dataesr/scientific-paragraphs-categorization huggingface dataset:
The images are shown after the preprocessing to identify the disc and re-sample them to account for the disc's ellipticity (when applicable) and convert the historical data to density values. The images have been roughly aligned to show the solar north pole at the top. (Haigh 2007; Gray et al. 2010; Ermolli et al. 2013; Solanki et al. 2013). This requires long and reliable solar activity indices (e. g. Kopp et al. 2016; Yeo et al. 2017; Shapiro et al. 2017; Wu et al. 2018). One such dataset comes from the collection of full-disc Ca II K observations. Numerous Ca II K archives, recorded and stored at various observatories all over the world, were digitised over the last 3 decades. Whether in physical or digital form, such archives have been used to derive information about the evolution of plage regions (e.
In this light, we present here results from the up-to-now most comprehensive analysis of historical and modern Ca II K observations taken between 1892 and 2019 from 43 different datasets to produce a composite plage area series. The paper is organised as follows. In Section 2 we present the data analysed in our study and the methods applied on the data. Our results for the plage areas from individual archives as well as the composite series are presented in Section 3. In Section 4 we discuss our results. In Section 5 we summarise the results of our study and draw our conclusions.
Ca II K observations We analysed solar full-disc Ca II K observations from 43 datasets, which include series of photographic images and data series acquired with CCD cameras. Table  1  summarises information on the datasets analysed in our study and their main characteristics. For the sake of clarity, we also list here all analysed datasets and the corresponding abbreviations used in the text to refer to the various series: Arcetri (Ar), Baikal (Ba), Brussels (Br), Calern (CL),     1  shows examples of observations from all datasets except for MM and Sc, examples of which can be found in  Chatzistergos et al. (2018a Chatzistergos et al. ( , 2019b)). In addition to the 38 datasets listed in Table  1 , we also analysed the 5 datasets included in Table  2. These 5 datasets include observations centred at different locations of the wing of the Ca II K line.
The majority of the analysed datasets stem from observatories located in Europe. However, there are datasets from Asia and North America that provide an overall good temporal coverage. All in all, there are 290,147 images taken between 25/06/1892 and 31/12/2019 covering 41,163 days. Figure  2  shows the fraction of days within a year with at least one observation among all datasets considered in our study. We also show the total annual coverage by the sum of all the datasets analysed here, the coverage for the case when only Ko and MW are used as well as for the plage area composite presented in Paper 2. Figure  2  reveals that the data analysed in our study offer a nearly complete coverage, with the exception of the period before  1925, 1944-1946, and 1986-1987. The coverage is on average 88% for the whole period of time since 1892.
However, it is on average 98% and is above 76% for all years if only the period after 1907 is considered. In contrast, the annual coverage when only Ko and MW are considered is on average 80%, while it drops down to 5% in the 1990's. The composite provides full annual coverage since 2010 and for 21 more years. In contrast, the Ko and MW series together provide a full coverage only for 1967. This illustrates the huge benefit of using multiple datasets to achieve a better coverage over the entire 20th century, but also the need to recover more historical data. The missing data from Abastumani (Khetsuriani 1981), Anacapri  (Antonucci et al. 1977) , Baikal, Cambridge  (Moss 1942) , Catania, Crimea, Ebro  (Curto et al. 2016) , Huairu  (Suo 2020) , Kandilli (Dizer 1968), Kenwood, Kharkiv, Kislovodsk, Locarno  (Waldmeier 1968 ), Madrid  (Vaquero et al.
2007 ), Manila, Meudon, Yerkes, Wendelstein, and Schauinsland would be invaluable for this purpose. Tables  1  and 2  illustrate the diversity of the analysed data in terms of, e. g. , instruments, bandwidth used, central wavelength, and the resulting pixel scale of the images. The observations from 14 of these datasets were stored on photographic plates (we will refer to all physical photographs as plates, even though celluloid film was used by some archives), a CCD camera was exclusively used for 25 datasets, while 4 datasets include data taken with a CCD camera as well as stored on photographic plates. We note that a few datasets include images obtained at the same observatory, but with different telescope or instrument, e. g. Kodaikanal and Kodaikanal Twin or Meudon spectroheliograms and Meudon filtergrams.
