dataesr/scientific-paragraphs-categorization huggingface dataset:
Observations and data reduction and processing We used a total of 19 HST orbits (period 95 min) over about 19 days to observe the Dimorphos ejecta (Extended Data Table  1 ). The first orbit (orbit 0o) was before the DART impact. The second orbit to the seventh orbit (orbits 01-06) started about T + 15 min and continuously observed the ejecta except when Earth occluded the view of the target. In the next five orbits (orbits 11-15), we observed the ejecta roughly once every 12 h, and then once every day in the subsequent three orbits (orbits 16-18). In the final phase (orbits 21-24), observations were made once every 3 days. The observations concluded 18. 5 days after impact. In each orbit, images were collected at several exposure levels, in which the central core of Didymos was unsaturated in short exposures and long exposures saturated Didymos to image the relatively faint ejecta and tail.
We then removed the sky background measured from a square 100-400 pixels wide and 100-300 pixels from the top right corner, depending on the image size. This area is in general 20 arcseconds away from Didymos and shows no sign of any ejecta. Aperture photometry was measured in all short, unsaturated exposures that have been corrected for charge transfer efficiency  31  but not geometric distortion (. flc files, available from the HST data archive; see 'Data availability'). The centroid was defined by a two-dimensional Gaussian fit with a 5 × 5 pixel box centred at the photocentre. The pixel area map was used to correct pixel area variations in the image  31. The total counts were measured with circular apertures of a radius of 1-130 pixels (0. 04-5. 2 arcseconds). We converted the total counts to flux density and Vega magnitude based on the photometric calibration constants (PHOTFLAM = 5.
3469 × 10 -20 erg Å -1 cm -2 per electron, PHOTZPT = 26. 78) provided in the image headers and the HST photometric calibration website. The total brightness of Didymos including the ejecta and the total brightness of ejecta are shown in Extended Data Fig. 4. We used the images corrected for charge transfer efficiency and geometric distortion (. drc files) to study the morphology of the ejecta. To increase the signal-to-noise ratio of the faint ejecta features, we stacked all long exposures in each orbit because no change is visible in the ejecta morphology with each orbit. The centroid of long exposures that are saturated in the centre was determined by the cross-section of the diffraction spikes. Some long exposures with pointing-induced drift were included in the stack, but those with more than ten pixels of drift were discarded.
This is confirmed by our derived cone geometry described in the following. We measured the position angles of the two edges of the ejecta cone from both the original and the enhanced images (see 'Observations and data reduction and processing'). The uncertainty range of the position angles is defined by the apparent width of the linear feature. Our measurement resulted in an ejecta cone centred within 5º of the incoming direction of DART with an opening angle of about 130º. Because of the fuzziness of the ejecta rays and their slight curvature, the uncertainty of the measured position angles could be as high as ±8º, resulting in an uncertainty of the opening angles up to ±12º. Taking the mean of these two edges and the maximum value of the uncertainty yields the ejecta cone axis at a position angle of 67° ± 8° under the assumption that the ejecta cone is axisymmetric about the cone axis.
Acknowledgements This work was supported by the  DART mission ,  NASA  contract no. 80MSFC20D0004  and by the  Italian Space Agency (ASI)  through the  LICIACube  project ( ASI-INAF  agreement AC no. 2019-31-HH. 0 ). Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  NASA. J. -Y. L. acknowledges the support provided by  NASA  through grant  HST-GO-16674  from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy , under  NASA  contract  NAS 5-26555. L. K. acknowledges support from the  NASA   DART Participating Scientist Program , Grant no. 80NSSC21K1131. R. L. , D. A. G. and T. J. S. acknowledge funding from the  NASA/GSFC Internal Scientist Funding Model (ISFM) Exospheres, Ionospheres ,  Magnetospheres Modeling  (EIMM) team, the  NASA Solar System Exploration Research Virtual Institute (SSERVI)  and  NASA  award no.
