dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Audrey Hessel  for help with sample preparation,  Bruno Franzetti  for providing the plasmid encoding the TET2 protein, and  Alicia Vallet  for recording a solution-NMR spectrum. This work was financially supported by the  Agence Nationale de la Recherche  ( ANR-10-PDOC-011-01  to P. S. and  ANR-17-CE11-0018-01  to J. -P. C. ) and the  European Research Council  ( ERC-Stg-2012-311318  to P. S. and  ERC-CoG - 2010-260887  to  J. B. , and  FP7-I3-BIO-NMR 261862 ). This work used the high-field NMR and EM facilities at the  Grenoble Instruct-ERIC Center (ISBG ;  UMS 3518 CNRS CEA-UGA-EMBL ) with support from the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05-02 ) and the  Grenoble Alliance for Integrated Structural Cell Biology (GRAL ;  ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology.
The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the  Fonds Européen de Développement Régional (FEDER) , and the  GIS Infrastrutures en Biologie Santé et Agronomie (IBiSA). Financial support from the  IR-RMN-THC Fr3050 CNRS  for conducting the research at the 1 GHz spectrometer at the  CRMN Lyon  is gratefully acknowledged. C. D. S. is supported by the  Intramural Program of the Center for Information Technology, National Institutes of Health.
Data availability The NMR assignment has been deposited in the BioMagResBank (entry 27211). The 4. 1 Å EM map has been deposited in the EMDB (entry 4179). The atomic model resulting from the three steps described herein, using in the final step the 4. 1 Å EM map, has been deposited in the Protein Data Bank (PDBid: 6F3K). Furthermore, we have used Phenix to further refine the structure, using all NMR and EM data (see Supplementary Table  6 ). This structure has been deposited in the Protein Data Bank (PDB ID: 6R8N). Other data are available from the corresponding authors upon reasonable request.
Money wise, Wooclap app is the most expensive and is very limited in its free use (only 30 students per session). Free license software Votar seems to be the most interesting if we do not want to use a complete system and ask only a few simple questions during a lesson. One of the important advantages of Votar is that its use requires very few materials. Answer sheets can be directly printed in color and used during class. Wooclap is a very complete solution that offers many varied activities, however it is necessary to use these activities sparingly. Indeed, we observe that after a while, fewer students answer the question as the class time progresses. To complete, additional studies are needed to establish its efficacy after several month, during exam for example to complete the work of Stoddard published in 2010  [9].
MiRNAs play pivotal roles in regulating monocyte development and functions, including differentiation, tissue recruitment, activation, initiation, and resolution of inflammation  (19) ; however, very little is known about their involvement when considering the heterogeneity of monocytes. An attempt to close this gap in knowledge has been addressed by miRNome analyses of monocyte subsets. Nevertheless, few miRNome analyses of monocyte subsets have been performed with human or mouse samples. To our knowledge and from free-access databases, we identified only three studies  (20) (21) (22) ; two focused on miRNAs differently expressed between classical and non-classical monocytes, in humans and mice, without considering "intermediate" monocytes  (20, 21). After showing differences in DNA methylation in the three human monocyte subsets-classical, non-classical, and intermediate (  23 )-Zawada et al.
Analysis of the miRNome of Classical and Non-classical Monocyte Subsets Briefly, we collected miRNome datasets (GSE52986 and GSE32370) from the GEO bank (http://www. ncbi. nlm. nih. gov/ geo/). For each GEO dataset, we compared miRNA expression profiles for classical and non-classical monocyte subsets (i. e. , CD14 ++ CD16 -and CD14 + CD16 ++ for human blood samples, Ly6C high and Ly6C low for mouse blood samples) to obtain a list of miRNAs differentially expressed between the subsets in both species. The technical platforms used in these two studies were Illumina Human v2 and Mouse v1 MicroRNA expression beadchips, respectively. Also, we performed largescale miRNA screening using a TaqMan low-density array to identify miRNAs differentially expressed between classical and non-classical monocyte subsets in human and mouse.
