dataesr/scientific-paragraphs-categorization huggingface dataset:
(2015) Indreica et al. (2017) AS-CN-001 EU-00-EU-RO- China Forest-Steppe Ecotone Database European Mire Vegetation Database Romanian Grassland Database Hongyan Liu Tomáš Peterka Eszter Ruprecht Fengjun Zhao Martin Jiroušek Kiril Vassilev 117 2,560 82 Liu et al. (2000) Peterka et al. (2015) Vassilev et al. (2018) AS-CN-002 AS-CN-003 EU-AL-001 EU-RS-002 AS-CN-004 EU-AT-001 EU-RU-EU-BE-EU-RU- Tibet-PaDeMoS Grazing Transect Vegetation Database of the BEF China Project Vegetation Database of Albania Vegetation Database Grassland Vegetation of Serbia Vegetation Database of the Northern Mountains in China Austrian Vegetation Database Lower Volga Valley Phytosociological Database INBOVEG Vegetation Database of the Volga and Karsten Wesche Helge Bruelheide Michele De Svetlana Aćić Sanctis Zhiyao Tang Wolfgang Valentin Golub Willner Els De Bie Tatiana Lysenko Yun Jäschke Giuliano Fanelli Zora Dajić Stevanović Christian Berg Andrey Chuvashov 58 24 31 217 2,310 383 124 119 174 Wang et al.
(2017) De Sanctis et al. (2017) Aćić et al. (2012) Bruelheide et al. (2011) Willner et al. (2012) Golub et al. (2012) Lysenko et al. (2012) AS-EG-EU-BG-EU-RU- Vegetation Database of Sinai in Egypt Bulgarian Vegetation Database the Ural Rivers Basins Vegetation Database of Tatarstan Mohamed Z. Hatim Iva Apostolova Vadim Desislava Sopotlieva Maria 143 160 206 Apostolova et al. (2012) Hatim (2012) Prokhorov et al. (2017) AS-ID-001 EU-CH-005 AS-IR-001 EU-SI-001 AS-KZ-AS-MN-001 Southern Gobi Protected Areas Sulawesi Vegetation Database Swiss Forest Vegetation Database Vegetation Database of Iran Vegetation Database of Slovenia Database of Meadow Vegetation in the NW Tien Shan Mountains Database EU-CZ-Czech National Phytosociological EU-SK-001 Slovak Vegetation Database Database EU-DE-VegMV EU-UA-Ukrainian Grasslands Database EU-DE-VegetWeb Germany Adjacent Parts of Russia EU-UA-Vegetation Database of Ukraine and Michael Kessler Thomas Prokhorov Jalil Noroozi Wohlgemuth Urban Šilc Viktoria Wagner Henrik von Wehrden Milan Chytrý Milan Valachovič Jozef Šibík Kozhevnikova Parastoo Mahdavi Filip Küzmič Karsten Wesche Ilona Knollová Florian Jansen Christian Berg Anna Kuzemko Yulia Vashenyak Florian Jansen Jörg Ewald Onyshchenko Kolomiychuk Viktor Vitaliy 2,134 24 1,029 277 13 1,032 1,287 2,394 15 301 587 96 Wohlgemuth (2012) Šilc (2012) Wagner (2009) von Wehrden et al.
(2009) Chytrý & Rafajová (2003) Šibík (2012) Kuzemko (2012) Jansen et al. (2012) Ewald et al. (2012) AS-RU-EU-DE-NA-00- Wetland Vegetation Database of Baikal Siberia (WETBS) German Vegetation Reference Database (BIOTREE-NET) (GVRD) Tree Biodiversity Network Victor Chepinoga Ute Jandt Luis Cayuela Helge Bruelheide 9 762 241 Chepinoga (2012) Jandt & Bruelheide (2012) Cayuela et al. (2012) (Continues) (Continues) TA B L E 1.
Datasets are ordered based on their ID in the Global Index of Vegetation Databases (GVID ID). No. open- GIVD ID Dataset name Custodian Deputy custodian access plots Reference NA-CA-003 Database of Timberline Vegetation in Viktoria Wagner Toby Spribille 63 Wagner et al. (2014) NW North America NA-CA-004 Understory of Sugar Maple Dominated Isabelle Aubin 13 Aubin et al. (2007) Stands in Quebec and Ontario (Canada) NA-CA-005 Boreal Forest of Canada Philippe Yves Bergeron 57 Harper et al. (2003) Marchand NA-GL-001 Vegetation Database of Greenland Birgit Jedrzejek Fred J. A. Daniëls 441 Sieg et al. (2006) NA-US-002 VegBank Robert K. Peet Michael T. Lee 14,965 Peet, Lee, Jennings, et al. (2012) NA-US-006 Carolina Vegetation Survey Database Robert K. Peet Michael T. Lee 3,263 Peet, Lee, Boyle, et al.
