dataesr/scientific-paragraphs-categorization huggingface dataset:
Transparent Methods 2. Treatment of multistate taxa The treatment of multistate taxa remains contentious  (Kornet and Turner, 1999). Two setting options (uncertainty and polymorphism) are available for the treatment of multistate taxa in PAUP  (Swofford, 2020) , and their choice affects the results and interpretation of the parsimony analysis. To clarify, let us assume that a taxon has been coded with two states 0 and 1. For the uncertainty setting PAUP chooses the state 0 or 1 that minimizes the tree length and assumes that the ancestral taxon at the node could have either one of the two states. For the polymorphism setting, the program chooses both states 0 and 1 to find the most parsimonious tree which might result in a longer tree; conceptually the polymorphic taxon is split into 2 subtaxa, one with the state 0 and the other with 1, the terminals are "polymorphic", and the ancestors are still assumed to have one state or the other.
Reweighing of morphological data sets produces substantial increases in jackknife frequencies, and results compared to equal weighing become more stable with lower error rates  (Goloboff et al, 2008). This supports the affirmation that "it is not that parsimony does not preclude weighing, but rather that it requires weighing"  (Goloboff, 1993). In the present study, we execute 16 runs with the PAUP software  (Swofford, 2020)  based on 4 character types (Fitch, Wagner, Camin-Sokal or Dollo, the last two with "Up" polarity), two settings for weights and two settings for multistate taxa (uncertainty or polymorphism). With regard to character weights, in a first search we will use equal weights while in a second search we will reweigh the characters with the character RCs obtained from the first search  (Caparros, 1997; Mounier and Caparros, 2015).
Transparent Methods 5. Comparative equivalence of MP bootstrap P boot v. s. BMCMC P post Bootstrapping is a random resampling method developed by  Efron (1979)  to evaluate the accuracy of statistical estimates. Felsenstein (1985)  proposed to use bootstraping to assess the confidence intervals in phylogenetic analyses, and PAUP has implemented this method. In a PAUP bootstrap analysis, the number of taxa of the dataset is held constant while the characters are randomly sampled with replacement to build a series of new data sets the same size as the original dataset which are called bootstrap replicate datasets (BSD). As indicated by  Felsenstein (1985) , each BSD consists of a new data table with the same set of species, but with some of the original characters duplicated and others dropped by the process of sampling n characters from the original dataset with replacement.
Most systematists view bootstrap values as a relative assessment of clade support rather than a strict statistical confidence statement of the nodes, or joint confidence for the entire tree. The BMCMC method estimates stochastically the posterior probability P post to assess the strength of support of the individual clades of the resulting tree topology  (Dembo et al. , 2016). The question is: are the values of P boot and P post comparable? Simulations of maximum likelihood bootstrap and BMCMC analyses performed on the same sequence dataset show that over various model spaces values P boot and P post are not significantly different  (Cummings et al. , 2003). These findings are corroborated in a set of simulations showing that P post estimated by Mr Bayes software and P boot calculated by PAUP software were almost exactly the same  (Goloboff and Pol, 2005).
Thus, in the two elliptic representations of Figure  2 , values of P boot of MPMAX tree and P post of BMCMC Dembo tree will be deemed to be equivalent in the comparison of support of the various clades of both phylogenies. P post values of the BMCMC Dembo tree are low but comparable to other Bayesian phylogenetic studies  (Dembo et al. , 2016) , and the same qualification may be applied to P boot values of the MPMAX tree; however, bootstrap values in general are negatively affected by the number of characters, the number of non-informative characters, the number of missing characters in some taxa, and by the sample size and the nature of the search algorithms  (Soltis and Soltis, 2003). One should not exclude the possibility that a tree topography with very low support values of an extensive and complex dataset might still be phylogenetically closer to the "true" tree than a tree topology with higher support values from a limited and related dataset.
