dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants were seated on the haptic chair on which two Clark Synthesis T239 Gold tactile transducers have been attached  (Fontana et al. , 2016)  for vibrotactile feedback (Figure  4 ). Spatial sound was relayed using Iiyama screen speakers to provide a physical spatial sound due to the physical positions of the eight speakers. User's head orientation has been retrieved using a Microsoft HoloLens mixed reality headset (visual augmentation facilities were not used here and the participants were asked to use it with the glasses raised upon their head). The 3D environment was made using real photographs of Muret airport (south of France) mapped onto a 3D scene that was developed with Unity editor. The different software modules for the augmentations (Audio Focus, Runway Incursion vibrations, Call from Secondary Airport vibrations and Spatial Sound alert) were written in C# language using Microsoft.
Behavioral Measurements Behavioral data were acquired by automatically computing response times during the experiment: since an event was raised, a timer was started until it was taken into account by the user. For the Spatial Sound alert, the timer was started when an aircraft moves on the ground without authorization, until the moment when the participants' head was aligned with the azimuth of the event. For the Runway Incursion alert, the timer was started when an aircraft crosses the holding point while another one was on the final leg, ready to land, until the moment when the participants presses the corresponding button on the radar HMI in front of him to tell that they had taken into account the alert. Finally, for the Call from Secondary Airport event, the timer was started when a message comes from the secondary airport, until the moment when the participants answered to this message by clicking on the secondary airport radio communication button end starting to speak to the pilot.
Since all the scripts contained at minimum one of each event, average values were computed from each type of event, for Single and Multiple contexts, and when the augmentations were activated or not. In total, there were 4 mean values per participant for Spatial Sound and Runway Incursion events: one for Single RT context without augmentations, one for Single RT context with augmentations, one for Multiple RT context without augmentations, and one for Multiple RT context with augmentations. Call from Secondary airport events could be tested only in Multiple RT context. Therefore, for this particular event, 2 average values were computed for each participant: one in Multiple RT context without augmentations, and another one in Multiple RT context with augmentations.
Another quasar lies at the same redshift at ≈ 8 arcmin separation, making this system a quasar triplet  (Francis & Hewett 1993). Upon its completion, this programme will acquire ≈ 200 hours of MUSE data (corresponding to ≈ 150 hours on source) in a 1. 5 × 1. 2 arcmin 2 region around the two quasars (ESO PID 1100. A-0528). This programme is complemented by deep high-resolution spectroscopy of the quasars using the UV and Visual Echelle Spectrograph (UVES  Dekker et al. 2000)  at the VLT (ESO PIDs 65. O-0299, 68. A-0216, 69. A-0204, 102. A-0194), and by the deepest spectroscopic survey (90 orbits in a single field) in the near infrared using the Wide Field Camera 3 instrument on board the Hubble Space Telescope, together with deep 8-orbit near UV imaging (HST PIDs 15637 and 15968). These combined datasets will enable us to achieve several goals.
In the future, once we have the full dataset, we will search for the presence of ionized gas in this putative filament. The depth of the observations will also provide spectra of exquisite quality for a few hundred objects. Thanks to our multiwavelength dataset from the near-UV to the near-IR, we will study the properties and structure of low-mass galaxies across a large fraction of cosmic time. In this paper, we present the survey design and the details of the MUSE observations and data reduction. As a first application we focus on the connection of enriched cool gas (T ∼ 10 4 K), as traced by the Mg ii λλ 2796, 2803Å absorption doublet in the quasar spectra, with the galaxy population and its environment at 0. 5 < z < 1. 5. Even though the full MUSE dataset is still being collected, the observations available to date already provide an excellent dataset for an accurate reconstruction of the local galaxy environment, and of the physical properties of galaxies at z 1.
