dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to measure the quality of the recovered MW only, we also compute the FSC over the MW support ("constrained" FSC or cFSC). For dataset D, we have both single-axis and double-axis versions of the data. A double-axis volume has less missing Fourier coefficients than a single-axis volume. Therefore, when processing the single-axis volume, the additional Fourier coefficients of the double-axis volume can act as an experimental ground truth. We evaluate the result with the CCC score, as illustrated in Fig. 13.
Analysis of restoration results The result on dataset B shows that noise is reduced and a significant part of the MW was recovered (see Fig. 10 ). Even though the recovery is not complete, it is enough to reduce the MW artifacts while preserving and enhancing image details. The ray and side artifacts induced by the high contrast of the gold particle are reduced and its sphericity has been improved, bringing the image closer to the expected object shape. The result on this dataset shows that the method is able to handle experimental noise in cryo-ET. The dataset C (ribosomes, Fig. 11 ) is challenging because the objects have a more complex shape and less contrast, i. e. the SNR is lower. Nonetheless, the method significantly enhanced the contrast and, according to the FSC criteria, the signal has indeed been improved.
Although visually it is more difficult to conclude that the MW artifacts have been affected, the Fourier spectrum shows that Fourier coefficients were recovered. As expected, the amount of recovered high frequencies is less than for dataset B, because of the lower SNR. It is now necessary to provide a proof that the recovered coefficients carry a coherent signal, therefore the cFSC has been measured. The black curve in Fig. 11  depicts the cFSC between the unprocessed image and the reference: given that the MW contains no information, the curve represents noise correlation. Consequently, everything above the black curve is signal, which is indeed the case for the processed data (red curve in Fig. 11 ). To illustrate how the method can improve visualization, a simple thresholding has been performed on the data (3D views in Fig.
11 ). While it is difficult to distinguish objects in the unprocessed data, the shape of ribosomes become clearly visible and it can be observed how they are attached to the membrane. In addition, in order to demonstrate that our procedure is not limited to ribosomes (considered as easy targets because of their good contrast), we perform the same evaluation procedure (i. e. dataset C) on subtomograms containing proteasomes (see Fig. 12 ). In both processed proteasome sub-tomograms, the contrast has been enhanced and the FSC and cFSC curves provide proof of an improved signal. Dataset D has a broader field of view than precedent datasets. As can be observed in Fig. 13 , the double-axis volume has a better contrast than the single-axis volume, the reason being that it has more sampled Fourier coefficients, and therefore contains more signal and less noise.
For this dataset, we processed the single-axis volume and investigated how close to the double-axis volume the restoration results are. We compare the results obtained with the MWR algorithm to those obtained with BM4D (Step #1 and Step #2). From Fig. 13 , MWR tends to enhance contrast, which facilitates the identification of membrane and. In (c) can be observed the ground-truth and the increase of the PSNR values over iterations. In (d) we compare our method to the original method  (Maggioni et al. , 2013). macromolecules. Also, the global contrast is higher in the images restored with MWR than those produced by BM4D. Moreover, we can notice that the output of Step #1 (D 1 in Fig. 13 ) has a better constrast than the output of Step #2 (D 2 in Fig. 13 ), which is surprising at first glance because Step #2 is generally used to "boost" the result of Step #1.
In terms of complexity (linear with respect to the number of pixels), the timings of MWR are actually 5. 5 min (with a non-optimized version of the algorithm (Matlab) with no parallel implementation) if the Fig. 9. Dataset A (2D): comparing our approach to competitive methods: i/sMAPEM, a regularized tomographic reconstruction method designed to achieve isotropic resolution; ii/ the Moisan's method designed to extrapolate missing regions in Fourier space; iii/BFLY, a filter designed to reduce MW artifacts. The sMAPEM method takes projections as input, therefore we used the same projections to produce the 2D input (via WBP) for the other methods. On the bottom we display the PSNR and CCC scores obtained for all tested methods. E. Moebel and C. Kervrann Journal of Structural Biology: X 4 (2020)  100013 number of iterations is set to 500.
Acknowledgments The authors thank the members of the  Max-Planck Institute, Biochemistry Department, Martinsried (Germany)  for providing the tomograms and for fruitful ssions:  W. Baumeister ,  F. Förter ,  A. Martinez ,  J. Ortiz ,  S. Pfeffer ,  S. Albert. We also thank  D. Larivière  et  E. Fourmentin  ( Fourmentin-Guilbert Foundation ) who participated to the design of the research project.
