dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability For convenience, all data sets (simulated and real) studied herein are included in the Supplementary Material. The models presented have been implemented in an experimental version (2) of PhyloBayes-MPI (https://github. com/bayesiancook/pbmpi2), allowing for a joint sampling across parameter space, auxiliary variables, and tree topology space. We have also implemented the models in a new software called BayesCode, which is focused on integrative comparative methods under fixed topology (https://github. com/ bayesiancook/bayescode). Example scripts demonstrating the use of the software are provided in the Supplementary Material.
Motivation: Trait data are fundamental to the quantitative description of plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides readyto-use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (a) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (b) maximized species coverage through data standardization within and among traits, and (c) implemented data quality checks.
, 2014; Freschet et al. , 2017; Kong et al. , 2019; Laliberté, 2016; Ma et al. , 2018; Valverde-Barrantes et al. , 2017) , they remain underrepresented in large-scale comparative studies and global models. Accordingly, root traits remain absent from nearly all existing studies that define the global spectrum of plant form and function  (Chave et al. , 2009; Díaz et al. , 2016; Reich, 2014; Wright et al. , 2004 ; but see  Averill et al. , 2019). Conceptual and methodological challenges have deterred widespread data integration of root traits into global trait databases. Conceptually, the functional importance of some root traits has yet to be established formally, which might preclude their use in large-scale analyses  (Aubin et al. , 2016). Methodologically, quantificatio of root traits is labour intensive, and there are technical difficulties in obtaining accurate measurements (e.
g. ,  Delory et al. , 2017). Furthermore, large variation in methodologies precludes data standardization and integration within traits. Specifically, although traits are characteristics measurable at the level of the individual plant  (Violle et al. , 2007) , root traits can be measured in different ways, increasing the number of trait variables. For example, data for root nitrogen uptake are separated into eight trait variables  (Iversen et al. , 2017). Although coordinated initiatives, such as the Fine-Root Ecology Database (FRED;  Iversen et al. , 2017)  and the Plant Trait Database (TRY;  Kattge et al. , 2011 Kattge et al. , , 2020)) , have compiled valuable root trait data, these databases still face many of these conceptual and methodological challenges associated with root traits. FRED has been essential in terms of mobilization of fine-root trait data and is the largest contributor of root trait data to TRY  (Kattge et al.
Therefore, using these data requires that one first disaggregates these traits (e. g. , by establishing links between trait names and definitions) and then standardizes trait values. Finally, accurate global assessments on root trait data availability, in terms of geographical or phylogenetic coverage, are essential to identify data gaps and to work towards increasing representativeness in largescale comparative studies and dynamic global vegetation models. To overcome these roadblocks, we have created the Global Root Trait (GRooT) Database. The main objective of GRooT is to make root trait data ready to use, particularly in the context of large-scale analyses. To do so, we first provide a set of core root traits that are considered to be relevant for describing plant form and function. Trait Spatial location and grain: Global coverage with data from arid, continental, polar, temperate and tropical biomes.
Data on root traits were derived from experimental studies and field studies. Time period and grain: Data were recorded between 1911 and 2019. Major taxa and level of measurement: GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with subspecies or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 subspecies or varieties, 6,214 species, 1,967 genera and 254 families. Owing to variation in data sources, trait records in the database include both individual observations and mean values. Software format: GRooT includes two csv files. A GitHub repository contains the csv files and a script in R to query the database.
