dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality: Only FAIR-compliant datasets will be used in this SP. However, because the nature of the project is primarily multidisciplinary and cross-domain, the only way to guarantee that the results deriving by the different disciplines/domains are comparable is to FAIR-ify the web services used, too. This way, the SP ensures that both the quality and process potential in the Author-formatted document posted on 16/05/2023. DOI: https://doi. org/10. 3897/arphapreprints. e106475 different disciplines and domains are comparable and therefore so are their results. For this to happen, the Consortium will engage existing frameworks, such as RO-Crate, developed by ELIXIR. Relevance: The SP contributes to the estimation of the impacts of the invasive species on the European Biodiversity and Ecosystems, which is directly linked to the mission of climate change but also to the new Biodiversity Strategy and therefore the European Green Deal.
Implementation, Plan of work Two approaches: (a) Combined metabarcoding with species occurrences data coming from EMBRC (ASSEMBLE Plus) and environmental data from multiple resources (e. g. multiple sources: COPERNICUS, ICOS, EMSO, EuroARGO, etc. ). These data don't go much back in time and so only statistical models can be applied; (b) Species occurrences from OBIS + environmental data. Because of the vast quantities of data, we may be able to apply mechanistic models (of SD) -if not, statistical models will take over. This approach will also be combined with an exploration of data available by ENA/BOLD (metabarcoding) and an attempt will be made in order to compare patterns (and inferred processes, where possible) from both approaches. Establish access to marine biotic and abiotic datasets from relevant data infrastructures, such as EurOBIS, ELIXIR-ENA, SeaDataNet, CMEMS (DIAS), EurOBIS, ICOS, EMBRC ERIC, EMSO ERIC, GBIF ENA and others, benefiting from ongoing efforts in the ENVRI FAIR H2020 cluster project for FAIR improvements, and in the Blue-Cloud pilot H2020 project for a federated Blue-Cloud discovery and access service, and integrating with the appropriate EOSC core technical layers and web services.
Acknowledgements This Science Project is part of the  EOSC Future Project , WP6. 3, co-funded by the  EU Horizon Programme  call  INFRAEOSC-03-2020 -GA 101017536. The  Science Project  has been largely supported by a collaboration between the core projects of  LifeWatch ERIC  and  EMBRC ERIC. Endnotes  Dr. Arvanitidis ,  Prof Basset ,  Dr. Obst ,  Dr. Pavloudi ,  Dr. Pade ,  Dr. Kotoulas ,  Dr. Santi  and  Dr. Huertas Olivares , have worked on the concept, design of the SP and the development of the article. Dr. González-Aranda ,  Prof. López ,  Dr. Fiore ,  Mr. Minadakis ,  Dr. Schaap ,  Dr. Exter ,  Dr. Portier  and  Dr. Vaira , have worked on the technical development of the SP and on the drafting of the technical part of the article. Dr. Carval ,  Dr. Giorgetti ,  Dr. Hebden ,  Dr. Meyer  and  Dr.
The COVID-19 (Corona Virus Disease 19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is causing enormous difficulties in the world's economies and there is uncertainty as to whether the current prophylactic measures will offer adequate protection globally after the appearance of virus variants that like that indicated as Omicron emerged in the presence of global vaccine-based immunization. While several studies are available describing the main differences in the spike protein of Omicron compared to the other variants previously emerged, there was no structural insights into the 3CL-protease (3CL pro ) associated to the new variant. Herein, we performed a computational study based on genomic data and amino acid sequences available in the most updated COVID-19-related databases that allowed us to build up in silico the 3D structure of Omicron 3CL pro.
While several studies are available in the scientific literature describing the main differences in the spike protein of VOC Omicron compared to the previously-detected SARS-CoV-2 variants  [25, 27]  , there is no structural insights into the 3CL-protease (3CL pro ) associated to the new variant. Aiming at opening the way to new anti-COVID-19 drug development, in this work we performed a computational study starting from genomic data and crystal structure available in the most updated COVID-19-related databases for the VOC, that allowed us to build up in silico the 3D structure of the Omicron 3CL pro. Moreover, by molecular dynamics simulation we investigated the potential inhibitory potential of the currently available drug Nirmatrelvir, that acts as an inhibitor of the SARS-CoV-2 main protease  [28]  , to estimate its effectiveness in the case of Omicron variant after the several amino acid mutations revealed.
