dataesr/scientific-paragraphs-categorization huggingface dataset:
Data management Before the launch of this study, all investigators and members of the investigative team took part in Good Clinical Practice (GCP) workshops. The sponsor's clinical research associates from the University Hospital of Nancy (France) will perform periodic monitoring throughout the trial. The data required to meet the research objectives will be collected in case report forms (CRF) specifically designed for this study and will come from the patient's medical records and the results of the analysis of biological samples. A database will be created specifically for this study and will use the double entry method. To ensure data quality and data concordance, two data entry technicians will independently feed data from the CRFs into the database. If inconsistencies are detected, a third investigator will decide on the data to be entered.
Methodology and Materials This systematic review follows the PRISMA Checklist (preferred reporting items for systematic review and meta-analysis protocols). As shown in Table  2  below, eight databases were searched covering several specialty areas to guarantee an interdisciplinary research strategy. A search strategy was developed and subsequently adapted for each database. No limitations on the publication date were defined. Search terms included the following key words: consumer attitudes towards olive oil, consumer behavior towards olive oil, consumer preferences for olive oil, consumer research on olive oil, consumer behavior towards olive oil carrying sustainability labels, consumer behavior towards olive oil carrying a PDO or PGI label (GI, region of origin, country of origin), eco-label, organic label, carbon footprint label, water footprint label, fair-trade label, consumer behavior towards purchasing olive oil produced by a cooperative/with a circular economy approach/in a short supply chain.
2 million sources across the three fields. We identified multi-wavelength counterparts to the radio detected sources, using a combination of the Likelihood Ratio method and visual classification, which greatly enhances the scientific potential of radio surveys and allows for the characterisation of the photometric redshifts and the physical properties of the host galaxies. The final radio-optical cross-matched catalogue consists of 81 951 radio-detected sources, with counterparts identified and multi-wavelength properties presented for 79 820 (>97%) sources. We also examine the properties of the host galaxies, and through stacking analysis find that the radio population with no identified counterpart is likely dominated by AGN at z ∼ 3 -4. This dataset contains one of the largest samples of radio-selected star-forming galaxies and active galactic nuclei (AGN) at these depths, making it ideal for studying the history of star-formation, and the evolution of galaxies and AGN across cosmic time.
However, such studies are typically limited to small-area fields with deep multi-wavelength and spectroscopic datasets, such as VLA-GOODS-N  (Morrison et al. 2010) , VVDS-VLA  (Bondi et al. 2003) , XMM-LSS  (Tasse et al. 2006) , and VLA-COSMOS  (Schinnerer et al. 2007; Smolčić et al. 2017b ). These deep surveys have helped to trace the history of star-formation, in a manner unaffected by dust absorption, thus constraining the dust-unbiased SFRD (e. g. Novak et al. 2017). They have also enabled the first studies of the evolution of the low luminosity AGN (e. g. Best et al. 2014; Pracy et al. 2016; Smolčić et al. 2017c; Butler et al. 2019)  as well as allowing the detection and characterisation of dust-obscured AGN (e. g. Webster et al. 1995; Gregg et al. 2002). However, even fields as large as COSMOS (∼2 deg 2 ) are subject to limited source statistics and cosmic variance effects; surveys covering large areas across many sight-lines are required to minimise these effects and to detect statistical samples of rare objects.
In the near future, the advent of the next generation of radio telescopes, such as the Square Kilometre Array (SKA;  Dewdney et al. 2009 ) and its pathfinders, in conjunction with other multi-wavelength facilities, such as Euclid  (Amendola et al. 2018 ) and the Vera C. Rubin Legacy Survey of Space and Time (LSST;  Ivezić et al. 2019) , will provide a revolutionary increase in survey speed, sensitivity, and source counts. The combination of these datasets will transform our understanding of the faint radio source population over the next decades, detecting orders of magnitude of more sources over large sky areas, down to sensitivities below what is even possible in the current small-area deep fields. The Low Frequency Array (LOFAR;  van Haarlem et al. 2013 ) Two Metre Sky Survey (LoTSS;  Shimwell et al.
