dataesr/scientific-paragraphs-categorization huggingface dataset:
Since in the simplest single-scattering scenario no dependence of polarization degree on photon energy is expected, the spectral shape of Q and U should resemble the shape of I refl,c. The IXPE image is in units of counts s -1 arcmin -2 cm -2 colour-coded on a linear scale (see the colorbar on the right). For IXPE, the edges of the image, where the exposure (including vignetting effects) drops below 15% of the maximal value, have been truncated. For Chandra, a logarithmic scale is used to show both diffuse and compact sources. The positions of the supermassive black hole Sgr A * and the Arches star cluster are labeled. The area where reflection emission is strong in the Chandra data is shown using a green 4. 5 ′ radius circle. This circle was used to extract I, Q, and U spectra from the IXPE data.
In the former case, a 1-2 years-long flare is needed, while in the latter, an hour-long outburst might be sufficient, since the observational data better constrain the fluence of the flare (product of the luminosity and duration of the flare) ∆t × L X ∼ a few 10 47 erg, rather than its luminosity, with an additional constraint that ∆t < 1. 6 yr  [29]. Looking at a landscape of possible classes of transient events  [30]  that can explain such parameters, one could mention Accretion Induced Collapse of white dwarfs, sub-luminous gamma-ray bursts, and Tidal Disruption Events. One could add a transient accretion event on Sgr A* (not directly related to a tidal disruption event) to this list, too. The second group of transients, which fall short a factor of 10-100 in terms of fluence, includes Giant Flares from Soft Gamma-ray Repeaters / Magnetars, Type II Supernova Breakouts, and major outbursts of Ultra-Luminous X-ray sources.
The data products are archived at the High-Energy Astrophysics Science Archive Research Center (HEASARC, at the NASA Goddard Space Flight Center), for use by the international astrophysics community. These level-2 event files were cleaned from additional environmental and background contamination  [33]. In the case of the observation of the Galactic center, the observation window corresponded to a period of increased solar activity which led to an increase of atmospheric noise (particle background). In addition, such activity tends to extend the South Atlantic Anomaly (SAA), above which the detectors are usually turned off to prevent observation in a highly particle polluted environment. However, as can be seen on Fig. 5  (top), several spikes were recorded in the light-curve right before the spacecraft entry in the SAA.
Chandra data reduction The Chandra data reduction follows a standard procedure based on the latest versions of the data reduction software (CIAO v. 4. 14) and calibration X-ray polarimetry of the Galactic center   2  The three more intense sources and their re-scaled flux in the extraction region we used in our paper (see Fig. 1 ). Fluxes are in erg s -1 cm -2. (CALDB v. 4. 9. 8). Our particular approach and analysis steps are described in detail in  [36]. Briefly, they include identification and removal of high background periods, correction of photon energies for the time and detector temperature dependence of the charge transfer inefficiency and gain, and creation of matching background datasets using blank sky observations with exposure times similar to the Galactic Center pointings. The Chandra observing program included multiple pointings with individual exposures 10-30 kiloseconds.
Archival observations with XMM-Newton In order to better constrain spectral de-composition of the X-ray emission from the region of interest, we have used all archival observations with XMM-Newton observatory accumulated between 2002 and 2012. These data have already been used for studies of the reflection component  [25]. The XMM-Newton data were prepared, reduced and processed in exactly the same manner. As the final step, the spectrum was extracted from the same region as used by IXPE. Thanks to large effective area above 4 keV and long total exposure time supplemented with superior spectral resolution around 6 keV, the XMM-Newton data allow us to better separate thermal and reflected components. Since only the contribution of the reflected component is expected to vary over time, one can combine the archival data with the quasi-simultaneous Chandra and IXPE observations by allowing only parameters of the reflected component model to vary while linking parameters of all other components when fitting the spectra of the total emission observed by all three observatories.
The APEC model 2  was used for the optically thin thermal components. The reflection component describes the spectrum emerging from a spherical cloud of molecular gas, illuminated by a beam of X-rays, having a power-law spectrum. For this purpose, a publicly available CREFL16 model  [25]  was used, and more specifically a version of it where reflected continuum and fluorescent lines are treated separately but with tied parameters. A combination of all components provides a reasonably good approximation of the IXPE, Chandra, and archival XMM-Newton spectra X-ray polarimetry of the Galactic center extracted from the reference region 3. We note in passing that IXPE and Chandra observations are quasi-simultaneous, while the XMM-Newton data were averaged over several observations spread over many years (from 2002 to 2012).