Funding This work was jointly supported by  Fourmentin-Guilbert Foundation , and  Région Bretagne (Brittany Council ). Experiments on real data (courtesy of  MPI Biochemistry, Martinsried, Germany ) were performed on the Inria Rennes computing grid facilities partly funded by  France-BioImaging infrastructure (French National Research Agency  - ANR-10-INBS-04-07 , Investments for the future).
Language is one of the most complex of human traits. There are many hypotheses about how it originated, what factors shaped its diversity, and what ongoing processes drive how it changes. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, https://chield. excd. org/), a tool for expressing, exploring, and evaluating hypotheses. It allows researchers to integrate multiple theories into a coherent narrative, helping to design future research. We present design goals, a formal specification, and an implementation for this database. Source code is freely available for other fields to take advantage of this tool. Some initial results are presented, including identifying conflicts in theories about gossip and ritual, comparing hypotheses relating population size and morphological complexity, and an author relation network.
Systematically collecting these hypotheses, storing them and converting them into statistical models is hard  (Bareinboim & Pearl, 2016). Extension: Language evolution is a field that has inspired much debate, and even reaching a consensus on interpretations of hypotheses is difficult. How can we support researchers in the continuing process of refining and extending them? How can we ensure that the tools to do this will be useful into the future? One possible solution is to harness the power of causal graphs. A causal graph is a graphical tool which breaks a complex hypothesis into individual causal links. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, pronounced like "shield", https://chield. excd. org/), a database of hypotheses expressed as causal graphs.
It allows users to apply computational search and visualisation methods, in order to express, explore and evaluate hypotheses. This paper describes the design and presents three case studies to demonstrate its functionality. Case Study 1 demonstrates how CHIELD can be used to explore connections between theories. Case Study 2 attempts to evaluate competing explanations of the connection between population size and morphological complexity, and demonstrates some issues with vocabulary. Of course, problems such as converging on the same vocabulary requires more than a database to solve, but CHIELD may at least provide a space for spotting potential areas of disagreement. Case Study 3 demonstrates extended uses such as constructing networks of authors working on the same topics. To be clear, the aim is not to build a list of theories that have been accepted by the scientific community as "correct", that are somehow more "prestigious" or supposedly have no counterevidence.
It is, of course, very difficult to prove a causal effect as being distinct from a correlation. However, at the heart of any research trying to explain a phenomenon is an idea about some kind of causal relationship. The aim therefore is to faithfully represent these ideas such that researchers can plan future work. Furthermore, the aim of this database is not to highlight one view over another, but to simply present them on an accessible platform. Its aim is description, not prescription. Finally, the database aims to be editable and maintainable into the future. We hope that future studies in language evolution will be enhanced by the insights provided by causal graphs.
g. , authors can check if they have provided justification or evidence for each causal link, or identify arguments that are tangential to the central claim, see section 5. 2 below). Expression of theories is an important part of teaching, and a unified approach to expressing causal hypotheses in language evolution could improve student understanding. For example, students could point to a particular causal link that they do not understand, and the database could give them a quote from the paper. Causal graphs also aid understanding in high-school students  (Hsu, Van Dyke, Chen & Smith, 2015). Of course, carrying out the experimental manipulations mentioned above might be impractical. In this case, researchers might depend on converging evidence from multiple sources, analogies with simulations or 'natural experiments' (see e.
Design of the database We reviewed various existing applications (see supporting materials S1) to check whether they address the problems discussed above. There appears to be no single tool that allows researchers to express hypotheses (visually), explore the way they interact, and evaluate them. CHIELD aims to fill this gap. In this section, we cover the design of CHIELD. The principle is to store data in a simple spreadsheet format, with scripts that convert them to a database that can be accessed and edited through a customised web interface.
Expression The aim of CHIELD is to allow researchers to express causal graphs for a particular theory, in a simple and intuitive format. CHIELD is based around 'documents' (usually a peer-reviewed, published paper), each of which have three associated files: bibliographic information stored as a bibtex file, a simple text file listing who contributed the data, and a spreadsheet file which stores information about the causal links in the document. These files are kept together in a folder, which is named after the unique bibtex key for the document. In order to make finding particular files easier, we borrow the design of Glottolog: document folders are sorted into parent folders for each year of publication, and then each year is sorted into parent folders for each decade of publication (see https://github.
com/CHIELDOnline/CHIELD/tree/master/data/tree/documents). For example, the files for Dunbar's (2004) paper on gossip is stored in 'documents/2000s/2004/dunbar2004gossip/'. This organisation is largely for the convenience of the database developer. For most users, the online interface for coding papers will automatically create files in the correct location, and documents can be searched using a userfriendly interface. Within the causal links spreadsheet, a single observation (a single row) represents a single causal link between two variables. The causal link has a number of associated properties, shown in table 1. The types of causal relation are shown in table 2. While a standard causal graph does not encode the direction of the relationship, this information can be encoded in CHIELD via the "Cor" (correlation) field, including positive, negative and non-monotonic relationships.
