dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: In this Commentary, we present our views on the key issues related to the current system for metadata submission in omics research, and propose the development of a global metadata system. Such a system should be easy to use, clearly structured in a hierarchical way, and should be compatible with all existing microbiome data repositories, following common standards for minimal required information and common ontology. Although minimum metadata requirements are essential for microbiome datasets, the immense technological progress requires a flexible system, which will have to be constantly improved and re-thought. While FAIR principles (Findable, Accessible, Interoperable, and Reusable) are already considered, international legal issues on genetic resource and sequence sharing provided by the Convention on Biological Diversity need more awareness and engagement of the scientific community.
Introduction The new age of modern "omics" technologies resulted in a tremendous amount of data generated by an everincreasing number of high-throughput processing methods for microbiome research (Schneider and Orchard 2011;  [36]. Omics technologies are defined as highthroughput biochemical assays that measure comprehensively and simultaneously molecules of the same type from a biological sample. They can be based on metagenome, metatranscriptome, metaproteome, or metabolome approaches  [5]. Metabolomics experiments generally result in more complex and less defined datasets, therefore we focus here on standards and concepts for DNA, RNA and protein sequence data. To date, thousands of datasets derived from microbiome research have been stored in diverse public and private repositories. Recent estimates indicate that the global market for microbiome sequencing will continue growing in the next years  [35].
Despite this impressive amount of data, the outcome in terms of products from microbiome research both in environmental sciences as well as in the medical field is still limited. In January 2022, only 8000 microbiome-related patents have been registered worldwide (https:// world wide. espac enet. com/ patent/). This low number is due to the fact, that most datasets are used only by the researchers that have generated the data for their particular study, and, due to the relatively young field of research  [5]. Thus, despite the availability of data in public depositories, microbiome research is scattered into showcases, and a generalization of findings by performing integrative analyses is difficult. Although comparisons of different datasets are still rare, tools based on artificial intelligence approaches and especially machine learning, which can be applied, develop fast.
This would allow nowadays assessments of large datasets and predictions for microbiome assembly as shown for the soil and gut microbiome  [3, 11]. In addition, comparative analyses of datasets obtained by different technologies are rare although especially combinations of different methods can increase the accuracy of results in microbiome research  [5]. Method development and corresponding standardization and vice versa is a continuously ongoing process. The main reason for this situation is a lack of common standards for data and metadata in microbiome research which is further exacerbated by missing or insufficient metadata in general  [2]. While substantial efforts were made to introduce standards for "wetlab work" in microbiome research in the last decades  [10] , including protocols for DNA extraction (ISO 11063  [37, 45] , storage of samples, primer use  [40]  and bioinformatics  [42, 43] , so far efforts to define minimum requirements for metadata in microbiome research are comparably rare.
Main text Here we discuss the most important questions connected with metadata, which were identified as the following: do we need minimum requirements or standards; which ethical and legal issues of the metadata have to be considered; do we need a common digital identifier and ontology, and how can we implement the FAIR concept, which includes the four principles "findable", "accessible", "interoperable" and "reusable" of data  [56] , while considering legal issues. In an additional chapter, we present selected use cases demonstrating added value of re-using microbiome datasets.
Most of the well-known repositories used for microbiome data, such as MG-RAST, ENA and SRA/NCBI are all based on the MIxS (minimum information about any (X) sequence) checklist for reporting information about a nucleotide sequence  (Yilmaz et al. 2011 ) (Additional file 1: Box 1). This checklist was developed by the Genomic Standards Consortium. The suggested standards specifically describe metagenomic and marker gene-based data sets, as part of the wider MIxS standard  (Yilmaz et al. 2011 ). The respective website (Genomic Standards Consortium 2016) maintains up-todate metadata checklists for genomes (MIGS), metagenomes (MIMS) and for marker genes (MIMARKS). The metadata suggested includes 11 items that are required for all kinds of submitted data: investigation type, project name, geographic location (latitude, longitude, country and/or sea, region), collection date, environment (biome, feature, material), environmental package and sequencing method.
Similar DELSA Global (Data-Enabled Life Sciences Alliance) propose a simplified, yet informative and flexible multi-omics checklist in order to capture the essential aspects of omics studies  (Kolker et al. 2014 ). It contains a number of questions regarding experiment information, design and methods, as well as data processing. Mainly in microbiome research, where the progress in available methodology has strongly influenced the field from the very beginning, it seems essential to implement a detailed description of methods and instruments used, to ensure the best possible reuse of data. When data is submitted to ENA/NCBI in addition to the MIxS requirements, a number of additional data must be added, depending on the environmental package selected. The minimal required data will consequently differ, depending on the research question.
