dataesr/scientific-paragraphs-categorization huggingface dataset:
When considered appropriate for particular purposes (e. g. regional studies), the EIVs for these aggregate taxa can easily be re-calculated by individual users from the EIVs we provide for subordinate taxa. Furthermore, we only applied the aggregation when the source EIV system did not provide EIVs for the higher level itself. Instead of aiming at an unachievable "perfect" taxonomic backbone, we developed decent and well-documented solution. With only 0. 03% "unresolved" combinations of original taxon name × EIV systems, our rate is almost surely lower than that of pure taxonomic databases, such as Euro+Med (2022) or  WFO (2022). We also worked intensively on concept synonymy (Jansen and Dengler 2010), i. e. cases where the exactly same name refers to a different taxonomic content in different EIV systems.
While this pattern needs to be confirmed by testing the correlations of mean EIVE vs. mean EIV values against measured environmental factors of vegetation plots in regional contexts, a test doing so with a "beta version" of EIVE, based on a much smaller number of source EIV systems than EIVE 1. 0, found indeed a superiority of the EIVE approach over the regional EIV system in the majority of datasets with measured soil variables (volumetric water content, C/N ratio and pH in H 2 O; Moeys 2020). One explanation for the good performance of EIVE might be that each of the included EIV systems is best understood as a single expert assessment, and every expert necessarily over-or underestimates niche positions of many species equivalent to "random measurement errors". The more such independent assessments are combined, the closer they should get to reality, which is supported in our comparison (Figure  4 ) by a higher correlation for species occurring in at least four lists (r = 0.
Potential of EIVE 1. 0 The main motivation for the creation of EIVE was the demand to have plot-based assessments of environmental conditions carry a broader set of meaningful predictors in macroecological studies of vegetation plots. Since the largest vegetation-plot databases globally, EVA  (Chytrý et al. 2016 ) and sPlot  (Bruelheide et al. 2019) , do not provide in situ measured environmental variables (except slope aspect, inclination and elevation), researchers were hitherto forced to find "work-arounds". The main approach was to use modelled environmental data for grid cells. This is well-established for climate data, e. g. WorldClim  (Fick and Hijmans 2017)  and CHELSA  (Karger et al. 2017 ), but less developed and explored for other environmental variables, some of which may be expected to show more local variation.
Therefore, the majority of continental-scale studies of vegetation in Europe, be it macroecological studies (e. g. Thuiller et al. 2005; Boonman et al. 2021; Dembicz et al. 2021)  or characterisation of vegetation units (e. g. Marcenò et al 2018 Marcenò et al , 2019;; Bonari et al 2021)  restricted themselves to coarsely gridded climate data, despite extensive knowledge that soil variables and disturbance regime are at least as decisive in shaping plant community composition. In one of the few attempts thus far to include gridded data of other environmental variables in a broad-scale analysis of vegetation-plot data, such as soil properties,  Bruelheide et al. (2018)  found a very low predictive power. This was probably driven by the fact that the global or continental geodatasets have modelled, not measured variables, and they are provided at resolutions of ca.
Despite the fact that we found 31 EIV systems for this study, country-specific EIV systems are still missing for most European countries. For some EIV systems, such as Poland  (Zarzycki et al. 2002)  and Slovenia  (Košir 1992) , the complexity of the coding/symbology largely prevented automated use for calculation of mean indicator values and, thus, researchers tended to prefer EIV systems from neighbouring areas. Here, EIVE offers two solutions: one can either use the European EIVE indicators or use the regional EIV indicators in their harmonised and ready-to-use editions also provided in the EIVE database (Suppl. material 2). This taxonomic "backbone" is another central feature of EIVE and is provided open access to facilitate further improvements in a well-documented manner. While the EIVE backbone for vascular plants is based on Euro+Med (2022), the most comprehensive and up-to-date European checklist currently available, it aims to overcome some shortcomings of the current Euro+Med Plantbase with the addition of taxa that are regularly recorded by vegetation ecologists.
