dataesr/scientific-paragraphs-categorization huggingface dataset:
MetamORF pipeline and database development Inclusion criteria for publicly available sORF-related data A total of 18 data sources, either Homo. sapiens and Mus. musculus original datasets or re-processed publicly available sORFs repositories, have been considered for inclusion in our database (Supplementary Table  S1 )  (5, 7, 11, 12, 14, 15, (17) (18) (19) (20) (21) (22) (32) (33) (34) (35) (36) (37). These data sources provide results from computational predictions, Riboseq experiment analyses and mass spectrometry (proteomics/proteogenomics) analyses. The data sources not providing the absolute genomic coordinates of the ORF start and stop codons  (5, 17, 20, (32) (33) (34)  or fully included in another data source considered here  (21)  have been discarded. Databases that did not allow export of their content in a single file or automating the download of all the files from their website have also been discarded  (19, 35).
The missing information (sequences and transcript biotypes) has been downloaded from Ensembl, and the ORF relative coordinates have been computed. Finally, the cell types and ORF classes have been normalized, and the Kozak contexts have been computed using the sequences flanking the start codons. information regarding (i) the splicing of the ORF and (ii) ORF length  (15, 36)  have not been included as well. Hence, the database has been made by collecting data from six distinct sources (Table  1 ), including either original datasets (Table  1  and Supplementary Table  S2 )  (11, 12, 14, 18, 22)  or reprocessed data  (37) , and discarding 12 of them (Supplementary Table  S1 ). Notably, we have included data from sORFs. org  (37) , considered as the main and most comprehensive repository of sORFs identified by genome-wide translation profiling (Ribo-seq), that currently integrates re-processed data from 73 original publications.
For each of these sources, a set of features essential to properly characterize the sORFs, related to their location, length, sequences, environmental signatures and cell types (i. e. cell lines, tissues or organs) in which they are expressed,   S1  for more information about these data sources. have been collected (see Table  2  for a full list of features considered for inclusion). When it was not provided by the source, the symbol of the gene related to the sORF was recovered using the transcript identifier (ID, if provided) or searching for the gene(s) or ncRNA(s) overlapping with the sORF coordinates in the original annotation version by querying Ensembl databases  (38)  in their appropriate versions (v74, 75, 76, 80, 90) with pyensembl (v1. 8. 5, https:// github. com/openvax/pyensembl).
In addition to these features, information regarding the transcript(s) harboring the ORFs have been collected from the data sources when available. This is of particular interest as some ORF features, such as the ORF class, may depend on the transcript they are located in (e. g. an ORF may be located in the 5 ′ UTR of a transcript and be overlapping with the CDS of another transcript). Finally, 3 379 219 and 2 066 627 entries from these six data sources have been collected and inserted in MetamORF for H. sapiens and M. musculus, respectively (Table  3 ).
Homogenization of genomic coordinates As the data sources were providing genomic coordinates from different genome annotation versions (e. g. GRCh38 and GRCh37), all the genomic coordinates registered in our database have been lifted over the latest annotation version (GRCh38 for H. sapiens and GRCm38 for M. musculus) using pyliftover (v0. 4, https://pypi. org/project/pyliftover). The liftover has been considered as failed for an entry if (i) at least one of the coordinates (i. e. start, stop or one of the start or end exon coordinates) was located on a strand different from all the others or (ii) the chromosome of the position changed during the liftover or (iii) the distance (in nucleotides) between the sORF start and stop codons has changed after the liftover. All the entries for which the liftover failed were removed from the database.
Merge of redundant information As our database aims to provide a repository of unique identified sORFs of the human and mouse genomes, all the redundant entries describing the same sORFs have been merged. In a first step, we identified all the sORF entries for which all the identification features were provided (chromosome, strand, start position, stop position, splicing status and splicing coordinates). sORFs sharing the same feature values were merged. In a second step, we identified all the remaining entries with only partial identification features provided: the chromosome as well as either (i) both the strand and the start positions or (ii) both the strand and the stop positions or (iii) both the start and the stop positions. Those entries were merged to the best matching fully described entries identified in the first step.
