dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This research is sponsored by the  NSERC Canadian Healthy Oceans Network  and its Partners:  Department of Fisheries and Oceans Canada  and  INREST  (representing the Port of  Sept-Îles and City of Sept-Îles). Mairi C. Meehan  and  Gerald G. Singh  acknowledge support from the  Nippon Foundation Ocean Nexus Center  at the  University of Washington EarthLab. Natalie C. Ban  was supported by  NSERC. Joachim Claudet  was supported by  Biodiversa+  (METRODIVER and MOVE) and  Fondation de France.
26-27 Ma in Masquelet et al. , 2022] , whereas volcanism in Anjouan, Moheli, and Grande Comore started later at c. a. 10 Ma  [Michon, 2016]. Therefore, there is no simple decrease or increase of the age of volcanism along the Comoros archipelago, as expected in the case of hotspotrelated intraplate volcanism. The link between tectonic deformation and volcanic development is under study  [Michon, 2016 , Famin et al. , 2020 , Feuillet et al. , 2021 , Boymond et al. , 2022 , Thinon et al. , 2022] , this issue. Geological, geochronological, geomorphological and geophysical datasets tend to confirm the hypothesis of a strong magmatic supply reaching the surface through fractures induced by lithospheric deformation  [Michon, 2016 , Tzevahirtzian et al. , 2021 , Famin et al. , 2020 , Feuillet et al.
Seismic network evolution and data availability In order to build the catalog for the first ten months of the Mayotte seismic sequence (Figure  3 ; Table  S1 ), we first estimated the amount of detected seismic events by performing a STA/LTA method on the vertical component of station YTMZ (Figure  4a  and Figure  S1 ). Due to the high level of noise in the signals of this continuous strong-motion station, we applied a Butterworth filter between 1. 5 and 15 Hz. Then we selected only events with a STA/LTA ratio above 6. 0 and a peak-to-peak amplitude of more than 200 counts. Those parameters reduced the detections of non-seismic sources, such as those of the Mayotte background noise, but likely of small seismic events too. Then, we considered two distinct time periods characterized by different monitoring seismic networks.
For the first four months of the sequence from 10 May to 31 August 2018, we completed the initial catalog of  Lemoine et al. [2020a]. In addition to the regional stations (Figure  2a ) in Madagascar (GE. SBV, II. ABPO, GE. VOI), Kenya (GE. KIBK), and sparse data from Grande Comore (KA. SBC, KA. CAB, KA. MOIN, Figure  2b ), we used additional data from stations located in Seychelles (II. MSEY), La Réunion (G. RER), Madagascar (G. FOMA), and Grande Comore (KA. DEMB). Furthermore, we completed the dataset with GE. SBV signals from May and June 2018, and added missing data from the Grande Comore network (the whole month of May, and short time intervals between June and August 2018). During periods not covered by the GE. SBV station located to the east, we only analyzed events with a well-identified P phase at II.
ABPO, to reduce the azimuthal gap. For the local network, since  Lemoine et al. [2020a]  had integrated MDZA signals only for magnitude M > 4. 0 events, we enriched the dataset with MDZA signals for smaller events (Figure  2 ). The local Mayotte network has developed from one to five available stations between May 2018 and February 2019 (see Supplementary Material, Table  1 , Figure  2 ). The picking of the P and S phases was done manually on the continuous signals, using the Seiscomp software [Helmholtz-Centre Potsdam -GFZ German Research Centre for Geosciences and  GEMPA GmbH, 2008]. We also checked the previously picked phases from the  Lemoine et al. [2020a]  catalog and searched for new small events, not previously detected. From 1 September 2018 to 24 February 2019, events were directly extracted from the database used in  Lemoine et al.
We used the regional velocity model of this work and the database of P and S pickings  [Lemoine et al. , 2020a , Bertil et al. , 2021]. The results were compared to the well-constrained hypocentral locations of the corresponding events from  Saurel et al. [2022]  (Figure  S2 ). Despite the challenging geometry of the onshore network, uncertainties are similar for both catalogs, remaining below 5 km along horizontal and vertical axes for most of the events. However, there is a mean horizontal shift of around 4. 1 km and 5. 5 km westward of our locations for the proximal and distal clusters, respectively, compared to a network including OBS stations above the seismic area, as well as an upward shift of around 4 km of our locations for the proximal cluster [similar results in  Aiken et al. , 2021].
