dataesr/scientific-paragraphs-categorization huggingface dataset:
Astronomers are nowadays required by their funding agencies to make the data obtained through public-financed means (ground and space observatories and labs) available to the public and the community at large. This is a fundamental step in enabling the open science paradigm the astronomical community is striving for. In other words, tabular data (catalogs) arriving to CDS for ingestion into its databases, in particular VizieR, is more and more frequently accompanied by the reduced observed dataset (spectra, images, data cubes, time series). While the benefits of making this associated data available are obvious, the task is very challenging: in this context "big data" takes the meaning of "extremely heterogeneous data", with a diversity of formats and practices among astronomers, even within the FITS standard.
Vizier VizieR is the reference database for tabular data from astronomical catalogues and tables published in scientific papers. It contains reference catalogs such as Gaia and SDSS, and also provides access to more advanced data products, such as the CoRoT time series  [1]. Its workflow and usage is described in  [2]. Tables are stored, along with homogeneous "metadata". This metadata consists of a range of information describing the data, such as the magnitude system used, the coordinate system and its epoch, the units of the columns, the type of data (magnitudes, sizes, positions. ), stored as Unified Content Descriptors. This set of metadata can be queried, allowing users to discover catalogs relevant for their research. Another strength of this approach is that VizieR allows for instance to perform cone queries in all of the >14 000 catalogs stored in one simple click.
Vizier is fully VO-compatible and communicates with a number of important astronomical resources and software such as the CDS SIMBAD database, Aladin and TOPCAT. These features have made VizieR a very popular resource among astronomers, and as a result, there have been 300 000 queries/day during the last year. Thousands of astronomers have decided that VizieR is the right place to make their data available to the community, and institutional data providers such as ESO and ESA have also trusted the service to distribute their surveys. Significant developments to improve VizieR pipeline for non-tabular data attached to papers were pursued, based on the ObsCore VO standard  [3].
AAS to use ObsCore as a common description framework. The VizieR associated data service has been been developed using the Saada data publishing tool  [4]  in collaboration with Laurent Michel (High Energy Team), and has been available in test mode in 2016. Among the datasets already implemented are the CoRoT data (150,000 time series), and the LAMOST DR1 spectra. These are queriable with an interactive interface, and a feature of the service is that these are also made automatically available to the VO using the relevant IVOA standards. 2 Associated data: the challenge of automatic indexation.
Heterogeneous FITS headers At the moment we consider as associated data only reduced data, in the form of FITS files, because of the presence of directly accessible metadata in the FITS headers. In these FITS headers, we try to find, in order of priority, the pointing of the observation, the time of observation, the electromagnetic wavelength range, and the instrument in use. Although FITS headers can be quite verbose, they are extremely heterogeneous. Indeed, telescope design and software, instruments data reduction pipelines, guidelines and tasks in MIDAS or IRAF and custom user-written routines have an impact on the content of the FITS headers and keywords. Therefore, the latter have evolved significantly since the introduction of the FITS standard. For instance, among the common keywords for the target name, we find : "TARGET", "OBJECT", "NAME", and all possible abbreviations and combinations of these basic words, linked with possibly hyphens or underscores, "OBJ", "OBJ_NAME", "TARG-NAME", "OBJNAME", etc.
Authors to the rescue Since the most accurate knowledge of the data resides with the authors themselves, the VizieR catalog submission webpage (http://cdsarc. u-strasbg. fr/vizier. submit/) has been significantly overhauled to allow users to deposit and index their associated data along with the catalogs. Once the associated data is uploaded, the automatic mapping engine provides a first guess, which the author can check, edit and correct if need be, as shown on the left of Fig. 1. Since this feature is pretty recent, we cannot tell yet how successful it will be at properly indexing the data and removing some of the indexation workload from the shoulders of the CDS staff. Only time will tell.
The Vizier associated data service Once the mapping of FITS keywords onto the ObsCore data model has been successfully performed, the associated data is ingested into a dedicated database which can then be queried. The new service has been released to provide easy and interoperable access to data associated with journal publications. These associated data (images, spectra, time series) have always been part of VizieR in addition to the catalogue collection, but they are now much easier to find and use. The Saada database engine is used in collaboration with L. Michel (OAS XMM-SSC). The data can be queried by an interactive interface and the service automatically publishes the data via VO protocols. Important data sets already in the system include 170,000 CoRoT time series and 2. 4 million LAMOST DR1 spectra.
High O32 galaxies are also found to have tentative non-zero LyC escape fractions ( f esc (L yC)) based on u band photometric detections. These results are consistent with samples of highly ionized galaxies, including confirmed LyC emitting galaxies from the literature. We also perform a detailed comparison between the observed emission line ratios and simulated line ratios from density bounded H ii regions modeled using the photoionization code MAPPINGS V. Estimates of f esc (L yC) for our sample fall in the range from 0. 0-0. 23 and suggest possible tension with published correlations between O32 and f esc (L yC), adding weight to dichotomy of arguments in the literature. We highlight the possible effects of clumpy geometry and mergers that may account for such tension.
