dataesr/scientific-paragraphs-categorization huggingface dataset:
(2019). These records have filled a data gap in the existing datasets and are consistent with other sunspot observers' data, such as those of Schwabe  (Fujiyama et al. , 2019). New records were recovered from Antonio Colla, a meteorologist and astronomer at the Meteorological Observatory of the Parma University (Italy). Colla's records cover the period from 1830 to 1843, just after the Dalton Minimum  (Carrasco et al. , 2020a). Colla recorded a similar number of sunspot groups as his contemporary sunspot observers regarding common observation days. However, as is the case for Hallaschka, sunspot positions and areas recorded by Colla seem unrealistic and should not be used for scientific purposes. William Cranch Bond was the director of the Harvard College Observatory in the midnineteenth century.
The Astronomical Observatory of the Coimbra University published a catalog with sunspot observations (including G and S) from 1929 to 1941  (Lourenço et al. , 2019). In addition, a dataset of sunspot drawings made at the Sacramento Peak Observatory (SPO) from 1947 -2004 has been recently digitized by  Carrasco et al. (2021f). This work is the first step for the publication of the complete SPO sunspot catalog that will include information on sunspot positions and areas. Carrasco et al. (2018c)  digitized this catalog and reconstructed the corresponding total and hemispheric S N series from Coimbra data. The published sunspot counts of Hisako Koyama  (Koyama, 1985; Knipp, Liu, and Hayakawa, 2017) , a staff member at the Tokyo Science Museum (later renamed the National Museum of Nature and Science (NMNS)) from 1947 -1985, have been used for one of the backbones of the group-number reconstruction of  Svalgaard and Schatten (2016).
Recent surveys of the archives of the NMNS in Tsukuba have located Koyama's sunspot drawings and logbooks from 1945 to 1996  (Hayakawa et al. , 2020a). Hayakawa et al. (2020a)  described and analyzed a full digital database (encoded by Toshihiro Horaguchi and Takashi Nakajima) of Koyama's sunspot observations and diagnosed a previously undetected inhomogeneity in the resulting sunspot counts affecting the later part of the series, after 1983. Hayakawa et al. (2022b)  have analyzed Hitoshi Takuma's sunspot drawings from 1972 -2013 in the Kawaguchi Museum. Comparisons with the contemporary records have shown Takuma's observations to be one of the most stable data sets over this ∼40-year time period.
3 -is the scaling procedure: linear scaling of yearly averages vs. a nonparametric mapping of daily values of a pair of observers. The latter allows for nonlinear relations between counts of a pair of observers, and avoids side effects of temporal averaging (the different distributions of observing dates within a year, the linearization effect of temporal averages). While the two series agree reasonably well after ∼1880, they diverge beforehand (Figure  2 ). Recently, Velasco Herrera et al. (  2022 ) announced a reconstruction of the G N series using wavelet and machine-learning techniques that supports the validity of the original HoSc98 series. However, rather than recalibrating the G N series or exploiting the recent revised G N database  (Vaquero et al. , 2016) , they just produce a harmonic model of the original HoSc98 series, which essentially replicates the characteristics of this input series, based on a limited set of variable periodic components.
Such approaches also suffer from the stochastic nature of solar activity (e. g. ,  Cameron and Schüssler, 2019; Charbonneau, 2020; Petrovay, 2020). This technique may help in interpolating intervals with scarce data or predominantly spotless days, on the base of periodicity assumptions, but it cannot provide diagnostics of possible flaws in the original series. On the other hand, this paper does not address any of the above issues identified in the daisy-chaining principle, as well as the homogeneity issue in the early part of the RGO photographic catalog. For the above reasons and the fact that the Velasco Herrera et al. (  2022 ) G N series is not a recalibration of actual data, but rather a model, we will not further consider this series here. Individual Nonlinear Scaling, Based on the Active-Day Fraction, Relative to a Degraded "Perfect" Observer  Usoskin et al.