If no matching fully described entry was found, then the entries were removed. In order to keep track of the number of times a same sORF has been described in the original data sources, the initial number of entries merged together was registered for each sORF. During this merging, information regarding the transcripts that harbor the sORFs has been registered too. Hence, when several sORFs were merged into one single entry in MetamORF, the resulting new sORF entry was registered as harbored by all the distinct transcripts related to the original entries. After this removal of redundant information, we were finally able to identify 664 771 and 497 904 unique sORFs for H. sapiens and M. musculus, respectively (Table  3 ). It should be noticed that all unique sORF entries generated at this stage have been kept, including the ones describing ORFs longer than 100 amino acids.
Completion of missing information and computation of relative coordinates In the original data sources, the only information provided (when provided) on the transcripts was the transcript ID. Detailed information was retrieved from Ensembl databases (v90) through their REST API and inserted in our database: (i) the transcript biotype, (ii) the transcript start and end genomic coordinates, (iii) the codon of the canonical CDS (for protein-coding transcripts only) start and stop genomic coordinates and (iv) the full nucleic sequence. In addition, the sequence flanking the start codon  (20)  has been recovered. As the sORF nucleic and amino acid sequences were not systematically provided by the data sources, these were downloaded from the Ensembl databases using their genomic coordinates. Moreover, when the transcript ID was available, sORF start and stop relative coordinates have been computed on each of their transcript using AnnotationHub (v2.
Cell types Original data sources do not use a common thesaurus or ontology to name the cell types (e. g. 'HFF' and 'Human Foreskin Fibroblast') or use non-biological meaning names (e. g. sORFs. org (37)  provides the name of the original publication as a cell type). In order to provide an uniform informative naming, we manually recovered the name of the cell line, tissue or organ used in these datasets and defined an unique name to be used in our database for each cell line, tissue or organ, trying to use the most commonly used nomenclature for cell lines (Supplementary Table  S3 ). In addition, in order to ensure interoperability with other biological resources, we recovered the matching ontology terms from the following ontologies when feasible: the Cell Ontology (40), the Cell Line Ontology  (41) , the BRENDA Tissue Ontology  (42) , the Human Cell Atlas Ontology  (43) , the Foundational Model of the Anatomy Ontology  (44) , the Ontology for Biomedical Investigations  (45) , the NCI Thesaurus OBO Edition  (46) , the Experimental Factor Ontology (47), the BioAssay Ontology  (48)  and the Ontology for MIRNA Target (49), using the Ontology Lookup Service (EBI) (50) (Supplementary Table  S4 ).
MetamORF software and languages The pipeline used to build MetamORF has been developed using Python (v2. 7) with SQLAlchemy ORM (sqlalchemy. org, v1. 3. 5). The database has been handled using MySQL (mysql. com, v8. 0. 16). Docker (docker. com, v18. 09. 3) and Singularity (singularity. lbl. gov, v2. 5. 1) environments have been used in order to ensure reproducibility and to facilitate deployment on high-performance clusters. The MetamORF web interface has been developed using the Laravel (laravel. com, v7. 14. 1) framework with PHP (v7. 3. 0), JavaScript 9, HTML 5 and CSS 3. The NGINX (v1. 17. 10) web server and PHP server (v7. 3. 0) were deployed with Docker (docker. com, v18. 09. 3) and Docker-compose (v1. 24. 0) to ensure stability.
Gene lists The list of genes harboring at least one uORF has been collected from MetamORF as a list of Ensembl identifiers using a SQL query. The list of ATF4 and CHOP targets identified by ChIPseq comes from Han et al. (52)  (available as supplementary data on the editor's website). Genes congruently and translationally upregulated under endoplasmic reticular (ER) stress have been provided by Guan et al. (53)  (upon request). As these lists of genes were provided as gene symbols, matching Ensembl IDs have been recovered using the g:Convert tool available on the gProfiler web interface  (54). The universe contains all protein-coding genes annotated at least once in Gene Ontology  (55, 56)  (downloaded from the g:Profiler web interface on 3 November 2020).