SAMPLE SELECTION In this Section we describe the selection methodology of this pilot survey. The goal of our photometric selection approach is to identify star-forming galaxies at z 3 that are likely to be leaking LyC photons into the IGM. This selection uses 30band photometry from the Cosmic Evolution Survey (COS-MOS,  Scoville et al. 2007 ) field, including FourStar Galaxy Evolution Survey (ZFOURGE  Straatman et al. 2016 ) deep medium-band imaging. The depth and wavelength coverage of this photometric dataset provides photometric redshifts with accuracies of ∆z <2% due to deep medium-band filter observations probing the Balmer break. Using galaxies from the ZFOURGE survey enables us to reliably select galaxies at z > 3. 0. LyC emitting galaxies at z > 3. 0 will have some contribution from LyC photons in the u band, making this the critical observation band for our selection.
Finally, we comment on the possibility of whether any of the eight galaxies in our sample harbour active galactic nuclei (AGN). This issue has been explored by the ZFOURGE team in  Cowley et al. (2016)  where galaxies are examined in X-ray, radio, and infrared (IR) data for evidence of AGN activity. All eight galaxies in our sample are not identified as AGN using any these datasets. It should be noted though, that the redshift of our sample is near the upper limit for AGN identification by these methods. Cowley et al. (2016)  estimate a lower limit for detecting radio AGN of L 1. 4GHz = 1. 9 × 10 24 W Hz -1 and for X-ray AGN of L X = 7. 0×10 42 ergs s -1 at 1. 8 < z < 3. 2. Galaxies in our sample are also only weakly detected in the IR 4. 5, 8. 0, and 24 µm bands (or not detected in the case of 13459) thus, from ZFOURGE data, it was not possible to rule out the possibility of weak AGN activity in our sample at the time of sample selection.
0. The LRIS data reduction was performed using the standard IRAF software procedures, the basic steps are as follows. First, a conversion is performed using the multi2simple task in the KECK. LRIS IRAF package, and during this process the overscan region is removed and bias corrections are performed. Master science frames for each target are produced by averaging all single exposures using imcombine and each master frame is flat fielded using a master flat produced from our twilight spectra. Next, apall is used to extract the 1D from the 2D spectra, and background subtraction is also performed during this step. We then apply a wavelength solution extracted from arc lamp observations. Finally, we apply a flux calibration based on standard star observations performed on the same night as our science observations.
Analysis of the full LRIS dataset is ongoing. More details on our data reduction process and a full spectral analysis of LyC emission in our full sample will be the subject of future work  (Meštrić et al. in preparation). In this work, Lyα properties of our LRIS spectra are used to produce matched template spectra for estimating f esc (L yC) from photometric observations in Section 4. 4. A careful assessment of LyC emission in stacked LRIS spectra will be included in  Meštrić et al. (in preparation).
Photoionization Modeling We calculate a variety of photoionization models using the publicly available MAPPINGS V code (see  Allen et al. 2008 , for the most recent publication regarding a previous release). MAPPINGS V is a photoionization modeling code that performs one dimensional radiative transfer calculations in order to model the emission line fluxes of various astrophysical sources including H ii regions, planetary nebulae, and active galactic nuclei. MAPPINGS V inputs include a source spectral energy distribution (SED), nebular abundances, physical parameters (e. g. density, pressure, ionization parameter, etc. ), atomic line data from the laboratory, and dust depletion data (among others). For this work we calculate two types of H ii region models for comparison with our MOSFIRE sample.
Comparison with Photoionization Modeling Here we present the emission line ratios calculated in our photoionization models, described in Section 4. 3. Separate metallicity and f esc (L yC) tracks in R23 vs O32 at fixed ionization parameter, q ion , are shown in Figure  8. Solid lines represent ionization bounded models ( f esc (L yC) = 0. 0) at various metallicities, whereas dashed lines represent density bounded models at a fixed metallicity from f esc (L yC) = 0. 0 to f esc (L yC) = 1. 0. Tracks are shown over observed values for our sample, as well as average values for comparison samples described in Section 5. 1. Results presented for ionization bounded models in Figure  8  mirror those presented in  Nakajima & Ouchi (2014). This is not surprising as R23 vs O32 tracks from  Nakajima & Ouchi (2014)  were based on values from a previous version of the MAPPINGS code presented in  Kewley & Dopita (2002).