Acknowledgments. The Imaging X ray Polarimetry Explorer (IXPE) is a joint US and Italian mission. The US contribution is supported by the  National Aeronautics and Space Administration (NASA)  and led and managed by its  Marshall Space Flight Center (MSFC) , with industry partner  Ball Aerospace  (contract  NNM15AA18C ). The Italian contribution is supported by the  Italian Space Agency (Agenzia Spaziale Italiana, ASI)  through contract  ASI-OHBI-2017-12-I. 0 ,agreements  ASI-INAF-2017-12-H0  and  ASI-INFN-2017. 13-H0 , and its  Space Science Data Center (SSDC)  with agreements  ASI-INAF-2022-14-HH. 0  and  ASI-INFN 2021-43-HH. 0 , and by the  Istituto Nazionale di Astrofisica (INAF)  and the  Istituto Nazionale di Fisica Nucleare (INFN) in Italy. This research used data products provided by the  IXPE Team (MSFC, SSDC, INAF , and  INFN ) and distributed with additional software tools by the  High-Energy Astrophysics Science Archive Research Center (HEASARC) , at  NASA Goddard Space Flight Center (GSFC).
Data availability. The IXPE data that support the findings of this study are freely available in the HEASARC IXPE Data Archive (https://heasarc. gsfc. nasa. gov/docs/ixpe/archive/). The XMM-Newton data can be found on the same website, while the Chandra data will be public after one year from the observation date. Code availability. The analysis and simulation software ixpeobssim developed by IXPE collaboration and its documentation is available publicly through the web-page https://ixpeobssim. readthedocs. io/en/latest/?badge= latest. 494. XSPEC is distributed and maintained under the aegis of the HEASARC and can be downloaded as part of HEAsoft from http://heasarc. gsfc. nasa. gov/docs/software/lheasoft/download. html.
10 - 6 10 - 5 6 10 - 5 Fig. 2 1065652 Fig. 2Top panel: Spectra of the X-ray emission extracted from the circular region shown in Fig. 1in the data of Chandra (red), IXPE (green) and archival XMM-Newton (black) observations, after division by energy-dependent effective area of each telescope. The solid lines correspond to the best-fitting models convolved with the spectral responses of the telescopes resulting in strong smearing of the emission lines in the IXPE data. Parameters of the stationary emission components were fixed among all three datasets, and only normalization of the reflection component was allowed to vary between them. Faint lines show contribution of the reflected component to the combined model (see Methods for individual description of each component). As demonstrated by the residuals shown in the bottom panel, the model provides a good fit for the soft part of the spectra for all three datasets, while contributions of the time-variable reflection component in the quasi-simultaneous Chandra and IXPE observations are consistent with each other, given the current level of cross-calibration uncertainties.
g. , on 8th February (Fig. S4 ), with very low depolarisation ratio (<2%), indicating that the dispersed plume is dominated by spherical sulfate aerosols. Relatively small colour ratio might indicate an increase in aerosol size, likely mainly due to sulfate aerosols water uptake/coagulation processes. Impact on the global stratospheric aerosol layer. Using the information content in long time series of different satellite observations, insights into the HT plume longer-term evolution (weeks to months timescale) and the comparison of its impact with recent stratospheric eruptions can be obtained. A clear but limited enhancement of SO 2 , with respect to background SO 2 levels, is apparent from zonal average IMS/IASI SO 2 observations (Fig. 3a ). Using this dataset, the SO 2 burden associated with the main eruption of 15th January is estimated at 0.
