dataesr/scientific-paragraphs-categorization huggingface dataset:
The overall calibration accuracy of the IIR measurement is specified to be better than 1 K in all channels. In-flight performances were assessed by CNES at the beginning of the mission  (Trémas, 2006; Tinto and Trémas, 2008). The in-flight short-term gain stability was found to be better than 0. 1 K of equivalent brightness temperature, except during the day-to-night transitions, during which the gain varies by up to 0. 45 K. The in-flight noise equivalent differential temperature (NedT) at 210 K was between 0. 2 and 0. 3 K, similar to values measured before launch and better than the specified value of 0. 5 K. The NedT is of the order of 0. 1 K at 250 K. The calibrated radiances are reported in the CALIPSO Version 1 IIR Level 1B products  (Vaughan et al. , 2015)  available from the Atmospheric Science Data Center of the NASA Langley Research Center and at the AERIS/ICARE data center in France.
SEVIRI/Meteosat Comparing IIR and MODIS/Aqua observations, both from the A-Train, ensures a very large sampling for statistical analyses. A complementary view is available through comparisons with SEVIRI on-board the geostationary second generation Meteosat satellites. SEVIRI provides radiometric data every 15 min in three spectral bands in the thermal infrared (channels IR8. 7, IR10. 8, and IR12) with a 3 km resolution at the sub-satellite point, which is at 0 • in both latitude and lon- NedT 0. 2-0. 3 K @ 210 K < 0. 025 K < 0. 12 K 0. 1 K @ 250 K  (Xiong et al. , 2015)  (EUMETSAT, 2007a) Specification: < 0. 5 K.
Selection of IIR, MODIS, and SEVIRI companion channels The IIR companion channels were chosen before launch among the various spectral channels of MODIS/Aqua and SEVIRI/Meteosat following a specific procedure. For each IIR channel, the selected MODIS or SEVIRI companion channel is the one that not only minimizes the brightness temperature differences (BTDs) with IIR, but also shows a similar sensitivity to the atmosphere and to the surface. This evaluation was conducted before launch by simulating the IIR and candidate companion channels using the forward radiative transfer model 4A (4A: Automatized At-mospheric Absorption Atlas) and more than 2300 atmospheres from the Thermodynamic Initial Guess Retrieval (TIGR) input dataset. MODIS/Aqua ISRFs can be retrieved from the MODIS Characterization Support Team website (http://mcst.
Handling these differences is more difficult over land where altitude and nature of the ground contribute to enhance the inhomogeneity of the observed scenes. We have chosen to minimize these issues by restricting our study to observations over ocean. It is worthwhile to note that this choice allows a robust calibration assessment, in a wide range of brightness temperatures, at most latitudes and at any season, which is compatible with the aim of our study. Future work will include analyses at the highest latitudes, which are currently not examined because they are covered by ice, and analyses in extreme conditions of land surface temperatures. The simulated BTDs between IIR and the selected companion channels are shown in Sect. 3. 2. 3, after a brief description of the simulation model and of the auxiliary datasets in Sect.
3. 2. 1 The forward radiative transfer model: 4A The radiative transfer model used at all stages of this study is 4A/OP, the operational version of 4A, adapted and maintained by NOVELTIS (http://4aop. noveltis. com/) in collaboration with CNES and Laboratoire de Météorologie Dynamique (LMD). The 4A model is a fast and accurate lineby-line (LBL) radiative transfer model initially developed at LMD  (Scott and Chédin, 1981). As recalled in Anthony Vincent and Dudhia (2017), 4A was among the pioneer radiative transfer models to bypass LBL processing time by calculating once and for all a set of compressed look-up tables (LUTs) of monochromatic optical depths. These LUTs are generated by the nominal line-by-line STRANSAC model  (Scott, 1974; Tournier et al. , 1995)  coupled to the Gestion et Étude des Informations Spectroscopiques Atmosphériques (GEISA) spectroscopic database.
