dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank all COVID Symptom Study Sweden participants whose participation, engagement, and feedback were essential to the study. We also thank  ZOE Limited  for excellent collaboration and development and maintenance of the app. We thank  NOVUS  for generously sharing their data with us. We thank the  CSSS team  for their dedication and engagement during the set up and running of the study. In particular, we thank  Anna-Maria Dutius Andersson  and  Mattias Borell  for administrative and technical support;  Ulrika Blom-Nilsson ,  Pernilla Siming , and  Riia Sustarsic  for project management support;  Sara Liedholm ,  Johanna Sandahl ,  Lars Uhlin ,  Caroline Runéus , and  Katrin Ståhl , along with  Lund University  and  Uppsala University communication teams , for dissemination and outreach.
Data availability Primary data in this study were collected by ZOE Limited and provided to CSSS under a data-sharing agreement. Additional anonymized data originated from the National Board of Health and Welfare, the Public Health Agency of Sweden, Statistics Sweden, and NOVUS. Restrictions apply to the availability of these data, which were used under license and ethical approval and are not publicly available. Pseudonymized individual-level data are, however, available from the authors upon reasonable request and with written permission from the Swedish Ethical Review Authority. Grouplevel prevalence estimates calculated from individual-level data, hospitalization data, and cases of COVID positivity are available as the Source Data (Figures. zip), which were used to generate Figures.
Code availability All code necessary for the replication of our results, including reproducibility instructions, is available on GitHub at https://github. com/ulfha881/App-based-COVID-19-syndromic-surveillance-andprediction-of-hospital-admissions-The-COVID-Symptom-S, or, in archived form, at  40. R and Stata codes are provided that will allow readers to generate Figures  2 3 4 5 from Source Data (Figures. zip). Codes to calculate prevalence estimates and the prediction models are provided, but without unmodified individual level datasets, they can't be executed in full.
The profiles of backyard poultry owners were computed using the MCA followed by a hierarchical cluster analysis (HCA)  (Costard and Porphyre, 2009; Delpont et al. , 2018; Martínez-García et al. , 2015). The MCA method allows the number of variables to be reduced by creating synthetic variables (also known as dimensions or factors) which maximise the dataset variance in a lower dimensional Euclidian space. The synthetic variables are then used in the HCA. For the MCA, out of 49 variables, 34 variables were considered to be active variables and 14 were considered as supplementary, such as clinical signs and treatments, and were used to help interpret the data. The HCA was performed based on the minimum number of factors from the MCA accounting for 50 % of the data variance. The HCA used Ward's method and was consolidated with the K-means method.
The over-representation of variable outcomes in each profile generated by the HCA was assessed with a hypergeometrical test  (Husson et al. , 2017). P-values indicate the strength of the category related to the profile's population: * when the p-value is < 0. 05, ** when the p-value is < 0. 01, *** when the p-value is < 0. 001 (Tables Appendix 2. 1-2. 5 in Supplementary materials). All statistical analyses were computed on R 4. 0. 2 and RStudio software Version 1. 3. 1093 (R Core Team, 2020; RStudio Team, 2020). The MCA and the HCA analyses and graphical outputs were computed with "FactoMineR"  (Lê et al. , 2008).
Backyard poultry flock profiles The first two dimensions of the MCA accounted respectively for 8. 39 and 5. 18 % of the total variance, and the first 16 dimensions accounted for 53. 48 % of the cumulative variance of the dataset. The HCA revealed five poultry owner profiles: urban poultry, traditional poultry, student poultry, pet poultry and hobby poultry. Data obtained by MCA are graphically represented as a cloud of dots in a high dimensional Euclidian space summed up in a lower dimensional approximation given by synthetic axes (or dimensions). Groups of points are identified by HCA in the MCA. frequency and the food origin. Details of variable distributions within each profile and whether their outcomes were over-represented are presented in supplementary materials (Tables Appendix 2. 1-2.
