dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that the redundant computations involved in the modified program are not implemented with a mere additional let-in such as The results are given in Table  3  for vm compute and native compute. The tested operations are addition and multiplication, as they constitute the vast majority of the arithmetic computations performed during a Cholesky decomposition. It is worth noting that these speedups should be taken as coarse orders of magnitude rather than precise measurements. Indeed, the time difference "Op×N -Op" also includes the cost of the duplication machinery itself, i. e. , some extra function calls such as select. As a consequence, the speedups are presumably largely underestimated, as this extra cost is far from negligible in the case of hardware floating-point arithmetic ("Op×1001-Op").
In particular, hardware floating-point numbers bring such a large speedup over emulated ones that using native compute is often detrimental, except for a handful of examples, as shown in Fig. 4. Finally, Fig. 5  shows that the 10× to 20× speedup from hardware floatingpoint numbers observed in Fig. 1  also holds when using native compute instead of vm compute, but only for the longest examples. For shorter examples, the native OCaml compilation dominates the timings, so any speedup brought by hardware floating-point numbers goes unnoticed. In addition to the figures described above, Appendix A provides the raw data for all the timings involved.
While the approach is similar on the implementation side, there is a large difference on the specification side. Indeed, while both integer and floating-point computations are axiomatized using their operational semantics, floating-point arithmetic is so peculiar that one should not blindly believe that the semantics expressed in the logic of Coq matches the behavior of the floating-point hardware. To restore the trust in the formal system, this operational semantics has been proved equivalent to that of the Flocq library, which had already been proved to comply with the IEEE-754 standard  [7, 12]. But as usual with any software implementation, a few bugs were introduced along the way. Those are now fixed and, barring such implementation bugs, the approach is theoretically sound and does not allow any incorrect proof.
Acknowledgements We are grateful to  Guillaume Bertholon  for his early work on extending the kernel of Coq with hardware floating-point numbers. This work was partly supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme -Grant  Agreement n •  101001995. This work was partly supported by the  NuSCAP  project ( ANR-20-CE48-0014 ) of the  French national research agency (ANR).
Stimuli and apparatus Stimuli were squares, size 0. 9 × 0. 9 (Experiment 1) or 0. 7 × 0. 7 degrees of visual angle (Experiments 2-4). In Experiment 1, the squares were grey, presented on the white background. In Experiments 2 and 4, the squares were black, and the background was mid-grey. In Experiment 3, the background was mid-grey, and the luminance of the squares varied in four steps across trials. In Experiment 1, stimuli were presented on a Dell CRT screen, resolution 1280 × 1208 pixels and 60 Hz. The experiment was run in Matlab 2008. In Experiments 2-4, stimuli were presented on a Trinitron Sony CRT, resolution 1280 × 1208, and Matlab 2011 was used for programming and displaying stimuli.
Data analysis We analysed data with generalised linear mixed-effect models, implemented in the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015)  for R Studio environment. This method of analysis has the ability to model between subjects variability and treat unbalanced designs  (Bates et al. , 2015). Here we describe a general structure of the models, and details concerning specific experiments are given in the results section of the experiment. Responses of observers were the dependent variable (binary responses), and predictors were the duration of the contact and square layout (filled or contour-only, Experiment 1) and speed of stimulus movement (Experiment 4). In Experiment 3 there were several analyses, described in detail in the results section. Observers were treated as a random factor, to account for the additional variability.
To test models, we used ANOVA function implemented in R, which tests the significance of a fixed effect, by comparing the goodnessof-fit between the full model (including that fixed effect) and the model that does not include that variable as the fixed effect, by means of a Chisquare test. In order to obtain an estimate of how well the model explains the data, we used marginal R 2 as outlined in  Nakagawa and Schielzeth (2013). In Experiment 2, we used the quickpsy package for R to fit psychometric functions to the data  (Linares & López-Moliner, 2016).
2009; Bennett & Sijacki 2020). Among the structures for which the connection to the cosmic web has been investigated in the most detail is the Coma cluster of galaxies. The Coma cluster has been studied for several decades  (Hubble & Humason 1931; Biviano 1998) , with extensive data sets in all wavelengths, from the X-rays  (Briel et al. 1992; Neumann et al. 2001 Neumann et al. , 2003) )  to the Sunyaev-Zel'dovich signal (Planck Collaboration Int. X 2013), radio  (Brown & Rudnick 2011), and optical (den Brok et al. 2011; Adami et al. 2005). The connections of the Coma cluster to other structures of the surrounding cosmic web are already evident from the galaxy distribution and the position of the surrounding clusters (e. g. , A1367,  West 1998; Mahajan et al. 2018). In  Malavasi et al. (2020b)  we explored the connections of the Coma cluster to the filaments of the cosmic web.