It would also be possible to allow empirical measures of causal strength to be stored for each causal link, which are important for causal studies in fields like medicine. However, they are not part of the core design goals here, since many studies of evolutionary linguistics do not estimate these kinds of measures directly, but use experiments, models or case studies to make analogies. The most important design feature here is a low barrier to adding data, and causal strength estimates may not be very helpful for many researchers in evolutionary linguistics. Having said this, it would be easy to add this kind of information to a future version of the database.
Exploration The web interface for CHIELD includes several ways of searching the data. PHP scripts fetch data from the compiled SQLite version of the database and serve them up into a dynamically searchable tabulated format (using the Datatables library, https://datatables. net). For example, users can view a list of documents, and search by title, author or year. Each document links to a document page, which displays the bibliographic information, the list of causal links (with the specification information above) and a list of other documents that have variables in common (found using live queries of the SQLite database). CHIELD also includes an interactive visualisation of the causal graph using the vis. js javascript library (http://visjs. org). Users can view tables which list all the causal links or all variables.
These link to similar pages which give overviews of the variable (e. g. , all causal links involving 'linguistic diversity'). These search features make it easy to find documents or causal links, and to explore links between documents. Collaboration is an important part of language evolution research (see  Bergmann & Dale, 2016; Youngblood & Lahti, 2018). There are now various approaches for automatically suggesting collaborations between authors based on network models (e. g. ,  Lopes et al. , 2010; Xu et al. , 2010; Yan & Guns, 2014; Guns & Rousseau, 2014; Kong et al. , 2017). By combining the bibliographic information with the causal links data, CHIELD may be able to find connections between authors beyond co-authorship. Users can search a list of authors, with links to author pages showing all documents and causal links by an author, a list of their coauthors and a list of potential collaborators.
The potential collaborators are defined following a method similar to the AXON database (see supporting materials S1): find authors whose causal graphs overlap (have nodes in common), but who have not published any papers together. CHIELD includes an "Explore" mode, where users can load multiple causal graphs into a single visualisation. This includes various tools: • An interface for combining multiple causal graphs into a single visualisation. • Interactive manipulation, allowing adding a node from the database, adding all nodes from a particular document, or removing nodes or edges from the current visualisation. • Expand links from the currently selected node (display all causal links that connect to a particular node). • Find evidence from other documents for the currently displayed links.
If conflicts are detected, the relevant edges are highlighted and the visualisation zooms in to display them. The final causal graph can be exported to the DAGitty web interface  (Textor, Hardt & Knüppel, 2011)  or as model definition code for the R package phylopath (von Hardenberg & Gonzalez-Voyer, 2013; van der Bijl, 2018), which performs phylogenetic path analysis on multiple competing models (each document is listed as a different model). The full database is openly available in a range of formats, for further manipulation by statistical software.
Extension Constructing a comprehensive database of theories of language evolution is not a feasible task for a single person. Consequently, CHIELD has been designed to be extendable by large numbers of contributors. This makes it important to be able to curate contributions and keep track of changes. It is not expected that everyone will agree on interpretations, but these issues are worth debating, and the database should include space for discussion and revisions. If the database is to be useful in the long-term, there are also the practical questions about how best to store the data. Moreover, we want the basic application to be extendable to other fields and by other developers. Therefore, the design goals here are: • Integration with version control software for keeping track of changes.
• Tools for discussion and curation. • Simple file formats that can be easily edited. • Non-proprietary data formats for longevity. • Open source code for extension to other fields. Data can be added to CHIELD by coders through the GitHub repository, but the website also includes a simpler customised interface for adding data (using the javascript library js-grid, http://js-grid. com/). This guides coders through the process of contributing a document to CHIELD, including how to draw the causal graph visually or to upload a causal links template spreadsheet from their computer (more information at https://chield. excd. org/Help_AddingData. html). The interface suggests existing variable names for the coder to re-use, in order to maximise convergence on variable names.
CHIELD is integrated with Once the coder has entered their data, a script creates the standard file formats for CHIELD and sends them to the GitHub repository (it creates a new branch, commits the new files to it, and then makes a pull request to merge these into the main branch). This sends an alert to the owner of the repository who can review the contribution and accept it into the database. The web manager can now pull these changes down to the server, and run the compiling script that creates an updated SQL database and deploys it to the website. This system is perhaps one of the more successful parts of the database design that allows anyone to contribute without needing to know how to use version control software. This system also takes advantage of the existing tools of GitHub, including user logins, bug reports, discussion threads and tracking changes which reduces the development load.