Transparent Methods 7. MP consensus trees and treatment of ambiguities Consensus trees are used when the maximum parsimony MP run generates more than one most parsimonious tree such as in the analysis with 74 apomorphous characters (Figure  3 ), or in runs with the entire dataset such as R1 & R2 (Figure  S3A ), R5 & R6 (Figure  S4A ), R9 (Figure  S5A ) and R13 (Figure  S6A ). Consensus trees are hierarchical summaries of the information common to a set of "rival" trees and represent a summary of agreement among trees. When the rival trees possess a certain number of similar branches (monophyletic sister groups), these are retained as such, while distinct branches not in agreement are expressed as polytomies. We use the Majority-rule model for the runs just mentioned and rely on 50% as percentage that allows one to retain all groups found in over half of the rival trees.
The 50% Majority-rule consensus tree is the most robust commonly used method. Consensus trees summarize the concordance of trees of minimal length, and theoretically should not be interpreted as a phylogenetic tree. Their explanatory power comes from the fact that they identify informative coherent subgroups of taxa and provide a good indication of the information content of the dataset  (Anderberg and Tehler, 1990). The multiplicity of MP trees of equal length obtained with 74 apomorphous characters (summarized by a consensus tree in Figure  3 ), as compared to the unique MPMAX tree with 391 characters (Figure  1A ), is explained empirically by the fact that removing homoplastic characters in the analysis increases substantially the number of minimal trees  (Caparros, 1997). Further to a numerical run, some apomorphies and homoplasies may be subject to ambiguities regarding the possible assignment of character-state changes at nodes of the MP tree.
Ambiguities may be resolved by a process of selective arguments, or by one commonly used optimization algorithm, such as ACCTRAN or DELTRAN  (Agnarsson and Miller, 2008). In MPMAX, the most consistent scenario based on Dollo parsimony where homoplasies are accounted for by reversals, we use for illustration purposes the ACCTRAN optimization algorithm that assigns changes along the branches of the phylogenetic tree as close to the root as possible and minimize hypotheses of parallelism and favour reversals  (Figures S9 and S10). A word of caution is required given that ACCTRAN (and also DELTRAN) may result in numerous equivocal resolutions when there is substantial amount of missing data as in the present dataset. For example, in Figure  S10 , character 55 (Projection nasal bones above frontomaxillary suture) is missing in S.
Phylogenetic trees are used to summarize taxonomic relationships between biological entities, splits networks are used to represent incompatibilities in data sets reflected for example by polytomies of consensus trees, and reticulate networks are used to explain evolutionary histories with edges representing lineages of descent or reticulate events, and internal nodes representing hypothetical ancestors. We run the PN analysis by using the SplitsTree software  (Huson and Bryant, 2006)  with the set of most parsimonious trees MMPT as input generated from the MP analysis run with 74 apormorphous characters, and produce graphically a consensus tree, a consensus network and a reticulate network (Figure  4 ). It is important to distinguish between datadisplay networks such as consensus trees and consensus networks, from evolutionary networks such as reticulation and hybridization.
erectus stem species (tree node 32 of Figure  1 ) from which three species have emerged ((African H. erectus, Georgian H. erectus), Asian H. erectus), and the stem species itself belongs to the sister group H. erectus = Her. The elliptic boundaries illustrate more accurately than the tree-based representation the formation of higher taxa monophyletic groups in conformity with the hierarchic system of phylogenetic systematics, and facilitate the interpretation of the ancestor-descendant relationships among the various hominin species under study. Refer to the legend of Figure  1  for sister group definitions. The examples are based on character diagnostics parameters from output of MPMAX run 15 (Table  S1 ). Projection of characters on MPMAX tree were produced with MacClade software  (Maddison and Maddison, 2005).
ran a MP bootstrap analysis with a dataset split of 288 unordered characters and 103 ordered characters (Figure S7 of their Supplementary Online Material [SOM]). For comparative purposes, we applied our MP protocol to their data split; the results of the 4 MP runs (equal weight and RC reweighing with uncertainty or polymorphism setting for multistate taxa) and the corresponding bootstrap runs executed with this mix are shown in Figure S11. The bootstrap run with equal character weights and uncertainty setting for multistate taxa (Figure S11B.
