dataesr/scientific-paragraphs-categorization huggingface dataset:
This lack of a unique definition generates confusion when it comes to the comparison between the nestedness values of different networks. In this work, we have performed a systematic comparative study of the performances of six different metrics and the variants of two of them, addressing their dependency on various network parameters. Based on a large database of real systems, our results clearly put in evidence that the different metrics show diverse dependencies on size, density of contacts, eccentricity and degree degeneracy. Therefore, if the same group of networks is ranked according to their nestedness, the outcome will depend on the metrics used. Understanding these dependencies for each metrics has helped us to explain, as well, the systematic shifts between the real values of nestedness and the average over a null model based on a maximum-entropy, maximum-likelihood ensemble.
While a dependency with the size is undesired and ought not to appear when using a proper normalization, some authors have claimed that a positive correlation between nestedness and fill is in fact expected  [25]. Our work aims at providing a useful guide addressed at practitioners that compiles the different characteristics, advantages and drawbacks of the most popular nestedness metrics. We also extended the use of maximum-entropy-based null models  [18, 30, 41]  to these metrics. Finally, this work is accompanied by a package that allows to calculate all the nestedness indicators studied, generate the null ensemble for any network, as well as a database with the already calculated probabilities allowing to generate the null models for the 199 networks studied here.
Acknowledgements Y. M. acknowledges partial support from the  Government of Aragon, Spain  through grant  E36-17R  (FENOL), by  MINECO  and  FEDER  funds ( FIS2017-87519-P ) and from  Intesa Sanpaolo Innovation Center. C. P. B. acknowledges support of the  LABEXMME-DII  (Grant No. ANR  reference  11-LABX-0023 ). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
VI. EXPERIMENTAL RESULTS The Parrot AR Drone 2. 0 was chosen as the prototype for performing tests in real time. This commercial vehicle has been adapted to work under our framework which runs into a Linux-based operation system, capable of implementing a wide range of control algorithms. Observer gains for  (15)  and (  29 ) are obtained by solving the LMI of the Theorem 2 using the Yalmip Toolbox  [36]. The attenuation level for each observer is γ = 0. 064. This value guarantees an adequate performance of the observer and its robustness against disturbances. The parameters of the nonlinear AO and the nominal controller are introduced in Table  4. For testing the effectiveness of the proposed scheme, four scenarios were considered. We refer the reader to Appendix, for videos of experimental results.
The Bologna process has led European universities to review quality assurance in Higher Education. Active learning, competency-based education as well as outcome-based teaching and learning have been encouraged as means to maintain quality assurance in Higher Education. The aims of this paper are (a) to introduce the context of European Higher Education quality assurance challenges and review existing initiatives in game design in Higher Education, and (b) analyze the game design process in the Game Based Learning course of the MSc SmartEdTech program through the prism of constructive alignment. The analysis of student deliverables, in the form of Game Design Documents, shows improvement on how students approach educational game design but also points out some aspects for improvement on the course structure.
Context of the Game Design Activity The MSc SmartEdTech of the University of the Cote d'Azur is an online Higher Education program, all of the courses for which are fully offered online. A set of comprehensive materials, including different forms of audiovisual content is available to students, who can also contact the teaching personnel of the course anytime during the academic year for questions or clarifications. Additionally, once per semester, students are invited to join a week-long event, called an "intensive week", during which teachers from different courses prepare activities regarding the whole program curriculum. In the scope of the MSc SmartEdTech program, a course has been designed, in which students are engaged in learning game design through developing a Game Design Document (GDD).
