dataesr/scientific-paragraphs-categorization huggingface dataset:
A scene is determined to be cloudy when the retrieved surface pressure differs by more than 30 hPa from the estimated ECMWF surface pressure. This relatively loose threshold is allowed as the proxy retrieval approach remains relatively robust in the presence of nearsurface clouds. The average difference between our retrieved surface pressure and ECMWF after filtering for cloud is approximately 3 hPa with a standard deviation of below 10 hPa, with the offset from 0 hPa being attributed to spectroscopic uncertainties in the O 2 cross-sections. The Proxy XCH 4 retrieval is performed for all scenes that are deemed to be sufficiently cloud free. After filtering for signal-to-noise, cloud, and data quality we are left with 1 032 760 XCH 4 retrievals over land between April 2009 and December 2013.
Validation of the Proxy XCH 4 and XCH 4 /XCO 2 ratio This section presents the validation of the University of Leicester GOSAT Proxy XCH 4 v5. 0 data through comparison to observations from the ground-based TCCON. In addition, for the first time the XCH 4 / XCO 2 ratio itself, the core component of the Proxy XCH 4 data, is validated against the corresponding TCCON data. TCCON is a global network of ground-based highresolution Fourier transform spectrometers recording direct solar spectra in the near-infrared spectral region  (Wunch et al. , 2011a). The TCCON data are calibrated to World Meteorological Organization (WMO) standards by calibration against aircraft measurements  (Wunch et al. , 2010). Although it should be noted that this aircraft calibration does not measure the whole column, the TCCON data are the standard against which current satellite observations of greenhouse gases are validated  (Cogan et al.
, 2012; Wunch et al. , 2011b; Dils et al. , 2014). To date, all previous validation of satellite greenhouse gas observations against TCCON has used TCCON data that were affected by instrumental biases relating to a laser sampling error which resulted in an XCO 2 error of approximately 0. 26 % (1 ppm)  (Messerschmidt et al. , 2010). Although the corresponding XCH 4 error was not quantified, it is expected that it would be of similar magnitude (i. e. 1 part in 400). The latest, recently released, version of the TCCON data (GGG2014) incorporates a correction for the laser sampling errors and any remaining bias is expected to be small. Figure  2  shows the GGG2014 TCCON XCH 4 data and the Proxy XCH 4 plotted as time series for each TCCON site. The mean GOSAT-TCCON difference, the standard deviation of the GOSAT-TCCON difference, the correlation coefficient, and the number of soundings are all provided for each site.
4 • N to Lauder, New Zealand, at 45. 0 • S. The correlation between the GOSAT and TCCON data is reasonable/good across all sites, ranging from 0. 54 at Karlsruhe to 0. 79 at Lauder with an overall correlation coefficient of 0. 87 between 22 619 points. The overall bias is found to be 4. 8 ppb with an overall single measurement precision of 13. 4 ppb (ranging from 8. 3 ppb at Darwin to 14. 9 ppb at Garmisch). The station-to-station bias, which is an indication of the relative accuracy, is calculated as the standard deviation of the individual site biases and is found to be just 4. 2 ppb. In addition to the validation of the Proxy XCH 4 data, we also present for the first time the validation of the XCH 4 / XCO 2 ratio. This ratio is the quantity directly retrieved from the satellite measurement, is independent of any model XCO 2 , and has recently itself been used directly within a flux inversion study  (Fraser et al.
Assessing the CO 2 model ensemble component In Sect. 3 the final Proxy XCH 4 and the XCH 4 / XCO 2 component were both validated against the TCCON data. In this section we validate the remaining component of the proxy product from Eq. (  1 ), namely the model XCO 2. As discussed in Sect. 2, this update to the University of Leicester GOSAT Proxy XCH 4 data uses an ensemble of model XCO 2 data to act as the model XCO 2 component. We utilise the XCO 2 from three state-of-the-art global transport models which all assimilate surface in situ measurements; GEOS-Chem (University of  Edinburgh -Feng et al. , 2011, v1. 50), MACC-II  (Chevallier et al. , 2010, v14r1)  and CarbonTracker (NOAA -  Peters et al. , 2007, vCT2013B). These model runs have assimilated similar surface measurements but not necessarily from all of the same data sets or the same locations.