However, we do not make a distinction for the medium used to store the original data, whether it was a CCD camera or photographic plates. We note that this categorisation is merely to simplify the discussions in this manuscript. The arrangement of the datasets for the calibration procedure to produce the plage area composite is different and is outlined in Appendix B. The majority of the data stored on photographic plates were acquired with a spectroheliograph, with only Ro including images taken with an interference filter. In contrast to that, most data taken with a CCD camera were obtained with interference filters, with only Co, MD1, Kh, and Ki images resulting from a spectroheliograph. These four datasets include data taken with a CCD but also stored on photographic plates. We point also that MD1 data over 2002-2017 are provided in data cubes with observations taken at the core of the Ca II K line as well as centred at four different wavelengths away from the core.
In particular, quite indicative are the observations from MD1 and SF1 which were taken on the same day with nominal bandwidth of 0. 15 and 9 Å, respectively. Consequently, the network regions are enhanced in the MD1 image, while they are barely discernible in the one from SF1. Conversely, sunspots are clearly visible in the SF1 image, but barely hinted at the MD1 one. Comparing RP1 and RP2, taken with a bandwidth of 2. 5 and 1 Å, respectively, the sunspots are only minutely reduced in size in RP2, while the CLV has also been reduced. It is noteworthy that modern datasets include observations taken in general with broader bandwidths than the ones used for the historical data. Figure  3  shows the annual mean value of the nominal bandwidth from all datasets included in this study for which we have information on the bandwidth and for which the observations were centred at the core of the line.
However, we note that the central wavelength also affects the brightness of the magnetic features in the images and consequently their disc coverage. We also note that some datasets appear to include observations taken slightly offset from the core of the Ca II K line. This affects, for instance, the observations from ML, which have a cen-tral wavelength of 3934. 15 Å instead of 3933. 67 Å. Unfortunately, the precise values of the central wavelength used for the observations from most archives are not available, so that we cannot show its change with time. Furthermore, the observations from Ka, PM, and VM have almost the same nominal bandwidth as RP1 (3. 0, 2. 5, 2. 4, and 2. 5 Å, respectively), however, the CLV is stronger in the Ka, PM, and VM observations compared to the RP1 ones, hinting that Ka, PM, and VM observations might have been taken outside of the line centre or that the actual bandwidth is broader.
A similar evaluation is more difficult for the historical data, which suffer from more artefacts than modern data. We only mention here that SP observations, taken with a nominal bandwidth of 0. 5 Å, have the lowest CLV Fig. 6. Segmentation masks derived from the observations shown in Figs. 1  and 5. Plage are highlighted in yellow, while quiet Sun and network regions together form the blue background. We stress that the same threshold was used for all datasets to identify the plage regions, which is why the different datasets seem to give rather different plage coverage depending on the employed bandwidth or central wavelength. among all datasets analysed in our work. This suggests that the actual bandwidth used at SP might be narrower than the nominal value. Furthermore, we note that in addition to a bandwidth different from the nominal one, there are other parameters of the observation that can affect the data.
Indeed, parameters such as stray light contribution, blurring due to atmosphere seeing, over-or underexposure of photographic plates, vignetting, instrument-and setup-specific filter transmission profiles, potential contamination from secondary lobes in the filter transmission profile may have affected the CLV. We point out that also modern data are affected by problems. For instance, a few ML images taken with a CCD were found to be saturated. Finally, observations from various sites have been copied and shared with other observatories. Therefore, it could also happen that images have been scanned and erroneously attributed to a wrong observatory. It is also worth mentioning that some of the datasets listed in  In particular, we used the Ko data from the more recent 16bit digitisation, MD1 data over the period  1939-1948 and 1964-1967  which were not available before, and recently recovered data from the MW dataset.
