dataesr/scientific-paragraphs-categorization huggingface dataset:
8 nT. Furthermore, frequency response functions from mHz up to kHz can be recorded. All these calibration measurements can be conducted in a wide temperature range from -196 • C up to +200 • C using a computer-controlled nonmagnetic thermal box. Also offset measurements in the mentioned temperature range can be performed using a sophisticated rotation mechanism inside the box. The major purpose of the ground calibration is the determination of the temperature dependent transfer function Φ which defines the relation between the known calibration field B c and the measured field B m B c = Φ B m. (1) The measured field B m comprises the real raw data B r , the coil system residual field B res and the instrument offset B off as B m = B r -B off -B res. (2) The complete calibration transfer matrix Φ is defined by φ = ρ ω σ , ( 3 ) where σ (T ) represents the temperature dependent sensor sensitivity, ω(T ) describes the temperature dependent internal sensor misalignment (orthogonalisation matrix), and ρ(T ) describes the real rotation of the sensor against the coil axes.
xml label files. This is a completely automated process providing the so called CALIBRATED data. The needed transformation into various coordinate systems is accomplished using the SPICE s/w  (Acton (1996) ,  Acton et al. (2018) ) provided by the In a further, semi-automatic process, the Cal2Der s/w (calibrated to derived data conversion software) generates a best effort cleaned and inflight calibrated magnetic field data product as well as averaged magnetic field data. Here, s/c disturbances like e. g. reaction wheel interference, magnetic thruster signatures, solar panel rotations and the influence of the ion engine are eliminated on best effort basis or at least flagged in the data files. All CALIBRATED and DERIVED data files are sent back to SGS, which cares about the proper ingestion into the public archive.
Data Products Various kinds of data products are available at PSA for different purposes. The directory structure of the MPO-MAG data is displayed in Fig. 8. All data are available as ASCII data and grouped in RAW, CALIBRATED & DERIVED data, stored in the directories data_raw, data_calibrated & data_derived accordingly. Below this layer data are grouped by mission phases e. g. near_earth_commissioning or cruise. The next logical layer separates the data by housekeeping hk and science sc data. And finally the real data can be found in the ib and ob folders for the inboard and outboard sensor data, respectively. According to the PDS4 regulations *. tab files with the data tables and related *. xml context label files are written to the archive. The data tables all contain at least UTC time stamps, onboard time entries (OBT) and specific data columns (positions, magnetic field, temperatures,.
). The RAW data comprises unprocessed (ADC counts) HK and SC data for the inboard (IB) and outboard (OB) sensor in instrument coordinates (URF) only. They are used mainly for diagnosis purposes. The CALIBRATED data represent observations in physical units made by the IB and OB sensor after application of the described ground calibration process. These data are available in URF, spacecraft (SCF) and suitable celestial coordinate frames. Dependent on the actual mission phase data will routinely be provided in the following frames: The DERIVED data contain an inflight calibrated data product (inflightcal_sc), representing the magnetic field as best as possible. Thus, all known s/c disturbances have been eliminated or at least flagged here. No distinction between IB and OB is made here anymore, there is only one magnetic field data product.
Region 2 field-aligned current systems associated with substorm current wedges (e. g. Glassmeier 2000; Poh et al. 2017)  have not been observed so far with MESSENGER data. The BepiColombo flybys 4 and 6 could prove useful to detect these for the first time. Figure  40  displays the flybys in a different way: Here, the satellite positions are shown in the ρ zz MSO plane in the MSO-frame with ρ z = x 2 MSO + y 2 MSO ). In this figure, the Bepi-Colombo flyby coordinates are compared to the MESSENGER positions (light grey area) from its orbital phase. At the pericenters of flybys 1 and 2, it is clearly seen, that we are able to sample the Hermean magnetic field at previously unknown locations within the southern hemisphere. At these points, the Hermean internal field models that were created using MESSENGER magnetic field data (e.
