dataesr/scientific-paragraphs-categorization huggingface dataset:
Phenological metrics A set of phenological metrics has been computed from each annual [Chl a] remotely sensed time series. Annual time series were defined from July of year n to June of year n + 1 in order to better display the winter-to-spring transition. For each pixel, 14 annual time series could be defined ranging (b) q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 100 200 300 400 Depth of nitracline (m) q q q EMTEC99 POEM-BC-A92 POEM-BC-O91 y = 0. 68x + 15. 1, r 2 = 0. 93 (c) Figure  3. Climatological maps of the estimation of the depth of the isopycnal 28. 9 kg m -3 for cyclonic (a) and anticyclonic (b) regimes. Data were spatially interpolated with kriging, and black dots indicate in situ data used for kriging.
Relationship between the depth of the nitracline and the depth of the isopycnal 28. 9 kg m -3 in the north Ionian Sea (c) from in situ data collected during three oceanographic cruises (see colors). from July 1998 to June 2012. Hereafter, the annual time series are referred to as year n + 1 (e. g. , 2000 for the annual time series 1999-2000). Phenological metrics were defined to describe the main characteristics of the phytoplankton annual cycle. They were selected to focus on the [Chl a] content and on the date of the main growing period. The metrics CHL_Year, CHL_March and CHL_Max refer to the yearly average [Chl a], the March average [Chl a] and the [Chl a] annual maximum, respectively. CHL_March has been defined here because previous studies demonstrated that the Mediterranean Sea trophic situation is highly variable in March, spatially and interannually ranging from oligotrophy to bloom conditions  (D'Ortenzio et al.
These patterns indicate a cyclonic circulation. In 2006 (Fig. 2c ), high ADT values extend northward and the lowest ADT values are pushed along the Italian and Greek coasts. This pattern represents the transition towards an anticyclonic regime, which is characterized by a higher ADT value in the center of the north Ionian basin than at its periphery. From Fig. 2d  to f, the transition from an anticyclonic (Fig. 2d ) to a cyclonic (Fig. 2f ) circulation regime is represented. Indeed, similarly to Fig. 2c , in Fig. 2d   In the Coriolis database, 26 318 potential density profiles were available to describe the anticyclonic situation  (data collected between 1990 and 1996 and between 2006 and 2009)  and 34 753 to describe the cyclonic situation  (data collected between 1998 and 2005 and between 2011 and 2012).
Optimal interpolation applied to each dataset provides an estimation of the depth of the isopycnal 28. 9 kg m -3 for cyclonic and anticyclonic regimes (Fig. 3a  and b , respectively). The resulting maps exhibit very different features. In the cyclonic pattern (Fig. 3a ), the depth of the isopycnal 28. 9 kg m -3 ranges between 40 and 120 m north of 37 • N. South of 37 • N, a steep gradient, implying isopycnal southward deepening, is observed consistently with the position of the mid-Ionian jet, which is reinforced during the cyclonic regime of the NIG  (Bessieres et al. , 2013; Gačić et al. , 2014). The shallowest isopycnals are located around 39 • N and 18 • E, offshore of the Italian coast, representing the center of the cyclone. During the anticyclonic phase, the isopycnal 28. 9 kg m -3 is very deep (240 m) in the center of the Ionian Sea and is shallower at the border of the basin.
This isopycnal depth pattern is consistent with an anticyclonic circulation. As previously demonstrated for the global ocean  (While and Haine, 2010; Omand and Mahadevan, 2013) , the depth of an isopycnal can be representative of the nitracline depth in the case of a stratified water column. This is confirmed in the north Ionian from the obtained linear relationship between the depth of the nitracline and the 28. 9 kg m -3 isopycnal (Fig. 3c, r 2  = 0. 93). The data used here were collected in different seasons (i. e. , in October 1991 and April-May 1992 and 1999) but always during the long period during which the water column is stratified. As soon as vertical mixing reaches the nitracline depth, this relationship is no longer valid. Nevertheless, such events are rare and short in the north Ionian Sea.