Continuum In addition to the emission lines, the continuum flux densities of the sources have been extracted from up to eight available polarization-averaged 7. 744 GHz wide sidebands, centered, depending on receiver configuration, on the following frequencies:  88. 3, 96. 0, 103. 7, 111. 5, 133. 5, 143. 2, 149. 0, and 158. 6  GHz (see Tables  2  and 3 ). All the sources in the Pilot Program detected in at least two emission lines are also detected in the continuum in at least four sidebands, with three in all eight sidebands (one of which, HerBS-95, being double). The NOEMA continuum flux densities together with the Herschel and SCUBA-2 data are displayed in Fig. 12  for HerBS-58 and HerBS-89a. The NOEMA continuum measurements are summarized in Table  3 , where the quoted flux density uncertainties account for both the noise level in the maps and the uncertainty in the absolute flux calibration scale (see Section 3).
Precise measurements of the amplification factors and the derivation of the properties of these sources will require higher resolution follow-up observations in the submillimeter, and at optical-near-infrared wavelengths to study the characteristics of the foreground amplifying galaxy. The observations presented in this study have enabled the first systematic measurement of redshifts of high-z galaxies using NOEMA. Measurements of a larger and complete sample of galaxies selected from the Herschel surveys will provide a useful database for exploring in detail the properties of these sources and, using follow-up observations, the properties of the lensing systems in the case of gravitational amplification. Building upon the success of the Pilot Program, we started a comprehensive redshift survey of a sample of 125 of the brightest (S 500 µm > 80 mJy) galaxies from the Herschel surveys.
Acknowledgements. This work is based on observations carried out under project numbers  W17DM  and  S18CR  with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). The authors are grateful to IRAM for making this work possible. The anonymous referee is thanked for providing useful comments. A. J. B. and A. J. Y acknowledge support from the  National Science Foundation  grant  AST-1716585. L. D. acknowledges support from the  ERC  consolidator grant CosmicDust (PI:  H. Gomez ). C. Y. acknowledges support from an  ESO  Fellowship. H. D. acknowledges financial support from the  Spanish Ministry of Science, Innovation and Universities (MICIU)  under the  2014 Ramón y Cajal program   RYC-2014-15686  and  AYA2017-84061-P , the later one co-financed by  FEDER (European Regional Development Funds).
D. A. R. acknowledges support from the  National Science Foundation  under grant numbers  AST-1614213  and  AST-1910107  and from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers. S. J. acknowledges financial support from the  Spanish Ministry of Science, Innovation and Universities (MICIU)  under grant  AYA2017-84061-P , co-financed by  FEDER (European Regional Development Funds). The National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenomebased RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14.
INTRODUCTION Rfam is the database of non-coding RNA (ncRNA) families  (1) , each represented by a multiple sequence alignment (known as the seed), a consensus secondary structure, and a covariance model to annotate non-coding RNAs in nucleotide datasets using the Infernal software  (2). Rfam and Infernal are commonly used to annotate ncRNAs in newly sequenced genomes  (3, 4) , and are the core components of the genome annotation pipelines in Ensembl  (5) , Ensembl Genomes  (6) , NCBI Prokaryotic and Eukaryotic Gene Annotation  (7, 8)  and other resources. For example, PDBe uses Rfam to enable searching for RNA chains, such as tRNA or rRNA, in 3D structures  (9) , while RNAcentral uses Rfam to detect incomplete sequences, RNA type annotations errors, and provide other quality controls  (10).
The manually curated multiple sequence alignments from Rfam are also used for training and benchmarking new software, such as secondary structure prediction algorithms  (11, 12). Here we describe community-driven improvements and updates that are available in Rfam 14 (releases 14. 0-14. 3), including new RNA families, new features on the Rfam website, and new tools for expert users to contribute families to the database. Rfam 14. 3 contains 3444 families, rep-resenting a 28% increase since version 13. 0. The majority of the new families have been generated in collaboration with other RNA resources and experts, in particular ZWD, miRBase and European Virus Bioinformatics Center (EVBC), supplemented by in-house literature curation. Following the transition from annotating a subset of ENA  (13)  to annotation of a collection of non-redundant and complete genomes in Rfam 13.