Summary & Conclusion MPO-MAG is a very well calibrated instrument that will provide high-precision measurements of the Hermean magnetic field. This will give us deep insights into the planetary structure and the dynamics of the highly dynamic magnetosphere under the influence of the solar wind. The tools for magnetic field analysis and modelling of the planetary interior will have to be continuously advanced until arrival in 2025 in order to evaluate the expected data down to the last detail. The cooperation with the sister instrument Mio-MGF will allow us to take a new look at the Mercury magnetosphere and its reaction as a tightly coupled system to the changing solar wind conditions.
Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, Phy-loFisher (https://github. com/TheBrownLab/PhyloFisher), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations.
Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic "single-copy orthogroup" datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing.
Introduction Molecular phylogenetic analyses have transformed our understanding of evolutionary relationships among eukaryotes. While the use of single to a few genes provided a wealth of information when the practice was in its infancy, these small datasets lacked sufficient phylogenetic signal to resolve the deepest nodes in the eukaryotic tree of life  [1, 2]. In order to clarify these ancient relationships, molecular phylogenetic datasets have grown in size from single genes to hundreds of genes (i. e. , tens of thousands of homologous sites)  [3] [4] [5]. There are existing tools that reduce the intense labor and time needed by automating some or all parts of phylogenomic dataset construction (see, for example,  [6, 7] ). However, as phylogenomic analyses rely on combining evolutionary signal from multiple genes into one phylogeny, it is essential to ensure that included sequences share an evolutionary history comprised only of speciation events (orthologs) rather than gene duplications (paralogs) or lateral/horizontal gene transfers (xenologs).
Therefore, manual curation of orthologs is highly recommended to ensure that the dataset is free of paralogs, xenologs, or contaminants with conflicting signals that would otherwise confound phylogenetic estimation. In addition to using a high-quality set of curated orthologs, it is important that, as part of phylogenomic analyses, homogeneity of signal and potential artifacts affecting estimation is identified by exploring perturbations of the original dataset in terms of genes, sites, or taxa sampled. Despite a dramatic rise in the number of publications that include phylogenomic analyses, there is no widely accepted standard protocol for assembly of such datasets. Similarly, there are no publicly available, manually curated, starting phylogenomic databases designed for addressing deep relationships (>100 million years) in the tree of eukaryotes.
To address these problems, we have designed a protocol for phylogenomic dataset construction and data exploration and incorporated it into a software package, PhyloFisher. This publicly available software package (https://github. com/TheBrownLab/PhyloFisher) aids in the construction, maintenance, and curation of protein sequence-based phylogenomic datasets from a user-defined set of starting protein sequences. It provides tools to conduct postdataset construction phylogenetic analyses and aids in the visualization of results. PhyloFisher also includes a manually curated starting database of 240 proteins from 304 eukaryotic taxa, representing the full breadth of known diversity in the eukaryotic tree of life (Fig 1  , Table A  and Fig A in S1 Text). Importantly, this database also includes identified paralogs of each of the 240 proteins from all investigated taxa, which is crucial for the identification of probable orthologs in newly added taxa.
Although PhyloFisher includes this pan-eukaryotic dataset, the tool is flexible and can work with any predefined dataset consisting of protein sequences derived from eukaryotes. To demonstrate the utility of PhyloFisher and its companion starting database to address phylogenomic questions at different depths of the eukaryotic tree of life, we performed phylogenomic reconstructions of ancient relationships in the tree of eukaryotes as a whole, as well as phylogenomic analyses of the much more recently diversified Saccharomycetaceae clade of budding yeasts using 3 different gene sets. Additionally, we show the increased accuracy of the PhyloFisher workflow over widely used methods of ortholog collection and phylogenomic dataset construction.