Ciliegi et al. 1999; Ibar et al. 2009). The current LoTSS Deep Fields dataset, covering ∼ 26 deg 2 (including multi-wavelength coverage) and reaching an unprecedented depth of S 150MHz ∼20 µJy beam -1 , is comparable in depth to the deepest existing radio continuum surveys (e. g. VLA-COSMOS) but with more than an order of magnitude larger sky-area coverage. With this combination of deep, high-quality radio and multi-wavelength data over tens of square degrees, and along multiple sight-lines, the LoTSS Deep Fields are now able to probe a cosmological volume large enough to sample all galaxy environments to beyond z ∼ 1, minimise the effects of cosmic variance (to an estimated level of) ∼ 4% for 0. 5 < z < 1. 0;  Driver & Robotham 2010) , and build statistical radio-selected samples of AGN and star-forming galaxies, even when simultaneously split by various physical parameters.
To achieve this, we build a forced, matched aperture, multi-wavelength catalogue in each field spanning the UV to mid-infrared wavelengths using the latest deep datasets. This higher quality multi-wavelength catalogue is then used for crossidentification of radio sources in this paper, for photometric redshift estimates (see  Duncan et al. 2020, subm. ; hereafter Paper-IV) and, for detailed SED fitting to allow source classification and characterisation (see  Best et al. 2020, subm. ;  hereafter Paper-V). The identification of genuine counterparts to radio sources as opposed to random background objects is a challenging task. Emission from radio sources can be extended and the typically lower resolution of the radio data can lead to poor positional accuracy (and large, asymmetric positional uncertainties).
Radio data The details of the LOFAR observations used, along with the calibration and source extraction methods employed, are described in detail in Paper-I and Paper-II. Here, we summarise these steps and list the key properties of the radio data released (see Table  1 ). The LOFAR observations for the LoTSS Deep Fields were taken with the High Band Antenna (HBA) array, with frequencies between 114. 9-177. 4 MHz. The ELAIS-N1 data were obtained from LOFAR observation cycles 0, 2, and 4, consisting of 22 visits of ∼ 8 hour integrations (total ∼ 164 hours). The Lockman Hole data were obtained from cycles 3 and 10, with 12 visits of ∼ 8 hour integrations (total ∼ 112 hours). The Boötes dataset was obtained from cycles 3 and 8 with total integration time of ∼ 100 hours. The total exposure times, pointing centres and root mean square (RMS) sensitivities of calibrated data are listed in Table  1.
Multi-wavelength data in ELAIS-N1 The ELAIS-N1 field has deep multi-wavelength (0. 15 µm -500 µm) observations taken as part of many different surveys, covering up to 10 deg 2. The ELAIS-N1 footprint illustrated in  Fig. 1 (top)  shows the coverage of some of the key optical-IR surveys used, as well as the region imaged by LO-FAR (plot limited to the 30% power of the primary beam). In total, we generate photometry from 20 UV to mid-IR filters, with additional far-IR data from Spitzer and Herschel. The typical depths and areas covered by the multiwavelength imaging datasets are listed in Table  2.
Selected survey area for Boötes In this paper, subsequent analysis is performed for the overlap of the NDWFS and SDWFS datasets, covering ∼ 9. 5 deg 2 , as shown in Fig. 1  (bottom). This area was chosen as the largest area with coverage in most of the optical-IR bands. At the largest extent of the selected area from the radio field centre, the primary beam correction factor is ∼0. 39.
n(m, c) represents the sky density of all galaxies of magnitude m and colour c. f (r) is the probability distribution of the offset between the radio source and the possible counterpart, while accounting for the positional uncertainties of both of the sources. A full description of the theoretical background and method of the LR technique is given in W19 and is not reproduced here. Instead, we focus mainly on the specific application of the LR technique to the LOFAR Deep Fields dataset.