We suggest that it can be used in European projects independent from the indicator values and, in combination with the regionally interpreted taxon names from the EUNIS-ESy, might help the big European vegetation-plot databases such as EVA  (Chytrý et al. 2016)  and GrassPlot  (Dengler et al. 2018)  to provide their content in a more harmonised way in the future. Our documented additions to Euro+Med (2022) are also an invitation to the team of the Euro+Med Plantbase to incorporate these taxa or a subset of these directly in future releases of their database.
For the light indicator (L), the EVA database might provide suitable proxies, such as slope, aspect and inclination and tree and shrub layer cover (for a possible approach, see  Tichý et al. 2023). By contrast, for the soil indicators, EVA lacks well-curated and readily available in situ measured environmental variables, such as pH value, C/N ratio or average depth of the water table. However, other vegetation-plot databases that are specialised in this field, such as the Ecological Conditions Database  (Wamelink et al. 2012)  and GrassPlot  (Dengler et al. 2018) , can provide the relevant data. Here, one could ask whether and how species cover should be used in the calculation of mean indicator values of a plot: not at all (i. e. only presence/absence), fully (i. e. weighting by % cover) or an intermediate solution (e.
With these qualities, we are convinced that EIVE 1. 0 will open new analytical avenues and become an important tool for vegetation ecologists, conservation biologists, species distribution modellers and macroecologists working on the European vegetation and flora. The implementation of EIVE is facilitated by the fact that the system, its underlying data and R scripts are provided freely. EIVE is an open-source, community-based database that will be released with fixed version numbers following improvements Therefore, readers are invited to send to the lead authors information about overlooked, new or updated EIV systems of any taxonomic group of plants and any niche dimensions, as well as any suggestions for further improvements.
Acknowledgements We thank the  Bayreuth Center of Ecology and Environmental Research (BayCEER) ,  University of Bayreuth , for a grant (" Anschubfinanzierung ") that supported the initial steps of this project. We thank  Hartmut Dierschke  ( †),  Nikolai Ermakov ,  Anna Maria Fosaa  ( †),  Stephan M. Hennekens ,  Mark O. Hill  and  Niels Böhling  for providing their regional EIV systems with relevant background information and the  Euro+Med Secretariat Berlin  for providing the Euro+Med database enabling us to use it as the basis for the taxonomic backbone of EIVE. Hallie Seiler  provided thorough linguistic editing. We thank  Wolfgang Willner  as Subject Editor and three anonymous reviewers for a fast and careful review that allowed us to further improve the manuscript.
2021 ). We placed constraints on the unobscured and obscured star formation using lowresolution photometric data obtained with the Galaxy Evolution Explorer (GALEX) in the UV and the Wide-field Infrared Survey Explorer (WISE) at 22 µm. However, we had no arcsecondresolution data to constrain, for example, the star formation history and the local star formation rate densities. To provide such constraints, we used the Multi-Unit Spectroscopic Explorer (MUSE) of the Very Large Telescope (VLT). Here we present a detailed analysis of optical imaging spectroscopy as well as a detailed description of the gas-heating processes and other optical spectroscopic properties of the bulge and disk in this outstanding source. We also advance the proposition that regions of high electron density and reduced star formation in J2345-0449 represent regions where the jets significantly interact with the interstellar medium (ISM).
The paper is organized as follows: First, in Sect. 2, we present the observations and data reduction, before describing our analysis of the stellar continuum and providing an upper limit on the bolometric luminosity of the AGN in Sect. 3. We discuss the emission-line diagnostics in Sect. 4, as well as our estimates of the electron densities and optical extinction. In Sect. 5 we investigate the past and present star formation in J2345-0449, and demonstrate that most stars were formed at least 10 Gyrs ago. We also estimate the current global star formation rate of the galaxy and investigate the properties of individual star forming regions in more detail in Sect. 6. We subsequently extract the kinematic properties of the warm ionized gas in Sect. 7, and use kinemetry to show that the gas velocities are dominated by disk rotation.
