dataesr/scientific-paragraphs-categorization huggingface dataset:
Cotte (1774)  was involved with establishing and maintaining an extensive network of weather stations for the society. He gave information about how to make daily instrumental observations in his Traité de Météorologie and similar instructions on instrumental exposure, observational procedure, and methods of recording and summarizing the daily reports were issued by the Royal Society so as to ensure that standardized and comparable observations would be made by the team of correspondents. The original manuscripts containing daily observations have been preserved in the archives of the Académie de Médecine's library. The book of  Cotte (1774)  and the registers of daily observations can be downloaded at meteo. academie-medecine. fr. The other European station data (Bologna, Copenhagen, Mannheim, Munich, Prague and Rome) were extracted from the Ephemerides of the Societas Meteorologica Palatina of Mannheim 1781-1792.
These publications contain the daily meteorological observations and can be downloaded at bibliotheque. meteo. fr. The outdoor temperature was observed and recorded three times a day at all stations at 7, 14 and 21 h. The thermometer was to be mounted outside in a sheltered north-facing exposure, in order to record air-shade temperature  (Kington, 1988). The thermometer was of the mercury-in-glass pattern, and graduated in degrees Réaumur. We computed daily averages of temperatures, which could be biased if data are available for the morning or the evening. An average seasonal cycle was computed from those daily time series, and then removed to the daily means. Monthly means of the anomalies were then computed for the 10 locations. The original digitized times series can be downloaded at http://www-lscedods.
Reanalysis data The base predictand data set for our study is the NCEP reanalysis  (Kalnay et al. , 1996)  between 1948 and 2012 on a daily timescale. We used the SLP, near-surface temperature (at 1000 hP height), and wind speed at 1000 and 500 hPa for the climate reconstructions. The reanalysis data were extracted over the North Atlantic region (80 • W-30 • E, 30-70 • N), interpolated on a spatial grid of 2. 5 • by 2. 5 •. For comparison purposes with the Kington data set, the NCEP SLP was regridded by averaging onto the Kington grid (smaller domain and lower spatial resolution). The effect of the spatial resolution is illustrated in the Supplement movie (the caption of the movie in provided in the Supplement). We also computed reconstructed fields for surface temperature and surface wind speed from the NCEP data.
Flow analogues We use the methodology and terminology of  Yiou et al. (2013)  to obtain atmospheric reconstructions based on analogues of atmospheric circulation. This type of approach was also proposed by  Schenk and Zorita (2012)  in a different context. A general goal of the method of analogues is to reconstruct an ensemble of meteorological fields (e. g. temperature, wind speed, geopotential height) from observations of sealevel pressure. For each day in 1781-1785, the simple form of the analogue method determines the days in 1948-2012 (in the NCEP reanalysis) that minimize a Euclidean distance (or root mean square: RMS) to those of 1781-1785. Since all the meteorological 3-D fields are known in a reanalysis data set, one can infer "analogues" for those fields in the predictor period 1781-1785.
Hence, the RMS is optimized on 5-day sequences, and the correlation score is computed on daily reconstructions. The choice of the window length is a tradeoff between the regularity of the the reconstruction (hence avoiding too many strong discontinuities in the dates of analogues) and quality of the analogues (in terms of correlation). This choice can be guided by the temporal persistence of atmospheric structures. Synoptic structures generally have an average persistence of a few days. We assessed this persistence by estimating the autocorrelation observed in the Kington SLP data set. For each grid point, we computed the autocorrelation function of SLP, with lags between 1 and 20 days (Fig. 1 ). The median autocorrelations drop within 9 days to an asymptotic value near C = 0. 18. We bootstrapped the dates in the SLP data set in order to obtain confidence intervals for the "background" autocorrelation of the data set.
The gridded data set actually contains many cells where there are no data. The computation of analogues is done on a subregion of the predictor maps (i. e. over 5 × 5 cells with no missing data). Then the analogues in the predictand set (NCEP reanalysis) are considered on their original 2. 5 × 2. 5 resolution over the whole North Atlantic. In this paper, we chose to reconstruct anomalies of SLP, surface temperature and surface wind speed. The anomalies are determined with respect to the 1971-2000 reference period. The reason for not reconstructing absolute fields, rather than anomalies, is that we do not have 30 yr climatologies during the 18th century for all fields (apart from SLP and surface temperature). This statistical approach to meteorological reconstructions complements reanalysis initiatives over long periods  (Compo et al.
