dataesr/scientific-paragraphs-categorization huggingface dataset:
The European Centre for Medium Range Weather Forecasts (ECMWF) global reanalysis (ERA-Interim) fields are used for initial and boundary meteorology conditions, while another CAM-chem simulation is used for initial and boundary chemical conditions  (Kumar et al. , 2022). The WRF-Chem simulation used the global Emission Database for Atmospheric Research developed for Hemispheric Transport of Air Pollution (EDGAR-HTAP v2) for anthropogenic emissions and the Fire Inventory from NCAR version 1. 5 (FINNv1. 5)  (Wiedinmyer et al. , 2011)  for fire emissions. The WRF-Chem output is saved hourly, however we only use 3-hourly output to match the MUSICAv0 simulation.
Overall, the mean biases (Figures  11  and 12 ) suggest that the simulation agrees better with ATom observations in the Southern Hemisphere than in the Northern Hemisphere, and in Jan-Feb than in Sep-Oct (Figures  11  and 12 ), consistent with  Gaubert et al. (2016). In both seasons and both hemispheres, the model in general overestimates O3 in the stratosphere/UTLS (upper troposphere and lower stratosphere) by up to 38 ppb (above 200 hPa). In the troposphere (below 200 hPa), the model overall agrees well with the ATom data over the Pacific Ocean in the Southern Hemisphere (in most cases the bias is less than ±5 ppb). However, over the Atlantic Ocean in the Southern Hemisphere, MUSICAv0 tends to overestimate O3, especially in Jan-Feb. In the troposphere of the Northern Hemisphere, MUSICAv0 consistently overestimates O3 over both oceans and both seasons.
Model application: identifying key regions in Africa for future in situ observations and field campaign(s) As a demonstration of the application of MUSICAv0, here we use the results of modelsatellite comparisons to identify potential regions where the atmospheric chemistry models need to be improved substantially. More field campaigns and more in situ observations would not only provide observational benchmark dataset to understand and improve the modeling capability in the region, but would be also useful for the validation and calibration of satellite products. Here we use Taylor score to quantify model-satellite discrepancies. Taylor score  (Taylor, 2001)  is defined by S = !(#$%) (' ( ! $#/' ( ! ) " (#$% # ) where σ $ * is the ratio of σ * (standard deviation of the model) and σ + (standard deviation of observations), R is correlation between model and observations, and R , is the maximum potentially realizable correlation (=1 in this study).
Acknowledgement This material is based upon work partially supported by the  National Aeronautics and Space Administration  under Grant No. 80NSSC23K0181  issued through the  NASA   Applied Sciences SERVIR program. We thank  ATom ,  WDCGG ,  IAGOS ,  NASA/GSFC SHADOZ  teams, and the  U. S. State Department  and the  U. S. EPA  for in situ observations. We thank  Anne Thompson  and  Gonzague Romanens  for detailed explanation of SHADOZ Ozonesonde data format. We thank  MOPITT , MODIS AOD,  OMI NO2  and  OMI HCHO  teams for the satellite products. The  NCAR  MOPITT project is supported by the  National Aeronautics and Space Administration (NASA) Earth Observing System (EOS)   program. We thank the  QA4ECV  project. We thank  Sabine Darras  for CAMSv5. 1 emissions. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.
Code and data availability The MUSICAv0 model source code and the model documentation can be downloaded through https://wiki. ucar. edu/display/MUSICA/MUSICA+Home (last access: 3 April 2023). CAMS-GLOB-ANTH version 5. 1 emissions can be found at https://eccad3. sedoo. fr/data (last access: 3 April 2023). QFED emissions can be found at https://portal. nccs. nasa. gov/datashare/iesa/aerosol/emissions/QFED/ (last access: 3 April 2023). MERRA-2 data can be found at https://disc. gsfc. nasa. gov/datasets?project=MERRA-2 (last access: 3 April 2023). ATom data are available at https://espoarchive. nasa. gov/archive/browse/atom (last access: 3 April 2023). WDCGG data are available at https://gaw. kishou. go. jp/ (last access: 3 April 2023). IAGOS data are available at https://www. iagos. org/iagos-data/ (last access: 3 April 2023).