Database content, accessibility and web interface A new repository of short ORF-related data MetamORF describes 664 771 and 497 904 unique ORFs in the human and mouse genomes, respectively, providing at least the information necessary to locate the ORF on the genome, its sequence and the gene it is located on (excepted for intergenic ORFs). Extensive information related to the transcripts is provided for 614 997 (∼93%) and 497 904 (100%) sORFs for the human and mouse genomes, respectively. These features allowed us to classify 630 953 (∼95%) human ORFs and 497 904 (100%) mouse ORFs in at least one class (Table  3 , Figure  2 , Supplementary Figure  S1 ). Interestingly, it should be noticed that a large proportion (36% and 52% for H. sapiens and M. musculus, respectively) of ORFs are using an alternative frame to the main CDS.
User-friendly web interface and genome tracks To provide users with a clear, fast and easy-to-use database, MetamORF can be queried through an user-friendly web interface at https://metamorf. hb. univ-amu. fr. A tutorial as well as a documentation page are available online. Briefly, the users may search for sORFs contained in the database starting with a gene symbol (symbol, alias, ID), a transcript ID (ID, name) and an ORF ID or screening a particular genomic area. The data are made accessible through four types of pages: (i) a 'gene' page (Figure  3 ) to allow visualizing information related to all transcripts and sORFs on a gene, (ii) a 'transcript' page to allow browsing information related to a transcript gene and all its sORFs, (iii) an 'ORF' page to allow fetching information related to all transcripts and gene that harbor the chosen ORF and finally (iv) a 'locus' page to allow getting information related to all sORFs located in a particular locus.
In addition, the user may also browse across all sORFs related to a species or detected in a particular cell type. It is possible to navigate from one to another page easily to get extensive information about a sORF, a gene or a transcript (Supplementary Figure  S2 ). In each page, the results can be filtered on (i) the identification method (computational prediction, ribosome profiling or mass spectrometry), (ii) the start codon, (iii) the Kozak context (as previously defined), (iv) the genomic length (defined as the sum of lengths of each exon constituting the ORF), (v) the transcript biotype (according to the Ensembl definitions), (vi) the ORF annotation (as previously defined) and (vii) the cell type (Supplementary Tables  S3  and S4 ). All results can be exported in an easily parsable format (comma-separated values file, CSV), as well as in FASTA or BED format.
On ORF, transcript and locus pages, a link allowing the user to easily visualize all the ORFs localized in a particular area on the UCSC Genome Browser (57) is proposed. We also implemented genome track hubs, to allow using UCSC Genome Browser advanced options, such as filtering on ORF categories, transcript biotypes, cell types and transcript IDs. In addition to this user-friendly interface, it is possible to download from the website the content of the full MetamORF database in BED and FASTA formats.
Discussion and conclusion MetamORF contains data about 1 162 675 unique sORFs for the human and mouse genomes identified by both experimental and computational approaches. While the Ribo-seq is considered by most as the 'gold standard' method to identify sORFs experimentally, the added value of predictive computational approaches, proteomics and peptidomics to characterize such biological sequences remains certain. Because these technologies are offering complementary information at genomic, transcriptomic and proteomic scales, we decided to include data from both experimental and computational experiments in our database. Nevertheless, data coming from distinct data sources may be difficult to compare, in particular because they are not necessarily using the same genome annotation and definitions of ORF classes and Kozak contexts, for instance.
By homogenizing this information, MetamORF offers the possibility to compare datasets coming from different sources. We noticed that information regarding the Kozak context is missing most of the time, and start flanking sequences are usually not provided. Hence, MetamORF provides a new interesting set of information. It is noteworthy that we discarded 12 of 18 datasets because they lack crucial information regarding their integration into MetamORF. Although this is a rather drastic method, this is performed for the sake of data quality. In these conditions, the confidence in the data and the reliability in the existence of the sORF of interest can be assessed by the number of original experiments that identify the sORF (column 'EXP. COUNT' in the tables of the web interface). It is noteworthy that >97% of the unique ORF entries registered in MetamORF have been identified by at least one experimental method.