ACKNOWLEDGMENTS We thank  Mana Dembo  and  Arne Mooers  for providing the workable Nexus file of the dataset. We thank  David Swofford  for his explanations regarding the numerical treatment of multistate characters in PAUP, and we assume all errors of interpretation. We thank the three anonymous reviewers for their comments and suggestions that were very helpful in improving the presentation of our work, and  Josephine Herbelin /atelier Nous Travaillons Ensemble for her help in the graphic design of Figure 4. We would also like to express our appreciation to the editor  Dr. Alessio Bolognesi  for his guidance during the review process. S. P. is partly supported by  Agence Nationale de la Recherche (France)   ANR-17-CE27-0005. One of us (M. C. ) would like to pay homage and express thanks to the late professors  Pierre Rosenstiehl  and Gerard Worms who taught him the intricacies of operations research and graph theory during his years as undergraduate at  HEC.
Character-state changes (apomorphies) in support of late Miocene -Pliocene stem species elliptic boundaries 46 to 41 (tree nodes -LCAs) of MPMAX scenario. Related to Figure  1  § Figure  S11. MP and bootstrap trees from PAUP  (Swofford, 2020)  heuristic searches with dataset split of 288 unordered characters and 103 ordered characters as per  Dembo et al. 2016  MP analysis in their Supplementary Online Material. Related to results concerning H. naledi and Au. sediba in main text 2. Comparative validation of MPMAX optimal scenario, and Table  S5  § Table  S1. Character diagnostics from output of MPMAX run 15. Related to Figures 1 and S6E § Table  S2. List of 74 apomorphies in ascending identifying numbers in support of tree nodes / elliptic boundaries (LCAs) of MPMAX scenario. Related to Figures 3, S9 and S10 § Table  S3.
Apomorphies in support of stem elliptic boundaries/tree nodes 40 to 26. Related to Figures  1, 2A  and S9 § Table  S4. Summary of H. naledi and Au. sediba MP phylogenetic positions. Related to results concerning H. naledi and Au. sediba in main text 2. Comparative validation of MPMAX optimal scenario, and Figures S3-S6 § Table  S5. Inconsistencies for the Popperian elimination of evolutionary scenarios applied to dataset split of 288 unordered characters and 103 ordered characters as per  Dembo et al. 2016. Related to results concerning H. naledi and Au. sediba in main text 2. Comparative validation of MPMAX optimal scenario, and Figure  S11  Paet 0/0 0. 000 1. 000 8 2 2 2 4 1. 000 1. 000 1. 000 0. 000 1. 000 9 1 1 1 1 1. 000 0/0 0/0 0. 000 1. 000. 000 0/0 0/0 0. 000 1. 000 359 1 1 1 2 1.
MP and bootstrap trees from PAUP (Swofford, 2020) heuristic searches with dataset split of 288 unordered characters and 103 ordered characters as per Dembo et al. 2016 MP analysis in their Supplementary Online Material. Related to results concerning H. naledi and Au. sediba in main text 2. Comparative validation of MPMAX optimal scenario, and Table S5 Consensus MP and bootstrap trees include groups compatible with 50% majority-rule. Replicates Pboot are indicated for all the clades in the bootstrap trees. (A) MP majority consensus tree with equally weighed characters under multistate taxa uncertainty or polymorphism settings. (B) Bootstrap consensus tree of run with equally weighed characters and uncertainty setting. (C) Bootstrap consensus tree of run with equally weighed characters and polymorphism setting.
Motivated by the current COVID-19 pandemic, which has spurred a substantial flow of structural data, the use of molecular-visualization experiences to make these data sets accessible to a broad audience is described. Using a variety of technology vectors related to the cloud, 3D and virtual reality gear, how to share curated visualizations of structural biology, modeling and/or bioinformatics data sets for interactive and collaborative exploration is examined. FAIR is discussed as an overarching principle for sharing such visualizations. Four initial example scenes related to recent COVID-19 structural data are provided, together with a ready-to-use (and share) implementation in the UnityMol software.
