dataesr/scientific-paragraphs-categorization huggingface dataset:
Results for the Real Dataset of Epileptic Patients The overall functional connectivity organization was extracted in all 27 seizures using the BTND method. This means that the seizures (from 1 to 4) of one patient are processed together according to BTND. Table  2  provides the qualitative comparison between the set of activated structures between visual analysis and the BTND method for patients 1 and 2. Also, Tables  3, 4  show the same qualitative comparison for, respectively, patients 2-4 and patients 5-9. Using this method, we found, in the case of 6 patients, that 6 distinct functional subgraphs characterized the organization of seizures; 7 subgraphs for 1 patient; 5 subgraphs for another one; and only 4 subgraphs characterized seizures for the last patient. However, for each patient, some subgraphs were more strongly activated before seizure onset or were continuously activated before seizure onset and remained active during the course of the seizures.
DISCUSSION This study investigated a new method named BTND to decompose the multi-seizure brain-wide time-varying network obtained by means of FC. The dataset is visualized as FC subgraphs characterizing the dynamic of all seizures from the same patient. The FC measure used was the PLV, estimated at different time steps of the seizure and applied on large band signal (20-100 Hz). We compared the obtained decomposition of ictal events from 9 patients who have drug-resistant focal epilepsy (observation of a total of 27 seizures) to the visual interpretation from the clinician. Overall, for every patient, results consisting of spatially localized FC subgraphs with stepwise activation were easily interpretable. For 8 of the 9 patients, the decomposition matched with the clinical observation entailing the BTND method as a relevant tool to visualize the multi-seizure brainwide time-varying network.
However, in addition to finding a measure characterizing each FC graph, one must know how to analyze the temporal evolution of the proposed scores. It becomes even more complicated when several modalities are used, as in  (43) , where several seizures are analyzed for different frequency bands over time. The present study proposes another strategy, consisting of decomposing all modalities characterizing the dataset simultaneously. Thus, the main advantages of the proposed method are that the analysis pipeline provides both the subgraphs and their temporal activations. The output of the process highlights the main components of the connectivity structure and summarizes a large amount of data with an automatic approach. A similar approach has already been employed in the context of epilepsy in  (44)  to decompose FC matrices from seizures into FC subgraphs with their respective activation profile.
Bats possess extraordinary adaptations, including flight, echolocation, extreme longevity and unique immunity. High-quality genomes are crucial for understanding the molecular basis and evolution of these traits. Here we incorporated long-read sequencing and state-of-the-art scaffolding protocols 1 to generate, to our knowledge, the first reference-quality genomes of six bat species (Rhinolophus ferrumequinum, Rousettus aegyptiacus, Phyllostomus discolor, Myotis myotis, Pipistrellus kuhlii and Molossus molossus). We integrated gene projections from our 'Tool to infer Orthologs from Genome Alignments' (TOGA) software with de novo and homology gene predictions as well as short-and long-read transcriptomics to generate highly complete gene annotations. To resolve the phylogenetic position of bats within Laurasiatheria, we applied several phylogenetic methods to comprehensive sets of orthologous protein-coding and noncoding regions of the genome, and identified a basal origin for bats within Scrotifera.
We extracted a comprehensive dataset of 12,931 orthologous protein-coding genes using TOGA (21,468,943 aligned nucleotides in length and 7,911,881 parsimony-informative sites) and 10,857 orthologous conserved noncoding elements (5,234,049 aligned nucleotides and 1,234,026 parsimony-informative sites) from 48 mammalian genomes (Supplementary Note 4. 1). We concatenated each of these datasets, identified the optimal model of sequence evolution with ModelFinder 27 (Supplementary Table  6 ), inferred the species tree under maximum likelihood using the model-partitioned dataset with IQ-TREE  28  , rooted using Atlantogenata  29  , and obtained 1,000 bootstrap replicates to estimate branch support (Supplementary Note 4. 2). For each protein-coding gene, we also compared the optimal gene tree inferred under maximum likelihood to the species tree, using the Robinson-Foulds distance to identify gene alignments with possibly incorrect homology statements  30  (Supplementary Note 4.
