dataesr/scientific-paragraphs-categorization huggingface dataset:
This information is captured in the location_to_keep column appended to each species' raw data file (1 = keep, 0 = discard). Step 4. data filtering. Each track in the standardized dataset was visually inspected by the Data Editorial Group, and flagged for removal (using the keepornot column in the metadata file) if location estimates appeared unreasonably noisy relative to the length and extent of the track, and/or the location estimates were very irregular in time. Next, automated quality-control checks were used to remove individual deployments that: (1) were flagged for removal (keepornot column in the metadata file); (2) had fewer than twenty location records; or (3) had deployments lasting less than 1 day. Additionally, individual deployments were checked to ensure that: (1) near-duplicate records in PTTs (locations occurring within 2 min of each other) were removed; (2) PTT Argos Z-class locations were reclassified as B-class locations (the least precise Argos location quality class that has an associated error variance  38  ); and (3) locations implying unrealistic travel rates during the preceding time step (over 10 m s -1 for penguins and marine mammals and over 30 m s -1 for flying seabirds) were removed.
If re-fitted tracks continued to fail to converge they were removed from the final filtered dataset. For converged tracks, longitude and latitude residuals were examined for systematic trends indicative of lack of fit. Tracks that failed this inspection were removed from the final filtered dataset. Step 5. data publication. RAATD established a data-sharing and publication agreement with all data providers in 2017. The standardized (trimmed) and filtered data are held in a data repository hosted at the Australian Antarctic Division (AADC) (see details below, in the 'Standardized Data' section). The filtered data are also according to the OBIS-ENV guidelines  40  published in international repositories through the SCAR Antarctic Biodiversity Portal (see details below, in the 'OBIS-ENV compliant data' section).
For this purpose, and to ensure standardized file structure, secure (meta)data storage and the facilitation of community access to the data (where appropriate), the resulting datasets have been uploaded to the biodiversity. aq IPT instance (Integrated Publishing Toolkit; www. ipt. biodiversity. aq), the accepted route for publishing data to the SCAR Antarctic Biodiversity Portal (www. biodiversity. aq). This should ensure a seamless flow to the Ocean Biogeographic Information System (OBIS) and the Global Biodiversity Information Facility (GBIF).
Standardized data. Standardized data are provided in files of aggregated comma-separated values (CSV). They are made available as (i) a single metadata file containing a description for each individual in the dataset and (ii) a set of seventeen CSV files, one for each species, which aggregate all of the respective individual location data (Online-only Table  1 ). Records in the two files can be linked by the common 'individual_id' field, as each animal in the study has a unique identifier. The data and metadata are available to the public through the Australian Antarctic Data Centre: standardized data  41  ; state-space model-processed (filtered) data  42.
OBIS-ENV compliant data. The standardised data will also be provided as a set of Darwin Core Archives using the Darwincore Event core (Fig. 1 ) in compliance with the OBIS-ENV-DATA format  40. All field definitions (Darwin Core Terms) are available on the Darwin Core website (at: http://rs. tdwg. org/dwc/terms/index. htm#occurrenceindex). The Darwin Core aims to share data about taxa in a simple structured way. It includes a glossary of terms and is primarily based on their occurrence in nature as documented by observations, specimens, samples, and related information. Documents describing how these terms are managed, how the set of terms can be extended for new purposes, and how the terms can be used can be found on the website. The OBIS-ENV compliant data data are made publicly available through the Antarctic Biodiversity Portal Integrated Publishing toolkit (http://ipt.
Geographic coverage. All species considered in this dataset have circumpolar Antarctic distributions (Fig. 2 ; species-specific distributions are given in Supplementary Fig. S1 ) with a longitudinal range spanning 180 °W to 180 °E. The species breed either on the coast of the Antarctic continent or on the sub-Antarctic islands to the north (see Supplementary Table  S1  for a list of the main study sites). Species with geographically limited distributions (such as chinstrap penguins Pygoscelis antarcticus) were not included; instead we concentrated on species whose distribution covers a large portion of the Southern Ocean. In addition, a number of deployments in the Antarctic (crabeater seals Lobodon carcinophaga and Weddell seals Leptonychotes weddellii) were conducted in the pack ice at un-named locations.