VLT/MUSE observations and data reduction Observations were carried out on June 10, 2021, with the Multi-Unit Spectroscopic Explorer (MUSE) in Service Mode at ESO's VLT. The program ID is 105. B-0299 (PI Nesvadba). MUSE is an imaging spectrograph with a field-of-view of 1 × 1 operating in the optical at wavelengths between 4650 and 9300 Å with a spectral resolving power, R ∼ 2600 at 7000 Å. We used the MUSE/WFM mode with the extended wavelength range and with the adaptive optics as a seeing enhancer. Due to the shutdown of the VLT in 2020 caused by the COVID-19 pandemic, these observations were carried over into period 107. Observing conditions were good and stable. In total we observed the source with four exposures with individual observing times of 600 s onsource, with a total on-source observing time of 2400 s.
Data reduction was done following the prescription given in the MUSE Pipeline User Manual 1. ESO makes MUSE raw data and static calibration data available in the archive. We chose the ESO Recipe Execution Tool Esorex, a command-line tool to process the data. The data reduction is organized into two stages. The first stage includes the basic data reduction steps such as the subtraction of bias and dark frames, and flat-fielding, wavelength calibration, and the illumination correction. These steps are done for each individual integral-field unit. In the second stage, data are converted into physical quantities using the flux calibration and astrometric solution. Finally, the data taken with each exposure and each individual integralfield unit are transformed into a single data cube. The flux scale was set by observing the spectrophotometric standard LTT_7987 right after the science observations.
Ancillary data sets We also used ALMA CO(1-0) observations obtained in ALMA Cycle 7 through program 2019. 1. 01492. S (PI Nesvadba). The reduction and analysis of these data were previously described by  Nesvadba et al. (2021) , and we refer the reader to this work for details on the data acquisition, reduction, and analysis. The ALMA and MUSE data do not share the same astrometry, and in particular data sets obtained with the VLT have absolute astrometric uncertainties of about 1. To register both images within the same coordinate frame, we assumed that the AGN, which can be seen in the 115 GHz continuum observed with ALMA, falls into the center of the bulge of J2345-0449 observed with MUSE. Moreover, we used low-frequency radio imaging obtained with the Giant Metrewave Radio Telescope (GMRT) obtained at 323 MHz (PI Bagchi).
Stellar and AGN continuum emission We used the publicly available software package Starlight 2  (Fernandes et al. 2005)  to fit and subsequently subtract the continuum emission from our data cube. Starlight fits the observed spectral energy distributions (SEDs) of galaxies with a linear combination of synthetic SEDs obtained with stellar population synthesis codes, in our case that of  Bruzual & Charlot (2003). Using the "Base. BC03. S" set of 150 SEDs provided by the Starlight package is adequate in our case. These spectra cover an age range between 1 Myr and 18 Gyrs, more than the age of the Universe (to account for potential systematic effects), and are calculated for solar metallicity. Extinction was modeled with a Galactic extinction law with R V = 3. 1  (Cardelli et al. 1989 ). The Table  1.
Discussion In the previous sections we have presented new results of J2345-0449 extracted from an optical imaging spectroscopic data cube obtained with VLT/MUSE. Our results add to previous observations obtained in the radio and with optical longslit spectroscopy  (Bagchi et al. 2014 ), X-ray observations with Chandra and XMM  (Walker et al. 2015; Mirakhor et al. 2021) , and millimeter CO observations obtained with the IRAM 30-m telescope  (Dabhade et al. 2020; Nesvadba et al. 2021 ) and ALMA  (Nesvadba et al. 2021). We now discuss our observational results further, to put them into a broader context of galaxy evolution.
Acknowledgements. We would like to thank the anonymous referee for their insightful comments, which helped improved the paper. We also thank the staff at  ALMA , and  GMRT  for carrying out the observations. DM and NPHN acknowledge partial financial support through  CEFIPRA program   6504-2. Based on observations collected at the  European Organisation for Astronomical Research  in the Southern Hemisphere under  ESO programme (s)  105. B-0299. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2019. 1. 01492. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and  NAOJ.
Deep Learning has been also applied at the preprocessing step, in particular to estimate the trans-formation function between sets of acquired images and their enhanced counterparts through supervised learning. One of the first and most successful methods was introduced with the CARE network  (Weigert et al. , 2018) , designed to restore fluorescence microscopy data without the need to generate manual training data. The authors showed that it is possible to learn the mapping between low-intensity and high-intensity image pairs using a U-Net based neural network. In the case of live-cell imaging, this makes possible to restore the image quality. However, two characteristics of this network limit the generalization capacity of CARE to new types of images. First, CARE network follows the supervised training paradigm and thus requires matching pairs of the same image and the corresponding nuclei masks, which is time-consuming.