NASA/GSFC SHADOZ data are available at https://tropo. gsfc. nasa. gov/shadoz/ (last access: 3 April 2023). The surface PM2. 5 data https://doi. org/10. 5194/gmd-2023-50 Preprint. Discussion started: 4 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Long considered as transcriptional noise, long non-coding RNAs (lncRNAs) are emerging as central, regulatory molecules in a multitude of eukaryotic species, from plants to animals to fungi. Yet, our knowledge about the occurrence of these molecules in the marine environment, namely in planktonic protists, is still elusive. To fill this gap of knowledge we developed LncPlankton v1. 0, which is the first comprehensive database of marine plankton lncRNAs. By integrating the predictions derived from ten distinctive coding potential prediction tools in a majority voting setting, we identified 2,210,359 lncRNAs distributed across 414 marine plankton species from over nine different phyla. A userfriendly, open-access web interface for the exploration of the database was implemented (https://www. lncplankton.
However, besides their lack of protein coding potential, lncRNAs exhibit certain characteristics that distinguish them from mRNAs. These include a low GC content, low number of exons, short sequence length, low sequence conservation, and low expression levels  (4). In terms of function, lncRNAs have been shown to play important regulatory roles in various biological processes and diseases, namely cancers, which involve the control of epigenetic modifications as well as gene and protein expression regulation  (1, 3). With the booming of high-throughput sequencing techniques and the exponential rise of transcriptomics data in public repositories, several databases dedicated to lncRNAs have been developed. Some are exclusively devoted to human lncRNAs, like GeneCaRNA  (5) , and LNCipedia  (6) , while other databases have grouped together a significant number of lncRNAs coming from photosynthetic organisms such as CANTATAdb  (7)  and GREENC v.
2  (8). The NONCODE knowledge database v6. 0 on the other hand compiled lncRNAs from 39 species including 16 animals and 23 plants  (9)  and the RNAcentral catalogue is an extensive database of lncRNA sequences from a broad range of organisms  (10). Despite such extensive work on the identification of lncRNAs in animals and plants, the ocean remains largely unexplored. To fill this gap of knowledge, our study took advantage of the publicly available transcriptomic data originating from more than 400 marine protists from the Marine Microbial Eukaryote Transcriptome Sequencing Project MMETSP  (11) , to perform a thorough screening to identify and annotate lncRNAs in marine plankton. In order to do this, a computational pipeline using a majority voting-based ensemble learning technique was developed and applied.
The predicted lncRNAs and their annotations, including the nucleotide sequence, the encoded ORF(s), the folding energy, and the predicted secondary structure were organized and stored in a database, LncPlankton, with the aim to update it periodically on the basis of new knowledge and a potential expansion in the number of planktonic species screened. A user-friendly, open-access web interface for the exploration of the database was also implemented (https://www. lncplankton. bio. ens. psl. eu/). The user interface provides modules for browsing, searching, and downloading lncRNA data per species and/or per phylum, as well as interactive graphs, and an online BLAST service. Additionally, a SHINY application was integrated which allows the user to customize and visualize the classifications. With this user-friendly interface, we anticipate that LncPlankton will provide a rich source of information about lncRNAs for small-and large-scale studies in a variety of plankton taxa and contribute significantly to future efforts aimed at deciphering the biology and evolution of lncRNAs in diverse eukaryotic lineages.
In addition, the tools should accept sequences presented in a FASTA format. The tools selected and the key characteristics of each of them are summarized in supplementary Table  S1. The selected tools were applied using their default parameters and their default pre-trained classification models. The Python package ezLncPred (V. 1. 0)  (30)  was used to run the following tools: CPC2, CPAT (-p Human), LGC and longdist. The remaining tools were run using an in-house script with the following parameters: LncADeep (-MODE lncRNA, and the coding potential probability of a transcript was calculated as the mean of probabilities generated by the 21 intrinsic models of the algorithm), lncFinder (svm. model="human"), mRNN (mRNN_ensemble which used the weighted average prediction of the five best single mRNN models), RNAmining (-organism_name Homo_sapiens), and RNASamba (Full and partial weighted models were included).
