dataesr/scientific-paragraphs-categorization huggingface dataset:
In both datasets, we removed singletons (i. e. taxa occurring at one site only) prior to calculation of the DNCI values. We employed the Kruskal-Wallis test followed by posthoc Dunn tests with p-value adjustments (method = 'bh') to study whether the DNCI differed across diatoms, macroinvertebrates and fish in the US Mid-Atlantic hydrologic subregions, and the Wilcoxon rank sum test to study whether the DNCI of bacteria and macroinvertebrates differed from each other in the Chinese catchments. Separately for bacteria and macroinvertebrates, the Kruskal-Wallis test followed by posthoc Dunn tests with p-value adjustments (method = 'bh') was used to study whether within-and across-catchment DNCIs differed from each other in the Chinese dataset. With Mantel tests, we studied whether the DNCI of Chinese bacteria and macroinvertebrates varied with distance using pairwise DNCI values and pairwise spatial distances of the site groups.
2020) , vegan  (Oksanen et al. 2018) , FSA  (Ogle et al. 2020 ), ggplot2  (Wickham 2016) , igraph  (Csardi and Nepusz 2006)  and dichromat  (Lumley et al. 2013)  with the R statistical software (<www. r-project. org>). The functions and codes for DNCI calculation and examples for preparing plots are available in GitHub (<https://github. com/Corentin-Gibert-Paleontology/DNCImper>) and as Supporting information.
Robustness tests If only niche effects are simulated, removal of 40% of the taxa or 30% of the sites in one site group of the dataset can lead the DNCI analysis to fail in identifying the correct assembly mechanism (Fig. 2a , 3a, blue boxplots). For assemblages simulated solely via dispersal effects, the removal of 50% of the sites in one site group can also lead to the identification of the wrong assembly mechanisms (Fig. 3a , orange boxplot). Consequently, the DNCI is more robust to variation in number of taxa than in number of sites but can also be highly sensitive to specific variation in number of sites (Fig. 2, 3 ). These results are consistent with preliminary robustness tests conducted on the qualitative PER-SIMPER method  (Gibert and Escarguel 2019). However, the computation of robustness tests on a quantitative index provides a more precise understanding of the consequences of sampling effort variation on the method's ability to detect assembly mechanisms and the intensity of their respective contributions.
First, the computation of a single, robust DNCI value requires groups with a limited difference in number of taxa and sites (Fig. 2a, 3b ). Specifically, comparing groups with deviation in number of taxa exceeding 40% is not recommended as it leads to underestimation of the niche processes (Fig. 2a ). In addition, to discriminate the respective contribution of niche and dispersal processes, the DNCI must be computed for site groups with less than 30% differences in site numbers, which is especially important for communities assembled by both niche and dispersal processes (Fig. 3a ). The DNCI is, therefore, to some extent sensitive to the symmetry of the  occurrence matrix of interest. Second, DNCI values can be compared accurately across datasets of different size both in terms of taxa and site numbers, as DNCI values are insensitive to symmetric removal of sites or taxa (at least up to 70% differences; Fig.
2b, 3b ). Consequently, random re-sampling  (Sanders 1968 , Raup 1972 , Gotelli and Colwell 2001 ) may be necessary when computing DNCI between site groups of dissimilar size within a dataset, but not when comparing DNCI values between distinct datasets of dissimilar size. This is a significant improvement in terms of precision compared to PER-SIMPER, which confounds the effect of matrix symmetry with the effect of unequal sample size.
Discussion Here, we developed a novel quantitative index, DNCI, to disentangle the importance of niche and dispersal processes in community assembly, with distinct advantages over existing methods, including earlier null model approaches  (Chase et al. 2011) , variance partitioning in constrained ordination  (Borcard et al. 1992 ) and PER-SIMPER's original qualitative inference procedure  (Gibert and Escarguel 2019). The advantages of the DNCI include, 1) reliance on taxa distributions rather than summary statistics, with the potential to better assess assembly processes (cf. Chase et al. 2011) , 2) relative simplicity, given that it does not require abundance, environmental or spatial data and, 3) high comparability across datasets, even those that differ greatly in numbers of sampled localities and taxa.
Thus, the DNCI allows evaluating whether the dominant assembly mechanism and its strength vary across different datasets. Based on observational data from the US and China, the DNCI-based results suggested that communities of distinct organismal groups, from microbes to fishes, were mainly assembled by dispersal processes. Differences in the DNCI were partly associated with organismal groups and geographical contexts. In the empirical datasets from the US and China, DNCI values varied among organismal groups, with dispersal assembly being slightly but significantly stronger for the metacommunities of smaller organisms. Furthermore, in the Chinese data, our results implied that the intensity of dispersal assembly may increase at greater distances for microbes. Although the DNCI does not inform us about actual dispersal rates, it can perhaps be reasoned that the distance-related DNCI pattern observed for the Chinese microbes may suggest that the occurrences are, to some degree, limited by dispersal in the two catchments.
