dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was partly based on results obtained from projects ( JPNP14004 ,  JPNP16007  received by M. T. ) commissioned by the  New Energy and Industrial Technology Development Organization (NEDO)  and partly supported by  JST ,  CREST  ( JPMJCR2004  received by M. T. ). Part of this work was conducted at  Takeda Sentanchi super cleanroom, The University of Tokyo , supported by " Nanotechnology Platform Program " of the  Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan , Grant Number  JPMXP09F20UT0021. The authors thank  Dr. H. Yagi ,  Dr. Y. Itoh , and  Dr. H. Mori  of  Sumitomo Electric  for providing InP epitaxial wafers.
, 2015). The current study utilizes data from Solar Orbiter (SolO) to study MM storms at heliospheric distances between 0. 5 and 1 AU. The goal and motivation for this study were to contribute to filling this gap and shed light on some unresolved questions. This was achieved by employing the novel SolO observations to investigate characteristics such as physical properties (e. g. , amplitude, frequency, peaks/dips, and spatial scale), dependence on local plasma conditions, and connection with solar wind structures (e. g. , SIRs and shocks), and their occurrence across heliocentric distances. The study first analyzed several case studies in detail before conducting an automated search for events. This produced 25 events that were used to investigate the occurrence rate, dependence on solar wind conditions, and location in the inner-heliosphere.
Data and Instrumentation SolO  (Müller et al. , 2020)  measurements collected between 15 April 2020 and 31 August 2021 are used to conduct this investigation. The fluxgate magnetometer instrument (MAG)  (Horbury et al. , 2020)  provides full 3D magnetic field vectors and is used to characterize the magnetic field properties of the large-scale structure and MM waves. The magnetic field data are also used to automatically detect MMs later in the paper. The radio and plasma wave experiment (RPW)  (Maksimovic et al. , 2020)  measures the probe-to-spacecraft potential (ScPot), which can be calibrated to estimate the local electron density (N e )  (Khotyaintsev et al. , 2021). Hereafter, N e refers to electron density from ScPot and is not calculated from moments of velocity distributions. This high temporal resolution is needed since MM storms are typically around 0.
The CDF file versions used to analyze individual MM events (and compute solar wind statistics) were V1-V3 (V1-V5) for MAG and V3 (V3) for SWA-PAS. Measurements from the Magnetospheric Multiscale Mission (MMS) were also employed from the fluxgate magnetometer  (Russell et al. , 2016) , Fast Plasma Investigation-Dual Ion Spectrometer (FPI-DIS)  (Pollock et al. , 2016). OMNI data was used to infer ion temperature for the MMS event since FPI-DIS was not intended to measure the solar wind.
Data Availability Statement Solar Orbiter data is publicly available at the ESA Solar Orbiter archive (https://soar. esac. esa. int/soar/). MMS data is freely available from the MMS science data center (https://lasp. colorado. edu/mms/sdc/public/). The OMNI data were obtained from the GSFC/SPDF OMNIWeb interface at https://omniweb. gsfc. nasa. gov.
Long recycle times were used to ensure quantitative measurements. All the spectra were normalized taking into account the number of scans, the receiver gain and the mass of the sample. 7 Li and  19  F integrated intensities were determined by using spectral simulation Dmfit software,  [70]  taking the spinning sidebands into account. The absolute quantification of detected species was performed using i) a numerical fit and integration of each set of peaks corresponding to a chemical species, ii) a calibration curve obtained from reference LiF-Si or -Si/Gr mixtures. [43] 6. Scanning transmission electron microscopy (STEM) -Valence electron energy-loss spectroscopy (VEELS) Scanning transmission electron microscopy (STEM) experiments were carried out using the Nant'Themis, a (S)TEM Themis Z G3 Cs-probe corrected microscope (Thermo Fisher Scientific) operated at 300 kV and equipped with a high-angle annular dark field (HAADF) detector.
For region 2, probably because of the importance given to the spectrum in the 10-30 eV range, the fit is much worse around the Li K-edge. This leads to the attribution of a strong "Organic no Li" contribution (pink line, Figure  S6a ), which seems unjustified to the naked eye. If one performs the MLLS without this contribution, the fit appears actually better and is consistent with the presence of PAALi in this region as well (Figure  S6b ). Consequently, it seems unjustified to take into account a contribution of a polymer not containing lithium for quantification of phases in the VEELS-SI data cubes. It is impossible to rule out any presence of such contribution, but it is most probably a minority phase and would lead to erroneous weights for the phase quantification. STEM imaging of initial electrodes.