The case of the COVID-19 pandemic provides a stringent example for the need to render the latest research data explorable by the broad scientific community. If such data were only to be provided in its raw form, the accessibility would be quite limited. The Worldwide Protein Data Bank (PDB)  (Berman et al. , 2003)  indeed provides quite a few visualizations along with the actual data files. Recently, the PDB in Europe (PDBe) also proposed a visual analytics platform that is FAIR compliant  (PDBe-KB consortium et al. , 2020). Here we try to go one step further in the democratization of such data by preparing curated visual experiences as a valuable support for cross-disciplinary scientific exchange. In related fields, similar initiatives have recently emerged. Now more than ever, sharing data from molecular simulations and modeling is becoming a priority to ensure reproducibility and accelerate discovery  (Amaro & Mulholland, 2020 ).
An   (Tiemann et al. , 2017)  for apprehending such data remotely to avoid unnecessary data transfers. By streaming trajectory data upon demand, a systematic transfer of such files, which can easily range between several megabytes to hundreds of gigabytes or more, is avoided. Yet to the best of our knowledge, current initiatives focus on the actual data, not so much on the visualizations and annotation thereof. Here we present an initial prototype using the UnityMol software. We place the focus on collaborative virtual reality (VR) as well as sharing 3D models and scenes to intuitively convey the spatial complexity of these molecular objects. Shape-related features have particular relevance for drug design and hence for expert users, yet they can also be understood by an inexperienced person, in particular through a visual experience such as those described here.
The features of the workflow we are seeking to implement are depicted in Fig. 1. We start from the original raw data that should encompass experimental structural data sets, molecular modelling results and bioinformatics findings. Crystallographic and EM data can be obtained from many portals, such as https://www. covid19dataportal. org/proteins?db= uniprot-covid19. We aspire to prepare new shareable visual content to be set up and curated by an experienced scientist knowledgeable in structural biology. Once prepared, viewing this content could be accessible to a much broader community. To do so, we propose that one could use a molecular visualization software package to describe the visualization scene and all its annotations through command scripts. The experience can be customized through add-ons, for example providing data-set-specific user menus leading to a visual scene that can be explored directly in the software.
A more classical approach is to provide the derived media, which may consist in videos or images for which sharing platforms exist, although this is not enough to optimize FAIR compliance  (Weber & Kranzlmuller, 2018). We chose the UnityMol package to prototype a workflow following the scheme in Fig. 1  and implemented a proof-ofconcept set of visualization experiences. We have been developing and redesigning the UnityMol software, which provides specific advantages for the tasks at hand in its latest version  (Lv et al. , 2013; Laureanti et al. , 2020). Many alternative software options of high value could be considered, such as those recently reviewed for protein visualization  (Martinez et al. , 2020). Particularly relevant packages in the opinion of the authors include VMD  (Humphrey et al.
A brief selection are listed in Table  1  and served as a testing ground for our visualization experiments. Many of the listed portals provide very direct access to the data that we require for our visualization experiences, while a few provide more general search engines with a less direct path to the actual data set of interest. We did not include the classical routes to search data such as the PDB, GigaDB  (Sneddon et al. , 2012; Xiao et al. , 2019)  or Zenodo, and we only included one of the many relevant initiatives by individual scientists or laboratories as an example.
Materials and methods Here, we will briefly describe the key functionalities implemented for the purpose of sharing molecular-visualization experiences. More detailed technical information is provided on github through the project overview page (https:// github. com/bam93/fair_covid_molvisexp/tree/master/overview). As an overarching principle, we want to be able to generate a broad variety of media, including still images and videos, based on these visual scenes describing the core of the contents, capturing them from within the running software, ideally in an executable build and, for a handful of technically more involved features, from within the Unity3D editor environment. For this purpose we implemented, extended and stabilized several features. A certain number of those features are still only experimental.
We added a Python console and API for scripting within UnityMol, providing an easy way to formalize and share the narrative of a given experience, as well as facilitating the reproducibility of all necessary steps leading to a scene. The interactive Python console can be toggled on and off, appearing on the right-hand side of the screen. Scripts can be loaded from the console or through a UI menu button. We furthermore implemented the possibility of generating custom user menus so that complex and fine-tuned functionality can be achieved with a one-click action. The Python-based menu uses the zmq and tkinter modules and needs to be started separately from the command line. A possible practical use could be to group shortcuts to relevant structural ensembles in the light of COVID-19 for visual analysis, comparison or even manipulation.
