dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to acknowledge the help with the sampling of all the technical staff at  Teagasc Research Centre, Athenry, Ireland. In Norway , the personnel working at the Section for Small ruminant research and herd health,  NMBU -Faculty of Veterinary Medicine, Sandnes, Norway. The authors would also thank  Gunn Charlotte Østby  at the  NMBU  for her work on the immunohistochemistry preparation.
Funding The project was funded through the  European Research Area Network, on Sustainable Animal Production (SusAN ; Grant no. 16/RD/SusAn/ERA-NET ). National funding was provided in Ireland by the  Department of Agriculture, Food  and the  Marine as well as Teagasc  ( RMIS 0299 ) and in Norway by  The Research Council of Norway  ( NFR 272338 / E50 ).
Data and Methods CTD observations from across the Arctic are used in this study, from four different sources: the ice-tethered profiler (ITP) program  (Krishfield et al. , 2008; Toole et al. , 2011 , http://www. whoi. edu/itp) and the Beaufort Gyre Exploration Project (BGEP, https://www. whoi. edu/beaufortgyre)-both based at the Woods Hole Oceanographic Institution, with the latter in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences-the Nansen and Amundsen Basins Observational System (NABOS, https://uaf-iarc. org/nabos), and data from the NOAA World Ocean Database (WOD, https://www. ncei. noaa. gov/products/world-ocean-database; Boyer et al. , 2018). The WOD collates oceanic observational data from a wide range of sources. The WOD data used here are those from CTD profiles, drifting buoys, and ocean stations.
Any ITP, BGEP, or NABOS data were removed from the WOD data set before use to avoid duplication. The BGEP and NABOS data sets include data from both moorings and ship surveys. Throughout the paper, salinity is given in practical salinity units, and potential temperature (when not available directly from the observational data product), heat content, and potential density are computed using the thermodynamic equation of Seawater 2010 (TEOS-10)  (IOC et al. , 2010). All data used in this study are processed versions of the raw data gathered in the field. Details of these procedures can be found in the sources referenced above, but all involved calibration, sensor-correction, and the removal or flagging of obviously erroneous data. In addition to this initial processing, further routines were applied to the data and profiles were smoothed for much of our analysis-details of which are given below.
, 2003)  and is the focus of much of this manuscript. The AW core in a given profile is defined as the point at which the maximum potential temperature is reached in the portion of the profile with salinity greater than 34. 7 (in order to avoid surface temperature maxima), following  Lique and Steele (2012). Figure  1  shows the distribution in time and space of profiles where the AW core was identified, with mooring locations shown as black squares. To ensure the AW core identified in each profile was not an artifact of limited sampling, profiles were required to start above 100 m and cover a depth range of at least 500 m before being used to identify the AW core (this also eliminated data from the surrounding shelf seas, allowing the study to focus on AW within the Arctic basin only). This latter step resulted in about 44,000 AW core data points.
Before identifying the core, profiles were smoothed over a vertical distance of 80 m by taking the mean of the profile data within 40 m of each data point. This length scale was chosen as it was the best at preserving the general shape and magnitude of the temperature profile, while removing the spikes due to features such as thermohaline intrusions and eddies (although please note that this smoothing was not applied to the profiles in Figure  3 ). This is important as the main aim of this study is to get a general picture of patterns and long-term trends in AW core properties, and features such as intrusions can disproportionately affect the depth of the AW temperature maximum in basin interiors in such a way as to detract from this. Any profiles used in the analysis should be assumed to be smoothed unless stated otherwise.
The depth coverage varies between data sources-ITP profiles cover the upper 800 m of the water column, whereas many data from CTD stations and moorings extend down to around 2,000 m. This variation in depth range does not affect the analysis in this study given that the AW core can be identified in both cases, and any profiles that do not sample the whole AW layer are omitted from the AW heat content analysis in Sections 5 and 6. Although ITPs and moorings provide year-round measurements, there remains a spring/summer bias in data from other sources. However, this is unlikely to impact results due to the negligible seasonality of AW when compared to its overall variability in space and time  (Lique & Steele, 2012). Heat content, HC, was computed for various portions of the vertical temperature profiles according to 𝐻𝐻𝐻𝐻 = ∫ 𝑏𝑏 𝑎𝑎 𝜌𝜌𝜃𝜃(𝑧𝑧)𝑐𝑐𝑝𝑝𝑇𝑇 (𝑧𝑧) 𝑑𝑑𝑧𝑧 (1) where ρ θ (in kg m -3 ) is the potential density, c p (in Jk g -1 °C -1 ) is the specific heat, T (in °C) is the potential temperature, z (in m) is the depth, and a and b are the depth bounds defining the layer in question.