Taxonomic coverage. Seventeen species of meso-and top predators were selected for analyses (Table  1  and Fig. 3 ), five marine mammals (one baleen whale, one otariid and three phocid seals) and twelve seabirds (five penguins, five albatrosses, and two petrels). These species cover a diverse range of ecological niches and life-history traits and include dietary specialists (e. g. , crabeater seals), deep divers (e. g. , elephant seal Mirounga leonina and emperor penguin Aptenodytes forsteri), wide ranging, highly migratory species (e. g. , wandering albatross Diomedea exulans), nearshore foragers (e. g. , Adélie penguin Pygoscelis adeliae) and capital (e. g. , Weddell seal) versus income (e. g. , Antarctic fur seal Arctocephalus gazella) breeders. In total, 4,060 individuals were included in the standardized dataset before quality control (some individuals may have been counted more than once in this total, as repeat deployments on the same individuals are not taken into account in this summation), with 1,482 marine mammals and 2,578 seabirds, providing 2,964,245 location fixes before filtering (Table  1 ).
After filtering and quality control processes, the total number of individuals used was 2,823, providing 2,328,772 location fixes (a 21% decrease in the number of location fixes) (Table  1 ). Temporal coverage. The data are not distributed evenly in time (Fig. 3 ). While the time frame ranges from 1991 to 2016, most data were collected during the period 2007 to 2014. This is a reflection of increased research effort in the Southern Ocean and advances in technology since the early 1990s, and also the timeline of the RAATD project which stopped actively seeking new data inputs in 2016. Further, some data providers primarily contributed older datasets that were already published or soon to be published, rather than unpublished data. The lack of even coverage in terms of taxa, space and time is a function of several factors.
Technical Validation The standardized data were subjected to a range of quality checks before undertaking further processing (Fig. 1 ). These included: • Counts of unique deployment positions and longitude/latitude variability were calculated for each dataset, and used as a check for errors in deployment position. • The distance from the recorded deployment position to the first few track points was calculated, and any distances greater than 10 km were flagged for manual inspection and verification. Similarly, the deployment date was compared to the date of the first point of the track, and differences were flagged for manual verification. • Various cross-checks were conducted to identify other data errors or discrepancies, including checking for multiple device identifiers associated with a single individual animal identifier, checking for identical individual identifiers on different species or in different datasets, checking that redeployed devices (i.
e. the same device deployed on multiple individuals) did not have temporal overlap, and checking for data missing from the 29 th of February of leap years (perhaps indicating data that had been discarded by accident). A number of additional quality-control checks were implemented prior to the data filtering; these are described in the Methods (Steps 3-4). State-space models (SSM) are now the standard approach for dealing with observation errors in electronic tagging location data  38, 43. The SSM filtering protocol that was applied to all the data provided essential quality control and validation. It was a variation of what was used by Jonsen et al. 38  , which was implemented in the statistical computing language R 44 via the Template Model Builder package (TMB package)  45. The TMB package provides extremely fast and stable maximum likelihood estimation, via autodifferentiation and the Laplace approximation, for non-Gaussian and nonlinear SSM's  46.
This was essential for filtering the large amount of tracking data compiled herein. The SSM filtering accounted for observation errors in the tracking data and, unlike the raw track data, provided location estimates and standard errors at regular time intervals along estimated tracks  38, 47. These location (error-filtered and time-regularised) outputs are essential for determining species' habitat preferences from tracking data (see Usage Notes) and other types of ecological inferences. We encourage users of these filtered outputs to evaluate the level of uncertainty in the estimated locations for their ecological inferences, as our methods for filtering are specific for our purposes. Example filtering code is provided so that users can reproduce our filtered data from the raw data or produce a new set of filtered data using, for example, different time steps (https://github.
com/SCAR/RAATD). Following SSM filtering, estimated tracks were evaluated for goodness of fit by examination of (1) maps of estimated and observed locations and (2) residual plots of latitude and longitude. Tracks associated with obviously poor fits to the data, unrealistic estimated movements and frequent extended periods without observations (relative to the step length duration) were discarded from the final output dataset. This examination was conducted independently by three people. Estimated tracks were discarded when at least two examiners were in agreement to discard. In cases where the optimisation algorithm (nlminb in R) failed to converge to a global minimum, up to ten attempts with different initial values were made in an effort to obtain convergence. Tracks for which convergence could not be obtained were discarded from the final output dataset.