Sequence retrieval and structure modelling of M pro Omicron VOC The GISAID database EpiCoV TM searched for the NSP5 sequences of SARS-CoV-2 Omicron variant of concern (VOC)  [29]. The mutations reported in the non-structural protein (NSP5) which encodes main protease enzyme (M pro ) vital for viral replication were mined with the following Accession IDs in supply table1. The mutations were modelled in a crystal structure PDB ID: 7VH8 complex with its inhibitor Nirmatrelvir (oral antiviral developed by Pfizer). In this study, the crystal structure of the SARS-CoV-2 main-protease enzyme (PDB ID: 7VH8) was obtained with a resolution of 1. 59Å from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB-PDB) (https://www. rcsb. org/)  [30, 31]. Four separate 3D structures of the main protease were used to incorporate mutations, including variant1 (P132H, Y161T, M162I, H163W, H164N, M165Y, E166Q, F185L, V186L) reported from the USA and variant2, variant3-4 (P132H, RI88I, Q189G, A191S, A194D, G195F), (P132H, R188G, Q189R), and (P132H, G195N) reported from Italy, respectively.
WHO Label Pango Lineage GISAID clade Mutations of (3CL-protease) A 52-Year-old male patient in the USA has reported the highest number of amino acid mutations (P132H, ins159VT, Y161T, M162I, H163W, H164N, M165Y, E166Q, F185L, V186L) in the 3CL-protease Omicron sequences. Meanwhile, the second to fourth highest number of mutations have been found in 8-Year-old female, 81-Year-old female, and 77-Year-old female from Italy respectively with mutations such as (P132H, R188I, Q189G, T190stop, A191S, A194D, G195F), (P132H, R188G, Q189R), and (P132H, G195N) as shown in table-S1. Alpha B. To better understand the impact of critical mutations in the 3CL-protease of Omicron VOC, we utilized Insilco techniques to model the mutations into four 3CL-protease variants in the crystal structure PDB ID: 7VH8 (3CL-protease-Nirmatrelvir complex).
Introduction Intensive research efforts are currently underway to identify novel carbon allotropes with the goal of achieving specific mechanical and thermal properties approaching those of diamond. For this purpose, modern materials research programs based on evolutionary crystallography are used to predict novel allotropes  [1]. Regarding the carbon database, SACADA  [2]  is recognized as a reliable one available to all researchers. The novel allotropes are subjected to topology analysis using TopCryst software  [3]  and then published in the Cambridge Structural Database (CCDC). Despite the help provided by AI in generating carbon stoichiometries and structures, the role of the physicist and chemist remains essential, especially in the crystal chemistry that underlies structural engineering.
Computation framework The computational methodology was developed in previous works  [8, 9]. Essentially, the search for the ground state structures with minimal energies was systematically performed using unconstrained geometry optimizations within the DFT-based Vienna Ab initio Simulation Package (VASP)  [9, 10]  using the projector augmented wave (PAW) method for potentials  [10]. The DFT exchange-correlation (XC) effects were treated using a generalized gradient approximation (GGA)  [11]. The plane wave energy cut-off was 500 eV. The study of the mechanical properties was based on the calculations of the elastic properties, which were determined by performing finite distortions of the lattice and deriving the elastic constants from the strain-stress relationship. The calculated elastic constants C ij are then used to obtain the bulk (B) and the shear (G) moduli with the Voigt averaging method  [12]  based on uniform strain.
In addition to the mechanical properties, the dynamic stability was determined from the phonons, which are described as vibrational quanta. They are represented by phonon band structures obtained using the interface code "Phonopy" based on the Python language  [13]. The representations of the structures and charge density projections were obtained with the graphical program VESTA  [14]. For the evaluation of the electronic properties the band structures were obtained with the all-electrons augmented spherical wave method ASW  [15].
C3(4i) at z = ½ forming the central square (green spheres) are connected to C2 to form two edge-sharing tetrahedra (Table  1 ). As the original occurrence, the structure then consists of both corner and edge sharing tetrahedra. Note that the tetrahedra are significantly distorted, as can be seen from the angles. Starting from the square configuration of C 9 with C3(4i) at x,x,½ transformed to C1(4h) at x,x,0 (brown spheres), the structure was completed with C2(8i) (white spheres) and removal of the corner atoms, then subjected to full geometry optimization. The crystal data are given in Table  1. The structure of C 12 shown in Fig. 1c  now consists only of edge-sharing C4 tetrahedra. The CIFs (crystal information files) of two structures were submitted to topology studies performed with the TopCryst program  [3].
While C 4 used as template is designated with dia topology, C 9 was assigned with "4,4,4T6902-HZ" topology, and C 12 was identified with "sqc5532" topology. We note here that there are three allotropes with sqc topology in the SACADA database: sqc1427 (space group No. 125), No. sqc3051 (space group No. 114), and sqc6952 (space group No. 133)  [2]. The first two contain sp 2 carbon, while the third one shows only tetrahedral C(sp 3 ) with corner sharing tetrahedra. Structures with carbon squares are documented in the SCACADA database as crb topology with bct space groups (cf. No. 60-64  [2] ). The novel C 9 and C 12 , not cataloged in databases, were subsequently deposited, curated, and then deposited in CCSD: C 9 and C 12  [17]. From the densities, the diamond-like C 4 structure is found to have the highest density, i.