0 (1), we have expanded the set of genomes in the Rfam sequence database (Rfamseq) by 76% in Rfam 14. 0 to reflect the increase in number of genomes. Rfamseq now includes 14 772 genomes from all domains of life. Here, we also describe the newly-developed Rfam Cloud curation pipeline, which marks a further major step towards ensuring that Rfam remains a core open and sustainable resource for the whole RNA community.
NEW FAMILIES FROM ZWD Rfam releases 14. 1 and 14. 3 included 253 new families from the Zasha Weinberg Database (ZWD) that were discovered by a systematic computational analysis of intergenic regions in Bacteria and metagenomic samples  (14) , as well as newly identified ribosomal leaders (r-leaders)  (15). ZWD stores the original manually curated multiple sequence alignments produced in the Breaker and Weinberg groups over the last decade. ZWD is a git-based resource that currently includes 417 alignments and is available at https://bitbucket. org/zashaw/zashaweinbergdata. Examples of the new and updated ZWD-based families are shown in Figure  1. Prior to version 14, Rfam had incorporated 108 families from ZWD, but in Rfam 14 a new process was developed to automate the addition of ZWD alignments.
Since the inception of Rfam in 2003, it has been a strict requirement that sequences in seed alignments were derived from Rfamseq, the underlying sequence database that is searched by covariance models for all families with each release. The Rfamseq database sequences must also exist in public databases like GenBank or ENA and each seed sequence is automatically checked to ensure that it is a valid subsequence of a publicly available sequence. However, many of the ZWD families come from environmental samples, so the sequences were not found in the INSDC archives. Previously such sequences were replaced with closely related ones from INSDC or removed, which required modifying the user-submitted alignments and could result in smaller alignments missing covariation compared to the originals. In order to preserve the manually curated alignments as much as possible and avoid the error-prone manual steps, we imported all ZWD sequences into RNAcentral  (10)  to create stable accessions and allowed for the RNAcentral identifiers to appear in Rfam seed alignments.
Furthermore, we have updated the Rfam pipeline to allow seed alignment sequences to derive from any valid ENA or GenBank accession. This added flexibility not only allowed us to import the ZWD families, but will facilitate the construction of seed alignments, especially using the new cloud-based Rfam family building pipeline (see below). To confirm the completeness of the import, we systematically compared Rfam families with ZWD using the Infernal cmscan program  (2)  to search all ZWD sequences with Rfam covariance models. Ninety eight percent of ZWD alignments have now been imported into Rfam except for seven families lacking covariation support as determined by R-scape  (16)  and 43 families marked 'Not for Rfam' in ZWD. The mapping between ZWD alignments and Rfam accessions is provided in Supplementary Information.
In releases 14. 2 and 14. 3, Rfam created 22 new families, focusing on Coronaviridae and Flavivirus structured RNAs. These releases are the first of many planned extensions for viral RNA families in Rfam. We will continually update the functional RNA structures of viral clades in Rfam and aim to provide a comprehensive database for virologists interested in RNA secondary structures. This effort is the first of its kind to bring bioinformaticians and virologists together to publish detailed and specific RNA alignments and secondary structures for a broad range of RNA viruses, and thus complements protein-based databases like vFam  (22)  and RVDB  (23).
We also reviewed and updated the existing Coronaviridae Rfam families, including Coronavirus packaging signal (RF00182), Coronavirus frameshifting stimulation element (RF00507), Coronavirus s2m RNA (RF00164), and Coronavirus 3 -UTR pseudoknot (RF00165). Two Rfam families were superseded by the new whole-UTR alignments and removed from the database: the Coronavirus SL-III cisacting replication element (RF00496) representing a single stem that is now found in Alpha-and Betacoronavirus 5 -UTR families (aCoV-5UTR and bCoV-5UTR) and Coronavirus 5p sl 1 2 (RF02910) representing two stems from aCoV-5UTR. The new and updated Coronaviridae Rfam families are available at https://rfam. org/covid-19.
