dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). There was a significant change in the quality of sunspot drawings due to a new observation method used by Galileo from May, 1612. A student of Galileo, realized that one could project the solar image on a paper sheet in order to observe the Sun at any time of the day  (Carrasco et al. , 2020). This projection method greatly increased the number of groups recorded by Galileo. This fact can be seen from the comparison between the number of groups recorded by Galileo and Harriot  (Carrasco et al. , 2020). Mun ˜oz-Jaramillo and Vaquero (2019) describes the challenges encountered when trying to stitch together historical records, and thus when using said records. The challenge described here is mostly linked to the sparsity of coverage over certain key periods and can be partly overcome by adding more historical data to the time series as described in  Arlt and Vaquero (2020)  (see also the Belgian FARSUN project 3 ).
One of the remaining issues are the still imprecise uncertainties. The GN series has been reconstructed using seven different methods  (Hoyt and Schatten, 1998a; Hoyt and Schatten, 1998b; Cliver and Ling, 2016; Svalgaard and Schatten, 2016; Usoskin et al. , 2016; Willamo et al. , 2017; Chatzistergos et al. , 2017; Dudok de Wit, personal communication). Fig. 1  shows the results of these reconstructions for the 1700-2010 period. The reconstructions show larger differences mostly in the pre-Greenwich era (before 1867). They also have different time resolutions depending on the cadence of data employed: daily data  (Cliver and Ling, 2016; Usoskin et al. , 2016; Chatzistergos et al. , 2017; Dudok de Wit, personal communication)  or yearly data  (Svalgaard and Schatten, 2016). Hoyt and Schatten (1998a) ; Hoyt and Schatten, 1998b created the first GN series in 1995 and an associated database.
This series is known to suffer from many problems in data interpretation  (Vaquero and Trigo, 2014)  and compilation methods  (Clette et al. , 2014; Cliver, 2015). Cliver and Ling (2016)  tried to reinterpret the Hoyt & Schatten database and created a revised GN that appears in blue in Fig. 1. However, it still uses the old method of simple kfactors, which is known to introduce bias. Svalgaard and Schatten (2016)  present a reconstruction of a complete GN series using a backbone 6 method. Contrary to the excessive use of daisy chaining 7  used in previous reconstructions, the backbone-method enables to actually minimize the number of links in the chain. Lockwood et al. (2016a) ;  Lockwood et al. , 2016b  discuss the use of daisy chaining and the propagation of possible errors made on the linear fit between two observers.
Usoskin et al. (2016)  describe a method using the active day fraction (in a month) to compute an individual observational threshold for each observer that can be applied to a reference dataset  (Greenwich data)  in order to rescale (without overlap) all observers to a common reference. Despite the clear advantages, the approach may suffer from a hard-toavoid dependency on the level of solar activity for nonoverlapping observations. Willamo et al. (2017)  tried to correct this method by addressing the known issues, but the dependency on the activity level still remains unresolved. Chatzistergos et al. (2017)  developed a backbone reconstruction of group numbers that is based on the correspondence matrices described in  Usoskin et al. (2016)  instead of using a ''simple" linear dependency between independent observers as in  Svalgaard and Schatten (2016).
, 2021a ; see also link to ''sunspot numbers data" in NSO's Historical Archive 8 ). These corrections are described in  Clette and Lefevre (2016). However, recent works by  Clette et al. (2021)  and  Bhattacharya et al. (2021) ;  Bhattacharya et al. (2023)  have shown that it is possible to correct the remaining inconsistencies by reconstructing the SN series from the available raw sunspot and group data. It is the case for the scale transfer from Schwabe to Wolf in 1849  (Bhattacharya et al. , 2023)  and the transition between Wolf and Wolfer over the 1860-1893 period  (Bhattacharya, personal communication). WDC-SILSO SN2. 1 time series has been recently described by  Bhattacharya et al. (2023). The historical part of the next revision of the WDC-SILSO SN series (V3) will be released by the end of 2023, and is based on all available sunspot and group number data over the 19th and the beginning of the 20th century, including recently recovered data due to work by Drs.