Therefore, in light of these results, we consider the locations of our catalog reliable for the periods that include the data from GE. SBV. For periods when data from GE. SBV were not available (Phase 2 and the beginnings of Phase 3 and Phase 4), the results should be analyzed more carefully  (Figures 4, 6, 7, 9 , and Figure  S2 ), particularly for seismicity upward migration and the lack of events a few km west from Fani Maoré during Phase 2 (Figure  7d ). However, the shallower depth (above 25 km) of the events and the southeastward migration of the seismicity (Figures  3, 6,  and 7 ) is confirmed by the evolution of the S-P values at YTMZ station (Figure  6d ). Moreover, the shallowness of the seismicity during Phase 2 is documented from various catalogs using distinct methods and data sets  [Cesca et al.
Acknowledgments The authors thank  Carole Berthod ,  Pierre Briole , and  Chastity Aiken  for constructive discussions, which improved this article. They are also grateful to  Angèle Laurent  and  Charles Masquelet , which shared generously unpublished results. This study is part of the  ANR COYOTES  project ( ANR-19-CE31-0018 ). The authors thank the three anonymous reviewers and the editors for their comments, and  Gregory Owcarz  for improving the quality of the English. A large part of the data have been acquired in the framework of the first response team to the Mayotte seismic crisis, including in particular members of the  BRGM  DRP/RSV unit:  A. Colombain ,  G. Dectot ,  C. Gracianne ,  E. Maisonneuve ,  K. Meza Fajardo ,  A. Roullé ,  F. Tronel. This work was possible thanks to the seismic data shared by the  Karthala volcanologic observatory (OVK).
Study design and population The CAPRI (clinical safety of the ENO ® , TEO ® , or OTO ® pacing system when used under 1. 5 and 3 T specific MRI conditions without scan exclusion zone) study (ClinicalTrials. gov Identifier: NCT03811691; EUDAMED: CIV-19-04-027,946) was an interventional, prospective, multicenter, open-label, two-arm parallel non-comparative nonrandomized study conducted in Europe and Asia-Pacific to confirm the clinical safety and performance of the ENO ® , TEO ® , or OTO ® MRI conditional single chamber rate response (SR) or dual chamber rate response (DR) pacemakers with VEGA ® pacing leads (Model R45, R52 or R58) in the MRI environment. The study took place between July 2019 and October 2021. MRI conditional devices were implanted according to current applicable guidelines  [12, 13].
Proper functioning of the automated MRI mode Auto MM was assessed by the pacing system's capability of automatically switching (1) to programmed asynchronous mode when detecting the external magnetic field, and (2) to initial settings after termination of the scanning. Switch to asynchronous mode and to initial settings was confirmed by device file analysis. Data was classified as evaluable when the MRI mode was programmed to "auto" before the MRI scan with a pacing rate higher (+ 10 bpm) than subject's spontaneous pacing rate and the device file including the Holter information was available.
Sample size and statistical analysis To provide a statistical power of at least 80% for the primary safety endpoint, a sample size of 114 patients was required for each study arm (1. 5 or 3 T) to reject the null hypothesis of insufficient safety of the device, defined as the proportion of patients free from MRIrelated-complications at 1 month post-MRI ≤ 90%, assuming an expected performance of 97%, and using a 0. 025 one-sided alpha. With an expected dropout rate of 15%, the number of patients to be included was therefore 135 for each study arm. The primary safety and secondary performance endpoints were evaluated in the Full Analysis Set (FAS) population, i. e. , patients of the included population who underwent MRI examination, partially or totally, and with evaluable data at 1-month post-MRI visit.
Primary and secondary endpoints are presented with 95% CI. Descriptive statistics were applied to assess all other study endpoints. Continuous quantitative parameters are presented as mean ± standard deviation (SD). Median and 1st and 3rd quartiles (Q1, Q3) are used for variables not normally distributed. Qualitative parameters are presented as number and frequency (%). Statistical analyses were performed with SAS version 9. 4 or above (SAS Institute).