). Where the model does not cover the full GOSAT time period studied here, the data from the previous year are used and adjusted by the NOAA annual growth rate. The main concern with using modelled XCO 2 data for the proxy method is that the additional uncertainty added to the final proxy data product is difficult to determine. Where the model XCO 2 data are constrained by surface data there can be a high degree of confidence that the model data are close to representing the true value of CO 2 ; however, it is away from such regions where there is a possibility of adding additional biases into the Proxy XCH 4 data. The TCCON stations are mostly in regions that are also well constrained by surface in situ measurements and hence the model CO 2 data should be well constrained, at least at the surface level, and it is therefore expected to reasonably reproduce the TCCON column data.
In more remote regions where we neither have validation data nor surface measurements to constrain the models, this inconsistency between the models becomes more pronounced. It is this uncertainty in model XCO 2 in regions away from the available validation data that we attempt to address through the use of the XCO 2 model ensemble. Each of the three XCO 2 models are sampled at every GOSAT time and location and convolved with the scene-specific GOSAT averaging kernels. The median value of the three model values is used as the model XCO 2 in calculating the final Proxy XCH 4. However, we also define the uncertainty on this median XCO 2 as the maximum of the absolute differences of each individual model to the median value. We have already demonstrated that the models all well reproduce the validation data at TCCON sites without any one model identified as being better than the others from our comparisons.
Where the models all agree well with each other away from the validation sites, the assumption is that the models are accurately representing the true atmosphere. Where the models disagree with each other, we do not know which model is correct in the absence of further validation data and in some cases the discrepancy between models can be very large (i. e. > 4 ppm). In such cases where no validation is possible, the best estimate of the uncertainty in the model XCO 2 data is obtained by examining the difference of the model data around the median value. Figure  5  shows global maps of this estimated model uncertainty for each season. There are clear spatial/temporal patterns in the distribution of this model uncertainty. During March-May (boreal "spring"), there is a large uncertainty (> 2 ppm) over India and the African regions typically associated with biomass burning.
E. Asia showing a moderate level of uncertainty. In the Southern Hemisphere, again South America and southern Africa show moderate uncertainty which appears to be linked to emissions from biomass burning. This section has shown that the estimated uncertainty of the model XCO 2 can vary greatly in time and space. When considering the implication of this uncertainty on flux inversions of the Proxy XCH 4 data, the relative importance of the different uncertainties must be considered. The following section investigates the distribution of the model XCO 2 uncertainty and judges its relative importance against the a posteriori error from the retrieval itself. Finally, both of these uncertainties are assessed against the difference to modelled XCH 4 already constrained by surface observations to determine the utility of the satellite data despite the presence of these uncertainties.
However, that in itself does not preclude the data from adding useful information to a CH 4 flux inversion. The MACC-II model XCH 4 (v10-S1NOAA) data have assimilated NOAA surface measurements at background sites and hence are well constrained in the remote atmosphere  (Bergamaschi et al. , 2013). Here we calculate the difference between the MACC XCH 4 model field and the GOSAT Proxy XCH 4 data for each GOSAT measurement (referred to from here as XCH 4 ). We then aggregate these differences in the same way as the model XCO 2 uncertainties. Note that the MACC XCH 4 model data are currently only available until the end of 2012. As some inversion systems will perform a simple (e. g. latitudinal) bias correction, the calculated retrieval a posteriori and model XCO 2 uncertainties can potentially be much lower than the XCH 4 value but still not provide information to the inversion.
The σ XCH 4 does exhibit more seasonality than the µ XCH 4 and is again considerably higher than the estimated uncertainties (7-20 ppb vs. 5-6 ppb). In combination, this suggests that the GOSAT observations over South America should add considerable information to the inversion. For Northern Africa, both the a posteriori error and the uncertainty related to the XCO 2 model are small due to the high SNR over the Sahara and the low CO 2 variability respectively (with seasonal average values ranging from 3. 3 to 3. 6 ppb for the a posteriori error and from 2. 8 to 4. 6 ppb for the model XCO 2 error). In contrast, the µ XCH 4 (7. 2-12. 3 ppb) and σ XCH 4 (4. 9-8. 4 ppb) values over this region are relatively large with a high degree of temporal variability, suggesting that the satellite data should add considerable value in constraining the inversion over this region.