5 µm detected and optical detected 3  sources (separately). For the optical dataset, we use the PS1 i-band in ELAIS-N1, the NDWFS I-band in Boötes, and the SpARCS r-band in Lockman Hole. While there exist i-band data from RCSLenS in Lockman Hole, the survey coverage had gaps in the field between different pointings due to the survey strategy employed. Therefore, we compromise slightly on the choice of optical filter for LR analysis in favour of area coverage. The method of  Fleuren et al. (2012)  was then used to compute Q 0 in each filter. We list the Q 0 values in the optical and 4. 5 µm bands for each field from this first stage in Table  7. The differences in these Q 0 values are largely driven by the relative depths of the optical and Spitzer surveys between the three fields. The Q 0 values were then used to derive the corresponding q(m) distributions following the method of  Fleuren et al.
The flag values and their definitions are listed in Table  10  along with the numbers in each category per field. In addition to studying the nature of these sources, an advantage of assigning the NoID flag is that with upcoming spectroscopic surveys (e. g. WEAVE-LOFAR;  Smith et al. 2016) , a fibre could well be positioned at the position of those radio sources with secure positions to obtain spectra of (and of any emission lines from) the host galaxies where existing optical to MIR imaging data is too faint. A large fraction (typically > 70%) of the radio sources without an identification were un-resolved (or barely resolved) sources with a secure radio source position. The host galaxy, however, was below the survey depths of our multi-wavelength dataset (albeit in some cases, low significance emission may be present).
This is likely due to the slight difference in the PyBDSF source extraction parameters used for Lockman Hole (and for Boötes), where a significantly larger fraction of the sources were fitted with multiple Gaussian components, resulting in ambiguity in the decision tree and requiring source association or de-blending. The effect of the difference in the PyBDSF source extraction parameters is less prominent in Boötes, likely due to the shallower radio data depth. It is important to note that these differences should not affect the final source-associated, cross-matched catalogues, but simply result in a difference in the method of the identification: visual classifications were more often used to form the correct source associations for sources split into multiple Gaussians. The LOFAR Deep Fields value-added catalogue released contains properties of the correctly associated radio sources, and the multi-wavelength counterpart identifications and properties (where available).
The radio source properties are as follows: -The IAU source identification ("Source Name") based on source position. -Radio source position and uncertainties ("RA", "E RA", "Dec", and "E Dec"). -Radio source peak and total flux densities and corresponding uncertainties ("Peak flux", "E Peak flux", "Total flux", "E Total flux"). -Ellipse shape parameters and corresponding uncertainties ("Maj", "Min", "PA", "E Maj", "E Min", "E PA"). These are blank for associated sources; see below for properties of associated sources. For de-blended sources, these are taken from the PyBDSF Gaussian catalogue. -A code to define the source structure ("S Code"; 'S' = single-Gaussian, 'M' = multi-Gaussian, 'Z' = associated/compound source) -Overlap bit flag indicating the coverage of the multiwavelength surveys at the radio source position ("flag overlap radio").
The trend of decreasing overall identification rate (see Fig. 10 ) with decreasing radio flux densities noted by W19 (down to ∼ 1mJy in W19) in LoTSS-DR1 (see Fig. 8  of W19) is not observed in these deep fields. This is expected as this decrease in identification rate was attributed to the shallow PanSTARSS and WISE data available for cross-matching by W19. In the LoTSS Deep Fields, although the typical redshift of sources probed increases with decreasing radio flux density, the significantly deeper multi-wavelength data available allows us to effectively identify counterparts down to lower radio flux densities than LoTSS-DR1, where the LR method starts to dominate the identification rates.
5 µm. Fig. 12 : Optical to mid-IR stacked SED of the unidentified radio sources with secure radio positions in ELAIS-N1 (571 sources; black points). The stack for the subset of the 'FIR-bright' sources (50 sources) is shown as red squares. Overlaid on top are typical star-forming galaxy templates, computed using models of  Bruzual & Charlot (2003)  at z = 2, 3 and 4 (blue, orange and green solid lines, respectively), scaled to the median SFR detectable for these sources at the depth of the LOFAR data in ELAIS-N1. The dashed lines show two-component templates of galaxies with an old stellar population undergoing a recent burst of star-formation (see text) at z = 3 and 4 (red and purple, respectively). the  Chabrier (2003)  initial mass function (IMF) and the  Calzetti et al. (2000)  attenuation curve.
