dataesr/scientific-paragraphs-categorization huggingface dataset:
9. Curate empirical datasets and develop data analysis and visualization tools through community-driven challenges (e. g. , the DREAM single-cell transcriptomics challenge;  Alonso et al. , 2019; Pham et al. , 2020). 10. Establish mechanisms for fostering new collaborations and funding for data generation, data integration as well as new technology and method development or adoption for plants. Some of these goals can be accomplished quickly, as already existing technologies such as single-cell and single-nucleus RNA-seq make them realistic. Others require the development of technologies to collect information at the single-cell level. As technology and discovery reshape the vision of the PCA, new aspirations should emerge from the community. Nonetheless, these ambitious milestones represent key gaps in knowledge that will accelerate plant research (if addressed) and are feasible to achieve given the opportunities presented by recent technological advances.
The PCA vision First, the participants in the PCA Research Coordination Network envision a one-stop, userfriendly website with illustrated and graphical details about cellular organization across different developmental stages. This information, modeled after findable, accessible, interoperable and reusable (FAIR) data principles, could be widely accessed and used to advance research and develop educational tools (Figure  3 ;  Wilkinson et al. , 2016). Although single-cell genomics data (mostly transcriptomic) have been generated and analyzed in individual studies for several years, how well the broader community can use these datasets in comparative analyses has been limited. This is largely due to the difficulty in integrating and comparing data generated by different methods, growth conditions and collection methods.
The PCA will be critical in harnessing the power of single-cell data by establishing and promoting communitysupported best practices in sample preparation, data generation, analysis and integrationincluding data benchmarking platforms, evaluation metrics and reference cell maps for major organ/cell types in model species. Important considerations for such infrastructure include how data will be gathered, curated and stored in order to integrate resources and contributions from different laboratories. It is necessary to develop and incentivize standards for both data collection (i. e. experimental protocols) and curation to allow integration of disparate datasets into a unified resource such as the PCA. This should include application programming interfaces (APIs) for programmatic access to ensure that PCA is accessible to all in a FAIR format.
The multidisciplinary nature of the PCA has enormous potential for accelerating the fundamental understanding of plant cells and how they connect to the whole organism, and for translating fundamental discoveries to agriculturally important crops across the globe. The dream of the PCA initiative in this regard is to actively engage applied plant scientists and breeders by facilitating the formation of joint national and international funding schemes across scientific disciplines. This will allow diverse communities Building the PCA will require cooperation and coordination between plant science, technology and data infrastructure. Plant scientists will use new technologies to generate data at the single-cell level, which will be integrated by data scientists into the PCA infrastructure through manual and automated curation.
This data integration will enable the development of new technologies and drive new hypotheses and investigations for plant scientists, who will then contribute additional data to the PCA resource. to benefit from each other's expertise and for ideas and technologies to be transferred from lab to land. Enhancement in research translatability will address globally relevant societal questions that are aligned with the UN Sustainable Development Goals  (Biermann et al. , 2017; Costanza et al. , 2016; Griggs et al. , 2013) , including food sustainability (Goals # 1-3), resources to tackle climate change (Goal #13) and the overall survival of the biosphere (Goals # 14-15). We envision that starting with a comprehensive cell atlas of model organisms will help to better understand non-model species and eventually expand to an ecosystem-scale analysis of interactions between organisms.
Inspiration from similar projects What the participants in the PCA Research Coordination Network envisage is ambitious and not yet available for any system. However, examples of components of our vision will serve as guides. Among the animal whole organism atlases  (La ¨hnemann et al. , 2020) , the mouse brain and adult mouse cell atlases are models for comprehensive coverage and community accessibility  (Rosenberg et al. , 2018; Saunders et al. , 2018; Zeisel et al. , 2018; Tabula Muris Consortium, 2018; Han et al. , 2018). The Human Cell Atlas' data portal already includes data from 55 organs and 12 million cells, and it is becoming a model for making community-generated single-cell data available at a single portal, along with APIs and data analysis pipelines made easily accessible. In addition, the OpenWorm project provides an excellent example for modeling, visualizing and simulating various aspects of the biology and behavior of an organism  (Szigeti et al.
The envisioned PCA community The PCA will be developed, maintained and used by a global community with diverse scientific, technical, creative and educational backgrounds (Figure  4 ). Helped by rapidly evolving online tools that increase inclusivity and transparency, the initiative should seek ways to facilitate the engagement, communication and collective decision-making among all groups involved. This work will aim to be grounded in active listening, psychological safety, teamwork, mutual respect and integrity. An inclusive and bottom-up ethos will underpin the development of global biological databases built with scientific rigor and transparency. The participants in the PCA Research Coordination Network anticipate that the success of the PCA will hinge on an open and frequent dialogue between developers and users, with facilitation and guidance from a steering group.