(2012) NA-US-014 Alaska-Arctic Vegetation Archive Donald A. Amy Breen 771 Walker et al. (2016) Walker SA-00-002 VegPáramo Gwendolyn Xavier Font 2,010 Peyre et al. (2015) Peyre SA-AR-002 Vegetation Database of Central Melisa Giorgis Alicia T. R. Acosta 86 Argentina SA-BO-003 Bolivia Forest Plots Michael Kessler Sebastian Herzog 44 SA-BR-002 Forest Inventory, State of Santa Alexander André Luís de 1,561 Vibrans et al. (2020) Catarina, Brazil (IFFSC Project) Christian Gasper Vibrans SA-BR-003 Grasslands of Rio Grande do Sul, Brazil Eduardo Valério D. Pillar 306 Vélez-Martin SA-BR-004 Grassland Database of Campos Sulinos Gerhard E. Valério D. Pillar 147 Overbeck SA-CL-002 SSAForests_Plots_db Alvaro G. 155 Gutiérrez SA-CL-003 Chilean Park Transects -Fondecyt Aníbal Pauchard Alicia Marticorena 44 Pauchard et al.
Variable Range/levels Unit of measurement No. of plots with information Type Only dominant species, Dominant woody plants >= 2. 5 cm dbh, All woody plants, Woody plants >= 1 cm dbh, Woody plants >= 2. 5 cm dbh, Woody plants >= 5 cm dbh, Woody plants >= 10 cm dbh, Woody plants >= 20 cm dbh, Woody plants >= 1 m height, Not specified GIVD_ID see Table 1 95,104 n Dataset see Table 1 95,104 n Continent Africa, Asia, Europe, North America, Oceania, South 95,104 n America Country 95,104 n Biome Alpine, Boreal zone, Dry midlatitudes, Dry tropics and 95,104 n subtropics, Polar and subpolar zone, Subtropics with year-round rain, Subtropics with winter rain, Temperate midlatitudes, Tropics with summer rain, Tropics with year- round rain Date_of_recording 05-07-1888 -03-02-2015 dd-mm-yyyy 80,085 d Latitude -54.
(ii) Variations in ion formation by a given compound during ESI are often observed. For instance, the formation of adducts, dimers, multiple charges and in-source fragmentation are frequently observed leading to MS/MS data amplification (about 40-80% of the collected MS/MS spectra  [18] ) and loss of information of the MS/MS spectra related to the original compound. If not treated correctly, dataset dereplication with MS/MS databases of known compounds is further compromised, since spectral identification can be misled by spectral similarities to ionization variants instead of to the real metabolite. In addition to proprietary and publicly available LC-MS/MS data processing and mining tools, many advances in LC-MS/MS data handling have been recently reported, such as TidyMS for data extraction  [19] , mzAdan  [20]  and feature-based molecular networking  [21]  to account for ionization variants, and CANOPUS for systematic compound class annotation based on MS/MS data  [22].
While these works were being reported, we developed our own tool, the NP 3 MS workflow, for processing and mining of LC-MS/MS data obtained from unpurified natural product chemical libraries. In this work, we report a pilot library-the elicited pre-fractionated bacterial (EPfB) chemical library-which was prepared using a new methodology to select chemical elicitors aiming to modulate deep-sea bacterial metabolism. The investigation of the chemical diversity of the EPfB library was performed by frontier cheminformatic methods using MS/MS spectra, compounds and networks derived from the NP 3 MS workflow data processing, assisted by the Global Natural Product Social Molecular Networking (GNPS)  [23]  and the Universal Natural Products Database (UNPD)-In-Silico MS/MS DataBase (ISDB)  [24]  databases for compound identification.
The [M + H] + spectra were further dereplicated against available spectra databases of known natural products (GNPS and UNPD-ISDB), returning possible chemical structures representing the bacterial metabolites contained in the EPfB library. The collection of annotated [M + H] + spectra was counted according to their detection in each sample group (control vs. elicited vs. both; crude extract vs. enriched fractions), and further for their identification against spectra databases (identified vs. non-identified spectra). This was done aiming to numerically dissect the overall contribution of bacterial elicitation and crude extract fractionation on the production and detection of bacterial metabolites, respectively, and further on the novelty of the library (Section 2. 2. 1). The annotated [M + H] + spectra collection was further visualized using spectra similarity molecular networking (SSMN) and principal component analysis (PCA).
