dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of the primary measurement data included in the aggregated NetCDF data records are archived in the primary (no harmonization of data as applied in this work) data repository EBAS (http://ebas. nilu. no) in Nasa Ames 1001 format, the required format for ACTRIS and GAW-WDCA data. The files also include comprehensive metadata and quality measures defined within ACTRIS and GAW-WDCA. All submissions of ACTRIS and GAW-WDCA near-surface data are identified in the EBAS database with a unique dataset identity number (ID-numbers). This makes potential changes and revised versions traceable; the latest revision date is included in the data files. The ID-numbers for all data available through EBAS that are also used in this aggregated data record are available upon request from EBAS. The link to the last version of data is always available online through the EBAS web interface and the ACTRIS data portal.
Technical Validation The quality of the datasets was assured and assessed based on the three steps described below. First, all data originators confirmed that they applied SOPs and recommendations provided by ACTRIS (available after registration at http://actris. eu/) or equivalent standards or user community established best practices. An overview of the ACTRIS SOPs is included in the ACRTIS Data Management Plan: (http://www. actris. eu/Portals/46/Publications/DataCentre/ACTRIS_Data_Manage-ment_Plan. pdf). The main points of each SOPs and recommendations are described in the 'Methods' section. ACTRIS provides documents for the operation of aerosol inlets, and operation and data processing of aerosol size distribution and cloud condensation nuclei measurements. ACTRIS recommendations are based on or complementary to the World Meteorological Organization Global Atmosphere Watch, the Atmospheric Composition Change: a European Network, the European Monitoring and Evaluation Program and the European Supersites for Atmospheric Aerosol Research recommendations.
Second, in the case of not having published the data in a peer-reviewed journal, each originator checked their data manually for plausibility and time periods that do not reflect ambient values (e. g. , removal of calibration periods or local contamination) as recommended by the World Data Center for Aerosols (WDCA: http://www. gaw-wdca. org/SubmitData. aspx). In case of prior publication, quality assurance has been described in the references as listed in Tables  3, 4 ,5 and 9. Note that for all aerosol chemical data the instruments have either participated in an intercomparison exercise or the dataset has been published already. Table  9  includes also information on whether data were already publicly available on the EBAS data base as a level 1 dataset. Level 1 data means that all steps in measuring and data processing are quality assured, invalid data have been removed and the data are provided in standard temperature and pressure and in native time resolution (http://www.
gaw-wdca. org/SubmitData/ AdvancedDataReporting/Level1. aspx). Third, even when operators follow SOPs and recommendations as closely as possible there is always a chance of instrumental drifts or minor malfunctioning. This can especially be the case for monitoring type observations where instruments are not constantly overseen and visits occur only several times a year. Hence, we here provide general quality check metrics. We specifically emphasize CCN data checks since there has not yet been an intercomparison or calibration exercise for this type of instrument. For the chemical composition data, we summarize the validation results from the literature since all instruments participated either in an intercomparison exercise or datasets have already been published.
From a detailed comparison of the SMPS with a CPC it is known that the bias stems from the CCNC. In addition, the under-and overestimations are consistent over the range of applied supersaturations which suggests a systematic quantification error. For that reason, the dataset has been corrected as shown for 1% supersaturation in Fig. 4. At CES the CCN number concentration is strongly underestimated. The fact that many small particles are present at this station can only partly explain the bias. Examination of the bias at various supersaturations revealed that the underestimation is a function of the applied supersaturation. At higher supersaturations the underestimation becomes larger suggesting that small particles, activating at higher supersaturation, were not sufficiently accounted for by the CCNC.