The MW dataset stems from the 16-bit digitisation by  Lefebvre et al. (2005) , which is the same as the one used in Paper 2. The complete original dataset was, however, considered lost due to a failure of the storage hardware. Luckily, it was recently recovered, and we found that it includes 3463 images which were missing from the dataset considered in Paper 2. However, 164 images from the dataset considered in Paper 2 are still missing in the new series. In this study, we analysed the recently recovered series of 16-bit MW data, but included the missing 164 images from the analysis by Paper 2. Furthermore, observations from CT, Ko, Ma, Ro, and WS were found in 35mm celluloid films, which were distributed as the "Photographic journal of the Sun". These were produced by the observatory of Rome over the period 1967-1978 as supplementary material to their monthly bulletins.
We digitised with 8-bit accuracy all the observations missing from our collection with the reflecta RPS 10M commercial film scanner, which is the same scanner previously used for the Ro observations  (Chatzistergos et al. 2019a ). The datasets from CT, Ke, Kh, Ki, Ma, MD1, Sc, WS, and YR have only been partially digitised. Considering the large gaps in observa-tions in the BB and MS datasets, it is possible that more data were taken over the 1980's, which, however, we were so far unable to recover. Finally, although the datasets from Ba, CL, Ka, PS, Te, and VM have multiple observations per day, for our analysis we used either the "best" observation of the day, as selected by the observers of the BK and VM datasets, or for the CL, Ka, PS, and Te datasets we used an automatic process to randomly select 1-3 images per day unaffected by cloud coverage.
We did one exception for the first 7 days in June 2014. For this period we analysed all available observations from all datasets to study the sensitivity of our results to daily variations of seeing (see Sec. 4 for more details). The period was chosen randomly with the only requirements to be an active period and to be during summer for improved seeing conditions. This way we provide a lower estimate of the uncertainty in the derived plage areas due to seeing variations. Within these 7 days there are 36, 62, 89, 1640, 79, and 561 images in the Ba, Br, CL, Ka, ML, and Te datasets, respectively.
Methods We consistently processed all images with the methods described by  Chatzistergos et al. (2018b Chatzistergos et al. ( , 2019b Chatzistergos et al. ( , 2020)). Briefly, we started by identifying the solar disc to extract the information on the coordinates of the disc centre and the radius  (Chatzistergos et al. 2019b (Chatzistergos et al. , 2020)). We applied the calibration for the digitisation device, where relevant data were available. In particular, the Ko, KW, and MS datasets include information to perform the calibration of the CCD employed for the digitisation and observation, respectively. The CCD calibration has also been applied on BB (only over the period between 07 July 2000 and 21 September 2006), Co, KT, MD2, Mi2, RP1, and RP2 data, but it has not been applied, or it is unclear whether it has been applied, on the images from the other datasets.
Besides, for all datasets we applied a data selection  (Chatzistergos et al. 2019b ) merely to exclude pathological cases with severely distorted discs, missing parts of the disc, or strong artefacts over the disc. Moreover, the solar disc in BB, Co, Kh, Ki, MD1, MW, SP, and Te images was re-sampled to account for its ellipticity following  Chatzistergos et al. (2020). Then, the images from the datasets with data stored on photographic plates were photometrically calibrated  (Chatzistergos et al. 2018b)  to account for the non-linear response of the photographic material. All images, historical and modern, were compensated for the limb-darkening as described in Paper 1. In particular, we define a contrast image as C i = (I i -I QS i )/I QS i , where C i , I i are the contrast and intensity values of the calibrated image at pixel i, while I QS i is the intensity of the quiet Sun (QS, hereafter) at the pixel i.
Ky and YR data suffer from a very specific artefact manifesting itself as bright/dark arcs on the solar disc. Therefore, to improve the accuracy of the analysis of the Ky and YR data we added one further processing step. The process applied on these data is described in detail in Appendix A. Figure  5  displays examples of the calibrated and limb-darkening compensated contrast images after the preprocessing to correct for the elliptical discs (where needed) and to convert the historical images to density values for the same observations as shown in Fig. 1. More details about the processing of the Ar, MM, MD1, Mi1, MW, RP1, Sc, and WS datasets can be found in Paper 2, about processing the CT and Ro datasets in  Chatzistergos et al. (2019a) ,  Ko in Chatzistergos et al. (2019c) , and Ky and SP in  Chatzistergos et al.