This tree challenges the Pegasoferae hypothesis  31  , which groups bats with Perissodactyla, Carnivora and Pholidota, but agrees with a previous study of concatenated phylogenomic data  32. Evolutionary studies of 102 retrotransposons, which considered incomplete lineage sorting, also supported a sister-group relationship between Chiroptera and Fereuungulata, but differ from the present study in supporting a sister-group relationship between Carnivora and Cetartiodactyla  25, 26. Next, we considered potential phylogenetic problems with our data and methods. First, as the number of homologous sites increases in phylogenomic datasets, so too does bootstrap support  33  -sometimes even for an incorrect tree  34. Therefore, we estimated the maximum likelihood support of each protein-coding gene (n = 12,931) for the 15 bifurcating trees that represent all possible topologies of the 4 key clades (Supplementary Fig.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at     9b  gives alignment of mature miR-337-3p sequences across 40 mammals. c, Specificity differences of human and bat miR-337-3p activity is shown using species-specific sensors in luciferase reporter assays (n = 9 biologically independent samples in 3 independent experiments). Significance calculated using two-way analysis of variance test, followed by post hoc Tukey calculation. ****P < 0. 0001, ***P = 0. 0008; for bat-miR-337, not significant (NS) P = 0. 9269; for human-miR-337, NS P = 0.
Gene annotation To comprehensively annotate genes, we integrated different evidence. First, we used GenomeThreader (v. 1. 7. 0)  72  to align protein and RNA transcript sequences from NCBI or Ensembl for one other closely related bat species that has annotated genes. Second, we projected genes contained in the human, mouse and Myotis lucifugus Ensembl 96 annotation  73  and our M. myotis annotation to other bats. To this end, we generated whole-genome alignments as described in ref. 74  and used Tool to infer Orthologs from Genome Alignments (TOGA)a method that identifies the co-linear alignment chain(s)  75  aligning the putative orthologue using synteny and the amount of intronic/ intergenic alignments-and annotated genes with CESAR 2. 0 76 in multi-exon mode. Third, we generated de novo gene predictions by applying Augustus 77 in single-genome mode with a bat-specific gene model trained by BRAKER (v.
2. 1)  78  and extrinsic evidence provided as hints. In addition, we applied Augustus in comparative mode to a multiple genome alignment generated by MultiZ (v. 11. 2). Fourth, we used transcriptomic data from both publicly available data sources and our own Illumina short read RNA-seq data. Additionally, we generated PacBio long-read RNA sequences (Iso-Seq) from all six species to capture full-length isoforms and accurately annotate untranslated regions (UTRs). RNA-seq reads were stringently mapped using HISAT2 (v. 2. 0. 0)  79. Transcriptomic data were processed using TAMA  80. All transcriptomic, homology-based and ab initio evidence were integrated into a consensus gene annotation using EVidenceModeller (v. 1. 1. 1)  81. High-confidence transcripts and TOGA projections were added if they provided novel splice site information.
Transposable elements We annotated each genome for transposable elements (TEs) following previous methods  82  that incorporate de novo TE discovery with RepeatModeler 83 followed by manual curation of potentially novel TEs (putative elements with mean K2P divergences <6. 6% from the relevant consensus). Starting consensus sequences were also filtered for size (>100 bp). To classify final consensus sequences, each TE was examined for structural hallmarks and compared to online databases: blastx to confirm the presence of known ORFs in autonomous elements, Rep-Base (v. 20181026) to identify known elements and TEclass  84  to predict TE type. Finally, duplicates were removed via the program cd-hit-est (v. 4. 6. 6)  85, 86 if they did not pass the 80-80-80 rule as described in ref. 87. The final de novo curated elements were combined with a vertebrate library of known TEs in RepBase (v.