This score was used to calculate the reliability (confidence level) of the lncRNA transcripts identified. The transcripts with significant hits in either the Pfam (V. 35. 0) or SwissProt (V. 2023. 01) databases were filtered out. A series of stringent filters was then applied to the "NonCoding-like" transcript sequences to classify them as long noncoding RNAs "lncRNAs" or small noncoding RNAs "sncRNAs" on the basis of transcript length and peptide length information.
Evaluation of the performance of the majority voting-based procedure The majority voting-based pipeline was tested on different sets of coding and non-coding datasets related to 18 different organism species and compared to the state-of-the-art coding potential prediction tools. Each tested dataset was independent of the training sets used in the construction of the pre-trained classification models related to each method. Among the 18 organisms tested, 10 datasets were perfectly balanced, containing the same number of coding and non-coding RNA transcripts. Furthermore, in order to test the robustness of the tools towards biased datasets, 7 highly imbalanced testing sets were considered. The description of the tested datasets is provided in the supplementary data (Table  S2 ). Each tool was applied to each tested dataset to predict the classes of the transcripts.
The default parameters and pre-trained models were used for each tool. Tool outputs were parsed and analysed with custom R scripts. A cross-tabulation of observed and predicted classes was generated, and the performance metrics including the accuracy, sensitivity, and specificity were calculated using the "confusionMatrix" method of the caret package (https://CRAN. R-project. org/package=caret). The Receiver operating characteristic (ROC) curve as well as its AUC (Area under the curve) value was also computed using the ROCR package (http://rocr. bioinf. mpi-sb. mpg. de). All tested tools considered coding transcripts as positive and non-coding transcripts as negative sets.
Architecture of the database The 3-tier client/server architecture model containing data, logic, and presentation layers has been implemented for LncPlankton as shown in (Figure  3A ). The data layer represents the data storage part which is handled by a relational database (Figure  3B ) setup with the popular MySQL (version 5. 7. 36) open-source relational database management system (RDBMS). The data layer is expanded with NoSQL file storage. The logic layer represents the core of the architecture, and is responsible for the communication between the user queries from the presentation layer, fetching the data from the data layer, processing the data, and formatting the response to the presentation layer. The JSONbased (JavaScript Object Notation) data structure is mainly the most used format.
Performance of the majority voting algorithm We compared the performance of the proposed majority voting-based method with the ten tools selected individually (Supplementary Table  S1 ). We used as input all coding and non-coding RNA sequences from the testing datasets of the 18 species described in Supplementary Table  S2. Our method showed the highest mean accuracy across all the datasets with a mean = 0. 95 (Figure  4A ), and low inter-dataset variability (variance: 0. 0014). At the species level, our method outperforms the other tools in seven organisms, and performs well in cases where the other tools presented poor performances; for example, for Homo sapiens the majority voting procedure yields an accuracy of 0. 95 while longdist and RNAmining yield only 0. 56 and 0. 54, respectively.
For the remaining organisms, our method gets approximately the same accuracy as the other tools (Supplementary Table  S3 ). In addition, our method showed comparable AUC performance (AUC: 0. 92-1. 00) with the best performing coding potential methods like CPAT and CPC2 (Figure  4C ). Among the 18 datasets tested, our method showed the highest AUC score in 13 datasets (Supplementary Table  6 ). The AUC values obtained by our method for the 18 organisms are shown in Figure  4B. The detailed results regarding the accuracy, the sensitivity, the specificity, and the AUC of all the tools can be found in Supplementary Tables S3, S4, S5, and S6, respectively.
Data content of LncPlankton V1. 0 Data in the current version of LncPlankton v1. 0 was based on transcriptomic datasets from 414 planktonic species ranging from the Cercozoa, representing 3. 6% of the total number of species screened, to the most abundant phylum Bacillariophyta, with 24. 4% (Figure  1A ). Using a majority voting-based method combining the 10 coding potential tools with strict prediction criteria (Figure  2 ), 2,210,359 lncRNA transcripts were identified from an input set of 11,623,179 transcripts. Among them, 239,116 were predicted as non-coding by all the tools and have a non-coding potential score = 1, we dubbed them "high-confidence lncRNAs" as their non-coding potential status was indisputable (Figure  5A ). The distribution of the predicted lncRNA transcripts across the groups is shown in Figure  5B.