Usage Notes Thanks to an unprecedented sharing effort from the SCAR EG-BAMM community, a benchmark dataset has been assembled that fills important gaps in spatial occurrence of various species for areas of the world that are traditionally data-poor. The dataset compiled for RAATD is used in the analytical project described in the Background section to determine Areas of Ecological Significance for the 17 species of predators considered in the dataset. To this end, a habitat selectivity procedure is one possible modelling method, aiming to identify the particular environmental conditions that are favoured by the animals, relative to the range of conditions that are available. This first requires estimation of the geographic space available to a given animal, which can be assessed using various methods, e.
g. 48. This region of geographic space has an associated range of environmental conditions over the period in which animals were tracked. Regression modelling can then be used to identify the environmental covariates that discriminate areas that are preferentially utilized. For particular analyses, tracking data may need to be subdivided, for example by breeding stage, depending on whether or not the animals' interactions with the environment differ by breeding stage. The individual habitat preference models can then be combined to provide a multi-species view of important regions of habitat including their underlying environmental processes, e. g. 17, 18. Following this analysis and production of a scientific article, the dataset will be available for re-use to help address emerging research questions or pressing conservation issues.
The SCAR EG-BAMM is pleased to make this dataset openly available for the Antarctic and broader scientific communities. It is organized and curated using the best principles and practices of recent biodiversity informatics practices  49. In this framework, the final version of the dataset is fully compliant with Darwin Core body of standards and can be downloaded through the Global Biodiversity Information Facility (GBIF) and Ocean Biogeographic Information System (OBIS) data portals. In regard to the use of the dataset, the RAATD consortium promotes the CC-BY (Creative Commons Attribution License), this being the standard practice for citing GBIF-mediated data, believing that it reflects an established norm that the communities we serve use to cite original work. Users are expected to comply with the guidelines of the SCAR/SCADM Data Policy: https://www.
Acknowledgements The  RAATD  project would not have been possible without the dedication of the many scientists, students and field assistants who helped collect data in the field or process them, including, non-exhaustively,  S. Adlard ,  A. Agüera ,  M. Biuw ,  M. -A. Blanchet ,  J. Clarke ,  P. Cock ,  H. Cox ,  M. Connan ,  A. R. Carlini ,  S. Corsolini ,  M. Cottin ,  J. D. Le Croquant ,  G. A. Danieri ,  D. Davies ,  B. Dilley ,  R. Downie ,  M. Dunn ,  B. M. Dyer ,  H. O. Gillett ,  S. Haaland ,  L. Jonsen ,  B. A. Krafft ,  C. Kroeger ,  C. A. E. Lemon ,  G. Mabille ,  M. Marczak ,  T. McIntyre ,  J. A. Mennucci ,  T. Nordstad ,  C. Oosthuizen ,  R. Orben ,  F. Pezzo ,  T. Photopoulou ,  B. Picard ,  O. Prud 'homme,  T. Raclot ,  S. Ramdohr ,  D. H. Raymond ,  G. Robertson ,  T. Rogers ,  K.
The experimental data for the remaining residues, with the 1 H- 15 N NOE values exceeding 0. 75 and increased R 2 relaxation rates, is consistent with the presence of the folded 3D structure. The 15 N relaxation data for FLASH resembled that of YARP, indicating that backbone dynamics in both structures are similar. However, some differences in the dynamic processes between the two proteins should be pointed. In particular, the R 2 relaxation rates measured for YARP are higher than those obtained for FLASH (Figure  3C, D ). Moreover, for YARP, the higher R 2 values are observed specifically for residues in α-helix I. Moreover, the experimental data measured at 18. 8 T demonstrated increase R 2 for the fragment proximal to Leu2164 and Thr2165 (Figure  3D ). A similar but less pronounced effect was observed for residues in α-helix III (Met2200, Gln2201, and Phe2203) (Figure  3D ).