Conclusions In this paper, we have presented the value-added catalogue of multi-wavelength counterparts to the radio sources detected in the first LoTSS Deep Fields Data Release by  Tasse et al. (2020, subm. )  and  Sabater et al. (2020, subm. ) , covering the ELAIS-N1, Lockman Hole, and Boötes fields. The value-added radio-optical cross-matched catalogues presented contain 81 951 radio sources, with counterparts identified and matched aperture optical to infrared properties presented for 79 820 sources (> 97%), covering ∼ 26 deg 2 in total, across multiple sight-lines. To achieve this, we first built new multi-wavelength catalogues in both ELAIS-N1 and Lockman Hole, consisting of forced, matched aperture photometry across 20 and 16 bands, respectively, from UV to mid-IR. These catalogues were built using deep χ 2 detection images, using information from the optical and IR bands, to maximise the catalogue completeness and generate clean, robust photometry and colours; this provides a significant improvement to catalogues existing in literature for photometric redshifts and SED fitting.
In contrast, deeper optical data are required to achieve higher identification rates and probe the optically faint counterparts to beyond the peak of host-galaxy magnitude distributions in these optical filters. The scientific potential of the catalogues presented in this paper is further increased by the availability of photometric redshifts. Rest-frame colours and photometric redshifts for both the multi-wavelength catalogues and the radio-optical cross-matched catalogues in the three fields are presented in Paper-IV. This enables one to determine physical properties of host galaxies (such as luminosities, stellar masses, star-formation rates, etc. ), which are used to perform source classification as presented in Paper-V. We performed a stacking analysis of the radio sources without an identification (but with secure radio positions) and compared the resultant average SED to typical starforming and passive AGN templates.
This revealed that the unidentified radio source population is likely dominated by a significant fraction of obscured AGN at moderate to high redshift (z > 3). For future LoTSS Deep Fields data release, the inclusion of deeper optical data from the HSC-SSP DR2 in ELAIS-N1, reaching depths of 27. 5, 27. 1, and 26. 8 mag in the g-, r-, and i-bands, respectively, should allow us to identify counterparts to a majority of the currently unidentified radio sources. We are continuing to acquire and calibrate more LO-FAR observations of the first three deep fields, ultimately aiming to achieve a target sensitivity of 10 µJy beam -1. The increase in source density offered by the deeper radio data will require more efficient and automated methods for deblending, such as the XID+ software  (Hurley et al.
2017 ) used for FIR Herschel data, with the capability of modelling the extended nature of radio sources. Furthermore, future data releases will also include deep radio imaging of the North Ecliptic Pole (NEP) field, reaching comparable sensitivity to the first three deep fields. This will be com-plemented by the ongoing and planned multi-wavelength observations from the next generation of telescopes such as Euclid and eROSITA and will achieve optical and IR depths capable of identifying the host galaxies currently undetected in the first three deep fields.
Acknowledgements This paper is based (in part) on data obtained with the  International LOFAR Telescope (ILT)  under project codes  LC0 015 ,  LC2 024 ,  LC2 038 ,  LC3 008 ,  LC4 008 ,  LC4 034  and  LT10 01. LOFAR (van Haarlem et al. 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and which are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris and Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland.
RKC acknowledges funding from the  John Harvard Distinguished Science Fellowship. JHC and BM acknowledge support from the  UK STFC  under grants  ST/R00109X/1 ,  ST/R000794/1  and  ST/T000295/1. KJD, WLW and HJAR acknowledge support from the  ERC  Advanced Investigator programme New-Clusters 321271. AG acknowledges support from the  Polish National Science Centre (NCN)  through the grant  2018/29/B/ST9/02298. MJH acknowledges support from  STFC  via grant  ST/R000905/1. MJ acknowledges support from the  National Science Centre, Poland  under grant  2018/29/B/ST9/01793. MJJ acknowledges support from the  UK Science and Technology Facilities Council  [ ST/N000919/1 ] and the  Oxford Hintze Centre for Astrophysical Surveys  which is funded through generous support from the  Hintze Family Charitable Foundation.