(2020). All the processed images were segmented to identify plage areas with a multiplicative factor, m p = 8. 5, to the standard deviation of the QS regions  (Chatzistergos et al. 2019b ). The segmentation was applied consistently with the same multiplicative factor to all the datasets. The observation time for all archives was converted to Universal standard time (UT). Figure  6  shows the corresponding segmentation masks of the observations shown in Fig. 1 , singling out the plage regions, which are the solar features mainly considered in this study.
Individual Ca II K series Figure  7  shows the plage areas series derived from each dataset analysed in our study. For clarity, we split the results into three panels, each one showing periods of roughly 4 solar cycles (SC). The temporal profile of the evolution of plage areas is, in general, similar. Thus, we can recognise the same features in all datasets during various periods, e. g. the period 1971-1975 or the double peaks of SC 22 maximum over the period 1989-1992. However, there are also some obvious differences. For instance, the plage areas from Ka, KW, Mi2, PM, UP, and VM are considerably lower than from all other datasets. This is in agreement with the comment in Sect. 2 that these data were probably taken off-band or with a broader bandwidth. The plage areas from Co are considerably lower than from other datasets over SC 20.
Plage areas composite In Paper 2 we presented a plage area composite derived from the analysis of 9 Ca II K archives. The composite of plage areas was the average series of those obtained from using the results from the 8-bit Ko and 16-bit MW series as the references. In this study, we present a plage area composite obtained on the basis of the results from 38 datasets. These include the data from the 16-bit digitisation of the Ko dataset  (Chatzistergos et al. 2019c)  as well as the recently recovered data from the 16-bit digitisation of MW. Furthermore, we use a different methodology to create our plage area composite series, employing the "backbone" approach  (Svalgaard & Schatten 2016; Chatzistergos et al. 2017). In particular, we split the datasets into two backbones, roughly representing the historical and modern data separately.
This way we construct one backbone for the modern data and two for the historical ones. We crosscalibrated the two historical backbone series and the RP1 backbone series in the same way as done for the individual series. Then the two historical backbone series were averaged to create the average historical backbone series. Figure  8  shows the RP1, Ko, MW, and the average historical backbone. We note that the Ko and RP1 backbones are at similar levels over SCs 22 and 23, with the latter being slightly lower. The variation of the plage areas in the MW backbone has higher amplitude than in the other backbone series by ∼ 0. 15 in disc fraction. The average his- We stress that the same threshold was used for all datasets to identify the plage regions, which is why the different datasets seem to give rather different plage coverage.
Discussion Here we focus on the uncertainties of our results due to characteristics of the analysed data, i. e. seeing conditions, or central wavelength used for the observations, as well as those due to our method of producing the composite plage area series. The accuracy of the methods applied to the processing of the observations was tested and discussed by  Chatzistergos et al. (2018b Chatzistergos et al. ( , 2019b,c),c). First, we estimate the sensitivity of daily plage areas from individual datasets to the observational conditions. These include the varying seeing conditions at different times of day and different locations, as well as stray-light. To this end, we analysed all available observations of the first week of June 2014. Figure  10  shows the derived plage areas over that period.
Over those days, the plage areas from all datasets as well as the sunspot areas show a roughly steady increase. We also notice the derived plage areas to increase through the course of each day, which is consistent with the worsening seeing conditions towards local noon, that smears the plage regions. However, at least part of this increase is due to the increase of solar activity over the course of that week. This increase is expected to be greater for the archives with observations taken with narrow bandwidths, such as Te. We find the areas from Ka to show more stable values within a given day, with only a slight increase of the areas around 06:00 UTC followed by a slight decrease after that. The results in Fig. 10  allow a rough estimate of the uncertainty of the derived plage areas due to the seeing conditions during the acquisition of the images.