Outstanding questions Computing SN and GN historical time series is never simple. First, the datasets are not homogeneous and could be affected by various observing biases, including changes in the observers' cohort with different levels of experience. Observing biases may also include differences in telescope aperture, the amount of scattered light (quality of optics and atmospheric seeing conditions), and even human vision (e. g. ,  Karachik et al. , 2019). The effect of (small) telescope apertures could be different for SN, GN, and total 8 https://nso. edu/data/historical-archive/. area of sunspot groups (e. g. ,  Nagovitsyn and Georgieva, 2017). Second, data coverage is not homogeneous, with relatively good coverage in some periods and fewer or even no observations in other periods. Finally, the methods of reconstruction are still under development.
Updates and revisions of the PMOD and ACRIM composites were more recently published by  Schmutz (2021)  and  Scafetta et al. (2019) , respectively, but the changes are not significant. The reported secular change ranges from a rise between the activity minima in 1986 (solar cycles 21/22) and 2009 (solar cycles 23/24) in the ACRIM composite to a weak decline in PMOD. The rise in the ACRIM composite is, however, entirely due to the unaccounted slips in the Nimbus-7/ HF data  (Lee et al. , 1995) , and is not featured by any other composite, models, or indeed alternative solar activity proxies. In addition to these three TSI composites produced by different instrumental teams, two alternative composites were recently produced, essentially by statistical analysis and weighting of data from individual instruments (Dudok de  Wit et al.
, 2022b and references therein). Chatzistergos et al. (2020b)  have presented the first composite of plage areas from 43 available datasets since the late 19th century. Being a good tracer of the solar surface magnetic field, Ca II images have the potential to provide the missing information and allow critical constraints on the secular variability  (Chatzistergos et al. , 2021). Reconstructions going yet further back in time, before the start of telescopic observations of sunspots, use concentrations of cosmogenic isotopes in terrestrial archives, such as 10 Be in ice sheets or 14 C in ancient plants, as a proxy of solar magnetic activity. This is possible because cosmogenic isotopes are produced by galactic cosmic rays (GCRs), whose flux is modulated by the solar open magnetic flux (see  Usoskin, 2023, and Sections 2.
However, it is still unclear how reliably the results of an analysis of a large-ensemble data (around 5,000 stars) can be projected to a single star, our Sun, which is less active than the majority of the Sun-like stars (e. g. ,  Reinhold et al. , 2020). Analyses of flaring stars in the Kepler data (and Gaia dataset) use different filters, whenever they are available, such as age, temperature, rotational period 25  , metallicity, absence of companions (see, e. g. ,  Okamoto et al. , 2021; Vasilyev et al. , 2022) , but other important parameters (e. g. , differential rotation, depth of the convection zone, meridional flow) cannot be defined straightforwardly for the stars. Sporadic cosmic rays, transients, or unresolved overlapping flaring objects can also contaminate the results  (Vasilyev et al.
Outstanding questions Models related to cosmogenic-isotope transport/deposition were primarily developed for slowly changing GCRrelated production and may be invalid for fast ESPEs, potentially leading to large errors (see, e. g. ,  Koldobskiy et al. , 2023 vs. Mekhaldi et al. , 2015). Additionally, stellar-solar projections are done by extrapolating simple scalings leading to huge uncertainties in estimates of the superflare occurrence probability of the Sun  (Notsu et al. , 2019; Reinhold et al. , 2020; Okamoto et al. , 2021; Vasilyev et al. , 2022). Thus, dedicated sophisticated analysis methods need to be developed to eventually resolve the existing controversy between the results from solar (proxy) and stellar (direct) datasets and methods. Different reasons can be related to the large uncertainties, and several outstanding questions stand up in this respect: Do extreme solar events and stellar superflares reflect the same type of events? Does the Sun have limits in the energy of eruptive phenomena? Is there an intrinsic limit (streaming limit) in accelerating SEPs for extreme events? Can the statistics of stellar superflares be directly projected to the Sun? Beyond purely academic questions, a crucially important one is related to the possible technological and societal consequences of such an extreme once-in-a-millennium event for our highly technological society.