This is especially relevant for datasets where high levels of pathogens or antibiotic resistance genes are found. Then again researchers from the industry sector might not accept if data on specific field sites is publicly available, for example if new plant cultivars are tested in trials in which new breeding efforts are made. Another important aspect, in the context of biological data and microbiome research, is the concern to publish collection coordinates for endangered species (e. g. those on the red lists). There is currently an ongoing debate on how to protect these species from poaching / illegal collection. Finally, also governmental organizations might not want exact locations of geopolitically important locations or contaminated sites, etc. , to become public. Other metadata information asked by the repositories may be a subject of intellectual property protection.
For example, defined microbial consortia or the way how the sample was processed may be a non-disclosure information due to a patent protection  [49]. Thus, we believe that the ideal minimum requirements for the metadata should allow for a maximum of the necessary information to be provided and, at the same time, tolerate alternative answers to the posed questions. Examples of such a solution may be permitting skipping the GPS coordinates question or exchanging it, for example, with the "country and region" field, or other relevant information. The sharing of sequence data is currently seriously challenged by a considerable number of countries that claim sovereignty over all nucleotide sequences originating from genetic resources within their national borders and aim to gain control over the access to these sequences and that have therefore excluded them from public databases  (Scholz et al.
The need for a common digital identifier for metadata A digital identifier is a unique name for the metadata record that allows finding metadata and connecting them to the respective dataset. In order for the metadata to be accessible even if the URL or the physical repository changes the address, a globally unique timeless identifier should exist for each metadata record  [14]. Different organizations worldwide supply various globally unique persistent identifiers. For example, http:// ident ifiers. org, provides resolvable identifiers in the form of URIs and CURIEs, while http:// www. doi. org provides Digital Object Identifier (DOI). The DOI system run by the official DOI Registration Agency Crossref (www. cross ref. org) is one of the most commonly used possibilities to identify the metadata and to connect it to the actual data.
The DOI system is currently used mostly for crosslinking the metadata with the corresponding written materials such as journals, articles and books. There is a large number of different metadata identifiers in microbiome research that are associated with diverse data repositories. This large variation in metadata identifiers makes taxonomic identification and comparisons between the studies rather complicated  [53]. The most prominent example of digital identifiers in biology was established by the NCBI database that uses Sequence Read Archive (SRA) accession numbers for its repository of high throughput sequencing data. The NCBI SRA accession number is the most common identifier for sequencing studies as it is valid across the three most commonly used databases that are part of the International Nucleotide Sequence Database Collaboration (INSDC).
INSDC includes NCBI SRA, European Bioinformatics Institute (EBI), and DNA Database of Japan (DDBJ). Other examples of the digital identifiers for omics data are the MG-RAST ID for data from DNA/RNA sequencing experiments processed by the MG-RAST pipeline, and the UniProt database for protein sequences. Standardization of the use of digital identifiers among the omics-related data would greatly improve our ability to crosslink the metadata and to re-use available data. However, we endorse the establishment of one common globally valid digital identifier across all available sequencing databases. As the DOI system has established itself throughout the world as the most commonly used identifier for various kinds of information, some authors recommend using it also as a global persistent digital identifier for all sequencing studies  [53].
Common ontology Common ontology or technically correct words that are used to describe the host organism or environment are a key for metadata re-use. Ontologies are hierarchies of well-defined and standardized vocabulary interconnected by logical relationships  (Bodenreider and Stevens 2006). Their main purpose is to make metadata searchable, comparable and machine-readable. In practice, however, researchers often complain about a non-stringent use of common ontologies. For example, in the case of plant root endophytes, the origin of the same sample may be described as "roots", "root interior", "root system" or more technically like "surface sterilized roots". Also, information about the exact location might be added, like "root hair zone, " "lateral root zone", etc. In the field of gut microbiome research, a sample dataset deposited as "mouse gut metagenome" could be of a gnotobiotic mouse colonized with a few bacterial species or with a microbial community of different origin (e.
g. , soil, human feces), which is completely misleading. Finding the correct ontology for submitting metadata is therefore challenging as it needs to include all information but should at the same time allow for the retrieval of the data using simple search strings. As a consequence, many researchers submit only minimum required data instead of adding rich metadata, or use incorrect terms to describe their experimental data. One possibility for an easy and non-redundant ontology search is to structure the finding of a correct ontology in a hierarchical way. An example of such a hierarchical search for correct ontology is shown in Fig. 1. One solution is already available: synonyms in prokaryotic nomenclature are already linked in the LPSN-database (https:// lpsn. dsmz. de). So, by connecting to this database, the issue of synonyms can be readily solved.