K E Y W O R D S Belowground ecology, functional biogeography, macroecological studies, plant form and function, publicly-available database, root traits selection builds on the compilation of standardized trait measurements in a new handbook on root traits  (Freschet et al. , 2020)  and an assessment by experts on root traits. In addition, we improve data coverage by compiling information from existing databases, mobilizing new data and standardizing data across methodologies within and among traits. Furthermore, we curate and perform data quality checks for each root trait in GRooT and make these data publicly available. Secondly, we provide within GRooT a unique overview of global root trait availability in terms of geographical and phylogenetic coverage. We envision that our advanced root trait database will be informative to global traitbased models and help to guide future measurement initiatives.
| Data acquisition and compilation GRooT includes root trait data provided directly from researchers, extracted from literature, or from large databases, such as FRED v. 2. 3 (https://roots. ornl. gov/; Iversen et al. , 2017 Iversen et al. , , 2018) )  and TRY v. 4. 1 (https://www. try-db. org; Kattge et al. , 2011 Kattge et al. , , 2020)). In total, GRooT includes data from 919 publications via FRED, 38 datasets via TRY and 12 additional datasets (Appendix 1, Data References). GRooT was assembled by initially determining which root traits are most relevant in terms of describing plant form and function (Table  1 ; Supporting Information Tables  S1  and S2 ). To build towards an ontology of root traits, we standardized trait names across data sources (Supporting Information Table  S3 ) and matched them with names from the new handbook of root traits  (Freschet et al.
After this process, we retained those relevant traits with data for > 50 plant species in the database (Table  1 ), because traits with lower species coverage seemed less helpful for large-scale analyses involving many species. Traits below this threshold, but still relevant, are currently excluded from GRooT (Supporting Information Table  S2 ). In GRooT, we included only trait records for which taxonomic information was available and excluded trait records where data was taken at the community level (i. e. , from species mixtures). Trait records varied in their taxonomic resolution, with subspecies or varieties and genera being the highest and lowest taxonomic resolution available, respectively. We used the generic term of "root", which includes any type of root entity (e. g. , established using diameter cut-offs, orders or functionality).
Moreover, we have included additional information for each trait record, such as species growth form, photosynthetic pathway and woodiness (Supporting Information Table  S5 ). We extracted this information from TRY and the Global Inventory of Floras and Traits (GIFT; http://gift. uni-goett ingen. de/home; Weigelt et al. , 2019)  or from general Web research [e. g. , Flora of China (www. eflor as. org), SEINet (swbiodiversity. org), United States Department of Agriculture (USDA; plants. usda. gov), and Southwest Desert Flora (southwestdesertflora. com)] when the information was not available in the aforementioned databases. We also included the present or absent ability to grow clonally and bud-bearing information at the species level on GRooT based on the CLO-PLA Database (CLO-PLA; http://clopla.
butbn. cas. cz/; Klimešová & Bello, 2009; Klimešová et al. , 2017 Klimešová et al. , , 2019)). For data collected in field conditions, biome classification according to Köppen-Geiger was included using the "kgc" R Package  (Bryant et al. , 2017). We added information on qualitative root traits as mycorrhizal association type and nitrogen (N 2 )-fixing capacity by interconnecting existing databases. For mycorrhizal type, we extracted data from the "FungalRoot: Global online database of plant mycorrhizal associations"  (Soudzilovskaia et al. , 2020). Mycorrhizal assignments were made at the genus level for plant species for which the mycorrhizal status is, according to current knowledge, conserved at this level  (Soudzilovskaia et al. , 2020). We included both standardized mycorrhizal types (named: mycorrhizalAssociationTypeFungalRoot) and mycorrhizal type from the original source (named: mycorrhiza-lAssociationType) in the database.
| Data curation and quality control We cross-checked references associated with each dataset to avoid data redundancy, which was mostly generated by: (a) a dataset being TA B L E 1 Root traits included in GRooT ). In addition, we obtained plant taxonomic order from the "taxize" R package v. 0. 9. 4  (Chamberlain & Szöcs, 2013). We checked trait records to ensure that data were in standardized units. Potential mistakes were checked in the original sources, corrected when possible or excluded when values were unreasonable (e. g. , negative values for nutrient concentrations or percentages > 100). We calculated the error risk as the number of mean standard deviations (across all species within trait) from the respective species mean (named: errorRisk), following the TRY protocol (TRY;   2017 ) and  Freschet et al.