Acknowledgements We are grateful to the  ANR  for the funding of the  SILMARILION  project ( ANR-16-CE05-0015 ) and to  M. Chakir ,  L. Croguennec ,  C. Tessier ,  M. Ulldemolins , and  B. Mortemard de Boisse  for the fruitful discussions. N. Stephant is warmly t an e or t e i a es ac isitions A so n in b t e renc ontrat P an tat-ion an t e ro ean  Regional Development Fund of Pays de la Loire , the  CIMEN Electron Microscopy Center in Nantes , is greatly acknowledged. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
These two main components of the subsurface mooring are complemented by current meters in between the vertical range of the ADCP and the MMP or, since September 2016, below the lower end of the MMP range. In addition, lowered ADCP (LADCP) profiles, carried out during the mooring service cruises, were included in the presented data set. In the following, we provide a brief description of these individual mooring components. A more detailed assessment of the processing methods of the individual instruments is beyond the scope of this data report.
Moored ADCP Measurements Upper-ocean current velocity data were mainly acquired by moored ADCPs. From December 2001 to December 2002 an upward-looking 300-kHz ADCP was installed at about 150 m. After a gap of 14 months, the subsurface mooring was reestablished in February 2004 with the deployment of two ADCPs: one upward-looking 300-kHz ADCP located at a nominal depth of about 90 m and one downward-looking 75-kHz ADCP at a nominal depth of about 110 m. During the following mooring period from 2005 to 2006 this setup did not change, but the nominal depths were adjusted to about 50 m and 60 m, respectively. The first two moorings operated by GEOMAR included one 150-kHz and one 75-kHz upwardlooking ADCP located at about 130 m (200 m) and 620 m (700 m) between  2006 to 2008 (2008 to 2009). The upper ADCP of the mooring period between 2008 to 2009 was later identified by the manufacturer to have electronic problems and could not be used after an evaluation of the data records.
, 2021). Post-processed data were interpolated to a vertical resolution of 10 m and current velocities were 40hour low-pass filtered to remove tidal velocities and then subsampled to a temporal resolution of 12 hours. The vertical gap that occurs between adjacent ADCPs was filled by linear interpolation. Current velocity data from the upward-looking ADCP of the most recent Kiel Physical Oceanography (KPO) mooring KPO-1210 is still under examination since the instrument compass was found to have not worked properly. Consequently, these data are not included in the data set presented here, but will potentially be included in a future update, once a satisfactory solution is found. As part of a case study to investigate the transfer of wind momentum across the air-sea interface and the diurnal cycle of the upper ocean, a downward-looking 600-kHz ADCP was attached to the PIRATA moored surface buoy for eight months between October 2008 to June 2009  (Wenegrat et al.
, 2008). For the final data product, only quality-controlled current meter data were included leading to an exclusion of five RCMs between 2006 to 2014. Analogously to ADCP data, current meter velocities were 40-hour low-pass filtered to remove tidal velocities and then interpolated to a 12-hour temporal resolution. Several current meters were equipped with a pressure sensor. If pressure was recorded, the temporal mean of the pressure time series sets the equivalent depth at which the current meter data were included in the final data product. Otherwise, the nominal depth/pressure was used. Furthermore, current meter data were excluded if they substantially intersected with ADCP data. This was the case for one RCM, three Argonauts, and three Aquadopps all located between 750 to 850 m. In Uncertainty Assessment, we use these overlapping velocity time series to assess the uncertainty of the current velocity measurements.
MMP Measurements At 0°N/23°W, the deep ocean is covered by vertical profiles of the McLane moored profiler (MMP). The MMP was first deployed in June 2006 and forms an integral part of the KPO moorings ever since. However, no MMP was installed during mooring deployment KPO-1023 and, unfortunately, during mooring deployment KPO-1063 the instrument failed. Apart from that, the MMP provides an overall good data coverage and improved full-depth coverage in comparison to current meter data as installed before 2006 (see Figure  1 ). In general, the MMPs are equipped with an acoustic current meter (ACM) and a conductivity-temperature-depth (CTD) sonde  (Doherty et al. , 1999). From June 2006 to September 2016, the MMP was installed to measure current velocities between about 1000 to 3500 m. It was then moved up to cover the water column between 850 and 3350 m and to further close the gap between ADCP and MMP measurements.