Automatic GNSS data processing The GNSS data are routinely and automatically processed by both OVPF-IPGP in charge of the 24/7 continuous and real-time monitoring of Mayotte, and IGN, which is in charge of data distribution (see Data availability statement). OVPF-IPGP calculates daily solutions with the GipsyX precise point positioning (PPP) software  [Bertiger et al. , 2020] , using the best quality JPL's orbits and the clock products available at the time of calculation (ultra, rapid, then final orbits after a delay of ten days) and provides daily solutions in the ITRF2014 reference frame. The standard deviations of the daily coordinates of MAYG for the years preceding the crisis, detrended from the secular velocity of the station, are 1. 9, 1. 8, and 5. 6 mm for eastern, northern, and vertical components, respectively.
The other stations have similar scores. The daily solutions are distributed, processed and displayed on WebObs, a web-based integrated system for data monitoring and networks management routinely used for the crisis management in a dozen volcano observatories worldwide, including the IPGP observatories  [Beauducel et al. , 2020a]. WebObs is an open source project that includes several specific modules for seismological, geophysical and geochemical data processing in near real-time, one of these modules is dedicated to GNSS solutions. The resulting outputs from these processes are: • coordinate files for all the stations used, in the ITRF2014 reference frame; • time series of the station coordinates with respect to the reference coordinates, for which the horizontal tectonic motion is subtracted using the theoretical velocity given by the tectonic plate model provided by the ITRF; IGN calculates the daily solutions with the Bernese GNSS software in double difference mode, including the GNSS stations in and around Mayotte, as described above, and a large number of stations located in Africa, Asia, Madagascar, and on some islands in the Indian Ocean or in the Subantarctic zone.
, 2005 , Loomis et al. , 2020]. We add back the atmospheric and non-tidal oceanic contributions to ensure comparison with the GNSS dataset. Deformation induced by surface loads, decomposed in the temporal and spatial domains, on a spherical elastic layered Earth model  [Dziewonski and Anderson, 1981]  is computed based on the Love number formalism  [Farrell, 1972 , Chanard et al. , 2018]. We model the elastic deformation resulting from variations in surface loading measured by the GRACE and GRACE-FO missions at the GNSS MAYG site in Mayotte (Figure  7b, c ). Note that considering the large scale resolution of the GRACE and GRACE-FO measurements (∼300 km) compared to the Mayotte GNSS network distribution, the loading model predicts similar deformation at all sites and local effects at sites are not accounted for.
Automatic processing and modeling From mid November 2018, automatic and daily modeling of PPP solutions were set up at IPGP using the WebObs dedicated module "GNSS"  [Beauducel et al. , 2019 [Beauducel et al. , , 2020a]] , in order to characterize the source of deformation and its evolution in real time. The characteristics of a single source (location, depth and volume variation), fitting the observations over a considered period of time, are modeled by a point source of isotropic pressure at depth, in a homogeneous and elastic medium  [Mogi, 1958] , with topographic effect approximation using the varying-depth formulation  [Williams and Wadge, 1998 ]. These simple models are the most suitable, given the limitations caused by the current geometry of the geodetic measurement network, with stations mostly west of the source  [REVOSIMA, 2019].
Acknowledgements Since June 2019, monitoring of Mayotte eruption is funded by  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI) ,  Ministère de la Transition Ecologique (MTE) and Ministère des Outremers (MOM)  with the support of  Ministère de l'Intérieur (MI) and Ministère des Armées (MINARM)  through the  REVOSIMA (Réseau de surveillance volcanologique et sismologique de Mayotte ). Data shown on this paper have been acquired, processed and modeled in the framework of the REVOSIMA, https: //doi. org/10. 18715/MAYOTTE. REVOSIMA. The most recent GNSS stations installed on Mayotte and  Grande Glorieuse  (KNKL, PMZI, MTSB, GLOR) have been funded by the  French state via  dedicated funding specially released by  INSU-CNRS  ( Institut national des sciences de l'univers-Centre national de la recherche scientifique ) after the beginning of the Mayotte seismic crisis.