, 2004; Luneva et al. , 2020; Melling & Moore, 1995) , suggesting that interaction with upper AW is feasible. Due to limitations in our data set, we are unable to quantify the relative importance of these two potential mechanisms through which the properties of the AW core are modified. Although observational evidence of interaction between shelf flows and AW is limited in the western Arctic, it is likely that both heat loss from the top of the AW layer and interaction with shelf flows contribute to the AW core becoming cooler and denser along the AW advection pathway. The AW core depth exhibits a bimodal structure, as shown in Figure  2a , being much deeper in the Canada Basin (approximately 500 m) than the Eurasian Basin (approximately 300 m) due to the Ekman pumping associated with the winds that drive the Beaufort Gyre.
In Figure  14e , AW core salinity is compared to AW core potential temperature. In the EEB, this reflects what is seen in the integrated AW layer (Figure  14d ). In the west, however, a stronger (moderate) correlation exists between the AW core data than that between the AW layer data. This could be related to the thermohaline intrusions which spread the warm AW core anomaly into the Canada Basin during the study period  (McLaughlin et al. , 2009). However, it also highlights once again the freshening of the AW core, relative to the AW layer, as it is advected around the Arctic.
Acknowledgments This study was funded by the  Natural Environment Research Council (NERC)  via a  DTP studentship  under grant number  NE/L002612/1. HLJ is grateful for funding from the  NERC   ArctiCONNECT  project (grant number  NE/V005855/1 ). We thank the two anonymous reviewers and the associate editor for their useful insights and constructive comments.
Data Availability Statement Profile data used in this study are available from http://www. whoi. edu/itp (for ITP data;  Krishfield et al. , 2008; Toole et al. , 2011) , https://www. whoi. edu/beaufortgyre (for BGEP data), https://uaf-iarc. org/nabos (for NABOS data), and https://www. ncei. noaa. gov/products/world-ocean-database (for WOD data;  Boyer et al. , 2018). The Atlantic Water core data computed for this study are available as an Open Access data set at the Oxford University Research Archive via https://doi. org/10. 5287/bodleian:wxv8GA7Mk. Note that this data set includes all mooring data rather than monthly mean mooring data.
While producing the actual distribution maps is relatively easy, collecting the data into a structured and harmonized database and curating the records is more time-consuming, especially when original data are recorded in different formats. Further, the database requires continuous updates as the distribution status of a species in an administration unit can alter between absent, introduced and established over time due to ecological change or trade, a phenomenon well-known for Ae. albopictus. Over the years, over 150,000 vector location records have been acquired and curated, and regular updates continue to add 20,000 records each year. All records are collated and validated by VectorNet core team members and are subjected to a three-stage error checking process before being added to the archive.
The maps provide geographical information on presence, absence, or unknown status, summarized for relatively large subnational units so that the maps can be easily visualized. The underlying database contains more detailed information that can be made available on request, which can be used for more complex mapping of, for example, vector abundance, or technical analysis such as spatial modeling. The VectorNet maps are validated by VEN members and local entomologists, which helps to assure quality and reliability. The multinational nature of the datasets also helps to engender a sense of joint ownership and promote the synergistic value of data sharing between countries and regions. In a sense, this is perhaps the most important impact of this initiative in that it helps to produce outputs that are supported by the professional community at a continental level.