LADCP Measurements The final data product is complemented by 10 full-depth vertical profiles of LADCP measurements carried out during the subsurface mooring service cruises (see Table  1 ) and another 10 deep LADCP profiles carried out during the annual service cruises of the moored surface buoy (PIRATA-FR-22 to PIRATA-FR-31). LADCP profiles were recorded as part of the CTD casts by attaching an upward- looking and a downward-looking 300-kHz ADCP to the CTD rosette frame. The processing of raw data followed the inverse solution approach developed by  Visbeck (2002).
DATA AVAILABILITY STATEMENT The combined current velocity data set (v1. 0) generated for this study (including zonal (U) and meridional (V) current velocity, datenum time (DTIME), julian time (JTIME), pressure (P) and depth (Z), as well as instrument type (IT)) is provided as a NetCDF file. Together with a list of the individual data sets as described in Table  1  it is stored in the PANGAEA data repository under: https://doi. pangaea. de/10. 1594/PANGAEA. 941042. Future updates of this data set will also be published through the PANGAEA repository and linked to the initial version (v1. 0) of the data set presented in this data report with successive version numbering after each mooring recovery. The MATLAB code and detailed instructions how to load, merge, combine and interpolate the individual data sets are provided via zenodo: https://doi.
org/10. 5281/zenodo. 6000560. PIRATA-FR LADCP data and processing protocols are provided by the IRD IMAGO team via: https://www. seanoe. org/data/00601/71295/. PIRATA moored surface buoy current meter data can be accessed via: https://www. pmel. noaa. gov/tao/drupal/disdel/ and high vertical resolution PIRATA ADCP data between October 2008 and June 2009 can be accessed via: www. pmel. noaa. gov/tao/drupal/disdel/ adcp_0n23w/index. html.
ACKNOWLEDGMENTS We thank the captains, crews, scientists and technical groups involved in several research cruises to the tropical Atlantic that contributed to collecting shipboard and moored data and making them freely available. We thank  M. Dengler ,  S. -H. Didwischus ,  J. Fischer ,  A. Funk ,  V. Hormann ,  R. Kopte  and the  IRD IMAGO team  for their help with acquiring and processing parts of the velocity data. We further thank  R. C. Perez  for comments on an early draft of the manuscript and two reviewers for their helpful feedback.
FUNDING This study was funded by  EU H2020  under grant agreement  817578   TRIATLAS  project, by the  Deutsche Forschungsgemeinschaft  as part of the Sonderforschungsbereich754 " Climate-Biogeochemistry Interactions in the Tropical Ocean " and through several research cruises with RV Meteor, RV  Maria S. Merian ,  RV L'Atalante , and  RV Sonne  and by the  Deutsche Bundesministerium fu ̈r Bildung und Forschung (BMBF)  as part of the projects RACE ( 03F06518 ) and by the  European Union   7th Framework Programme  ( FP7)  under Grant Agreement 603521. Moored velocity observations were acquired in cooperation with the  PIRATA  project supported by  NOAA (USA) ,  IRD  and  Meteo-France (France) ,  INPE (Brazil)  and the  Brazil Navy. This research was performed while FPT held an  NRC   Research Associateship Award  at  NOAA's Atlantic Oceanographic and Meteorological Laboratory.
03. 2008 RV Meteor M80/1 04. 11. 2009 10. 1594/PANGAEA. 924780 10. 1594/PANGAEA. 811718 KPO-1044 RV Meteor M80/1 05. 11. 2009 RV Maria S. Merian MSM18/2 02. 06. 2011 10. 1594/PANGAEA. 924781 10. 1594/PANGAEA. 846777 KPO-1063 RV Maria S. Merian MSM18/2 03. 06. 2011 RV Maria S. Merian MSM22 05. 11. 2012 10. 1594/PANGAEA. 923604 10. 1594/PANGAEA. 846763 KPO-1089 RV Maria S. Merian MSM22 06. 11. 2012 RV Meteor M106 03. 05. 2014 10. 1594/PANGAEA. 923986 10. 1594/PANGAEA. 869634 KPO-1125 RV Meteor M106 04. 05. 2014 RV Meteor M119 22. 09. 2015 10. 1594/PANGAEA. 908544 10. 1594/PANGAEA. 877351 KPO-1140 RV Meteor M119 23. 09. 2015 RV Meteor M130 15. 09. 2016 10. 1594/PANGAEA. 924247 10. 1594/PANGAEA. 915871 KPO-1176 RV Meteor M130 15. 09. 2016 RV Meteor M145 24. 02. 2018 10. 1594/PANGAEA. 904026 10.