The temporary GNSS stations deployed on the three sites of Mayotte in 2019, before their perpetuation, came from the  GPSmob from Résif-Epos. Résif-Epos is a Research Infrastructure (RI ) managed by the  CNRS-INSU. It is a consortium of eighteen French research organisations and institutions, included in the roadmap of the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation. Résif-Epos RI  is also supported by the  Ministère de la Transition Ecologique. The stations NOSY and DSUA were installed by the  LACy laboratory (Université de La Réunion ) in the framework of the  INTERREG-5 Océan Indien 2014-2020 « ReNovRisk Cyclones et Changement Climatique » project , funded by the  Europe, Région Réunion  and the  French state [Bousquet  et al. , 2020]. The station MAYG is part of the REGINA network funded by  CNES  and co-operated by  CNES  and IGN (https://regina.
It was located on top of the tape system, inside the vacuum pipe, in such a way that the implantation in the tape could take place through a 10 mm aperture on the side of the detector cup. The MONSTER array used 48 cylindrical cells of 200 mm diameter and 50 mm height, filled with either BC501A or EJ301 scintillating liquid. Each cell is coupled through a light guide of 31 mm thickness to either a R4144 or R11833 PMT. In this experiment the cells were arranged in two di↵erent setups with 30 and 18 cells at 2 and 1. 5 m flight paths, respectively. The signals from the plastic detector and MONSTER are used as the start and stop signals, respectively, to measure the neutrons' time-of-flight (TOF). All the detector signals were registered with a custom digital data acquisition (DAQ) system developed at CIEMAT based on ADQ14 digitizers from Teledyne SP Devices with 14 bit resolution and 1 GSample/s sampling rate.
The complete detector setup required 60 channels. Custom pulse shape analysis software was developed at CIEMAT to analyze the registered signals online. The -detector signals were fitted to the average signal in order to resolve pileups. MONSTER signals were analyzed using the digital integration method in di↵erent regions. The pulse shape analysis routine does not add any dead time. The data was collected in cycles of implantation and decay adapted to the half-lives of the isotopes involved for maximizing the activity of the neutron emitter with respect to the other isobars consisting 95 % of the mass separated beam, see Section 4. 1. During the cycle, the ion beam was switched on and o↵ by electrostatic deflection at the switchyard of the IGISOL beam line. The cycle also included an initial time interval for background measurement and a final time interval for removal of the long-lived activity of the decay products.
Efficiency characterization The detection e ciency of the di↵erent detection systems has been determined by Monte Carlo simulations in combination with experimental measurements that served as validation of the simulations. In the case of the -detector, the e ciency has been determined experimentally at two di↵erent endpoint energy values from the 92  The MONSTER neutron detection e ciency has been determined through Monte Carlo simulations and validated with the data from a measurement with a 1. 00 ± 0. 15 GBq 252 Cf source. The -rays from the spontaneous fission of 252 Cf detected in the LaBr 3 detectors served for establishing a TOF between the emission and the detection of the spontaneous fission neutrons. Neutron signals in MONSTER were selected by performing pulse shape discrimination, which allowed for a background reduction due to -rays of one order of magnitude.
Summary This work entails a successful commissioning and measurement with MONSTER and the custom DAQ and pulse shape analysis routines. MONSTER is a neutron TOF spectrometer with good neutron/ -ray discrimination capabilities and excellent neutron energy resolution. Accurate Monte Carlo simulations of the complete detection system have been performed and validated by reproducing the e ciency of the di↵erent detectors (neutrons and -particles) with known sources and decays. An innovative analysis methodology based on the fit of the -activity curve with the Bateman equations and the unfolding of the neutron TOF spectrum with the iterative Bayesian unfolding method has been developed. Lastly, the -delayed neutron energy spectrum of 85 As has been procured. This result is in excellent agreement with previous experimental data  [15]  and evaluations  [10].