80GSFC21M0002. R. M. acknowledges support from a  NASA Space Technology Graduate Research Opportunities (NSTGRO)  Award (contract no. 80NSSC22K1173 ). P. M. acknowledges funding support from the  Horizon 2020  research and innovation programme of the  European Union  under grant agreement no. 870377  (project  NEO-MAPP ), the  CNRS  through the  MITI interdisciplinary programmes ,  CNES  and  ESA. F. F. acknowledges funding from the  Swiss National Science Foundation (SNSF) Ambizione  grant no. 193346. J. O. has been funded by grant no. PID2021-125883NB-C22  by the  Spanish Ministry of Science and Innovation/State Agency of Research   MCIN/AEI/10. 13039/501100011033and  by the  European Regional Development Fund "A way of making Europe". G. T. acknowledge financial support from project  FCE-1-2019-1-156451  of the  Agencia Nacional de Investigación e Innovación ANII (Uruguay).
T. Kohout is supported by the  Academy of Finland  project  335595  and by institutional support RVO 67985831 from the  Institute of Geology of the Czech Academy of Sciences. F. M. acknowledges financial support from grants  CEX2021-001131 -S funded by  MCIN/AEI/10. 13039/501100011033and PID2021-123370OB-I00. Research by M. G. is supported, in part, by the  Academy of Finland  grant  345115. J. M. T. -R. acknowledges financial support from the project  PID2021-128062NB-I00  funded by  Spanish   MCIN/AEI/10. 13039/501100011033. We thank  J. DePasquale  ( STScI ) for generating the animation included in Supplementary Video 1.
Data availability All raw HST data associated with this Article are archived and are publicly available at the Mikulski Archive for Space Telescopes (https://mast. stsci. edu/search/ui/#/hst/results?proposal_id=16674) hosted by the Space Telescope Science Institute. The stacked long exposures in Figs. 2 3 4 are available from a website hosted at JHU/APL (https://lib. jhuapl. edu/ papers/ejecta-from-the-dart-produced-active-asteroid-dimo). Other related data are available from the corresponding author upon request.
Material and methods Cyclothone spp individuals were collected during four multi-disciplinary research cruises aboard RV ''Ramón Margalef'' at an oceanic station (5 • 40' W and 44 • 20' N) with a bathymetry of 4600 m in the Bay of Biscay (BoB), eastern North Atlantic. The cruises took place during winter (13-20 February), spring (13-27 April), summer (10-28 July) and autumn (12-24 November) of 2015. Macroplankton samples were obtained by Multi Net tows at a maximum of 10 vertical strata down to 2000 m depth and Cyclothone spp were sieved immediately after samples were recovered onboard. For more information see  Peña et al. (2019 Peña et al. ( , 2021)). The Opencv library in Python  (Bradski, 2000)  was employed to process the images and extract the fish contours. Individuals with the mouth open or any kind of damage were discarded.
Acknowledgements This work was funded by the  Spanish Ministerio de Economía y Competitividad (MINECO)  through project  SCAPA  ( CTM2013-45089-R ) and by  Instituto Español de Oceanografía, Spain  through project ' MESOPELAGICOS '. We thank the collaboration of all scientists and crew involved in the surveys and the feedback provided by the anonymous reviewers that contributed to improve the manuscript. LA was funded by an  ERASMUS internship grant. Sven Gastauer  kindly provided guidance on the use of the acoustic model and gave feedback on the manuscript.
X Ray Diffraction X-ray diffractograms of individual beds from two samples of the Brockman Iron Formation (samples DD98-26A and DD98-30A; see Figs. 2  and 5 ) were acquired using a PANalytical Empyrean X-Ray Diffractometer using Cu Κα1/Κα2 radiation, a 0. 1 x 4 mm collimated beam, and with a PIXcel3D linear position-sensitive detector. Diffraction spectra were analysed using the X'Pert Highscore software and the associated crystallographic database PAN-ICSD (Inorganic Crystal Structure Database).
Micro X-ray Fluorescence Micro x-ray fluorescence (μXRF) was performed at the University of Brest using a Bruker M4 Tornado operating with an excitation energy of 50 kV with the source at 600 μA. Three different spot analyses were performed per sample and averaged to obtain representative concentrations. Concentration calibration was performed using the Fundamental Parameters algorithm built into the M4 Tornado software package.
We also examined grain sizes (mixed with lattice distortion effects) using the Scherrer method  (Scherrer, 1918)  as implemented by the HighScore software (Malvern Panalytical, Ltd, Malvern, UK). This method evaluates peak broadening compared with a standard analyzed under the same conditions to infer crystallite size. The results are presented graphically in Fig. 6  as box and whisker plots showing the maximum and minimum sizes for each mineral, which are also tabulated in Supplemental Table  1. The quartz-rich sample DD98-26A generally shows a significantly larger range in indicated grain sizes, with quartz crystallographic domains reaching up to 3 μm compared to 1 μm in DD98-30A. Both samples show quartz crystallographic domains reaching down to 100 nm or below. Magnetite crystallographic domains reached a similarly higher range in DD98-26A relative to DD98-30A (1000 nm vs.
