dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifying metadata and/or data is a practice that is different across disciplines. The Working Group decided that both metadata and data should be identified with a persistent identifier (PID) because both metadata and data were considered important in their own right. However, it was acknowledged that requiring separate PIDs to be assigned to both data and metadata might not align with existing practices where a PID resolves to a landing page that contains the metadata of the object and a link (e. g. URL) to the actual data contents. The main argument behind this was that machines should be able to automatically locate the actual data without needing a human to interpret the landing page. Assumptions on the way that data and metadata objects are identified in practice will influence the implementation of the FAIR principles.
Discussion The FAIR principles represent a collection of best practices regarding data management that is general enough to be valid across domains and has brought visibility on how poorly curated data can hinder data reuse and delay scientific advancement. The work of the FAIR Data Maturity Model Working Group has strengthened the realisation that community involvement and input is crucial to identify and build the standards and approaches that are required to enable FAIR data in a range of disciplines. These are also the standards and approaches that are required down the track for automated assessment of the FAIRness of data. At present, there is a lack of such standards and approaches in most disciplines to make such automated assessment possible. Specifically, the work carried out in the FAIR Data Maturity Model Working Group has been essential in identifying the relevant indicators when understanding the degree to which a digital object is FAIR.
For any indicators to be accepted by the broad scientific community, they must come from the community itself and, as such, the outputs of this RDA Working Group are the result of extensive consultation of and dialogue between domain experts from across disciplines. They represent a solid community-endorsed foundation on which further work can be built. From a policy maker's perspective, the indicators are essential in raising awareness of the FAIR principles, and in ensuring that the different research communities build comparable tools with which they can assess their respective FAIR data maturity. From a funder's perspective, these community-approved indicators are a necessary first step towards building a sound methodology to assess the FAIRness of publicly funded research outputs. As data management and FAIR become increasingly important elements in the assessment of proposals, and in the periodic assessments of ongoing projects, funders can only benefit from effective, unbiased and inclusive methodologies with which to evaluate the FAIRness of the research data produced.
In North America, particularly within some private funders, the value of well-documented, shared data is promoted and sometimes required. Notably, the U. S. Geological Survey (USGS) has taken decisive steps toward making their data FAIR and also including the role of repositories in providing necessary FAIR services on behalf of researchers, such as registering datasets for Digital Object Identifiers  (Lightsom, 2019). We are hopeful that as USGS progresses in their work, they will consider using the FAIR Data Maturity Model to evaluate their approach to FAIRness. Furthermore, work at the National Oceanic and Atmospheric Administration (NOAA) on a method to optimize the reuse of NOAA data through a various criteria of completed information positioned them to better support their researchers and help this Working Group as one of the adoption stories demonstrating that efforts made to make data FAIR benefit researchers and their communities.
4 rom the European Commission's perspective, with regards to policy, the FAIR principles, are instrumental in promoting the Open Science policy 5  of the Commission, which includes the wide and early sharing of knowledge (e. g. data) and tools by researchers within and across disciplines, and with society at large. The FAIR principles are at the heart of the European Open Science Cloud, 6  an initiative to build a trusted, open and distributed system for the scientific community, providing researchers with a seamless access to a web of FAIR data and services built on top of those data. With regards to the Commission's role as a funder, the FAIR principles are going to be very prominent under the upcoming framework programme for Research and Innovation, Horizon Europe  (Commission, 2018). The Commission will advocate for "responsible research data management in line with the FAIR principles".
Applicants will be evaluated at proposal stage on their plans with respect to data management, and specifically, on how they plan on making their data FAIR. This will continue to be relevant during the lifetime of a project, as beneficiaries will have to report on their data through a data management plan that will need to be updated during the project. As the scientific community values datasets that underpin research findings, the need to ensure the quality, understanding, and consistency of how these datasets are prepared for others to discover and experience needs a method of measurement. The FAIR Data Maturity Model provides a way for these community-based FAIR assessments to have comparable results and provide consistent feedback as to how well communities are doing in making research data FAIR.