0 (Ref. 20) at 675 nm and the integrated stratospheric AOD (SAOD). With respect to previous datasets versions, this dataset exhibits significant retrieval improvements, especially in the Southern Hemisphere (SH), when compared with independent datasets  34. The OMPS-LP SAOD time series is extended back, to compare the HT eruption to earlier Pinatubo and El Chichon eruption, using the GloSSAC (Global Space-based Stratospheric Aerosol Climatology) database v2. 0  35. In situ measurements of volcanic aerosol size distributions with LOAC. The Light Optical Aerosol Counter (LOAC) is a multi-angle in situ optical particle  36  which, thanks to its very limited weight (~300 g), has been operated on small balloon measurement flights at the Observatoire de l'atmosphère du Maïdo at La Réunion island (21.
1 S, 55. 3E), after the HT eruption. Using a laser at 650 nm wavelength, aerosol concentration is derived with LOAC for particles between 150 nm and 30 μm, in 19 size classes. The aerosol extinction is also estimated. For more details please refer to Ref. 19. In this work, we show LOAC measurement for 23 January 2022 (20-21:30 UTC). Multi-station analysis of ground-based AERONET sunphotometric data. A multi-station analysis of L1. 5 cloud-screened and quality assured sunphotometric data from the AERONET (AErosol RObotic NETwork)  37  is carried out at stations in Australia and La Réunion Island, using the VolcPlume portal for the 4D multiscale monitoring and analysis of volcanic plumes. The contribution with time of the fine (sub-micron) and coarse (super-micron) modes to the total AOD at 500 nm, derived from the spectral deconvolution algorithm  38  , is analysed to detect volcanic sulfate aerosols as in Ref.
Acknowledgements This research has been supported by the  Agence Nationale de la Recherche  (grants no:  21-CE01-0007-01 , ASTuS;  21-CE01-0016-01 ,  TuRTLES ;  21-CE01-0028-01 , PyroStrat;  15-CE04-0003-01 ,  VolcPlume ), the  Centre National d'Études Spatiales (CNES)  via  TOSCA/IASI  grant, the  Centre national de la recherche scientifique-Institut National des Sciences de l'Univers (CNRS-INSU PNTS  ( Programme National de Télédétection Spatiale)  via  MIA-SO2  grant, and the  Laboratoire d'Excellence VOLTAIRE (VOLatils -Terre ,  Atmosphère et Interactions -Ressources et Environnement ;  ANR-10-LABX-100-01 ) and  CaPPA (Chemical and Physical Properties  of the  Atmosphere ;  ANR-11-LABX-0005-01 ). The providers of the libRadtran suite (http://www. libradtran. org/)are gratefully acknowledged.
Data availability The OMPS-LP data are freely available from EarthData centre at: https://disc. gsfc. nasa. gov/datasets/OMPS_NPP_LP_L2_AER_DAILY_2/summary. The CALIOP data v3. 41freely are available from the NASA LARC website at: https://doi. org/10. 5067/ CALIOP/CALIPSO/CAL_LID_L1-VALSTAGE1-V3-41. The Himawari observations are freely available from the AERIS-ICARE data centre, at the following link: https://www. icare. univ-lille. fr/asd-content/browse/geoview. The IMS/IASI SO2 and SA OD datasets used in this work can be accessed for the following data record: https://doi. org/10. 5281/ zenodo. 7102472. The LOAC in situ observations can be accessed from the following data record: https://zenodo. org/record/6522689#. YrBjAd869hE. The AERONET data are freely available at the following website: http://aeronet.
Code availability A python package to read and process the IMS/IASI data is available at the following link: https://github. com/bernard-legras/ASTuS/tree/master/IMS and in the IMSreader. tgz archive at the following link: https://doi. org/10. 5281/zenodo. 7102472. The LibRadtran software can be freely downloaded at the following link: http://www. libradtran. org/doku. php?id=download.
the biogeography of bacterial communities is a key topic in Microbial Ecology. Regarding continental water, most studies are carried out in the northern hemisphere, leaving a gap on microorganism's diversity patterns on a global scale. South america harbours approximately one third of the world's total freshwater resources, and is one of these understudied regions. To fill this gap, we compiled 16S rRNA amplicon sequencing data of microbial communities across South America continental water ecosystems, presenting the first database µSudAqua[db]. The database contains over 866 georeferenced samples from 9 different ecoregions with contextual environmental information. For its integration and validation we constructed a curated database (µSudaqua[db. sp]) using samples sequenced by Illumina MiSeq platform with commonly used prokaryote universal primers.
