dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020) , while image-based identifications are confined by the available taxonomic expertise and image resolution. These limitations decline as genetic and image reference libraries continue to grow and computational image-based identification methods improve in the coming years. This will likely result in an increased number of identified species from ARMS. Our case study shows that ARMS are well suited for hard-bottom monitoring as they provide standardized genetic data that can be continuously enriched and re-analyzed to document benthic community composition, obtain quantitative estimates from image analysis, and detect non-indigenous species. Although the species composition measured by ARMS might not always accurately reflect the biotic composition of surrounding natural substrates due to e.
Given that ports and marinas constitute major introduction hotspots and dispersion pathways for NIS  (Ulman et al. , 2017) , the establishment of ARMS-MBON observatories in such environments is expected to enhance the timely detection of new NIS across the European regional seas. So far, at least nine observatories in the network are deploying ARMS for this purpose, while at least four observatories are part of national MSFD monitoring schemes in Sweden, Denmark, Greece, and Spain. Another potential application area is the collection of data for calculating Essential Biodiversity Variables (EBV), as suggested by  Kissling et al. (2018). EBVs emphasize standardized repeated measures of the same community in the same location at short to long time intervals required for reporting biodiversity change  (Pereira et al.
, 2013). In the future, ARMS-MBON time-series data should be tested for such applications in EBVs. Presence surveys provided by ARMS-MBON can provide occurrence data for prioritized taxa, including the AZTI Marine Biotic Index  (Borja and Muxika, 2005)  and Non-indigenous species (NIS). Finally, species traits can be derived from plate images recording the prevalence or relative composition of calcifying species or filter-feeding groups that might indicate important trends in ocean acidification or nutrient levels.
EMBRC is currently developing an "Omics Observation network" to be deployed across its 9 member states, which is expected to cover pelagic and benthic sampling. Through the network of RIs in Europe, EMBRC will also work to develop new bioinformatics tools and analysis pipelines with its partners, such as ELIXIR and LifeWatch. The ambition is to ensure integration of ARMS-MBON (the European pilot described in this paper) into global efforts, such as the Global ARMS Program (www. oceanarms. org), Genomic Observatories Network  (Davies et al. , 2014) , Global Omics Observatory Network GLOMICON  (Buttigieg et al. , 2019) , as well as regional infrastructure networks in other regions, such as those under the umbrella of the World Association of Marine Stations (IOC) or the Partnership for Observation of the Global Ocean (POGO) to expand the ARMS-MBON further in the future.
The EMBRC "Omics Observatory" aims to ensure long-term, sustainable observation of marine biodiversity, integrated in a global setting. As such, the European research infrastructure can support the ARMS-MBON by providing increased visibility of the initiative to researchers and stakeholders in Europe and beyond. It will provide a platform for training and maintaining standards in the network, as well as expanding the coverage by integrating new sites from EMBRC. Furthermore, it is likely that the coordination and sequencing costs of the ARMS-MBON sites in EMBRC can be covered by its "Omics Observatory, " ensuring data collection from these sites for the foreseeable future. Such an initiative provides an important demonstration and prototype for mainstreaming biodiversity observations at the molecular level into biodiversity monitoring programs and supports efforts led by a merger of the Genomic Observatories Network and GLOMICON (https://glomicon.
org/) to establish an "Omics BON"  (Buttigieg et al. , 2019). data management, and analysis. Funding for ARMS observatories in the North Sea Region was provided by the INTERREG project GEANS (North Sea Program of the European Regional Development Fund of the European Union) and the Swedish Agency for  Marine and Water Management (grant no. 3181-2019) , and the Flanders LifeWatch contribution (Research Foundation Flanders grant I000819N). The ARMS observatory in Roscoff also received support from the Aquanis 2. 0 project (FONDATION Total). Data management and analysis was funded by Swedish LifeWatch grant from the Swedish Research council (grant no. 2017-00634) as well as the EOSC NORDIC project (grant no. 857652). Guiding documents to obtain ABS clearance for access to genetic resources were developed in the framework of projects INTERREG EBB (EAPA_501/2016) and H2020 EOSC-Life (grant no.