At the ISSI workshops, Muñoz-Jaramillo introduced a segmented-ADF method in order to improve the method published by  Usoskin et al. (2016). The general idea of this new method is to determine the threshold in the same way, but to compensate for points (5 -7) by applying a temporal window based on the data coverage of the imperfect observer and to make it move (in time) with regard to the reference dataset to account for the level of activity within the "imperfect observer" data. Then, this threshold is applied to the reference data to count groups. Here, the actual group numbers play again a role, next to the ADF itself. Note also, that the reference dataset is slightly modified to account for possible drifts in the first years. Like the original ADF methodology for G N reconstruction, this refined segmented-ADF approach remains to be fully developed and still requires an end-to-end validation.
Expectation (2): Observers Should Improve, and Correction Factors Should Decrease, over Time Cliver (2016) defined a "correction factor" time series [CF i ] for a given G N time series (Equation  3 ), obtained by dividing the annual group count [G Ni ] by the corresponding yearly   Cliver (2016)  was produced from all observers in the V16 database and applied in Equation 3 to various G N series regardless of which database  (Hoyt and Schatten, 1998a,b or Vaquero et al. , 2016)  (and/or which observers within these databases) they were constructed from. Here, we produced correction factors (in essence, ensemble-averaged k-and k'-factors of observers for a given year) by considering the data used by each series and the corresponding database of raw counts. Specifically, we consulted the tables of observers listed by  Cliver and Ling (2016) ,  Chatzistergos et al.
(2017) ,  Usoskin et al. (2021) , and DuKo22 (in preparation), and produced a [G Nraw ] series by averaging the raw counts of those specific observers from the database used in each case. For HoSc98 we used all available observers in the  Hoyt and Schatten (1998a,b)  database, while for SvSc16 we used all observers in the V16 database, but in all cases we excluded the time series from Mt Wilson derived from just the center of the solar disk. We would expect that in general CF values for all series would increase more or less monotonically from values ∼1 in the twentieth century to higher values 2 when moving back to the early nineteenth century and before (see Section 5. 1) because of (1) inferior telescope technology for earlier centuries, and (2) the change in sunspot counting procedure from Wolf to Wolfer (see Section 2.
g. ,  Usoskin et al. , 2021). In general, proxy data can be used both to corroborate S N and G N time series and to raise questions about their validity, particularly when abrupt discontinuities separating two extended stable periods (jumps) are observed between sunspot-number time series and those for proxy parameters. The diagnostic gains in robustness if the same offset is found in comparisons of the same sunspot-data series with multiple unrelated proxies or benchmarks.
Ca II K Plage Areas Plage-area is another facular parameter with a connection to sunspot-number series. Plage areas are determined from full-disc Ca II K (393. 367 nm) observations  (Chatzistergos et al. , 2022b). Such observations exist since 1892 and continue to be performed from many sites around the world  (Chatzistergos et al. , 2022b) , thus being one of the longest direct solar datasets. Various studies compared sunspot-number series to plage areas (e. g. ,  Kuriyan, Muralidharan, and Sampath, 1982; Foukal, 1996; Fligge and Solanki, 1998). Recently, Chatzistergos et al. , 2022a compared plage-area series from 38 archives as well as a composite series of plage areas from all available data  (Chatzistergos et al. , 2020)  to S N (1. 0), S N (2. 0), SvSc16, and CEA17 sunspot series. A power-law relation between plage areas and sunspotnumber series was found to represent the data best, while a slight dependence of the relationship on the activity level was also reported.
This new dataset together with an improved semiempirical model describing the evolution of the Sun's global total and open magnetic flux  (Krivova et al. , 2021)  made the first high-resolution millennium-long (970 -1900) sunspotcycle reconstruction possible  (Usoskin et al. , 2021). Figure  20  compares the 14 C-based sunspot number of  Usoskin et al. (2021)  from 1610 -1900 (gray shading indicates 67% confidence intervals) with the S N (2. 0), HoSc98, SvSc16, CEA17, and UEA21 time series. Overall, it best matches intermediate sunspot series, like CEA17. Discrepancies between the amplitudes and timings of the sunspot and 14 C-based time series increase as one goes back in time, making the comparison less reliable. Note the high minimum in the 14 C-based S N record ca. 1780. Although the properties of individual solar cycles cannot be reliably established by this method during grand minima of activity  (Usoskin et al.