Atmospheric inputs: the TIGR dataset The simulations have been conducted for the 2311 atmospheres of the TIGR climatological library  (Chédin et al. , 1985; Chevallier et al. , 1998). The 2311 atmospheres are sorted into five air mass types according to their virtual temperature profiles  (Achard, 1991; Chédin et al. , 1994) , which are namely (1) tropical, (2) mid-lat1 for temperate conditions, (3) mid-lat2 for cold temperate and summer polar conditions, (4) polar1 for very cold polar conditions, and (5) po-lar2 for winter polar conditions. The tropical air mass type is composed of 872 atmospheres, mid-lat1 and mid-lat2 are composed of 388 and 354 atmospheres, respectively, and po-lar1 and polar2 are composed of 104 and 593 atmospheres, respectively.
Brightness temperatures simulations The 4A model and the TIGR atmospheres input data have been used to simulate the brightness temperatures of IIR and of the candidate companion channels. Each of the five TIGR air mass types includes one individual simulation for each individual atmosphere included in the air mass type (i. e. , 872 simulations for the tropical type, 388 for mid-lat1, 354 for mid-lat2, 104 for polar1, and 593 for polar2). Each TIGR air mass type is then characterized through the mean BTD between IIR and MODIS or SEVIRI channels and associated standard deviations derived from the individual simulations (hereafter "TIGR_BTD"). The simulations presented here have been obtained using the 2009 version of the 4A/OP model described above, which does not call into question the initial evaluations conducted before the 2006 launch.
Clear sky brightness temperatures simulations Clear sky simulations of the collocated observations (Sect. 4. 1) are carried out using the 4A/OP model (see Sect. 3. 2. 1) and the temporally and spatially closest atmo-spheric profiles and ocean skin temperatures given by ERA-Interim (ERA-I) reanalyses generated at the European Centre for Medium-Range Weather Forecast (ECMWF). We have chosen to use outputs from reanalyses over outputs based on radiosondes measurements (e. g. , the LMD Analyzed Ra-dioSoundings Archive, ARSA, database) because of the low density of the radiosonde network over sea. ERA-I reanalyses are available every 6 h with a nominal resolution of 0. 75 • in latitude and longitude. The 4A/OP-simulated radiances are computed for each clear pixel found at a distance smaller than 5 km from the closest ERA-I input to ensure the highest possible coherence for the comparisons with the observations.
Acknowledgements. The authors are deeply grateful to CNES, the  NASA Langley Research Center , and  SSAI (Science Systems and Applications, Inc. ) for their support. This work benefited from the support of the  Centre National de la Recherche Scientifique (CNRS)  and of  Institut National des Sciences de l'Univers (INSU). Numerous people have contributed to this work over the years. The experience gained at the occasion of the  NOAA/NASA Pathfinder Program  and related research since then has paved the way for this study. Our warmest thanks go to  Alain Chédin  and colleagues from  LMD  for stimulating discussions at all the stages of this work. We thank  Thérèse Barroso  and  Pascale Ferrage , former and current CNES CALIPSO mission coordinators, for their helpful discussions about the IIR instrument and the  CALIPSO team  for their encouragement.
Gene Accession number Forward Reverse EF1-α DQ312264 5'-gcctggtatggttgtgacct-3' 5'-gaagttagcagcacccttgg-3' 18S rRNA X16077 5'-cggataaccgtagtaattctag-3' 5'-gtactcattccaattaccagac-3' BnaSultr1. 1 AJ416460 5 0 -agatattgcgatcggaccag-3 0 5 0 -gaaaacgccagcaaagaaag-3 0 BnaSultr1. 2 AJ311388 5 0 -ggtgtagtcgctggaatggt-3 0 5 0 -aacggagtgaggaagagcaa-3 0 BnaAsy XM009145653 5 0 -tcacgctcatctcccaaagt -3 0 5 0 -agtcctacacacgcctcaaa -3 0 BnaMot1 NM128127 5 0 -ctcgccaggatttggactta-3 0 5 0 -agatccccaacacgaacaag-3 0 AtSULTR1. 1 At4g08620 5'-cggccatctacttttccaac-3' 5'-gcattttcttgctcctctcg-3' AtSULTR1. 2 At1g78000 5'-atccgttttcaaagcagctc-3' 5'-tcaagaatgatgcaccaatga-3' AtSDI1 At5g48850 5'-tccctgtggagacactcctt-3' 5'-ccatctccgggttcttctct-3' AtAPR3 At4g21990 5'-ccaatcaagtatccatcagagaag-3' 5'-ccgaacaagattcaagaaagatg-3' AtUBQ10 At4g05320 5'-ctgcgactcagggaatcttc-3' 5'-ttgtgccattgaattgaacc-3' doi:10.