Phylogenomics Human transcripts were projected to 41 additional mammal species resulting in 12,931 genes classified as 1:1 orthologues by TOGA (Supplementary Data 2). Non-homologous segments were trimmed and CDS sequences were aligned. The best-fit model of sequence evolution for each alignment was found and used to infer a maximum likelihood (ML) gene tree using IQTREE  28. Individual gene alignments were also concatenated into a partitioned supermatrix, which was used to estimate the mammalian species tree. Branch support for this tree was determined using 1,000 bootstrap replicates. This species tree was rooted on Atlantogenata and used to determine the position of Chiroptera position within Laurasiatheria. Individual gene trees were compared to the species tree using Robinson-Foulds (RF) distances  30.
5), and required that less than 80% of the ORF was intact in all six bats, excluding genes classified as lost in more than 20% of non-Chiroptera Laurasiatherian mammals contained in our 120-mammal multiple genome alignment  88  (Supplementary Note 4. 5). We confirmed the presence of inactivating mutations in independently sequenced bat species. To investigate expansions and contractions of protein families, we used CAFE  89  with a false discovery rate < 0. 05 cut-off. As input for CAFE, we clustered Ensembl-annotated proteins into families using POrthoMCL  90  and the PANTHER Database (v. 14. 0)  91  and our ultrametric time tree, generated using r8s.
We further used luciferase assays  96, 97  to test the functionality of candidate miRNAs in vitro. A full description is provided in Supplementary Note 5. The dashed line at 4 kb marks the minimum read length that was used in the assemblies. b, Detailed Bat1K assembly pipeline, listing the steps and methods used to assemble the genomes. c, Hi-C maps for M. myotis prior to (left) and post manual curation (right). Hi-C maps were created by mapping and filtering the Hi-C read pairs by using the tools bwa, pairsamtools, pairix and cooler following the Hi-C data processing pipeline on https://github. com/hmsdbmi/hic-data-analysis-bootcamp. Left, ellipse 1 shows that scaffold 2 contains a false join. It was split in the manual curation step. Ellipse 2 highlights two scaffolds, which were not joined in automated scaffolding steps but were manually integrated into scaffold 3.
Acknowledgements E. W. M. and M. P. were supported by the  Max Planck Society  and were partially funded by the  Federal Ministry of Education and Research  (grant  01IS18026C ). All data produced in Dresden were funded directly by the  Max Planck Society. S. C. V. , P. D. and K. L. were funded by a  Max Planck Research Group  awarded to S. C. V. from the  Max Planck Society , and a  Human Frontiers Science Program (HFSP)  Research grant awarded to S. C. V. ( RGP0058/2016 ). M. H. was funded by the  German Research Foundation  ( HI 1423/3-1 ) and the  Max Planck Society. E. C. T. was funded by a  European Research Council   Research Grant  ( ERC-2012-StG311000 ),  UCD Wellcome Institutional Strategic Support Fund , financed jointly by  University College Dublin  and  SFI-HRB-Wellcome Biomedical Research Partnership  (ref  204844/Z/16/Z ) and  Irish Research Council Consolidator Laureate Award.
G. M. H. was funded by a  UCD   Ad Astra Fellowship. G. J. and E. C. T. were funded from the  Royal Society/Royal Irish Academy  cost share programme. L. M. D. was supported by  NSF -DEB 1442142  and  1838273 , and  NSF-DGE 1633299. D. A. R. was supported by  NSF -DEB 1838283. E. D. J. and O. F. were funded by the  Rockefeller University  and the  Howard Hughes Medical Institute. We thank  Stony Brook Research Computing  and  Cyberinfrastructure , and the  Institute for Advanced Computational Science at Stony Brook University  for access to the high-performance SeaWulf computing system (which was made possible by a  National Science Foundation  grant (no. 1531492 )); the  Long Read Team of the DRESDEN-concept Genome Center ,  DFG NGS Competence Center, part of the Center for Molecular and Cellular Bioengineering (CMCB) ,  Technische Universität Dresden ;  S.
Data availability All data generated or analysed during this study are included in the Article and its Supplementary Information. All genomic and transcriptomic data are publicly available for visualization via the open-access Bat1K genome browser (https://genome-public. pks. mpg. de) and for download at https://bds. mpi-cbg. de/hillerlab/Bat1KPilotProject/. In addition, the assemblies have been deposited in the NCBI database under BioProject PRJNA489245 and GenomeArk (https://vgp. github. io/ genomeark/). Accession numbers for all the miRNA-seq and RNA-seq data used in this study can be found in Supplementary Tables  17  and 34 , respectively.