Further work With the publication of the RDA Recommendation, the RDA FAIR Data Maturity Model Working Group reached the end of its charter. As it was considered useful to continue the work, the Working Group has transitioned to a maintenance Working Group dedicated to maintaining the FAIR data maturity model. As part of the ongoing work, feedback will be solicited from the community and other interested parties to take care of the further development and revision of the model. The focus will be on (1) coordinating efforts to ensure the adoption of the FAIR data maturity model by developers of assessment approaches and tools, (2) maintaining the deliverables produced taking into account feedback from the communities, and (3) proposing a governance model for handling maintenance activities. The maintenance Working Group will operate without time limit and will be home to discussion forums to address specific problems related to the outputs and recommendations produced during the inception phase.
Acknowledgements Throughout the development of the FAIR data maturity model, many people significantly contributed to the discussions and meetings. The Chairs, the  European Commission  and the editorial team is truly grateful for the work they have done and would like to thank them. Below you can find the names of the contributors, in no particular order;  Athanasios Karalopoulos ,  Alejandra González Beltrán ,  Alicia Fátima Gómez Sánchez ,  Andras Holl ,  Anusuriya Devaraju ,  Barbara Sierman ,  Carole Goble ,  Françoise Genova ,  Ge Peng ,  Gerry Coen ,  Helen Parkinson , Hervé L'hours,  Keith Jeffery ,  Kerry Levett ,  Kevin Long ,  Jean-Eudes Hollebecq ,  Jolanda Strubel ,  Jonathan Petters ,  Leyla Garcia ,  Marco Molinaro ,  Maggie Hellström ,  Mark Wilkinson ,  Marta Teperek ,  Michel Dumontier ,  Nichola Burton ,  Nick Juty ,  Mustapha Mokrane ,  Oya Deniz Beyan ,  Peter McQuilton ,  Rob Hooft ,  Romain David ,  Susanna Sansone  and  Yann Le Franc.
2011; Sharon et al. 2016). Also, without properly resolved datasets, the magnification of the molecular gas component is unknown, even in those cases where the lens model is robust, which limits the interpretation of the observations of gravitationally lensed galaxies  (Deane et al. 2013). In this paper, we use the gain in angular resolution and sensitivity provided by strong gravitational lensing to carry out a resolved study of the CO (1-0) molecular gas properties of two radioloud AGN on unprecedented angular scales. These data are coupled with high angular resolution continuum imaging at optical/nearinfrared (NIR) and radio wavelengths to investigate the distribution and kinematics of the molecular gas relative to the stellar emission and the non-thermal jets produced from the central engine.
Hubble Space Telescope High resolution optical/NIR observations of MG J0751+2716 and JVAS B1938+666 that were taken with the HST were retrieved from the archive and re-processed. The datasets were mainly obtained as part of the CfA-Arizona Space Telescope Lens Survey (CAS-TLES; www. cfa. harvard. edu/castles/) and the Cosmic Lens All-Sky Survey (CLASS;  Browne et al. 2003; Myers et al. 2003)  programmes. For both targets, data were taken using the Wide-Field Planetary Camera 2 (WFPC2) through the F555W and   F814W filters (GO-7495; PI: Falco, GO-8268; PI: Impey) and with the Near-Infrared Camera and Multi-Object Spectrograph (NIC-MOS) using the NIC2 camera through the F160W filter (GO-7255; PI: Jackson, GO-7495; PI: Falco). These data have been published most recently by  Alloin et al. (2007)  and  Lagattuta et al.
The central observing frequency for the observations of MG J0751+2716 was ν obs = 27. 4455 GHz, while for JVAS B1938+666 it was ν obs = 37. 6826 GHz. The A-configuration data for JVAS B1938+666 were taken using 2 spectral windows with 128 MHz bandwidth and 64 channels each using dual polarization. However, the spectral windows overlapped to reduce the effect of the sharp bandpass edges on any possible broad component of the emission line. A visibility averaging time of 3 s was used for all datasets. In addition, we re-analyzed archival VLA observations in D-configuration for JVAS B1938+666 (Project ID: 15B-329, PI: Sharon). These observations were for 3 h in total, and were taken using 16 spectral windows with a bandwidth of 128 MHz each. Note that 8 spectral windows covering frequencies that included the line emission were divided into 128 spectral channels, whereas 64 channels were used for the 8 spectral windows that covered the continuum emission (see  Sharon et al.
