dataesr/scientific-paragraphs-categorization huggingface dataset:
We also expected optimal results when large proportion of the sequence data is associated with the trait of interest, to avoid the potential artifact of non-associated positions affecting the overall fit of the model to the data. The results further showed that for the TraitRELAX model an empirical likelihood ratio (LR) test is better suited to test trait-related change in selection intensity. An alternative that is more compatible with the case of using real data, yet time consuming, approach is to use a full parametric bootstrapping procedure  (Whelan and Goldman 1999) , which can obtain for each examined dataset a more compatible distribution of LR values and consequently result in reduction of the FPR. The results also indicated that the method is sensitive to the confounding effect between  and the  parameters (underestimation of the inferred values of the  0 parameter may be compensated by overestimation in the inference of the  parameter, such that the induced value of  0  is similar and thus the obtained likelihood score is nearly unchanged).
Availability TraitRELAX was implemented as an open-source program in Bio++  (Guéguen et al. 2013 ) and its code is available at https://github. com/halabikeren/TraitRELAX/. The input to the program is a tree in Newick format, as well as sequence and character data files in Fasta format. The program outputs the maximum-likelihood score for the null and alternative models as well as their inferred model parameters.
a synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients.
Background & Summary Several research groups have advocated for a trait-based approach to ecology of bacteria and archaea  [1] [2] [3] [4] [5] [6] [7] [8] [9]  , but so far this has remained at the level of conceptual discussion or interpretation of particular study systems. Here we describe a scripted workflow that generates a unified microbial trait dataset suitable for investigating which traits are correlated across species versus which vary independently. The dataset spans the full range of bacterial and archaeal habitats, including fresh and marine waters, soils and sediments, animal and plant hosts, and thermal environments. Data sources include well-established repositories, such as GenBank 10 , Bergey's Manual of Systematics of Archaea and Bacteria  11  , and a number of compilations published in the literature (Online-only Table  1 ).
We believe this data product will prove useful to other research groups in several ways. Some may use the current version of the dataset for their own data analyses. They may adjust the scripted workflow to adopt different merger rules; for example, about how data sources are aggregated or prioritized when multiple records are available. Some may choose to update the dataset, since among the contributing data sources several are continuing to receive new data. Some may choose to add further data sources or merge their own data sources, which should be made easier by the scripted structure we provide. Once scripted into the workflow, new or updated data sources can be merged with the current data product in GitHub resulting in a new version of the data product. Trait data can have a variety of research purposes.
We have included information from culture on metabolic pathways and carbon substrates. However, we have not yet included metabolic pathways inferred from genomes, and consequently the question of reconciling genome-inferred pathways with culture-observed pathways does not arise. Also we have not yet included presence or absence of specific genes as qualitative traits, for a combination of reasons. First, there are potentially a very large number of such traits. Second, the number of complete genomes available continues to increase rapidly, and so such data will be out of date quickly. Third, there exist a number of databases (MIST  14  , MACADAM 15 , ANNOTREE  16  for example, and more emerging all the time) that specialize in annotations from genomes. When users wish to ask questions involving these genome-derived traits it will be better for them to link those databases to ours, which can be done using NCBI Taxon IDs.
Methods The scripted workflow was developed to reproducibly (a) prepare datasets to be merged; (b) combine datasets; (c) condense similar or the same traits into columns; and (d) condense rows into species based on either the NCBI taxonomy  17  or the Genomic Taxonomy Database (GTDB) taxonomy  18  (Fig. 1 , Online-only Table  1 ). This workflow generated five data products  17  for the 23 phenotypic, genomic and environmental traits shown in Online-only Table  2. The first two products are record level, which includes taxonomic levels below species (e. g. , strain) and based on the NCBI taxonomy and GTDB taxonomy, respectively. A reference table was generated to track provenance of raw data through the workflow. The last two products are aggregated at species-level for the NCBI taxonomy and GTDB taxonomy, respectively.
Trait coverage across the phylogenetic tree is shown in Fig. 2  and the trait distributions are shown in Fig. 3. Table  1  shows species-level trait data derived from original datasets. Prepare. The preparation steps removed unwanted columns from raw datasets, ensured standard trait (column) naming, and established that each record (row) had an NCBI taxon ID and reference. In cases where NCBI taxon IDs were not provided in the raw dataset, taxon mapping tables were created using the NCBI taxonomy API, which could retrieve IDs by fuzzy searches of name or accession number, depending on what was available  10, 17. In cases where the API did not resolve to a single taxon, the NCBI taxonomy browser was used to manually look-up parts of names in case of misspellings or name fragments (e. g. , strain names that were truncated to species level).