It is increasingly common in natural and social sciences to rely on network visualizations to explore relational datasets and illustrate findings. Such practices have been around long enough to prove that scholars find it useful to project networks in a two-dimensional space and to use their visual qualities as proxies for their topological features. Yet these practices remain based on intuition, and the foundations and limits of this type of exploration are still implicit. To fill this lack of formalization, this paper offers explicit documentation for the kind of visual network analysis encouraged by forcedirected layouts. Using the example of a network of Jazz performers, band and record labels extracted from Wikipedia, the paper provides guidelines on how to make networks readable and how to interpret their visual features.
It discusses how the inherent ambiguity of network visualizations can be exploited for exploratory data analysis. Acknowledging that vagueness is a feature of many relational datasets in the humanities and social sciences, the paper contends that visual ambiguity, if properly interpreted, can be an asset for the analysis. Finally, we propose two attempts to distinguish the ambiguity inherited from the represented phenomenon from the distortions coming from fitting a multidimensional object in a two-dimensional space. We discuss why these attempts are only partially successful, and we propose further steps towards a metric of spatialization quality.
Introduction Networks are not only mathematical but also visual objects. If network computation has existed since the 18th century, the last decades have seen the rise of network visualization as a tool of scientific investigation  (Correa and Ma, 2011; Freeman, 2000). This visual renaissance is particularly noticeable in digital humanities and social sciences-where the increasing availability of relational datasets has fueled the interest in graph charts-but it has also touched other disciplines such as ecology, neuroscience, and genetics. In general, it has become common to illustrate social relations, economic fluxes, linguistic co-occurrences, protein interactions, neuronal connections, and many other relational phenomena as points-and-lines charts. The function of such charts, however, is often unclear.
, 2013) , or dismissing this type of visualization altogether  (Aris and Shneiderman, 2007; Henry et al. , 2012; von Landesberger et al. , 2001). This paper proposes an alternative approach: instead of trying to overcome the ambiguity of points-and-lines charts, it considers it positively. Not as a burden but as an asset. The same ambiguity that makes network charts unfit for hypothesis confirmation, we contend, makes them invaluable for exploratory data analysis. This is particularly true for medium-sized networks-graphs of hundreds or thousands of nodes often found in social and biological phenomena. Alongside the metrics and models typically employed by network science and social network analysis, there exists a practice of visual network analysis (VNA), which allows to explore the richness of relational datasets and exploit their inherent ambiguity  (Decuypere, 2020).
This practice is widespread but remains mistrusted because of lack of documentation  (Jokubauskaite, 2018). The working hypothesis of this paper is that, by making explicit the heuristic bases of VNA and investigating its way of dealing with relational ambiguity, we can build trust in this practice and make it even more useful as a technique for exploratory data analysis. To address this hypothesis, this paper offers an account of VNA practices and an explicit discussion of its foundations. Because this technique is yet unsettled, we will alternate theoretical and practical considerations and unfold our argument through examples, using the software Gephi (http://gephi. org; Bastian et al. , 2009 , but see Cherven, 2015 or Khokhar, 2015  for a more how-to introduction to Gephi). (1) We start by reviewing the standards of points-and-lines charts and retracing the history of force-directed layouts.
How to read networks and make them legible To exemplify VNA techniques, we were inspired by a network of jazz musicians created by  Gleiser and Danon (2003). As observed by  McAndrew et al. (2014) , "as a music form, jazz is inherently social" and thus particularly propitious to network analysis (cf. also  Sonnett, 2016; Vlegels and Lievens, 2017 ). Yet, the original jazz network contains only 1473 nodes and is limited to bands performing between 1912 and 1940. We thus produced an updated and expanded jazz network (https://github. com/tommv/ ForceDirectedLayouts): • We used Wikidata. org to extract from English Wikipedia: 1. 6796 "humans" and 976 "bands" with "genre ¼ jazz", together with their: • "birth year" or "inception" date • "citizenship" or "country of origin" (when multiple, we kept the first one).