, 2011). Rather than running a climate model that assimilates SLP, we compute analogues of SLP and determine the corresponding atmospheric fields in a reference data set (here the NCEP reanalysis). Although the reconstruction yields poorer scores than what can be obtained by a climate model simulation, we are able to produce relatively large ensembles of conditions at a low computational cost. We also emphasize that the quality of the reconstruction depends on the predictor data set (here the Kington, 1988 set), and the reconstructed ensemble samples the uncertainty due to the analogue method itself.
We show that SLP analogues that are optimized for RMS can yield very low spatial correlation values (sometimes below 0. 4), although the distribution is centred above 0. 7 (red line). This is further illustrated in the Supplement movie comparing the predictor data set and the reconstructions from the daily 1st and 10th analogues, between 1783 and 1784. As shown on this illustration, the analogues are optimized over a relatively small proportion of the eastern North Atlantic and European region (because the  Kington, 1988  is mainly based on land data), but the inferred circulation patterns cover the North Atlantic. The summers of 1783 and 1784 are the ones with the highest average RMS values (Fig. 2 ). During those two summers, we find that the analogue reconstructions can yield rather different behaviours and hence low correlation values with the predictor SLP data set.
, 2011). This recent reanalysis data cover 1871 to 2012. We used an SLP field interpolated on a 2 • × 2 • grid. Then we performed the same processing as for the NCEP reanalysis. We find very similar scores for RMS and correlation (Fig. S4  in the Supplement). The distributions of the dates of analogues are different, by construction because the 20CR reanalysis is much longer than NCEP, but we find a qualitative coherence (Fig. S5  in the Supplement) although the differences could be due to issues that are beyond the scope of this paper  (Krueger et al. , 2013).
Acknowledgements. We thank  P. D. Jones  for providing the data files for the Kington SLP data and a discussion on its production. We acknowledge Germany's National Meteorological Service (DWD)  for providing the images of  Ephemerides Societatis Meteorologicae Palatinae. This work was supported by the  French ANR   CEP  project  CHEDAR. We thank  C. Eschstruth  for digitizing some of the historical temperature data. All computations were performed with the R language (http://www. r-project. org). The two anonymous referees brought many constructive comments that helped clarify the manuscript. Edited by:  E. Zorita The publication of this article is financed by  CNRS-INSU.
The sediment settling effect was attenuated in post-processing by forcing the velocity data to be horizontally periodic and   18 ) to the ripple profiles (with coefficients in Table  1 ). removing the horizontally averaged vertical velocity (which must be true from flow continuity). Note that, though van der Werf et al. (  2007 ) did calibrate their data against other measurements of suspended sediment, the ABS concentration measurements are accurate only within a factor of 2, which limits the degree to which the suspended sediment transport rates can be expected to agree with LES results. Van der Werf et al. (  2008 ) did, however, provide error estimates of the sediment transport measurements, and from that we can expect that the ABS results are not so inaccurate. After measurements were made and the water had become still, high-resolution measurements of the ripple geometry were made with a laser displacement sensor.
Acknowledgements. R. Street  and  O. Fringer  are thanked for providing the initial LES code, and  J. van der Werf  and  J. Malarkey  are gratefully acknowledged for providing us with a copy of the experimental data used. This work was partially supported by the  US Office of Naval Research (ONR)   Coastal Dynamics Program  under grant  N00014-00-1-0440  and the  ONR Coastal Geosciences Division Mine Burial Program  under grant  N00014-05-1-0068  (code  321CG ). We thank the  URI Energy Partnership  for making the 72-core cluster Spock available for the numerous preliminary simulations. Edited by: H. J. Fernando Reviewed by: two anonymous referees.