Data and simulations In this section we describe our constrained cosmological simulation, which we use to reproduce the Coma cluster and analyze the physical properties of its surrounding LSS, and the observational data with which we compare our simulation. As this work heavily relies on what previously done in  Malavasi et al. (2020a,b) , our observational data essentially amount to what has been used in those works. We recapitulate and summarize the essential information in Sect. 2. 1, where we also introduce further data sets we used for specific purposes in this analysis.
(2002)  for further details. For the rest of this paper we refer to this selection of objects as the Legacy MGS. In this work, we have enhanced this data set with a measurement of the galaxy masses and K-band magnitudes. In the following, we mainly make use of K-band apparent magnitudes, which we compare with the same quantity available for the halos of our numerical simulation. We use this comparison to inform our selection of a galaxy sample from our halo population. As for galaxy masses, they are derived as shown in Appendix A and used only as a further confirmation of our simulated galaxy selection from our halo population and not to derive physical conclusions. K-band apparent magnitudes for SDSS galaxies were obtained from the New York University Value-Added Galaxy Catalog (NYU-VAGC,  Blanton et al.
Acknowledgements. The authors would like to praise and thank the anonymous referee, whose on-point and in-depth comments helped greatly improve the quality of the paper. Part of this research has been supported by the funding for the  ByoPiC  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  grant agreement  ERC-2015-AdG 695561. K. D. acknowledges support by the  COMPLEX  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement  ERC-2019-AdG 882679  as well as support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy - EXC-2094-390783311. J. S. , K. D. , and N. A. acknowledge support by the grant agreements  ANR-21-CE31-0019/490702358  from the  French Agence Nationale de la Recherche/DFG  for the  LOCALIZATION  project.
The CosmoSim database used in this paper is a service by the  Leibniz-Institute for Astrophysics Potsdam (AIP). The MultiDark database was developed in cooperation with the  Spanish MultiDark Consolider  Project  CSD2009-00064. The authors gratefully acknowledge the  Gauss Centre  for Supercomputing e. V. (www. gauss-centre. eu) and the  Partnership for Advanced Supercomputing in Europe  (PRACE, www. prace-ri. eu) for funding the  MultiDark  simulation project by providing computing time on the  GCS Supercomputer SuperMUC at Leibniz Supercomputing Centre (LRZ , www. lrz. de). The Bolshoi simulations have been performed within the Bolshoi project of the  University of California High-Performance AstroComputing Center (UC-HiPACC)  and were run at the  NASA Ames Research Center.
, 2006; Antão et al. , 2020). Biodiversity data are therefore an important tool for monitoring ecosystem health  (Rodrıǵuez-Ezpeleta et al. , 2021). Significant initiatives that organise and improve access to biodiversity data and information are the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility  (GBIF). OBIS provides access to data and information for more than 120,000 marine species and supports numerous projects and initiatives studying biodiversity 1. In addition to hosting biodiversity data and information, GBIF works on standards, best practices and tools that support global biodiversity research 2. The demand for generation of concrete, welldescribed and reusable biodiversity data increases the need for marine biodiversity observations at a European level, especially from established, long-term, monitoring programmes  (Balvanera et al.
, 2022). Spatially extensive and long-term time series will allow the understanding of how various human pressures affect marine communities and their biodiversity  (Boero et al. , 2015; Hughes et al. , 2017). The physico-chemical parameters and the pressures to the marine environment are being measured effectively and data are broadly available, however, marine biodiversity observation remains uncoordinated, immature, and scattered across various initiatives  (Canonico et al. , 2019; Claudet et al. , 2020; Satterthwaite et al. , 2021). This weakness in measuring biodiversity as a minimum to access the biological communities makes it impossible to deduce and understand causalities. To move towards a sustainable and environmentally conscious society, it is urgent that we rapidly develop and organise extensive and integrated biodiversity observation research to complement the sophistication and scope of the physico-chemical observation networks.
All EMO BON observatory stations are operating under the same well-described and publicly available strategies and workflows. This way, the delivered samples, datasets, and information are standardised and comparable, and provide highlevel insights into regional biodiversity. Modules, protocols, and guidelines are included in the EMO BON Handbook which is published and openly shared on the EMBRC website 3  , and discoverable on the Ocean Best Practices System (OBPS) repository  (Santi et al. , 2021). The Handbook of EMO BON contains the genomic sampling Standard Operating Procedures (SOPs) and the network's operational guidelines. The protocols are re-evaluated following feedback from the observatory stations and minor adjustments may be made to ensure that the dynamics of each station are appropriately accommodated.