f Mycorrhizal colonization intensity is based mostly on data for arbuscular mycorrhizal colonization. TA B L E 1 (Continued)  Kattge et al. , 2011 Kattge et al. , , 2020)) , but not implemented across root traits in TRY. We reported the number of data entries used to calculate the error risk per species (named: ErrorRiskEntries), with error risk robustness increasing when based on multiple replicates (preferably > 10 data entries). Normal distribution was checked for each trait, and logarithmic transformations were used before calculating error risk scores when required. Large error risk scores can indicate potential measurement errors, but they can also reflect intraspecific variation. Thus, we did not use error risk scores to remove trait records from the database but provide them to be used at the users' discretion.
| Data use guidelines and data availability GRooT contains two csv files and an R script. S4 ) or relevant covariables, such as root vitality  (McCormack et al. , 2015). GRooT is publicly available but should be referenced by citing this paper. We suggest citing the original data sources that contributed a substantial proportion to the analysis. GRooT is located and will be maintained and updated in a GitHub repository (https://groot -datab ase. github. io/GRooT/). We encourage users to report mistakes and suggestions to improve the database and to contribute data. of species-by-site mean values). Root trait coverage from field studies varies geographically across the globe (Figure  1 ). Regions such as North America, Europe and Asia are well covered, whereas there are consistent gaps in other regions, such as Africa and South America.
GRooT can be used for (but is not restricted to) studying macroecological and functional biogeography  (Violle et al. , 2014) , assessing global belowground trait-environmental relationships as known from aboveground approaches  (Bruelheide et al. , 2018) , and detecting fundamental ecological patterns, such as the root economic space  (Bergmann et al. , 2020)  or trade-offs and coordination among organs in the plant economic spectrum  (Freschet et al. , 2010). Furthermore, GRooT facilitates the integration of root traits into studies of related scientific disciplines, such as soil science and agronomy  (Martin & Isaac, 2018; Wood et al. , 2015). The completion of this standardized, curated and publicly available database provides immediate benefit to the research community from ready-to-use data  (Gallagher et al.
traits in large-scale comparative studies and global models by integrating expert knowledge, data mobilization, standardization, curation and open accessibility. In terms of geo-referenced data from field studies, GRooT has highest coverage in North America, Europe and Asia, especially for chemical and morphological traits, reflecting the capability for large-scale studies in these regions. In terms of phylogenetic coverage, data in GRooT include the major clades of vascular plants with, on average, four traits included per species. F I G U R E 1Maps depicting all georeferenced data from field studies included in the Global Root Trait (GRooT) Database. Circles indicate the range of species per site (e. g. , one or two species, two to four species, successively) or traits per site (e. g. , one or two traits, two to four traits, successively) Thereby, phylogenetic coverage in GRooT provides the possibility of using the data in large-scale phylogenetic studies, such as analyses of trait conservatism (Averill et al.
Grant/Award Number: vidi  016. 161. 318 ; Deutsche Forschungsgemeinschaft , Grant/Award Number:  FZT 118 ,  RI 1815/22-1  and  RU-1815/20-1 ;  Robert Schalkenbach Foundation , Grant/Award Number: # 16-14-10208 ;  Department of Energy's Office of Science  are supported by the  Biological and Environmental Research program  in the  Department of Energy's Office of Science , and  Joana Bergmann  was supported by  DFG  grants  RU-1815/20-1  and  RI 1815/22-1. Nathaly R. Guerrero-Ramírez  thanks the  Dorothea Schlözer Postdoctoral Programme of the Georg-August-Universität Goettingen  for their support;  Nadejda A. Soudzilovskaia  thanks vidi grant  016. 161. 318issued by the  Netherlands Organization for Scientific Research ; and  Vladimir G. Onipchenko  thanks  RSF  for financial support (# 16-14-10208 ).
The study has been supported by the TRY initiative on plant traits (http://www. try-db. org). The TRY initiative and database are hosted, developed and maintained at the  Max Planck Institute for Biogeochemistry, Jena, Germany. TRY is currently supported by  DIVERSITAS/Future Earth  and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. Open access funding enabled and organized by Projekt DEAL.