1594/PANGAEA. 915873 KPO-1201 RV Meteor M145 24. 02. 2018 RV Meteor M158 11. 10. 2019 10. 1594/PANGAEA. 924782 10. 1594/PANGAEA. 941181 KPO-1210 RV Meteor M158 12. 10. 2019 RV Sonne SO284 13. 07. 2021 10. 1594/PANGAEA. 941190 10. 1594/PANGAEA. 941186 Moored current velocity data from the subsurface mooring and LADCP measurements (from the 2008 cruise onward) can be accessed through their individual links in the PANGAEA repository.
Ecological metrics to design and predict long-term biodiversity-based cropping system performances: insights from trait-based ecology A "functional trait" is a physiological, morphological or life-history characteristic of an organism that can respond to environmental changes (response trait) or modify the process of an ecological function (effect trait)  (Garnier and Navas, 2012). Trait-based ecology relies on this trait concept, and many studies describe how to measure plant functional traits, either root traits  (Fort and Freschet, 2020; Freschet et al. , 2021)  or aboveground traits  (Cornelissen et al. , 2003; Ordoñez et al. , 2009). Currently, considerable knowledge is available in databases such as the TRY Plant Trait Database  (Pakeman, 2014). These databases can be used to design biodiversity-based cropping systems that combine short-and long-term issues.
Using multi-year data to calibrate models, however, is a good practice to improve the accuracy of modelled scenario results  (Abi Saab et al. , 2015; Angulo et al. , 2013). In such approaches, long-term experiments are crucial, but they are encountered mainly in Europe and North America, and do not yet consider a wide variety of cropping systems. An open international database of these long-term experiments could serve to improve crop models in order to reconstruct trajectories of agroecosystem structure and functioning under climate change to project scenarios in the future. Metamodelling 3 has been applied successfully to crop models and should be encouraged for scenario projection and regional assessments  (Florin et al. , 2011; Ramanantenasoa et al. , 2019). However, metamodels require a huge experimental design to test them before implementation and depend on the knowledge embedded in crop models.
10. 1029/2022GL098522 3 of 12 The central and four inner moorings were also instrumented with 75-KHz unit upward-looking Acoustic Doppler Current Profilers (ADCP) at about 450 m, which measured horizontal velocity in 8-m bins and formed ensembles every 60 min. In addition, the mooring measurements were complemented by hydrographic observations acquired by two ocean gliders that navigated in a bow-tie pattern across the mooring array for the entire sampling period  (Damerell et al. , 2016; Thompson et al. , 2016). The mixed layer depth, H ML , is calculated from coincident glider data using a threshold value of potential density increase (▵ρ = 0. 03 kg m -3 ) from a near-surface value at 10 m  (Damerell et al. , 2016). Mooring measurements captured the mixed layer during winter and early spring months, and the pycnocline plus part of the ocean interior throughout the year.
Wind Data The buoy of the OSMOSIS moorings contained meteorological sensors. Unfortunately, the buoy sank shortly after deployment. Wind data are instead taken from the European Centre for Medium-Range Weather Forecasting (ECMWF) ERA-Interim reanalysis surface wind fields  (Dee et al. , 2011). Zonal and meridional reanalysis winds are obtained at the grid point closest to the central mooring site with a time interval of 3 hr for the record year. Wind measurements from the K1 buoy, 250 km away from the OSMOSIS mooring site (Figure  1a ), are used to validate the reanalysis wind fields. The K1 buoy was sampled at hourly intervals from 5 September to 28 December 2012. Wind speed was converted to stress using a speed-dependent drag coefficient  (Large & Pond, 1981). The reanalysis winds show a good agreement with the measured winds, with a correlation coefficient of 0.
Acknowledgments We are grateful to the officers, crew, scientists, and technicians of the  RRS Discovery, RRS James Cook , and  R/V Celtic Explorer , for their hard work in deploying and recovering the OSMO-SIS moorings and gliders. This work was funded by grants from the  Natural Environmental Research Council  ( NE/ I019999/1  and  NE/I01993X/1 ). We thank two anonymous reviewers for their insightful feedback.
Data Availability Statement All OSMOSIS mooring and glider data are freely available, and are archived at the British Oceanographic Data Centre. Moored observations can be obtained from https://www. bodc. ac. uk/projects/data_management/uk/ osmosis/, and glider data are accessible at https://doi. org/10. 5285/6cf0b33e-a192-549f-e053-6c86abc01204. ERA-Interim data were obtained from the European Centre for Medium-Range Weather Forecasts (downloaded from http://www. ecmwf. int/en/research/climate-reanalysis/era-interim).