It should be noticed that a large amount (∼80%) of the sORFs contained in our database have been described in the sORFs. org repository  (37). Despite being the most prominent sORF database and offering the community data processed in a normalized way using their own workflow, sORFs. org does not provide metagene analyses (1). In addition, such analysis is made difficult by the absence of gene names and transcriptomic coordinates as well as the high redundancy of information contained in the sORF. org database  (37) , issues that we addressed with MetamORF. It is noteworthy that another sORF resource, namely Open-Prot (59), does not contain ORFs shorter than 30 amino acids, whereas in MetamORF, sORFs of such size represent ∼50% of the dataset. Of note, 54% of them have been detected in at least two data sources, therefore reinforcing their probability of existence.
Hence, in comparison with existing resources (Supplementary Table  S8 ), MetamORF is complementary and allows analyses at ORF, transcript, gene and locus levels. In addition, it opens the possibility of studying sORFs as a group, at a global scale. The resource is accessible at https://metamorf. hb. univamu. fr and provides an intuitive querying interface to enable wet-laboratory researchers to easily question this large set of information. The web interface comes with advanced filters, notably on computed ORF classes, ORF start codons, identification methods, Kozak contexts and cell contexts. Such filters should help end-user biologists without computational skills to identify and collect information about the sORFs important for their topic of interest. Moreover, the implementation of MetamORF content in track hubs allows both quick and advanced visualization of data through the UCSC Genome Browser.
Finally, the database content may be exported in various convenient formats widely used by the scientific community (e. g. FASTA and BED). We believe that MetamORF is of interest not only to bioinformaticians working on short ORFs but also to a wider community, including any biologist who may benefit from knowledge regarding the sORFs located on their gene, transcript or region of interest. As ribosome profiling becomes more appreciated and proteomics starts allowing accurate identification of short peptides, new data describing sORFs in various conditions will be published in the next years, and our database is expected to grow accordingly. In particular, the next release of MetamORF is expected to include data describing the sORFs of other organisms such as Drosophila melanogaster. As a conclusion, we believe that MetamORF should help to address new questions in the future, in particular regarding the regulatory functions of the sORFs as well as the functions of the short peptides they may encode.
Funding The project leading to this publication has received funding from the  « Investissements d'Avenir » French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University  -A*MIDEX. It has also received funding from the  Excellence Initiative of Aix-Marseille University  -A* Midex a French "Investissements d'Avenir programme"-Institute MarMaRa   AMX-19-IET007.
Data availability Data sources are available on the editor's website or using the links provided in their original publications. The source code used to create the database and the full technical documentation (source code documentation, manual, database structure and dockerfiles) are available on GitHub (https://github. com/TAGC-NetworkBiology/ MetamORF). Full content of the database can be downloaded in BED and FASTA formats from the Meta-mORF website, and up-to-date version of track hubs may be downloaded and/or used with your favorite genome browser from the link https://metamorf. hb. univ-amu. fr/ hubDirectory/hub. txt. The dump of the database is available on request.
Inductive Analysis The focus groups were entirely transcribed and anonymized. The data analysis was carried out with the R software (version 3. 5. 1, RQDA package). An inductive content analysis approach was used  [28]. After a global reading of the verbatims, the minimum meaning units were identi ed. They were then coded according to the different aspects relevant to patient decision-making and were labelled using shortened titles. Double coding was carried out independently by two researchers (AF and CB). Doubts or disagreements were discussed before pooling the nal analysis. These meaning units were grouped into categories that were then presented and discussed during steering meetings in order to establish the speci cations of the PtDA.