Chemical Composition of the EPfB Chemical Library LC-MS/MS data from all samples were jointly processed using the NP 3 MS workflow (vide Methods), aiming to determine the chemical composition of the collection, as well as the contribution of chemical elicitation and crude extract fractionation to the detected chemical diversity of the EPfB library. For this, the MS/MS consensus spectra relative to the putative [M + H] + ions were extracted and globally analyzed using molecular networking. These spectra were further compared to external MS/MS databases for initial metabolite identification, and unmatched spectra were associated to the novelty of the chemical collection. The identified compounds were additionally used for mapping the chemical diversity and the biological relevance of the EPfB library.
Global Analysis of the Bacterial Metabolites Contained in the EPfB Library The LC-MS/MS dataset processed from the 180 library samples resulted in the detection of 1137 MS/MS [M + H] + consensus spectra linked to unique bacterial metabolites (Figure  3a ). The elicited conditions contributed to 45% (514/1137) of these metabolites, which were exclusively detected in samples from bacterial cultures grown in the presence of the chemical elicitors and would not be observed under typical growth conditions. At the same time, 24% (271/1137) of the bacterial metabolites were only found in the control exclusive conditions, being thus downregulated in the presence of chemical elicitors. Finally, about 31% (352/1137) of the metabolites were detected in both control and elicited samples of the EPfB library, thus not being influenced by chemical elicitors.
Compound Novelty Introduced by Eliciting Bacterial Cultures The [M + H] + consensus spectra retrieved with the NP 3 MS workflow data process were compared to GNPS  [23]  and to UNPD-ISDB  [24]  databases. From the 1137 spectra related to bacterial metabolites, 424 (37%) matched with MS/MS spectra of known natural products. Considering that UNPD-ISDB alone contains 213,210 out of the ~300,000 natural products already reported, it is possible that the remaining 713 (63%) non-identified spectra found in the EPfB library represent novel bacterial metabolites (Figure  3a ). The elicited conditions added 49% (348/713) of the novel MS/MS spectra. In terms of relative novelty, 68% (348/514) of the spectra found exclusively in the elicited conditions are unmatched with databases versus 62% (167/271) of the spectra exclusive to control, and 56% (198/352) of the common spectra (found in both control and elicited conditions)-Figure  3a , showing that chemical elicitors could mostly modulate the expression of novel bacterial metabolites.
Interestingly, a large cluster of possible biotransformation products of ampicillin was also found, composed of 16 nodes (large cluster colored blue in Figure  4a ). Three bacterial strains were cultivated in the presence of ampicillin: LAMA915, LAMA585 and LAMA627; however, the biotransformation products were mainly found in LAMA915, which is a bacterial strain rich in beta-lactamases (GenBank assembly accession: GCA_001235865. 1). Four spectra clusters from the SSMN presented in Figure  4  were extracted to exemplify clusters of bioactive and rare natural products present in the library (Figure  5 ). Despite the identification given by the MS/MS databases used for comparisons, the data were also manually curated. This was done because some factors, such as coherence with the reported origin of the compounds (marine environment or bacterial natural products) and featured isotope occurrence (in case of halogens, for example) are not taken into account by the NP 3 MS workflow at this point-or by any other MS/MS dereplication tool that we are aware of-when handling spectra identification.
Biological Relevance of the Library The chemical analyses described above highlighted the diversity and potential novelty of the bacterial metabolites produced by deep-sea bacteria, which were further induced by chemical elicitors under laboratory growth conditions. The analysis of selected clusters and identified compounds (Figure  5 ) point to the relevant biological activity of these known natural products. We then questioned whether the global diversity of natural compounds obtained in this work might also hold biological significance, especially in the context of drug discovery. For this, we first used Principal Component Analysis (PCA) based on molecular descriptors, using the 424 consensus spectra pointed as [M + H] + ions in our dataset that were identified by the GNPS (n = 12) and UNPD (n = 412) databases.
