dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, our data reveals Wikipedia was highly se-lective in regards to the existing scientific output dealing with COVID-19 (See supplementary dataset (1)). We next analyzed all the citations and references included in the complete Wikipedia dump from May 2020, using mwcite. Thus, we could extract a total number of about 2. 68 million citations (2,686,881) comprising ISBNs, DOIs, arXiv, PMID and PMC numbers (Supplementary figure  S1D ). Among the citations extracted were 860K DOIs and about 38K preprints IDs from arXiv, about 1. 4 percent of all the citations in the dump, indicating that the server hosting non-reviewed studies does contribute sources to Wikipedia alongside established peer-review journals. These DOIs were used as a separate group that was compared with the EuroPMC 30K DOIs (30,720) and the extracted DOIs (2,626 unique DOIs) from our initial Wikipedia COVID-19 set in a subsequent analysis.
An analysis of the journals and academic content from the 2,626 DOIs that were cited in the Wikipedia COVID-19 corpus reveals a strong bias towards high impact factor journals in both science and medicine. For example, Nature -which has an impact factor of over 42 -was among the top cited journals, alongside Science, The Lancet and the New England Journal of Medicine; together these four comprised 13 percent of the overall academic references (Figure  1A ). The Cochrane database of systematic reviews was also among the most cited academic sources (WPM and Cochrane have an official partnership). Notably, the papers cited tended to not just to come from high impact factor journals, but also have a higher Altmetric score compared to the overall average of papers cited in Wikipedia in general.
Among the preprints that were cited on Wikipedia was an early study on Remdesivir  (18)   two of the preprints focusing on the benefits of contact tracing  (23, 24). The number of overall preprints was in line with the general representation of preprints in Wikipedia (1. 5%), but lower than would be expected considering the fact that our academic database of EuroPMC papers had almost 3,700 preprints -12. 3 percent of the roughly 30,000 COVID-19 related papers in May 2020. Thus, in contrast to the high enrichment of preprints in COVID-19 research, Wikipedia's editors overwhelmingly preferred peer-reviewed papers to preprints. In other words, Wikipedia generally cites preprints more than it was found to on the topic of COVID-19, while COVID-19 articles cited open-access paper by more 10% (from 29% to 39%).
Taken together with the bias towards high-impact journals, our data suggest that this contributed significantly to Wikipedia's ability to stay both up to date and to maintain high academic standards, allowing editors to cite peer-reviewed research despite other alternatives being available. Due to the high selectivity of Wikipedia editors in terms of the percentage of COVID-19 academic research actually cited on Wikipedia's COVID-19 articles, we also focused on non-academic sources. Popular media, we found, played a substantial role in our corpus. Over 80 percent of all the references used in the COVID-19 corpus were non-academic, being either general media or websites (Figure  2A ). In fact, a mere 16 percent of the over 21,000 references supporting the COVID-19 content were from academic journals.
The prominence of articles like "Coronavirus disease 2019" or "COVID-19 drug development" -both of which were locked (supplementary dataset (3)) and fell under the auspices of the COVID-19 task force -in our network underscore the role academic media had in their references. Furthermore, it highlights the effects of the editing community's centralized efforts: for example, by allowing key studies to find a role both in popular articles reached from the main articles and in scientific articles linking out from them, thus creating the network we describe. In our network analysis, an additional smaller group of nodes (with a lower degree) was also found. It had to do almost exclusively with China-related issues. As such, it exemplified how Wikipedia's sourcing policy -which has an explicit bias towards peer-reviewed studies and is enforced exclusively by the community -helps fight disinformation.
In general, all factual claims on Wikipedia need to be supported by a verifiable source. Specifically, biomedical articles affiliated with the WikiProject Medicine (WPM) are bound by a specific policy known as MEDRS (which requires meta-analysis or secondary sources for medical content  (26) ). However, the mere existence of this policy does not necessarily mean it is respected. However, our findings indicate that this policy, aided by the infrastructure provided by the community to enforce it, likely played a key role in regulating the quality of coronavirus articles. One mechanism used generally by the WPM to enforce the MEDRS sourcing standards and specifically deployed by the COVID-19 task force during the pandemic was locking articles to public editing (protected pages, supplementary dataset (3)).