The PhyloFisher protocol The standard PhyloFisher workflow begins with the user-provided set of predicted proteins from the organism(s) they wish to add to an existing database. Alternatively, the user can create a new phylogenomic database from a set of orthologs, with or without corresponding paralogs, collected in advance prior to addition of new organisms. Users can choose between 2 fundamental protocols, one where candidate sequence selection will proceed in a novel "phylogenetically informed" way or a more traditional default manner where profile hidden Markov models (HMMs) generated from the set of orthologs in the starting database are used to collect candidate sequences from an input proteome (Fig 2 ; Materials and methods). Our unique "phylogenetically informed" method prioritizes putative orthologs if they branch with known The PhyloFisher package consists of a manually curated starting database of 240 protein-coding genes and their paralogs from 304 eukaryotic taxa; a series of tools to perform the essential steps of phylogenomic dataset construction (homolog collection, single-protein tree construction, removal of paralogs and contaminants, and matrix concatenation); and many pre-and post-construction analyses necessary for a publication-quality phylogenomic study.
Materials and methods) is appended to the corresponding alignment of homologs, through a ranking system. The sequence receiving the highest priority by the algorithm is demarcated as the probable ortholog, and any other surviving sequences are denoted as probable paralogs. This is followed by removal of nonhomologous sites, alignment, trimming, sequence length filtering, and phylogenetic tree reconstruction of homolog datasets. The PhyloFisher workflow forces manual inspection of all single-protein trees after the addition of candidate homologs from new data to prevent the inadvertent inclusion of paralogs and sequences derived from contaminating organisms in the final phylogenomic datasets. PhyloFisher includes a graphical user interface tool (ParaSorter) designed to ease the arduous task of manual ortholog selection in large numbers of genes.
ParaSorter color-codes taxon names based on their taxonomic affiliations, highlights clades or sequences suspected to be paralogs, and appends specific sequence information to each taxon name in the phylogenetic tree. Users manually inspecting the tree can change the original designation of each sequence to either an ortholog, paralog, or "to delete" (a setting reserved for permanent removal of known contaminations) by simply clicking the corresponding box next to the sequence header (Fig  1 ). After the removal of "to delete" sequences, final single ortholog alignments are then generated from ortholog-or paralog-curated datasets. Phylogenomic studies are often accompanied by additional analyses in which users manipulate or examine their dataset in various ways to reveal potentially artifactual signals.
Phylo-Fisher is equipped with a unique set of utilities to conduct these exploratory analyses (see Table  1  for more details). These include the prediction of alternative genetic codes, removal of genes and/or taxa based on occupancy/completeness of the dataset, testing for amino acid compositional heterogeneity among sequences, removal of heterotachious and/or fast evolving sites, removal of fast evolving taxa, and supermatrix creation from randomly resampled.
genetic_code_examiner. py Checks stop-to-sense and sense-to-sense codon reassignment signal in transcriptomic/ genomic data heterotachy. py Within-site rate variation (heterotachy) has been shown to cause artifactual relationships in molecular phylogenetic reconstructions. This tool will remove the most heterotachious sites within a phylogenomic supermatrix in a stepwise fashion, leading to a user-defined set of new matrices purge. py Deletes taxa and/or taxonomic groups from the database permanently mammal_modeler. py Generates a MAMMaL site heterogeneous model from a user input tree and supermatrix with estimated frequencies for a user-defined number of classes using the methods described in  [11]  random_resampler. py Randomly subsamples the gene set included in the supermatrix into a set of new matrices.
org/10. 1371/journal. pbio. 3001365. t001 proteins from the starting dataset. Furthermore, PhyloFisher can consolidate multiple proteomes from the same operational taxonomic unit to produce a single "most complete" final proteome. This is useful for single-cell data or to generate a single, better-sampled "hybrid taxon" representing several closely related taxa. Many of these provided utility scripts have been designed to act as "stand-alone" programs and can be used on datasets generated outside of the main PhyloFisher workflow as was done in  [8].