Statistical analysis For each measurement, hydroponic experiments on B. napus were conducted with four independent biological replicates constituted of four individual plants, except for q-PCR analysis, which was conducted with three biological replicates. Data are given as mean ± SE for n = 4 (or n = 3 for q-PCR data). All data of the hydroponic experiment were analyzed by Student's test (Excel software) and marked by one or several asterisks when significantly different between controls and S-deprived plants ( Ã P<0. 05, ÃÃ P<0. 01, ÃÃÃ P<0. 001). Under field conditions, for the study of different fertilization rates on an S deficient field, three replicates corresponding to a pool of 20 leaves of 20 independent plants were collected. Data are given as mean ± SE for n = 3 and were analyzed by Student's test (Excel software) and marked by different letters when they were significantly different between treatments at a given date, at P<0.
05. All experiments on different species in controlled conditions were conducted with four independent biological replicates, except for P. sativum culture with three replicates. Data are reported as mean ± SE for n = 4 (or n = 3 for P. sativum data). All data of the multispecies experiment were analyzed by Student's test (Excel software) and marked by one or several asterisks when significantly different between controls and S-deprived plants ( Ã P<0. 05, ÃÃ P<0. 01, ÃÃÃ P<0. 001).
Acknowledgments The authors thank  M. Che  ´deville for providing the experimental site at Ondefontaine and for technical help to fertilize plots, and all farmers identified by DATAGRI who collaborated in this study. The authors acknowledge the  PLATIN' (Plateau d'Isotopie de Normandie) core facility  for the element analysis used in this study and also appreciate the technical help of  Marie-Paule Bataille, Frederine Marie and Josette Bonnefoy. The authors are grateful for the technical help of  Julie Levallois  for molecular analysis and of  Bastian Welter  for Arabidopsis growth. The authors thank  Dr Laurence Cantrill  for improving the English in the manuscript.
The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps. ucsd. edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations.
Introduction Natural products (NPs) from marine and terrestrial environments, including their inhabiting microorganisms, plants, animals, and humans, are routinely analyzed using mass spectrometry. However a single mass spectrometry experiment can collect thousands of MS/MS spectra in minutes  1  and individual projects can acquire millions of spectra. These datasets are too large for manual analysis. Further, comprehensive software and proper computational infrastructure are not readily available and only low-throughput sharing of either raw or annotated spectra is feasible, even among members of the same lab. The potentially useful information in MS/MS datasets can thus remain buried in papers, laboratory notebooks, and private databases, hindering retrieval, mining, and sharing of data and knowledge.
Although there are several NP databases -Dictionary of Natural Products 2 , AntiBase  3  and MarinLit  4  -that assist in dereplication (identification of known compounds), these resources are not freely available and do not process mass spectrometry data. Conversely, mass spectrometry databases including Massbank  5  , Metlin  6  , mzCloud  7  , and ReSpect 8 host MS/MS spectra but limit data analyses to several individual spectra or a few LC-MS files. While Metlin and mzCloud provide a spectrum search function, unfortunately, their libraries are not freely available. Global genomics and proteomics research has been facilitated by the development of integral resources such as the National Center for Biotechnology Information (NCBI) and UniProt KnowledgeBase (UniProtKB), which provide robust platforms for data sharing and knowledge dissemination  9, 10.
Recognizing the need for an analogous community platform to effectively share and analyze natural products MS data, we present the Global Natural Products Social Molecular Networking (GNPS, available at gnps. ucsd. edu). GNPS is a datadriven platform for the storage, analysis, and knowledge dissemination of MS/MS spectra that enables community sharing of raw spectra, continuous annotation of deposited data, and collaborative curation of reference spectra (referred to as spectral libraries) and experimental data (organized as datasets). GNPS provides the ability to analyze a dataset and to compare it to all publically available data. By building on the computational infrastructure of the University of California San Diego (UCSD) Center for Computational Mass Spectrometry (CCMS), GNPS provides public dataset deposition/retrieval through the Mass Spectrometry Interactive Virtual Environment (MassIVE) data repository.