Calibration method All of the datasets were reduced with the Common Astronomy Software Application package (CASA;  McMullin & et al. 2007 ) using scripts that applied standard calibration procedures. Here, we summarize the steps. We first perform the standard a-priori calibrations, which included corrections for the antenna positions, the tropospheric opacity and the antenna gain curves as a function of elevation. We then inspect the visibilities in order to flag potential bad data. For the observations of MG J0751+2716, we found radio frequency interference (RFI) between 27. 4 and 27. 9 GHz that strongly affected the visibilities in the LL circular polarization. Therefore, we use only the visibilities in the RR polarization for the MG J0751+2716 dataset. We generally had to flag 2 to 3 antennas out of 27 antennas per observation that were not operational, and the initial 21 s of each scan, when the antennas were recording data while still slewing on-source.
Imaging and self-calibration of the continuum emission We perform phase-only self-calibration for the continuum emission separately for each dataset using the line-free spectral windows. We first make an image based on the calibration procedure described above and then use this model for the source to determine the phase solutions as a function of time. We start with a solution interval that is equal to the scan length, and then decrease this iteratively to a solution interval of 60 s. Note that during this process we apply the phase solutions to all spectral windows, including those with the spectral line. We then concatenate all of the self-calibrated datasets in order to perform the final imaging. We do not use the edge channels of the spectral windows, which are often noisier than the central channels.
Continuum subtraction and spectral line imaging The first step in extracting the CO (1-0) emission consists of subtracting the continuum emission from the interferometric data. This is done by fitting a first order polynomial function in the Fourier space to the real and imaginary parts of the line-free spectral windows only. The model of the continuum is then subtracted from the visibilities and a continuum-subtracted dataset is generated. We then made a dirty cube image in order to estimate the rms noise per channel. Subsequently, we interactively CLEAN the cube using a threshold that is 3 times the noise of the line-free channels and a natural weighting scheme. We also made cubes by applying a uv-taper and also using Hanning smoothing of the uv-data (at  For the A-configuration image, the off-source rms noise level is 40 µJy beam -1 and the peak surface brightness is 9 mJy beam -1.
However, as the highest angular resolution observation in A-configuration for JVAS B1938+666 and in B-configuration for MG J0751+2716 did not detect the CO (1-0) emission line (see be-low and Fig. A1 ), the surface brightness sensitivity, and hence, the significance of our detections went down for the combined datasets. Therefore, for our analysis, we use only the B-and C-configuration datasets for JVAS B1938+666 and MG J0751+2716, respectively. The spectra of the CO (1-0) emission, integrated over the extent of both lens systems, are presented in Figs. 5  and 6  for MG J0751+2716 and JVAS B1938+666, respectively. Finally, we con- struct moment maps for the integrated line intensity (moment zero) and for the line-intensity-weighted velocity (moment one), which are shown in Figs. 7 and 8, and will be described in detail in Section 4.
We believe the radio emission coincident with the AGN host galaxy to be the AGN core. However, higher angular resolution observations with spectral index information are necessary to confidently identify the radio core (expected to have a flat radio spectral index) in this system. The total continuum flux density is S 27. 4 GHz = 24 ± 2 mJy and S 27. 4 GHz = 25±3 mJy for the C-and B-configuration datasets, respectively. This suggests that even at these high frequencies the In each image we also overplot the moment zero contours (white in the left column, grey in the right column), which increase by factors of two. The first contour level is the off-source rms, which is 23 mJy km s -1 beam -1 for the naturally weighted map and 30 mJy km s -1 beam -1 for the tapered map. MNRAS 000, 1-22 (2020) In each image we also overplot the moment zero contours (white in the left column, grey in the right column), which increase by factors of two.