The Correlation Between f esc (L yC) and O32 In this Section we discuss the recent suggestion by Faisst (2016) and  Izotov et al. (2018b)  of an empirical correlation between O32 and f esc (L yC) for galaxies with detected LyC emission. Here we comment both on the predicted f esc (L yC) for our galaxy sample based on the observed O32 values as well as the comparison with density bounded models computed using the MAPPINGS V code as described in Section 4. 3. The left panel of Figure  10  compares the observed distribution of confirmed LCEs and the empirical fits in O32 vs f esc (L yC) space while the right panel shows the theoretical predictions of our MAPPINGS V density bounded H ii region models. Our fixed metallicity models were calculated on grids with 9 values of decreasing HI optical depth, τ H I , simulating f esc (L yC) values from ∼0.
Shocks associated with merging may explain their low observed O32 values. Difficulties in interpreting f esc (L yC) estimated from u band photometry has informed our selection of future samples to shift to higher redshifts. In doing so, we expect to be able to select a cleaner sample of LCEs as beyond z = 3. 4 the u band contains only LyC photons. We note that the selection for this pilot survey pre-dates the CLAUDS survey, thus using this new data set allows us to more reliably select LCEs in our on-going work (e. g. Meštrić et al. in preparation). In addition to the observational results presented above, we also performed 1D photoionization modeling using the MAPPINGS V code to predict the line ratios of H ii regions with a variety of nebular conditions. Models explore metallicities in the range 7.
This may hamper the predictive power of this line ratio given the average uncertainties in its measurement at high redshift. • The above point, as well as Figure  10 , clearly shows that in order to reasonably estimate f esc (L yC) from optical emission line ratios, an accurate estimate of galaxy metallicity is required. In the future, estimates of galaxy metallicities at z > 3-4 will be provided using observations of [NII], Hα, and [SII] emission lines using JWST with the caveat that these values may also be affected by density bounded conditions in H ii regions. We stress here that photoionization models of MAPPINGS V, as well as the commonly used photoionization code CLOUDY, typically employ homogenous spherical or planeparallel geometries. Observations of nearby H ii regions show extremely complex geometries implying large spatial variation in LyC escape.
ACKNOWLEDGEMENTS This research was conducted by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. The authors wish to thank  E. Zackrisson  for useful discussion in the early stages of this work,  D. Nicholls  for assistance in constructing photoionization models using MAPPINGS V, and the anonymous referee for their comments that have helped to improve the focus, clarity, and relevance of this work. JC acknowledges research support from  Australian Research Council   Future Fellowship  grant  FT130101219. TG is grateful to the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Universit Ãl' de Lyon  for its financial support within the  programme 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
Integrated mapping-modelling approaches: These combine spatial approaches with an element of bio-physical modelling to extrapolate from spatial datasets to ecosystem services. They are often designed specifically to address ecosystem services and include established methods such as InVEST and ESTIMAP  (Zulian et al. , 2014). Land-use scoring approaches: This includes approaches based primarily on mapped data that produce ES outputs by applying some kind of expert-scoring. Referred to here as the ''matrix approach" these methods include both the simple matrix  (Burkhard et al. , 2012)  which uses only land-use as a proxy for ecosystem service provision, and more advanced versions such as GreenFRAME  (Kopperoinen et al. , 2014)  which build in more biophysical understanding by using additional datasets.
Monetary valuation was applied in only eight cases. How was it addressed in the OpenNESS case studies?. Values were assessed by combining different methods from across the overarching method classes illustrated in Fig. 1. Eight cases combined at least one monetary method with a socio-cultural method and either a biophysical, integrated mapping-modelling or land-use scoring method to assess biophysical values. Of the other cases, all but four combined at least one of the three method classes for assessing biophysical data and at least one socio-cultural method. Furthermore, 19 of the 24 cases (Fig. 3 ) used participatory/ deliberative mapping approaches which facilitate the capture of sociocultural views and values (through their participatory nature) as well as being capable of capturing biophysical data related to the location and quality of ES-providing structures.
Of these issues, i-vii) are discussed in Section 3. 3 which details the ways that methods were combined in practice within the cases. Here we focus on the final issue where a second method was applied to increase the level of biophysical realism within the case study. This is particularly important for biophysical methods, as the application of methods with weak links to ecosystem processes increases the uncertainty in ecosystem service assessments  (Eigenbrod et al. , 2010; Lavorel et al. , 2017). As such, the level and detail of the biophysical data that underlies assessments influences the ability of a method to accurately represent ecosystem service provision. Lavorel et al. (2017)  differentiate between five classes of biophysical method with increasing levels of biophysical realism: i) proxy methods -which use land-use data alone as the biophysical units from which ES are provided; ii) phenomenological methods: which integrate additional understanding of the biological mechanisms which underpin ES supply (e.
g. landscape configuration, species type/richness, soil quality etc. ) iii) niche-and iv) trait-based models that consider distributions and abundances of species or traits and v) full process models that explicitly represent ecosystems using mathematical formulations of ecological, physical and biogeochemical processes that determine the functioning of ecosystems. How was it addressed in the OpenNESS case studies?. Fig. 4e  illustrates that whilst five cases used proxy-based methods to explore ecosystem services, none of these cases did so without an accompanying approach with a higher level of biophysical realism. Fig. 3  shows that the cases using the matrix approach based on land-use data alone (the proxy method) combined this with the integrated mapping-modelling model ESTIMAP in Slovakia and Barcelona, species distribution modelling in Warwickshire, and an STM in the Carpathians.