3 388. 2 416. 7 455. 5 concentration Root nitrogen mg/g 1,719 3,619 12. 5 7. 8 10. 8 15. 5 concentration Root phosphorus mg/g 486 1,284 1. 33 0. 74 1. 10 1. 61 concentration Root carbon-to- mg/mg 925 1,308 41. 6 25. 3 35. 8 51. 5 nitrogen ratio Root nitrogen-to- mg/mg 154 154 13. 7 6. 0 10. 6 19. 0 phosphorus ratio Root calcium mg/g 169 915 5. 10 2. 44 3. 38 5. 57 concentration Root potassium mg/g 167 891 6. 88 3. 65 5. 95 8. 92 concentration Root magnesium mg/g 146 862 2. 55 1. 39 2. 09 3. 27 concentration submitted to multiple databases; or (b) databases including the Root manganese dataset from different sources (e. g. , submitted by the main authors mg/g 52 89 concentration versus extracted from literature, or the same data being used in Dynamics and decomposition multiple papers). When datasets or references appeared in multiple 0.
22 Global Compositae Checklist (GCC; http://compo sitae. landc arere 0. 10 0. 16 0. 29 search. co. nz/Defau lt. aspx; accessed 21 August 2015), International Legume Database and Information Service (ILDIS; http://www. ildis. Root production c data sources, we performed manual checks to ensure the removal g/m 2 /year 116 201 397. 5 org/; accessed 21 August 2015), Missouri Botanical Garden's Tropicos 116. 6 230. 0 436. 8 Root lifespan mean of redundant measurements while ensuring that complementary in-days 80 98 Root lifespan median days 60 65 formation was not removed. In some cases, data contributors were contacted directly to avoid dataset overlaps. Despite these efforts, 696. 2 Database (http://www. tropi cos. org; accessed 19 December 2014) 252. 5 377. 0 734. 5 324. 8 149. 5 255. 7 and the USDA's Plant Database (http://plants.
In a study focused more on real-time applications, Oh et al. [11]  use deadline meet ratio as one of the criteria. Tornell et al. [12]  perform analytical evaluation of their algorithm and validate the results using experiments. In addition to the delay criterion, they also examine the number of exchanged messages. In contrast to our proposal, all of the approaches mentioned so far in this section focus on the technical aspects and try to objectively measure parameters of the network. However, exchanging lots of data with minimum latency does not automatically imply that these exchanges are useful. Our approach addresses this problem and aims at capturing the actual value that communication provides for the application at hand. Many techniques are being developed for measuring Quality of Experience (QoE) (e.
The network emulator used in the presented experiments is a fork of Mininet (http://mininet. org/) with added support for Docker containers: Containernet (https://containernet. github. io/). As wireless connections are not supported in Containernet, we decided to model the network in our scenario as a router to which all devices (drones and a base station) are connected (a star topology). In the described case, this simplification is acceptable, because the single communication line between the router and the base station assures only one packet can be transmitted at a time. In real life, channel access methods used in both LTE and 802. 11 serve a similar role as this router. The link bandwidth was always set to the minimum available value of 1 Mbps. The simulation framework developed by us first starts a Docker container for each agent in the system, connects them through a virtualized network, and runs a single instance of the experiment.
When it detects the end of the experiment, it shuts down all containers and cleans the network configuration. It is also possible to run multiple experiments in parallel. In order to avoid high CPU usage, which might make the results less reproducible, we limit ourselves to run 8 experiments at a time. After each experiment all outputs from the programs are stored together with a log of all captured packets and the experiment's metadata. This data can be then further analyzed. The experiment running on each agent was implemented in Python and Robot Operating System 2 Crystal Clemmys (https://index. ros. org/doc/ros2/) with two Data Distribution Service (DDS) implementations being tested: eProsima Fast RTPS (https://github. com/eProsima/Fast-RTPS) and RTI Connext DDS (https://www. rti. com/).