Overall, dispersal was the dominant assembly process in the studied regional datasets, but further research is needed to assess whether the intensity of dispersal assembly changes across larger spatial distances and environmental gradients in other datasets. For the Chinese data, some pairs of site groups were very strongly structured by dispersal both within and across catchments, while some pairs of site groups were structured by a more balanced mix of dispersal and niche processes within and across catchments (Fig. 5e-f  ). These two examples illustrate some of the limitations of the DNCI approach related to the use of occurrence matrices and pairwise comparisons. First, the use of a single occurrence matrix without additional environmental information makes the use of DNCI extremely straightforward, but it also implies a lack of information when it comes to the effect of niche mechanisms on the distribution of taxa.
Only the assumed effect of niche mechanisms on the presence/absence of taxa and, therefore, on the richness of localities (i. e. row sums) are compared with the assumed effect of dispersal potential of taxa (i. e. column sums). This bias might be important especially for small organisms, such as bacteria, where large differences in abundance between sites rather than richness may be observed. Ecological differences between and within the two catchments were not strong enough compared to differences in dispersal potentials to explain the dissimilarity structure and thus the composition (in terms of occurrence) of assemblages. This was evidenced by the highly negative DNCI values obtained from the datasets collected from the two catchments. For macroinvertebrates, the three site groups within the Mekong catchment showed a slightly more balanced situation between dispersal and niche processes, which was indicated by higher DNCI values and, therefore, a comparatively increased role of niche conditions on the presence and absence of the sampled taxa.
Second, the DNCI approach analyses pairs of assemblages. Therefore, a single analysis does not allow us to conclude on the nature of the structure of one community but on the nature of the assembly processes that differentiate groups of local communities. Here, network plots can be useful for interpreting results. By simultaneously observing the DNCI values of multiple pairs of site groups, we can identify the pairs intensely structured by dispersal (such as S-MID2-M-HIGH for bacteria and S-LOW-M-MID for macroinvertebrates) as well as those comparatively more marked by the effect of niche conditions (such as M-HIGH paired with all other site groups for macroinvertebrates). Our general results that communities from different spatial site groups were more differentiated by dispersal than niche processes can be partly seen as a consequence of the pairwise nature of the method and, in our example datasets, of a comparatively weak environmental effect, assumingly portrayed by the local taxonomic richness, on the presence or absence of taxa.
Although the DNCI values based on our observational example datasets were all negative, indicating that dispersal assembly was dominant for all tested aquatic metacommunities from two continents, it is likely that datasets from different biological, environmental, spatial and temporal settings may produce both positive and negative DNCI values. Exploring the sensitivity of DNCI can guide future research to assess how community assembly is determined by the following factors, for instance: 1) spatial extent of studied areas (i. e. site groups in the analysis); 2) spatial distances among studied areas (site groups) and within studied areas (sites); 3) physical connectivity within and across site groups; 4) organismal groups, taxonomic levels and functional traits (e. g. dispersal ability, stress tolerance or feeding behaviour), 5) environmental heterogeneity versus homogeneity; 6) successional phase; 7) seasonality; 8) temporal environmental trends, such as progressive eutrophication, land use changes and climate change.
Fourth, distances between sites and site groups and, more generally, the overall spatial setting, are not directly accounted for (but indirectly via the occurrence matrix structure), potentially complicating interpretation of results. Fifth, unlike methods which use numerous environmental variables as predictors (e. g. variation partitioning), the DNCI alone cannot show which types of environmental variables contribute to variation in community structure across sites and site groups. Keeping in mind the requirements for shared regional species pools and limited size variations among site groups as well as the caveats described above, the proposed DNCI approach can shed light into the importance of niche and dispersal processes by measuring the strength of community assembly processes in a way that is simple, quantifiable and easily comparable across datasets.
In datasets C and D and the illustrated dataset, DNCI is significantly less than zero, indicating that dispersal assembly is the main driver of the empirical species distributions. Dataset D and the illustrated dataset do not have significantly different DNCI values, i. e. the control of the empirical species distributions by dispersal assembly is similar. Dataset C has a DNCI significantly higher than dataset D and the illustrated dataset, i. e. dispersal assembly control in dataset C is significantly weaker than in dataset D and the illustrated dataset. SES d = standard effect size of E d. SES n = standard effect size of E n. Modified from Gibert and Escarguel's (2019) Supporting information.