Summary and conclusions We present details of the update to the University of Leicester GOSAT Proxy XCH 4 v5. 0 data set with 5 years of GOSAT data now processed. The data are validated against the latest ground-based TCCON data and found to agree well with on average a small bias of 4. 8 ppb (∼ 0. 27 %), a single-sounding precision of 13. 4 ppb (∼ 0. 74 %), and a relative accuracy of 4. 2 ppb. For the first time the XCH 4 / XCO 2 ratio component of the proxy retrieval is validated and also found to agree well with TCCON with a bias of 0. 014 ppb ppm -1 (∼ 0. 3 %) and a single-sounding precision of 0. 033 ppb ppm -1 (∼ 0. 72 %). A major unknown uncertainty in previous Proxy XCH 4 products was the uncertainty associated with the model XCO 2. In this work we validate three separate state-of-theart chemistry transport models against the TCCON data and find that although the models can differ greatly (> 4 ppm) away from the TCCON stations, at the validation locations it is difficult to distinguish which model performs better from our comparisons.
It is important to note that the MACC data are already constrained by NOAA background sites. One final consideration for users of the Proxy XCH 4 data who are performing atmospheric inversions is that, should they have their own XCO 2 model which they believe is consistent with their XCH 4 model, it may be beneficial to only take the GOSAT XCH 4 / XCO 2 ratio and apply their own model XCO 2 (with appropriate averaging kernels) in order to minimise transport model errors between the different models. Alternatively the XCH 4 / XCO 2 ratio can also be inverted directly as shown in  Fraser et al. (2014)  and  Pandey et al. (2015). Table  A1. Table showing the comparison statistics between each XCO 2 model (sampled as per the GOSAT measurements) within ± 2 • of each TCCON site against the TCCON validation data.
Acknowledgements. We thank  Japanese Aerospace Exploration Agency ,  National Institute for Environmental Studies , and the  Ministry of Environment  for the GOSAT data and their continuous support as part of the  Joint Research Agreement. R. J. Parker  is funded through an  ESA   Living Planet Fellowship. The work at the  University of Leicester  and  University of Edinburgh  was supported by funding through the  UK National Centre for Earth Observation (NCEO) , the  Natural Environment Research Council (NERC) , and the  ESA Greenhouse Gas Climate Change Initiative (GHG-CCI ). Part of this work was funded by the  NERC Amazonian Carbon Observatory project  ( NE/J016284/1 ) and the  NERC   GAUGE  project ( NE/K002465/1 ). This research used the  ALICE High Performance Computing Facility at the University of Leicester.
The authors would like to thank  Paul Wennberg  as the  TCCON PI  for provision of TCCON data. The European TCCON groups acknowledge financial support by the  EU project InGOS. The RAMCES team at  LSCE (Gif-sur-Yvette, France)  is acknowledged for maintenance and logistical work for the Orléans TCCON site. The  University of Bremen  acknowledges support from the  Senate of Bremen , the  EU -project  ICOS-INWIRE , and operational funding from the  National Institute for Environmental Studies (NIES, Japan). A part of the work at  JAXA  was supported by the  Environment Research and Technology Development Fund  ( A-1102 ) of the  Ministry of the Environment, Japan. A part of this work has been supported by the  European Commission Seventh Framework Programme  (FP7/2007-2013) projects  MACC  under grant agreement  218793  and  MACC-II  under grant agreement  283576.
The Double Focusing Mass Spectrometer (DFMS), part of the ROSINA instrument package aboard the European Space Agency's Rosetta spacecraft visiting comet 67P/Churyumov-Gerasimenko, experiences minor deformation of the mass peaks in the high resolution spectra acquired for m/Z = 16, 17, and to a lesser extent 18. A numerical deconvolution technique has been developed with a twofold purpose. A first goal is to verify whether the most likely cause of the issue, a lack of stability of one of the electric potentials in the electrostatic analyser, can indeed be held responsible for it. The second goal is to correct for the deformation, in view of the important species located around these masses, and to allow a standard further treatment of the spectra in the automated DFMS data processing chain.