The GNPS analysis infrastructure further enables online dereplication  6, [11] [12] [13]  , automated molecular networking analysis  [14] [15] [16] [17] [18] [19] [20] [21]  , and crowdsourced MS/MS spectrum curation. Each dataset added to the GNPS repository is automatically reanalyzed in the next monthly cycle of continuous identification (see Living Data by Continuous Analysis below). Each of these tens of millions of spectra in GNPS datasets is matched to reference spectral libraries to annotate molecules and to discover putative analogs (Fig. 1a ). From January 2014 to November 2015, GNPS has grown to serve 9,267 users from 100 countries (Fig. 1b ), with 42,486 analysis sessions that have processed more than 93 million spectra as molecular networks from a quarter million LC-MS runs. Searches against a combined catalog of over 221,000 MS/MS reference library spectra from 18,163 compounds (Supplementary Table  1 ) are possible, and GNPS has matched almost one hundred million MS/MS spectra in all public and private search jobs using an estimated 84,000 compute hours.
GNPS Spectral Libraries GNPS spectral libraries enable dereplication, variable dereplication (approximate matches to spectra of related molecules), and identification of spectra in molecular networks. GNPS has collected available MS/MS spectral libraries relevant to NPs (which also include other metabolites and molecules), including MassBank  5  , ReSpect  8  and NIST  22  (Table  1 , Fig. 2a , and Supplementary Table  1 ). Altogether, these third party libraries total 212,230 MS/MS spectra representing 12,694 unique compounds (Fig. 2b ). While this combined collection of reference spectra, provides a starting point for dereplication, only 1. 01% of all spectra public GNPS datasets has been matched to this collection, indicating insufficient chemical space coverage. Although the NP community is working to populate this "missing" chemical space, there is no way to report discoveries of chemistries in an easily verifiable and reusable format.
This indicates that the GNPS libraries contain compounds that are complementary to the chemical space represented in other libraries (Fig. 2c, d ). Moreover, in contrast to third party libraries, spectra submitted to GNPS-Community libraries are immediately searchable by the whole community, such that submissions seamlessly transfer knowledge between laboratories (Fig. 1a ) in a process that is akin to the addition of genome annotations to GenBank  9. In order to create a robust library, it is important for submissions to be peer-reviewed and, if necessary, annotations corrected or updated as appropriate. Reference spectra submitted to the GNPS-Community library are categorized by the estimated reliability of the proposed submissions. Gold reference spectra must be derived from structurally characterized synthetic or purified compounds and can only be submitted by approved users.
Approval is given to contributors who have undergone training. Training is initiated by contacting the corresponding authors or CCMS administrators. Silver reference spectra need to be supported by an associated publication, while Bronze reference spectra are all remaining putative annotations (Supplementary Table  3 ). This type of division of spectra is reminiscent of RefSeq/TPA/GenBank 9,23 (genomics) and Swiss-Prot/TrEMBL/UniProt  24, 25  (proteomics), allowing for varying tradeoffs between comprehensiveness and reliability of annotations defined as Gold, Silver, and Bronze (Fig. 2e ). To enable refinements or corrections of annotations, GNPS allows for community-driven, iterative re-annotation of reference MS/MS spectra in a wiki-like fashion, to progressively improve the library and converge towards consensus annotation of all MS/MS spectra of interest.
These ratings are 4 star (correct), 3 star (likely correct, e. g. could also be isomers with similar fragmentation patterns), 2 star (unable to confirm the annotation due to limited information) and 1 star (incorrect) (Supplementary Table  6 ). So far, of the 3,608 matches that have been rated, 139 (3. 9%) matches were given 1 or 2 stars (insufficient information (2. 9%) or incorrect (1%)) by user ratings. These percentages are consistent with the false discovery rates estimated using spectral library searches of benchmark LC-MS datasets with compound standards (Supplementary Note 3, Supplementary Fig. 1 ,2 and Supplementary Table  7 ). Furthermore, these 3,608 match ratings were associated with 2,041 library spectra, therefore the average rating of a library spectrum can offer insight into the reliability of its reference annotation, not unlike Yelp ratings for restaurants.