We further used 208,009 natural products available in the UNPD-ISDB and 2078 approved drugs present in the DrugBank (https://www. drugbank. ca (accessed on 25 April 2019))  [38]  as references for the chemical space of natural products and drugs, respectively. The molecular descriptors of each compound were calculated using CDK ® v2. 0 package (https: //cdk. github. io/ (accessed on 25 April 2019))  [39] , including three groups: topological (n = 195), constitutional (n = 17) and geometric (n = 49). Only uncorrelated descriptors were considered to perform the analyses, resulting in the following 18 selected variables: ALogp2, C1SP2, C1SP3, C3SP2, C3SP3, LipinskiFailures, MDEC. 22, MDEC. 33, MDEN. 12, MDEN. 22, MDEN. 23, MDEO. 22, nAcid, nBase, nRingBlocks, nRings7, WTPT. 5 and XLogP. The PCA plot resulted in a map corresponding to the chemical space covered by the identified compounds ([M + H] + consensus spectra) from the EPfB library in comparison with the space covered by reported natural products (UNPD-ISDB) and approved drugs (DrugBank)-Figure  6.
Chemical Redundancy in the Relevant Biological Space Next, we expanded the chemical space analysis of the EPfB library by introducing unidentified analogues of known natural products present in the spectra collection. For this, non-identified [M + H] + consensus spectra that are linked in the SSMN to one of the 424 [M + H] + consensus spectra identified in the MS/MS spectra databases used were also considered. This was done to further include part of the chemical novelty (the 713/1137 [M + H] + consensus spectra not identified in databases), from which we could reference chemical descriptors by linking 57/713 of them to its most similar identified [M + H] + consensus spectra in the chemical space (for more details see the Methods section). For a better visualization of this data, the PCA plot of the EPfB library is shown in Figure  7a , being further split by sample type and growth condition Figure  7b.
Bacterial crude extracts were obtained and fractionated by high performance liquid chromatography to construct the library, as enriched fractions are more suitable to per-form high throughput biological screenings  [51]  and LC-MS/MS data collection  [23, 24]. Indeed, fractionation of the crude extracts evidenced about 90% of the bacterial metabolites (Figure  3c ) and the biological activity (Table  1 ) detected from the samples comprising the EPfB chemical library. The NP 3 MS workflow was applied to extract the [M + H] + MS/MS consensus spectra relative to bacterial metabolites, retaining the information of the sample of origin, its biological activity and chromatographic information. Finally, the GNPS and UNPD-ISDB databases were used to identify the [M + H] + consensus spectra and the NP 3 MS workflow selected the top results based on theMS/MS spectra matching scores.
Improvements in MS/MS data mining tools are being pursued in the current literature  [21] [22] [23] 47]. Although none of them are currently perfect at retrieving the correct [M + H] + ions or the correct identification of compounds from MS/MS spectra databases, untargeted metabolomics have increasingly proven to be a valuable asset for mining compound collections and comparing of biological groups. The [M + H] + consensus spectra retrieved from the EPfB library by the NP 3 MS workflow were counted in each bacterial group, revealing that the elicitation of bacterial cultures had almost doubled (~45%) the number of compounds detected in the data set (Figure  3a ). Analysis of the library's [M + H] + consensus spectra in comparison with databases revealed that most (63%) of these spectra were new to the MS/MS public collections accessed, half of them (49%) being only observed in the elicited growth conditions.
The bacterial collection further concentrated nitrogenated compounds (Q3), which are important molecular features of drugs. Overall, the compounds identified in the EPfB library span the chemical space of approved drugs, bringing chemical diversity in the relevant biological space. What is worth noticing is that the bioactive fractions derived from the biological screenings reported here concentrate compounds at Q3 and Q4, highlighting the importance of these regions of the chemical space also for in vitro bioactivity. However, the abovementioned analyses were carried out with less than 40% of the bacterial metabolites identified from the EPfB library as MS/MS spectra database matches, from which chemical descriptors could be extracted. Part of the remaining [M + H] + consensus spectra in our collection could be linked to at least one identified compound, covering 65% (71/109) of the SSMN clusters, further landmarking the chemical space covered by the EPfB library (Figure  7a ).
0 and included bio tools CompassXport. Data processing and analysis using the NP 3 Mass Spectrometry (MS) Workflow: This workflow is an in-house collection of scripts to enhance untargeted metabolomic research focused on drug discovery with optimizations towards natural products. The beta version is freely available in our repository (https://bitbucket. org/cnpemlqpn/np3_ms_workflow/ src/master/ (accessed on 18 December 2020)), with a detailed manual and a command line interface. The NP 3 MS workflow uses a metadata file manually constructed by the user to describe the LC-MS/MS files used in the job, the files types, their grouping information and bioactivity scores. For this job a metadata file with 229 mzXML files was compiled and the samples codes summarized the EPfB chemical library samples.