5. Several studies have used the Mg ii doublet to trace gas with similar column densities to that detected through 21-cm atomic hydrogen observations (e. g. Bergeron 1986; Kacprzak et al. 2008; Steidel et al. 1994; Chen & Tinker 2008; Chen et al. 2010; Gauthier 2013; Bordoloi et al. 2014a; Nielsen et al. 2015; Schroetter et al. 2016; Nielsen et al. 2018; Rubin et al. 2018a,b). These studies have found that Mg ii absorbers trace the CGM of galaxies and possibly outflowing gas up to a distance of ∼ 100 kpc  (Kacprzak et al. 2008; Chen et al. 2010). This transition is therefore ideal to study the CGM of galaxies in groups and in isolation in the MUDF, for the first time in a very deep and complete dataset. The paper is structured as follows: first we present the MUDF survey strategy, the data reduction procedures, and the quality validation of the MUSE data (Section 2), and of the high-resolution UVES spectroscopy (Section 3).
2018 ) outside the MUSE footprint. The survey design prioritises the collection of deeper data in the area between the two quasars, where our science goals warrant maximum sensitivity. The outer black dashed contours mark the regions with at least 15 hours and 30 hours of exposure, respectively. The instrument setup makes use of the MUSE Wide Field Mode with extended wavelength coverage in the blue (4650-9300Å), in order to search for Lyα emitting galaxies down to z ≈ 3. We also take advantage of the Ground Layer Adaptive Optics module (GALACSI) which uses artificial laser guide stars to improve the image quality by partially correcting for atmospheric turbulence. In this way, the observations can be performed under a wider range of natural seeing conditions without compromising the image quality of the final mosaics.
Data reduction The data reduction procedure follows the methodology described in previous works by our team  (Fumagalli et al. 2016 (Fumagalli et al. , 2017a;; Lofthouse et al. 2019). In brief, we use the ESO pipeline (v2. 4. 1 Weilbacher & et al. 2014)  to reduce the calibrations (bias, flat, arcs, and standard stars) and apply them to the individual exposures. We also use this software to resample the detector values into cubes that are then sky subtracted using models of the sky continuum and sky lines that are matched to the data extracted from the darkest pixels in each frame. The individual exposures are then aligned by using the position of point sources in the field, and we generate a first coadd of the frames. We register this initial coadd to match the WCS coordinates of the two quasars from the Data Release 2 of the GAIA survey  (Gaia Collaboration et al.
2017). To correct for these effects, we post-process the individually aligned, reconstructed and unskysubtracted exposures using routines from the CubExtractor package (v1. 8, CubEx hereafter, Cantalupo in prep. ; see  Cantalupo et al. 2019  for a description of the algo-rithms). The adopted procedure follows earlier work using MUSE data  (Borisova et al. 2016; Fumagalli et al. 2016 Fumagalli et al. , 2017a)). In brief, we use the CubeFix tool to correct residual differences in the relative response of the 24 MUSE IFUs and of individual slices, which are not fully corrected by flat-field calibration frames. CubeFix improves the illumination uniformity by measuring the average illumination in each stack (the MUSE FoV is composed of 24 IFUs which are further made of 4 stacks of 12 slices), as a function of wavelength on white-light images generated on-the-fly from the cube.
To increase the wavelength coverage (down to ≈ 3600 Å and to fill the current gaps) and to increase the S/N of the fainter quasar, we have been awarded a total of 23 hours of new UVES observations (PID 102. A-0914). At the time of writing, observations for QSO-SE have been completed for a total of 7h on-source, while for QSO-NW only 15. 5h were obtained, and 17h are still to be observed. In this paper, we therefore make use of the full dataset for the brighter quasar, relying only on a partial dataset for the fainter one. We will describe the spectra obtained from the complete observations in a forthcoming paper. All data were reduced with the current version of the UVES pipeline (v. 5. 10. 4), using default parameters and procedures. At the end of the standard reduction process, the non-merged, non-rebinned spectra were reformatted with a custom script and input to the ESPRESSO Data Analysis Software (DAS,  Cupani et al.