Empirical models, Mazhnik-Oganov (MO)  [22]  and Chen-Niu (CN)  [23] , are based on elastic properties, namely bulk and shear moduli. Fracture toughness (K Ic ) was evaluated using the Mazhnik-Oganov model  [22]. The results are summarized in Tables  3  and 4. Table  3  shows the crystal parameters, density and Vickers hardness calculated from the thermodynamic model. For template C 4 , all values are close to those of diamond and lonsdaleite, as expected. The hardness of tetragonal C 9 and C 12 is lower, but in both cases it exceeds 80 GPa, which allows us to assign these allotropes to the family of ultrahard phases  [28]. We also calculated hardness of two sqc allotropes from the SACADA database, sqc3051 and sqc6952. The hardness values were found to be 51 and 67 GPa, respectively, which is a correlation with the density.
Primary biodiversity data related to occurrences -which reports a named organism observed or collected in a given time and place-can be then organized in datasets and published in supplementary materials, and then may become available in data repositories such as DRYAD  [12] , Zenodo  [13] , among others. In some cases, these datasets are available in standardized and fit-for-use formats following the FAIR principles (findability, accessibility, interoperability and reusability)  [14, 15]. For example, the Darwin Core standards offers a stable framework for compiling biodiversity data from variable sources, with structures terms that facilitate data access and reuse, which can be then aggregated into portals, platforms, and facilities  [11, 16, 17]. Among these aggregated platforms, the Global Biodiversity Information Facility (GBIF) was formed in 2001 as an intergovernmental initiative, following the recommendation of the Working Group on Biological Informatics of the Mega-Science Forum of the Organization for Economic Cooperation and Development (OECD)  [18] , with the purpose of promoting the development of infrastructures for diverse, high quality and integrated biodiversity data access.
Methods We generated two lists of scientific studies related to human health that reuse biodiversity data, separated into those with GBIF-mediated data (positive list) and those that used other data sources (negative list). The positive studies were obtained from the scientific literature database tracked and maintained by the GBIF Secretariat since 2015 (details in Appendix A). After exclusion filtering (duplicates, out of the scope), the final positive list was generated by selecting those specifically related to human infectious diseases. The negative list was generated by searching in the Dimensions database (www. dimensions. ai, August 2021), using a keyword string based on terms obtained from the positive list (including 'zoonoses', 'bat borne disease', 'rodent borne'; Appendix A). Negative list was generated by randomly selecting studies from these results mirroring the positive list size.
We excluded studies with pathogens only related to humans, those not reusing data from other sources, and studies with data without GBIF scope (e. g. , only with captive domestic animals). We did not consider pathogen variables based on serology testing, as the presence of antibodies may not necessarily represent pathogens' occurrence. Analyses were developed at study-and variable-levels (Fig. 1 ). The studies were grouped according to GBIF usage in three categories: (a) only using GBIF-mediated data; (b) studies using GBIF together with other data sources; and (c) studies not using GBIF (from the negative list). As targets for analyses, studies explored single species or taxon or, alternatively, explored multi-species groups, and therefore could be disaggregated in different analytical entities or variables (variable-level).
For example, one study may explore mosquitoes and rodents, representing two different variables, which could have different analytical approaches, data sources, and represent different epidemiological levels, i. e. , hosts (e. g. , rodents), vectors (e. g. , mosquitoes), or pathogens (e. g. , viruses) (a. k. a. disease compartment  [26] ). We only included variables related to biodiversity, and occurrence data of pathogens and vector species recorded in domestic animals but not the occurrence of domestic animals themselves. We first developed a bibliometric analysis using the Biblioshine platform (R Bibliometrix package)  [27] , including parameters such as journals, authors' affiliations, among others. To characterize and compare topics and research areas, we used three approaches, starting with the Bibliometrix theme analysis, which combines performance analysis and science mapping, and identifies conceptual subdomains and thematic structure based on the co-occurrence of key terms  [27].
The two groups presented remarkable differences in the epidemiological levels of the variables explored (Fig. 1 , Fig. 4 , Fig. 5 ). Overall, a 40. 5% of the variables were related to pathogen species (145; 40. 5%), with 58 of them (40. 0%) obtained from the positive studies; however, GBIF was seldomly used as the data source (only in 3 variables), and in none of these used as an exclusive source (i. e. , GBIF was used with other sources). We identified 115 variables related to vectors (32. 1%), 68 of them (59. 1%) found in positive studies. GBIF was the data source in 52 (85. 2%), being particularly relevant for variables related to Aedes spp. and Culicidae mosquitoes. Finally, 90 variables (25. 1%) were related to host/reservoirs, mostly obtained from the positive studies (67; 74. 4%), with GBIF-mediated data used in 58 of them (86.