ACKNOWLEDGMENTS We thank the  LifeWatch ERIC  and the  Global ARMS Program  for the helpful advice, especially  Laetitia Plaisance at the Smithsonian Institution in Washington D. C. The ECIMAT Marine Station  would like to thank  Alberto Meizoso  and  Guillermo Díaz-Agras  ( Marine Biology Station of A Graña, University of Santiago de Compostela ),  Enrique Poza ,  Roberto Gómez  and  José González  for their help on ARMS construction and deployment, and  Prof. Pedro Gomes ,  Prof. Filipe Costa  ( CBMA, University of Minho ) for help with sampling and lab processing and technical support. The  MBA  wish to thank  John Bishop  and  Christine Wood  for sample processing and technical support. HCMR thanks  Argyro Zenetos  for help confirming of NIS observations. We are grateful to the participants of  JRA5 (Scientific Diving) of the AssemblePlus program  for their support in ARMS deployment in several of our observatories.
With the major increase in the volume of the spectroscopic line lists needed to perform accurate radiative transfer calculations, disseminating accurate radiative data has become almost as much a challenge as computing it. Considering that many planetary science applications are only looking for heating rates or mid-to-low resolution spectra, any approach enabling such computations in an accurate and flexible way at a fraction of the computing and storage costs is highly valuable. For many of these reasons, the correlated-k approach has become very popular. Its major weakness has been the lack of ways to adapt the spectral grid/resolution of precomputed k-coefficients, making it difficult to distribute a generic database suited for many different applications. Currently, most users still need to have access to a line-by-line transfer code with the relevant line lists or high-resolution cross sections to compute k-coefficient tables at the desired resolution.
In this work, we demonstrate that precomputed k-coefficients can be binned to a lower spectral resolution without any additional assumptions, and show how this can be done in practice. We then show that this binning procedure does not introduce any significant loss in accuracy. Along the way, we quantify how such an approach compares very favorably with the sampled cross section approach. This opens up a new avenue to deliver accurate radiative transfer data by providing mid-resolution k-coefficient tables to users who can later tailor those tables to their needs on the fly. To help with this final step, we briefly present Exo_k, an open-access, open-source Python library designed to handle, tailor, and use many different formats of k-coefficient and cross-section tables in an easy and computationally efficient way.
To circumvent this problem we present a way to accurately bin down precomputed k-coefficients to an arbitrary spectral grid without any significant additional loss in accuracy. This method is faster than computing the k-coefficients directly from line-byline data. It therefore opens up a new way of disseminating accurate, reference molecular opacities: Spectroscopists only need to compute k-coefficients once with a resolution that is higher than the highest resolution they envision their users need (e. g. , the resolution of the instrument you want to compare your model to). Users can then easily bin down the data to their spectral grid of choice, possibly even on the fly. To help with the latter, we developed Exo_k 2 , an opensource Python 3 library that can directly load precomputed kcoefficient tables, change their spectral resolution as well as pressure-temperature grids, and save them back on disk.
Finally, the library includes methods to directly interpolate and combine the opacities, and even compute transmission and emission spectra for 1D planetary atmospheres. Leveraging on the Numba library, these operations are performed in a very computationally efficient way. So Exo_k can be imported directly into any radiative transfer code to handle molecular opacities easily and efficiently. In this article, we first briefly compare the relative accuracy and efficiency of the sampled cross section and correlated-k approaches in Sect. 2. After introducing the necessary concepts and notations, Sect. 3 demonstrates rigorously why and how to bin down precomputed k-coefficients to an arbitrary resolution. Then, in Sect. 4, we validate the numerical algorithm presented in this work and implemented in Exo_k.