Interactions of the NPAT Peptide with FLASH and YARP Fragments The residues in FLASH or YARP fragments involved in interactions with NPAT were mapped using chemical shift perturbation (csp) analysis of 1 H- 15 N HSQC spectra upon titration with the NPAT peptide. Step-wise addition of small amounts of the NPAT peptide to protein solutions, resulted in significant changes in the 1 H- 15 N HSQC spectra (See Figure  S8  and S9 ). Unfortunately, FLASH-NPAT and YARP-NPAT mixtures are precipitated at equimolar conditions. The best quality NMR spectra were obtained with two fold molar excess of FLASH or YARP, these data were further analyzed. Upon titration of the FLASH fragment with the NPAT peptide, some FLASH signals dislocated in the 1 H-15 N HSQC spectrum, while some others broadened or disappeared (Table  S1 ).
Multidimensional NMR Spectroscopy All NMR samples were prepared in 25 mM TRIS-HCl buffer (H 2 O:D 2 O 9:1 v/v) in the presence of 200 mM KCl for FLASH and 250 mM KCl for YARP samples at pH 6. 2. 50 mM Arg+Glu and 40 mM Tris(2-carboxyethyl)phosphine (TCEP) were additionally added to FLASH and YARP solutions to improve protein solubility and prevent cysteine oxidation. All NMR experiments were carried out on Agilent DDR2 800 NMR spectrometer operated at 18. 8 T ( 1 H resonance frequency 799. 94 MHz) equipped with four channels, Performa IV z-gradient unit and inverse 1 H/ 13 C/ 15 N probe. The NMR spectra were acquired at 298 K, processed with NMRPipe software  [65]  and further analyzed in the Sparky program  [66]. The 13 C and 15 N chemical shifts were referred in the indirect manner according to the external sodium 2,2-dimethyl-2-silapentane-5-sulfonate (DSS) reference using Ξ = 0.
Evaluation of High-Resolution 3D Structures of the FLASH, YARP, and NPAT Fragments In case of FLASH and YARP fragments, 3D 15 N-and 13 C-edited NOESY-HSQC spectra were acquired for 15 N-or 13 C, 15 N-labeled forms. As a result, 541 (219 intra-residue, 147 sequential, 94 medium range, and 81 long range) and 606 (224 intra-residue, 201 sequential, 141 medium range, and 40 long range) distance constraints were yielded for FLASH and YARP, respectively (Table  1 ). These data were additionally supplemented with angular restraints deduced from the chemical shifts of 15 N, 13 C, 1 H, 13 Cα and 13 Cβ resonances using TALOS-N software  [38]. In the final rounds of simulations, each identified hydrogen bond was converted to restraints for r NH-O (1. 5-2. 8 Å) and r N-O (2. 4-3. 5Å) distances  [39]. The initial step of calculations was performed using the CYANA (version 3.
95) software  [68]. The refinement with an explicit solvent was conducted for the 20 highest-scored structures with the aid of YASARA software [42] using Yasara2 force-field and isothermal-isobaric (NPT) conditions. The resulting set of 20 structures was finally validated with PROCHECK-NMR  [40]  and MolProbity  [69]  programs, and visualized with either MOLMOL  [70] , Chimera  [71] , or YASARA software.
15 N Relaxation of the Backbone Amide Groups Longitudinal (R 1 ) and transverse (R 2 ) 15 N relaxation rates, together with 1 H- 15 N heteronuclear NOE enhancement were determined for uniformly 15 N-labeled FLASH and YARP samples on Varian Unity+ 500 and Agilent DDR2 800 spectrometers at 11. 7 and 18. 8 T, respectively. The standard pulse sequences [72] included in BioPack software (Agilent Inc. , USA) were applied. The longitudinal magnetization was recorded at ten evolution times: 10, 90, 170, 290, 410, 550, 690, 850, 1010 and 1250 ms. Transverse relaxation was measured with Carr-Purcell-Meiboom-Gill (CPMG) pulse train (refocusing time of 650 µs) at 10, 30 50, 70, 90, 130, 170, and 210 ms evolution times. The cross-correlation effect was suppressed by the delay between π( 1 H) pulses of 5 and 10 ms for R 1 and R 2 measurements, respectively  [73].
The steady-state 1 H- 15 N NOE was measured with 6 s delay. All relaxation rates were estimated using a two-parameter model of a single exponent decay with the RELAX software  [74]  (version 4. 0. 1). Performed NMR experiments provide to extract R 1 , R 2 and 1 H- 15 N NOEs for 45 and 54 residues of FLASH and for 52 and 55 residues of YARP at 11. 7 T and 18. 8 T, respectively.