Source detection We identify continuum sources using the SExtractor  (Bertin & Arnouts 1996)  software on the white light image reconstructed from the MUSE datacubes. We input a variance image and we use a conservative threshold of 3σ above the local noise, and a minimum area of 10 pixels for detection. The minimum deblending parameter DE-BLEND CONT is set to 0. 0001, chosen to enable the detection of sources in crowded regions of the mosaic. We restrict source extraction to the area where we collected more than 10 exposures, corresponding to an observing time of ≈ 4 hours, to avoid spurious detections at the noisy edges of the field of view. In future publications we will use deep HST imaging for source detection in the field. This procedure, identified 250 sources. For each of them, we extract the magnitude in the detection image (m MUSE ) in an elliptical aperture with size equal to 2.
8 arcsec. To extract IRAC photometry from these images we use the t-phot software  (Merlin et al. 2015) , which optimally deals with the lower resolution of IRAC data in crowded fields by means of source priors from higher resolution imaging. We use the MUSE white light image, PSF model and source catalogue as the high resolution dataset. We then derive a Gaussian convolution kernel for each IRAC channel, to transform the MUSE PSF into the IRAC one. Lastly we resample the IRAC data onto the MUSE pixel grid and run t-phot. Due to the shallowness of the IRAC data, only the relatively bright sources can be detected robustly, especially in crowded areas of the MUDF. In the end, we visually inspected the t-phot results for each source to assess if results: i) can be used and the detection is at least with 3σ significance; ii) can be used as an upper limit; iii) cannot be used because the source is faint and it is highly blended with a nearby bright source.
The multidimensional likelihood space is sampled by using PyMultiNest  (Buchner et al. 2014) , a python wrapper for the MultiNest code  (Feroz & Hobson 2008; Feroz et al. 2013). Figure  7  shows an example of the results obtained with this fitting procedure for a galaxy in the MUDF. With the current wavelength coverage, some degeneracy remains between dust extinction and star-formation rate. Rest-UV data from HST will break this degeneracy, however, in this work we primarily make use of the stellar mass estimates from the fits which are found to be well converged and free from degeneracies with other parameters.
Going beyond the survey edges The unprecedented depth of the MUSE observations in the MUDF comes at the price of a relatively small survey area. Therefore, it is possible that at least some of the groups identified in the previous section are part of a larger scale structure, or that some of the group members are missed by our footprint. Unfortunately, this area of the sky is not covered by wide-area archival spectroscopy, thus we can only attempt to characterise the large scale environment around the MUDF with photometric redshifts (photo-z). For this purpose, we downloaded source and magnitude catalogues from the DES DR1  (Abbott et al. 2018 ), and we computed photo-zs from the g, r, i, z fluxes using the EAZY code  (Brammer et al. 2008). Following  Hoyle et al. (2018)  we do not include Y-band magnitudes, because the observations are too shallow to improve the photo-z estimate.
SUMMARY AND CONCLUSIONS In this work, we have presented the design, observations, and data reduction methodology for the MUSE Ultra Deep Field (MUDF) survey, together with results from the cold gas content of galaxy groups at 0. 5 < z < 1. 5. The MUDF survey is a 150-hour (on source) large programme on the MUSE instrument at the VLT that is observing a 1. 5 × 1. 2 arcmin 2 region of the sky characterised by the presence of two quasars at z ≈ 3. 22 separated by ≈ 60 arcsec. The MUSE data are also complemented by HST imaging programmes in the near-UV and in the near-IR, and by the deepest HST near-IR spectroscopic campaign in a single field. Deep highresolution spectroscopy of the quasars is also being collected with the UVES instrument at the VLT. These rich datasets will enable us to reach several goals, including: an investigation into the connection between gas and galaxies reaching the low-mass regime at z ∼ 2 -3; a search for gas filaments of the cosmic web in emission; and a study of the build-up of the Hubble sequence with cosmic time with accurate information on the morphology, kinematics, gas budget, and star-formation history of galaxies.