As Muñoz-Jaramillo and Vaquero (2019) point out, there is a marked drop-off in both the quality and quantity of sunspot data before 1825 (Figure  8  and Figure  21 ). Bridging the data-sparse period of the Dalton Minimum to scale Staudacher, who counted spots from 1749 -1799, to Schwabe (1826 -1867) is a key challenge for any sunspot-number series. Recently recovered datasets for the Dalton Minimum will help to address this problem  (Hayakawa, Besser, and Iju, 2020; Hayakawa et al. , 2021g). In general, the relative performance/accuracy of the various reconstruction methods in sparse data environments needs to be examined/assessed (e. g. ,  Usoskin, Mursula, and Kovaltsov, 2003). Amateur astronomer Johann Casper Staudaucher made 1146 drawings of the spotted solar disk from 1749 -1799. Quoting from Svalgaard (2020): "Haase (1869).
The last decade has shown that this process of acquiring consensus via applications and discussions of multiple approaches followed by reviews of their results cannot be rushed. The  Hoyt and Schatten (1998a,b)  time series, valuable both for the introduction of a G N series that could be extended to 1610 and for the creation of the first publicly available digitized database, taught the lesson of the necessity for due diligence regarding modifications/revisions of the S N. The now-apparent issues with the Hoyt and Schatten G N time series were not independently examined for more than a decade  (Cliver and Ling, 2016) , during which time it became entrenched as an alternative to S N , in part because of its extension to the Maunder Minimum. In Appendix 1 of  Hoyt and Schatten (1998a,b) , the k'factors for  Wolf (1.
Once the new/revised time series have been developed, they will need to be independently reproduced and evaluated using benchmarks and proxy series. Certain methods may work better in data sparse environments, so composite methodologies may be required. This will take time. We estimate that new databases will be available/released by early 2023, with the key reconstruction methods brought to maturity and corresponding series created by the end of that year. At that point, an evaluation team chaired by the SILSO Director, will select next versions of S N and G N for sanction by an International Astronomical Union (IAU) reviewing body, with formal release targeted for conjunction with the IAU General Assembly in 2024. Even with the release of S N (3. 0) and a consensus G N (3. 0), it is possible that the two series may not be complete reconstructions for years before 1750, given the broader uncertainties and more complex and indirect validations required for these early years.
Acknowledgments This work was facilitated by an  International Space Science Institute (ISSI) International Team  selected in 2017 as number  417 , " Calibration of the Sunspot Number Series ", organized by  Matt Owens  and  Frédéric Clette. We thank  ISSI  for support of the team. We thank  John Leibacher  for encouraging us to use this status report to update the Solar Physics 2016 Topical Issue on Sunspot Number Recalibration edited by  F. Clette ,  E. W. Cliver ,  L. Lefèvre ,  J. M. Vaquero , and  L. Svalgaard. This research has made use of  NASA 's Astrophysics Data System. The  National Solar Observatory (NSO)  is operated by the  Association of Universities for Research in Astronomy (AURA), Inc. , under cooperative agreement with the  National Science Foundation. H. Hayakawa  thanks  Chiaki Kuroyanagi  for archival investigations for the Eimmart Collection in  St.
Funding Open Access funding enabled and organized by  Projekt DEAL. F. Clette  and  L. Lefèvre  are supported by the  Belgian Solar-Terrestrial Center of Excellence (STCE)  funded by the  Belgian Science Policy Office. L. Lefèvre  acknowledges funding from the  Belgian Federal Science Policy Office (BELSPO)  through the  BRAIN VAL-U-SUN project  ( BR/165/A3/VALU-SUN ). T. Chatzistergos acknowledges support by the  German Federal Ministry of Education and Research  (Project No. 01LG1909C ). H. Hayakawa  acknowledges.
Differences in phytohormone concentrations between elevations and dormancy stages The endogenous concentrations of three main phytohormones (indole-3-acetic acid (IAA), abscisic acid (ABA) and cytokinin (CTK)) were determined on three biological replicates for each dormancy stage, population and valley. We first performed an ANOVA on the whole dataset, to determine whether the two valleys could be considered as biological replicates. We used the following fixed-effects model: Y ijk = µ + D i + E j + V k + εi ik , where D i is dormancy stage (i = "endodormancy" or "ecodormancy"), E j is elevation (j = "low", "medium" and "high" elevation) and V is the valley factor (k = "Ossau" or "Luz"). The results are shown in Additional File 1 Fig. S1 panel A. No significant valley effect was detected, as for the date of leaf unfolding (data not shown).