The past few decades have seen a marked acceleration in the amount of marine observation data derived using both in situ and remote sensing measurements. For example, high-frequency monitoring of key physical-chemical parameters has become an essential tool for assessing natural and human-induced changes in coastal waters as well as their consequences on society. The number and variety of data acquisition techniques require efficient methods of improving data availability. The challenge is to make ocean data available via interoperable portals, which facilitate data sharing according to Findable, Accessible, Interoperable, and Reusable (FAIR) principles for producers and users. Ocean DAta Information and Services (ODATIS) aims to become a unique gateway to all French marine data, regardless of the discipline (e.
g. , physics, chemistry, biogeochemistry, biology, sedimentology). ODATIS is the ocean cluster of the Data Terra research infrastructure for Earth data, which relies on a network of data and service centers (DSC) supported by the major French oceanic research organizations (CNRS, CNES, Ifremer, IRD, SHOM; Marine Universities). ODATIS, through its components, is involved in European and international initiatives such as Copernicus, SeaDataCloud, and EMODnet. The first challenge of ODATIS is to catalog all open ocean and coastal data and facilitate data collection and access  (discovery, visualization, extraction)  through its web portal. A specific task is to develop tools for handling large amounts of data and generate products for policymakers, practitioners, and academics. This study presents the strategy used by ODATIS to implement the FAIR and CoreTrustSeal requirements in each of its DSCs and promote adherence within the scientific community (the main data producer) regarding the upload and/or use of data and suggestion of new products.
) has become an essential tool for assessing the natural and humaninduced evolution of coastal waters, as well as their societal and management implications  (Schmidt et al. , 2017; Nichols et al. , 2019). The number and variety of data acquisition techniques require effective tools to ensure that such large volumes of data are available to and usable by the research community and stakeholders. Observations are required at all stages of the scientific process: description, understanding, modeling, and forecasting. Technological progress provides us with increasing capabilities to generate richer datasets  (Buck et al. , 2019). However, accessing, using, or combining these datasets can be complicated by the large variety of data types, their volume and format, the complexity of their underlying processing, their distribution, and their location.
In order to make the most of this unprecedented flow of data for the benefit of knowledge and society, the procedures and policies of all data centers need to be harmonized. This is the objective of the findable, accessible, interoperable, and reusable (FAIR) data principles  (Wilkinson et al. , 2016; GO-FAIR, 2020). Appropriate approaches must also be defined to acquire, process, archive, and distribute the validated data and products issued from Earth observations. The implementation of such an approach must be coordinated and implemented at least at national and international levels  (Miguez et al. , 2019; Tanhua et al. , 2019; Braud et al. , 2020). Here, we illustrate the national initiative of a portal dedicated to French marine data, named ODATIS (Ocean DATa Information and Services).
We briefly present the challenges to be considered when developing an operational system that not only meets the needs of data producers and users but also the FAIR requirements. Created in 2017, ODATIS is based on existing data and service centers (DSCs), which is extremely complex in terms of its managed databases and services provided. After an introduction to the challenges involved in assessing marine data, we detail the structure of ODATIS before presenting the strategy implemented to help DSCs apply FAIR principles and guide them toward certification. Finally, we define a classification system of DSCs to optimize human and IT costs with respect to the services offered to users.
Large multi-national and multi-fieldwork programs have also suffered from a lack of centralized coordination (and funding) for data management. For example, the Joint Global Ocean Flux Study  (JGOFS, 1987 (JGOFS, -2003) )  program was a pioneer in setting a data policy based on national JGOFS data managers. However, the final JGOFS International Data  Collection (discrete datasets, volume 1, 1989 Collection (discrete datasets, volume 1, -2000) )  was available on DVDs, whereas some countries published their data online. This limited the discovery of this important dataset regarding the fluxes of carbon between the atmosphere, surface ocean, and ocean interior, as well as their sensitivity to climate change. Fortunately, an initiative of the World Data Centre PANGAEA permitted the compilation and harmonization of JGOFS datasets in Pangaea (for example, see  Schmidt et al.