Developers The mission of the PCA community will be achieved by groups who will contribute to the development and maintenance of the database, as well as those who generate or use the data and provide inputs for improvement. Developing the PCA database will be done with the expertise of scientists, software engineers, modelers, scientific illustrators and animators coming together to fine-tune the display of cellular organization and collecting information. Data and computational scientists will have to be involved in the curation and annotation of data. Engagement will extend to consortia pursuing similar activities in different biological systems (e. g. , Human Cell Atlas). As the PCA will heavily depend on data visualization, user interface and experience design will also benefit from advisors in art and design fields.
Users The participants in the PCA Research Coordination Network expect several types of users. One of the major user groups will be the data generators, which will involve plant scientists from many disciplines, including single-cell profiling, protein-protein interactions, cell and developmental biology, imaging or synthetic biology. To engage scientists from different career stages and areas of plant science, it will be important to develop mechanisms attributing and giving credit to data contributors. Another type of users will be researchers who wish to explore the PCA data to make discoveries. Engaging these users through a bottom-up process driven by their needs will be required for a vital and thriving PCA. Finally, the third group of users, both within and outside of the research community, are those who will access the PCA for educational purposes.
Integrating the PCA in the wider bioinformatics community Robust computational infrastructure development will be essential to support the research, community-building and outreach activities of the PCA (Figure  5 ). We envision the PCA infrastructure (that is, the technologies, standards and computational platforms used by developers to build a framework to house and make available the PCA-relevant data) to have a seamless integration of multi-omics data with localization data at multiple scales of the plant. A governance structure, contribution guide and code of conduct for participants in infrastructure development should be established to define and support community values. These values include open-source development, FAIR principles and outcomes-based planning and implementation. Stakeholders should be included in the decision-making process.
To implement these values, existing efforts with similar values and goals should be leveraged. For example, the DREAM challenges group could be leveraged for developing data analysis, visualization and access tools  (Pham et al. , 2020). While new tools and resources will be needed to build a PCA community platform, existing resources should be strong guiding principles when developing the PCA infrastructure. The PCA should make efforts to liaison with data Figure  6. Knowledge gaps to fill for the PCA. Several limitations inherent to plants (shown as pieces of a jigsaw puzzle) must be overcome to enhance the understanding of plant cells as biological systems. To characterize the unique molecular attributes of each cell type composing a plant, plant biologists will need to develop reliable methods to broadly access the different cell types across various plant species.
et al. , 2007; Sullivan et al. , 2019; Howe et al. , 2020; Tello-Ruiz et al. , 2021; Portwood, 2018; Berardini et al. , 2015; Seren et al. , 2016; Barrett et al. , 2005; Papatheodorou et al. , 2020; Waese et al. , 2017). For example, OMERO is a global consortium effort aiming to standardize biological imaging data, which includes data depository as well as image processing software to translate image files into a universal format. Cross-species information may be mapped to the latest phylogenetic tree updated by the Tree of Life Web Project and Bioschemas. Engagement should also extend to consortia that are undertaking related work in other organisms, such as the Human Cell Atlas. To create a collaborative research environment, projects such as Synapse, Cyverse and KBase could be evaluated for strategic partnership.
Driving the community forward The PCA will receive diverse sets of data from different types of techniques and experiments, many of which are constantly pushing the boundaries of the state of the art. As such, the PCA needs to position itself and its community at the frontier of plant biology, looking out for emerging findings and technologies. This could be achieved through online workshops that enable pioneering researchers and developers to introduce emerging technologies to the community. Other training activities might include hands-on workshops on single-cell, spatially resolved experimental techniques, data annotation and analysis tutorials, as well as hackathons and virtual office hours to help novice code developers, data scientists and other researchers. Gaps in knowledge: How the PCA could address unresolved questions in plant science Many unresolved questions in plant biology require new technologies and approaches that the PCA could drive forward, as well as benefit from.
These platforms serve as data infrastructure blueprints as well as knowledge banks and inspiration. This would allow the PCA to be extended from an atlas of a single reference species and clone, to a set of atlases capturing natural variation (within and between species) in cell types and functions. For example, the interactive PCA platform could hyperlink the gene found in each tissue or cell type to the web portal of that gene in natural variation panels of the species of interest (such as the AraGWAS Catalog,  Togninalli et al. , 2020). Data structure scaffolds in the PCA could also be created so that the template can be replicated across genotypes of a species (e. g. different ecotypes) or different species, and allow users to select their desired organism or compare between the atlases.