In the second analysis, we therefore used the whole dataset, with the following fixed-effects model Y ijk = µ + D i + E j + D i *E j + ε ij , where D i is the dormancy stage, and E j the elevation effect, to identify significant dormancy, elevation and dormancyby-elevation interaction effects. The results are summarized in Additional File 1 Fig. S1 , Panel B for the three phytohormones analyzed. IAA were higher in EcoD samples (i. e. ecodormant buds) at all elevations. CTK content were higher in EcoD samples only at low and high elevations, respectively. Finally, ABA concentrations were lower in EcoD samples at all elevations (Additional File 1 Fig. S1, Panel B ). These results are consistent with those obtained by  [49]  and  [50]  for ecodormant poplar and endodormant pear buds, respectively, where similar hormone dynamics results were obtained over the winter period.
We used PCA (Principal Component Analysis) to explore transcriptome-wide changes in our dataset. This exploratory analysis showed close clustering within biological replicates, supporting the quality of the RNAseq data (Fig. 1A ). It also showed clustering of the samples into two distinct groups corresponding to each dormancy stage (Blue and Yellow dots spread along the first PCA axis in Fig. 1A ). Finally, it should be noticed that elevations separated also along the PCA 2 axis within a dormancy stage (except for two replicates sampled at 800 m of elevation) suggesting a stronger influence of temperature rather than photoperiod on dormancy regulation in our experimental design. The adjusted data were also evaluated by a clustering approach, with the complete method available in Expender software (Fig.
2% of the 17,676 genes between O-08 and L-08 in EndoD samples. These results confirmed that the two valleys could be considered as biological replicates. Increasing the number of biological replicates (from 2 to 4) decreased the false-positive rate, facilitating the distinction between noise and true biological signals in our dataset. We then used likelihood ratio tests to identify genes displaying significant dormancy, elevation and interaction effects. Genes displaying differential expression (at least a two-fold difference in expression) with a corrected P-value < 0. 01, and with adjustment for a false discovery rate (q-value) < 0. 05 were considered to display significant differential expression. The results of the differential expression analysis are shown in Fig. 2 , panel A. The overlap between the three effects is shown in Fig.
Genes regulated during dormancy induction and release (Geneset#1) By comparing models M1 and M2, using the threshold presented above, we identified 2,709 genes differentially regulated between EndoD and EcoD samples (Fig. 2,  panel A, B  and C ). For this first gene set, most of the variation identified was quantitative (i. e. all the genes displaying differential expression were covered by reads from both the EndoD and EcoD libraries). We identified a single gene (Qrob_P0090160. 2, encoding a riboflavin synthase) covered only by reads from EcoD samples. The results are presented in detail in Supplementary File 1. Gene set enrichment analysis was performed with the TopGo Package for this first gene set. Our analysis revealed different GO terms between EndoD and EcoD regulated genes. The results are shown only for the first 100 GO (ranked according to their p-value) in Supplementary File 1.
Transcriptomic studies identified two biological networks corresponding to gene set#1 related to either EndoD or EcoD samples (Supplementary File 1). As expected, important hubs related to cold tolerance, freezing tolerance, drought tolerance and defense response were identified in the EndoD network, whereas biological hubs related to meristem functioning (meristem identity, flower development, floral organ identity, etc. ) were found in the EcoD network. Similar results have already been published for oak  [45]  and beech  [42] , but the dataset reported here is much broader than those previously published, extending to genes with lower levels of expression (e. g. genes encoding transcription factors). The upregulation of molecular mechanisms involved in dehydration and cold tolerance during endodormancy is consistent with the greater tolerance to ice formation of cold-tolerant dehydrated tissues, making it easier for the plant to cope with cold winter temperatures  [55].
Gene set and subnetwork enrichment analysis GO term enrichment analyses were first performed with the TopGO R package  [119, 120]. We considered corrected P-values below 0. 05 (after adjustment for multiple testing with a false discovery rate (FDR of 5%)) to indicate significant enrichment in a particular GO term in our dataset. We then performed subnetwork enrichment analysis with Pathway Studio software (Pathway Studio ® , Elsevier 2017), as described by  [115].