Data DEScRIptoR opEN into sub-Antarctic latitudes. Due to the high habitat heterogeneity and a complex geological history, South America is considered a hotspot of biodiversity, being the most species-rich region on Earth  17, 18. This fact, added to its location in a predominantly maritime hemisphere, offers a unique opportunity for comparing empirical patterns found originally for Northern Hemisphere aquatic ecosystems. Pioneering studies have shown a geographically biased picture of the aquatic microbiome, leading to overlook differences in structure and functioning of microbial communities  19. That limits, for instance, our understanding of the expected scenarios resulting from the current pressures experienced by the aquatic ecosystems  20. In order to reduce this gap, we performed an exhaustive bibliographic search, collected and annotated data of bacterial communities from South American continental waters.
We constructed the first 16S rRNA amplicon sequencing database of South America, the µSudAqua[db] containing 866 georeferenced samples. For its integration in further works, preventing biases by the sets of primers and sequencing methodology used, we constructed a curated database (µSudAqua[db. sp]) which contains over 509 samples of the V3-V4 region of the 16S rRNA gene sequenced by the Illumina MiSeq technology with the commonly used primers proposed by Herlemann & collaborators  21. This work is a result of the µSudAqua collaborative network, a Latin American network in Aquatic Microbial Ecology. This network emerged as an initiative to nucleate researchers of the field to join efforts in consolidating a regional critical mass. Its main objectives are to strengthen and expand the interactions between aquatic microbial ecologists, to contribute to the development of a community feeling at the regional level, and to provide a fruitful space for long-term collaboration in research and training of human resources.
Data compilation. The µSudAqua[db] database was constructed with samples from published papers and new data generated in the scope of this work (Table  1 ). We only considered those studies fitting with the following criteria: 1) samples were obtained from continental water systems of South America; 2) the whole bacterial community was studied using high-throughput amplicon sequencing of the 16S rRNA gene; 3) the 16S rRNA gene was subject to amplification using universal primers (i. e. studies using group-specific primers or functional genes were not included); 4) sequencing data were publicly available or provided by the authors of the study upon request; 5) the samples could be georeferenced. Sample metadata were collected from the published papers or provided by the authors of the current work.
The altitude was automatically extracted based on the sampling location, using the QGIS geographic information system software (https://qgis. org/). Each sample was assigned to an environmental type (e. g. shallow and deep lakes, rivers, streams, reservoir, swamps) and an ecoregion (section Ecoregions description). Besides, the georeferenced location and procedures adopted for the sampling and sequencing were fully recovered. The complete list of metadata recovered and its description is presented in Table  2. The samples information used to build the database is available as an accessed as plaintext (TSV format) at Zenodo repository  22. The µSudAqua[db] was used as a seed to construct the curated database, µSudAqua[db. sp], which contains a subset those samples sequenced with 1) Illumina MiSeq technology and; 2) the commonly used set of primers proposed by Herlemann & collaborators  21.
Fig. 1  Global distribution of amplicon sequencing samples from continental water systems using HTS. The information was acquired from MGnify (https://www. ebi. ac. uk/metagenomics/) resource by searching for non-marine aquatic samples, obtained by amplicon or metabarcoding experimental types. The geographical coordinates were retrieved for 4,691 samples from a total of 7,832 using the metadata available from each sample. The microbial communities were obtained with different filtration strategies. In some environments, water samples were pre-filtered to exclude larger particles, or to split the microbial community in free-living and particle attached fractions (Table  3 ). Even though different DNA-extraction methods were used (Table  3 ), the V3-V4 regions of the 16S rRNA gene were amplified using the same set of bacterial universal primers 341 F (5′-CCTACGGGNGGCWGCAG-3′) and 805 R (5′-GACTACHVGGGTATCTAATCC-3′)  21.
Only samples with more than 10,000 reads were analyzed. To increase sensitivity to rare variants and avoid chimeras and sequencing errors, we used the "pool" option from dada function. The chimera sequences were excluded after merging the different projects using the functions removeBimeraDenovo and mergeSequenceTables, respectively. The taxonomic classification was performed using BLAST v2. 5  26  with the blastn algorithm (e-value = 0. 0001) and the SILVA database (SSU Ref 132 NR 99  27  ) as a reference. The Amplicon sequence variants (ASVs) were classified into 7 different taxonomic groups. The contribution of each group was calculated as their relative abundance to the total number of reads, and the richness was defined as the total number of ASVs. The scripts used for DADA2 and sample description are available in GitHub (https://github.