The first is performed in a tool called Graph Recipes (tools. medialab. sciences-po. fr/graph-recipes) through a script (https://github. com/tommv/ForceDirectedLayouts) that transforms a network chart in a density heatmap where denser zones are highlighted by darker backgrounds (Figure  6 ). The second characterizes the different areas of the heatmap through a set of "qualifying nodes" (in our example, jazz subgenres and record labels) and a "double spatialization. " We spatialize the network with only bands and individuals and freeze the position of these nodes. We then add the subgenres and record labels and run the spatialization again but only on qualifying nodes. 4 The qualifying nodes can then be used as labels for the clusters in which they end up being located.
, 1996) , force-directed layouts have outlived their origins. Nowadays, they are no longer used to follow paths in small networks, but rather to explore large relational datasets and eyeball relational structures such as clustering, centrality, or betweenness. We call this second perspective topological as its objective is to provide an overview of topological structures (see  Grandjean and Jacomy, 2019). While diagrammatic and topological perspectives coexist in practice, the two approaches come from different traditions-algorithmics for graph drawing and information design for network visualization-and serve different needs. A diagrammatic stance suits small networks, whose configuration is simple enough to be qualitatively appreciated, while a topological attitude is more appropriate for larger networks, where pattern detection and exploratory data analysis  (Behrens and Chong-Ho, 2003; Tukey, 1977)  are preferred.
This explains why, in the last few years, the attention of scholars has gradually shifted from the diagrammatic to topological approach. Diagrams, favored in the early years of network visualization, are becoming obsolete when confronted with the growing size of relational datasets  (Henry et al. , 2012). Reviewing an assessment of spatialization algorithms by  Purchase et al. (1996) ,  Gibson et al. (2013)  note for instance: The type of tasks she [Helen Purchase] asked her users to complete. were finding shortest paths, identifying nodes to remove in order to disconnect the graph and identifying edges to remove in order to disconnect the graph. It is unclear as to if this type of accurate, precise measurements are typical analysis tasks for graphs with hundreds or thousands of nodes. If those kinds of tasks become infeasible due to the volume of nodes and edges then the better layouts should support the user for a different set of tasks.
Toward a measure of spatialization quality Effective in practice, visual network analysis remains conceptually underdeveloped. As observed by Bernhard Rieder and Theo R€ ohle: "tools such as Gephi have made network analysis accessible to broad audiences that happily produce network diagrams without having acquired a robust understanding of the concepts and techniques the software mobilizes"  (Rieder and R€ ohle, 2017). To master VNA, it is crucial to appreciate not only its strengths, but also its biases, many of which come from the difficulty to separate the "positive ambiguity" inherited from the represented phenomenon from the distortions coming from fitting a multidimensional mathematical object in the two dimensions of a computer screen (or piece of paper). Figure  9  illustrates the problem.
Conclusion This paper starts from the empirical observation that scholars in a variety of disciplines in social and natural sciences are increasingly relying on network visualizations to eyeball their relational datasets and to convey their findings. The growing popularity of these charts suggests that, far from being merely decorative, pointsand-lines visualizations have a distinctive heuristic force. Their use constitutes a fully-fledged form of network analysis, though one that differs from the metrics and models typically used in social network analysis and network science. This visual analysis, however, has so far remained a sort of "trick of the trade," whose virtues (but also whose limits) are seldom acknowledged or explicitly discussed. This lack of documentation explains the mistrust that many scholars still maintain against network visualizations.
3. Part of the mixing is due to the fact that, while the inception date corresponds directly to the moment in which bands started to be active on the jazz scene, this is not the case for the birth date, which is obviously offsets by several years. However, as our dataset spans over almost 150 years (the earliest date being 1870 and the latest 2014) the distribution of the two timescale remains consistent in the network and does not require correction. 4. A last detail: although the Wikipedia pages related to the subgenres and record labels have hyperlinks connecting them, we removed these edges from our network, so that the qualifying nodes are only positioned according to their connections to the primary nodes (and not between themselves). 5. We thank Emiliano Neri, whose jazz expertise was instrumental in this analysis.