There has been little quantitative analysis of the successful careers of music artists in the literature given the importance of music in world culture. The Billboard 200 database in the U. S. (BB200, 200 best-selling weekly albums) is freely available for the period 1963-2019, a long period encompassing the entire careers of many musicians. An analysis of this database enabled us to establish some rules of musical success for artists, which are examined in this article. Our results show that the time distribution of success for artists reaching high chart positions follows a Frechet-like distribution, with a decline principally due to the progressively shorter lifespan of successive charted albums from the same artist. We then show that the surmised difficulty of the second album exists. Our findings also confirm that success is more diverse than it was a few decades ago, in the sense that more artists reach the charts every year.
, 2007; Jarynowski & Buda, 2014; Strobl & Tucker, 2000) , prediction of musical success  (Cosimato et al. , 2019; Kang et al. , 2022) , or songs' success across cultures  (Buda & Jarynowski, 2015) , and very few of these studies focused on artists. As a source for artistic success, the Billboard 200 database in the U. S. (BB200, 200 best-selling weekly albums) is freely available for the period 1963-2019 (Acoustic and meta features of albums and songs on the Billboard 200, 2022), a long period encompassing the entire career of many musicians. By analyzing this database, we can establish some rules of musical success for artists, which are examined in this article. These rules provide an interesting, albeit imperfect, point of entry into music marketing, but also into the more complex notions of musical quality and creativity.
Methods We analyzed the BB200 database with a freely available Matlab (Mathworks) code (https://gitlab. pasteur. fr/idapublic/musical-success) that imports data and generates each figure. The BB200 database contains lists of the top 200 albums for every week from May 1, 1963 to January 19, 2019. We converted each ranking (between 1 and 200) into a score between 200 (rank 1) and 1 (rank 200). For each artist, we obtained weekly scores, for all of the artist's albums included in the charts, which we added together over either a period of one year or the entire career of the artist. The distribution of these cumulative scores over a career follows a roughly normal distribution when plotted on a logarithmic scale (Figure  1A ) although it fails the Jarque-Bera test (p = 1e-3). Indeed, it has a kurtosis slightly below 3 (2.
The Life Cycle of an Artist By studying this score at the scale of a week, a month or a year, we can trace the evolution of an artist's success. Indeed, even for artists with long careers, there are relatively predictable ups and downs, and this is one of the lessons we can learn from this database. Success over the years approximates an asymmetric Frechet-type law. Smoothing out the artistic ups and downs over a period of five years reveals a "pattern" of rapid growth and then slow exponential decline. Frechet-type distributions gave the closest fit to the data of all the asymmetric distributions tested. For example, the Rolling Stones' cumulative annual score clearly peaks at the beginning of their career (in 1966 to be exact), before decreasing inexorably over the following 50 years (Figure  2A ).
Thus, 1966 was the year in their career in which they had the largest number of albums in high chart positions for the longest. This model works for all the artists and groups explored (Figure  2B , Supp. Figure  1A  and B ), provided, of course, that only complete careers are considered, the group has not disbanded (if appropriate), and the artist did not die prematurely, which would distort the model. An artist dying tends to boost their sales in the short term, and even in the longer term, even for artists no longer considered trendy, as illustrated by the recent examples of Prince and Michael Jackson (Supp. Figure  2A  and B ). Finally, this rule of success is more regular and easy to identify in the BB200 database than in the BB100 database of best-selling singles (Supp. Figure  3 ).
The Rise and Fall of an Artist It is easy to imagine that an artist's success would grow and then decline, but these distributions are surprisingly consistent in their pattern: rapid success, a relatively brief career "peak," and then a slow and inevitable decline, with no significant recovery. Unexpectedly, the same law seems to apply to artists who write only some of their own songs, such as Madonna (Figure  3Ci ) and artists who only perform songs written by other people, like Barbra Streisand (Figure  3Di ). It might be supposed that the possibilities for diversification and renewal open to pure singers would protect them from such a fate, but this is not the case. The relative earliness of the "peak" of success, in particular, is clearly demonstrable on a large scale. An analysis of career peaks over the entire database (9,372 artists, without excluding artists who die early or groups with that disband) clearly showed that, whatever the length of the career, the peak generally occurs between years 2 and 6-7 (Figure  2C ).