Acknowledgements. The authors thank the  ESA Ocean Colour CCI team  for providing OC-CCI chlorophyll data,  NASA  for providing SeaWiFS, MODIS and MERIS chlorophyll data, the  ECMWF  for providing meteorological data and AVISO for support from  CNES  for distributing altimeter products produced by  SSALTO-Duacs. The  Coriolis Centre  for in situ Oceanographic Data and the  National Oceanographic Data Center of the OGS (NODC-OGS ) are acknowledged for the collection of the CTD and nitrate profiles that were made available for this study. This study was conducted using  EU Copernicus Marine Service Information. We are also grateful to  Dhyan Aranha  for proofreading an earlier version of this paper. We acknowledge the  European Commission  (co-funded by the  European Union  under " FP7 -People -Cofunding of Regional, National and International Programmes ", GA no.
An e-mail containing quicklooks of the different spectral bands as well as performance metrics is sent to the operators every 2 h. The mail provides some product metadata (sensing date, orbit number, platform reference…) and a geographic context (position on a world map as well as the name of the location at the centre of the image) in order to help image analysis. An additional benefit of the S2check tool comes from the recording of all inspections in a database. This improves traceability and robustness of the quality control process, and allows to compute history of performance metrics.
Processing chain overview Figure  1  presents a synthetic view of the S2check process. The S2check tool is implemented as a 'cron' job which starts a new inspection every 2 h. This periodicity allows inspecting approximately one product per orbit for one satellite. Note that the inspections are performed after dissemination. S2check is implemented as a set of python scripts, using common python libraries (requests, numpy and matplotlib). S2check uses the Sentinel Data Hub API (Application Programming Interface, see  Martin & Knowelden, 2015)  to select a tile recently ingested in the archive which is suitable for inspection, then a previous acquisition of the same tile to be used as a reference. S2checks generate quicklooks of the image for a fast human visual inspection, then some automatic checks are performed: • Relative registration performance between the current and reference image for the reference spectral band • Multi-spectral registration performance between two spectral bands of the current product • Average radiometric gain and offset between the current and reference products for R,G,B bands.
Sampling strategy The first step is to obtain from the Sentinel Data Hub archive the list of all products ingested since the last inspection, using the archive API. Some filters are then applied to select products which are suitable for quality control: • Filtering on cloud cover percentage (<20% currently) • Filtering on data coverage (less than >50% of 'No Data' pixels): this filter uses the size of the product as a proxy for data coverage. • Filtering on latitude to avoid snow or ice covered tiles • Filtering with a land/water mask to avoid tiles located completely over the sea The first product found matching these criteria is selected for inspection. This ensures in practice a random sampling in the sense that the probability of choosing a particular sense depends only on the local cloud statistics.
End-to-end performance metrics All inspections are recorded in a database, which allows to monitor trends on the mission performances. A synthetic timeline of multi-spectral and relative co-registration performances is generated (see Figure  5 ). On this plot, the size of the circle is proportional to the standard deviation of shift measurements among the various features. A large standard deviation can signal an anomaly affecting a part of the image. Red stars indicate failed inspections (no matching points have been found between the reference and current image). This is generally the result of high cloud or snow coverage, low land or data coverage, or a combination of these factors. The inspection database can also be used to produce end-to-end mission performance plots for availability (Figure  6 ) and timeliness (Figure  7 ).
The timeliness plot details the contribution of the space segment, ground segment and user dissemination segment. The availability plot reports outages of the Copernicus data hub (red flags) and ingestion or production failure (orange flags). Yellow flags correspond to the case where no reference product has been found for comparison. This happens generally when sampling a very cloudy area where few cloudfree products are available. Note that the S2check service itself may have outages (internet access failure for instance) in which case no flag is produced.
Conclusion and perspectives S2check has proved an efficient tool to support quality control analysis. The concept of automatic random sampling for quality control inspection seems a valid approach that could be implemented in future Payload Data Ground Segments. It complements the simple quality checks applied systematically on the production as well as detailed investigations that can be performed by human operators. The key ingredients of the method are as follows: • Use a powerful API to download products suitable for inspection • Robust and fast performance analytics • Efficient reporting strategy (summary plots, email and web page) • Recording of inspections in a database for trend detection and long-term performance monitoring The main drawback of the present approach is that quality checks are performed after dissemination in order not to impact the timeliness performance.
