive. The main advances in the ERA-Interim data assimilation relative to ERA-40 are the 12-h 4D variational analysis, the T255 horizontal original resolution, improvement in the humidity analysis, model physics and data quality control, variational bias correction of satellite radiance data, and other improvements in bias handling  (Dee et al.
d. Interannual variability and decadal trend of the jet stream The frequency of jet stream events per year is given in Fig. 12. The Opme yearly series varies between 0. 7% (in 2006) and 6. 3% (in 2014). The Lannemezan series shows a slightly larger variability, between 0. 8% in 2011 and 8. 7% in 2010. The mean interannual variability of the frequency of jet stream events is 1. 5% in Opme and 2. 9% in Lannemezan. One can observe quite large differences between the trends calculated from the VHF and ECMWF data after 2010. The trends at Opme, on the contrary, have some large differences in early years but generally seem to agree after 2009. Linear trends have been calculated from monthly occurrence time series for the whole VHF profiler dataset of the two sites, and as function of the seasons (Table  3 ).
The trends are positive and slightly significant in the two sites, 11. 6 6 1. 2% decade 21 for Opme, and 12. 4 6 2. 2% decade 21 for Lannemezan. If we separate the datasets by seasons, the trends are maximum in winter, and significant. For the rest of the year, the trends remain positive, but not significant. The positive trend observed at the two sites is in agreement with a global study based on NCEP-NCAR and ECMWF ERA-40 reanalysis  (Strong and Davis 2007). This study shows increased eddy-driven jet core speeds (115%) and probabilities (130%) over the midlatitudes, collocated with increases in baroclinicity driven by a spatially heterogeneous pattern of largescale warming with cooling centers embedded poleward of 608N. However, these global increasing trends, larger than local trends observed over Opme and Lannemezan in our study, are based on longer series , and present important differences as function of longitude, and also depend on the model used (ERA-40 and NCEP).
Acknowledgments. The authors deeply thank the three reviewers and the editor for the constructive comments and useful suggestions. This study has been performed in the framework of ACTRIS-FR (previously SOERE ROSEA/ATMOS). The operation of VHF wind profilers is supported by the  Observatoire de Physique du Globe de Clermont-Ferrand/Laboratoire de Météorologie Physique (OPGC/LaMP ; http://wwwobs. univ-bpclermont. fr/opgc/index. php) and  Observatoire Midi Pyrénées/ Laboratoire d'Aérologie [OMP/LA ;  Pyrenean Platform for Observation of the Atmosphere (P2OA) ; http:// p2oa. aero. obs-mip. fr]. P2OA facilities and staff are funded and supported by the  University Paul Sabatier, Toulouse, France , and  Centre National de la Recherche Scientifique (CNRS). OPGC/LaMP  facilities and staff are funded and supported by the  University Clermont-Auvergne, Clermont-Ferrand, France ,  CNRS , and  CNES (EECLAT).
D'autre part, la démocratisation des pratiques naturalistes associée à l'essor des outils numériques facilitent la constitution de bases de données de grande ampleur, issues de contributions distribuées. Quels que soient les lieux, les formes, les objectifs de ces dispositifs participatifs dans la production des savoirs naturalistes et plus généralement dans la gestion de l'environnement, des questions communes émergent. Au-delà des méthodologies, elles concernent les partenariats à l'oeuvre dans les programmes de recherche et les motivations explicites ou non des institutions, associations ou chercheurs impliqués. Comment sont associées les populations et à quelles étapes du processus ? Existet-il une appropriation au cours du processus, une restitution, une co-construction ? Ce processus fait-il, ou non, l'objet d'une narration ? Plus spécifiquement, qu'en est-il dans des systèmes peu ou non démocratiques ? Ces interrogations sont également liées aux résultats de recherche obtenus, concernant à la fois leur robustesse et leurs usages dans les arènes de discussion : quels sont les statuts (notamment juridiques) des savoirs produits ? Ces partenariats obligent à réfléchir au statut des données, c'est-à-dire à la propriété et à la diffusion des connaissances produites.