The latter can be achieved through interactive simulations such as interactive molecular dynamics (IMD). In such simulations, the user may actively manipulate the molecular system and steer the exploration of conformational space to interesting regions by adding custom forces to the simulation. UnityMol implements the generic IMD protocol for steering molecular simulations through MDDriver  (Delalande et al. , 2009) , but also provides a few in-app implementations such as a rigid protein-protein docking module. The scenes can be annotated by the user through several functionalities such as text bullets or free-hand drawing. A guided-tour function has been implemented to walk the user through a set of predetermined key frames in a fully interactive experience. These features are demonstrated in two supplementary movies, which are accessible through the Zenodo and figshare pages.
A particular focus concerns the possibility of generating effective high-quality graphics, for instance in order to convey the complex shapes of molecular surfaces using ambient occlusion (AO) or to draw the user's attention to a certain part of the structure of interest using photographic effects such as depth of field (DOF). In addition to the molecular structures themselves, their properties (such as electrostatic field) can also be depicted  (Laureanti et al. , 2020). In the latest version of the software we added an experimental feature enabling interactive CPU-based raytracing using the OSPRay package  (Wald et al. , 2017) ; GPU raytracing is not yet fully implemented. This is illustrated for our first example of the spike trimer complex with angiotensin-converting enzyme 2 (ACE2) from cryo-EM data (Fig.
Raw data and targeted visual experiences To illustrate the sharing of such visual experiences, we have prepared four typical data sets, depicted in Figs. 2  and 3 : a simple structural view, a collection of small molecules binding to a viral protein, an MD trajectory and a bioinformatics sample with conservation as well as mutation data. The first example, depicted in Fig. 2 , aims at setting up a simple structural view of the SARS spike glycoprotein complex with human ACE2. The model we use is derived from cryo-EM density measurements and has been deposited in the PDB. This trimeric complex, accessible as PDB entry 6cs2  (Kirchdoerfer et al. , 2018) , was used as the starting point for many early SARS-CoV-2 studies. We then have a comparative look at where drug molecules bind to the main protease of COVID-19 in our second example (Fig.
3a ). The visualization is inspired by an animation of small molecules in 92 PDB structures (https:// www. rbvi. ucsf. edu/chimerax/data/sars-protease-may2020/) prepared by the ChimeraX team  (Goddard et al. , 2018). All of the PDB structures were obtained by X-ray crystallography (the PDB codes can be found in the scripts that we provide). We also created a 3D print of the protease monomer. Molecular-dynamics simulations provide another inestimable resource for insight into molecular mechanisms. Example 3 (Fig. 3b ) is based on a trajectory depicting a binding event of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the human ACE2 receptor (DESRES-ANTON-10857295 and DESRES-ANTON-10895671; D. E. Shaw Research; https://www. deshawresearch. com/downloads/ download_trajectory_sarscov2.
cgi/). The underlying structural data were determined by X-ray crystallography (PDB entry 6vw1). The fourth example illustrates a visual experience related to representing the results of bioinformatics analyses. Our example is built upon the freely available data from a recent study on cross-species transmission of SARS-CoV-2, highlighting the species variability of viral-host protein interactions  (Rodrigues et al. , 2020). We visually map this data onto the cryo-EM-derived ACE2-RBD complex structure (Fig. 3c ).
From raw elements to created content The first level of sharing these four examples is to make the software and the scripts themselves available. We considered the Zenodo and GigaDB platforms, and chose Zenodo as the main platform for our experiment. Typically, no dedicated category for 'visual experiences' exists in these databases, whereas the classical categories such as software, data set, image, video, workflow or other only partially reflect this case. As a first approach, we created a Zenodo community (Fig. 4a ) for the FAIR sharing of molecular-visualization experiences at https://zenodo. org/communities/fair-molvisexp/. We then linked a github repository for our visualization scripts to Zenodo and the collection. Upon execution of the raw elements, the scripts that we designed implement the targeted visual experiences for the four example systems described above, thereby giving access to the created content.