Some of the amazing contributions brought to the scientific community by the Protein Data Bank (PDB) are described. The focus is on nucleic acid structures with a bias toward RNA. The evolution and key roles in science of the PDB and other structural databases for nucleic acids illustrate how small initial ideas can become huge and indispensable resources with the unflinching willingness of scientists to cooperate globally. The progress in the understanding of the molecular interactions driving RNA architectures followed the rapid increase in RNA structures in the PDB. That increase was consecutive to improvements in chemical synthesis and purification of RNA molecules, as well as in biophysical methods for structure determination and computer technology. The RNA modeling efforts from the early beginnings are also described together with their links to the state of structural knowledge and technological development.
Structures of RNA and of its assemblies are physical objects, which, together with genomic data, allow us to integrate present-day biological functions and the historical evolution in all living species on earth. The Protein Data Bank (PDB) is an icon for structural biologists. In its virtual vaults, the PDB stores the structures of biological macromolecules obtained painstakingly by thousands of crystallographers, electron microscopists, and nuclear magnetic resonance spectroscopists over the last 50 years or so. This note is primarily written to honor and acknowledge those who set up the PDB and who have strived throughout all these years to improve and maintain the database. We thank the Journal of Biological Chemistry for organizing the publication of such pieces that provide a mixture of personal recollections, historical records about nucleic acids structure, and some general comments about databases and their future.
The evolution of structural databases When one of us was a postdoctoral fellow with M. Sundaralingam at the Department of Biochemistry of the University of Wisconsin in Madison, the only way to work with a published molecular structure was to first type in the atomic coordinates at the console of a PDP computer (Programmed Data Processor from Digital Equipment Corporation, Massachusetts). Only then could one deduce or check atomic distances, angles, stereochemistry, contacts and visualize the 3D structure using programs developed in-house. This was a tedious job, but very instructive to students and postdoctoral fellows. In those days, atomic coordinates were part of the paper and presented with temperature factors in tables (for an example, see (1) and for general guidelines (  2 )). The number and diversity of errors that escaped detection before publication, while rarely casting doubt on the crystallography itself, led generally to inaccurately described molecular structures with, for example, some wrong chiralities, or atoms of the molecular unit in different unit cells.
This manual handling of data constituted for sure an excellent training ground. Happily, the nucleic acid structures that could be tackled at that time were restricted to the bases (often modified), nucleosides, and nucleotides, with the first structure of a dinucleotide appearing in 1971 (3, 4). In 1965, Olga Kennard, inspired by J. D. Bernal, had started to work on the Cambridge Structural Database, CSD (5) and, in 1971, the PDB was established jointly by the Crystallographic Data Centre, Cambridge and the Brookhaven National Laboratory (Protein Data Bank). PDB originally served as a repository system where crystallographers could mail in their data on punch cards, then computer tapes, for archiving and distribution. The announcement of the founding of the PDB in Nature New Biology was a simple little insert of 256 words (6)! Full publications appeared later (6, 7).
The CSD archived structures of individual nucleotides, as well as di-and trinucleotides. These structures are not in PDB. In fact, the first release of PDB did not include any nucleic acid structures, just seven protein structures (7). Later, in 1992, the Nucleic Acid Database (NDB) was founded by Berman et al. (8) and structures of RNA units were included in the NDB. In the early days of the development of structural databases, the main objectives were to prevent the loss of data by archiving solved structures and making them freely available upon request. The deposited data were checked for atom names, numberings, and geometry by database curators, but not as systematically and thoroughly as it is done today. Dictionaries and defined formats were developed over the years and applied to standardize nomenclature, file formats, and metadata.
that could accommodate much larger structures and much more metadata, which were eventually adopted by PDB. Nowadays, these databases are rich in metadata and tools for assessing and describing structures. Very valuable tools for structural validations  (9)  and corrections  (10)  of nucleic acid structures were introduced. The PDB now offers various validation tools with compelling metrics for all types of structures  (11). While the PDB remains, understandably, protein-centric, other databases and tools are available for nucleic acids  (8, 12, 13) -see also https://www. bgsu. edu/ research/rna/.