In this paper, we have discussed in detail the survey design, the data reduction procedure, the extraction and validation of source catalogues, and the derivation of the physical properties of galaxies from the partial dataset observed to date. As a first application, we investigated the galaxy environments in the MUDF, finding seven groups three or more members at 0. 5 < z < 1. 5 covering a large range in inferred dark matter halo mass (log(M h /M ) ≈ 11-13. 5). We explored the correlation between galaxies and galaxy groups with cold gas detected via Mg ii absorption in the quasar sightlines. We found five absorption systems in the spectrum of the bright quasar and only one in the faint quasar spectrum. All but one of these systems are at the redshift of a galaxy group, while for the last one we find a massive galaxy at a small velocity separation, which we speculate could itself be a member of a group falling outside the MUDF footprint.
This evidence, combined with previous examples in the literature of strong Mg ii absorbers in group-like environments, suggests that the absorbers reside in gas once bound to individual galaxies (or their CGM) that has been stripped at larger radii, boosting the cross section of Mg ii. Gravitational interactions and tidal forces, in analogy with what is seen in nearby groups, are indeed effective in galaxy groups and obvious mechanisms to strip gas and stars from the galaxy disks, leading to the observed phenomenology. So far, our analysis relied on deep MUSE data in a double quasar field. These data will soon be complemented by deep spectroscopic and imaging data from HST, extending the wavelength of our observations from the near-UV to the near-IR. These data will provide us with accurate redshifts for galaxies in the redshift desert (1.
ACKNOWLEDGEMENTS We thank  J. T. Mendel  for the developement of the MC-SPF code used in this work and the anonymous referee for their comments which improved the quality of the manuscript. M. Fumagalli acknowledges support by the  Science and Technology Facilities Council  [grant number  ST/P000541/1 ]. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  757535 ). SC acknowledges support from  Swiss National Science Foundation  grant  PP00P2 163824. RC was supported by a  Royal Society University Research Fellowship. CP thanks the  Alexander von Humboldt Foundation  for the granting of a Bessel Research Award held at MPA. MR acknowledges support by  HST Program GO-15637  provided by  NASA  through grants from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
Acknowledgements. The authors wish to acknowledge all of the ORACLES science team and the  NASA  P-3 flight and maintenance crew for the successful deployment. Financial support. ORACLES is funded by  NASA   Earth Venture Suborbital-2  grant  NNH13ZDA001N-EVS2. The Henties Bay and  Gobabeb AERONET  stations are maintained by the  French Centre National de la Recherche Scientifique (CNRS)  and the  South African National Research Foundation (NRF)  through the  Groupement de Recherche Internationale Atmospheric Research in southern Africa and the Indian Ocean (GDRI ARSAIO) , the  "Projet International de Coopération Scientifique" (PICS)  " Long-term observations of aerosol properties in Southern Africa " (contract n. 260888), and the  Partenariats Hubert Curien (PHC) PROTEA  of the  French Ministry of Foreign Affairs and International Development  (contract numbers  33913SF  and  38255ZE ).
Data availability. All ORACLES 2016 in situ data used in this study are publicly available at https://doi. org/10. 5067/Suborbital/ORACLES/P3/2016_V1 (ORACLES Science Team, 2017). This is a fixed-revision subset of the entire ORACLES mission dataset. It contains only the file revisions that were available on 15 June 2018. The 4STAR raw data have been analyzed to produce AOD in this dataset using the open-source software published by the 4STAR Team (2018, https://doi. org/10. 5281/zenodo. 1492912).
Over much larger extents, such as countries, counties, islands or national parks, large-area inventories like multispecies 'checklists' or state-level databases for select species of national interest (for example, pests or nonnatives) are often based on multiple data sources and protocols and are considered 'summary inventories'  38. Depending on effort and rigor, they may provide both reliable presence and absence information, but usually with limited spatial or temporal specificity. Despite the fundamental role of inventories for ascertaining potential absences, especially in the context of growing modelling methodology  47  , they have seen limited mobilization and integration with other data types. Key past causes for this include the lack of (1) data and metadata, (2) infrastructure to facilitate the capture and use of this information and (3) community-wide appreciation and incentives for data and metadata sharing.