Between m/Z = 15 and 16 amu/e there is a major step in the coarse potential, requiring the fine potential to be at its highest value for m/Z = 16 amu/e and progressively smaller at subsequent masses, and thus close to its design limits. The working hypothesis is that the fine potential then fluctuates around the desired value. The spectra therefore are deformed, and this is most pronounced in the HR spectra. The goal of the present paper is (1) to model the peak deformation so as to verify whether it is compliant with the working hypothesis, and (2) to offer a way to deconvolve the spectra so that they can be processed afterwards by the normal DFMS data processing chain. This is particularly relevant in view of the importance of the species detected at m/Z = 16-17 amu/e for cometary chemistry.
By examining how the deformed peak shape changes across the spectrum, it has been possible to confirm, at least to first order, that the beam pattern indeed is only shifted without changing its shape. The analysis presented here therefore appears to corroborate the conclusion that a deviation of the electric potential in the electrostatic analyser is indeed the cause of the problem. At the same time the analysis suggests a technique for correcting the peak deformation. An algorithm has been developed and implemented, and the results for high resolution spectra at m/Z = 16 and 17 look promising. In this way, the processing of such spectra can be easily incorporated in the DFMS data analysis chain. This gives access to a few important ions, in particular 16 O + and 16 OH + , that play a role in the water chemistry, which is essential for understanding the composition of cometary atmospheres.
Acknowledgements The authors thank the following institutions and agencies, which supported this work: Work at  BIRA-IASB  was supported by the  Belgian Science Policy Office  via  PRODEX/ROSINA PEA 90020  and an  Additional Researchers Grant  (Ministerial Decree of 2014-12-19), as well as by the  Fonds de la Recherche Scientifique  grant  PDR T. 1073. 14 " Comparative study of atmospheric erosion ". Work at  UoB  was funded by the  State of Bern , the  Swiss National Science Foundation , and by the  European Space Agency PRODEX Program. Work at Southwest Research institute was supported by subcontract no. 1496541  from the  Jet Propulsion Laboratory. This work has been carried out thanks to the support of the A* MIDEX  project (n •  ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR).
This work was supported by  CNES  grants at  LATMOS  and  LPC2E. Work at the  University of Michigan  was funded by  NASA  under contract  JPL-1266313. The results from ROSINA would not be possible without the work of the many engineers, technicians, and scientists involved in the mission, in the Rosetta spacecraft, and in the ROSINA instrument team over the past 20 years whose contributions are gratefully acknowledged. We thank herewith the work of the whole  ESA/Rosetta team. Rosetta  is an  ESA  mission with contributions from its member states and  NASA. All ROSINA data are available on request until they are released to the PSA archive of  ESA  and to the  PDS archive of NASA.
More than 20 years ago, the French Muséum National d'Histoire Naturelle 1 (MNHN, Secretariat of the Fauna and Flora) published the fi rst part of an atlas of the fl ora of France at a 20km spatial resolution, accounting for 645 taxa  (Dupont 1990). Since then, at the national level, there has not been any work on this scale relating to fl ora distribution, despite the obvious need for a better understanding. In 2011, in response to this need, the Federation des Conservatoires Botaniques Nationaux 2 (FCBN, http://www. fcbn. fr) launched an ambitious collaborative project involving eleven national botanical conservatories of France. Th e project aims to establish a formal procedure and standardized system for data hosting, aggregation and publication for four areas: fl ora, fungi, vegetation and habitats.
In 2014, the fi rst phase of the project led to the development of the national fl ora dataset: SIFlore. As it includes about 21 million records of fl ora occurrences, this is currently the most comprehensive dataset on the distribution of vascular plants (Tracheophyta) in the French territory. SIFlore contains information for about 15'454 plant taxa occurrences (indigenous and alien taxa) in metropolitan France and Reunion Island, from 1545 until 2014. Th e data records were originally collated from inventories, checklists, literature and herbarium records. SIFlore was developed by assembling fl ora datasets from the regional to the national level. At the regional level, source records are managed by the national botanical conservatories that are responsible for fl ora data collection and validation.
SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories In order to present our results, a geoportal was developed by the Fédération des conservatoires botaniques nationaux that allows the SIFlore dataset to be publically viewed. Th is portal is available at: http://sifl ore. fcbn. fr. As the FCBN belongs to the Information System for Nature and Landscapes' (SINP), a governmental program, the dataset is also accessible through the websites of the National Inventory of Natural Heritage (http:// www. inpn. fr) and the Global Biodiversity Information Facility ( http://www. gbif. fr). SIFlore is regularly updated with additional data records.