(2018). Collision-induced-absorptions (CIA) are taken from the HITRAN database  (Richard et al. 2011). Then, the high-resolution cross sections are sampled at various resolutions (R sp = {1 000, 3 000, 10 000, 30 000}) and a new set of spectra are computed at each of these resolutions. The spectra obtained from the sampled cross sections are then binned (preserving area) at selected final resolutions (R fin = {10, 20, 50, 100, 200, 500, 1 000}). The transmission spectra computed in the hot-Jupiter case are shown in the left column of Fig. 2. Finally, these spectra are compared to the properly binned reference spectrum (right column of Fig. 2 ). The root mean squared (RMS) error is shown for each (R sp , R fin ) in Fig. 1. The emission spectra computed in the Mars-like case are shown in Fig.
Conclusions We have demonstrated mathematically how the k-distribution of the opacity in a large band can be obtained from the kdistributions inside each of the smaller bands that it encompasses. Then we have presented a numerical algorithm to use this property to bin down precomputed tables of k-coefficients to any arbitrary resolution. Finally, we showed, in two concrete cases, that the numerical error added by this procedure is relatively small compared to the errors due to the more general use of the correlated-k approach. To facilitate the implementation of this new method, we developed Exo_k, an open-source Python library that has been designed to handle many different sources of radiative data (including, of course, k-coefficient tables, but also cross section and collision-induced-absorption tables).
This of course makes us wonder what would be the optimum resolution and quadrature for a flexible and versatile correlated-k database. Our test show that R = 1000 k-coefficients computed on 20 gauss points, when combined with the flexibility brought by a tool like Exo_k, seem to be sufficient for most 1D-3D atmospheric model applications 5  as well as modeling synthetic spectroscopic observations of planetary atmospheres up to that resolution. This method can thus be used to model HST, Spitzer, Ariel  (Tinetti et al. 2018) , and low-resolution JWST data. For high-resolution JWST observation modes, higher resolution radiative data will be needed.
, 2018; Vörösmarty et al. , 2010 ). An understanding of many of these processes requires a spatially explicit approach, such as how pollution and chemicals are transported in riverine networks  (Helton, Hall, & Bertuzzo, 2018) , how organisms spread along rivers and invade riverine ecosystems  (Giometto, Altermatt, & Rinaldo, 2017; Mari, Casagrandi, Bertuzzo, Rinaldo, & Gatto, 2014) , or how the modification of network structures across drainage basins affects local diversity  (Leuven et al. , 2009). Consequently, there has been a rapid increase in ecological and evolutionary studies considering the effect of river-like network structures on ecological dynamics over the last two decades  (Altermatt, 2013; Campbell Grant, Lowe, & Fagan, 2007; Fagan, 2002) , paralleled by an increase in methodological tools to analyze such spatial datasets  (Duarte et al.
Despite the long-standing establishment of the OCN concept, its application especially in ecology and evolutionary biology has been lagging behind, likely because easily accessible code or appropriate tools have been lacking. This is particularly regrettable considering the recent bloom of tools allowing the statistical analysis of data from real dendritic networks (e. g. , the R-package SSN; Ver Hoef,  Peterson, Cliord, & Shah, 2014). However, such tools are specifically designed for real river networks, while their applicability to virtually generated networks is limited. To fill this gap, we here describe the methodological and mathematical frameworks that underly OCNet, an R-package for the generation and analysis of optimal channel networks, and provide guidelines and examples to facilitate the use of this tool.
| Overall setup of the package The OCNet package consists of a series of functions that allow constructing river-analogue networks as well as calculating a number of (1) H = N ∑ i = 1 A i. nodes, and lengths thereof), rivergeometry_OCN (hydraulic geometry of the river network, following  Leopold and Maddock (1953) ) require as necessary input the output list produced by the previous function, in the aforementioned order (except rivergeometry_OCN, which can be executed after aggregate_OCN). The output of these functions is a list where all objects of the input list are copied, and to which new objects are added. Note that output lists contain all input values, to avoid inconsistencies in the sequential application of functions. A group of functions (identified by the prefix "draw_", see examples in Figure  3 ) are devoted to graphical representations of the OCN.