Data from national forest inventories (NFI) are particularly relevant for modelling pure and mixed stand dynamics with a view to assisting forest managers. The wide geographical coverage of these data makes it possible to study the dynamics of a large variety of pure and mixed stands under diverse environmental and silvicultural conditions (e. g. ,  Fortin & Langevin, 2012; Lessard et al. , 2001; Li et al. , 2011). Such datasets make it possible, in particular, to compare the dynamics of pure and mixed stands and thus to estimate the effect of mixture on stand productivity for different species assemblages, while controlling for the effect of environmental conditions on this composition -productivity relationship. NFI data are therefore suitable for modelling various stands in a variety of contexts, which is useful for managers looking to compare different strategies before defining a management strategy.
On the other hand, landscape models are too large in scale to plan silvicultural operations, while tree-level models can be too data-or prescription-intensive. Landscape models are more relevant for large-scale spatial planning, while tree-level models can be more relevant for studying interactions among trees, for example. Here we present the Salem (for StAnd LEvel Model) simulator. Salem predicts the dynamics of even-aged pure and mixed stands and makes it possible to simulate management operations. Its purpose is to be a decision support tool for forest managers and stakeholders as well as for policy makers. Salem is calibrated on stand-level data from the French National Forest Inventory (NFI) as well as on environmental data and it simulates standlevel processes. The mixture effect on stand productivity is explicitly implemented in Salem using the multiplier approach (3) mentioned above.
Salem uses species-specific models to generate tree diameter distributions from the stand level values Dg and N of each species (Section 3. 5). From these predicted diameters as well as from environmental and stand variables, tree height is predicted (Section 3. 6). Finally, a two-entry (diameter and height) volume equation is applied to predict tree volume (Section 3. 7). The modelling framework presented here is the result of successive developments, some of which have been presented in previous studies. Table  1  summarises the differences between previous work and the work presented in this article. 3 Model descriptions 3. 1 Growth models in pure stands 3. 1. 1 Data. We calibrated our growth models using French NFI data collected between 2006 and 2013 in pure stands according to the following criteria: • Stands had to be classified as even-aged stands by the French NFI.
• Stands must have not been logged in the five years preceding the measurements. • A single species was to make up the total stands BA. French NFI data are collected on temporary plots that are measured only once. Trees equal or greater than 7. 5 cm in diameter at breast height (dbh) are identified and measured. Tree radial growth in the five years preceding their measurement is assessed using tree cores. In order to develop a predictive growth model, we calculated the stand dendrometric values BA and Dg at the beginning of the five-year growing periods using the diameter measurements and the radial growth measurements. All pure stands were considered, regardless of their location. However, we removed all pure P. pinaster stands with a Dg at the beginning of the five-year growing periods smaller than 5 cm.
(2018). The climate data comes from the French national meteorological service and were processed following the Aurelhy method  (Benichou & Le Breton, 1986)  DI is then given by: N DI N = max (6) where N is the number of stems and N max is the maximum number of trees given by the self-thinning boundary for the corresponding Dg. DI is therefore expected to range between 0 and 1. In Salem, when DI reaches 1, i. e. when N reaches N max , mortality is triggered (see Section 3. 4). This DI has been commonly used in pure stands  (Charru et al. , 2012; Reineke, 1933)  but also in mixed stands  (Del Río et al. , 2016). 3. 2 Models of the mixture effect on growth 3. 2. 1 Data. We modelled the effect of mixture on stand growth using French NFI data collected in mixed stands according to the following criteria: • Stands had to be classified as even-aged stands by the French NFI.
We had at our disposal, for nine species or groups of species and for four to five dbh classes: the number of sampled trees, their mean dbh and the mean and variance of their bark proportion. All data can be found in  Bouvet & Deleuze (2013). In this dataset, Q. robur and Q. petraea are not differentiated and no data are available for P. halepensis. We therefore built a single bark model for both Quercus species and Salem applies the P. sylvestris bark model to P. halepensis because these two species have a very similar bark proportion (Deleuze, personal communications).