The impossibility for an artist of avoiding the following decline is fascinating in itself. The BB200 database sheds light on certain aspects of this decline. A first way of approaching this aspect is to look at the evolution of the highest score achieved-i. e. , the highest ranking in the BB200-by successive albums from the same artist (Figure  3A ). This evolution actually depends on the number of albums released by the artist, which is not necessarily obvious a priori. Artists who have placed many albums in the charts, and therefore have a longer career, generally achieve a higher ranking for each of their albums than artists whose success is more ephemeral, who make up the bulk of the rankings (Figure  1A ). More surprisingly, these prolific artists achieve increasing success up to their sixth album, and hold their own until about the tenth album, when the inevitable decline accelerates.
Does the Surmised Difficulty of the Second Album Exist? It is possible, through analyses of chart data, to address the famous myth of the "cursed" second album of an artist. Is it really difficult for artists to achieve the same success for a second album as for the first? Here, we transform this basic question by considering the difficulty of placing a second album in the charts. The database does not indicate whether the first album from a given artist appearing in the charts was actually the first album of that artist. 1 However, we can obtain the first part of the answer to our question by considering the number of albums placed in the charts by the artists (Figure  1B ). The scale of the percentage of artists is logarithmic, and this percentage decreases more rapidly than a simple straight line, and is perfectly predicted by a logarithmic law  (Johnson et al.
Production Rate The BB200 can also be used to analyze the relationship between production rate and success. In recent decades, it has become increasingly obvious that the rate at which artists produce albums has slowed. The Beatles recorded 13 albums in seven years between 1962 and 1969 and the Rolling Stones recorded 12 albums in 10 years between 1964 and 1974. By comparison, the best-selling bands of the twenty-first century, Maroon 5 and Coldplay (List of best-selling music artists, 2023), have released only seven albums in 19 years and nine albums in 21 years, respectively. This trend can be seen in the BB200 data (Figure  4E ): the mean gap between two (charted) albums increased from five months (!) in 1963 to two years at the turn of the 1990s. It subsequently stabilized, but variability has increased over the last two decades (dotted lines).
Has the inspiration of artists dried up since the 1960s? This is a question debated in the field  (Mauch et al. , 2015; Why Has Overall Musicianship in Popular Music  Been in Decline since the 60s and 70s?, 2022). We cannot answer this particular question with the BB200 database, but we can at least address the question as to whether there is a production rate that would increase the success of an artist. The answer to this question is "Yes!" And that production rate is a new album every two years (Figure  4F ). It should be noted that the cumulative score of an album is generally lower than that of the previous one, by about 20% in most cases. That is not very encouraging for either the artist or the producer. However, this decline is least pronounced for a two-year gap between successive albums.
A Little-Explored Area The Billboard 200 database has been freely available for years. Strikingly, while music charts have long been identified as interesting sources of scientific data  (Hesbacher et al. , 1975) , few studies actually use them. More generally, there are relatively few quantitative analysis of artists' success  (Boughanmi & Ansari, 2021; Buda & Jarynowski, 2015; Carroll, 2015; Cosimato et al. , 2019; Davies, 2002; Giles, 2006; Hesbacher et al. , 1982; Kang et al. , 2022; Schneider & Gros, 2019; Strobl & Tucker, 2000)  in the literature given the importance of music in world culture. It is, of course, possible that the three music major record companies (Warner, Universal, Sony), which accounted for 70% of global music sales in 2018 (Global Music Production and Distribution Industry Trends (2016-2021), 2021), have actually performed their own analyses and are jealously guarding their findings.
After all, album sales are a well-kept secret in the music industry. Access to the database of direct sales in the U. S. , owned by the RIAA (Recording Industry Association of America), for example, is not free. Perhaps the most explored area of the success of artists is that relating to superstars  (Chung & Cox, 1994; Giles, 2006; Hamlen, 1991; Rosen, 1981; Strobl & Tucker, 2000). This phenomenon is generally interpreted as a small difference in talent generating a disproportionate difference in success  (Rosen, 1981). Its causes remain a matter of debate and there are two principal historical theories  (Haampland, 2017). According to the first, consumers prefer to invest in the most talented artists, and only the careers of the most talented endure, leading to an accumulation of income that generates disproportionate inequalities between artists  (Rosen, 1981).