Acknowledgments: We would like to thank all members of the  KPK NAV  for providing ideas, discussions and feedback about this work. We are also immensely grateful to  Mahin Atiq  from the  Institute of Networked and Embedded Systems at the University of Klagenfurt  and  Nikolaos Tsiogkas  from the  Department of Mechanical Engineering at KU Leuven  for their inputs during the revision process. Finally we would like to acknowledge all the editors and reviewers working with MDPI for their involvement in improving the quality of this manuscript.
Polycomb (PcG) and Trithorax (TrxG) complexes are two evolutionarily conserved epigenetic regulatory components that act antagonistically to regulate the expression of genes involved in cell differentiation and development in multicellular organisms. The absence of PcG in both yeast models Saccharomyces cerevisiae and Schizosaccharomyces pombe suggested that polycomb proteins might have evolved together with the emergence of multicellular organisms. However, high throughput sequencing of several microalgal genomes and transcriptomes reveals an unprecedented abundance and diversity of genes encoding the components of these complexes. We report here the diversity of genes encoding PcG and TrxG proteins in microalgae from the Marine Microbial Eukaryote Transcriptome Sequencing Project database (MMETSP) and detected at broad scale in Tara Oceans genomics datasets using a highly sensitive method called eDAF (enhanced Domain Architecture Filtering).
Our focus here is on the first group of TrxG complex proteins considering their role in antagonizing PcG mediated repression. Both complexes have been studied intensively in multicellular species but only scarcely investigated in unicellular organisms such as microalgae or yeast model species in which PRC1 and PRC2 complexes do not exist. Here, we draw a comprehensive picture of the diversity and abundance of both complexes in the Marine Microbial Eukaryote Transcriptome Sequencing Project database (MMETSP) and metagenome/metatranscriptome samples from Tara Oceans using the enhanced Domain Architecture Framework, eDAF, which is an ensemble of algorithms to process, enhance and expand the output of DAMA  (Bernardes J. S. et al. , 2016)  or any other domain architecture algorithm  (Terrapon et al.
Most Specific Gene Ontology Terms After gene predictions, search is performed for their functional annotations. "PFAM2GO" that provides the Gene Ontology terms  (Mitchell et al. , 2015)  for each domain detected by CLADE is used. Domains are represented by a set of GO terms organized in a hierarchical representation. To look for the most specific gene ontology terms of each domain, the postgresql database is used to execute a depth first search  (Even, 2011)  over the gene ontology tree terms  (Carbon and Mungall, 2018). This search algorithm retains only the terms located on the leaves by avoiding generic terms, such as "cellular activity, " that do not provide conclusive knowledge of protein function. However, generic terms can also be found on the leaves; thus, only terms whose depth is higher than the three levels are retained.
Architecture Extended Information (AEI) Architecture Extended Information displays some DAMA and CLADE outputs not explored before. Each CLADE prediction is associated with one or more models and each model is associated to a specific taxonomy (species level). AEI recovers the corresponding taxa of each gene prediction and displays them to the user. The frequency of observed domain co-occurrence (internally part of DAMA database) are shown to highlight exotic (rarely observed) composition of conserved regions in the predictions.
eDAF Analysis We conducted an extensive set of scans over more than 11,720 transcriptomes distributed among 414 species (the MMETSP dataset) using eDAF. First, we used eDAF to interrogate the MMETSP transcripts by first translating each into the 6 possible frame translations. Next, eDAF fed the translations into CLADE and DAMA to identify the conserved domains. Then, eDAF selected the most probable translation of each transcript (as described in the previous sections). eDAF outputs the resulting proteins and the domain architecture of the corresponding MMETSP transcripts. Finally, to identify the true positives among the MMETSP sequences, we used eDAF to identify the domain architecture of both Polycomb and Thrithorax reference proteins (sequences reported as true positives in the literature).