Functional modules of LncPlankton V1. 0 LncPlankton v1. 0 provides a very useful user interface (UI) accessible from the browser. The UI offers various ways to browse and search lncRNA resources (Figure  6 ). Furthermore, users can download the data deposited in LncPlankton v1. 0 in FTP bulk or programmatically through dedicated APIs. Search per species. The current release of LncPlankton allows querying a given species to explore the content of its transcriptome. The user is required to select a phylum and a species from dropdown lists. A page will then appear (Figure  6B ) displaying a summary table with the number of transcripts classified as lncRNA-like, sncRNA-like, and Coding-like by the majority voting procedure described above. A pie chart reporting the percentage of lncRNAs found is also displayed.
The user can also visualize the distribution of lncRNA features such as transcript length, peptide length, GC content and Fickett score. Additionally, all the information about the lncRNAs found for the selected species are reported in a table with one row per lncRNA. The presented data include lncRNA id, length, peptide length (if any), Fickett score, iso-electric point, longest ORF length (if any), coverage ORF (if any), GC content, class determined by the pipeline above, and the probability of coding potential generated by the majority voting procedure. To get access to more information on the selected lncRNA the user can click upon the "More. " button in the last column of the table. lncRNA details webpage. The detailed information about the lncRNA selected is displayed on this page (Figure  6C ).
To allow the user to customize their predictions, for example using a preferred coding potential prediction tool, and/or to modify the cut-off value of the filters, a SHINY application was developed and embedded to the UI. The application contains two panels (Figure  6A ): i-The input panel in which the user can select the phylum, the species, and the coding potential tool from dedicated drop-down lists. Other inputs can be set by the user such as the transcript length, the peptide length, and whether or not to include the reverse strand in the prediction. The input panel offers also the possibility to select the manner in which the result will be displayed, namely as a histogram, polar plot, table, or map (with the sampling location coordinates of the selected species); ii-The output panel, which renders the results and displays it according to the choice of the user.
All the figures and tables generated can be optionally downloaded and saved. The output panel was implemented using the plotly package (V4. 10. 1). BLAST module. The user can perform a sequence-based search of data stored in LncPlankton using BLAST (Figure  6D ). The input sequence should be in a FASTA format; and the user can select between two tools, BLASTN and MEGABLAST. Except for the expectation value (E-value) and the number of target hit sequences which can be selected in dedicated drop-down lists, the default arguments were used. The BLAST search outputs a raw report which includes pairwise alignment, BLAST hits based upon alignment scores and other measures of statistical significance. To interactively display the BLAST result, a viewer module was implemented using the BlasterJS library  (32) , and was integrated to LncPlankton (Figure  6E ).
API. A representational state transfer application programming interface (REST-API) was implemented using the plumber package, and made available to allow programmers to interface with LncPlankton programmatically. The API returns documents in the JSON format and can be used in any programming language. In the current version of LncPlankton, three APIs were implemented and documented to access and use data:  GET /lncRNA_details: information about lncRNA transcript including: sequence length, peptide length, reliability (confidence calculated by the majority voting-based procedure), GC content, ORF coverage, MFE, Fickett score, isoelectric point, nucleotide sequence, peptide sequence.  GET /search_per_species: detailed information about species including the number and the percentage of transcript predicted.
As reported in the supplementary data (Figure  S2 ), the lncRNA content of the LncPlankton database varies according to the coding potential prediction tool used; for instances only 7. 73% of the transcripts of the MMETSP datasets were predicted as lncRNAs when using mRNN but this number reached 33. 64% with LGC. Such difference in findings is inevitably caused by the intrinsic algorithms used by each method, the training set, and the fine-tuned learning features and parameters (see supplementary Table  S1 ). In our pipeline, we opted for the use of a combination of multiple tools in a majority voting setting, inspired from the ensemble method technique used in machine learning. With this approach, 19% of the whole planktonic transcriptomes surveyed in this study was labelled as "lncRNAs".