The only differences we identify are that MD1 data have marginally lower errors (up to 0. 017 instead of 0. 019 found in RP1 results when considering the common observations to MD1) and that there are images for which the plage areas decrease slightly more than RP1 ones (minimum value of -0. 007 instead of -0. 002 found in RP1 data for the common days to MD1). The values for the differences in the plage areas are consistent with the results of the actual datasets. We also estimate the uncertainties in the derived plage areas due to variations in the central wavelength of the observations. All Ca II images can exhibit such variations, irrespectively of whether they were taken with a spectroheliograph, or with an interference filter. In the case of the spectroheliograph, the variations are due to the positioning of the slit, e.
g. , because the observers intentionally or by mistake took observations off-centred from the line core. In general, the cases when the observers intentionally took observations off-centred are documented, as in the archives from MD1 and Co for which observations centred at K1 and K3 are separated (the former are labelled MDW and CoW respectively, in this paper, while the latter are abbreviated as MD1 and Co). However, this practice was not always documented consistently for all of the datasets considered in our study. In the case of the interference filters, the variations are more consistent and mostly due to the replacement of the filters or filter degradations, manifesting as a single offset or a drift in time, respectively. In order to test the effects of the different central wavelength on our derived plage areas we used the offband observations from Coimbra, Meudon, and Mauna Loa.
Meudon has observations over the period 2002-2017 taken at four different wavelengths around the core of the Ca II K line along with the data taken at the core of the line. All of those observations were taken with the same bandwidth. Here we use only those taken at the centre of the line and the two extreme cases which correspond to offsets of ±0. 3 Å. We will refer to those series as MDV and MDR for the data taken in the violet and red part of the wing of the line, respectively. Coimbra and Meudon have also observations centred beyond the K1V minimum, with an offset of -1. 4 Å of the Ca II K line (CoW and MDW, hereafter), taken with the same bandwidth as those centred at the core of the line. The Mauna Loa dataset includes data taken at the core of the Ca II K line as well as data taken 2.
For MD1-MDR and MD1-MDV we notice the distribution of the differences to be quite narrow, compared to the other cases tested here. The differences in the derived plage areas for these cases are on average (RMS difference) less than 0. 003 (0. 003). However, we also notice that even though the wavelength offsets for MDR and MDV are equal in absolute value, the results for the plage areas are not exactly the same. The difference in the plage areas for MDR are greater than for MDV. This is unsurprising considering the similarity of MDV and MD1 images compared to the MDR ones (see Fig. 12 ). a) b) Fig. 14. Plage area composites (a) and their difference to our main composite series (b) when the historical backbone series was computed with various individual historical datasets as the backbone reference instead of using the average of the calibrated Ko and MW ones to the RP1 one.
RP1 is always taken as the overall reference for all composite series. The datasets used as backbones are those from Ar (red), Ko (blue), Ky (pink), MD1 (orange), MM (purple), MW (green), Ro (dark green), and SP (brown) sites. The composite plage area series derived in this study is also displayed in black. Shown are the annual median values (lines) of the final plage area composites along with the asymmetric 1σ interval (shaded surfaces). The numbers below the curves denote the conventional SC numbering. Next, we tested to which degree the composite series is affected by our choice of individual backbone datasets. In this process we used all historical datasets with sufficiently long periods to act as backbone references. These are the Ar, Ko, Ky, MD1, MM, MW, Ro, and SP datasets. Figure  14  shows the resulting plage area composite series.
We did not consider Co, Mi1, or Mi2 in this test due to the poor overlap with many of the other datasets. Table  B. 1 lists the assignment of the various plage series to the individual backbones. For consistency, all series are normalized to the level of the RP1 backbone. All produced composites agree almost perfectly over SCs 22-24, owing to the RP1 backbone. However, there are disagreements for the remaining cycles. The differences are greatest for the composites created with MM, Ky, and SP as the backbones for the historical data. This is consistent with the rather low amount of data within those datasets, rendering the calibration of the various other (non-backbone) datasets to their level more uncertain. The remaining reconstructions show results that are very similar to our proposed series.