The FAIR principles  [56]  (https:// www. go-fair. org/ fair-princ iples/), which are summarized in Fig. 2  would adequately address all these needs. Microbiome metadata following the FAIR principles will be: (1) easy to use and clearly structured in a hierarchical way; (2) compatible with all existing microbiome data repositories; (3) follow common standards for minimal required information; (4) allow replacement of some fields even belonging to the minimal required data with similar alternative questions (e. g. exact GPS coordinates of the sample location can be replaced with a region where the sample has been collected). The questions implemented in the 'environmental packages' developed by the Genomic Standards Consortium  (Yilmaz et al. 2011 ) could be used in the application to guide the researcher through the questions related to his/her study in a hierarchical manner, while the fields relevant for the specific research area have to be mandatory.
The redundancy of the classification could be minimized by a hierarchical search option for a correct ontology integrated in the application. Finally, the Fig. 1  Example of the hierarchically built structure of the questions in the metadata application allowing for an easy and non-redundant ontology search for plant-associated microbiomes output of the entry could be either a direct submission of the metadata in the selected repository or a correctly filled metadata spreadsheet that can be used for the same purpose. Implementing the basic criteria of FAIR principles would allow an easy and uncomplicated entry of the complete metadata to each specific kind of a research question and at the same time ensure that datasets can be easily found by using simple search strings. At least, datadeposition is now a stringent criterion for publishing and use of FAIR principles should be enforced by publishers, editors and reviewers as well.
Selected use cases demonstrating added value of re-using microbiome datasets Omics datasets are mainly produced to answer a specific scientific question. Only a small proportion of microbiome datasets were re-used after their initial publication or found in meta-studies and global assessments. Due to the fact that global standards for minimal required metadata in microbiome research are not yet available, re-use of available datasets can be a challenging and time-consuming task. It often requires that the dataset creators be contacted for missing information, which is especially a lengthy task when not only one but multiple datasets are re-used. Due to advancements in big data analyses and artificial intelligence, which are occurring in parallel to microbiome research  [3, 11] , it will become increasingly important to create added value by combining both.
Integrating large numbers of available datasets to newly developed algorithms based on machine learning with improved analytical capacity can help to improve our understanding and therapeutic possibilities of poorly understood diseases that might be linked to the microbiota, for example inflammatory bowel disease in humans  [9]. In a recent study,  Pinart et al. (2021)  compared observational studies with data on nutrition and gut microbiome composition based on a template from the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) consortium. Only a few studies were found which contain information on both dietary intake and the gut microbiome. Thus, development of more readily retrievable information from metadata will be essential for combined applications in the future.
Although data generated in microbiome research is currently sparsely re-used, the benefit of Fig. 2  An overview of FAIR principles ( adapted from  [56]  that could be implemented in metadata requirements to facilitate search and re-use of deposited microbiome data in the future such attempts is illustrated by selected use cases that led to important discoveries. In plant microbiome research, microbial communities inside seeds came recently into the spotlight due to their implications for host health and fitness, as well as their potential for biotechnological applications  (Berg & Raaijmakers, 2019). Simonin et al. [50]  have conducted the first meta-analysis of seed microbiome datasets by retrieving data from 63 studies that encompassed 50 different plant species. They re-analyzed raw data from a number of metabarcoding studies, which were obtained from public databases and used 16S rRNA gene fragments, gyrB, and the fungal ITS region s genetic markers for microbial community profiling.
In order to verify this so-far undescribed and surprising observation, the authors had to compare their data with other publicly available datasets from the same ecosystem but geographically distant sites. Their data were compared with previously established culture collections from Germany and Norway  [32, 31]  as well as with Austrian and Swedish metagenomes  [8, 7, 27, 29, 57]. This comparative study confirmed the initial hypothesis of naturally occurring antimicrobial resistances in pristine environments and facilitated the development of a new theory about resistome development in natural habitats. Metagenomics data sets mentioned above were further exploited, and yielded in basic discoveries for plant microbiome science, e. g. deciphering role of Archaea on plants  (Taffner et al. 2018) , and understanding different plant-microbiome coevolution for mosses and vascular plants  [55].
Metagenome-assembled genomes (MAGs) provide the possibility to analyze short read-based datasets at species-level or even strain-level by applying certain assembly and binning strategies. In a recent study, 303 metagenomes obtained from fermented foods were used for MAG reconstruction and compared to 9445 publicly available human metagenomes that were subjected to the same processing strategy  [34]. The authors discovered that a certain proportion of lactic acid bacteria in the human gut is likely obtained from fermented foods, while other bacteria from this group are acquired via different routes. In another study that was based on comparative MAG analyses, the authors have reconstructed 498 MAGs from paleofaeces samples and compared them to publicly available metagenomes obtained from recent stool samples from different geographic regions  [54].