With false discovery rate-adjusted P ≤ 0. 05, we found 25 miRNAs differentially expressed between classical and non-classical monocytes. We then used a Venn diagram to visualize common miRNAs between all four datasets (http://www. irp. nia. nih. gov/ bioinformatics/vennplex. html). Only miR-146a was commonly downregulated in classical monocytes, for all available human and mouse datasets, independent of the technological platform used (Figure  1A ). At the intersection of human datasets, we identified nine miRNAs (miR-132, miR-106a, miR-19b, miR-18b, miR-20b, miR-146a, miR-342-3p, miR17, and miR18a): miR-132, miR146a, and miR-342-3p showed lower expression in classical than non-classical monocytes. At the intersection of mouse datasets, we identified 4 miRNAs (miR-146a, miR-130b, miR-150, and miR-148a); miR-148a and miR-130b were upregulated in Ly6C high versus Ly6C low monocytes.
Zawada et al. identified a fourth panel, including miR-150, with downregulated miRNAs in intermediate monocytes as compared with both classical and non-classical monocytes (p < 10 -10 , and > 10-fold difference in expression). Our Venn diagram analysis identified miR-150 as the only miRNA with differential expression between classical and non-classical monocyte datasets in mouse but not human datasets (Figure  1A ), which agrees with the Zawada et al. miRNome data (Panel 4). Using OmicsNet, a web-based tool for the creation and visual analysis of biological networks  (35) , we uploaded the list of nine monocyte subset-specific miRNAs identified in our human analysis together with the list of 182 genes found overexpressed in classical or non-classical monocytes identified in five independent transcriptomic microarray datasets  (36).
We aimed to create and merge different types of biological networks that could provide a clue to the pathways involved in the functional heterogeneity of monocyte subsets. Figure  4A  shows the 3D OmicsNet biological networks highlighting connections between the nine miRNAs and putatively targeted genes according to the TarBase software [Table  2 ;  (37) ]. Gene Ontology analysis with the Reactome pathway database  (38)  showed enrichment of biological process categories such as signal transduction, small GTPases of the Rho family (Rho GTPases), p75 NTR receptormediated and Sema4D in semaphorin signaling (Figure  4B  and Table  3 ). The trafficking of monocytes into tissues requires the activation of integrins via signal transduction induced by Rho GTPases such as RHOA or RAP1, which results in cell adhesion to the blood-vessel wall  (39).
FUNDING This work was supported by  INSERM (Institut National de la Santé et Recherche Médicale) , the  University of Montpellier , the  Arthritis Fondation Courtin , the  European Union  (grant no. 115142 ), the  French National Research Agency  ( ANR-16-CE14-0030-02 ), and the  E-Rare -3  Joint Transnational Research Project on Rare Diseases INSAID that is a joint initiative between EC and ANR  ( ANR-15-RAR3-0013-05 ).
, 2012). However, the water around the still-moving slide includes highly turbulent threedimensional effects that cannot be represented correctly in a Boussinesq model. To remove the residual flow (that is not expected to generate significant waves) near the slide, we apply an ad hoc filter, as determined by numerical experimentation. It consisted in multiplying the output of THETIS (i. e. , free surface elevation and each velocity component) by a spatially varying function, removing the interior flow while keeping a smooth initial condition for FUNWAVE. This function is Gaussian, with a standard deviation of 15 km and the center is located at coordinates (10 km, 10 km). For more details, including validation of this approach, see  Abadie et al. (2012). After this filter is applied, local Boussinesq wave modeling is conducted on a 500-m resolution bathymetric grid taken from the Global Multi Resolution Topography (GMRT) database  (Ryan et al.