Dominant climatic conditions prevailing in the study area are that of typical Mediterranean climate, with contrasted hot and dry summers, and mild and rainy winters. The strongest winds and waves are from the NE and E. Mean significant waves height is less than 1 m, although during storm condition it can reach 4. 5 m (WANA Puertos del Estado wave data network). The lagoon is isolated from the Mediterranean Sea by a 25 km long sandy barrier crossed by an artificial inlet recently opened and stabilized (2011). The NW area of this lido is marked by many overwashes, which indicate how intensely it has been exposed to the adverse sea events through history.
Back Close Full spectrum of the grain size percentages vs. grain size was plotted; the results show unimodal distribution with one mean grain population ranging between 133 and 282 µm. The percentages of this grain size class in surface samples decrease considerably from the sea to the lagoon (Fig. 6 ), therefore, it can be used successfully to trace marine sand in lagoon sediments. The geochemical data obtained by XRF portable analyzer allowed detection of 11 elements (Ti, Fe, Sr, Cu, Zn, Zr, Mn, Co, As, Rb, and Pb). Variation of the concentration of the most important detected elements from sea to continental samples (Fig. 7 ) shows that the elemental composition of barrier beach sediments are enriched in Sr and depleted in Fe, Ti, Mn and Rb. Although, these results are based on a limited number of samples and detected elements, they are in agreement with previous studies in the area.
POLARCAT-IPY: objectives and main activities Although airborne campaigns prior to 2008 provided very useful first data about the composition, and possible origin of pollution in the Arctic, many scientific questions remained  (Law and Stohl, 2007) , and major advances in airborne measurement techniques and modeling capabilities had occurred. These factors, coupled with the arrival of several key satellite instruments capable of making aerosol and trace gas measurements in the Arctic (e. g. Aqua Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)), and the announcement of an International Polar Year (IPY) in 2007/08, motivated the atmospheric chemistry community to design an international project, POLARCAT, aimed at further improving our understanding of Arctic pollution and impacts on climate.
, 2011) , and Arctic Summer Cloud Ocean Study (ASCOS)  (Tjernström et al. , 2014)  studying low-level Arctic clouds and aerosol-cloud-ocean interactions. In this review, it is not possible to cover all issues listed in Table  2 , such as aerosol-cloud interactions, and to which POLARCAT also contributed. Briefly, gas-phase measurements on board the POLARCAT aircraft included O 3 and its precursors (NO x , CO, VOCs), intermediate VOC oxidation products (e. g. carbonyls, peroxides), tracers of combustion (CO), industrial emissions (sulfur dioxide, SO 2 ), and biomass burning (acetonitrile, CH 3 CN), and greenhouse gases. Detailed data were also collected on aerosol chemical composition, and their physical and optical properties. Additional data were provided by observations from satellites and ozonesondes (e.
Conclusions and future perspectives POLARCAT-IPY provided a wealth of new data on Arctic air pollution. In particular, data were collected on the vertical distributions of aerosol chemical composition and trace gas species throughout the free troposphere and lowermost stratosphere during spring and summer 2008. These data have been used, and are continuing to be used, to evaluate and improve chemistry-climate models, for example, as part of POLMIP, and by the Arctic Council AMAP (Arctic Monitoring and Assessment Program) expert group on Black Carbon and Ozone, which is examining the impact of different sources of short-lived pollutants on climate. Analysis of POLARCAT data highlighted important contributions from agricultural and boreal fire emissions, as well as anthropogenic emissions from east Asia, to aerosols and trace gases observed over the Arctic in spring and summer 2008.
Acknowledgements. French  support is acknowledged for POLARCAT-France and CLIMSLIP projects from the  French Agence National de Recherche (ANR) ,  CNRS-LEFE  and  CNES. POLARCAT-Norway  was supported by the  Norwegian Research Council. The ARCPAC project was supported by  NOAA Climate Change and Air Quality Programs. ARCTAS was supported by  NASA   Tropospheric Chemistry and Radiation Sciences Programs. YAK-AEROSIB measurements were supported by  CNRS ,  ANR ,  RFBR  and  SB-RAS , and relies on B. D. Belan ,  M. Yu. Arshinov  and colleagues at  Institute of Atmospheric Optics, Tomsk, Russia and P. Nédélec at Laboratoire d'Aerologie, Toulouse, France. The Falcon aircraft deployment for POLARCAT-GRACE was funded by  DLR. HS and AR acknowledge support from  Deutsche Forschungsgemeinschaft (DFG)  under  SPP 1294  ( SCHL1857/2-1 ) and  PAK 348  ( SCHL1857/3-1 ).