VECTORNET BEYOND MAPPING Besides the collection of spatial and temporal data on vector distributions, VectorNet provides scientific support on entomological topics to EFSA and ECDC for their joint as well as separate activities related to VBDs, in the form of technical reports (systematic), literature reviews, fact sheets, modeling and advice on risk assessments  (8) (9) (10) (11) (12) (13). VectorNet also builds One Health capacity in public health experts, animal health experts and entomologists responsible for national surveillance and control through scientific and technical webinars and face-to-face training sessions (available on ECDC Virtual Academy, https:// eva. ecdc. europa. eu).
Sterl et al. (2020)  found that the impact of barotropic tidal currents on microplastic surface transport and accumulation was very small. Tidal currents have also been considered using the SMOC dataset  (Drillet et al. , 2019)  to understand the trajectories of Sargassum in the Tropical Atlantic by  van Sebille et al. (2021). Moreover, the Lagrangian transport due to internal tides has been studied by  Sutherland and Yassin (2022) , but they found it to be negligible when averaged over an inertial period. Technological advances in the past years have allowed recent OGCMs to include tidal forcing, improving the representation of internal waves and tide fields  (Le Sommer et al. , 2018). Tidally forced OGCMs allow not only to consider tidal currents, but also the impact tides have on the flow structures, like eddies, filaments, or fronts.
Moreover, this area has been found to be important in terms of marine pollution with a high exposure to marine floating debris, which reaches the islands through filaments and eddies generated from the Gulf Stream  (Sala et al. , 2016; Pham et al. , 2020; Cardoso & Caldeira, 2021). The objective of this paper is to investigate the effect of tidal forcing on the surface dispersion properties. In Section 2 we describe the dataset used and the methods to evaluate the data. Section 3 describes the results obtained and in Section 4 we discuss them and suggest future studies. 2 Data and methods With a spatial resolution of 1/60 • and a model time-step of 40 seconds, the simulation is submesocale permitting  (Verron et al. , 2020). For the scope of this study, we use the hourly model outputs, and though the model is 3D, we only use the 2D surface velocity fields.
Gaussian Kernel Density Estimation (GKDE) One of the methods used to calculate the density of particles is a 2-dimensional horizontal Gaussian Kernel Density Estimation (GKDE). This is a non-parametric (appli-cable to a non-Gaussian distribution) estimate of the probability density function of a field. The GKDE is applied monthly on all particles in the whole domain (black box in fig. 1 ) by calculating it on the particle distribution of day 28. We use the python SciPy stats gaussian kde algorithm with a default kernel size (bandwidth), which is calculated following Scott's Rule  (Scott, 1992). This bandwidth selector calculates the optimal bandwidth value that avoids both over-smoothing and under-smoothing.
Open Research Details on the model data used in this study, eNATL60, and its availability can be found here https://github. com/ocean-next/eNATL60 (Brodeau, Le Sommer, & Albert, 2020). The codes used to generate the Lagrangian simulations and analyse them can be found here: https://github. com/OceanParcels/Azores TidalForcing. surface mixing activity in the Western Mediterranean influence bluefin tuna larval habitats. Marine Ecology Progress Series, 685 , 69-84. doi: 10. 3354/meps13979 d'Asaro, E. A. , Shcherbina, A. Y. , Klymak, J. M. , Molemaker, J. , Novelli, G. , -28-manuscript submitted to JGR: Oceans.
The extended North Atlantic simulation at 1/60 • (eNATL60) eNATL60 is an extended version of the NATL60 run (see Ajayi et al. (2020); https:// github. com/meom-configurations/NATL60-CJM165) in which a larger spatial domain is covered. The model's domain includes the North Atlantic basin : from 6 • N to 66 • N and from 80 • W to 9. 5 • E, together with the whole Mediterranean and Black Seas and the Gulf of Mexico (fig. 1).
Acknowledgments LGN and EvS were partly funded by the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation programme  (grant agreement No  715386 ) and the  ESA   World Ocean Circulation project ,  ESA  Contract No. 4000130730/20/INB. EvS was also partly supported through the  IMMERSE  project from the  European Union Horizon 2020 Research and Innovation Program  (grant agreement no. 821926 ). VMM was supported by the  MORHOC ' H2  project, funded by the  French "Agence Nationale de la Recherche " and " Direction Générale de l'Armement ", grant number  21-ASM1-0003. 0025-326X ) doi: 10. 1016/j. marpolbul. 2011. 04. 016McWilliams,  J. C. , Gula ,  J. ,  & Molemaker ,  M. J. ( 2019 ). The gulf stream north wall: Ageostrophic circulation and frontogenesis.