Nevertheless, as this fit has a similar reduced χ 2 value to that of the single Gaussian fit, we cannot consider this asymmetry statistically significant. Therefore, for deriving the CO (1-0) line luminosity we use the single Gaussian fit parameters (peak of 1. 5 ± 0. 1 mJy and a FWHM of 378±33 km s -1 ). The CO (1-0) line luminosity of MG J0751+2716 is µ CO × L CO(1-0) = (25. 7 ± 5. 1) × 10 10 K km s -1 pc 2 by following  Solomon & Vanden Bout (2005) , where µ CO is the magnification   3 ). These observed line properties are in excellent agreement with the results obtained by  Riechers et al. (2011) , who used the GBT and the VLA in D-configuration, and demonstrate that our VLA C-configuration dataset is recovering all of the CO (1-0) molecular gas associated with the lensed AGN. Our VLA C-configuration observations are about 3 times better in angular resolution (equivalent to an order of magnitude improvement in beam area) than those carried out previously and show that the CO (1-0) molecular gas emission from MG J0751+2716 is smoothly extended in a north-east to south-west direction.
JVAS B1938+666 The optical and NIR imaging of JVAS B1938+666 clearly resolves the early-type foreground lensing galaxy and the almost complete Einstein ring of the AGN host galaxy (see Fig. 2 ), as was previously reported by  King et al. (1997)  and  Lagattuta et al. (2012). We find that the radio continuum emission at 37. 7 GHz is not coincident with the AGN host galaxy, but is extended beyond the stellar halo (see Fig. 11 ): the offset between the radio and NIR emission is significant considering the astrometric precision of the Keck adaptive optics (4 mas) and the VLA A-array observations (30 mas; see below). Both the doubly and quadruply imaged parts and the extended gravitational arc that were found at lower frequencies  (King et al. 1997 ) are also detected in our B-and Aconfiguration datasets, with measured continuum flux-densities of S 37.
Target Config. S ν ∆v FWHM I CO µ CO × L CO µ CO Major axis Minor axis (mJy) (km s -1 ) (Jy km s -1 ) (10 10 K km s -1 pc 2 ) (kpc) (kpc) MG J0751+2716 C 1. 5 ± 0. 1 378 ± 33 0. 56 ± 0. 12 25. 7 ± 5. 1 2. 1 ± 0. 8 20 ± 3 6 ± 2 JVAS B1938+666 B 0. 9 ± 0. 4 683 ± 280 0. 94 ± 0. 23 20. 2 ± 4. 0 8. 7 ± 3. 1 5 ± 2 2 ± 1 the surface brightness distribution of the background source simultaneously in a fully Bayesian framework. We find the light profile of the lensing galaxy is well described by two elliptical Sérsic profiles for MG J0751+2716 and one elliptical Sérsic profile for JVAS B1938+666 (consistent with  Lagattuta et al. 2012  for the latter). Crucial to carrying out a multi-wavelength analysis is the correct alignment of the different datasets, which have uncertainties in the absolute astrometry of up to ∼ 0.
3 arcsec. We align the optical, NIR and radio data using the inferred positions of the lensing mass, which provides a common reference point between all datasets. We use the best quality lens mass models from  Lagattuta et al. (2012)  and  Spingola et al. (2018) , keeping all of the lens mass model parameters fixed except for the lensing mass position, which is allowed to vary, and apply a backward ray-tracing approach to determine the maximum likelihood source whilst optimising for the position of the lensing mass. This approach provides a precise measurement of the relative astrometry between the datasets, and is another advantage of multi-wavelength studies that use gravitationally lensed objects. The uncertainty in the lensing mass position, which translates to the uncertainty in the multi-wavelength alignment, is determined using MULITNEST  (Feroz et al.