They not only found that the ancient samples (1000-2000 years old) were more similar to present-day nonindustrialized human gut samples, but they also located important symbionts that have undergone changes during adaptation processes over the past centuries. The presented use cases are not exhaustive, but they clearly show that re-use of multiple datasets produced in different microbiome studies can be of added value by providing insights that would not be possible by solely relying on datasets that were obtained in one specific study. Their increased implementation will become more feasible if global standards for minimal required metadata based on FAIR principles are developed and broadly applied in the future. In future, re-using microbiome datasets will contribute to promoting complex and mechanistic microbiome studies.
Conclusions The science community needs globally accepted common standards, especially in fast-developing areas such as microbiome research to ensure best possible use, re-use and integration of data. To make the process of metadata submission uncomplicated, database-independent and thus allow for a higher quality of submitted metadata, we propose the development of a globally recognized metadata submission tool. The proposed application should fulfill important FAIR Guiding Principles, be user-friendly, compatible with all existing repositories, follow common standards for minimal required information, allow alternative answers, allow complementary rich data entries, be hierarchically structured to avoid duplications in ontology and allow for direct submission to a respective database. Developing of global standards for the minimal required metadata in microbiome research should involve international collaboration between research institutions, bioinformaticians and data repositories all over the world.
Stained cells were passed on the BD Symphony A5 cytometer with Diva software or a Beckman Cytoflex LX with CytExpert software. The formula (number of cells acquired)×(volume of resuspended cells after staining and washing/volume of cells acquired)×(volume of cell suspension in blocking buffer prior to staining/volume of cells plated for staining) was used to quantify the numbers of cells per tissue. Results were analyzed and plots generated using FlowJo 10. 8. 1 software. Gating strategies are provided in the supplementary Materials and Methods. The UMAP v3. 1 plugin of FlowJo 10. 8. 1 software was used with default parameters; Euclidean distance function with nearest neighbors=15, minimum distance=0. 5 and number of components=2  (McInnes et al. , 2018). Compensated parameters for CD71-AF647, CD11b-AF700, Kit-APC-Cy7, CD34-eF450, Sca-1-BV510, Ly-6C-BV605, F4/80-BV786, CD41-BUV395, CD45-BUV661, CD16/32-BUV737, Flt3-PE, CD115-PE-CF594 and Itgb7-PE-Cy7 were selected from Lin neg (Ter119 neg CD19 neg CD8 neg CD4 neg CD3e neg NK1.
Single-cell RNA-sequencing analysis Raw data files for the RNA-sequencing can be accessed at GEO (GSE108097). Digital Expression Matrixes are available at https:// figshare. com/s/865e694ad06d5857db4b, with an R package for scMCA analysis on GitHub (https://github. com/ggjlab/scMCA) as reported in  Han et al. (2018). Data from the Mouse Cell Atlas  (Han et al. , 2018)  were provided at https://satijalab. org/seurat/v3. 0/mca. html. Analysis was performed using Seurat 4. 0. 2. Cells from tissues of interest ( placenta, fetal liver and fetal brain) were selected using the subset function. Data were normalized, scaled, clustered and UMAP dimensionality reduction performed. Some of the cluster annotations were renamed in order to simplify the labeling of the UMAP plot in Fig. 3B. Mononuclear phagocytic cells were further subdivided to generate a heatmap of the pairwise Pearson correlation coefficient between the average cluster expression profiles and to compare variable features among placenta macrophages.
Funding This work was funded by the  Institut Pasteur , by the  Centre national de la recherche scientifique , by  Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ), by a  European Research Council   investigator award  ( 2016-StG-715230  to E. G. P. ), by  Fondation Schlumberger  ( FRM FSER 2017 ), by the  Emergence (s ) program  from  Ville de Paris  ( 2016 DAE 190 ), and by the  Florence Gould Foundation  and the  Pasteur Foundation  (fellowship to L. F. ). Open access funding provided by  European Research Council. Deposited in PMC for immediate release.
Source data are provided in the Source data file, see Data availability. Data are represented as the mean values +/standard deviation, n = 3 independently prepared samples. E, F Cleavage sites for the indicated proteases were determined by Edman sequencing (E) or LC-MS/ MS (F), showing that most tested proteases cleave within the predicted bait region sequence with the exception of the Arg-specific proteases trypsin and plasmin. very recent finding that the corresponding BR-C residues in the native A2Moo subunit threads through a similar channel  23. In summary, two orthogonal techniques provided consistent atomic structures of native A2ML1. Our structures confirmed that within the CTM8 body, the domains interact in a very similar manner as in prior structures of native A2MF proteins. These structures also provide insight into dynamic properties, as the MG3 and MG4 domains appear to be somewhat mobile in native A2ML1.