2. 1 1 The extended North Atlantic simulation at 1/60 • (eNATL60) eNATL60 is an extended version of the NATL60 run (see Ajayi et al. (2020); https:// github. com/meom-configurations/NATL60-CJM165) in which a larger spatial domain is covered. The model's domain includes the North Atlantic basin : from 6 • N to 66 • N and from 80 • W to 9. 5 • E, together with the whole Mediterranean and Black Seas and the Gulf of Mexico (fig. 1).
Phase 2: Data Collection and Definition of the Population Under Study We administered the DVL tool to participants from an open online cohort (CONFINS)  [27]. All participants were aged more than 18 years, living in France, and were able to read and understand French. CONFINS is a cohort collecting data on the impact of confinement on the health and well-being of the French population  [28]. It included, among others, variables on opinions about vaccination and the DVL items. It also comprised sociodemographic information (age, gender, having children, being vaccinated against influenza) used in this study. Items were defined by a group of public health experts through several rounds of corrections and refinement. CONFINS consisted in a baseline questionnaire and repeated monthly follow-up questionnaires.
Participants could decide whether to be contacted or not for the following phases of the survey. This study used data from the baseline questionnaire and the first follow-up questionnaire, covering the period from April to May 2020. This was a convenience sample. CONFINS participants were recruited on a voluntary basis with no incentives through different communication channels. Posts were published on the social media (LinkedIn, Twitter, Facebook) of the University of Bordeaux and the partner contract research organization hosting the database. A total of 3 press releases were addressed to journalists. The coprinciple investigators were interviewed to promote the study. Three newsletters and weekly emails and SMS text messages were sent to the participants to remind them to complete the follow-up questionnaires.
Acknowledgments We wish to thank all members of the CONFINS group including the i-Share,  Kappa Santé , and Kap Code team members: we especially acknowledge  Garance Perret  and  Mathilde Pouriel  for data analysis;  Julie Arsandaux ,  Shérazade Kinouani , and  Mélissa Macalli  for paper writing;  Raphaël Germain  and  Clothilde Pollet  for regulatory affairs; and  Vanessa Marie-Joseph ,  Adel Mebarki ,  Elena Milesi , and  Marie Mougin  for the study communication. Kevin Ouazzani Touhami  is also gratefully acknowledged. The authors are also grateful to all the participants who volunteered to take part in the study. The i-Share team is currently supported by an unrestricted grant of the  Nouvelle-Aquitaine Regional Council (Conseil Régional Nouvelle-Aquitaine , grant  N°4370420 ). It has also received grants from the  Nouvelle-Aquitaine Regional Health Agency (Agence Régionale de Santé Nouvelle-Aquitaine , grant  N°6066R-8 ),  Public Health France (Santé Publique France , grant  N°19DPPP023-0 ), and  The National Institute against cancer INCa  (grant  N°INCa_11502 ).
| Open science policy 0] [151] [152] [153] [154]  The policy of sharing of data, often preliminary models (beta-versions), has helped in popularizing Martini, and provided valuable feedback from the community to the main developers. The Martini web portal (http://cgmartini. nl/)  149 plays a central role in this respect, with currently >6000 registered users. A Martini twitter account (@CG_Martini) was established in 2020 to further expand the communication options. Currently, we are in the process of establishing a Martini database (MAD, https://mad. ibcp. fr/),  155 which will serve the need for a well-documented repository of both validated and developmental Martini topologies across the molecular sciences. Due to its popularity, Martini has been implemented not only in its traditional host Gromacs,  156  but also in other software packages including NAMD,  157  LAMMPS,  158  ddcMD,  159  openMM,  160  CEMD,  161, 162  Materials Studio,  163  and Genesis.
