dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). M. Zhao et al. (2013)  pointed out that Ni and V were enriched in submicron particles in the Shanghai port. Recently,  Liu et al. (2017)  also reported that ship plumes contributed 2-7 µg m -3 to fine particles within the coastal area of the Shanghai port, accounting for 20 %-30 % of total PM 2. 5. Known as the "golden canal", the Yangtze River was an important route for trade and travel. However, there are seldom data related to air quality and the influence of ship emissions along the Yangtze River channel. Meanwhile, related observations with the synchronous trend in aerosol in the MLYR region remain insufficient. To characterize air quality in this region, a round-trip field observation voyage, namely the Yangtze River Campaign (YRC), was carried out between Shanghai and Wuhan.
Satellite data and ship traffic data The satellites, including the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 10×10 km, Measurement of Pollutants in the Troposphere (MOPITT), and the Ozone Monitoring Instrument (OMI) reaching a spatial resolution of 13×24 km at nadir, and the Earth Observing System (NASA EOS) Aura satellite, were applied to provide spatial distribution of aerosol particles and trace gases  (Xu et al. , 2011; Huang et al. , 2012a). The column levels of CO, NO 2 , SO 2 , and aerosol optical depth (AOD) were retrieved over the MLYR region. In this study, all data from satellite datasets were interpolated and averaged into grid cells with a 0. 25 • × 0. 25 • resolution. Ship positions and numbers in the Yangtze River channel were decoded by Automatic Identification System (AIS) databases, which were obtained from the Maritime Safety Administration of Shanghai.
Chemical composition of fine particles during the YRC and comparisons with other published data The concentrations and mass fractions of the major ions and levoglucosan in PM 2. 5 are shown in Fig. 3. The water-soluble ions constitute one of the dominant components in atmo- spheric aerosol and determine the aerosol acidity  (Kerminen et al. , 2001) , accounting for 37. 43 % and 40. 15 % in PM 2. 5 and PM 1. 0 during the YRC, respectively. To access the data quality, ion balance gained by the major anions (SO 2- 4 , NO - 3 , and Cl -) and cations (Na + , NH + 4 , K + , Ca 2+ , and Mg 2+ ) was calculated in this cruise. Both cations and anions are in units of equivalent concentration (µ eq. m -3 ). There is a good correlation (R 2 > 0. 99 and R 2 > 0. 98, P < 0. 01) between cation and anions (equivalent concentration) in PM 1.
Acknowledgements. This work was supported by the  Ministry of Science and Technology of China  (nos. 2016YFC0202700 ,  2014BAC22B00 ), the  National Natural Science Foundation of China  (nos. 91743202 ,  21527814 ), and the  Marie Skłodowska-Curie Actions  ( 690958-MARSU-RISE-2015 ). Edited by: Yuanhang Zhang Reviewed by: two anonymous referees.
Methods The inventory of active pollen monitoring stations was created by: (1) reviewing the relevant bibliography and (2) contacting authors or the network managers of pollen networks. Information on pollen monitoring stations was obtained by phone, email or post. A small questionnaire was sent, which requested information on what was monitored, since when, at which location, using which collection method (Hirst-type, brand type Burkard or Lanzoni, automatic or another sampling system), the availability of quality control, and the station owner's most recent contact data. This questionnaire is also accessible in an interactive format in the map application. The questionnaire makes it possible to fill in a. csv database which serves as input data for the map. The map was programmed in "R"  [23].
Maps are instantaneously displayed at https://www. zaum-online. de/ pollen-map. html. Stations that are currently running are displayed by default, but historic sites can also be made visible. New information for the map (changes of owner, a new station etc. ) can be submitted to either the Appadministrator or the map itself online via the "contact form (Modify info, add station, contact)" button. The information is then reviewed by the App-administrator and, if correct, entered in the database, after which the new information is displayed. It is not possible to make a modification without the App-administrator, thus preventing the misuse of the map.
shinyapps. io/ pollen_map (accessed 25-01-2018). Blue dots (Hirst trap), red (Automatic station), orange (other manual) monitoring station from which to obtain pollen or spore data for comparison with the patient's symptoms. This could improve diagnosis. Our review accounts for at least 879 open running pollen traps in the world from a total of 1020 records (Fig. 1 ). As can be observed, most of the traps are distributed in the northern hemisphere, the majority in Europe. The continent with the weakest coverage is Africa. The top six countries by number of active pollen stations are: Japan (143), Italy (88), USA (85), France (85), Spain (77) and Germany (44). The Environmental Agency of Japan owns the biggest network (120 automatic traps), while RNSA of France owns the biggest Hirst-based network (84).