The code is fully parallelized using the Message Passing Interface (MPI) protocol and efficient algorithms allowing a substantial acceleration of the computations with the number of cores. For operational uses, FUNWAVE-TVD has received many convenient implementations, such as the use of nested grids to refine the simulations in the interest areas, or the use of heterogeneous Manning coefficients to characterize bottom friction, the Manning coefficient is a constant 0. 025 s•m -1/3. In the framework of the U. S. NTHMP program, FUNWAVE-TVD has been validated for both tsunami propagation and coastal impact, through an important set of analytical, laboratory and field benchmarks  (Tehranirad et al. , 2011). Other recent applications have allowed the validation of the model on real cases, such as the Tohoku-Oki tsunami  (Grilli et al.
Run-up assessment An accurate assessment of the actual impact of the tsunami requires refined computations on nested refined grids including local friction coefficients and an accurate knowledge of the bathymetry and the topography. In the present study, this extensive work was performed in Guadeloupe and partly in selected areas of the French Atlantic Coast (Britanny, Gironde and Saint-Jean-de-Luz) with a lower level of complexity. Regarding the island of Guadeloupe, simulations were carried out with SCHISM with a refined grid of the nearshore area of about 10 m resolution along the shoreline and using variable friction coefficients dependent on the soil nature. For the submerged area, 10 classes of Manning values were used while 50 classes have been used for the aerial domain based on Corine Land Cover dataset  (Büttner et al.
Acknowledgements. This work has been performed in the framework of the  PIA RSNR French program  TANDEM (Grant no:  ANR-11-RSNR-00023-01 ) and as a part of the project  C3AF , funded by the  ERDF  and the Region Guadeloupe. Part of this work was supported by the  LRC Yves Rocard (Laboratoire de Recherche Conventionné CEA-ENS ).
Bibliographic/metric tools and their users Contemporary bibliometric tools are characterized by a double division, in terms of their design and use. These divisions reflect the ambiguity of bibliometric proposals from the outset, between the desire to predict the directions that science has taken and would take, tracing what would be "important" in different disciplines and strong practical limits on the information actually available  (Wouters 1999). On the database design side, there is a strong opposition between two models: on the one hand, the Web of Science and Scopus offer lists of specific outlets, for the vast majority journals, supposedly prestigious or important, but voluntarily limited; on the other hand, Google Scholar, which operates in an open electronic world containing books, reports, preprints, articles, presentations, but totally opaque about its limits even if its technical inclusion rules 1 are public.
In fact, the very recent vote of the European copyright directive by the European Parliament would lead to a wider and easier road for open citations. A mandatory exception to copyright for TDM is included in the current text, meaning that bibliographic data could soon be scrapped, stored and manipulated independently of its intellectual property protection, especially database protection. It order to grasp the potential effect of such a law, we can take the example of the very recent paper of the OCC team (Heidi, Peroni and Shutton 2019b), which has created the COCI RDF data with 445 million DOI-to-DOI citation links, based on 43 million Crossref documents. Based on a linear distribution of references, that means around 500 million links are still hidden from the public because of proprietary policies.
Strategies to make an open bibliographic/metric tool In the short term future, bibliographic data will thus become a public good or a common, at least from within the borders of the European Union. But what should we do with Open Citations? Until now, and especially within OCI and OCC frameworks, the answer seems to be: to build neat and clean databases that are available for API developers and other expert users. In this paper and in our current development, we give a different and complementary one, that is building a tool precisely not designed for scientometricians and experts in semantic web or scholarly communication, but for ordinary researchers. As we have seen, they do perform at least the three first kind of tasks (browser, search engine, mirror) with their current tools, so we have begun the construction of a bibliographic/metric tool, intended for lay users looking for documents and using basic citation-tracking, following a dual development strategy.
On the one hand, we have started by gathering theoretically available corpus, would it be in dumps or through dedicated APIs. That includes REPEC, PubMed, ArXiv and Crossref, all for metadata and some for full texts when available. For PubMed and Crossref, we have focused on its more recent part, in order to maximise the quality of metadata and the presence of references in the dedicated field at this stage of development. In fact, as it has been noted before, the Crossref data is highly variable in quality  (Van Eyck et al. 2018 ), as it does not depend on a centralized production and checking process, but on the good will and expertise of each publisher member of Crossref. Data curation is an ongoing long process, especially on the references field, as a lot of them are unstructured, don't include identifiers (DOIs or other ones) or contain incorrect ones.