2009 ) to sample the posterior density distribution. The uncertainty on the position of the lensing mass of MG J0751+2716 is inferred at 2. 5 mas (1σ) precision from the VLA dataset and at 1. 5 mas precision from the Keck adaptive optics dataset. For JVAS B1938+666, the precision on the lensing mass position is 30 mas from the VLA dataset and 4 mas from the Keck adaptive optics dataset. In order to reconstruct the radio continuum and spectral line emission in the source plane, we use a Bayesian pixellated lens modelling code that fits directly in the visibility space. This new extension of the  Vegetti & Koopmans (2009)  method will be presented in detail in a follow-up paper  (Powell et al. in prep. ). Due to the low signal-to-noise ratio detection of the CO (1-0) line, we do not use the full spectral resolution of the available line channels, but instead we average the data in frequency.
g. Venturini & Solomon 2004; Paraficz et al. 2018 ). However, we highlight that the estimate v max is likely affected by beam smearing (e. g. Di Teodoro & Fraternali 2015). Nevertheless, the low signal-to-noise ratio of the line emission prevents us to robustly model the source data cube using sophisticated kinematical modelling tools that take into account also the gravitational lensing effect  (Rizzo et al. 2018). Overall, JVAS B1938+666 has a somewhat similar sourceplane morphology to MG J0751+2716; there is a compact red galaxy that hosts a dust obscured AGN with small radio jets that can be seen just beyond the stellar component of the galaxy. The molecular gas distribution can be interpreted as a possible disc, but unlike in the case of MG J0751+2716, the extent of the molecular gas is much smaller, more regular and has a higher surface brightness.
2012a; Hodge et al. 2012). For instance, this value for the CO-H 2 conversion factor leads to gas fractions of f gas ∼ 80 per cent, which is much higher compared to what is predicted from simulations ( f gas ∼ 30 per cent). Moreover, independent estimates of α CO using different methods find that the CO-H 2 conversion factor covers a large range of values at high redshift (e. g. Daddi et al. 2010; Ivison et al. 2011; Hodge et al. 2012) , indicating that its value cannot be universally assumed, but must be estimated for each individual case. Alternatively, the conversion factor for lensed galaxies at high-redshift can be estimated by comparing the enclosed dynamical mass and the luminosity-derived molecular gas mass (e. g. Paraficz et al. 2018 ). However, this approach requires knowledge of the stellar mass and dark matter contributions to the total enclosed mass, which are not well constrained by our current datasets for MG J0751+2716 and JVAS B1938+666.
At this point, radiative feedback from the intense UV radiation field from the quasar should heat the gas and dust, possibly limiting star formation in the vicinity of the AGN, and perhaps further throughout the galaxy (up to kpc scales), until the quasar is no longer obscured. During this, and possibly later active phases for the super massive black hole, it is expected that radio jets inject mechanical feedback into the system, clearing the galaxy of a significant fraction of the dust and gas to the point that star formation is halted. Without significant gas accretion, the stellar population of the galaxy will continue to evolve to present day until a passive galaxy is formed  (Sanders et al. 1988). Our multi-wavelength dataset for MG J0751+2716 and JVAS B1938+666 is somewhat consistent with this picture.
ACKNOWLEDGEMENTS We thank the anonymous referee for their useful and constructive comments on the paper. CS is grateful for support from the  National Research Council of Science and Technology ,  Korea  ( EU-16-001 ). CS and JPM acknowledge support from the  Netherlands Organization for Scientific Research (NWO , project number  629. 001. 023 ) and the  Chinese Academy of Sciences  (CAS, project number  114A11KYSB20170054 ). SV has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  758853 ). MWA acknowledges support from the  Kavli Foundation. LVEK is supported through an  NWO-VICI grant  (project number  639. 043. 308 ). CDF acknowledges support for this work from the  National Science Foundation  under Grant No.
CESM-MAM7). Furthermore, the temporal samplings of satellite and model data also differ: for CESM-MAM7 the temporal sampling is twice a day (one simulation at noon and one at midnight), whereas MLS samples the Earth on distinct orbits, with a full global coverage every 3 days. Even though it is therefore possible that intensive short-time events are missed by either CESM-MAM7 or MLS, the sampling bias is not expected to present a significant source of 240 discrepancies for 3-month averages, as shown in Figures  1a  and 1b. Compared to MLS observations, the model underestimates the CO mixing ratio by about 30%. One possible reason for this underestimation could be an underestimation of biomass burning emissions in the model (obtained from GFED4), which are a significant source for CO. We have also compared CESM-MAM7 HCN mixing ratios (a strong biomass burning tracer) with ACE-FTS   , 2008) , with GFED at the low end of this variability.