A comprehensive inventory of solar and geomagnetic datasets relevant for long-term space weather and space climate research Both historical and more modern observations of the Sun are available to researchers and the general public via various datasets. The datasets based on NASA spacecraft data are better organized, although, due to the preferences of research communities, the data from different heliophysics and geophysics sub-fields are using different formats and metadata. The data can be accessed via the NASA Heliophysics Data Portal (see the first entry in Table  3 ). The dataset is organized by a mission (or spacecraft), and it even includes some ground-based instruments. Non-NASA data are less well organized, although NOAA's National Centers for Environmental Information (NCEI) contains a good collection of solar and heliospheric data.
Das et al. (2019). Right: A simulated 3D view of the steady-state imposed magnetospheric configuration of a planet without an intrinsic magnetosphere. The stellar wind eventually strips off the atmosphere of the planet in the course of its evolutionary timescale  (Basak and Nandy, 2021). Traditionally, historical datasets are maintained by projects that were involved in their collection or digitization. This decentralized approach does not guarantee a longterm curation of the datasets. Even for large observatories, historical datasets could be in danger of neglect as the priorities change and funding sources ''dry out". For example, as the NSO relocated to Colorado from its previous locations at New Mexico and Arizona, only selected historical data were transferred. Other data were either left behind to new observatory operators or were transferred to some other users.
Some historical data accumulated by NOAA in its Boulder, Colorado location were given away to individuals as new priorities for the warehouse led to the reduction of available space. Digitized observations of Ca K line taken at Mount Wilson Observatory (MWO) were partially lost as the data server was turned off due to a lack of understanding of the importance of the data and a lack of funding. Some MWO data were recovered using copies made by members of the international research community for their individual projects. The datasets maintained by individual projects are much harder to find and could be highly non-uniform with respect to the data format and metadata. Some data are provided as ASCII tables in the attachment to published papers. As an alternative, some (small) datasets are placed on free data repositories (e.
g. , Harvard Dataverse 27 , VizieR Online Data Catalog by Centre de donnees astronomiques de Strasbourg 28 ). The repositories provide the ability to create metadata and to assign DOI 29 numbers. However, the DOI numbers themselves need to be maintained and updated with new pointing information. Unfortunately, there are already cases when DOIs may end in a broken link. There are also several archives that collect information (links) about the datasets, which may improve the discoverability of some datasets. Table  3  provides brief information about selected datasets. NASA also funds the development of the Virtual Solar Observatory (VSO) to improve the accessibility of heliophysics data. However, VSO only serves as an interface for connecting the user with the data providers. Data curation remains the respon-sibility of data providers.
Observable Period Link into action a method for gathering, processing, and preserving historical sunspot data. Its goal is to make the raw historical sunspot data of national and international origin findable and accessible for all users. The project gathers, interprets, and valorizes the data. It includes data pre-processing, quality assessment, and standardization, as well as publicity towards end users. A detailed statistical study will provide quality criteria for these historical data, and the most pertinent criteria will be included in metadata describing the dataset. This project extends across multiple fields: the WDC-SILSO expertise as curator of the International Sunspot Number is complemented by statistical expertise, expertise on historical datasets, Virtual Observatory (VO) expertise, time-series expertise, and a team of experts in optical characters recognition 31.
The output of this project will be a compilation of historical sunspot numbers that will be made available via standard VO tools defined by the International Virtual Observatory Alliance (IVOA 32  ), and more specifically via an EPN-TAP (Europlanet-Table Access Protocol) service. This will allow this catalog to be findable (via the IVOA Registry) and accessible by queries from a variety of TAP clients. In addition, the standardization of this VO tool allows other tools (graphical viewers, editors, etc) to easily access the data, making them interoperable, while the rich catalog metadata will allow the data to be reusable, i. e. , the data will be FAIR-compliant  33. Making these validated historical sunspot data collections FAIR will allow solar physicists, Earth-climate modelers, experts in statistics, or anyone with a keen interest in solar variability, to analyze this unique natural record and will ensure its sustainability in the long term, while the association of specifically developed metadata will enable homogeneous processing for future additions to the dataset.
Continuity of long-term observations of the Sun While this ISWAT S1 cluster concentrates on long-term solar variability, the continuity of modern-day observations over extended periods of time (synoptic observations) is critical in building the datasets that future researchers would call ''historical data". Thus, for example, continuous observations are essential to study changes in solar differential rotation, the meridional flows, kinetic and magnetic helicities -all key ingredients for solar dynamo. A routine observation of Doppler velocities led to the discovery of socalled torsional oscillations, latitudinal bands with faster or slower solar rotation  (Snodgrass and Howard, 1985). Spatially, the torsional oscillation pattern coincides with latitudes of sunspot emergence, but the pattern also precedes the active region development, giving an indication of extended solar cycle.