Overall, 108 (74. 5%) of the 145 pathogen variables were recorded in humans, 52 (35. 9%) in non-human vertebrates, and 19 in vectors (13. 1%). Most of the non-human vertebrate records (33; 70. 2%) were obtained from wild species and 22 (46. 8%) from domestic animals (e. g. , cattle, dogs, pigs). In some variables pathogens were recorded in more than one species (e. g. , from humans and rodents), therefore, total percentages do not sum 100. We identified 172 additional sources used 517 times (Fig. 5 , Fig. 1. General framework of analyses at study-and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies).
In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
Discussion Our results give evidence of the growing contribution of biodiversity data for emerging infectious diseases research, which are frequently zoonotic and transmitted by vectors, influenced by the ecology and interactions of multiple species  [1, 3, 4, 21, [28] [29] [30]. Our results address this complexity, while most of the studies explored (especially those using  GBIF-mediated data) include the analyses of multiple variables, species, and epidemiological levels. Here, we characterize usage trends of biodiversity data for infectious diseases research and identify other data sources used for similar proposes. Our findings reveal usage-gaps and current limitations, summarized in Appendix F together with potential actions to be taken.
Data sources uses and gaps: In general, biological and biodiversity repositories (including GBIF) were relevant for facilitating data related to hosts/reservoirs and vector species (Fig. 4 , Fig. 5 ), with GBIF frequently used as the data source for mosquitoes and mammals such as bats and rodents. Mosquitoes are considered the most lethal species for humans, responsible for at least 700,000 deaths per year  [31] , and bats and rodents are well-known hosts and reservoirs of several zoonotic pathogens  [32]. Therefore, GBIF contributes with aggregated and standardized data to the understanding of these species' distribution and ecology, which may give important clues about disease potential distribution and risk. This contribution is observed independently of the distribution (proportion) of species' occurrences in the GBIF database.
In positive studies (i. e. , those using GBIF), GBIF presented an unpaired contribution depending on the epidemiological level. Even though the similar proportion of variables for the three epidemiological levels in positive studies (33. 4%, 33. 2%, 28. 7% for vectors, hosts/reservoirs and pathogens, respectively), GBIF had a remarkable lower use for pathogens (2. 5%; Fig. 4 , Fig. 5 ). In fact, GBIF was not used as the only source in any study exploring multiple variables, revealing a selective use in which even though authors were familiar with GBIF, they tended to exclude it for some variables, especially when related to pathogens. However, this unpaired use was not exclusive of GBIF. Most of the data sources were used for specific species groups related to only one or two epidemiological levels, i.
e. , not used to obtain data related to pathogens, hosts/reservoir, and vector species. However, GBIF and scientific literature represented the only data source that, exceptionally, were used to obtain data of species related to the three epidemiological levels. Therefore, even with its limited use for pathogens, GBIF represented the only individual data source with a wider usage. The selective use of data sources according to the species and their epidemiological levels may be influenced by the scope of the data provided. For example, biodiversity data repositories tend to exhibit taxonomic biases, in general towards social preferences and charismatic species (e. g. , birds, mammals)  [33, 34]. GBIF, for example, presents a Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen.
In the right panel: positive studies (i. e. , those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus.
(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) median of 317 occurrences per bird species, in contrast to only three for arachnids  [33]. In addition, biodiversity data tend to be biased towards free-living organisms, as opposed to mutualistic species, parasites and pathogens  [35, 36]. In fact, besides a few exceptions (e. g. , Biological Abstracts, GenBank), most of the additional biodiversity/ biological data sources were seldomly used to obtain pathogen data (Fig. 5, Appendix  D ). This bias contrasts with the following definition of biodiversity: 'the sum of all plants, animals, fungi, and microorganisms on Earth, their genotypic and phenotypic variation, and the communities and ecosystems of which they are a part'  [37].
For example, GBIF offers occurrence data of only three viruses known to infect humans (Zika, West Nile, and Hepatitis B; GBIF. org, February 2022). Nevertheless, despite this limited availability of virus data, we found that GBIF supported 49 studies related to viral diseases (Fig. 3 ), in which viral occurrences were not always required. In fact, near half of the positive studies and one-third of the negatives did not include any pathogen species data, revealing that disease research, modelling and risk assessments may be developed using vector or host species' occurrences. For example, the major threat suddenly imposed by SARS-CoV-2 motivated the access to ecological data concerning potential reservoirs of coronavirus and others with zoonotic potential, not necessarily requiring virus occurrences  [20, 32, 38].