Acknowledgments We thank  Jean-Pierre Oldra  for thin sections,  Jessica Langlade  at Microprobe and  Philippe Elies  from  UBO Plateforme d'Imagerie et de Mesures en Microscopie , all for assistance with sample preparation and analyses. We also thank  Marcel Koken  for stimulating discussions, and  Charbel Tannous  for his careful mathematical analysis of RPE curves. We are grateful to  IUEM  for providing financial support with UBO funds. Finally, we also thank  Bertrand Sichler , who was among the first participants in the  UBO Nano Research group , and critically read the text.
23). Thisisdueto differencesinsnowfall, humidityandairtemperaturebetweenbothforcingdatasets. Inconclusion,ERA5hasgreat potential to force snow models but the use of Crocus in the Arctic is affected by limitations such as inappropriate parametrisations for Arctic snowpack evolution, but also by lack of process formulations such as vertical water vapour transport. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.
g. , due to the absence of water vapour transport  (Domine et al. , 2016b; Barrere et al. , 2017; Domine et al. , 2019). However, complete and sufficiently long time series of meteorological measurements, especially reliable precipitation data, to force snow models are limited in the Arctic  (Boelman et al. , 2019). Therefore, snow models are often driven by atmospheric reanalyses delivering complete time series of meteorological data for given locations or areas  (Barrere et al. , 2017; Gouttevin et al. , 2018; Domine et al. , 2019). Such reanalysis data are physically consistent, i. e. , the individual variables of the reanalysis are consistent to each other and energy, mass, and momentum are conserved as the dataset is generated using physical equations in the model, which cannot be guaranteed for in situ observations  (European Centre, 2020; Hersbach et al.
In situ measurements We use in situ observations from Greenland at VRS, where meteorological data from November 2015 to August 2018 and snow depth from August 2014 to September 2018 were measured to answer the research questions. In addition, during a campaign in spring 2018 numerous snow parameters, as snow depth, snow density and SSA were measured. Meteorological data were measured at an automatic weather station  (Villum Reserach Station, 2021). We chose the study period to compare atmospheric variables of ERA5 and in situ and simulated snow depth according to the availability of measured forcing variables between 27 November 2015 and 8 August 2018. Table  1  gives an overview on the installed meteorological sensors, their accuracy and the occurrence of missing data. In the Supplementary Appendix an overview about the different time periods used for validation and simulations in this study is delivered (Supplementary Appendix Table  SA1 ) as well as a figure displaying the time series of most relevant atmospheric data (Supplementary Appendix Figure  SA1 ).
In addition, we conducted snow measurements during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign, in a distance less than 500 m away from VRS, between 10 March and 8 April 2018. Measurements included snow density, SSA and snow depth. The meteorological situation during and shortly before the campaign is described in Section 3. 2. 2. Vertical profiles of snow density and SSA were retrieved from data measured with a SnowMicroPen (SMP) Version 4, a snow penetrometer measuring the bonding force between snow grains with a vertical resolution of 1. 25 mm  (Schneebeli et al. , 1999). Note that there are different parametrisations to obtain density and SSA from penetration force and hence calculating snow density and SSA from penetration force introduces uncertainties.
Therefore, we deliberately set missing precipitation data to zero knowing that this is causing an underestimation of the accumulated precipitation. Additionally, we examined the timing and length of the precipitation data gaps. There are many short gaps over the entire study period, as visible in Supplementary Appendix Figure  SA2. The in situ control simulation (Insitu-CTRL) is forced with in situ measurements from VRS from 27 November 2015 to 8 August 2018, except for the measured incoming longwave radiation, which was replaced by the corresponding ERA5 variable for the whole study period because of the systematic errors in this measurements (see Supplementary Appendix Figure  SA1  for a comparison of measured and ERA5 longwave radiation). To reduce spin-up effects for the in situ simulation, we used the archived initial conditions from 26 November 2015 of ERA5-CTRL, as initial conditions for the in situ simulation.