Data visualization and exploration tools In addition to data analysis and interpretation, the PCA infrastructure should include tools that provide effective ways to visualize real or virtual plant cells, and the dynamic processes that form and define them. This could enable hypothesisgenerating in silico experiments that predict how plant cell types would respond to stimuli, or the properties of cells with a set of desired features. Creating these digital cells, tissues and other structures would require developing modeling, simulation, artificial intelligence, as well as data visualization and exploration tools similar to those being set up for other projects such as the OpenWorm  (Sarma et al. , 2018). Computational modeling and simulation-based predictions could then inform experimental work, the results of which could feed back into the PCA to improve models and predictions  (Radivojevic´et al.
Curating the PCA databases To achieve the PCA's vision of building a 4D representation of a developing plant, a wealth of existing data, resources and tools will need to be leveraged. For example, it is now routine to deposit next-generation sequencing, processed expression data, proteomics, metabolomics and protein structures in numerous repositories worldwide  (Deutsch et al. , 2017; Kaminuma et al. , 2010; Leinonen et al. , 2011; NCBI Resource Coordinators, 2018; wwPDB consortium, 2019). A combination of automated searches and manual curation of datasets from existing data repositories can be used to build the PCA data infrastructure. Reference genome databases such as TAIR, Phytozome, Gramene, EnsemblPlants and others that provide gene and pathway annotation resources (e. g. Gene Ontology) could enable an initial assessment of gene structure, location and function  (Berardini et al.
, 2015; Goodstein et al. , 2012; Howe et al. , 2020; Tello-Ruiz et al. , 2021; The Gene Ontology Consortium, 2019). Building a platform that brings these data together with data exploration and visualization tools that allow a user to explore at multiple scales, from molecular to cellular to organismal, is a challenge. To this end, single-cell datasets should be made available in both raw and processed data formats and linked from the publicfacing PCA website. This resource could additionally be populated using automated literature search tools, followed by manual curation by the PCA community. A selection of high-quality 'reference' single-cell datasets directly hosted by the PCA may also prove to be useful for benchmarking purposes. For all datasets, curation standards should be imposed to include minimal metadata falling into several categories: (1) experimental metadata should include a project name, details regarding how plants were grown, treated and sampled, as well as quality control metrics and definitions used to evaluate sample integrity; (2) cell metadata should include a cell identifier (where applicable), cell type annotations and other cell-level data that have come from standard or specific analysis of the dataset; and (3) gene metadata that include structural and functional annotations should be required.
In addition to providing links to data repositories, the PCA portal should include web-based tools to generate, access and query relevant data and metadata using a standardized ontology for metadata, similar to the Single Cell Expression Atlas  (Papatheodorou et al. , 2020)  and the Human Cell Atlas implementation of metadata types. This will facilitate interoperability of datasets. Depending on the data type, scripts that implement standardized pipelines for single-cell data analysis could also be made available that reflect the current best practices, including those that implement methods for cross-platform and multimodal data integration as well as label transfer (e. g. ,  Lotfollahi et al. , 2020; Hie et al. , 2019; Korsunsky et al. , 2019; Kang et al. , 2021; Stuart et al. , 2019). Versioncontrolled source code that demonstrates analyses using the information in the PCA as a starting point should also be made available.
These resources should promote greater use of the rapidly growing number of single-cell datasets. Multiscale networks can provide an intuitive method to discretize and integrate different data types  (Duran-Nebreda and Bassel, 2017). Other informatics frontiers that will be useful to explore include multi-dimensional image visualization and analysis tools such as Napari and Squidpy  (Sofroniew et al. , 2021; Palla et al. , 2021) , graph-based knowledgebases for integrating datasets (e. g. , Reactome;  Fabregat et al. , 2018) , natural language processing for automating curation  (Braun and Lawrence-Dill, 2020) , as well as machine learning and artificial intelligence methods for integrating heterogeneous data  (Ma et al. , 2020).
Challenges to the PCA infrastructure Despite the widespread availability of data types described above, additional data that need to be integrated to achieve the PCA vision (metabolomics, imaging and phenotypic observations) are currently available in a variety of heterogeneous sources such as data sharing platforms (e. g. EMBL-EBI, Dryad, Figshare, and Zenodo), institutional data repositories, researcher websites or embedded within scientific publication records. A lack of centrality in data deposition makes these data less accessible. In addition, methods for data standardization and curation will be critical tools to mitigate challenges with existing data, create best practices for collecting new data and ensure consistency to allow for data exchange and interoperability  (Sansone et al.