The NP 3 MS workflow (command "run") processed the input EPfB's mzXML files from the metadata file, and returned the following main files as outputs: (i) the Mascot Generic Format (MGF) file, containing the collection of consensus spectra extracted; (ii) the clean count table "*_peak_area_clean_annotated. csv", with the consensus spectra's quantification in each sample of origin (tracked by the sample codes) and identifications against the UNPD in-silico database; (iii) the edges file "*_molecular_networking_sim_06_topK_15_maxComponent_200. selfloop", with the connections of the raw Spectra Similarity Molecular Network (SSMN); (iv) the edges file "*_molecular_networking_annotations. selfloop", with the connections of the Ionization Variant Annotation Molecular Networking (IVAMN); and v) the table "*_molecular_networking_annotations_attributes_protonated_representative.
g. , [M+Na]+ -> [M + H] + ). Experimental MS/MS database dereplication with GNPS library: For including GNPS library identifications, the resulting MGF consensus spectra list from the NP 3 MS workflow was used as input in the GNPS website aiming dereplication of the EPfB library against the experimental MS/MS collection of GNPS  [23]. The GNPS spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0. 65 and at least 6 matched peaks. The identifications from GNPS were combined in the "*_peak_area_clean_annotated. csv" table (NP 3 output) using the NP 3 MS workflow command "gnps_result ". Selection of the [M + H] + consensus spectra relative to bacterial metabolites: The IVAMN was used to remove the ions from the chromatographic blanks, chemical elicitors, and culture media samples.
8. 0. Next, the "IVAMN_clean_ protonated. csv" (bacterial metabolites list and attributes) was imported and used to filter only the nodes present in this list. As a result, the filtered SSMN nodes contained only the [M + H] + consensus spectra relative to bacterial metabolites in the EPfB library. The SSMN edges file was exported as "SSMN_filtered_protonated. selfloops" for further use in the workflow. This filtered SSMN and its attributes from the "IVAMN_clean_protonated. csv" was used in the analyses presented in this work. Cytoscape regex filter, based on the sample code format described above, were used to select and distinguish (colors, formats, etc) the network nodes according to the sample of origin, quantity in the sample of origin, chemical elicitation and sample type. The GNPS and UNPD identifications columns were used to select and distinguish the identified [M + H] + consensus spectra relative to bacterial metabolites.
Spectra count: The quantification and identification columns of the "IVAMN_clean_ protonated. csv" were used to count the bacterial metabolites in the different samples of the EPfB library and to prune novel compounds. Python scripts, helped by pandas library and matplotlib, were used for retrieving the data and plotting the bar charts. Euler diagrams: A Python script counted all intersections, unions, and disjoints quantifications by sample and by database identification also using the "IVAMN_clean_protonated. csv" as input. The resulting numbers were the input for eulerr R library version 6. 1. 0 (https: //github. com/jolars/eulerr (accessed on 25 April 2019)). Eulerr generates area-proportional Euler diagrams displaying set relationships. Chemical space mapping: The evaluation of the chemical space was performed by Principal Component Analysis (PCA) using molecular descriptors extracted from each identified compound.
The "IVAMN_clean_protonated. csv" file, which also contains the database identifications, was used to retrieve the smiles of the compounds suggested by databases as spectra matches to the EPfB library metabolites. Only the top-ranked smile string, based on MQscore, were used in the PCA analyses. Chemical spaces were constructed based on the approach used in  [1]  with minor modifications. The compounds deposited at the UNPD-ISDB and at the Drug Brank (set approved drugs) were used, along with the compounds identified from the elicited pre-fractionated bacterial (EPfB) chemical library reported here. The calculation of all molecular descriptors for each dataset was preformed using the CDK ® v2. 0 package (https://cdk. github. io/ (accessed on 25 April 2019))  [39]. Duplicates were removed. The following descriptors were used: topological (n = 195), constitutional (n = 17) and geometric (n = 49) and only uncorrelated descriptors were considered for the PCA analysis, chosen through a Spearman's correlation matrix.
2, for the UNPD-ISDB (in silico), and MQScore ≥ 0. 65, for GNPS (experimental) databases, with the MQScore ranging from 0 (totally dissimilar) to 1 (completely identical). (b) Euler diagram distribution of themetabolites detected exclusively under the elicited conditions: EDTA 10 mM, ampicillin 100 µg/mL, sodium butyrate 50 µM, kanamycin 100 µg/mL and procaine 100 µM. (c) Distribution of the metabolites detected exclusively in crude extracts (extract), fractions and both extract and fraction samples. Bars are color coded according to compound identification in databases-the same criteria used in (a) was applied. (d) Distribution of the metabolites detected in crude extracts, fractions and both extract and fraction samples, colored according to their detection in control (white), elicited (blue) or both (yellow-control and elicited) growth conditions.