In that respect, preservation, recovery, digitization, long-term curation, and scientific exploration of historical data is essential. There is also a need to develop a better characterization of historical datasets in respect to their uniformity and error analysis. Long-term TSI time series has reached a community consensus. However, to develop SSI time series of similar quality as TSI would require longer SSI measurements with sufficient long-term stability. See Section 2. 2. 4 for outstanding questions related to solar total and spectral irradiance. Outstanding questions for large-scale heliospheric parameters/structure can be found in Section 2. 3. 4. Solving the open flux problem and developing a better understanding of long-term evolution of the global structure of the heliosphere are some of the most important questions.
High latitude in situ and remote observations would be extremely beneficial for achieving these goals. There are several outstanding questions related to extreme solar events (see Section 2. 4. 4) including the unknown physics of extreme events, their possible impacts on our highly technological society, and, not least, resolving the apparent controversy in the level of flare activity based on solar proxies and direct observations of solaranalogues. To answer these questions, new precise datasets in both terrestrial proxies and stellar observations are needed, along with the development of sophisticated analysis methods and new models. Currently, there is a lack of models for extreme events. In modeling of solar dynamos, further improvements need to be made in global 3D simulations to address shortcomings of current models.
Acknowledgments The  National Solar Observatory  is operated by the  Association of Universities for Research in Astronomy (AURA), Inc. , under a cooperative agreement with the  National Science Foundation. The  Center of Excellence in Space Sciences India (CESSI)  is funded by the  Ministry of Education, Government of India. C. S. is funded by the  Council of Scientific and Industrial Research, Government of India  through Grant No. 09/921(0334)/2020-EMR-I. A. S. B, B. P. and A. S. acknowledges funding by the  European.
Solar activity from the catalogs of the Zu ¨rich Observatory 1887-1920 https://doi. org/10. 1007/s11207-022-02015-3 BASS-2000: Solar survey archive Various https://bass2000. obspm. fr/home. php Databases at the Debrecen Observatory 1872-2014 http://fenyi. solarobs. epss. hu/en/databases/Summary/ Sunspot positions and areas 1612-1614 https://doi. org/10. 1007/s11207-020-01752-7 Sunspot number 1900-1911 https://doi. org/10. 1002/asna. 202013654 Sunspot positions and areas 1610-1613 https://doi. org/10. 1007/s11207-020-01604-4 Sunspot positions 1761, 1764-1777 https://doi. org/10. 1007/s11207-019-1466-y Sunspot positions 1761, 1764-1777 https://doi. org/10. 1007/s11207-019-1465-z Sunspot positions 1747 https://doi. org/10. 1002/asna. 201913544 Sunspot positions 1680-1709 https://doi. org/10. 1002/asna.
org/10. 7910/DVN/VF5BMO Sunspot positions (NSO/Sac Peak) 1947-2004 https://doi. org/10. 25668/9x5p-6d86 NSO/Sac Peak flare patrol Ha 1966-2000 https://nispdata. nso. edu/ftp/flare_patrol_h_alpha_sp/ NSO/Sac Peak spectroheliograph Ca K and Ha 1965-2002 https://nispdata. nso. edu/ftp/Evans_spectroheliograms/ https://nispdata. nso. edu/ftp/ESF_SHG_cdrom/ NSO/Sac Peak Ca II K-line monitoring program Nov. 1976 -Oct. 2015 https://nsosp-dev. nso. edu/node/15 NSO Ca K indices from the Kitt Peak vacuum telescope 1974-2013 https://solis. nso. edu/kpvt/CaK_Indicies/K-line_stats. dat NSO/SOLIS integrated sunlight spectrometer 2006-2017 https://doi. org/10. 25668/61PJ-WH14 Full disk Ha images (GONG) 2000-present https://doi. org/10. 25668/as28-7p13 Combined database of sunspot magnetic fields (USSR-Russia) 1957-1997 http://www.