DOIs or full text citations were used for referencing where possible, but in some cases only NCBI BioProject or accession numbers were available and were used to track provenance instead. All changes in the preparation stage were scripted and commented in dataset-specific preparation scripts. Other dataset-specific steps included splitting number ranges into different components (e. g. , 10-20 µm to 10 [min], 20 [max] and µm [unit]), and any general data translation issues (e. g. , spreadsheet software issues that manipulated characters, dates, and other inconsistencies). Only the traits summarised in Online-only Table  2  were retained for the steps where data are combined (next). Combine. All the raw datasets were placed into a single sparse matrix with zero overlap (Fig. 1b ). A column was added with the name of the dataset (Online-only Table  1 ) to keep track of dataset provenance.
All columns containing referencing information (reference and reference type) and NCBI taxon IDs were moved into dedicated columns. The basic taxonomic hierarchy was mapped onto each row using either of the NCBI or GTDB taxonomies, which added columns for species, genus, family, order, class, phylum and superkingdom. Condense traits. Condensing trait data involved moving values for the same trait from different datasets into one column (Fig. 1c ). The inherent assumption is that data for the same taxon from different datasets were observed independently (e. g. , cell sizes for a given strain or species that occurred in multiple datasets were considered different observations, and so are included as multiple rows). This assumption had little influence on the data following the condense species step (next).
To avoid clutter, only the six most speciose phyla are delineated at the outer rim (>100 species). Coloured bands represent the presence of traits in the dataset for 14,884 species. In order for the centre outwards, green are habitat traits (isolation source, optimum pH, optimum temperature, growth temperature), blue are organism trait (gram stain, metabolism, metabolic pathways, carbon substrate, sporulation, motility, doubling time, cell shape, any cell diameter), and red are genomic traits (genome size, GC content, coding genes, rRNA16S genes, tRNA genes). of approximately 100 environment labels. The scheme is hierarchical using up to four levels of specificity, for example a one-term label is "host", a two-term is "host_animal", a three-term is "host_animal_endotherm", and a four-term is "host_animal_endotherm_intestinal").
These concatenated strings were then manually translated into their most appropriate label in terms of our scheme and saved in a translation table. Given the large number of unique strings created in this way, only the most prevalent strings have at this stage been translated (>3,000), covering approximately 65% of the species in the species condensed dataset. These environmental labels were annotated with terms from the Environmental Ontology (ENVO) and stored in the "environments. csv" table in the GitHub project; however, ENVO annotations do not currently appear in the data products  19  because most environmental terms required the union of multiple ENVO terms. A step was also included to correct datum-specific errors. Some of these likely occurred during original data entry, such as wrong units or misspellings.
Others were values that seemed surprising, and also stronger or newer evidence was available from other sources. These corrections were scripted as a translation table that contained the original dataset, taxon, trait and value where the error occurred, and then the new, corrected value as well as a comment and reference as to why the change was made (see Technical Validation). The condense trait step generated three files  19  : "condensed_traits_NCBI. csv", "condensed_traits_GTDB. csv" and "references. csv". Condense species. At this stage, rows in the dataset represented both strains and species, and each strain and species could have multiple replicate rows for a given trait. Because every row could be mapped to species (but not vice versa), data were aggregated at either the NCBI  10, 17  or GTDB  18  species level.
If no dominant term could be found at a given level (not resolved), the process was stopped at that level. As such, an isolation source may contain 1 to 4 levels of information with increasing specificity. Bergey's Manual of Systematics of Archaea and Bacteria 11 contains a large amount of useful phenotypic trait detail, such cell size, sporulation, gram, metabolism and more, across the whole of Archaea and Bacteria, but is not stored as a dataset. Therefore, this data source was used at the final stage of the species condense step to fill in data gaps, especially for traits that were easily extractable using text matching (e. g. , cell size and metabolism; see scripted workflow for details). The condense species step generated two files 19 : "condensed_species_NCBI. csv" and "condensed_species_GTDB.
Data Records 1. "condensed_traits_NCBI. csv": A trait condensed data record containing all focal trait data (Online-only Table  2 ) from original datasets using the NCBI taxonomy  19. Rows represent strain-or species-level measurements, and there can be more than one row per taxon. On the whole, this is a strain-level, non-aggregated data record. 2. "condensed_traits_GTDB. csv": Same as "condensed_traits_NCBI. csv" but using the GTDB taxonomy  19. This trait condensed data record is smaller, because the GTDB protocol does not accept all NCBI taxa. 3. "references. csv": A table containing reference information for the data  19. Each row in the trait condensed data ("condensed_traits_NCBI. csv" and "condensed_traits_GTDB. csv") has a unique ID that points to a reference in the reference table for that particular data record.