Appropriate force constants and additional bonded terms such as angles or dihedrals should be calibrated with respect to structural data obtained from either reference atomistic simulations or a structural databank. Note that the bimodality of the distributions of bonds, angles, and asymmetric dihedrals may not be well-captured by Martini given limitations of potentials implemented in Gromacs (or other MD codes), although cosine-based functions such as the Ryckaert-Bellemans potential could be used in certain cases. Section C3 of the supplementary information of the Martini 3 paper provides more details. 83 4) Improving numerical stability and performance: Two main factors affect the numerical stability of Martini simulations: (i) the number of bond constraints and the use of virtual particles to manage this number; and (ii) the definition of proper dihedrals and collinear 3-body angles.
| High-throughput workflows The ability to perform high-throughput (HT) simulations is key to the rational design process of new materials, improved enzymes, or novel drugs. 373 In addition, HT simulations are important to generate fundamental understanding of a system's behavior by systematically probing many different state conditions. Furthermore, HT workflows can be used to create databases that are subsequently mined by bio-or chem-informatics tools. The inherent speed of simulations with CG resolution, in combination with the ease of generating CG topologies and starting structures, has prompted the use of Martini in a variety of HT pipelines, some of which are illustrated in Figure  8  and further discussed as follows. An early example of such a HT pipeline is from the folding@home project of the Pande group, utilizing the power of a large number of home computers to simulate lipid membrane fusion.
122 The generated ensemble, based on 10,000 short MD trajectories, was combined with MSM to provide the underlying energy landscape of the fusion process. A more recent example in this area is from Hub and coworkers. 377 In their study, they addressed the propensity of membranes to fuse from a computational lipidomics perspective. In particular, the CG resolution enabled computation of $200 free energies of stalk formation in membranes with different lipid headgroups, tail lengths, tail unsaturations, and sterol content. The results from this dataset were subsequently used to explain the finding that the inner leaflet of a typical plasma membrane is far more fusogenic than the outer leaflet. The group of Stansfeld has used HT Martini-based simulations to maintain a database of membrane proteins and associated webserver (http://memprotmd.
bioch. ox. ac. uk/). 374, 378 To fill the database, membrane protein structures are automatically identified in the PDB on a weekly basis and converted to Martini representation (Figure  8b ). This is followed by a 100 ns MD simulation in the presence of lipids and water to allow a lipid bilayer to self-assemble around the protein. The simulation is continued for another 900 ns simulation to study the dynamics of the assembled membrane, and subsequently converted to atomistic resolution and analyzed. Currently, the database contains almost 6000 simulations of intrinsic membrane proteins. The database can be explored in many ways, for example, to see how a specific protein is embedded in the membrane, or run statistics on a subset of proteins to explore protein-lipid contacts, for instance.
In a recent application, cardiolipin binding sites of a large set of 42 different Escherichia coli inner membrane proteins were analyzed and compared. 379 More than 700 specific binding sites were detected, leading to the identification of the molecular basis of a prototypical cardiolipin binding site. A related database that has just been released, SuPepMem,  380  aims to collect simulations of host defense peptides (HDPs) interacting with lipid membranes, at different resolutions including Martini. A first set of 403 systems resulting from the combination of 52 HDPs and six different membranes models, simulated using the Martini 2. 2 force field for 5 μs each, is currently available. Further examples of HT work in this area are studies of the Tieleman group comparing lipid fingerprints of 10 generic plasma membrane proteins  192  as well as of 20 different GPCRs,  221  and work from the Stansfeld group reporting affinities for cholesterol binding to a range of membrane proteins based on the computation of binding saturation curves, designed to mimic experimental protocols.
427 n important additional development is the construction of MAD, the Martini database,  155  allowing deposit and retrieval of all Martini models developed so far, with detailed version tracking, through a simple graphical interface. The MAD sever will also integrate and provide a graphical interface for the most commonly used Martini tools, such as martinize, insane, and polyply, therefore promoting adoption of the force field by new users. Finally, new and accurate automatic topology builders, properly including all the steps of Martini 3 parametrization protocol, should be of the paramount importance for high-throughput applications and for overall progress of the model. Finally, we would like to stress the importance of the continuous development and support of other CG models. As mentioned in the Introduction, different philosophies to CGing exist, each with their own merits and shortcomings.