infrastructure The SP EBVs require rethinking traditional approaches to producing knowledge products. At the heart of the SP EBVs is the recognition of harmonization and model-based integration of multiple types of biodiversity and environmental data from heterogeneous sources that address different scales and are stored in multiple formats. This requires workflows that connect data to models in order to produce and disseminate credible and transparent modelled products. Such workflows necessitate a network of tools and infrastructure to address four main steps (Fig. 4 ): (1) data generation, contribution and aggregation, (2) data integration, (3) modelling and production of SP EBVs and (4) delivery and use of SP EBVs. In step 1, biodiversity data producers, such as taxon-, region-or data-type-specific networks, sampling campaigns (for example, through citizen science) or national/institutional interests (for example, country atlases and national marine surveys) are improving the sophistication of their database tools and data size.
Unfortunately, much data still remain unavailable owing to lack of sharing or restrictive licensing, but data contributions are facilitated by a range of platforms (for a review of examples and associated standard and workflow issues, see ref. 36  ). For incidental records, data and metadata standards are enabling such data to effectively support EBV development. Key examples of such networks include GBIF and OBIS, which operate globally via connection to national nodes and thematic networks. Such global networks are important infrastructures to ensure availability, repeatability, standardization and archiving in support of downstream data integration and use  120. However, still nascent and of immediate need is infrastructure that can play a similar role for more complex data types, such as select inventory data that require detailed metadata to most effectively feed into models, data-type or model-focused effort.
Evidence base Dataset information value. With billions of organisms on this planet that are changing in distribution and abundance at any moment, it is clearly impossible to fill the space-time-species cube in fine spatial, temporal and taxonomic resolution for all life and at the planetary scale. Even with the aid of ongoing environmental data collection and models, the operational grain for a given species group will be finer (and typical uncertainty levels will be lower) for those with many and well-stratified records in space and time (and environment) and high detection levels, such as birds, and coarser for those with more limited and/or spatially clustered data, such as ferns (Fig. 5a, b ). This operational grain in turn is intimately connected to our ability to infer change -the coarser it is, the more constrained is our ability to infer species population trends, and particularly so for species groups with fine-scale spatiotemporal dynamics (Fig.
5c ). Critically, even in the best-known species groups, viable spatiotemporal prediction grains may only arise in a proportion of species, usually an ecologically and spatially highly non-random subset  103, 121, 122. Accordingly, taxonomic or functional representativeness of the predicted space-time-species cube may be high for some well-or easily-studied groups, but not others. Such limited taxonomic or functional coverage (representativeness) has strong repercussions for the relevance and generality of detected change (Fig. 5d ). Consequently, the value of a dataset on a given species group is not determined by a single attribute, but is instead driven by its minimum 'performance' across a range of attributes (Fig. 6 ). We advocate for an expanded assessment of existing and planned data collections that accounts for this multitude of components and that provides a valuation of their ability to inform modelbased predictions relative to other data.
Effective data contributions. What then are the most valuable data contributions from individuals, organizations and government agencies in the context of these grain and coverage limitations? It follows from above that the value of additional data sampling and mobilization activities must be seen in the context of After raw data is annotated with, for example, remotely-sensed environmental data, a default set of dynamic models, adapted to data type and quantity, is applied for predictions in space and (past to current) time, producing the EBV. Dashboards allow both taxon-and region-specific experts and modelling specialists to engage with, and iteratively improve, models and products. b and c, and parts of a concerning particularly model-relevant data and metadata, are enabled by infrastructure (such as Map of Life) that addresses global data integration, annotation, modelling and feedback to networks of experts.
3 ) and the formulation of required elements and steps would herald a new and global phase of species population information collection, synthesis, and use. To attain this vision, we make the following key recommendations: • Enhanced data and metadata publication and sharing by countries, agencies, conservation organizations, research networks and individuals. Too much collected species occurrence-relevant data remains locked up, or its use is restricted by licensing, a key cause behind existing spatial biodiversity data gaps. We urge all to contribute to publishing and sharing mechanisms Poor performance in a single criterion can result in limited overall value of a dataset for the purposes of global mapping and monitoring in this framework, and useful datasets would have at least medium performance in all criteria and would score high in as many as possible.