Between 17 February 2018 and 27 February 2018, ERA5 snowfall was about 5% of the in situ snowfall (ERA5: 28 mm w. e. , in situ: 530 mm w. e. ). Due to the short periods of air temperature above the freezing temperature there was also some rainfall measured. ERA5 rainfall during that time was close to zero at 81. 5 °N, 16. 75 °W, while neighbouring grid cells showed a similar amount of rainfall of 1 mm as the in situ measurements. Apart from the warm event the amount of precipitation in both datasets were similar, but the timing of precipitation events differed strongly. During PAMARCMiP, in situ measurements showed a snowfall event lasting over several days, which is not present in ERA5. Instead, ERA5 showed snowfall about a week before the campaign. Field observations during PAMARCMiP confirmed strong snowfall events.
Comparison of atmospheric in situ measurements and reanalysis data at Villum Research Station Atmospheric variables measured at VRS are point measurements representing local conditions. ERA5 on the other hand represents mean values in a grid cell. Due to the relatively coarse resolution of ERA5, the orographic relief is much smoother than in reality. This causes discrepancies in altitude and leads to differences, e. g. , in surface air pressure. After correcting for this ERA5 air pressure agrees well with measurements, which is in line with the findings of  Delhasse et al. (2020). We find a good correspondence between measured and ERA5 air temperatures, shortwave and longwave radiation (excluding the erroneous time period) in agreement with other studies  (Betts et al. , 2019; Wang et al. , 2019; Delhasse et al.
, 2018). In the Arctic, snow redistribution is important. However, we investigated the correlation of wind speed above 6 m s -1 and snowfall for both forcing datasets and calculated a Pearson correlation of 0. 46 and 0. 51 for in situ and ERA5 data, respectively. From this we conclude that the impact of snowdrift at our location is probably not large and that therefore the timing of snow incorporated into the snowpack is not a decisive process for the snow density profile at VRS. Otherwise, we would have expected a higher correlation between high wind speeds and precipitation for in situ data (containing the effect of wind drift) than for the ERA5 data. However, the impact on density and increased sublimation due to snowdrift is implemented into Crocus and leads to distinctively lower snow depths in our simulations.
Conclusion Working with snow in the Arctic is challenging due to limited availability of measured snow data. Detailed snow models can help to overcome this problem and can assist to interpret the measurements but require carefully prepared forcing data to ensure high quality of simulated snow data. We can show that already relatively small deviations in solid precipitation, specific humidity and air temperature lead to considerable differences in simulated snow depth. In situ atmospheric and snow measurements in the Arctic are rare, incomplete, and have to cope with difficult measurement conditions such as riming on the instruments and strong winds. However, models need complete time series to drive them. Here we present in situ atmospheric measurements for 2015 to 2018 and compare them with the modern atmospheric reanalysis ERA5 before driving the snow model Crocus with these datasets.
Acknowledgments We gratefully acknowledge the funding by the  Deutsche Forschungsgemeinschaft (DFG-German Research Foundation ; project  ID   268020496-TRR 172 ) within the  Transregional Collaborative Research Center project of  " ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes , and  Feedback Mechanisms  (AC)3". We thank  Marco Zanatta  for the snow sampling during the campaign and  Villum Research Station  for general support. Special thanks go to  Keld Mortensen  and  Andreas Massling  for providing observational meteorological data. The authors acknowledge the cooperation and the productive scientific discussion with  Christian Haas ,  Stefanie Arndt ,  Marco Zanatta  and  Olaf Eisen.
(2022). We present an extensive dataset that combines (i) compositional profiles in olivine crystals, (ii) analysis of olivine-hosted melt inclusions, and (iii) geochemical characterisation of matrix glasses, to constrain the timescales of the processes that preceded this eruption. This data is complemented by dissolved S, H 2 O, and CO 2 in melt inclusions to discuss the dynamics of the deep magma contribution and to add further pressure (i. e. , depth) constraints on the source region feeding these frequent, violent events. The integration of the geochemical data with gas plume time series from the monitoring network yields insights into the degassing behaviour and into precursor timescales preceding the explosive events that can be successfully detected by the monitoring network.