Download_ftp_R2 Ftp link to download the reverse raw fastq Table  2. Metadata associated to the samples used to build the µSudAqua[db] database. Each sample is identified by an sample indentifier and the corresponding Run accession number from the GenBank. Moreover, each sample was assigned to a geographical location, ecoregion, sub-ecoregion and environment. Technical information regarding of sampling procedure, nucleic acid extraction methodologies and sequencing strategies are also described. For samples from the µSudAqua[db. sp] database the number of high quality reads and Amplicon Sequences Variants (ASVs) defined is also indicated.
Data Records The µSudAqua[db] covers 866 individual samples of continental waters from South America (Table  1 , Fig. 2 ). It contains samples sequenced using 454, Ion Torrent and Illumina technologies, and targeting different hypervariable regions of the 16S rRNA gene. The raw samples files are freely available in the European Nucleotide Archive (ENA) database  73. They can be downloaded using the Run Accession Number from the metadata file provided in Zenodo repository  22.
µSudAqua[db. sp]: Bacterial distribution among the ecoregions of South America In total, 509 samples and 116,687,584 reads were processed with DADA2. In order to exclude possible remaining sequencing errors or chimeras, we filtered ASVs with less than 50 reads in less than 3 samples. Thus, the final ASV table consisted of 502 samples, 25,334 ASVs and 42,188,085 reads, from: Amazon and Coastal Lowlands (96 samples), Atlantic Forests (67), Cerrados (86), Humid Chaco (59), from Southern Flat Pampas (127), Valdivian Forested Hills and Mountains (7) and, Patagonian Tablelands (67). The information of sequence processing and quality check of samples is summarized in Table  3. The µSudAqua[db. sp] database was mainly represented by Bacteria (24,279 ASVs, 97. 7% reads) followed by chloroplasts (1,001 ASVs, 2.
Usage Notes The links to download the raw fastq data from µSudAqua[db] and µSudAqua[db. sp] are in the metadata file accessible in Zenodo  22. In addition, other files associated with the µSudAqua[db. sp] are available in the same repository: ASVs table (number of reads in each sample), taxonomy, nucleotide sequences in fasta format and ASVs table filtered with only Bacteria. Importantly, the database will grow as new samples and sequencing projects from the µSudAqua network appear. This information will be uploaded in the repository and the tables will be updated in future versions of the database. A bibliography revision and open call for new data submission will be performed once a year, and the database will be updated after data quality check, processing and integration. The µSudAqua[db] and µSudAqua[db.
sp] databases are the first to integrate information of microbial diversity from continental systems of South America, an important region that has been overlooked comparing to other regions and environments worldwide. These databases will open new avenues for studies on the temporal patterns and spatial distributions of microbial communities among the different ecoregions of South America. Besides, the integration of the curated data to meta-analysis of microbial communities from different ecosystems (comparison between South America and well-studied regions of the world), will be particularly important for exploring the novel microbial diversity, allowing to reveal regions with unknown organisms and functions, as well as hotspots of microbial biodiversity.
acknowledgements We acknowledge the funding agencies that supported  MicroSudaqua meetings in Uruguay (Centro Universitario Regional del Este -CURE-, International Society for Microbial Ecology -ISME-)  and  Argentina (National Agency for Scientific and Technological Promotion -ANPCyT-, National Council for Scientific and Technical Research -CONICET -, Municipality of Chascomús). We would like to thank all members of the  MicroSudAqua collaborative network  (listed by name on https://microsudaqua. netlify. app)for their suggestions on this study and collective effort to promote the research in Latin America, despite financial challenges and other political issues. Bioinformatics analyses were performed at the PIRAYU cluster (https://cimec. org. ar/c3/pirayu/index. php) via grants obtained from the  Agencia Santafesina de Ciencia, Tecnología e Innovación  (ASACTEI; Res  N° 117/14 ) and we thank  A.