Figure5. The DNCI values for bacteria (left column) and macroinvertebrates (right column) in the Chinese dataset. There was a significant negative relationship between DNCI of bacterial communities and distance (a), suggesting that intensity of dispersal assembly grew with increasing distances. There was no clear relationship between the DNCI of macroinvertebrate communities and distance (b). Based on the Kruskal-Wallis test, the DNCI of bacteria within and across catchments did not differ from each other (c; p = 0. 326). Based on the Kruskal-Wallis and post-hoc Dunn tests, the DNCI of macroinvertebrates was almost statistically significantly different within the Mekong and the Salween catchments (d; overall Kruskal-Wallis p = 0. 056, post-hoc Dunn p = 0. 050). Network plots illustrate the DNCI, i.
DOAB was launched as early as 2012 and, as of November 2018, it contains only about 13,390 books from about 285 publishers --obviously not broad coverage, given that the American university presses alone publish about 3,000 monographs per year, and BRILL, a heavyweight of the academic book market, around 2,000. 1 We would like to thank Javier Arias for his valuable comments on this paper. 2 [Gatti, 2017]  shows, among other critical points, a weakness in aggregation of the data concerning the usage of books, for example when downloads of full books and download of chapters are not clearly distinguished. The publication of monographs in open access is made more difficult by the fact that a complete digitization of the monograph is far more challenging than that of scientific journals and articles, whether in terms of reading habits, reputation gains or storage concepts.
In order to ensure to the authors gain in reputation through their publications, a certain level of quality certification is required. It is therefore necessary to develop a system for certifying the scientific quality of a publication that can be independent of the prestige of the publisher. For this purpose, it is also essential to enable the collection of bibliometric data that can depict the use and resonance of a particular book. Finally, a more intensive interaction with the publications -whether in the form of open annotations or content mining services -is an added value for authors and should motivate them to choose the open access format for the publication of their monographs. The EU Project HIRMEOS is dedicated to the development of such services which giving new value to Open Access digital monographs.
The different, independent publishing platforms participating in HIRMEOS are willing to use the same metadata as a result of the project and implement services accordingly. Thus, the publication system remains open to the participation of other platforms, a process which will be simplified by an implementation guide created during the course of the project. Such an integrated publishing system also strives to support scientific work by simplifying and accelerating basic research activities -the so-called scholarly primitives -functions such as writing, finding, annotating, referencing, assessing, exemplifying, or presenting, but also elementary activities in the digital field such as searching in browsers, connecting digital texts, collecting data, scanning and creating standards of data handling (data practices) (cf.
HIRMEOS Partners and Platforms The HIRMEOS project consortium consists of nine partners in six European countries and one transnational ERIC [ii] : 1. Centre National de la Recherche Scientifique (CNRS), France 3. ΕΚΤ Open Book Press (Greece), financed with its own and structural funds, is the service provider for electronic publishing for the Greek National Documentation Center. EKT offers advanced e-Infrastructures and services for institutional partners (universities, research centers, scientific societies, and memory institutions), in particular, to enable the OA publication of peer-reviewed journals, conference proceedings and monographs in the SSH. EKT works with Open Monograph Press (OMP), an Open Source software developed by the Public Knowledge Project to organize the peer review and editorial processes.
HIRMEOS Work Package, Data and Services Providers In order to improve the integration of OA monographs from the SSH, HIRMEOS provides the five These will facilitate the reuse, cross-linking and searchability of content, enable more intensive interaction with users, and strengthen confidence in the quality of published monographs. All in all, the incorporation of these standards across the platforms should significantly improve the findability of open access monographs, which is not always optimal today (cf. [McCollough, 2017] ).
Annotation of digital monographs HIRMEOS has already made it possible for users of monographs across the five platforms to add annotations using the open annotation tool Hypothes. is. Hypothes. is is an open platform for discussion on the web that allows annotations to be written at sentence or word level, such as criticism or notes on news, blogs, scientific articles, books, terms of use, campaign initiatives, legislative procedures, and more. Hypothes. is is based on an open source JavaScript library and annotation standards developed by the W3C Web Annotation Working Group. Hypothes. is has established broad partnerships with developers, publishers, academic institutions, researchers, and individuals to provide a platform for next-generation read-write web applications. The Hypothes. is software is developed by a non-profit organization, financed by the generosity of the the Knight, Mellon, Shuttleworth, Sloan and Helmsley Foundations.