Acknowledgements We would like to thank the many people who, with their personal contacts, helped us to collect all the necessary information regarding the pollen and spore monitoring stations, along with the  EAACI (European Academy of Allergy and Clinical Immunology) for funding Task Force 40108. We also thank all participant who submitted their data to the map. We acknowledge  Helmholtz ZentrumMünchen  for the Postdoctoral Fellowship Program III 2017-2019.
Images Analysis and Quantification Quantification of images was performed using a home-made macro on ImageJ software (National Institute of Health, United States). The macro steps included intensity threshold and count of metabolically active and permeabilized bacteria using "particle analysis" with shape parameter on 505-544 nm and 600-700 nm images, respectively. The number of bacteria was then divided by the image area size to obtain a density of events. Then, the damaged number of events was calculated by counting bacteria on logic AND image between the two acquired images.
Acknowledgements The authors are grateful to the Spanish Ministry of Science, Innovation and Universities (Project AGL2017-87755-R) for funding this work. The exposome concept refers to 'the totality of environmental exposures from conception onwards', proposed to complement the genome with all those factors that may be related with the human phenotypes and responsible of perturbation of biological processes  (Wild et al. , 2005). This novel vision involves different levels of complexity and dimensionality, providing an integrative overview of the relationship between the internal biological processes and the environment. Environmental external factors include radiation, infectious agents, chemical contaminants and environmental pollutants, diet, lifestyle factors (e. g. tobacco, alcohol), occupation and medical interventions.
2 ). Making use of a high resolution global forest cover and forest change dataset  27  , as well as the CHELSA climate dataset  28  , we generated consistent and comparable climate and land-use change indicators for each study to perform our analyses at two different resolution levels using either the data aggregated at the site level (n = 43; some of the 39 studies focused on several study sites that were treated independently here) or raw data at the species level (n = 1464). We found that apart from temperature changes and baseline temperature conditions, the rate of climate-related elevational range shifts is also affected by local habitat features such as baseline forest cover and recent forest cover change. The overall synergistic effects between climate and habitat change reveal the importance of considering multiple threats holistically when predicting biodiversity redistribution and for biodiversity conservation.
The elevational distance to the highest mountain summit within the study area was also found to be an important factor in explaining upslope shift rates at the species level: the greater the elevational distance to the highest summit, the more rapidly species shifted their range upslope. This finding supports the idea that limited physical space may constrain the rate of species' range shifts along elevational gradients  16, 37, 38. In addition, microclimate conditions and soil nutrition levels determined by montane mass (the Massenerhebung effect) could also limit species range extension  39, 40. One limitation to our work lies in the short time-span of the forest data used (2000-2015), especially given that most selected studies covered periods of several decades. On average, initial surveys were conducted 44 years prior to the period covered by the Global Forest Watch database.
Land-use history over the last decade is not likely to accurately mirror changes over previous decades, and this mismatch in time scale might have contributed to the unexplained variance in our models at the species level (cf. 87. 4% on average, with ± 0. 97% S. D. ). Nonetheless, the spatial consistency that the Global Forest Watch dataset offers is a breakthrough for large-scale comparisons in habitat features (see discussion on model limitations using various habitat data For details of the 30 competing models refer to Supplementary Table  3. All variables are scaled to allow direct comparison both in direction and in magnitude, ranked by importance (threshold = 0. 6). Sdist: distance to mountain summit, T: baseline temperature, CCR: climate change rate, Cover: forest cover percentage sources in Mantyka-Pringle et al.