, 2016) , which were recently outlined for marine data  (Tanhua et al. , 2019). The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reuse of scientific data, with zero or minimal human intervention due to their increasing volume and complexity. Table  1  describes each principle. The first principle is findable, i. e. , metadata and data must be easy to find and (re)use. The second is accessible, that is, users must know how it can be accessed. Third, the data must be interoperable, i. e. , able to be integrated with other datasets, especially in workflows for analysis and processing. Fourth, the data must then be reusable with welldescribed metadata. A second challenge is to encourage people to share data. Data producers are increasingly pressured, in particular by research funders, to release their data  (Gutmann et al.
The data repositories need to make data sharing easier for the data owner, not just the user/analyst. Decisions to archive data are still often made by individual researchers, who may renounce the decision because of individual costs  (Roche et al. , 2014). To ensure sufficient quality to meet specific user needs, it is necessary to popularize already existing guidelines and resources, such as tools to clean and normalize data, and to include metadata that clearly describe the datasets' content, prior to archiving, to enable faster and easier data sharing. Data owners also need feedback on data use, for example, through relevant metrics (e. g. , number of downloads, citation). In brief, in order to accelerate the collection, analysis, dissemination, and intelligent use of data, from national and international observations and across disciplines and Earth compartments, there is a need for interoperable infrastructures that help producers preserve and share their data and allow users to obtain relatively easy access to data.
THE FRENCH NATIONAL INITIATIVE FOR EARTH OBSERVATION DATA The French data and services hub for the Earth system, named Data Terra 3  , is the French response to the need for a research infrastructure for Earth observation data management and processing. The Data Terra research infrastructure is a priority of the national roadmap of the French Ministry for Higher Education and Research (MESRI). Data Terra includes four thematic clusters corresponding to each main Earth compartment: Ocean (ODATIS), Atmosphere (AERIS), Continental Surfaces (THEIA), and Solid Earth (FORM@TER) (Figure  1 ). The objectives of Data Terra, through its clusters, is to provide wide and unified access to data, products, software, tools, and/or services on the Earth system produced by the French scientific community. The terms of reference of Data Terra facilitate data access, improve data interoperability, and integrate data and knowledge.
The targeted users are not only the academic community but also socio-economic actors or stakeholders responsible for implementing or evaluating public policies. Indeed, in addition to promoting a better understanding of the Earth system, environmental data also have a significant socio-economic impact in many fields, such as protection against natural hazards, water quality, and management of mineral resources or living resources. Data   Terra also serves the international community through satellite missions, international monitoring networks, and partnerships for development. ODATIS is the "Ocean" cluster of Data Terra and the new national gateway to access all French ocean data (Figure  1 ). Its aim is to promote and facilitate the use of all marine observation data collected by in situ and remote sensing measurements in open and coastal waters.
The data managed by ODATIS include variables from all marine disciplines (physics, chemistry, biology, etc. ) measured from the coastal environment to the deep ocean by any technique (e. g. , satellites, in situ observatories, field cruises, lab analyses). The objective of ODATIS is to describe, quantify, and understand the entire ocean, including offshore and coastal environments, from the perspective of processes such as thermohaline circulation, distribution of chemical species including carbon, biogeochemical cycles, ecosystem functioning, ocean evolution, ocean-climate relationships, and interactions with other components of the Earth system. To achieve this ambitious objective, ODATIS must be able to offer users the following: -long-term preservation of datasets; -easier access through a single portal to fully described FAIR databases: completeness and easy comprehension of data and metadata are required to ensure that sufficient information is available for end users to assess the quality of data according to current scientific standards; -a global overview of in situ and remotely sensed observations and their derived products, in order to permit the user to fully discover data; -interoperability of datasets, regardless of location, time, discipline, and compartment; -the possibility of combining data of different type (in situ/satellite) and origin (observation networks/scientific experiments); -assistance with extracting information from the databases by proposing exploration, extraction, analysis tools, and computing facilities.