The WPM has formed institutional-level partnerships to provide editors with access to reputable secondary sources on medical and health topics -namely through its cooperation with the Cochrane Library. The Cochran Reviews' database is available to Wikipedia's medical editors and it offers them access to systematic literature reviews and meta-analyses summarizing the results of multiple medical research studies  (34). As well as the existence of this database on medical content, the practice of providing access to high-quality sources was also deployed specifically in regards to coronavirus in the form of the task force's list of "trusted" sources. Alongside Cochrane studies, the WHO, for example, was given special status and preference  (35). This was evident in our results as the WHO was among the most cited publishers on the COVID-19 articles.
In this study, which was limited to English, we retrieved most of our scientific literature metadata using Altmetrics  (14, 45) , EuroPMC  (16)  and CrossRef (15) R APIs. However the content of the underlying databases is not always accurate, and at a technical level, this method was not without limitations. For example, we could not retrieve all of the extracted DOIs' metadata. Moreover, information regarding open access (among others) varied with quality between the APIs  (46). In addition, our preprint analysis was mainly focused on MedRxiv and BioRxiv which have the benefit of having a distinct DOI prefix. Unfortunately, we found no better solution to annotate preprints from the extracted DOIs. Preprint servers do not necessarily use the DOI system (47) (i. e ArXiv) and others share DOI prefixes with published paper (for instance the preprint server used by The Lancet).
Corpus EuroPMC 30K Wiki Dump score Altmetric score A Scientific sources Nucleic Acids Research The Lancet Respiratory Medicine Virology PLoS Neglected Tropical Diseases bioRxiv Intensive Care Medicine Cell PLoS Pathogens Journal of Biological Chemistry Nature Medicine Journal of General Virology Annals of Internal Medicine Antiviral Research Scientific Reports Viruses (1999-4915) Journal of Infectious Diseases Nature Science The Lancet New England Journal of Medicine Proceedings of the National Academy of Sciences Journal of Virology British Medical Journal PLoS ONE Emerging Infectious Diseases JAMA: Journal of the American Medical Association Lancet Infectious Diseases Cochrane database of systematic reviews MMWR: Morbidity & Mortality Weekly Report Clinical Infectious Diseases 0 25 50 75 100 0 30 60 # Citations 90 120 B COVID-19 Corpus Paywall EuroPMC 30K 37% C Peer-reviewed COVID-19 Corpus EuroPMC 30K 90% Wikipedia dump 61% Wikipedia dump 99% Preprints 71% 98.
, 2003). Remote sensing datasets of wind waves go back to 1985  (Ribal and Young, 2019) , when the first satellite radar altimeter missions (Seasat in 1978 (the first satellite to provide data) and Geosat in 1985) were launched and started to provide ocean surface elevations with high temporal and spatial resolution. However, remote sensing data have to be validated against in situ measurements, typically available from buoys (such as NDBC buoys,  Swail et al. , 2010 , or NOWPHAS, Nagai et al. , 2005). Buoys measure vertical and horizontal displacements of the ocean surface (such as Spotter or Datawell buoys with up to 2. 5 Hz sampling frequency,  Raghukumar et al. , 2019)  and provide highly accurate estimates of wind wave characteristics, effectively now assimilated into Numerical Weather Prediction (NWP) models.
Assimilation of the significant wave heights from wave buoys in operational wave models decreases rootmean-square error in significant wave height forecasts by 27 % on average  (Smit et al. , 2021). However, buoy networks are sparse, with most deployments being in the coastal regions, and can only effectively serve for verification of all other datasets rather than for developing global or regional climatologies. Starting from the 1980s, considerable progress in wind wave modelling  (WAMDI, 1988; Hasselmann et al. , 1985; Cavaleri et al. , 2020)  resulted over the last decade in the development of multiple global and regional wind wave hindcasts generated by spectral wave models such as WAM  (WAMDI, 1988)  or  WAVEWATCH (WW3DG, 2019)  forced by atmospheric reanalyses or climate models, providing multidecadal wind wave fields with high temporal and spatial resolution  (Casas-Prat et al.