25  ), and the baseline forest cover data in year 2000 might also represent previous land-use histories that were missed in terms of forest change information. Apart from the forest cover dataset, the elevational shift data analyzed here are also likely biased towards species that have exhibited observable responses to climate change. Other factors such as precipitation change, individual species' physiology and biotic interactions may also greatly impact shift patterns  30, [41] [42] [43] [44]. Geographic bias, especially the relative lack of tropical studies, is another limitation here, which also characterizes former findings  7. Moreover, the fact that most tropical studies in our dataset were from islands, whereas temperate studies were largely continental could bring in additional bias in terms of biotic composition.
Methods Data collection. We selected 39 studies on climate-related elevational range shifts (Fig. 2 ) as synthesized by Lenoir and Svenning  7  and by searching literature published between 2014 and 2017 following the same protocol as in Lenoir and Svenning  7  (see Appendix 1 therein). Data were only included if observed elevational range shifts were reported in the original research. When more than one geographically distinctive site was surveyed in a study with shift information separately reported (e. g. , Freeman and Freeman  45  ), we treated them as different sites (n = 43). Sites with fewer than 5 species resurveyed were excluded for the sake of representativeness. In addition to some general information (e. g. , location, kingdom/taxon, species number, starting and ending year, study period calculated by subtracting start year from end year) at the study level, which is tabulated in Lenoir and Svenning 7 (Table  A3 ), we also recorded the elevation range covered by each focal study, together with detailed species' elevational range shifts as reported in the original focal study (positive values for upslope movements and negative for downslope shifts).
When only significant shifts were reported, we calculated the average shifts based on the ratio between shifting species and the total number surveyed. When raw data were not available, we used the "DataThief" program (http://datathief. org/) to extract the raw data from figures. In terms of different reference points (e. g. , upper limit, lower limit or midpoint), we used a simplified binary classification (center vs. margin); when possible, shift data on optimum position were preferred, as they are less sensitive to sampling effort  46  and statistically more robust compared with shift data at the distribution margins  47. Data on marginal reference points were included if there was no better alternative (order of preference: optimum/mean/midpoint > margins). As indicators of land-use change for each mapped location  5  , we extracted high resolution (30 m resolution at the equator) baseline forest cover (year 2000), forest cover loss (2000-2015) and forest cover gain (2000-2012) data from Global Forest Watch 27 (http://earthenginepartners.
appspot. com/science-2013-global-forest/ download_v1. 3. html). To assess habitat alteration and species distribution shift patterns for specific elevation ranges, we overlaid these forest layers onto fine scale digital elevation models (SRTM Arc-Second Global, 30 m resolution at the equator, https://lta. cr. usgs. gov/SRTM1Arc) for each site (in one Norway site with no SRTM data, a local digital elevation model with 10 m resolution was obtained from Norwegian Mapping Authority, Kartverket, https://www. kartverket. no/en/data/ Open-and-Free-geospatial-data-from-Norway/) and exported forest data at intervals of 10-m elevation bands (See Supplementary Fig. 3  for an example). Therefore, baseline forest cover percentage and forest cover change profiles by 10m elevation bands along the elevational gradient could be generated for each study site for subsequent analyses (Supplementary Figs.
4  and 5 ). Climate data were obtained from CHELSA Version1. 2  28 and CRU_TS4. 00  48. We used the annual mean temperature from 1979 to 2013 (cf. bio1 at 30 arcsecond which is about 1 km resolution at the equator, http://chelsa-climate. org/ downloads/) as the baseline temperature for each site. We also computed temperature data for each 10-m elevation band by overlaying it onto digital elevation models using the same method as we did for the forest data. Time-series data on temperature from CRU (0. 5°, ~55 km at the equator, https://crudata. uea. ac. uk/cru/data/hrg/cru_ts_4. 00/ge/) were analyzed to estimate site-specific climate change rate (°C per decade) by fitting the regression line of temperature (response variable) against time (predictor variable) over each study period, following Chen et al.