Not being due to insufficient droplet growth to the detection limit of 1 μm, the bias most likely owes to particle losses in the sampling line leading to the CCNC. Since this cannot be accounted for across the various supersaturations, the dataset has not been corrected. Users must hence apply caution when using and interpreting the data. Discrepancies are as large as a factor of 2. 8 in the median. At stations with monodisperse data not including 1% supersaturation, a different quality check approach was used. Figure  5  (right panels) shows the integrated particle number size distribution above the diameter indicated in the subscript versus the integrated CCN number size distribution above the same diameter at a certain supersaturation. The selected diameter is larger than the expected activation diameter while at the same time the number of externally mixed non-hygroscopic particles is very low since these types of particles can impair the comparison.
8% and measurements were polydisperse. Hence we plot the CCN 0. 8 number concentration against the integrated particle number size distribution larger than 43, 49 and 60 nm, corresponding to a hygroscopicity parameter kappa of 0. 3, 0. 2 and 0. 1, respectively (Fig. 6 ). A value of 0. 3 has been identified as a global average for continental and urban particles  74  , while several other studies found kappa to be around 0. 2, and even as low as 0. 1, in urban environments with fresh emissions  [75] [76] [77]. For Seoul, a low kappa value of 0. 1 seems realistic as the corresponding size cut-off yields the best correlation between the datasets and the slope of the linear fit is closest to the 1:1 line within the tested range, indicating that the activation diameter is near 60 nm. The width of the interquartile range is comparable to those of other stations with polydisperse measurements, indicating that the instruments ran reliably.
Usage Notes The standardized NetCDF format allows for quickly loading the data into software commonly used in the atmospheric science community including the freely available software 'R'. For more details on the format and how to access NetCDF data see http://www. unidata. ucar. edu/software/netcdf/. The files are selfexplanatory as they contain all metadata and data. The time series of the variables are designed such that they can be used without further processing. To derive the particle number concentrations from the size distributions, the area of the curve of dN/dlog(D) versus the bin diameter must be integrated taking the logarithm (log 10 ) of the bin spacing into account: Total number concentration data is provided so users can double check their integration methods. Depending on the specific purpose, the data can also be treated with a variety of statistical analysis methods.
However, users must keep in mind the uncertainties discussed in the 'Technical Validation' section. To determine whether atmospheric conditions were within the expected variability or exceptional for the available data record periods, we recommend users to work with complementary data such as ERA-Interim (a global atmospheric reanalysis product available from http://apps. ecmwf. int/datasets/) or similar products. For some locations, complementary data on aerosol optical properties or trace gas species can be obtained from the EBAS database (http://ebas. nilu. no/). We encourage users to utilize the data for the following purposes (this is a non-exhaustive list): N tot ¼ Z D max • Detailed understanding of aerosol properties: A synthesized analysis including all variables can be produced in which aerosol characteristics can be explored and compared among different environments (e.
g. , remote marine versus high-altitude), throughout various seasons and large scale Right panels: scatter plots for CCN number concentration at a certain supersaturation versus the integrated particle number from size distribution measurements greater than the indicated diameter in the subscript (in nm). The black line denotes the 1:1 line and the dashed black lines indicate the range of expected uncertainty from particle counting. Left panels: box and whiskers plots as explained in Fig. 4. weather patterns (e. g. , Monsoon or Arctic Haze). Also, further variables can be calculated from the datasets. For example, combining the size distribution data with the CCN number concentration, the fraction of activated particles can be derived whereby a common lower cut-off diameter (e. g. , 30 nm) can be defined for maximal comparability between sites.
• Evaluating models' representation of aerosol properties: Recent work  4, 81  has shown how important insitu aerosol measurements are to help constrain the uncertainty in aerosol-cloud processes related to radiative forcing. The dataset will allow the modelling community to test their results against particle number size distributions, CCN number concentration and chemical composition. Comparing global climate model output with measurements at this level of detail is necessary as the complex climate models rely on these variables for climate projections. Models that focus more on the chemical and microphysical aerosol processes as well as their transport in the atmosphere can also benefit from this dataset. In particular, the ACSM dataset can be harnessed to evaluate models' representation of particulate organics using the volatility basis set or similar schemes.
