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Aims: We present a detailed long-term study of the single M6 III giant RZ Ari to obtain direct and simultaneous measurements of the magnetic field, activity indicators, and radial velocity in order to infer the origin of its activity. We study its magnetic activity in the context of stellar evolution, and for this purpose, we also refined its evolutionary status and Li abundance. In general, for the M giants, little is known about the properties of the magnetic activity and its causes. RZ Ari possess the strongest surface magnetic field of the known Zeeman-detected M giants and is bright enough to allow a deep study of its surface magnetic structure. The results are expected to shed light on the activity mechanism in these stars. Methods: We used the spectropolarimeter Narval at the Télescope Bernard Lyot (Observatoire du Pic du Midi, France) to obtain a series of Stokes I and V profiles for RZ Ari. Using the least-squares deconvolution technique, we were able to detect the Zeeman signature of the magnetic field. We measured its longitudinal component by means of the averaged Stokes I and V profiles. In addition, we also applied Zeeman-Doppler imaging (ZDI) to search for the rotation period of the star, and we constructed a tentative magnetic map. It is the first magnetic map for a star that evolved at the tip of red giant branch (RGB) or even on the asymptotic giant branch (AGB). The spectra also allowed us to monitor chromospheric emission lines, which are well-known indicators of stellar magnetic activity. From the observations obtained between September 2010 and August 2019, we studied the variability of the magnetic field of RZ Ari. We also redetermined the initial mass and evolutionary status of this star based on current stellar evolutionary tracks and on the angular diameter measured from CHARA interferometry. Results: Our results point to an initial mass of 1.5M_⊙so that this giant is more likely an early-AGB star, but a lotaction at the tip of the RGB is not completely excluded. With a v sin i of 6.0 ±0.5 km s−1, the upper limit for the rotation period is found to be 909 days. On the basis of our dataset and AAVSO photometric data, we determined periods longer than 1100 days for the magnetic field and photometric variability, and 704 days for the spectral line activity indicators. The rotation period determined on the basis of the Stokes V profiles variability is 530 days. A similar period of 544 days is also found for the photometric data. When we take this rotation period and the convective turnover time into account, an effective action of an α-ω type dynamo seems to be unlikely, but other types of dynamo could be operating there. The star appears to lie outside the two magnetic strips on the giant branches, where the α-ω-type dynamo is expected to operate effectively, and it also has a much higher lithium content than the evolutionary model predicts. These facts suggest that a planet engulfment could speed up its rotation and trigger dynamo-driven magnetic activity. On the other hand, the period of more than 1100 days cannot be explained by rotational modulation and could be explained by the lifetime of large convective structures. The absence of linear polarization at the time the magnetic field was detected, however, suggests that a local dynamo probably does not contribute significantly to the magnetic field, at least for that time interval. 

Context. Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process ( s -process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star. Aims. We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the F RUITY and Monash stellar models. Methods. We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star’s companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removing s -process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion. Results. With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass of M = 2.23 M ⊙ and 2.34 M ⊙ and an average [Fe/H] = −0.21 and −0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns. 

Here, we present the data and MixSIAR code that corresponds to the manuscript “Interannual variability in the source location of North African dust transported to the Amazon.” African dust is seasonally transported to the western Tropical Atlantic Ocean (TAO) and South America (SA), including the Amazon Basin. Leading hypotheses suggest that either the Western North African potential source area (PSA) or the Central North African PSA (e.g., Bodélé Depression) is the main source of dust transported to the Amazon. However, these notions remain largely untested with geochemical data. Here, we present a more nuanced hypothesis: both PSAs contribute dust to SA with precipitation and wind patterns determining the dominant source. Our premise is based upon two years of isotopic measurements (strontium and neodymium) of African dust collected in SA integrated into a statistical model in a Bayesian framework. With this approach, we identified strong interannual variability: while the Central PSA supplied 48% in winter 2016, a region within the Western PSA, which we suggest may be located near Niger, Mali, and Algeria accounts for 54% of transport in winter 2014. We propose the variability is due to the strength of the Libyan High and differing amounts of precipitation in the Gulf of Guinea and TAO between the two years. We anticipate that our work will lead to better constraints of dust nutrient deposition and subsequent carbon sequestration in the TAO and Amazon as well as improved model predictions of dust transport. Due to the connection between dust, precipitation, and wind patterns, our work can be used to link changes in climate with past changes in the source and magnitude of dust transported to the Amazon and TAO. This data is associated with the article: Barkley, A.E., Pourmand, A., Longman, J., Sharifi, A., Prospero, J.M., Panechou, K., Bakker, N., Drake, N., Guioiseau, D., Gaston, C.J. Interannual variability in the source location of North African dust transported to the Amazon. Submitted to the Proceedings of the National Academy of Sciences ## Description of the datasets The `data/` folder contains three data sets. `ds01` contains the data collected in this study from 34 samples including the dates of collection and Sr and eNd isotopic ratios. ## Metadata of the trajectory file ds01 is a *csv* file that contain 12 columns. Column 1 presents the date in the format ‘MM:DD:YYYY’ (e.g., 01-30-2014) that sample collection was initiated. Column 2 presents the date ‘MM:DD:YYYY’ (e.g., 01-31-2014) sample collection ended. Column 3 shows the mean 87Sr/86Sr ratio (unitless) measured and Column 4 shows the 95% confidence interval (CI) for each sample run in triplicate. Column 5 shows the 143Nd/144Nd isotopic ratio reported as epsilon neodymium (unitless) and Column 6 presents the 95% CI of the mean epsilon Nd. Columns 7, 9, and 11 show the lead (Pb) isotopic ratios normalized to 204Pb with their corresponding 95% CI in Columns 8, 10, and 12.