S. Recorded Music Sales, 2022). On the other, album sales figures are not available from a free database. The changes in an album's ranking over the weeks and its lifespan in the charts, as provided by the BB200, at least provide interesting criteria for the comparison of albums from the same artist, as illustrated by our study of Michael Jackson's albums (Figure  4D ). It has also been hypothesized that the initial ranking of an album may determine its fate  (Schneider & Gros, 2019). Overall, charts provide individual success trajectories for albums and artists for a long period with stable metrics and economic circuits. In a few decades, such trajectories shall be compared to those emerging from the dramatic changes in music listening habits and consumption of people.
Equations for transferring from a rank (or score) to a value closer to sales have been proposed  (Carroll, 2015; Hesbacher et al. , 1982). We are aware of these equations, but the use of a score in this study was simpler and had the advantage of providing a comparable measurement scale for albums from different periods. Indeed, scores do not suffer from the overall variation in album sales over the decades, for example (U. S. Recorded Music Sales, 2022). Another structural limitation concerns the necessarily restrictive view of the U. S. market provided by the BB200, whereas music is a global phenomenon. However, databases equivalent to the Billboard 200 are not very common around the world, and were often founded more recently or are less comprehensive. For example, the French Top 50 only dates from 1984 (Syndicat National de l'Édition Phonographique, 2022).
Perspectives and Conclusions The BB200 database alone is not sufficient to answer many open questions about musical success. For example, we have studied the duration of success here, but were unable to link it to obvious factors, such as the age of the artist. There is a theory-not formalized but often mentioned  (Anderson, 2016; Forever Young, 2016 ; What Bands Have Kept Releasing Hits into Their 40s?, 2022) -that artists lose their inspiration and/or their ability to produce "hits" after the age of 40. The rare counterexamples sound more like exceptions proving the rule. Moreover, after the age of 50, examples become even rarer. 5 It can also be hypothesized that career length is at least as great a constraint as age in terms of inspiration. Thus, a later start may help to push back the 40-year barrier.
6 A fine-grained study of this kind of hypothesis requires the cross-referencing of hit databases, such as BB200, with artist databases, such as musicbrainz. Surprisingly, this area of research remains in its infancy, even in the age of BigData. As pointed out above, the study of factors influencing success (media coverage, distribution, political or social context, gender  (Lafrance et al. , 2011)  etc. ), which should be objectively quantified at the scale of hundreds of musical successes, also remains at a very early stage  (Cosimato et al. , 2019). The musicbrainz database also includes some metadata relating to musical content  (tempo, key, mode, harmonicity, timbre). With data of this type, it is already possible to quantify the diversity of musical styles over the decades  (Mauch et al.
, 2015)  or collaboration-based networks and social-based coverage predictive of musical success in the charts  (Cosimato et al. , 2019; Kang et al. , 2022). In the future, it may become possible to identify constants and patterns among the great hits and to finally "crack" the code defining a sure-fire hit. Artificial intelligence is already being used to compose fairly coherent pieces of music today. Will the machine eventually triumph over human creativity? Will human creativity eventually run out of steam with the intensive musical exploration of the last 70 years? Will we be able to create as many legendary songs in the coming years as we did in the 1960s? The future of music is exciting and intriguing, but perhaps also more analytical.
Context. We study the distribution of galaxies in nearby clusters to shed light on the evolutionary processes at work within clusters and prepare for a full dynamical analysis to be conducted in forthcoming papers of this series. Aims. We use the Wide-field Nearby Galaxy-clusters Survey (WINGS) database complemented with literature data. We assign galaxy membership to individual clusters, then select a sample of 67 clusters with at least 30 spectroscopic members each. 53 of these clusters do not show evidence of substructures in phase-space, as measured by the Dressler-Shectman test, while 14 do. We estimate the virial radii and circular velocities of the 67 clusters by a variety of proxies (velocity dispersion, X-ray temperature, and richness) and use these estimates to build stack samples from these 53 and 14 clusters, that we call "Reg" and "Irr" stacks, respectively.