Species condensed data (below) have multiple reference IDs. 4. "condensed_species_NCBI. csv": A species condensed data record contained all focal traits (Online-only Table  2 ) aggregated so that there is one row per NCBI-defined species  19. 5. "condensed_species_GTDB. csv": Same as "condensed_species_NCBI. csv" but using the GTDB taxonomy  19. However, this species condensed data record is smaller, because the GTDB protocol does not accept all NCBI taxa.
technical validation Approximately 80% of the time spent developing this bacteria and archaea trait data pipeline was consumed by searching for and fixing errors and inconsistencies in the raw datasets that were ultimately combined. When inconsistencies across datasets could not be resolved, the data were removed. These fixes necessarily involved human judgment, hence the large time expense. All fixes to datasets have been recorded into a data correction table (in "data/conversion_tables/data_corrections. csv") that is implemented by the script so that the decision-making process is transparent. In addition to basic error checking (e. g. , looking at unique lists of controlled terms, removing whitespace, etc. ), we paid particular attention to outliers, which sometimes (though certainly not always) turned out to be problematic.
Usage Notes The data records are available at figshare  19. The script that generated the data records is available at GitHub (https://github. com/bacteria-archaea-traits/bacteria-archaea-traits/releases/tag/v1. 0. 0). Two large files were not included with the GitHub project: the NCBI taxonomy translation table and PATRIC dataset. These files are automatically downloaded to their correct directories the first time the workflow script is run. If download problems occur, instructions for where to place these large files manually can be found in the project readme file. Please note that several of the raw datasets entering into the workflow were sourced from dynamic, growing databases (see Online-only Table  1 ). Therefore, users of the Data Records may consider obtaining fresh versions of the different sources from the links or data providers in Online-only Table  1 , and then re-applying the scripted workflow to build an updated data synthesis.
Additionally, the datasets we merge contain additional traits that we do not collect in our workflow, given our broader research goals. Adding these traits requires adjusting the project settings and editing dataset specific preparation files. Instructions for doing so are in the project readme file and dataset specific readme files ("data/raw"). Translation tables created to map trait variables, including isolation source, are in the "data/conversion_tables" directory. Additional quality control will be necessary following the addition of new or updated datasets and traits to the workflow. We encourage other groups who update or add new data sources to this data product to do so using our procedure outlined in the Methods (above) and in more detail at the GitHub project readme. This project uses GitHub's standard fork and pull request workflow, which is well documented at GitHub.
Such changes would follow this general pattern: • Forking the GitHub project. • Updating the existing or adding the new dataset in its raw form to the "data" repository. • Writing a data preparation script ("R/preparation"), which includes appending NCBI taxon IDs if not already in the dataset. • Identifying the traits to be merged ("R/settings. R"), and writing a conversion table if the trait is not in the same units of categories as the present dataset version ("data/conversion_tables"). • Looking for outliers and other errors, which can be removed or altered using the corrections table ("data/ conversion_tables/data_corrections. csv") • Running and testing the merger ("workflow. R"). • Submitting a pull request via GitHub, at which point we will review and test the changes. • Once the pull request is accepted, the project version will be updated.
25 M (as recommended  (Alberty et al. , 2011) ). Since energetic calculation using C 1 -tetrahydromethanopterin intermediates are not supported by eQuilibrator-API, we used formate as substrate for serine cycle and the modified serine cycle. The scripts and details can be found at https://gitlab. com/hi-he/wenzhangfujian within the "2020_Promiscuous aldolases" directory.
We further removed the ATP maintenance reaction (ATPM) as, rather than estimating growth rate, we used FBA to estimate the maximal yield. Biomass yield was calculated by the predicted maximal biomass reaction flux divided by the flux of methanol uptake. While modeling acetyl-CoA production, two unrealistic pathways were blocked by deleting reactions DRPA and PAI2T respectively. The full code, including changes to the model, reactions specific to the methanol assimilation pathways, and the reactions of each production route can be found at https://gitlab. com/hi-he/wenzhangfujian within the "2020_Promiscuous aldolases" directory.
Strains and genomic manipulation Strains used in this study are listed in Table  1. An E. coli MG1655 derived strain SIJ488  (Jensen et al. , 2015)  was used as the parental strain for genomic modifications. Iterative rounds of λ-Red recombineering  (Jensen et al. , 2015)  or P1 phage transduction  (Thomason et al. , 2007)  were used for gene deletions. For the recombineering, selectable resistance cassettes were generated via PCRprimers 50 bp homologous arms as in  (Baba et al. , 2006 )using the FRT-PGK-gb2-neo-FRT (Km) cassette (Gene Bridges, Germany) for kanamycin resistance (Km) and the pKD3 plasmid (GenBank: AY048742)  (Datsenko and Wanner, 2000)  as a template for chloramphenicol resistance cassettes (CAP). The procedures of the deletion, verification and antibiotic cassette removal are detailed in  (Wenk et al.