Flavivirus families In order to create new Flavivirus families, we identified a set of full-length, non-redundant Flavivirus genomes to serve as a source of sequences. All Flavivirus genomes marked as 'complete' were downloaded from the ViPR database (10 443 genomes as of August 3rd, 2020)  (29). Since most of the known flaviviral genomes are represented by the Dengue virus genomes (∼58%), the ViPR-based Dengue sequences were excluded and the curated high-quality RefSeq sequences were used instead. The genomes were scanned with the existing Rfam models for Dengue virus SLA (RF02340), Flavivirus capsid hairpin cHP (RF00617), and Flavivirus 3 UTR CRE (RF00185) to identify the genomes with complete 5 and 3 UTRs. The resulting 2661 genomes were used to refine the existing Flavivirus Rfam families as well as previously published models  (30)  to produce a set of 12 new and 2 updated Rfam families (Table  1 ).
SYNCHRONISING MICRORNA FAMILIES BETWEEN RFAM AND MIRBASE MicroRNAs are a class of ∼22 nt ncRNA that regulate gene expression at the post-transcriptional level. Animal and plant genomes contain hundreds to thousands of mi-croRNA genes, many of which have been implicated in processes such as development and disease  (38). For example, the mir-17-92 cluster and mir-155 have been shown to act as oncogenes [oncomirs; reviewed in  (39) ]. Understanding the evolutionary relationships between microRNAs of different species allows the transfer of gene annotation and functional information, for example from model organisms to human. MicroRNA sequences and annotations are aggregated in miRBase  (40) , the authoritative resource for published microRNA genes. miRBase is primarily a sequence database, but both Rfam and miRBase contain classifications of microRNA families.
However, before Rfam 14. 3 the two databases have not been coordinated or synchronised. Previously, miRBase used a semi-automated, clustering method relying on BLAST  (41). These sequence-only miRBase families have higher coverage but lower quality than the Rfam microRNA families. In release 14. 2, Rfam contained 529 microRNA families, while miRBase v22 annotated 1,983 microRNA families. Only 28% of the miRBase families matched one or more of the Rfam 14. 2 families. There was therefore an opportunity to create up to 1500 new families to increase the coverage of microRNAs in Rfam, as well as investigate and rationalise the entries that are unique to each database. Here, we present the first phase of a comprehensive review and classification of microRNA gene families in collaboration with miRBase.
Based on miRBase v22, we have manually curated an initial set of 1678 multiple sequence alignments. The remaining ∼300 families require more detailed consideration and curation, including merging and splitting, and will be revisited in a second phase. The manually curated alignments were used as seeds for building the covariance models, with which we searched the Rfam sequence database for homologs of these new microRNA families. At the time of writing, we have created and submitted 356 new microRNA families to Rfam and updated 40 existing families. Work is underway to create and review the remaining microRNA families, which will be made available in the subsequent Rfam release. The workflow established here will rationalise microRNA families in the key RNA database resources, and ensure consistency between Rfam, miRBase, and RNAcentral.
miR-Base will retain its focus on sequences, and Rfam will be the primary resource of microRNA family classifications. These Rfam microRNA family classifications will be made available in both miRBase and RNAcentral. The relationship between the databases will create a cycle of improvement. All Rfam microRNA seed alignments will contain only sequences that are validated as microRNAs by miR-Base. Rfam microRNA families will be used to identify new member sequences, and these new sequences will be reviewed by miRBase for inclusion in both miRBase and RNAcentral. The validated microRNA sequences will then be added to an updated version of the Rfam seed alignment. Since miRBase and Rfam are both RNAcentral member databases, the synchronisation of miRBase and Rfam is facilitated by consistent use of RNAcentral sequence identifiers for all sequences in microRNA seed alignments.