Acknowledgements The authors acknowledge funding from the  European FP7  project  BACCHUS  (grant agreement No. 49 603445 ) and the  Horizon 2020 research and innovation programme ACTRIS-2 Integrating Activities  (grant agreement No. 654109 ). Long-term measurements at Jungfraujoch are supported by the  International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat and MeteoSwiss  in the framework of the  Global Atmosphere Watch (GAW) program. AN acknowledges support from a  Georgia Power   Faculty Scholar  and a  Cullen-Peck Faculty Fellow funds. JO thanks the  HEA-PRTLI4 Environment and Climate: Impact and Responses programme and EPA-Ireland. The research at Cabauw has received funding from the  European Union Seventh Framework Programme  ( FP7 , grant agreement no.
Data Records The aggregated data files (Data Citation 1) are available in standardized Network Common Data Form from figshare. A link to the data is also provided on the ACTRIS secondary data portal: http://actris. nilu. no/Content/Products. The files are self-explanatory as they contain all data and metadata, i. e. information about the global attributes, dimensions and variables. A large number of data fields is included within the global attributes in each file. Many were adopted from the National Center for Atmospheric Research Research Aviation Facility file format (http://www. eol. ucar. edu/raf/software/netCDF. html) such as coordinates, time coverage, etc. Additional fields include date of file creation, contact information,.
59), and Mar. 2016 VAV n. a. passed, Oct. 2013 yes no instrument (denoted as n. a. = not available). *thefirst CCNC intercomparison will take place in fall 2016 at the European Center for Aerosol Calibration. †reports are available from the European Center for Aerosol Calibration (http://www. actris-ecac. eu/contact. php). ‡as of 24 August 2016 as a primary dataset on http://actris. nilu. no/,archived in EBAS. § ref. 68describes the data quality and instrument reliability for the site, but does not discuss the dataset included here as it covers an earlier time period.
Inorganic nutrients Inorganic nutrient data from the Land Ocean Interaction Study-Shelf Edge Study  (LOIS-SES; 1995-96 (Leeks and Jarvie, 1998; LOIS-SES Project Members, 1999) ) and from the World Ocean Database  (WOD; 1960 -2014 (Boyer et al. , 2013)) ) were collated and reanalysed. Nutrient observations from the upper 20 m of the water column and within 30 m of the seabed were used to construct monthly mean nutrient concentrations and nutrient stoichiometries for the mixed layer and benthic layer respectively, providing the seasonal context for the Hebrides Shelf region (55-60°N, 7-12°W; Fig. 1b ). Use of a constant mixed layer depth and/or benthic layer thickness may simplify seasonal variability in both layers but the aim here was to provide a simple seasonal index for comparative purposes.
Organic nutrients Organic nutrient observations for the Hebrides Shelf region were collated from the literature. Such observations are scarce for this region and strongly biased towards dissolved organic carbon (DOC) and nitrogen (DON) measurements, with few dissolved organic phosphorous (DOP) observations reported previously. Observations were collated from the LOIS-SES study  (LOIS-SES Project Members, 1999) , the OMEX study  (OMEX Project Members, 1997)  and from  Moschonas et al. (2015). In total between 109 and 446 measurements of DOC, DON or DOP from 86 unique positions across the shelf, shelf break and offshelf regions were identified (Fig. 1c ). In the majority of cases DON and DOP concentrations were calculated by difference between reported total dissolved nitrogen (TDN) or phosphorous (TDP) concentrations and concurrent measurements of inorganic NO 3 -(plus NO 2 -) and PO 4 3pools reported in the original source publication/dataset.
Particulate datasets Measurements of POC, PON and POP concentrations are also rare for this region. Consequently, a broader analysis of, and comparison to, coastal and oceanic particulate measurements collated and reported by  Sterner et al. (2008)  and  Martiny et al. (2013 Martiny et al. ( ,2014) )  was undertaken. These analyses focussed on comparisons of coastal and oceanic POC, PON and POP particulate concentrations and stoichiometric relationships to the 2014 cruise data to determine significant differences or notable agreements within the new observations reported here.