| Aggregation levels Before moving to the illustration of some possible applications of the package, we here clarify some concepts and terminology with respect to the aggregation of OCNs. Additional details are provided in the package documentation. Networks produced by the OCN algorithm can be used in a variety of fashions (see Table  1   (2013). In this case, the OCN was used to generate a realistic elevation gradient governed by fluvial erosion, on which, for instance, the structure of terrestrial metapopulations can be studied  (Bertuzzo et al. , 2016; Giezendanner et al. , 2019). The final output of the OCNet functions is a list of lists, each of which named after the corresponding aggregation level (N4/N8, FD, RN, AG, SC, CM) and containing relevant topological and morphological information for that level.
Numbers represent the cumulative drainage area (in number of pixels). At the FD level, all 64 pixels belong to the network. To obtain the RN level, a threshold area A T = 5 pixels is applied to distinguish pixels belonging to the river network. Bottom row: the same procedure is applied to a single-outlet, 20 × 20 OCN (obtained via the script presented in Generation of an OCN). Aggregation is performed with A T = 5 pixels. Note that river width is proportional to the square root of drainage area  (Leopold & Maddock, 1953)  formulation presented in Theoretical background holds when multiple-outlet pixels are imposed. Technically, this is done by preventing these pixels to drain into their neighboring pixels. In this case, the sum of the areas drained by all outlet pixels is equal to the lattice area.
| Compatibility with existing R-packages Specific functions of OCNet enable transformation of OCNs into objects that can be used by other commonly used R-packages in spatial ecology. In particular, compatibility with igraph  (Csardi & Nepusz, 2006) , a package for network analysis and visualization, is provided by function OCN_to_igraph. Moreover, function OCN_to_ SSN transforms an OCN at a desired aggregation level into an object that can be read by SSN, a package on spatial statistical modeling and prediction for data on stream networks  (Ver Hoef et al. , 2014). Examples for these functions are shown in Figure  6. Finally, output from OCNet can be used in combination with R-packages for geostatistical modeling such as gstat  (Pebesma, 2004) , based on the coordinates of nodes of an OCN given at any aggregation level.
| CON CLUS IONS The importance of adequately representing spatial processes in ecological and evolutionary studies cannot be overstated. In the realm of freshwater ecology in particular, it is essential to consider how geomorphology shapes the structure of dendritic river networks and the ensuing connectivity configuration, which in turn control the variability of physical habitats and environmental variables, the dispersal of species and pathogens, and the spatial patterns of biodiversity and ecosystem processes. To this end, we presented OCNet, an R-package that enables the generation of optimal channel networks, spanning trees that reproduce all scaling features of real river networks throughout the globe. These can be used as realistic riverine landscape analogues for a number of ecological, epidemiological, ecohydrological and evolutionary studies.
acknowledges funding from the Swiss National Science Foundation Grants No PP00P3_179089 and 31003A_173074 and the University of Zurich Research Priority Programme "URPP Global Change and Biodiversity". This is publication ISEM-2020-127 of the Institut des Sciences de l'Evolution -Montpellier. F I G U R E 7 Results from the application of a metapopulaton model on an OCN. (a) Dynamics of local population size in three nodes: green represents the headwater that is invaded at the beginning of the simulation; red, the outlet; blue, the headwater that is farthest from the green node (see circles in the top-left corner of panel c). Colored vertical lines represent the time steps when the respective local population has reached the equilibrium (arbitrarily imposed as 99% of the population size at t = 800).
DATA AVA I L A B I L I T Y S TAT E M E N T The code of the OCNet package is accessible on both CRAN (https:// CRAN. R-proje ct. org/packa ge=OCNet) and Github (http://doi. org/10. 5281/zenodo. 3669873 -development version). A code script generating the figures of the manuscript can be found in http:// github. com/lucar raro/OCNet -ECE-Creat eFigures.