Although this value has no fixed limit, the user can customize it in the SHINY prediction application developed and integrated to the LncPlankton user interface. As part of our pipeline, we used a meta-learner combining multiple and diverse protein coding potential tools. We assessed the performance of our meta-learner using gold standard metrics such as the accuracy and AUC-ROC, and compared to each single tool. Overall, our method showed similar performance to the top performing coding potential tools, and it maintained a good performance in challenging cases in which some tools found difficulties to discriminate coding from non-coding transcripts. For highly imbalanced datasets like B_taurus, X_tropicalis, and M_mulatta which have a 1:72, 1:32, and 1:22 ratio of the number of coding to non-coding instances, some tools performed poorly longdist and RNAmining, while our method preserved a high accuracy meaning that our method is not impacted by the imbalancedness between coding and non-coding classes.
The following aspects supported our choice to use the meta-learner proposed: i) the meta-learner used diverse and heterogeneous models covering a broad range of species since each model related to each tool was trained using transcripts of different species. Therefore, we believe that our method is a suitable choice for cross-species prediction; ii) our method achieved the highest accuracy. There are two reasons why the meta-learner outperforms others. First, our method benefited from the ensemble method principle since it combined multiple single tools. Second, the method integrated information from different types of features to enhance lncRNA prediction performances; iii) our method is robust showing a low variability of the prediction across the different testing datasets; iv) our method promotes the diversity which is coming from the different mathematics and algorithms used by each single tool, and fine-tuned features and parameters.
Indeed, by pooling all the tools together, our method allowed an in-depth analysis of the transcripts by using all possible features describing the sequences; v) the reliability of the prediction in our method can be measured by the non-coding potential probability of the ensemble. It reflects the agreement of the tools to recognize the correct class of a transcript. In our method, we set the threshold to a value = 0. 6 i. e. if at least 6 out of 10 tools recognized a transcript as non-coding RNA, it is more likely to be a non-coding RNA than a coding RNA. Increasing this threshold to 1 means that all the tools recognize it as a non-coding RNA. We believe the majority voting tool offers us a reliable choice for lncRNA identification, beyond what a single tool can offer at the moment. We collected all the reliable lncRNAs predicted by our pipeline and their information, and stored them in a MySQL database termed LncPlankton.
Many databases of lncRNAs have been implemented in the past few years but most of them are dedicated to humans, vertebrates and plants. To date, no other database of this scale collecting non-coding transcripts on planktonic organisms, with representatives from the major branches of the eukaryotic tree of life, has been developed. Although a few algal species have been included in plant lncRNA databases: 6 algae in GreenC (8), and 3 algae in Cantatadb  (7) , no database was completely dedicated to lncRNA in planktonic species. Compared with other lncRNA databases, the lncRNA transcripts of LncPlankton were predicted using the majority voting pipeline with stringent filters making them more reliable than what we could get with other pipelines using a single coding potential prediction tool.
Similar to other lncRNA databases, LncPlankton v1. 0 includes all the basic information of a given lncRNA including the nucleotide sequence, the longest ORF sequence, and the predicted secondary structure. Two missing features of lncRNA recorded in LncPlankton v1. 0 are the genomic coordinates (the start and stop position, and the strand information), and the expression profiles. This is due to the lack of an annotated genome for the species surveyed  (11). In addition to the regular update of the database, the following future developments will be considered: i) the genomic coordinates of lncRNAs will be incorporated to further classify the lncRNAs identified as long intergenic ncRNAs (lincRNAs), intronic-lncRNAs, or antisense as this information becomes available; ii) the expression patterns can be integrated for the functional study of lncRNAs identified; iii) even if lncRNAs do not show high conservation at the nucleotide level, a sequence clustering using Orthofinder algorithm will be performed to provide information about highly conserved lncRNAs; iv) given that lncRNAs of related functions often share similar short motif profiles, we will use a kmer-based approach called SEEKR proposed in  (34)  to quantify the nonlinear sequence homology and the evolutionary relationships between the lncRNAs predicted in LncPlankton.