Acknowledgements. We thank  C. Liquete  for providing Fig. 1, and  Compagnie Nationale du Rhône, Hydro France  and  Agència Catalana de L'Aigua  for providing hydrological data. We also thank all people involved in the water sampling of the French rivers ( D. Aubert ,  J. Carbonne ,  C. Sotin ,  N. Delsaut  and  D. M'PeletBoukidi ) and the two referees for their valuable comments. This research has been supported by the  PERSEUS  ( FP7-OCEAN-2011-3-287600 ),  GRACCIE-CONSOLIDER ( CSD2007-00067 ), and  ANR-CHACCRA  ( ANRVULN-06-001-01 ) research projects, and the  Zone Atelier ORME network (CNRS). We also received support from  Catalan Government  through a  Grups de Recerca Consolidats grant  ( 2009-SGR-1305 ). Edited by:  S. Bouillon The publication of this article is financed by  CNRS-INSU.
The NEPTUNE cabled observatory network hosts an ecological module called TEMPO-mini that focuses on hydrothermal vent ecology and time series, granting us real-time access to data originating from the deep sea. In 2011-2012, during TEMPO-mini's first deployment on the NEPTUNE network, the module recorded high-resolution imagery, temperature, iron (Fe) and oxygen on a hydrothermal assemblage at 2186 m depth at Main Endeavour Field (North East Pacific). 23 days of continuous imagery were analysed with an hourly frequency. Community dynamics were analysed in detail for Ridgeia piscesae tubeworms, Polynoidae, Pycnogonida and Buccinidae, documenting faunal variations, natural change and biotic interactions in the filmed tubeworm assemblage as well as links with the local environment. Semi-diurnal and diurnal periods were identified both in fauna and environment, revealing the influence of tidal cycles.
In this 21 st century, technology has progressed in such a way that the difficulties of accessing and working in the deep sea are less of a threshold than before. Scientifically, this is illustrated by the development and implementation of deep-sea observatories, operational in a variety of settings  [12]. In 2009, a regional-scale cabled observatory network, NEPTUNE (North-East Pacific Time-series Undersea Networked Experiments, an installation of Ocean Networks Canada www. oceannetworks. ca), came online. It consists of an 800 km electro-optic cable loop laid on the seabed over the northern Juan de Fuca tectonic plate, off the coast of British Columbia (Canada), granting real-time access to the data collected by the observatories. An ecological observatory module (TEMPO-mini) equipped with a video camera and environmental probes, is implemented at one instrument node of the NEPTUNE network and focuses on hydrothermal vent ecology.
g. 7, 16, 17, 18, 19]. The main advantages of a cabled observatory are that energy, battery life and hard drive space are no longer main limiting factors, as opposed to the autonomous, wireless observatories. The instrumented nodes of the NEPTUNE observatory are powered by the on-shore facilities of Port Alberni on Vancouver Island. Thanks to live streaming, data collected is available in real-time on the internet (www. oceannetworks. ca) and is stored and managed at the University of Victoria in British Columbia (Fig. 1b ).
Data Recordings and Observatory Set-up TEMPO-mini's first deployment on the NEPTUNE network took place in September 2011 and it was recovered in June 2012; both were carried out with the Remotely Operated Vehicle (ROV) ROPOS. Since its deployment on September 29, 2011, TEMPOmini transmitted data until the day of its recovery (June 19, 2012). The first week of the deployment was used to run tests in lighting, zoom of the camera, etc. , while for the 23 subsequent days (October 7 (8 h UTC) -October 30, 2011 (14 h UTC)), imagery was recorded continuously. Lights were on constantly during the continuous imagery recording period. After these 23 days, the recording frequency changed to one half-hour (30 min) recording every 4 hours and corresponded with a substantial change in zoom (zoom-out, enlarging the filmed surface by a factor of 1.