Compared to the already mentioned COCI corpus, we intend to systematically reallocate DOIs to references missing them, through the use of GROBID developed at INRIA  (Riondet and Foppiano 2017)  and BILBO (developed by OpenEdition 8 and adapted to variable forms of SEO). When this process succeeds, we harvest the metadata of these documents and if an open version of the document exists-directly available or found through Unpaywall 9 it is harvested and indexed. All the results produced are then enriched by a similar APIs to the one already developed in ISIDORE (for example, the language of the document, the disciplinary field, etc. ) On the other hand, we create a user interface that can be configured by users based on the APIs and data already produced via the ISIDORE search engine  (Dumouchel 2018 ).
It will include full-text search, contrary to legacy databases (Dimensions recently introduced that for commercial tools), and author pages. Its entire design aims at interoperability with existing tools-for example Unpaywall-or tools under development, which share the same open science vision, in order to place Matilda in the existing ecology. Above and beyond technical developments, which are essential for a tool, we would like in the final part of this communication to insist on two principles, which are for us quintessential to an open science tool.
All texts should be born equal The first principle of an open bibliometrics tool should be inclusivity, at the exact opposite of the foundation of previous generations bibliometrics tools. Since the Journal Impact Factor has become successful in some disciplines, the database on which it is calculated, owned by the Institute for Scientific Information (ISI), then Thomson Reuters, have acted as a triage for scientific quality of outputs in some communities, and by a rippling effect, on evaluation and careers (McKiernan e al. 2019). Conversely, journals and articles that were excluded have become invisible in some parts of academia. As an example, in the 1980s, an international movement tried to have a better representation of journals published in the Global South in ISI indexes, with no success (Moravscik 1985), as scarcity was at the heart of its constitution.
Thirty years later, even if the marketisation of Scopus has led to a less selective regime, the biases towards certain disciplines, english-language sources and US-produced science are still powerful  (Mongeon and Paul-Hus 2016) , and the divide between STS on one side, Humanities and Social Sciences on the other remains abyssal  (Archambault et al. 2006) , especially if non-journals outputs are considered. In 1993, Margaret Rossiter published an article entitled The Matthew Matilda Effect in Science, in which she discusses the systematic reduction of women's contributions to science. This reduction is the symmetrical and corollary of the Matthieu effect described by  Merton (1968) , which leads to the accumulation of scientific credit by a small number of researchers, usually men. She concluded by naming this effect by the first name of a 19th century American feminist figure, suffragette but also the first one to have identified this phenomenon of invisibilisation of women in science.
We have chosen this same name for our project, observing a similar invisibilisation process of a large number of scientific productions, because they are written in a language other than English, because they come from publishers with limited economic weight, because they are supposedly nonscientific or, at least, uncertified types of production. Matilda's promise is to remedy this situation by treating all available or identifiable texts and their metadata with equal dignity. The inclusive movement has already started in the tools made by newcomers: for example, Dimensions has included some pre-prints servers in its sources (BiorXiv, SSRN… ), as they attribute a DOI and thus are available through Crossref in its data. Some scientometricians 10 have started to consider Crossref data a basic source for their work, rather than WoS or Scopus.
Inclusion is the first condition of openness, but it should not only concern texts, but also users as true part of the tool. The beauty should be in the eyes of the users Bibliometric databases have no theory, they are purely empirical data, cleaned and rawified  (Denis and Goëta 2017). This view is commonly shared and certainly amongst most designers and users of evaluative bibliometrics. Though they have often searched for a theory of citation, scientometricians have failed in according on one (see for example  Cronin 1981; Leydesdorff 1998). The diversity of citation usage, though a very challenging research and practical question, only becomes a problem when adding citations to count them, compare texts or journals on this basis, would give an objectified view while meshing in fact very different communication and social processes  (Erikson and Erlandson 2014).