The area of the summer flux footprint of the Barrow station calculated using the FLEXPART Lagrangian particle dispersion model  31  was mainly restricted to the regions of Siberia and Alaska and the Chukchi and Beaufort Seas (Supplementary Fig. 4 ). A similar footprint area was found by a simulation of the sensitivity of the Barrow CO 2 measurements during the last week of August to terrestrial carbon fluxes 20 days before the measurements using the adjoint code of the Laboratoire de Météorologie Dynamique (LMDZ) atmospheric transport model (see Methods; Supplementary Fig. 5a-c ). We also performed 40-and 60-day back-trajectory calculations using LMDZ and found that the footprint area for fluxes influencing the SCD did not change significantly compared to a 20 days influence (Supplementary Fig.
R SCD-T also shifted when we used a different climatic dataset (Supplementary Fig. 7 ) and weekly rather than daily Barrow CO 2 concentration data (Supplementary Fig. 8 ). R SCD-T again decreased significantly for 1979-2012 (P < 0. 01) when calculated with a 15-year moving window (with both SCD and temperature detrended) (Supplementary Fig. 9 ). We also explored the robustness of the result to the method used to calculate SCD. SCD was initially calculated as the difference in the CO 2 concentration between the first week of July and the last week of August in the detrended CO 2 record, but CO 2 uptake decreased briefly in August (Supplementary Fig. 1 ), which could conceivably impact the results. Therefore, we calculated SCD using alternative methods. We first set SCD as the difference in CO 2 concentration between the climatological day of the year when the detrended CO 2 concentration crossed its long-term mean downwards (climatological spring zero-crossing date) and the climatological day of the year when detrended CO 2 concentration reached its annual minimum (climatological trough date).
We found that NDVI and temperature across ecosystems north of 50°N were always significantly positively correlated during the earlier period (R = 0. 82 ± 0. 07), but not the later period (R = 0. 08 ± 0. 19) (Fig. 2a ), supporting this hypothesis. A further analysis of the spatial pattern of changes in R NDVI-T indicated that the recent decrease in R NDVI-T occurred over a wide region comprising eastern Siberia and Alaska (Fig. 2d ), which constitutes most of the summer footprint of the Barrow station (Supplementary Fig. 4 ). In addition, analysis of an extended solar-induced chlorophyll fluorescence (SIF) dataset  35  also showed that the partial correlation between SIF and temperature across ecosystems north of 50°N was not significant during the period 2001-2012 (R SIF-T = -0. 19, P = 0.
61) (Supplementary Fig. 14 ). A 30-year global dataset of a satellite-derived NPP model  27  , also based on NDVI (Fig. 2b, e ) and of gross primary productivity (GPP) observation-based results for 1982-2011 based on flux-tower data  28  (Fig. 2c, f ) confirmed the loss of a positive correlation with summer temperature. By contrast, only two of nine terrestrial ecosystem models properly captured the loss of the positive correlation between NPP and temperature (Supplementary Fig. 15 ). The decrease in R NDVI-T may have been due to an increase in the number of extreme warm days, because vegetation growth is reduced by heat stress during extremely warm days. The number of extreme warm days (defined as temperatures higher than the 90 th percentile of the July and August temperature distribution for 1982-2012) increased during the later period (Supplementary Fig.
The decrease in R NDVI-T may also have been caused by lower soil-moisture contents during the later study period. We investigated this possibility using the values of root-zone soil moisture from the observation-based model of land water budgets constrained by multiple satellite datasets, including surface soil moisture detected by microwave sensors  37, 38. These values of soil moisture in summer were slightly higher during the later than the earlier period (Supplementary Fig. 18 ), suggesting that changes in soil moisture alone did not account for the decrease in R NDVI-T. In addition, increased temperature could induce plant water stress, through increasing atmospheric deficits, to such a degree that plants either lose water at a faster rate or close stomata. We also analyzed changes in the atmospheric vapor pressure deficit (VPD), which is indicative of atmospheric demand for water, between the earlier and later period.