RT-qPCR validation For the elevation and dormancy-by-elevation effects, we selected six genes each for RT-qPCR validation. All 12 genes analyzed are listed, together with their associated effect, in Additional File2 Table  S3. RT-qPCR quantification was performed with 1 µg total RNA, as described by  [121] , on a Lightcycler ® LC 384 (Roche Lifescience, Germany, EU) with standard parameters, as described by  [115]. Fluorescence data were normalized against two control genes (Qrob_P0530610 and Qrob_P0426000) and analyzed by STATQPCR (http:// satqp cr. sophia. inra. fr/ cgi/ home. cgi). All the primer pairs used here were designed with Primer3 software  [122].
Funding This work was supported by  France Génomique  ( ANR-10-INBS-09-08 ,  Oakadapt  project). IL received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP/2007-2013) /  ERC Treepeace Grant  Agreement n. 339728. Illumina sequencing was performed at the Genoscope sequencing facility (www. genos cope. cns. fr). The current publication benefitted also fundings from the  ANR  ( EPITREE ANR-17-CE32-0009-01 ).
Availability of data and materials All the sequences generated for this publication have been deposited in the short-read archive of NCBI (https:// www. ncbi. nlm. nih. gov/ sra) under accession PRJEB17876. All data generated or analysed during this study are included in this published article and its supplementary online information files (Additional File 1 and 2 and Supplementary File 1,2 and 3).
We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.
Phosphorylation of proteins at serine, threonine, tyrosine and histidine residues controls nearly every aspect of eukaryotic cellular function 1,2,5,6. Misregulation of protein kinase signalling commonly results in human disease 7. Deciphering the cellular roles of any protein kinase requires the elucidation of its downstream effector substrates. However, the majority of kinase-substrate relationships that have been published to date involve a relatively small number of well-studied protein kinases, while few, if any, substrates have been identified for the majority of the approximately 300 human protein Ser/Thr kinases within the human kinome  [8] [9] [10]. This lack of knowledge of kinase-substrate relationships limits the interpretation of large mass spectrometry (MS)-based phosphoproteomic datasets, which to date have collectively reported over 200,000 Ser and Thr phosphorylation sites on human proteins  3, 4, [11] [12] [13].
4b ). When quantitative phosphoproteomics data from HepG2 cells lacking FAM20C  44  were analysed using our kinome-wide dataset, the most downregulated kinase-recognition motif corresponded to that of FAM20C. Similarly, when skeletal-muscle-like myotube cells were stimulated for 30 min with isoproterenol  45  , the most upregulated phosphorylation motifs corresponded to multiple isoforms of cAMP-dependent protein kinase (PKA)-canonical effector kinases downstream of the β 1 and β 2 adrenergic receptors (Fig. 4c ). Notably, PKA motifs are highly similar to those of several other basophilic kinases, yet we could identify their enrichment in this scenario. Moreover, our comprehensive Ser/Thr kinome motif collection elucidated secondary signalling events in a dataset from HeLa cells arrested in mitosis using the PLK1 inhibitor BI 2536 (Fig.
Furthermore, we found up-and downregulation of less-appreciated DNA-damage-responsive kinases (MAPKAPK2 50,51 , PLK3  52  and LRRK2  53  ). The full collection of Ser/Thr kinome motifs also enabled the temporal dynamics of signalling to be resolved from time-resolved phosphoproteomic datasets. For example, motif-based analysis of phosphoproteomic data from insulin-treated 3T3-L1 adipocytes  54  revealed rapid activation of the phosphoinositide 3-kinase signalling pathway within 1 min after insulin stimulation followed by subsequent activation of the MAPK pathway, together with downregulation of AMP-activated protein kinases within 60 min (Fig. 4f ). Similarly, phosphoproteomic data analysis from lipopolysaccharide-stimulated dendritic cells  55  suggested marked upregulation at 30 min of a set of stress-activated kinases including the IKKs, JNK and p38 MAPKs, along with the MAPKAPK family of p38 effector kinases.