Matilda is built, in a sense, for non-counting but reading users, which would actually explore the meaning of these links between texts, if they are interested. More generally, while the database of source has to be as inclusive as possible, it is by no way objective for our users, because we wish to offer them the most possibilities to personalize their usage. More precisely, as Daston and Galison would have said it (2007), they don't search for mechanical objectivity, but a tool to help them to perform their "trained judgment" on what matters to them. It is remarkable that the literature on recommendation algorithms has almost been absent of the bibliographic/metric topic, while it has been blooming on cultural products  (Shay & Pinch 2006) , coming mainly from computer science, management science and sociology.
Redifining bibliometrics? 23 Until now, the Open Citations movement has been dominated by visions of building information systems for users making APIs or advanced code manipulators, with a strong connection to the tradition of bibliometrics. Matilda, though resting on the same kind of information, will enable search, recommendation and as often as possible reading, directly available instead of a giving access to a database. Matilda is therefore part of a twofold heritage of conception and usage with, on the one hand, Google Scholar, which has popularized free access to citation tracking, outside the circuit of libraries and publisher sites, to metadata of scientific content, intervention of users as enrichers and curators. On the other hand, it inherits from ISIDORE, Base and other search engines based on OAI-MPH harvesting, with full-text search, blindness to prestige or selection process.
The objective is therefore to complete/replace these various uses, as well as those of commercial databases (WoS/Scopus), which are widely used in certain disciplines. 24 By doing so, we wish to redefine bibliometrics tools as a technology. From the start, it has been ambiguous between description and evaluation, showing the past and predicting the future, aimed at funders, ordinary researchers or those studying the science of science, mapping its evolutions. It has become popularized in the 21st century through the web and its hyperlink core, the circulation of some in-fashion indexes well outside scientometrics circles (from Journal Impact Factor to h-index) and the large use of tools as narcissistic machines or search engines. It is time that these last uses, which will deeply benefit from inclusivity, become the basis for a bibliographic/metric tool, rather than diverting ones made for other purposes.
On a multidisciplinary basis, it is the outcome of the "open citations" movement: originally, in 2010, it was a reference data corpus, the Open Citation Corpus(Pironi et al. 2015), before these remarkable precursors were joined by various organizations demanding the release of Crossref citation data to publishers. The I4OC 3 collective has consequently obtained the availability, under a CC0 license, of a large part of the CrossRef database, which has led to the use of this data in a number of tools, including the VOS Viewer 4 developed by Leiden University. Beyond their common interest in "freeing" metadata and citation data, there is no shared agenda: they hope that other actors would take them and build services on this new shared resource. Like the OCC database, they often presuppose professional users, either experts in API manipulation or interested in very advanced bibliometric developments.
For example, the very intriguing proposition of crowdsourced open citations (Heidi, Peroni and Shutton 2019a) designs an incredibly expert user, able and willing to code DOIs and lines into a specific format in order to fill a shared database up. Though an elegant proposal, it would need the very participation of a large part of the global academic community in order to "free" their authored data, and almost all of them won't neither have the skills nor the time to do it.
Yet, those metadata are critical to ensure reproducibility of the measurements, and ultimately of the scientific findings  (White, Zeil, & Kemp, 2015). In this article, we present lightr, an R package that aims at offering a unified user-friendly interface for users to read reflectance, transmittance, and absorbance spectra files from various formats in a single line of code. Additionally, it provides for the first time a fully free and open source solution to read proprietary spectra file formats on all operating systems. lightr started as a fork from the popular R package pavo, which provides a large suite of colour analysis tools  (Maia, Eliason, Bitton, Doucet, & Shawkey, 2013; Maia, Gruson, Endler, & White, 2019).