The widely used nuclear 18S rRNA and mitochondrial COI markers were selected for metabarcoding biodiversity assessment to capture the eukaryotic diversity within both genomes (nucleic and mitochondrial). For all samples collected, the generated raw sequences undergo a basic quality control step, and both the raw and the qualitycontrolled data are uploaded to the European Nucleotide Archive (ENA)  (Cummins et al. , 2022)  and released to the public at regular intervals. The EMO BON DMP has been developed to ensure a unified approach to data management across EMO BON and harmonisation of dataset organisation including sample labelling, data publication, workflows, quality controls, storage, and data release  (Santi et al. , 2022). The first EMO BON sampling Flow chart overview of EMO BON (European Marine Omics Biodiversity Observation Network) strategy from sample acquisition to actions targeting data (re)usability.
e. , refer to particular activities within this project that cannot be described with standardised terms, as for example date a sample was shipped for analyses. The full metadata list is available in the EMO BON DMP and the templates used can be found in the EMBRC website  7. Data and metadata are submitted to suitable public databases at frequent time intervals and become openly available. Sequencing raw and quality-controlled data are submitted to ENA and are attached with ENA accession numbers as digital identifiers. The collected sampling and analyses metadata, the associated environmental (meta)data, and the ENA accession numbers indicating the sequencing raw data are submitted to the European node of the international Ocean Biodiversity Information System (EurOBIS) and from there passed on to European Marine Observation and Data Network (EMODNet).
EMO BON (meta) data entries destined for EurOBIS are provided in the standardised Darwin Core Archive (DwC-A) format. The data and metadata are assigned a Creative Commons licence with attribution (CC BY) with their release. A dataset publication follows shortly after the data and metadata release to describe the data, the metadata, and their online location. These measures not only make the outputs from EMO BON FAIR and machine-and human-operable and understandable, but also speed up the process from sampling to data publishing. EMO BON is developing a data management system to be used throughout the project that handles the regular creation of omics (meta)data in a structured way. The system use Research Object (RO) Crates  (Soiland-Reyes et al. , 2022)  reflecting the structure of EMO BON, and is designed to function on existing data platforms.
EMO BON is endorsed as a UN Ocean Decade project associated with the programme OBON and synergizes with other regional or thematic biomolecular observing activities. Therefore, EMO BON complements OBON and together aim to decipher coastal biodiversity and contribute to the building of a coastal ocean biomolecular observing system. To participate in global observation missions, any regional or local observatory must align its and operations to global scale frameworks and agree to accept data and metadata standards to ensure comparability and interoperability. EMO BON unites individual biological observation stations in Europe and provides the context and opportunity to participate in an advanced highly coordinated biodiversity observation framework. This network aims to become a paradigmatic observatory with its activities and procedures being able to be transposed to other regions and aspires to grow national observation capacities by engaging with national stakeholders and policy makers.
This paradigm can accordingly contribute to expanding marine observation efforts and to overcoming the existing geographical, social, and economic biases in FAIR biodiversity data production  (Ramıŕez et al. , 2022). The geographic expansion of marine genomic observation is necessary to fill the existing gaps in the sampling effort in Europe, as for example in the Eastern Mediterranean and Eastern Baltic Sea. EMO BON becomes part of the existing marine biological observation system and aspires to set the basis for the regular and standardised production of genomic data. EMO BON's openly shared protocols and guidelines contribute to building the capacity for marine biological observation beyond this network. The discoverable and FAIR observation data produced can promote the full integration of omics data into OBIS and GBIF, therefore expanding their marine omics component.
This would promote the reusability of the data and relevant information and add the biological component in environmental policy making in a systematic and standardized way. Outstanding examples of similar observation initiatives are the National Ecological Observatory Network (NEON 11 ) and Australia's Integrated Marine Observing System (IMOS 12 ). Both initiatives are considerably contributing to their related fields and are proving the value biological observation networks can provide. NEON is covering freshwater and terrestrial ecosystems in the US and produces ecological data for over 20 years; their data have fuelled different research areas such as litter patterns in forests  (Jevon et al. , 2022)  and plant biodiversity research  (Liang et al. , 2022). The focus of IMOS is the monitoring of marine coastal communities by employing different methodologies such as animal tracking and genomics; one of their focus areas is food security wherein marine data are directly integrated to decision-making regarding for instance highly productive coastal zones.
EMO BON is motivated by such well-established initiatives and aspires to increase its direct impact in a similar way. Through the large-scale, and long-term context of biodiversity observation, EMO BON will improve our understanding of the oceans, the impact of climate change and anthropogenic stressors, and provide the necessary resources to make informed decisions about sustainable actions for the European waters. It will facilitate the European Union's Missions, such as Healthy Oceans, Seas, Coastal, and Inland Waters 13 , Adaptation to Climate Change 14 and Mission Starfish 2030: Restore our Ocean and Waters 15. Moreover, environmental genomic data can be integrated with data generated through experimental research linking genomes to phenotypes under different environmental conditions.