The emerging technologies of wearable wireless devices open entirely new ways to record various aspects of human social interactions in a broad range of settings. Such technologies allow to log the temporal dynamics of face-to-face interactions by detecting the physical proximity of participants. However, despite the wide usage of this technology and the collected datasets, precise reconstruction methods transforming the raw recorded communication data packets to social interactions are still missing. In this study we analyse a proximity dataset collected during a longitudinal social experiment aiming to understand the co-evolution of children's language development and social network. Physical proximity and verbal communication of hundreds of pre-school children and their teachers are recorded over three years using autonomous wearable low power wireless devices.
The dataset is accompanied with three annotated ground truth datasets, which record the time, distance, relative orientation, and interaction state of interacting children for validation purposes. We use this dataset to explore several pipelines of dynamical event reconstruction including earlier applied naïve approaches, methods based on Hidden Markov Model, or on Long Short-Term Memory models, some of them combined with supervised pre-classification of interaction packets. We find that while naïve models propose the worst reconstruction, Long Short-Term Memory models provide the most precise way to reconstruct real interactions up to ∼90% accuracy. Finally, we simulate information spreading on the reconstructed networks obtained by the different methods. Results indicate that small improvement of network reconstruction accuracy may lead to significantly different spreading dynamics, while sometimes large differences in accuracy have no obvious effects on the dynamics.
This experiment, explained in details in Sect. 2, aims to understand the language development of children in their early age, by observing the verbal and social interactions of pre-school children and their teachers over three years. Physical proximity and verbal interactions of the participants are recorded using voice enabled decentralised Low Power Wireless tags. By focusing on the proximity data, and relying on ground truth data recorded simultaneously in controlled settings, we explore several supervised reconstruction methods of the temporal social interactions. As demonstrated in Fig. 1 , first we build a binary interaction sequence from the raw data of packet exchange between the LPW badges of a pair of individuals, and then use it to reconstruct the time and duration of the mutual interactions among the participants in order to obtain a temporal network representation of the social interaction dynamics.
Experimental setting and data collection Our primarily analysed dataset was collected during a large-scale social experiment, which employed IoT-LPWD technology to track human interactions. More precisely, it involved, in each year of the project, about 170 pre-school children (between age 2 1  2 and 6 1 2 ) in 7 classes, together with about 30 teachers and assistants. During this experiment, their physical proximity and verbal interactions have been recorded using voice enabled LPWD badges. The experiment has been ongoing in a French pre-school over three years, for one week in each month during school periods (i. e. 30 weeks in total). The original goal of this study has been to analyse the relations between child socialisation and language development in pre-school, by describing the co-evolution between social network dynamics (changes in the social relationships) and the language dynamics in the networks (interindividual influences and changes in language skills).
Data collection For the purpose of our analysis, we concentrate on the proximity data recorded by LPWD badges worn by each of the participants. In our architecture, developed by SEQUANTA  [23] , each badge is associated with a unique ID and uses the IEEE 802. 15. 4 low-rate wireless standard to communicate a. Badges broadcast a 'hello' packet with 0 dBm transmission power for 384 μs in every 5 seconds. For communication, they use the carrier-sense multiple access (CSMA) protocol, thus they first listen to the dedicated channel, and only transmit packets if it is clear to avoid collision. When a badge is not transmitting, it is listening to capture incoming packets from other devices in its vicinity. Due to the CSMA protocol, a random offset can appear in the timing of the packet transmission, which can accumulate and cause considerable delay as compared to a global clock signal.
For this reason, the time of each badge is synchronised with a central device, connected to a computer, which propagates the global time to all badges. Badges which fall off synchronisation of the time stop transmitting packets. This way, each sent packet is time-stamped. Beyond the broadcasted global time signal our architecture is completely distributed as it builds up from autonomous badges, which record data locally on their own flash memory card. Every day, badges are plugged on a USB board to re-charge their batteries overnight, and at the end of each measurement period (typically after a week), we gather data from their flash memory card to a computer, and re-initialise it ahead of the next recording session. Note that there are other wireless architectures developed for similar purposes, such as OpenBeacon  [17]  or Open badges  [30] , but they typically use RFID technology with centralised communication protocols and a different philosophy for data collection.