Faunal densities were quantified for each hourly image, while for one image every 4 hours the microbial coverage was assessed. To pursue the latter, the microbial cover was marked in white and the rest of the image rendered in black. Using the ''magic wand tool'' of the ImageJ image analysis software  [21] , the surface covered by microbes was quantified and converted to percentages. ''Heat maps'' were created for all mobile organisms. These maps are a graphical presentation of the numbers of observations using colour-codes. The colours thus reveal the most (dark red) and least (dark blue) frequented areas and distribution patterns for each taxon. For this purpose, the packages gplots  [22]  and hydroTSM  [23] , both in R 2. 15. 1  [24]  were used. Several functions were adapted within the packages to meet our requirements and to fit our objectives.
Pixel coordinates were attributed to individuals with the ImageJ ''point picker'' plugin on the hourly images for all images in the continuous imagery recording (n = 536). Subsequently, these coordinates were grouped into bins, each bin covering ,1. 125 cm 2 , and maps featuring spatial presence of fauna were produced. An index quantifying the measure of spatial segregation was calculated for each species and a pairwise segregation index was calculated between neighbouring species (Dixon package in R  [25] ).
Statistical Analyses Various frequencies were investigated to study faunal variations, going from hourly, every 4 hours, every 6 hours to every 12 hours. Since the probes that recorded the environmental variables were not positioned on the filmed assemblage itself, timestamps were relative and could not be considered representative; hence, the values of the environmental variables used as explanatory variables for faunal variations over time were averaged per hour for each probe separately. Only the last two Fe measurement cycles were used to calculate Fe concentrations and these were averaged per recording time (be it twice or once a day). Gaps in the recorded variables data series were a restricting and determining factor in the selection of analyses. Whittaker-Robinson (WR) periodograms, programmed in R by P.
Legendre  [26] , were used to unravel periods in environmental variables and faunal densities. During periodogram analysis, data time series are folded into Buys-Ballot tables with periods of 2 to a maximum of n/2 observations. The number of columns in the table can be restricted to a range of periods assumed to be of interest for the study  [27]. The underlying statistic, measuring the amplitude, used in this periodogram function is the standard deviation of the means of the columns of the Buys-Ballot table  [27] ; it is plotted in the resulting graph as a function of the periods. The WR periodogram can handle missing values in the datasets, when filled in with NA values (''Not Available''). Prior to periodogram analyses, datasets were tested for stationarity. In case of lack of stationarity, trends were removed by linear regression and residuals were used in the periodogram analysis.
Some degree of caution is needed during interpretation as this kind of periodogram also finds the harmonics of basic periods to be significant. Periodogram analyses were carried out for both temperature data and faunal densities using the hourly data for 23 days, with a maximum period of n/2 (,279. 5 h). Correlations between environmental variables and faunal densities (hourly resolution) were calculated using two-tailed correlations with permutations tests (n = 999), which is a correlation test that does not require normality of the data. P-values were subject to a Holm correction for multiple comparisons. Additional correlations using different frequencies (every 4 hours, every 6 hours and every 12 hours) were computed among faunal taxa. Variation partitioning, by multiple regression and partial canonical analyses  [28, 29, 30] , was carried out on the faunal densities (excluding the visiting fish species), using two types (subsets) of explanatory variables: (1) significant temporal eigenfunctions (2) a selection of temperature probe measurements, retained in both cases by forward selection on the faunal density dataset.
This analysis allowed a quantification of the proportion of variance in faunal densities explained by each subset of explanatory variables, while controlling for the effect of the other, as well as an estimation of the variation explained jointly by the two subsets. Temperature was recorded continuously (1 measurement every 30 seconds). For this period, Fe was measured once a day from October 20, 2011 onward. Oxygen was recorded continuously for the period, although it showed a steep decrease and other inexplicable patterns until day 83, rendering the data unusable (light-grey). doi:10. 1371/journal. pone. 0096924. g003.