gaoran. ru/database/mfbase/main_e. html Pulkovo catalogue of solar activity (various solar features and indices) 1932-1991 http://www. gaoran. ru/database/csa/main_e. html Sunspot magnetic fields (Crimean Astrophysical Observatory) 1957-present https://sun. crao. ru/observations/sunspots-magnetic-field Sunspot drawings of the Specola Solare Ticinese 1957-2023 https://www. specola. ch/e/drawings. html https://sunspots. irsol. usi. ch/db/ Sunspot Group Database of the Specola Solare Ticinese 1957-2022 https://doi. org/10. 5281/zenodo. 8117331 Historical archive of sunspot observations (HASO) at the University of Extremadura (Spain) Various http://haso. unex. es/ Archive of sunspot drawings at NOAA's NCEI 1816, 1817, 1859, 1860, 1880-1919, 1942-2019 http://www. ngdc. noaa. gov/stp/space-weather/solar-data/ solar-imagery/photosphere/sunspot-drawings/ Archive of sunspot drawings at Slovak Central Observatory Hurbanovo 1967-present a http://www.
Research Council (ERC) under grant #810238 Whole Sun, by the European Space Agency (ESA) under the Swesnet project and by the French Space Agency (CNES) Solar Orbiter & Space Weather grants and CNRS/INSU Sun-Earth research program, and by the french Agence Nationale de la Recherche (ANR) under grant STORMGEN-ESIS ANR-22-CE31-0013-01. L. L. gratefully acknowledges funding from the Belgian Federal Science Policy Office (BELSPO) through the BRAIN DeepSun project (B2/191/P2/DeepSun) and FARSUN project (B2/233/P2/FARSUN) and the Solar-Terrestrial Centre of Excellence, a collaboration between the Royal Observatory of Belgium, the Royal Meteorological Institute, and the Royal Belgian Institute for Space Aeronomy funded by BELSPO. I. U. acknowledges the support from the Academy of Finland (project ESPERA 321882).
Y. A. N. acknowledges the support from Grant 075-15-2020-780 of the Ministry of Science and Higher Education of the Russian Federation. C. C. acknowledges funding by NASA Living with the Star (LWS 80NSSC20K1819). M. A. D. acknowledges partial support from NASA LWS grant 80NSSC19K0079 and SWO2R award 80NSSC20K0290. Several authors are members of the international teams ''Solar Extreme Events: Setting Up a Paradigm" (team #510), ''Modeling Space Weather And Total Solar Irradiance Over The Past Century" (team #475), ''Reconstructing Solar and Heliospheric Magnetic Field Evolution Over the Past Century" (team #420), and ''Recalibration of Sunspot Number Series" (team #417) supported by the International Space Science Institute (ISSI), Bern, Switzerland and ISSI-Beijing, China. Fruitful discussions by members of the International Astronomical Union's Inter-Division B-E Working Group on Coordination of Synoptic Observations of the Sun (https://www.
Background Bronchiectasis is a heterogeneous, neglected disease with few multicentre studies exploring the causes, severity, microbiology, and treatment of the disease across Europe. This aim of this study was to describe the clinical characteristics of bronchiectasis and compare between different European countries. Methods EMBARC is an international clinical research network for bronchiectasis. We report on a multicentre, prospective, observational, non-interventional, cohort study (the EMBARC registry) conducted across 27 European countries and Israel. Comprehensive clinical data were collected from adult patients (aged ≥18 years) at baseline and annual follow-up visits using electronic case report form. Data from individual countries were grouped into four regions (the UK, northern and western Europe, southern Europe, and central and eastern Europe according to modified EU EuroVoc classification).
Research in context Evidence before this study We searched PubMed from inception until May 1, 2022, for articles related to causes, microbiology, severity, and treatment of bronchiectasis. We identified 5598 articles. Although we identified a marked increase in studies into the epidemiology of bronchiectasis in recent years, increasing from 193 references in 2011 to 406 in 2021, most studies were single centre and of small sample size. Prospective registry data was identified from Europe, the USA, Australia, India, and Korea, the largest of which had a sample size of 2596 patients and was limited to a small number of western European countries. There were no studies identified describing the causes, microbiology, severity of disease and treatment of bronchiectasis across Europe and particularly a paucity of data from central and eastern Europe.