The visualization of molecular networks in GNPS represents each spectrum as a node, and spectrum-to-spectrum alignments as edges (connections) between nodes. Nodes can be supplemented with metadata, including dereplication matches or information that is provided by the user, such as abundance, origin of product, biochemical activity or hydrophobicity which can be reflected in a node's size or color. It is possible to visualize the map of related molecules as a molecular network  21, [30] [31] [32] [33]  (Supplementary Fig. 3 ) both online at GNPS (Fig. 3c ) or exported for analysis in Cytoscape  31. Molecular networking analyses of 272 public datasets (Fig. 4a ) from a diverse range of samples reveals that on average 35. 2% of all unidentified nodes are significantly matched to other spectra of related molecules within a cosine score of 0.
Living Data by Continuous Analysis Funding agencies and publishers have called for raw scientific data, including mass spectrometry data, and analysis methods to be made publically available where possible. Consistent with this aim, GNPS datasets usually comprise the full set of mass spectrometry files produced during a NP research project or the full set of spectra analyzed for a peerreviewed publication (Supplementary Note 4). While it is potentially advantageous to the community for all data to be made public, GNPS user data can remain private until users explicitly choose to make it public (private data is also analyzable and privately sharable, with >93 million spectra in >250,000 private LC/MS runs already searched using GNPS). GNPS has the largest collection of publicly accessible natural product and metabolomics MS/MS datasets and is the only infrastructure where public data sets can be reanalyzed together and compared with each other (Table  1 ).
To date, GNPS has made 272 public GNPS datasets openly available which are comprised of more than 30,000 mass spectrometry runs with approximately 84 million MS/MS spectra. In common with other public repositories  34, 35  , GNPS datasets can be downloaded. However, data availability on its own does not serve to enable data reuse. GNPS is unique among MS repositories by enabling continuous identification: the periodic and automated re-analysis of all public datasets (Supplementary Note 5,6 and Supplementary Table 9,10). This continuous reanalysis, which incorporates molecular networking and dereplication tools, implements a 'virtuous cycle' as illustrated in Figure  1a. Because GNPS spectral libraries are constantly growing due to community contributions and continued generation of reference spectra, the number of matches made by successive re-analyses of public datasets has already grown and is expected to continue to grow over time (Fig.
4b ). GNPS users are periodically updated with alerts of new search results. For example, a Streptomyces roseosporeus project (MSV000078577) was deposited April 8, 2014. At first, only 7 MS/MS spectra were matched. However as of July 14, 2015 36 spectral matches have been made to GNPS libraries. Overall, the total number of compounds matched to GNPS datasets increased more than tenfold, while the number of matched MS/MS spectra in GNPS datasets increased more than twenty-fold in 2015 (Fig. 4b ). GNPS users can also subscribe to specific datasets of interest, rather like 'following' people on Twitter. When new matches are made, changed, or revoked, all subscribers are notified of new information by an email summarizing changes in identification. From April 2014 to July 2015, 45 updates were initiated by CCMS and automatically sent to subscribers (Supplementary Fig.
4 ). Update emails have led to substantially more views per dataset, compared to non-GNPS datasets (192 proteomics datasets) deposited in MassIVE. Continuous identification not only keeps a single dataset 'alive', it can create connections between datasets and users over time. Similarities between datasets could form the basis of a data-mediated social network of users with potentially related research interests despite seemingly disparate research fields, rather like the "People You May Know" feature on LinkedIn. On average each GNPS user already has 5 suggested collaborators (Supplementary Fig. 5 ).
Molecular Explorer Molecular Explorer is a feature that can only be implemented on 'living data' repositories and thus exists only in GNPS. Molecular Explorer allows users to find all datasets and putative analogs that have ever been observed for a given molecule of interest. We anticipate that this feature could guide the discovery of previously unknown analogs of existing antibiotics. Public NP data contains more than one hundred unidentified putative analogs of antibiotics such as valinomycin, actinomycin, etamycin, hormaomycin, stendomycin, daptomycin, erythromycin, napsamycin, clindamycin, arylomycin, and rifamycin, highlighting a clear potential to generate leads to discover structurally related antibiotics though the application of GNPS (Supplementary Fig. 6 , Supplementary Table  5 , and Supplementary Note 7).