, 2018; Semedo et al. , 2018; Morim et al. , 2020 Morim et al. , , 2022;; Sharmar et al. , 2021). Being currently a widely accepted source for estimating long-term climate variability in wave characteristics, wind wave hindcasts also suffer from the inaccuracy in the modelling of many aspects of wind wave dynamics, including e. g. extremely high wave peaks at high wind speeds during the storm passage  (Cavaleri et al. , 2020). In summary, all three sources of global wind wave information (VOS, satellite data, and model hindcasts) require data for extensive validation. Existing wave buoys deployed in a few locations cannot solve this problem to the full extent. Thus, investigating alternative sources of massive wind wave data remains a challenge. In this respect, ship navigation radars represent an option whose potential, especially in open-ocean regions, is not yet explored to its fullest extent.
There are also attempts to use images of the sea clutter revealed from X-band radars for estimating the current-depth profiles with an Eulerian approach  (Campana et al. , 2017) , to retrieve wind speed and wind direction  (Chen et al. , 2015; Dankert and Horstmann, 2007; Dankert et al. , 2003; Vicen-Bueno et al. , 2013)  and to derive surface characteristics  (Senet et al. , 2001). On this basis, several commercial systems such as WaMoS II (http://www. oceanwaves. de, last access: 2 August 2022), SeaDarQ  (Greenwood et al. , 2018) , and WaveFinder  (Park et al. , 2006)  were developed. The most widely used system nowadays is WaMoS II (software and hardware details provided in  Reichert et al. , 2006) ; it is focused on the operational monitoring of the sea state (wind wave and surface currents) and operational management of oil platforms and ships using nautical X-band radars.
Derkani et al. (2021)  provided a dataset of the wind, wave, and surface currents over the Southern Ocean collected with WaMoS II  (Alberello et al. , 2020c; Derkani et al. , 2020). In combination with other sources of the data (altimetric wave radar, vessel hydrodynamic simulator), wind wave estimates from navigational radar can be used to manage security of the offshore systems, assess ship fatigue due to mechanical environmental influence  (Drouet et al. , 2013) , or predict ship rolling in real time  (Hilmer and Thornhill, 2015). We present the design and pre-processing methodology of the SeaVision system along with the dataset collected during three research cruises (Fig. 1 ). SeaVision was developed in collaboration between the Shirshov Institute of Oceanology of the Russian Academy of Sciences (IORAS, https://ocean.
ru/, last access: 4 August 2022) and the Joint Stock Company "Marine Complexes and Systems" ("MC&S" J. S. C. , https: //www. mcs. ru/, last access: 4 August 2022). SeaVision is developed on the basis of the sea ice monitoring system with navigational marine radar -IceVision (https://ice. vision/en, last access: 4 August 2022). The pilot version of SeaVision was tested and validated on two North Atlantic cruises in 2020 and 2021 and on the Arctic cruise in 2021 (Fig. 1 ). The major advantage of the presented dataset is the provision of the co-located Spotter wave buoy data with SeaVision records at almost 50 locations and outputs of WaveWatch III (WW3) model experiments forced by ERA5 reanalysis  (Hersbach et al. , 2020)  for the corresponding domains. We present in this study the SeaVision system and dataset of the measurements of the wind waves in the open ocean and their comprehensive analysis.
The paper is organized as follows. In Sect. 2 we provide details of the research cruises, technical specifications of the SeaVision system, data collection, and analysis principles, as well as the description of the WW3 model set-up. Section 3 presents the results of the analysis and validation of the SeaVision dataset against Spotter buoy data and the comparison to the WW3 model output. The concluding Sect. 4 summarizes the results and discusses the perspectives of the use of ship navigation radars for a massively enhanced collection of wind wave information in the open ocean.
Ship cruises Figure  1  demonstrates ship tracks of the three research cruises, during which wind wave data were collected. Research cruises were carried out by IORAS research vessels (R/Vs) Academik Sergey Vavilov and Academik Ioffe. Table  1  provides general information about the cruises and detailed information on the coordinates and dates and is provided in Appendix A. The two cruises in the subpolar North Atlantic (Fig. 1a, b ) were focused on the regular survey of the 59. 5 • N oceanographic trans-Atlantic cross section and cross sections in the Denmark Strait  (Verezemskaya et al. , 2021). During these cruises the R/V does full-depth conductivitytemperature-depth (CTD) profiling. The distances between the hydrographic stations vary from ∼ 30 km in the open ocean to a few kilometres near the East Greenland coast, with the time allocated for each station (the ship is drifting) varying from 2 to 6 h.