The absorbance (OD 600 ) in each well was monitored and recorded after every three shaking cycles (~16. 5 min). Raw data from the plate reader were calibrated to normal cuvette measured OD 600 values according to OD cuvette =OD plate /0. 23. Growth parameters were calculated using MATLAB (MathWorks) based on three technical triplicatesthe average values were used to generate the growth curves. Checked in MATLAB, in all cases variability between triplicates measurements were less than 5%.
1 mm, 1. 8 μm; Waters). 0. 1% formic acid in H 2 O (A) and 0. 1% formic acid in acetonitrile (B) were the mobile phases. The flow rate was 0. 4 mL/min and the gradient was: 0 to 1 min -99% A; 1 to 5 minlinear gradient from 99% A to 82%; 5 to 6 minlinear gradient from 82% A to 1% A; 6 to 8 minkept at 1% A; 8-8. 5 minlinear gradient to 99% A; 8. 5-11 minre-equilibrate. Mass spectra were acquired using an Exactive mass spectrometer (Thermo Scientific) in positive ionization mode, with a scan range of 50. 0 to 300. 0 m/z. The spectra were recorded during the first 5 min of the LC gradients. Data analysis was performed using Xcalibur (Thermo Scientific). The identification of amino acids was based on retention times and m/z, which were determined by analyzing amino acid standards (Sigma-Aldrich) under the same conditions.
Acknowledgements The authors thanks  Änne Michaelis  and  Pauline Kuschel  for experimental assistance,  Elad Noor  for help in the MDF analysis, and  Charlie Cotton ,  Seohyoung Kim ,  Steffen Lindner ,  Nico Claassens , and  Andrew Hanson  for critical reading of the manuscript. This study was funded by the  Max Planck Society. Hai He  is funded by the  China Scholarship Council.
(  3 )). If misclassifications were random, reaching the observed MI of 2. 48 bits would require 53% correct classification. Thus, information for species identity is clearly present in the drumming signal and could provide reliable species discrimination as long as particular errors of classifications within related species could be avoided. We analyze this putative strategy in the section 'Information in ecological communities' below. Since our hierarchical clustering analysis of acoustic structure revealed a finite number of distinct drumming strategies, we began to explore the relationship between acoustic structure and species signature by examining the contribution of drumming type to the MI for species discrimination. We postulated that novel (in the sense 'newly emerged') drumming types might evolve to increase the MI during clade radiation, and examined this hypothesis based on the 92 extant species in our dataset.
within communities), showing an optimal use of acoustic strategies within communities (Supplementary Table  9 ). d Average Classification Index within species pairs, either taking all species together (black line; n = 92), or within communities (coloured lines). Classification Index ranges from -100 (no correct classification at all) to 100 (maximum correct classification). Compared with the family taken as a whole, sympatric species (i. e. within communities) are much better classified (Supplementary Table  9 ). e Cumulative distribution of the average acoustic distance within species pairs either taking distances within communities ('Real Communities'; black line), or randomly sampling species from our full community dataset (i. e. 6 species among 33; grey line), or selecting species each with different drumming types (red line), or selecting species which all have a similar drumming type (blue line).
Methods Acoustic data and analysis. Audio data were collected from online sound archives (Xeno-Canto-https://www. xeno-canto. org-and Macaulay librarieshttps://www. macaulaylibrary. org), creating a pool of over 2000 audio tracks. We assessed the sound quality of these audio tracks by listening and through visual inspection of sound spectrograms. To capture intra-specific variation, we limited audio extraction to one drum per audio track (which also avoided pseudoreplication) and only included species for which at least 3 high-quality drums could be extracted. We retained 736 high-quality drums suitable for further analyses. These drums were distributed among 92 species (out of the 217 recognized species of woodpeckers 50 and 22 genera, providing a representative sampling of the phylogenetic diversity found in this family (Fig.
1b )). Background noise and other artifacts were reduced by wavelet continuous reconstruction (R 'WaveletComp' package  72  ), following the methods and description outlined in previous work  73. The full script is available on demand. Finally, 22 acoustic variables were extracted from these filtered sound samples using the R 'Seewave' package  74. Given the pulsed-like nature of drumming, the chosen variables emphasized the temporal and amplitude-related (all normalized to the maximal amplitude within a given drumming signal) features of the sounds (Supplementary Table  1 ). These 22 variables were z-scored and then used in all subsequent analyses. Since these variables were partly correlated to varying degrees, we performed a principal component analysis (PCA) to reduce the number of descriptive variables quantifying drumming acoustic structure.