Solution 3D Structures of the FLASH-NPAT and YARP-NPAT Complexes Both structures were modeled in silico using the HADDOCK 2. 2 software  [75]  available at the European WeNMR grid facility (milou. science. uu. nl/services/HADDOCK2. 2). The 3D structures of YARP, FLASH, and NPAT fragments were used as templates for the modeled complexes. Weak conditional distance restraints between C-terminal residues of NPAT and FLASH/YARP residues, located in α-helices I and III and affected upon NPAT binding, were introduced to drive the relative orientation of components in the complex. After the standard assessment, the resulting structures were additionally evaluated according to the agreement with the contact surface deduced from the csp data. For both complexes, the best models were then subjected to simulated annealing procedure performed in the presence of explicit water with the aid of YASARA software.
For all proteins, only regions with a well-defined structure were analyzed: T1930 -R1982 for FLASH, T2156 -A2209 for YARP and A1412 -E1427 for NPAT. Both complexes were further tuned by 10 successive rounds of optimization of side-chain rotamers with FoldX software (ver 5. 0)  [76]. Finally, the variation of free energy of the dimer formation caused by particular amino acid replacements was assessed using FoldX. For both structures, the effect of F1419A, L1420A, L1423A, and Y1425A replacements in NPAT has been analyzed in the context of three independent pulldown experiments (Figure  S11 ). The observed exponential relation between ∆∆G and pulldown results (blue line) validates the proposed structural models of the NPAT complexes with FLASH and YARP. The only disagreement identified for L1423A replacement in FLASH-NPAT complex most probably reflects suboptimal packing of residues proximal to L1423 in the complex.
GST Pull-Down Assay For pull-down assays, the DNA of the C-terminal fragments of FLASH and YARP were cloned into the pET42a vector and expressed in bacteria as fusions with the N-terminal GST tag and two C-terminal HisTag 6. The proteins were purified on Glutathione Sepharose 4B resin (GE Healthcare) according to the manufacturer's protocol and Size Exclusion Chromatography (SEC). Binding efficiency was expressed as the percentage of the radioactive input (total amount of 35S-labeled NPAT used in the experiment) that was bound by FLASH-GST or YARP-GST (detected on glutathione beads). The percentage calculated for FLASH and YARP was reduced by subtracting the background level obtained by using GST alone and normalized as 100% for wild type NPAT. Each NPAT mutant was tested next to wild type NPAT in at least three independent experiments and the calculated data were plotted as a bar graph.
ACKNOWLEDGMENTS This work was supported, in part, by the  Grant-in-Aid for Scientific Research (C)  (Grant No. JP18042069 ),  Scientific Research on Innovative Areas  (Grant No. JP18H05493 ) from the  Japan Society for the Promotion of Science (JSPS). The collaboration between Japan and France was supported by  JSPS  and  MEAE-MESRI  under the Japan- France Integrated Action Program (PHC Sakura   40951TG ).
Context. Studies of long-term solar activity and variability require knowledge of the past evolution of the solar surface magnetism. An important source of such information are the archives of full-disc Ca II K observations performed more or less regularly at various sites since 1892. Aims. We derive the plage area evolution over the last 12 solar cycles employing data from all Ca II K archives available publicly in digital form known to us, including several as yet unexplored Ca II K archives. Methods. We analyse more than 290,000 full-disc Ca II K observations from 43 datasets spanning the period 1892-2019. All images were consistently processed with an automatic procedure that performs the photometric calibration (if needed) and the limb-darkening compensation. The processing also accounts for artefacts plaguing many of the images, including some very specific artefacts such as bright arcs found in Kyoto and Yerkes data.
The employed methods have previously been tested and evaluated on synthetic data and found to be more accurate than other methods used in the literature to treat a subset of the data analysed here. Results. We have produced a plage area time-series from each analysed dataset. We found that the differences between the plage areas derived from individual archives are mainly due to the differences in the central wavelength and the bandpass used to acquire the data at the various sites. We have empirically cross-calibrated and combined the results obtained from each dataset to produce a composite series of plage areas. "Backbone" series are used to bridge all the series together. We have also shown that the selection of the backbone series has little effect on the final plage area composite. We have quantified the uncertainty of determining the plage areas with our processing due to shifts in the central wavelength and found it to be less than 0.