The role of omics in marine biological observation Omics is an umbrella term for the wide range of highthroughput approaches used to analyse biomolecules; it includes, but is not limited to, genomics, transcriptomics, proteomics, and metabolomics (see also Supplementary Material 3 Text Box eDNA). Environmental omics approaches, where DNA is sampled from an environmental medium, provide a means of capturing the most representative amount of biodiversity information, and the data needed to implement reliable services to monitor ocean life  (Pawlowski et al. , 2018; Bush et al. , 2019). This observation network applies biomolecular omics methodologies, such as metagenomics and metabarcoding, to produce maximal information on community biodiversity and to provide means to access the pool of genes present in an ecosystem.
The EMO BON omics data will reveal the biodiversity of different communities from microorganisms to Metazoa, their temporal trends and shifts, the ecological relationships among species, and their responses to environmental changes. This information can be subsequently translated to the function and role of taxa in the environment and used to other applied research fields. Omics data can support the conservation of ecosystems and the sustainable use of marine resources (for example  Bowser et al. , 2020; Turunen et al. , 2021) , as well as the monitoring of wild animal populations such as fish  (Andruszkiewicz et al. , 2017)  or mammals  (Suarez-Bregua et al. , 2022). In addition, omics data can be integrated into ecosystem models to develop innovative investigations in food security (for example  Grützke et al.
, 2013; Muller-Karger et al. , 2018). EBVs were defined under the scope of a comprehensive description of biodiversity and can serve as a list of variables to be considered in monitoring programmes  (Pereira et al. , 2013; Balvanera et al. , 2022). GOs, such as EMO BON, can have multiple and pivotal roles in sustaining and evolving the use and value of EOVs and EBVs. Omics data are necessary for defining the emerging EOVs for microorganisms and can be used to complement the existing EOVs for non-microbial organisms (for example, EOV Zooplankton biomass and diversity)  (Miloslavich et al. , 2018). The systematic generation of omics data by GOs can serve as the data collection used to calibrate and operationalize EOVs and EBVs by attaining omics related variables from a variety of standardised datasets  (Kissling et al.
, 2018; Jetz et al. , 2019). The temporal resolution of GOs data is necessary for defining the functional biodiversity that can rapidly change with time, and for identifying the critical time intervals during which the dynamically changing biodiversity can be safely accessed, i. e. , within which time span can biodiversity of a given community be described. EMO BON produces omics biodiversity and functional data for numerous taxa simultaneously; this can similarly serve as the data collection to define the aggregation level useful for EOVs and EBVs, i. e. , which functional (e. g. , predatory-plankton, grazer meiobenthos) or taxonomic levels (e. g. microorganisms including bacteria, archaea, microbial eukaryotes) can be grouped and considered as one variable. EMO BON measures physical and biochemical EOVs as a common metric to connect biodiversity to its environment.
GOs such as EMO BON can promote the collection of EOVs and other EBVs together with omics data and therefore help make biological observation interoperable and comparable to a higher level while adding multidisciplinarity into the system. This would allow a parallel assessment of diverse essential and standardised values and thus contribute to predicting ecosystem changes and responses. This structured approach to EOVs and EBVs measurement can help escaping Data-Rich-Information-Poor (DRIP) datasets and move towards hypothesis-driven data collection. Omics technologies can apply to all the Bio-Eco EOVs and several EBVs, however, specialised methods need to be developed and agreed upon to measure them. The desirable high-level coordination of EMO BON and other GOs can contribute to, on one hand, testing methodologies, and, on the other hand, applying the suggested best practices across a broad spatial and temporal scale.
Data management needs to be initiated at the beginning of any project, and it is necessary to decide on the procedures for data and metadata collection, storing, handling, and sharing early on. EMO BON gave emphasis on FAIR data management from its preparatory phase, recognized the experts in the EMBRC network and worked directly with them and allocated resources to support the data management. Furthermore, the connection to the global level is significant here as it is important to adopt global standards and use public databases to ensure the interoperability and reusability of the data.
Sciences Centre (UmU), University of Bergen (UiB), University of Gothenburg (UGOT), Vigo University Marine Research Centre This work is also supported by the France Genomique National infrastructure, funded as part of the «Investissements d'Avenir» program managed by the Agence Nationale pour la Recherche (contract ANR-10-INBS-09). The development of metagenomic computational workflow (metaGOflow) received funding from programme EOSC Life (EU Horizon 2020 research and innovation programme under grant agreement No 824087). Prerequisite work on the ARMS sampling campaigns was supported by the infrastructure programs ASSEMBLE Plus (grant no. 730984). RC would like to recognise the support of programmes TechOceanS (Horizon 2020 research and innovation programme under Grant Agreement No 101000858) and EuroSea (Horizon 2020 research and innovation programme under grant agreement No 862626).