It cannot be overemphasized that the quality of the deposited structures is of primary importance; it is directly related to the crystallographic resolution of the X-ray data and on the refinement process, since only a minor fraction of X-ray structures is obtained at true atomic resolution (better than 1 Å). For example, can one really be confident in RNA structures at resolutions above 3. 3 Å with average B-factors around 200 Å 2 , bad clash scores, and poor PDB validation metrics? Only a systematic analysis of the various regions of the RNA molecule in electron density maps would allow a knowledgeable and interested person to reach an informed opinion. One key parameter for compiling reference databases for knowledge extraction is the nonredundancy of the structures that are included. Nonredundant structure databases, by reducing bias in the parameters deduced from structures, are extremely valuable for extracting knowledge about RNA as well as for benchmarking modeling strategies  (21).
F, nucleotides 32 and 38 immediately adjacent to the last base pair of the anticodon stem generally present a single H-bond (for details (242)). Figure  5. A native state of the hammerhead ribozyme (left drawing) that promotes cleavage at low magnesium concentrations (243) that is reached through intricate contacts (and variable depending on the type of ribozymes) between an apical loop and an internal loop (drawn in red color). Without these tertiary contacts, a different core of the three-way junction (drawn in magenta in both structures) is observed (compare the regions in magenta color). The nomenclature described in Figure  3B  is used for the non-Watson-Crick pairs  (240). swap or interchange and determine which interactions are opportunistic and not critical for form and function. Integrated databases with sophisticated techniques for guaranteeing the interoperability of data will need to be developed to exploit fully the one-dimensional and the three-dimensional data.
These modeled structures can no longer be found on the PDB but can be retrieved from the following website https://eric-westhof. ibmc. cnrs. fr/. Interestingly, wwPDB recently held a workshop on data standards for integrative structural models  (136) , and Protein Data Bank Japan (PDBj) recently launched the Biological Structure Model Archive (BSM-Arc)  (137). Figure  7  (top) shows a timeline of selected RNA models that have been published. It starts with the modeling work of Fuller and Hodgson  (138)  on the anticodon loop of tRNA. They manually assembled space filling (139) and Kendrew (140) models. They stated very clearly the objectives that are still of value today: "We do not necessarily believe that our models describe the actual molecular conformations to an accuracy of a few hundredths of an angstrom, but the analysis shows that a model with the general characteristics we propose can be built with acceptable stereochemistry.
Toward that aim, the RNA-Puzzles computational contests were created in 2011  (155) (156) (157) (158). The RNA-Puzzles consortium is a community-wide assessment of RNA 3D structure prediction that aims to expose the bottlenecks in current RNA 3D structure prediction and promote the improvement of these prediction methods. RNA-Puzzles attempts to provide the RNA modeling community what CASP (Critical Assessment of Methods of Protein Structure Prediction) has been doing for protein modeling  (159). All the data sets for the modeled structures and codes for assessment are now available as open source on GitHub (https://github. com/RNA-Puzzles) (160).
Databases play central roles in science In 2003, three organizations, the Research Collaboratory for Structural Bioinformatics (RCSB) in the United States, the Macromolecular Structure Database (MSD) at EBI, and the PDBj in Osaka, created the worldwide PDB (wwPDB; http:// www. wwpdb. org/) with the goal of maintaining a single archive of structure data of macromolecules that is freely and publicly available to the global community  (161). The Biological Magnetic Resonance Data Bank is now also part of wwPDB, and soon the China Protein Data Bank will join. This structure also facilitates unified deposition, curation, and distribution of structures to the growing global community of structural biologists and scientists that use 3D data  (161). At the same time, each independent center is free to innovate new visualization and analysis tools to promote the reuse of the data in creative and useful ways for nonspecialists.