Spatial analysis Both the new measurements from 2014 and the collated historical observations were analysed to obtain a mean regional picture of surface and benthic layers before being split into sub-regions (shelf, shelf break and ocean) and reanalysed to determine the presence and extent of intraregional variability in nutrient concentrations and stoichiometry, differences in timing of the seasonal cycle and region specific characteristics. The datasets were split by water column depth denoting shelf (< 150 m), shelf break (150-280 m) and open ocean (> 280 m) sub-regions with depth ranges determined by approximate shelf break frontal positions. This separation of data resulted in a 6 box conceptual model of a coupled shelf-ocean system that allowed identification of differences in vertical gradients between layers and horizontal gradients between sub-regions with the aim of investigating the relevance of these for carbon export from the shelf to the ocean.
Comparison to previous studies The relationship between carbon, silicate, nitrogen and phosphorous in particulate organic material is central to the stoichiometric themes explored here. The reliability of the derived stoichiometric relationships were assessed via log-log plots of the entire particulate dataset collected in this study (section 3. 1. 2) in comparison to other publically available datasets. For the coastal ocean we have incorporated the dataset of POC, PON and POP measurements compiled by  Sterner et al. (2008; hereafter S08) , and for the open ocean, the dataset reported by  Martiny et al. (2013 Martiny et al. ( , 2014 ; hereafter M14) and a smaller oceanic dataset also reported by  Sterner et al. (2008). For the coastal ocean comparison a significant difference in the relationship between carbon and phosphorous and between carbon and nitrogen was found between the Hebrides and S08 datasets (Fig.
11a, c ). In both cases the Hebrides dataset deviated away from the Redfield ratio at low particulate concentrations resulting in elevated C:P and C:N values (i. e. high C content; Fig. 5 ). As a result the linear regressions through the datasets report rather different relationships. As the Hebrides observations were towards the lower end of values reported in the S08 dataset it is possible that the influence of high C:N and C:P production under seasonally weak productive conditions, or differential remineralization of N and P are resulting in distorted relationships between carbon and nitrogen and carbon and phosphorous. In contrast a strong agreement exists between the two datasets describing the relationship between PON and POP (Fig. 11b ) with the regression through the Hebrides dataset almost perfectly matching the Redfield relationship.
The open ocean comparison produced conflicting results. The relationship between POC and POP in the Hebrides dataset was similar to that of the S08 oceanic dataset but both were different to that of the M14 dataset (Fig. 11d ). As both the Hebrides dataset and the S08 oceanic dataset were far smaller than the M14 dataset the differences may be indicative of undersampling, seasonal bias or reflect the balance between near coastal and open ocean observations within each dataset. In comparison, the relationships between PON and POP within the S08 and M14 datasets were similar but both were different to the Hebrides dataset, which produced a relationship very similar to the Redfield ratio (Fig. 11e ). Finally, the relationship between POC and PON indicated that the M14 dataset produced a mean relationship similar to the Redfield ratio whilst the S08 dataset was very similar but both were clearly distinct from the relationship present in the Hebrides dataset (Fig.
Discussion A detailed picture of the internal stoichiometry of the Hebrides Shelf has been presented which revealed strong vertical and horizontal gradients in dissolved nutrient and particulate concentrations and in the elemental ratios of those pools. Collectively the Hebrides data reveal increased C:N, C:P and N:P elemental ratios with depth in particulate, dissolved organic and inorganic nutrient pools due both to the preferential remineralization of P relative to N and of N relative to C and to  11. Comparison of the October-November 2014 particulate data with two similar particulate databases reported by  Sterner et al. (2008)  and  Martiny et al. (2014). In all plots we show Type II major axis regressions (coloured by dataset) with 95% confidence intervals (grey lines) in relation to Redfield relationships (dashed blue lines) through particulate data that has been log 10 transformed.