12. When the study area was too large for fine grid calculation, and considering that site-specific temperature data were obtained by averaging across all grid cells covering the study area (which were less sensitive to resolution differences), we used the 5°resolution temperature anomaly data which is ~550 km at the equator (CRUTEM4  49, 50  , https://crudata. uea. ac. uk/cru/data/crutem/ge/) to run the regression over time. Both climate and habitat data were obtained using Google Earth and ArcGIS 10. 2. Aggregated analysis. For each of the 43 sites (from 39 selected studies), the average elevational range shift (m) for all examined species across the spatial extent of the focal site was either reported as in the original paper or calculated from published raw data. To account for the fact that each study focused on a different period (cf.
starting and ending years were study-specific), we calculated the average elevational range shift rate (m per decade) as the response variable for cross-site comparisons. We ran ordinary least-square (OLS) regressions to assess the potential confounding effect of land-use change on "climate-driven" elevational range shifts. As explanatory variables, we used site-specific climate data (T: baseline temperature based on annual mean conditions between 1979 and 2013 (°C), CCR: climate change rate (°C per decade)), average forest data within the elevation range of the focal study (Cover: baseline forest cover (%), Loss: forest cover loss (%), Gain: forest cover gain (%)), as well as other recorded parameters, including taxon (plants vs. animals) and reference point (center vs. margin). All numeric variables were scaled for effect size comparisons  25, 51.
The best of these candidate models was determined based on AIC c (corrected for finite sample size), parsimony and adjusted R 2 values. We inspected diagnostic residual plots to assess goodness of fit of the best model (Supplementary Fig. 6 ). All analyses were performed in RStudio  55. Disaggregated analysis. Species within each mountain range were found to exhibit very diverse range shifts, both in magnitude and direction  41  , likely associated with their elevational locations. Therefore, we retrieved all species-level shift data (n = 2798) to construct disaggregated models of species range shifts, with individual species' shift rates as the response variable. The species-level shift dataset is dominated by records from a single study site (Dainese et al. 23  , n = 1334 out of 2798), of which the rapid shift rates were associated with proximity to roads and the occurrence of several non-native species.
When temperature data were missing for certain elevation bands (due to the coarser resolution compared with forest data), we estimated temperature values by calculating the mean local elevational lapse rate, based on the temperature at other elevations of the same site. Climate change rate, however, was a more regional feature and thus remained the same as in the aggregated analysis (assuming all species located in the same study site experience the same climate change rate). Applying the same VIF threshold to check for data collinearity, the forest gain variable (with VIF > 2) was excluded in the following analyses (see Supplementary Table  5  for the correlation matrix) due to its positive correlation with forest loss. According to the Global Forest Watch database, forest gain is defined as a change from a non-forest to forest state, and unlike the annually allocated forest loss information, forest gain percentage was calculated over the entire study period (2000-2012) due to the slow growing process  27.
Natural growth of existing longlived forest was not considered as forest gain  27. Therefore, forest loss and gain are often correlated in areas with temporary deforestation (e. g. , logging, harvesting, fire, etc. ) followed by replantation or natural regrowth. Based on this, we decided to focus on forest loss solely as it better reflects the dynamic of habitat disturbances. To account for the nested design of our disaggregated dataset (cf. several range shift values for a given single study site) and the potential pseudo-replication issue of having identical values for the set of variables available only at the study site level (e. g. , climate change rate), we ran linear mixed-effect models (LMMs) with "Site" as a random factor potentially affecting the intercept (i. e. , 1|Site) and the slope parameters of the elevation band-specific predictor variables (i.
e. , 1 + Cover|Site, 1 + Loss|Site, 1 + T|Site, as well as additive combinations including 1 + T + Cover| Site, 1 + T + Loss|Site and 1 + Cover + Loss|Site). We used the "lmer()" function from the lme4 package in R 56 to run LMMs. The optimal random component structure among the previously mentioned structures was determined by comparing AIC c values of LMMs fitted by restricted maximum likelihood (REML), while the fixed effects were kept constant with all possible explanatory variables and two-way interaction terms incorporated (cf. the "beyond optimal model" sensu Zuur et al. 51  ). Using REML is generally considered to give better estimates for the random effects. Candidate fixed effect variables were similar to the aggregated approach (all standardized), although most data were elevation-band-specific, whereas climate change rates were still averaged across the whole study.