Reconstruction of the eukaryotic tree of life with PhyloFisher To demonstrate the power of PhyloFisher and its accompanying database, we performed a full phylogenomic analysis of the tree of eukaryotes in IQ-TREE  [14]  (Fig 3  , Figs A, I , and O-Q in S1 Text). We use only tools available in PhyloFisher (including the third-party software listed in S1 Text) and datasets composed of the orthologs in the provided starting database (see Materials and methods for details regarding database construction). While much of the tree of eukaryotes has been resolved over the last decade (see  [15] ), several key relationships remain undetermined, and some groups, often referred to as "orphan taxa," remain difficult to place on the tree. Using the PhyloFisher workflow along with our post-dataset construction analyses, we have recovered deep relationships within the eukaryote tree, which are generally consistent with results of previous phylogenomic analyses.
For example, our analyses recover relationships such as Obazoa + Amoebozoa + CRuMs, Metamonada + Discoba, Haptophyta + Centroheliozoa, and Ancyromonada + Malawimonada  [15]. We recover a monophyletic Archaeplasatida (traditionally Glaucophyta + Chlorophyta + Rhodophyta) that includes Rhodelphidia and Picozoa and is sister to the Cryptista, although this potential relationship lacks strong statistical support (80% IQ-TREE maximum likelihood bootstrap support [MLBS]). The inclusion of Rhodelphidia and Picozoa in Archaeplastida and the group's potential sister relationship to the Cryptista has been shown recently in separate studies that used independent datasets for the phylogenomic analyses performed within  [16] [17] [18]. Interestingly, our analyses also recover some novel potential relationships that deserve further investigation.
For example, a clade made up of the enigmatic "orphan taxa" Telonemia and Hemimastigophora (Hemimastix and Spironema) is recovered. These 2 groups were included in phylogenomic analyses only recently, and our tree is the first to have all available data from them together in the same analysis. When the fastest evolving sites within the supermatrix are removed from the dataset using our accompanying tool fast_site_remover. py, this clade branches as sister to Stramenopiles + Alveolata + Rhizaria (SAR) with 95% MLBS, potentially comprising a monophyletic megagroup. Another interesting potential relationship is a group uniting the Haptophyta+Centroheliozoa clade with the "orphan" eukaryote Ancoracysta twista (i. e. , Haptista sensu  [19] ). Using all the tools provided through the PhyloFisher software package, many of the abovementioned groupings are frequently recovered including when (i) fast evolving sites (.
Reconstruction of the tree of Saccharomycetaceae budding yeasts and addition of Torulaspora globosa with PhyloFisher To demonstrate the utility of the PhyloFisher workflow to address questions surrounding more recent divergences in the tree of eukaryotes, we reconstructed the tree of the Saccharomycetaceae clade of budding yeasts using concatenation and coalescence-based methods. For reference, members of the Saccharomycetaceae are hypothesized to have been diverging from one another for 102 to 126 million years and exhibit a level of genetic diversity similar to that seen in flowering plants  [20]. Homologs were collected from 86 budding yeast genomes, the genome of the Saccharomycetaceae T. globosa, and 12 other fungal taxa provided with PhyloFisher, generating a phylogenomic dataset derived from 208 of the 240 orthologs (pruned due to gene sampling in the Saccharomycetaceae representative, Saccharomyces cerevisiae) found within PhyloFisher's provided starting database.
We also created a custom starting database constructed of a more computationally feasible subset of 128 genes from the 1292BUSCO dataset of  [20]  using tools provided in PhyloFisher. These 128 genes were selected by employing the rtc_binner. py tool from PhyloFisher to collect the top approximately 10% RTC scoring trees of the 1292BUSCO dataset of  [20] , also taking into account the taxonomic coverage within the 1292BUSCO dataset (>70%), resulting in 128 genes (128BUSCO). Using the PhyloFisher workflow, we then recollected homologs of these 128 genes from the same 86 budding yeast genomes as above. We also included an additional Saccharomycetaceae taxon, T. globosa, to illustrate PhyloFisher's ability to add new taxa to this database. During our reanalysis of the 128 genes in the 128BUSCO dataset, we discovered the inadvertent inclusion of paralogs in 6 genes (total 63 sequences) in the data analyzed in the original study of  [20]  (Figs S-X in S1 Text).