To test the hypothesis that the loss of temperature dependence of terrestrial respiration was responsible for the change in R SCD-T , we used a global respiration dataset that integrates a global soil respiration database  5  with a climate-driven empirical model of soil respiration  39. The interannual partial correlation of summer heterotrophic respiration (HR) with summer temperature (R HR-T ), while controlling for the effects of precipitation and cloudiness, was significant for both study periods. The coefficients are 0. 85 ± 0. 11 for 1979-1995 (P < 0. 01) and 0. 57 ± 0. 16 for 1996-2012 (P < 0. 05), respectively (Supplementary Fig. 20 ). Our analysis suggested that respiration continued to respond significantly positively to temperature in both study periods, and the response was not stronger during the later period, implying that the change in R SCD-T was not due to changes in the response of terrestrial respiration to temperature.
Some unreliable CO 2 observations can strongly affect estimates of the seasonal cycle and therefore the net summer uptake, so any data lying outside five standard deviations of the residuals between the original data and the fit were discarded from the original daily time series  17. We calculated summer CO 2 drawdown (SCD), which was adopted as an indicator of net summer CO 2 uptake in three ways. For the main analysis, SCD was calculated as the difference of CO 2 concentration between the first week of July and the last week of August. For the robustness tests, SCD was calculated as the difference of CO 2 concentration between the climatological day of the year when CO 2 crossed its annual mean level (the climatological spring zero-crossing date) and the climatological day of the year of minimum atmospheric CO 2 concentration (the climatological trough date).
Climatic and satellite-derived products. We used monthly climatic data (temperature, precipitation, and cloud cover) at a spatial resolution of 0. 5°for 1901-2012 from the Climate Research Unit, University of East Anglia (CRU TS4. 0 dataset)  50. Climatic observations at high latitudes are rare, so we also applied another climatic dataset, WATCH (WATer and global CHange) Forcing Data, to the ERA-Interim data (http://www. eu-watch. org/gfx_content/documents/ README-WFDEI. pdf)  51. Satellite-derived NDVI, used as a proxy for vegetation productivity, was retrieved from the third-generation of the Advanced Very High Resolution Radiometer (AVHRR) developed by the Global Inventory Modeling and Mapping Studies (GIMMS) group  26. The GIMMS NDVI for 1981-2014 has a spatial resolution of one-twelfth of a degree (~8 km) and a 15-day interval (available at https://ecocast.
arc. nasa. gov/data/pub/gimms/3g. v1/). Solar-induced chlorophyll fluorescence (SIF) that reflects photosynthetic signals from the molecular origin is increasingly used as a physiological-based proxy for gross primary productivity (GPP)  [52] [53] [54] [55]. Here we used an extended satellite-derived SIF dataset for the period 2001-2012  35. This dataset is generated by a trained neural network that integrates surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from the Orbiting Carbon Observatory-2 (OCO-2). The extended SIF dataset, which is available for the all-sky condition, has a spatial resolution of 0. 05 degrees and a temporal resolution of 4 days. Two other terrestrial productivity products were also used. The first was a 30-year global dataset of satellite-derived net primary productivity (NPP), which was calculated using the Moderate Resolution Imaging Spectroradiometer (MODIS) NPP algorithm driven by 30-year (1982-2011) GIMMS FPAR (the fraction of photosynthetically active radiation absorbed by the vegetation) and data for leaf area index (LAI)  27.
The second was the GPP product (0. 5°× 0. 5°, monthly, 1982-2011) generated by integrating a global network of eddy covariance sites, satellite remote sensing, and meteorological data in a machine-learning algorithm  28. This global product has been widely adopted to aid the understanding of the spatiotemporal dynamics of the global carbon cycle  56  and to benchmark process-based terrestrial models  28, 57, 58. In addition, we used monthly data for root-zone soil moisture and evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) version 3. 0a dataset at a spatial resolution of 0. 25°× 0. 25°3  7. This dataset assimilates microwave observations of surface-soil moisture from the European Space Agency (ESA)-Climate Change Initiative (ESA-CCI) dataset in a multi-layer water-balance module  38.