Such a design allows for the maintenance of at least 300 m distance between the buoys and the R/V. Then, both buoys were in the free-floating mode for at least 30 min, during which the recording was performed by both SeaVision and the Spotter buoy (Fig. 4a ). Lastly, both buoys were pulled back on board. The Spotter buoy measured vertical and horizontal displacements, starting from its release until being retrieved back on board. After completing measurements at each station, only the data recorded during the freefloating mode were used for the joint analysis of SeaVision and Spotter buoy records. During all SeaVision and Spotter buoy measurements, standard meteorological parameters were measured using the onboard meteo station. https://doi. org/10. 5194/essd-14-3615-2022 Earth Syst. Sci. Data, 14, 3615-3633, 2022  Development of the SeaVision system was based on a commonly accepted approach to the recording and analysis of the sea clutter images.
Using a similar approach, commercial systems such as WaMoS II (http://www. oceanwaves. de, last access: 4 August 2022), SeaDarQ  (Greenwood et al. , 2018) , and WaveFinder  (Park et al. , 2006)  were developed. These commercial systems provide customers with their original software and hardware. In our approach we are focused on the development of an independently operating, low-cost, and easy-to-install system compatible with the existing ship navigation radars. Research vessels Academik Sergey Vavilov (R/V ASV) and Akademik Ioffe (R/V AI) are equipped with the standard navigation X-band radars JRC JMA-9110-6XA and JMA-9122-6XA. Technical details of radar transmission and backscattering characteristics are given in Table  2. Both radars operate at 9. 41 GHz frequency (wavelength ∼ 3 cm) and are equipped with a 6 ft antenna with a directional horizontal resolution of 1.
By doing this, SeaVision converts the sea clutter image into a digital format and records the data onto the external storage. SeaVision is also connected to the ship navigation package and simultaneously records geographical coordinates from GPS, speed over ground (SOG), and course over ground  (COG). Each full turn of the antenna results in an ASCII file (∼ 16 MB) consisting of a 4096 × 4096 matrix (1. 875 m discretization at 4096 beam directions) representing the sea clutter digitized image with GPS information, SOG, and COG in the file header. These files are further consolidated and converted into NetCDF format at the postprocessing stage.
Analysis of the sea clutter images After the sea clutter images are collected and digitized, the next step is the post-processing focused on the computation of significant wave height (H s ), wave period (T m01 ), wave energy spectrum (S w ), and wave direction (D s ). Here we provide a short condensed description of the algorithm, with the full details given in Appendix B. The subset collected at each station (Fig. 1 ) consists of the 20 min SeaVision record, which is equivalent to at least 540 images of the sea clutter (27 antenna full turns per minute for the JRC JMA-9110-6XA radar). The methodology for estimation of wind wave characteristics relies on a well-established Fourier transform (FT) technique (Nieto-Borge and Guedes  Soares, 2000; Nieto-Borge et al. , 2006). For each station, pre-processing of the data begins with the choice of the processing squared area (squared area of 720 × 720 m).
Spotter wave buoy data To calibrate and validate SeaVision wave observations, we performed simultaneous measurements with the Spotter https://doi. org/10. 5194/essd-14-3615-2022 Earth Syst. Sci. Data, 14, 3615-3633, 2022   wave buoy (https://www. sofarocean. com/products/spotter, last access: 4 August 2022) in the locations shown in Fig. 1  and specified in Table  A1. Once the ship was drifting at the locations of the measurements, the Spotter buoy was deployed and started drifting away from the ship. Note that the ship drift is always faster compared to that of the buoy; thus, the distance between the buoy and the ship progressively increases. When the distance between the ship and the buoy reached at least 300 m, the "free-floating" mode of SeaVision and Spotter buoy operation was initiated for at least 30 min as described in Sect.