Metadata for metrics and legacy metrics The measurement of impact and resonance presents specific challenges for open access monographs. Keeping track of current downloads, readership, and reuse across multiple platforms is difficult, but important if one wants to understand and track their reach. The use of alternative metrics (Altmetrics), which measure the number of mentions of a document in social networks and other kinds of publications, has also increased significantly in recent years because it helps to better understand the impact of scientific publications by documenting the resonance of scientific content in broader communities and beyond the specific academic context. HIRMEOS partners Ubiquity Press and Open Books Publishers are enabling the platforms to collect usage metrics and alternative metrics and to display them directly on the documents.
The software collecting altmetrics will be hosted by Ubiquity Press and all code, APIs, and standards will be published Open Source. The altmetrics service will record the following measures for books: tweets, Facebook shares, Wikipedia quotes, and annotations. The service is designed to operate on a daily basis; it can therefore also make this data available in chronological order. Since the data sources often change their access methods, licensing and conditions, this is a maintenance-intensive system. Ubiquity Press will host, manage and maintain this system and make it available as a service to other platforms. The entire source code will be available under an Open Source license (MIT) for the participating platforms and the wider community to use, reuse and expand. Open Book Publishers, on the other hand, has developed its own software capable of collecting, processing, and standardizing usage metrics from third-party platforms.
A key component of their software stack is a database and API containing mappings of the different identifiers used by each platform (e. g. URLs, ISBNs, DOIs) to the pertinent work, whether it is a monograph, a chapter, an image, or any digital object constituting a publication on its own. The service then extracts usage data from the platform in question and uses the identifiers database to enforce the use of a particular type of identifier, such a DOI, i. e. , then translates the identifiers used by the reporting platform to the one desired by the data collector. The data collected is stored in the form of events, each of them recording the measure collected, the timestamp, the identifier of the work affected by the event, and the number of times the event was repeated, e. g. , there were four downloads of this book in this platform.
The standardization process not only normalizes identifiers, it also tags each event with a URI identifying the measurement it represents (e. g. views), the platform reporting the event (e. g. Google Books), and provides a location of the definition of the measure for further user-friendly description. The format described is provided as an open standard, allowing its adoption by any platform or publisher wishing to collect and display usage data from any distributing platform. Similarly, the software developed to collect metrics is provided Open Source, and containerized, as to allow its reuse.
Use cases Before discussing all the scenarios in detail, it is informative to detail the amount of data each partner had to work with: -4000 books in English and French from Open Edition -2000 titles in English and German from OAPEN -162 books in English from Ubiquity Press -765 books (606 in German, 159 in English) from UGOE -EKT had just one book in English, the rest was in Greek Although the preferred publishing format varies from platform to platform, whether it is XML, PDF, or HTML, entity-fishing is able to handle XML, PDF, and plain text. However, as each organization uses a different publishing platform, the design and methodology used in the implementation had to be tailored to each case. One of the most frequently implemented use cases was the improvement of the platform's search interface by using entities extracted from the library content.
This was done by extracting specific Named Entities and enabling users to filter their search with these complementary parameters. OpenEdition extended their Books Catalogue 4  by adding two additional facets to filter books by entities of type PERSON and LOCATION. Along the same lines, Goettingen State Library also extracted mentions of organizations from the library corpus in their book catalog 5  : Once the annotation data is collected and stored, it can also be used for automatic generation of word clouds at the repository level, with words displayed according to their importance (relevance, frequency, etc. ). Users are then able to access the most relevant concepts at library level. The underlying data is effectively the same used for facet searching, but with a different visualization.
Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an interrater agreement of about 85 % only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches to a new deep learning method, SimpleSleepNet, which reach state-of-the-art performances while being more lightweight.
We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 89. 9 % vs 86. 8 % on average for human scorers on DOD-H, and an F1 of 88. 3 % vs 84. 8 % on DOD-O. Our study highlights that stateof-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Considerations could be made to use automated approaches in the clinical setting.
Sleep stages are labeled by sleep technologist following the American Association of Sleep Medicine (AASM) rules  [2]. These rules set out 5 stages, based on the various waveforms observed on each signal of the PSG: wake, rapid eye movement (REM), non-REM sleep stage 1 (N1), 2 (N2) and 3 (N3). It typically takes a sleep technologist 30 minutes to an hour to perform sleep staging on a whole record, i. e. about one thousand 30-second epochs, making it time-consuming and expensive. Another important aspect of sleep staging is the relatively low interrater agreement. Indeed, by definition, the AASM rules act as guidelines but do not fully characterize the natural variability that a PSG signal can measure. Hence, a study conducted on the AASM Inter-scorer Reliability dataset shows an average inter-rater agreement of 82.