Sentinel-1 Images A database of Sentinel-1 (S1) images collected over the Orgeval Basin situated near Paris was used (centered on lat. : 48. 86 • E and long. : 3. 17 • N, Figure  1 ). Relatively flat, the Orgeval Basin is mainly composed of cereal fields (wheat, colza, maize, barley, etc. ) and forest. The soil texture is relatively uniform over the whole basin, with clay 20%, silt 70% and sand 10%  [29]. Sixty-one S1 images obtained from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation operating at the C-band (frequency = 5. 406 GHz, wavelength ~6 cm) were collected for the period between 1 November 2016 and 1 March 2017. Over our site, S1A and S1B provide three images every four days, on average. One image is in the Descending mode (acquisition time ~05:58 TU), with an incidence angle of approximately 38 • ; one is in the Ascending mode (acquisition time ~17:40 TU), with an incidence angle of approximately 42 • ; and one is in the Ascending mode (acquisition time ~17:31 TU), with an incidence angle of approximately 33 •.
Temperature Data The meteorological data available for our study site describe the air and soil temperatures. The hourly mean air temperature at 5 cm above ground, soil temperature at 5 cm depth, and 0 cm ground surface temperature were used. The continuous temperature series between 1 November 2016 and 1 March 2017 shows three periods with frozen soil conditions, namely 30 November 2016-9 December 2016, 30 December 2016-9 January 2017, and 16 January 2017-31 January 2017, with temperatures that dropped to -5 °C (Figure  2 ).
Temperature Data The meteorological data available for our study site describe the air and soil temperatures. The hourly mean air temperature at 5 cm above ground, soil temperature at 5 cm depth, and 0 cm ground surface temperature were used. The continuous temperature series between 1 November 2016 and 1 March 2017 shows three periods with frozen soil conditions, namely 30 November 2016-9 December 2016, 30 December 2016-9 January 2017, and 16 January 2017-31 January 2017, with temperatures that dropped to -5 • C (Figure  2 ). The meteorological data available for our study site describe the air and soil temperatures. The hourly mean air temperature at 5 cm above ground, soil temperature at 5 cm depth, and 0 cm ground surface temperature were used. The continuous temperature series between 1 November 2016 and 1 March 2017 shows three periods with frozen soil conditions, namely 30 November 2016-9 December 2016, 30 December 2016-9 January 2017, and 16 January 2017-31 January 2017, with temperatures that dropped to -5 °C (Figure  2 ).
In the past, some of the most prominent applications of SWIs have been in a paleoclimate context to infer past temperatures and moisture sources from natural archives, for groundwater studies, and in studies investigating the water vapour budget in the stratosphere  (Sherwood and Dessler, 2000; Vimeux et al. , 2001; Dessler and Sherwood, 2003; Jouzel et al. , 2005). The process-based insight provided by the isotope composition of atmospheric water have more recently been extended to synoptic and sub-diurnal timescales and to the lower troposphere, where most atmospheric water vapour resides. Thanks to a tremendous expansion in the number of datasets of water vapour isotopic composition and a substantially improved set of theories and models for interpreting them, the related studies have been expanded during the past several years (e.
SWI measurements are mainly obtained from space-borne retrievals (e. g. Schneider et al. , 2016; Lacour et al. , 2017)  and ground-based in situ laser spectroscopy (e. g. Aemisegger et al. , 2015). The space-borne measurements provide continuous datasets in space at the global scale with coarse vertical resolution and limited precision. On the other hand, groundbased measurements with high temporal resolution are only available from a few locations and from dedicated field campaigns. In particular, the data availability for the Mediterranean region is very limited. A notable exception is the airborne dataset acquired around Corsica  (Sodemann et al. , 2017)  during the first Special Observing Period of the Hydrological cycle in the Mediterranean Experiment (HyMeX SOP-1,  Ducrocq et al. , 2014).