Additional factors driving method selection Above we have discussed the major drivers for combining methods, according to the stakeholders. However a number of additional factors were shown to influence method selection (e. g. when deciding between two methods that assess cultural ES values). These included: a) practical constraints (time/resources and data); b) adapting to changes in circumstance; and c) research interests of the case study teams. Practical constraints (time/resources, data and expertise) tended to be most often mentioned with respect to certain methods that are recognised to be quicker/less data intensive than others, including participatory GIS, value transfer, cost-based methods, preference assessment, time use, expert-based mapping and photoseries analysis. In Barcelona, for example, limits on data availability and model scope led to the choice of expert-based mapping to assess soil erosion control, which was then combined with more sophisticated integrated mapping-modelling analysis of recreation and air quality using ESTIMAP.
How are methods combined within practical cases? The previous sections focussed on why different methods were combined to meet case study needs. Here we address how these methods were linked. Methods were linked in many complex and different ways, including i) input-output transfers of data between methods, but also ii) transfer of ideas, concepts and learning; iii) methods development to customise them better to the context; iv) cross-comparison of method outputs for cross-checking/validation and v) linkages of method experience across contexts. Fig. 6  illustrates the concepts behind the types of linkage. In the sections below we draw on experiences from across all 24 case studies (Table  SM4 ) that demonstrate these different types of linkage, with particular reference to two examples: Oslo and the Cairngorms.
Inputs may also come from methods outside the case study research, e. g. from existing datasets or prior research, including value transfer from other studies. In Oslo, for example, municipal blue-green space mapping by the Agency of Urban Environment forms an input to methods in both O1 and O3 (Fig. 7 ). This incorporation of existing knowledge/data can be crucial and has also been shown to increase the acceptance of the ES approach by local stakeholders (e. g. Barcelona).
Initially intended to be applied in a standardised manner customised at a European scale  (Paracchini et al. , 2014) , through testing across a number of Open-NESS case studies the methodology was adapted to be applicable at much finer resolutions with successful application in both national parks (e. g. Cairngorms; Costa Vicentina, Portugal) and urban areas  (Oslo, Trnava, Slovakia, Helsinki, Barcelona). This was not just a downscaling of the approach but an evolution of the method from one based on standardised datasets to one that could be customised to local needs.
Method evolution and development In 14 of the 24 cases, existing methods evolved into new and improved methods, or progressively more advanced methods were applied. In some instances this was a natural progression (Fig. 6-3a ). For example, in Slovakia, simple land-use scoring methods  (Burkhard et al. , 2012)  were improved by including stakeholder data from questionnaires and additional environmental datasets, to evolve the method into an advanced approach more akin to Greenframe  (Kopperoinen et al. , 2014). This was considered more scientifically sound and suitable for the case study's planning purposes. In eleven of the case studies, key aspects of different methods were combined to produce hybrid methods (Fig. 6-3b ). In Oslo, a web tool was developed that combined participatory mapping of favourite walking paths with a willingness-to-pay assessment of the value of city trees.
These were included along with spatial modelling, statistical approaches and participatory mapping outputs in a final workshop that led to policy recommendations. Finally, in some cases new methods had to be developed because existing methods were not available or contextappropriate. In Patagonia, where there was limited available data on cultural services and many of the methods proposed within OpenNESS were unsuitable because they were customised for Europe, the case study developed the photoseries approach to map and quantify cultural services. The key point is that approaches need to evolve dynamically and respond to feedback or new opportunities that arise within the case study; and that creativity and flexibility in combining approaches increase what can be achieved.
Pragmatic concerns Challenges faced. Practical constraints on time, cost, data availability and technical expertise (see Section 3. 2. 7) led to challenges for case study teams combining multiple methods: challenges that increase with the technical complexity of the methods combined. Time demands may also increase as case studies adapt and evolve due to changing understanding of the issue at hand and/or changing stakeholder interests. Successes and opportunities. There are an increasing number of networks, tools and training opportunities to help in selecting and applying new methods, including the OPPLA hub (www. oppla. eu) and the Natural Capital Protocol Toolkit (http://naturalcapitalcoalition. org/protocol/protocol-toolkit/). The OpenNESS case studies showed how face-to-face visits from method experts can strongly influence the successful uptake of a new method (Sections 3.