Electron microprobe analyses Compositional profiles for diffusion modelling were obtained on 34 olivine crystals through electron microprobe analysis. In addition, point analyses were obtained next to melt inclusions and embayments to characterise host olivine compositions for an additional 13 olivine crystals. A first dataset was obtained on both 500 µm and 250 µm olivine crystals performed using a WDS at JEOL 8200 Super Probe at the University of Milan (Italy). Analyses were performed at 15 kV with a focused beam current of 5 nA and steps that varied from 15 µm for apparently homogeneous olivine to 5 µm for complex (zoned) crystals. Profiles and spot analyses were also acquired using a CAMECA SX five and a CAMECA SX 100-electron microprobe in Paris (Camparis Platform, Sorbonne-University, France).
In most cases, multiple spots were analysed within one melt inclusion and were averaged subsequently. While overall melt inclusions and embayments in 33 olivine crystals were characterised by electron microprobe for their major element and S composition, a subset of melt inclusions and embayments in 13 olivine crystals was additionally analysed by FTIR spectroscopy. The complete datasets are reported in Table  S4. Equilibrium and potential post-entrapment crystallisation (PEC) were calculated using Petrolog v. 3  (Danyushevsky and Plechov 2011). A K D value of 0. 3 (cf. Toplis 2005) was used at NNO + 1, following the model of  Borisov and Shapkin (1990). For density and viscosity estimates, the build-in models of  Lange and Carmichael (1987)  and  Bottinga and Weill (1972)  were used, respectively.
e. , 3 May, 8 and 24 Nov 2009, La Felice and Landi 2011). Contrastingly, matrix glass compositions observed in previous small-scale paroxysms of 2003 and 2007 as well as in the 1998 major explosion show a limited variability both for matrix glass as well as olivine compositions (Fig. 5 , Fig. S3 ). S5 ). These compositions agree with previously obtained results for the 1998 major explosion and for the 2003 and 2007 small paroxysms but extend towards (and beyond) the 1456 LSP dataset. Embayments show compositions that overlap with matrix glasses but are generally more evolved than MIs, with CaO/Al 2 O 3 = 0. 57-0. 68, K 2 O/ Na 2 O = 0. 61-0. 86 (Fig. 6b ). Melt inclusion CaO/Al 2 O 3 ratios are variable, possibly due to minor clinopyroxene crystallisation in the melt that leads to decreasing CaO/ Al 2 O 3 ratios, while K 2 O/Na 2 O ratios remain relatively constant (e.
Further constraints to allow better discussion of the ponding depth of major explosions are gained from total volatile (CO 2 + H 2 O) pressures. It is well known that absolute values for pressure and depth estimates are strongly dependent on the used solubility model and therefore need to be treated with caution (see the supplementary material for a more detailed discussion). Previously published pressures for the 2007 paroxysm from  Métrich et al. (2010)  were recalculated with the same solubility model used for the 2020 major explosion data (Fig. S2 , Table  S3 ), to ensure the comparability of the different datasets. H 2 O and CO 2 contents identify two primary pressure modes at 275 MPa and 175 MPa for the 2020 eruption that correspond to the pressures recorded by MIs and embayments, respectively (Fig.
In this perspective, the geochemical signal of the CO 2 /SO 2 ratio and the CO 2 flux from the degassing plume  (Aiuppa et al. 2021 ) and its correlation with magmatic processes in the deep system from petrological studies (e. g. ,  Métrich et al. 2021)  revealed to be an effective tool to document perturbations of the deep magmatic system over timescales of days to months. For the majority of cases, this appeared to add crucial information to the long-term forecasting of major explosions occurrence. In a more general scheme, the presented comprehensive dataset on a violent major explosion adds further crucial insights in terms of magma storage depths and pre-eruptive timescales that will be integrated within the database that provides the framework for short-and long-term eruption forecasting.
Acknowledgements The authors thank  N. Rividi  and  M. Fialin  at the  Sorbonne-University, France (Camparis)  and  A. Risplendente  at  Università degli Studi di Milano  for help with microprobe analyses. M. Bitetto  is thanked for the photo in Fig. 1. This work was supported by the  Italian Civil Protection  in the framework of the agreements between the  Universities of Florence and Pisa and between INGV  and the  University of Pisa. We thank  J. Hammer  and  A. Harris  for editorial handling and helpful suggestions on the manuscript as well as  M. Myers ,  R. Spallanzani ,  M. J. Rutherford , and an anonymous reviewer for their detailed comments and suggestions. Finally, we would like to thank  F. Van Wyk de Vries  for help with handling the paper through the EdMan system.