Open Access *Correspondence: e. raebel@rd-rp. com deficiency affects intra-lysosomal degradation pathways and leads to the progressive accumulation of undigested mannose-oligosaccharides and subsequently, impaired cell function  [2, 3]. Alpha-mannosidosis is present worldwide and although its exact prevalence is not known, it is estimated at 1:500 000 live births  [4]. Currently, 162 mutations of the MAN2B1 gene are reported in The Human Gene Mutation Database  [5]. Children with alpha-mannosidosis appear normal at birth, with manifestations developing at an early age and the condition progressively worsening  [6]. The disorder encompasses a broad spectrum of clinical manifestations including mental and cognitive impairment, sensorineural hearing loss, facial dysmorphism, skeletal abnormalities, ataxia, motor function disturbances, immunodeficiency, recurrent infections and in older patients, behavioural problems and psychotic episodes  [6] [7] [8] [9].
Determining the causes of death in these patients is important to improve our understanding of the clinical course of the disease, to evaluate the effectiveness of current treatment therapies, to optimise their clinical management and, ultimately, to better understand the natural history of the disease and thus improve the quality of life of patients and their families. Although an international registry collecting prospective data in treated (with ERT) and untreated patients with alpha-mannosidosis is currently ongoing  [18, 19] , large datasets on these patients are scarce. The aim of this study is to review the age at death and the causes of death of patients with alpha-mannosidosis as reported by clinicians and patient organisations (POs), and to review causes of death reported in published studies which include deceased patients with this disorder.
Mortality in alpha-mannosidosis patients reported by clinicians and patient organisations Data from POs and clinicians were obtained by means of a specifically designed questionnaire which was emailed to participants and completed between April and May 2021. Variables collected included: country, gender, year of birth, year of death, age at death, cause of death and MAN2B1 mutation. The questionnaire also asked whether deceased patients had received alpha-mannosidosis therapy (e. g. , HSCT, ERT) and these patients were excluded from the analysis. Other collected variables are not presented in this study. To avoid duplications while maintaining patient anonymity, patients' initials were requested where available. The questionnaire to collect data from the UK MPS Society asked for the patient's full name, date of birth and date of death to be able to obtain a death certificate.
Mortality in alpha-mannosidosis patients reported in the literature A literature search was conducted in PubMed to ascertain published studies reporting on deceased patients with alpha-mannosidosis. The pre-determined search terms 'alpha-mannosidosis' , 'α-mannosidosis' , 'alpha mannosidase deficiency' and 'mannosidosis' were combined using the Boolean 'OR' operator. Studies were retrieved if the title/ abstract/ keyword contained at least one of the terms. The Boolean term 'NOT' was used to exclude animal studies. Reference lists from natural history studies and reviews were hand searched for potential sources of data and subjected to the above process. Searches were completed on 21 April 2021. The Rayyan Web app  [23]  was used to review titles/ abstracts by one researcher to identify potential alpha-mannosidosis studies for inclusion (e.
g. excluded biochemical, in vitro, molecular genetic analysis studies and those not reporting on patients). Further animal and plant studies were excluded. Full texts of relevant papers, including all case studies, were subjected to further scrutiny to include case descriptions or quantitative data of deceased patients with a genetically or laboratory confirmed diagnosis of alpha-mannosidosis (i. e. deceased patients for whom alpha-mannosidosis was only suspected were excluded, e. g. [24] ) with final papers being selected based on the inclusion criteria in Table  1. Studies were examined for repeat reporting of individual patients. Data extraction included year of death, age at death, sex, residing country at death and cause of death where available. Individuals were only included in the study if treatment status and cause of death were available.
Deceased patients from clinicians and patient organisations' records Responses A total of 15 deceased patients with a confirmed diagnosis of alpha-mannosidosis from clinicians and POs were included in the study (Fig. 1 ). Patient data were obtained by inviting 78 clinicians and 34 POs to participate, with responses received from 53 clinicians (response rate: 67. 9%) and 20 POs (response rate: 58. 8%). Ten clinicians reported 25 deceased patients with alpha-mannosidosis in their clinic or through historic records. Eleven of these patients were excluded from the analysis: seven had been lost to follow-up and the cause of death was not available and four patients died due to complications of HSCT. Fourteen deceased patients provided by four clinicians were included in the analysis (Fig. 1 ). Three POs had records of deceased patients on their database or knew of members that had died, with a total of six patients.