Additionally, during reanalysis, we were able to increase gene sampling in the original dataset for several taxa using the PhyloFisher approach (for example, see the Saccharomycodaceae clade in Fig U in S1 Text). To explore how these inaccuracies and missing data affect the structure of the tree of Saccharomycetaceae, we created a third dataset with our new decisions regarding ortholog selection and increased data sampling. Full details describing the construction of all 3 datasets can be found in the Materials and methods section. The tree topologies for both our concatenation and coalescence analyses of the 208 ortholog subset of PhyloFisher's provided starting database are congruent with the topologies of the original 1292BUSCO dataset of  [20] , although MLBS support for the topology is higher (100%) using the PhyloFisher 208 ortholog dataset in our concatenated analysis than the results using the 1292BUSCO dataset (53%).
However, the local posterior probability (LPP) value using gene tree coalescence is lower at this node when using the 208 ortholog dataset  (Fig 4). Interestingly, the concatenation-based ML trees using both of the 128 ortholog datasets show an alternative topology to that of  [20]  (labeled as Shen et al. 2018-BUSCO 1292 orthologs), while the coalescence-based trees are congruent with the topology of  [20] , although again the LPP values are lower than those in the original study.
Discussion To improve and ease the accurate collection of orthologs and subsequent phylogenomic dataset construction from eukaryotic proteomes, we designed PhyloFisher as a novel workflow for these purposes and provide it to the phylogenomic community in the form of the freely available open-source software package. Included is a manually curated starting set of 240 orthologs and their related paralogs from 304 eukaryotic taxa that cover known eukaryotic diversity. However, to broaden the use of the software, we provide tools to construct starting databases from alternative sets of orthologs and paralogs that can then be harvested from new taxa and processed via the remainder of the PhyloFisher workflow. To show the potential of our newly designed software package, we reconstructed the tree of eukaryotes using our provided starting database and the tree of the Saccharomycetaceae using both our starting database and a custom database created using tools provided in PhyloFisher from a 128 gene subset of 1292BUSCO dataset used in the phylogenomic analyses of  [20].
The phylogenomic tree reconstructed from the eukaryotic-wide dataset represents the most taxonomically comprehensive deep phylogeny of eukaryotes to date. Our results recover many previously known relationships as well as some potential groupings that are novel (Fig 3 Although the overall branching patterns were consistently recovered in most analyses we have conducted, many of the groups are sparsely sampled, and low bootstrap support values for deepest nodes remain. For example, because the Telonemia, Hemimastigophora, and Ancoracysta all represent sparsely sampled lineages on these trees that can be notoriously unstable and form artefactual groups. Thus, the foregoing relationships should be treated with caution. Notably, the exact relationships between the fully supported groups Chloroplastida + Glaucophyta, Rhodophyta + Rhodelphidia + Picozoa, and Cryptista remains unclear.
Notably, the results of a recent independent study using a larger dataset  [17]  and another that includes novel data from diverse picozoans  [18]  are both congruent with our results regarding these potential relationships, although with similar or higher statistical support, respectively. Whereas the recently discovered Rhodelphidia contain a non-photosynthetic plastid and can thus fit naturally into the Archaeplastida  [16] , Picozoa seem to be devoid of a plastid  [18, 21] , suggesting its secondary loss or the need to reconsider the concept of the Archaeplastida  [18]. These hypothetical relationships can be tested in the PhyloFisher framework once novel species that break these branches are eventually sampled. , Our resulting yeast phylogenies using datasets derived from PhyloFisher's provided set of genes and using a custom subset of genes derived from the 1292BUSCO dataset of  [20]  demonstrate the potential of the PhyloFisher dataset and the workflow applied to custom datasets as tools for resolving phylogenetic questions involving a particular group of eukaryotes.