For low wind speeds SeaVision tends to underestimate H s by up to 60 cm, and for moderate and strong winds the analysis shows an overestimation of SeaVision H s compared to buoy and model data. This can be explained by better-developed https://doi. org/10. 5194/essd-14-3615-2022 Earth Syst. Sci. Data, 14, 3615-3633, 2022   ripples (affecting the signal-to-noise ratio) at the ocean surface under stronger winds. We also identified three locations (2901, 2928, and 2937; see Table  A1 ) for which the differences between the Spotter buoy data and SeaVision reach more than 1 m (for 5 and 13 m s -1 winds). Weather conditions for these cases were not associated with severe weather and H s values were in the range between 1. 5 and 2 m. However, in these cases we recorded a strong drift of the vessel due to the local current that potentially impacted the angle of the electromagnetic signal reflection from the surface and hence affected the accuracy of the radar images.
Data availability Datasets that contain significant wave heights, wave periods, wave directions, wave energy frequency spectra, meteorological data, and other related parameters from both SeaVision and the Spotter buoy at the locations of every station (Table  A1 ) are available in the PANGAEA repositoryhttps://doi. org/10. 1594/PANGAEA. 939620 (Gavrikov et al. , 2021). In this dataset we provide wind wave statistics disregarding separation of the swell and wind waves at this stage of the SeaVision development. We plan to include this procehttps://doi. org/10. 5194/essd-14-3615-2022 Earth Syst. Sci. Data, 14, 3615-3633, 2022 dure in the next studies. At the same time, we provide a 1D spectrum that potentially allows us to see the first and second peaks associated with wind waves and swell (an example is shown in Fig.
Conclusions To broaden the avenue for widely needed broad-scale highquality observations of ocean wind wave estimates, we used a conventional navigation X-band ship radar equipped with a SeaVision recorder and software package. Here we present the evaluation of the instrument package for measuring wind wave parameters and comparing them to in situ observations and model results. The data were collected on three cruises in the subpolar North Atlantic and in the Kara Sea. All SeaVision records were co-located with in situ Spotter buoy measurements, which were used for validation. We demonstrate overall agreement of the estimates of significant wave height and wave period measured by SeaVision with the Spotter buoy measurements and with simulations using the WW3 spectral wave model. Estimates of significant wave height between SeaVision, WW3, and the Spotter buoy are in better agreement than those for the wave periods.
A broad implementation of SeaVision opens a potential for enhancing massive observations of wind waves over the open ocean. SeaVision is currently mounted on board two R/Vs operated by IORAS, but in 2022 five more IORAS R/Vs will be supplied with SeaVision systems. Data records will become operationally available on an opensource web page. In 2023 we also plan to develop a portable and cheaper version of SeaVision that can be easily mounted on board any commercial ship with navigational radar operating in the open ocean as well as on the platform, lighthouse, or any coastal infrastructure. After further validation in dif-   A1. ferent sea state and weather conditions, we plan to upgrade SeaVision to a portable device and to incorporate all postprocessing procedures into the internal software package that will make it possible for commercial ships on which the system is installed to provide real-time reporting of wind wave parameters through the Global Telecommunication System (GTS).
Theoretically the estimated data flow is formally one estimate per 2-3 s (one full turn of the radar antenna). Even with a reporting frequency of once per minute, the potential of the SeaVision data flow exceeds the current VOS data flow by 100 times. Contrasting with existing commercial systems for wind wave monitoring with navigational marine radars, such as WaMoS II (http://www. oceanwaves. de, last access: 4 August 2022), SeaDarQ (http://www. seadarq. com/seadarq?set_language=en, last access: 4 August 2022), and WaveFinder  (Park et al. , 2006) , SeaVision represents potentially a low-cost, portable, and easy-to-install alternative. Wide use of such a system on commercial ships can drastically increase the number of sea state observations available to users, including the National Meteorological Offices using this information as data assimilation input for NWP models and reanalyses.
The Global Climate Observing System (GCOS) and associated Global Ocean Observing System (GOOS) consider the sea state to be a critical climate variable highly demanded by global observing modules. We hope that SeaVision with its perspective to provide exceptionally high global coverage with online wave measurements will meet this urgent demand and help to satisfy GCOS given its mandate for systematic observations under the UN Framework Convention on Climate Change (UNFCCC), also including GCOS and GOOS responsibilities under the Subsidiary Body for Scientific and Technological Advice (SBSTA) and the Subsidiary Body for Implementation (SBI). Further integration over the frequency domain returns the zeroth moment m 0,image of the S image (f ) spectrum:.