, 2012). Experiments, datasets and other resources should be described in sufficient detail to be easily accessible and reproducible. Validating data with resource description frameworks is a means to ensure data compliance against the conceptual model it follows, data consistency and completeness. Data records and metadata should be machine-readable by using standardized vocabularies (i. e. cell type ontologies;  Bard et al. , 2005)  to enable interoperability. Existing solutions and concepts of such ontologies are broadly applied in vertebrates and are available for plants, though they need to be widely adopted by the PCA community  (Avraham et al. , 2008; Bard et al. , 2005 ; Diehl et al. , 2016; Ilic et al. , 2007; Xia and Yanai, 2019). Mapping ontologies to the 4D representation of the PCA will facilitate linking and visualizing the growing knowledge of plant cells.
with over 200 cell types, whereas the PCA community embraces cell atlas data from a diverse array of plants. A typical plant has about 100 cell types (https://www. ebi. ac. uk/ols/ontologies/po; Ilic et al. , 2007). Understanding the diversity and origin of cell types across the plant lineage will be a central part of the initiative, as illustrated in Box 2 with the chloroplast as an example. The PCA is an essential step towards realizing the vision of a Transparent Plant, a simulated environment where virtual plants can be built from parts and behaviors  (Henkhaus et al. , 2020). In order to materialize such a simulation environment, where the parts are and how they work together must be understood. Finally, we see the Virtual Plant database  (Katari et al. , 2010)  as an essential component of the PCA vision and infrastructure.
, 2020). In 2004, the evolutionary biologist John Avise wrote that ''despite nearly three decades of experience with recombinant DNA techniques, the ultimate contribution to the broader human enterprise remains uncertain''  (Avise, 2004). Two decades after this statement, in 2020, recombinant DNA technology ushered in the beginning of an end to a global pandemic that cost millions of lives, through a record-breaking runtime of development and deployment of mRNA and DNA-based viral vaccines. While still at its infancy, we hope that the PCA initiative has the potential to serve as a nucleator, bringing together scientists and engineers from a wide range of fields to solve fundamental problems in plant biology with innovative solutions from emerging technologies. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements The PCA community-building activities are funded in part by the  National Science Foundation  grant numbers  MCB-1916797  and  MCB-2052590 ,  Carnegie Institution for Science , and  BASF. We thank  Emily Fryer ,  Nick Melosh ,  Heather Meyer ,  Jason Thomas ,  Terri Tippets ,  Renate Weizbauer ,  Zhiyong Wang  and  Kangmei Zhao  for helping with organizing the first workshop. We thank  Emily Fryer  and  Julie Gosse  at the  Science Editors Network  for developing the PCA website. We are grateful to the first PCA workshop steering committee members  Jim Haseloff ,  David Jackson ,  Edward Marcotte ,  John Marioni ,  Marisa Otegui ,  Alberto Salleo ,  Waltraud Schulze ,  Edgar Spalding ,  Michael Sussman ,  Marja Timmermans  and  HS Philip Wong  for their guidance and support.
, 2021) , but potentially pathogenic microorganisms were also sporadically isolated  (Knox et al. , 2016; Singh et al. , 2018a). In 2015, several bacterial strains were collected from the observation deck (Cupola) of the ISS, and whole-genome sequences (WGS) were generated for these isolates  (Bharadwaj et al. , 2020a,b; Bijlani et al. , 2020a,b; Daudu et al. , 2020a,b; Solomon et al. , 2020; Simpson et al. , 2021). Based on preliminary genomic analyses, three ISS strains were identified as belonging to the Agrobacterium genomospecies 3 (also known as A. tumefaciens genomovar G3 or Agrobacterium G3). The first objective of this study is to describe the phylogenomic novelty and characterize the taxonomic affiliation of the three strains isolated from the ISS environment. We, therefore, assembled a dataset of complete genomes of Agrobacterium G3, including previously released sequences from five strains isolated from various geographical regions and environments: an eosin flask from a hospital environment in France (CFBP 6623, sequenced twice), a cave in Lechuguilla, United States (LC34), algae in the United Kingdom (SUL3), Arabidopsis roots in Germany (Root651), and bioprospecting for lignocellulolytic microbes and enzymes from natural, highly evolved plant biomass-degrading systems in the United States (UGM030330-04).
There are notably two genes encoding ankyrin-repeat domain-containing proteins, as well as a protein containing an extracellular HAF repeat domain (InterPro domain IPR014262)  and an autotransporter domain, all three exported to the cell surface and likely mediating attachment to an extracellular substrate. Together with the biosynthesis of a likely extracellular polysaccharide, these species-specific genes present a convergent function related to surface attachment and potentially resistance to stress from the external medium. This enrichment in the specific core-genome of A. tomkonis suggests natural selection was at work and that surface attachment may have a strong role in A. tomkonis ecology. Indeed, the first reported A. tomkonis strains CFBP 6623 and CFBP 6624 were isolated from within an eosin flask and from a human clinical sample (likely as an opportunist taking advantage of another pathogen infection), respectively  (Popoff et al.