MKB and AW acknowledge support from the  National Science Centre, Poland  under grant no. 2017/26/E/ST9/00216. VHM thanks the  University of Hertfordshire  for a research studentship [ ST/N504105/1 ]. IP acknowledges support from  INAF  under the  SKA/CTA PRIN "FORECaST " and under the  PRIN MAIN STREAM "SAuROS " projects. WLW also acknowledges support from the  CAS-NWO  programme for.
Many governments have begun to adopt aggressive targets for electric vehicles. However, studies of the drivers of electric vehicle (EV) adoption are scarce. Social media interactions can provide a new data-driven vantage point to explore such drivers. This study uses data from 36,000 public posts on Facebook to investigate intersectionality in EV-communication as per the Political, Economic, Social, Technological, Legal and Environmental (PESTLE) categories. A computational social science methodology was adopted using a mixed-method application of social network analysis and machine learning-based topic modelling through Latent Dirichlet Allocation algorithm on a 600,000-text corpus extracted from the Facebook posts. Results showed that political, economic, and legal posts had dense clusters around the technology policy of EV, the institutional discourse of electrification of the federal vehicle fleet, and tax and credit framework politics.
However, studies of its intersections with the Political, Economic, Social, Technological, Legal and Environmental (PESTLE) system are scant. With more EVs penetrating the U. S. market, it is crucial to identify such intersection points to sustain EV growth. This study fills this knowledge gap by investigating the demand-side PESTLE attributes of EV adoption and diffusion in the United States. The novelty lies in deriving an intersectional PESTLE-based overview of EV transition using crowdsourced public data from social media interactions of EV-related posts in 2020. A computational social science methodology was used to derive the PESTLE indicators using a mixedmethod approach of social network analysis (SNA) and machine learning-based topic modelling to extract latent topic clusters. The use of the above methodology contributes to the state-of-the-art discussions on social data science for public policy applications.
The technology, legal and environmental factors would accelerate the low-carbon transition in the transportation sector. Recently, Capuder et al. [22]  carried out risk analysis in EV integration policymaking using this framework. The authors found that high probability risks included technology and range anxiety risk among the consumers, whereas charging infrastructure risk, regulation and incentive reduction risks were categorised as low probability risk in EV integration policies. Similarly, PESTLE analysis was used to evaluate compact EV and gasoline cars  [23]. Methodological advancement in the PESTLE framework space indicates a growing theoretical scoping of mixed-method approaches using data science. Campbell et al. [24]  call for leveraging artificial intelligence (AI) in PESTLE and SWOT analysis in marketing research.
e. , 'Political', 'Economic', 'Social', 'Technological', 'Legal' and 'Environmental') with 'EV' and 'electric vehicle'. The bibliometric analysis was performed using the methodological guidelines of Aria and Cuccurullo  [26]. Bibliometrics is the use of statistical methods to analyse books, articles, journal articles and other publications. The bibliometric indicators used here were 'keyword/term frequency', 'word cloud' and 'co-occurrence network maps' [see  Kurtz and Bollen [27]  for a detailed definition of these indicators]. The bibliometric analysis data was extracted from the 'Web of Science (WoS)' database; it is the most authoritative citation database with publications of high quality. A similar approach was recently used by Debnath, Mittal and Jindal  [28]  to review the current challenges in the Indian Power System.
Gatto and Drago  [29]  derived the taxonomy of energy resilience using a similar bibliometric approach. Renewable energy finance was identified through a detailed bibliometric analysis by Elie, Granier and Rigot  [30]. A recent bibliometric analysis to identify suitable business models for EV provides critical clues for this section, see Secinaro et al. ,  [31]. A total of 11,478 published documents were identified in the WoS database between 2015 and 2020 on EV with individual PESTLE elements. The documents that had different words related to individual PESTLE elements and EV simultaneously in its title, abstract or journal keywords were classified as those of the individual PESTLE group. For example, the 'Policy' group consisted of 2689 articles that words 'political/policy/politics' along with 'EV/electric vehicle' in its title, abstract or journal keyword lists (see Fig.