We also used the sea-ice extent data that are derived from the Sea Ice Index Version 3 dataset (http://nsidc. org/data/G02135). Atmospheric CO 2 inversion system. We gathered estimates of monthly net biome production (NBP) from MACC (Monitoring Atmospheric Composition and Climate) (version v14r2, http://copernicus-atmosphere. eu/) for 1979-2011  49. The MACC CO 2 atmospheric-inversion system, based on the global tracer transport model LMDZ  29  , adopts a variational inversion formulation to estimate optimized surface CO 2 fluxes from nearly 130 CO 2 observing stations, with a spatial resolution of 3. 75°× 1. 875°and a temporal resolution of 8 days, separately for daytime and nighttime. Simulations of the terrestrial ecosystem models. We used simulated net primary productivity and heterotrophic respiration from nine process-based ecosystem models: Community land Model Version 4.
The linkage between atmospheric CO 2 and surface CO 2 fluxes. Atmospheric CO 2 concentration and net carbon fluxes are two different parameters: atmospheric CO 2 concentrations integrate net carbon fluxes from different regions through atmospheric transport. To quantify the linkage between atmospheric CO 2 concentration and net surface CO 2 fluxes, we use the LMDZ atmospheric transport model to convert net CO 2 fluxes into an estimate of atmospheric CO 2 concentration at Barrow station. The transport model is nudged with winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis. We performed an ensemble of transport simulations for the period 1979-2012, using different NBP simulations from the terrestrial ecosystem models participating in the Trendy project and the NBP dataset from the MACC inversion system.
Acknowledgements This study was supported by the  Strategic Priority Research Program (A)  of the  Chinese Academy of Sciences  (grant  XDA20050101 ,  XDA19070303 ), the  National Natural Science Foundation of China  ( 41530528 ,  41871104 ), the 13th Five-year  Informatization Plan of Chinese Academy of Sciences  (Grant  XXH13505-06 ) and the  Thousand Youth Talents Plan  project in China. P. C. , I. J. , and J. P. were funded by the  European Research Council Synergy  grant  SyG-2013-610028 IMBALANCE-P. We thank the  TRENDY modelling group  for providing the model simulation data.
Finally, proprietary software used for analysis of data from each device may apply unknown selection and other processing of data, resulting in differences in absolute values obtained by different software or different versions of the same software (see example in  [183] ). Total protein amount can be measured by various colorimetric assays [Bradford or micro-bicinchonic acid (BCA)] or fluorimetric assays, or by global protein stain on SDS-PAGE. The EV sample concentration must be within the linear range of the reference curve. However, protein quantification can result in overestimation due to co-isolated protein contaminants (such as albumin from culture medium or plasma/serum), especially when the less specific methods of EV separation are used, or conversely can prove not sensitive enough if highly specific methods yield pure EVs.
Furthermore, the knowledgebase can be revised and expanded as technologies and techniques develop, with input and assistance from the community. 1 Authors are also urged to submit EV profiling data to public databases such as those curated and maintained by the European Bioinformatics Institute, the US National Center for Biotechnology Information, and the Japanese Center for Information Biology. In addition, but not as a substitute, data may be submitted to field-specific databases such as EVpedia  [315, 316] , Vesiclepedia  [317]  (formerly ExoCarta  [318] ), and the exRNA Atlas  [319]. 2   Notes 1. EV-TRACK submission and EV-METRIC calculation may assist with but do not replace appropriate peer review. Interestingly, respondents to the MISEV2018 Survey were split between advocating mandatory EV-TRACK submission and reporting and recognizing EV-TRACK as a valuable but optional tool.
As a result, MISEV2018 strongly encourages but cannot mandate EV-TRACK submission, which most seem to acknowledge as highly valuable. 2. The utility of field-specific databases was questioned by some respondents, who felt that data from studies analysing EVs obtained through low-specificity methods renders these databases difficult to interpret in terms of specific association of a given molecule/sequence to EVs or an EV subtype. Submission to field-specific databases is thus important to update these resources, which are of value to identify different studies finding the same molecules, but not to demonstrate the exosomal nor EV nature of the analysed entities. It also does not substitute for deposition with publicly maintained repositories.