Rfam Cloud provides access to a command line environment that enables users to create or modify Rfam families. The Rfam family building process involves several steps: starting with a single sequence or a seed alignment containing known examples of a family, the user can search for similar sequences using a covariance model, refine the family by adding more sequences into the seed alignment, and identify a bit score cutoff (the gathering threshold) that separates homologous sequences that constitute an Rfam family from non-homologous sequences. This process can be iteratively repeated to improve the seed alignment and the associated covariance model. The user can also perform quality control checks to verify the format and ensure that there are no overlaps with existing families. Upon successful completion of quality control steps, new families can be submitted to the Rfam team for review and inclusion in the main Rfam database.
The Rfam Cloud documentation describes the curation tools and provides guidelines and tips for common tasks, such as selecting the gathering threshold for a family and is available at https://rfam. org/cloud. Rfam Cloud enables RNA family building using Rfam's Infernal-based search pipeline and requires some manual intervention between iterations. Two alternative RNA family building tools with more automation than the Rfam Cloud pipeline are GraphClust2  (42)  and RNAlien  (43) , both of which also use Infernal amongst other tools. However, Rfam Cloud is directly linked to the Rfam database where the nascent families can be deposited and accessed by the larger community. Following a testing period in 2019, Rfam Cloud was successfully used in a higher education setting in collaboration with the University of Paris-Saclay.
Getting credit for authoring Rfam families using ORCID The ORCID registry (https://orcid. org) provides unique identifiers that unambiguously link researchers with their scientific papers and other outputs. The Rfam database is now integrated with the ORCID system, allowing authors of Rfam families to get credit for their contributions by adding Rfam family accessions to their ORCID profiles. The ORCID identifiers have been manually associated with existing Rfam families and the new families. This feature was enabled by the 'Claim to ORCID' functionality provided by the EBI Search  (46). The process includes three steps: (a) search for an ORCID identifier on the EMBL-EBI website; (b) manually select all or a subset of listed entries and click 'Claim to ORCID'; (c) login to ORCID using the same ORCID identifier and agree to add the Rfam entries to the ORCID record.
New sequence search integrated with RNAcentral The Rfam sequence search has been updated to use the RNAcentral reusable sequence search component. The search is executed on the RNAcentral cloud infrastructure   and, in addition to annotating the query sequence with Rfam using Infernal, identifies similar sequences in the RNAcentral sequence database using nhmmer  (48). The new search also performs the clan competition procedure  (49)  which selects the longest and highest scoring hit if several Rfam families from the same clan match the query sequence. The new search can show related RNAs even if a query sequence does not match any Rfam families (e. g. most lncRNA queries will not have matches in Rfam). The search is also integrated with R2DT (50) to visualise RNA secondary structure in standard, reproducible, and recognisable layouts (Figure  4E ).
CONCLUSIONS After eighteen years of development, Rfam still continues to grow as new RNA families are regularly reported in the literature. The recent innovations and improvements described here are focused on collaboration with the RNA community and other RNA resources to share data and tools to classify RNA families. Furthermore, the new Rfam Cloud pipeline is designed to involve more RNA experts in the creation of new families and narrow the gap between cutting edge research and manual database curation. Our future plans, in addition to completing the microRNA and viral projects, include the integration of experimentally determined 3D structure information into seed alignments and connecting the existing RNA families with the latest literature using text mining. The new Rfam Cloud and ongoing collaborations with resources such as ZWD, EVBC and miRBase cements Rfam's position as the community resource for RNA families.
ACKNOWLEDGEMENTS The authors would like to thank the organisers of the Benasque 2018 meeting where several collaborations described in this paper have been established. We would like to thank  Michael T. Wolfinger  ( University of Vienna ) for providing the ISFV, TBFV and NKV Flavivirus alignments. We also thank  Ramakanth Madhugiri  ( Justus Liebig University Giessen ) for reviewing the Coronaviridae alignments. We thank the  Scientific Advisory Board  members for useful feedback.