In the upper row we compare the Hebrides Shelf data (red) to the coastal dataset (black) collated by  Sterner et al. (2008). In the lower row we compare the Hebrides Shelf data (red) against the oceanic datasets of  Sterner et al. (2008)  (black) and  Martiny et al. (2014) (blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) the variable biological demand for P, N and C. Similar observations were reported for the dissolved organic nutrient pool on Georges Bank  (Hopkinson et al. , 1997)  and more widely across several shelf and oceanic locations  (Hopkinson and Vallino, 2005)  suggesting the Hebrides results fit to a common principle of stronger biological N and P recycling but weaker C mineralization (e.
, 2011)  as well as coincident shelf-to-ocean changes within the phytoplankton community  (Siemering et al. , 2016). Separately, the analysis of historical nutrient data for the Hebrides region revealed important seasonality in both organic and inorganic nutrient pools with the prominent summer maximum in organic nutrient concentrations reducing DOC:DOP and DOC:DON (i. e. DOM was comparatively C poor) whilst the summer inorganic nutrient minimum elevated DIC:NO 3 and DIC:PO 4 (i. e. comparatively C rich). There were thus important seasonal stoichiometric variations in the inorganic and organic pools of this shelf sea system. As both pools are exportable to the ocean throughout the year  (Wakelin et al. , 2012; Painter et al. , 2016) , as well as being transferable between particulate and dissolved pools  (Butler et al.
Consequently, extrapolations from limited observational datasets using a fixed stoichiometric assumption are likely to lead to significant error. This is an important result because mass balance calculations of coastal ocean carbon fluxes do not normally consider the impact of variable elemental stoichiometry when assessing the trophic status of shelf systems. It is, for example, common practice to estimate the proportion of shelf wide net primary production supported by terrestrially derived P via Redfield scaling arguments (e. g. Smith and Hollibaugh, 1993; Ducklow and McCallister, 2004). As a result, even minor deviations in the C:P ratio could easily introduce errors into conclusions of autotrophy or heterotrophy derived from mass balance approaches.
Acknowledgments Cruise DY017 was a  NERC National Capability  funded cruise conducted in support of the  UK NERC/DEFRA co-funded Shelf Sea Biogeochemistry programme  ( NE/K001701/1  and  NE/K001884/1 ). Historical nutrient datasets were obtained from the  British Oceanographic Data Centre  (www. bodc. ac. uk), Pangaea  (www. pangaea. de)and the  World Ocean Database  (www. nodc. noaa. gov/OC5/WOD/or_wod. html). MODIS Aqua ocean colour observations were obtained from https://oceancolor. gsfc. nasa. gov. L. A. Salt was supported by post-doctoral grants from the  Conseil Général du Finistère and Région Bretagne. DY017 cruise datasets are available via www. bodc. ac. uk. We thank the reviewers for their detailed, constructive and helpful comments.
Acknowledgments We acknowledge financial support from the  ERC  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement  207467  and from  NERC  ( NE/I010734/1  and  NE/M00046X/1 ). We are grateful to  S. Tanaka  and two anonymous reviewers who helped to produce a much improved paper. The data used to produce the figures here can be obtained from the supplementary information of Badro et al. [2014Badro et al. [ , 2015] ] and from Siebert et al. [2013].
2(a ). It appears that either coarse particles or fine particles could be identified well by the image processing program developed in our lab based on Matlab and ImageJ. Particle inhomogeneity caused by that fluorescence sometimes originated from only a part of a particle was an issue should be paid attention, especially for coarse particles. We examined and showed that our method could recover the whole shape of the particles thanks to the high contrast in the fluorescence image, see Fig. 2(c ). So, this inhomogeneity did not cause incorrect estimation of the aspect ratio. Our image processing method achieved a precision of half pixel (approximately 80 nm) on determining the particle size. By dealing with 50 fluorescence images on one Teflon filter, a total of 9537 particles were captured.