Taxonomic coverage Note: Th e taxonomic and nomenclatural reference for the fi rst edition of the SIFlore dataset is the fi fth edition of the taxonomic repository for the fauna, fl ora and fungi of metropolitan France and overseas territories, TAXREF, which was developed within the framework of a convention between the French ministry of ecology, the MNHN, the FCBN and Tela Botanica. Th e overall methodological framework at the basis of the TAXREF repository is presented in  Gargominy et al. (2014). Th e version originally used for data aggregation is TAXREF v5. 0, posted online on July 20th, 2012. At the time of writing, the current version of TAXREF was v8. 0, posted online on December 1st, 2014. Data is available in the taxonomic and nomenclatural reference TAXREF v5. 0 on the http://sifl ore.
fcbn. fr web atlas. For practical reasons, data has been automatically linked to TAXREF v8. 0 on the GBIF website, which may generate some taxonomic errors. Th is work was carried out under the responsibility of the Service du patrimoine naturel (SPN/MNHN). In order to prevent any error of interpretation, amended taxa were tagged in the Darwin core archive within the fi eld datageneralization as follows: "taxon initially attached to the Taxonomic repository TAXREF v5. Th is taxon has undergone changes since". It has to be noted that the taxonomic coverage of La Reunion Island was incomplete in this 1 st version of SIFlore dataset as TAXREF v5. 0 did not include most of La Reunion Island taxa. Th is issue was adressed in TAXREF v7, and subsequent versions, with the integration of the " Index des Trachéophytes de La Réunion (ITR,  Boullet & al, 2012)  ".
Consequently, SIFlore completeness will be improved in the near future by including more taxa originating from La Réunion. General taxonomic coverage description: Th e taxonomic coverage of this dataset spans the phylum Tracheophyta (vascular plants) present in metropolitan France (excluding the departments of Alsace and Lorraine) and La Reunion Island. Taxa are fi rst identifi ed at the species level and, if appropriate, subspecies level. Th e largest number of data records belong to the Asteraceae family (2,317,247 records), followed by Poaceae (2,220,479 records), Fabaceae (1,436,758 records) and Rosaceae  (1,264,321 records). Th e families with the least number of records are Escalloniaceae, Malpighiaceae and Schizaeaceae with one data record each (Figure  1 ).
General geographic description Th is national dataset collates species occurrences from metropolitan France and Reunion Island. Four diff erent climates are covered in the metropolitan area: oceanic, continental, mediterranean and alpine. Reunion Island is an overseas department and region of France, located in the southwest Indian Ocean, in the Mascareignes archipelago, to the east of Madagascar. Th e island has a tropical climate. Th e records documented in the dataset are distributed across 21 (out of a total of 27) administrative regions of France: Aquitaine, Auvergne, Bourgogne, Bretagne, Centre, Champagne-Ardenne, Corse, Franche-Comté, Ile-de-France, Languedoc-Roussillon, Limousin, Midi-Pyrénées, Nord-Pas-de-Calais, Basse-Normandie, Haute-Normandie, Pays de la Loire, Picardie, Poitou-Charentes, Provence-Alpes-Côte d'Azur, La Réunion, Rhône-Alpes.
Th e Universal Transverse Mercator Projection 40 South (UTM 40S ) is an adaptation of the standard Mercator projection. Th is is a cylindrical and conformal projection using RGR92 geodetic system (compatible with WGS84). Th e central meridian is 57°E and the latitude of origin is 0. Prime coordinates are 500,000 meters and 10,000,000 meters. Grids are composed of cells of 10 km by 10 km. Continental and maritime metropolitan France (954, 500 sq. km) is divided into 9546 cells and La Reunion Island terrestrial area (2, 512 sq. km) is divided into 34 cells (information on maritime area is not given as it is disproportionate in relation to terrestrial one). All SIFlore records are georeferenced through the code of the corresponding grid square. French municipalities repository: Th e offi cial geographic boundaries of the municipalities and associated data (BD CARTO®) were provided by the National Geographic Institute (IGN) based on the National Institute of Statistics and Economic Studies (Insee) database.