Scope of data The condition for entry into the database is that the document proposes a hypothesis with causal claims that relates to some part of the evolution of communication and that it is published in a peer-reviewed publication (e. g. , journal paper, peer-reviewed conference proceedings, book chapter). This could be either biological or cultural evolution (or both) at any evolutionary stage. As a rule of thumb, the document should relate directly to language, or be "one link away" from a relevant language document. Entry into the database does not mean that the hypothesis is correct or widely accepted, or even empirically supported. The aim is not that the database be a single theory of the evolution of communication, but a reflection of the whole field. Potential sources for documents include the Language Evolution and Computation Bibliography (https://langev.
Moderating contributions CHIELD aims to be open and editable. Anyone can contribute documents to CHIELD or edit any existing documents (as long as they have a free GitHub account). This necessitates moderation for quality control. This is done through GitHub, with a central administrator being able to review each contribution or edit (as a pull request) before it is added to the database. An advantage of this system is that it creates a record of every change to the database. The document pages of the website include buttons for raising issues with the coding (through a pre-filled GitHub issues page), which may be taken up by other users or an administrator. There are various administrator tools for aggregate tasks, like replacing all occurrences of a particular variable label with another.
The CHIELD website is live (http://chield. excd. org/) and is updated continuously (https://github. com/CHIELDOnline/CHIELD). The results in this paper are based on version 1. 1. In general, we found coding of causal graphs challenging but productive. We found that a paper might take between 10 minutes and an hour to code, depending on the complexity of the theory, the methods used and the coder's familiarity with the subject. Version 1. 1 of CHIELD includes 400 documents and 3,406 causal links between 1,700 variables. These were contributed by 41 coders (see https://chield. excd. org/about. html). The ratio of unique variables to links is high, suggesting that many documents introduce new variables. Ideally, if the database was approaching a "full picture" of the field, the number of new variables being added would decrease over time.
Case study 1: gossip, ritual and language The first example of CHIELD's functionality demonstrates how theories can be compared against each other, and how CHIELD can be used to explore empirical evidence that might help resolve debates. Figure  4  shows two theories about the coevolution of group organisation and communication in human language emergence. The first theory,  Dunbar (2004)  relates population size, brain size and gossip: risk of predation drives individuals into larger groups for safety, but these groups require more time dedicated to social bonding in order to maintain alliances. Since gossip is more efficient at maintaining a larger number of alliances than oneon-one physical grooming, it was selected for in humans, leading to a coevolution between population size and brain size (required for gossiping).
In Figure  6 , we show 21 of these studies represented as causal graphs (supporting materials S3 include the R script for automatically generating this figure from CHIELD). This graph includes evidence from fieldwork, cross-cultural statistics, lab experiments and simulations. While this visualisation may look complicated, in tandem with the interactive features of the website it provides a way of systematically thinking about different explanations. For example, Nettle (2012) and Cuskley & Loreto (2016)'s explanation involves general processes, whereby larger populations change frequency distributions in ways that lead to simplification. In contrast,  Wray & Grace (2007)  and  Little (2011)  suggest that there are specific effects of the way adults simplify their speech when talking to strangers.
Another contribution to theory that can arise when constructing these graphs, is an understanding of differences in the way terminology is used. For example, while coding some of these studies, it became clear that "population size" was not the only way to refer to how many speakers a language has. Table  4  shows 10 different terms alongside their definitions. In anthropology, terms are usually more specific in order to capture distinctions, such as the total number of speakers of a language and the number of people in a community (the first may be very high while the second could be fairly low). In contrast, studies in modelling or demography tend to use terms derived from biology that refer to more abstract properties. Dissonance amongst terms is a known issue (at least by experts), but CHIELD provides motivation and a formal system for trying to achieve unification in terminology.
Case study 3: networks of authors Figure  7  shows a network of connections between authors, where each connection indicates that causal graphs from hypotheses by the two authors have at least one node in common. This network includes 500 of the 720 authors in the database and 6065 connections. This kind of network is unique, since it is built from hand-coded data about central components of hypotheses rather than publication of co-authorship statistics or textual analysis. Of course, these are based on just a small sample of papers in the field, and heavily biased by the research interests of the coders. Nevertheless, we can use standard network analysis tools like modularity to find clusters of authors. The network splits into three main clusters which might be characterised based on their methods: experimental (experimental semiotics, iterated learning, computational modelling), statistical (cross-cultural statistics, phylogenetics) and comparative  (animal communication, genetics).