dataesr/scientific-paragraphs-categorization huggingface dataset:
The Science Archive Webservers (SAW) provides visu-alisations of most of the reduced images and data products available on the SAS. The SAW offers the option to display spectra with their model fits, and to search spectra based on a variety of parameters (e. g. observing program, redshift, coordinates). These searches can be saved as permalinks, so that they can be consulted again in the future and be shared with collaborators. All SAW webapps are available from https://dr17. sdss. org/, and allow for displaying and searching of images (SDSS-I/II), optical single-fiber spectra (SDSS-I/II, SEGUE, BOSS and eBOSS), infrared spectra (APOGEE-1 and APOGEE-2), and MaStar stellar library spectra. Images and spectra can be downloaded through the SAW, and previous data releases are available back to DR8.
The SAW also offers direct links to SkyServer Explore pages (see below). The MaNGA integral-field data is not incorporated in the SAW due to its more complex data structure, and can instead be accessed through Marvin (https://dr17. sdss. org/marvin/; Cherinka et al. 2019). Marvin offers not only visualisation options through its web interface, but also allows the user to query the data and analyze data products remotely through a suite of Python tools. Marvin also offers access to various MaNGA value added catalogs, as described in §5. 5. Marvin's Python tools are available through pip-install, and installation instructions as well as tutorials and examples are available here: https://sdss-marvin. readthedocs. io/en/ stable/. No installation is required to use Marvin's Python tools in SciServer, as described later in this section and in §5.
3. Catalogs of derived data products are available on the SAS, but can be accessed more directly through the Catalog Archive Server (CAS,  Thakar et al. 2008 ). These include photometric and spectroscopic properties, as well as some value added catalogs. The SkyServer webapp (https://skyserver. sdss. org) allows for visual inspection of objects using e. g. the QuickLook and Explore tools, and is also suitable for synchronous SQL queries in the browser. Tutorials and examples explaining the SQL syntax and how to query in SkyServer are available at http://skyserver. sdss. org/en/help/ docs/docshome. aspx. For DR17, the SkyServer underwent a significant upgrade, which includes a completely redesigned user interface as well as migration of the back end to a platform independent, modular architecture.
Although SkyServer is optimal for smaller queries that can run in the browser, for larger ones we recommend using CASJobs (https://skyserver. sdss. org/casjobs). CASJobs allows for asynchronous queries in batch mode, and offers the user free storage space for query results in a personal database (MyDB) for server-side analysis that minimizes data movement  (Li & Thakar 2008). SkyServer and CASJobs are now part of the SciServer science platform  (Taghizadeh-Popp et al. 2020 , https:// www. sciserver. org), which is accessible with free registration on a single-sign-on portal, and offers server-side analysis with Jupyter notebooks in both interactive and batch mode, via SciServer Compute. SciServer is fully integrated with the CAS, and users will be able to access the data and store their notebooks in their personal account (shared with CASJobs).
SciServer offers data and resource sharing via its Groups functionality that greatly facilitates its use in the classroom, to organize classes with student, teacher and teaching assistant privileges. Several SciServer Jupyter notebooks with use cases of SDSS data are available through the SDSS education webpages (https://www. sdss. org/education/), some of which have been used by SDSS members in collegelevel based courses as an introduction to working with astronomical data. SciServer has prominently featured in the "SDSS in the Classroom" workshops at AAS meet-ings. Users can now analyze the MaNGA DR17 data in SciServer, using the Marvin suite of Python tools. SciServer integration enables users to use the access and analysis capabilities of Marvin without having a local installation. In the SciServer Compute system 155  , the MaNGA dataset is available as an attachable MaNGA Data Volume, with the Marvin toolkit available as a loadable Marvin Compute Image.
All spectra and models shown in SpecDash can be downloaded, shared, and then uploaded again for subsequent analysis and reproducibility. Although the web-based version shares the same functionality as the Jupyter widget version, the latter has the advantage that users can use the SpecDash python library to programmatically load any kind of 1-D spectra, and analyze or model them using their own models and kernels. All tools and data access points described above are designed to serve a wide range of users from undergraduate level to expert users with significant programming experience. In addition, Voyages (https://voyages. sdss. org/) provides an introduction to astronomical concepts and the SDSS data for less experienced users, and can also be used by teachers in a classroom setting. The Voyages activities were specifically developed around pointers to K-12 US science standards, and a Spanish language version of the site is available at https://voyages.
APOGEE-2 : FULL RELEASE The central goal of APOGEE is to map the chemodynamics of all structural components of the Milky Way Galaxy via near-twin, multiplexed NIR high-resolution spectrographs operating simultaneously in both hemispheres (APOGEE-N and APOGEE-S spectrographs respectively; both described in  Wilson et al. 2019). DR17 constitutes the sixth release of data from APOGEE, which has run in two phases (APOGEE-1 and APOGEE-2) spanning both SDSS-III and SDSS-IV. As part of SDSS-III, the APOGEE-1 survey operated for approximately 3 years from August 2011 to July 2014 using the 2. 5-m Sloan Foundation Telescope at APO. At the start of of SDSS-IV, APOGEE-2 continued its operations in the Northern Hemisphere by initiating a ∼6-year sur-vey (APOGEE-2N). Thanks to unanticipated on-sky efficiency, APOGEE-2N operations concluded in November 2020 with an effective ∼7.
5 years of bright time observations, with many programs expanded from their original 6-year baseline. In April 2017, operations began with the newly built APOGEE-S spectrograph and associated fiber plugplate infrastructure on the 2. 5-m Irénée du Pont Telescope at LCO; APOGEE-2S observations concluded in January 2021. A full overview of the APOGEE-1 scientific portfolio and operations was given in  Majewski et al. (2017)  and a parallel overview for APOGEE-2 is forthcoming (S. Majewski et al. , in prep. ). The APOGEE data in DR17 encompass all SDSS-III APOGEE-1 and SDSS-IV APOGEE-2 observations acquired with both instruments from the start of operations at APO in SDSS-III (September 2011) through the conclusion of SDSS-IV operations at APO and LCO (in November 2020 and January 2021, respectively).
Compared to the previous APOGEE data release (DR16), DR17 contains roughly two additional years of observations in both hemispheres; this doubles the number of targets observed from APOGEE-2S (see Table  1 ). DR17 contains APOGEE data and information for 657,135 unique targets, with 372,458 of these (57%) as part of the main red star sample that uses a simple selection function based on de-reddened colors and magnitudes (for more details see  Zasowski et al. 2013 Zasowski et al. , 2017)). The primary data products are: (1) reduced visit and visit-combined spectra, (2) radial velocity measurements, (3) atmospheric parameters (eight in total), and (4) individual element abundances (up to 20 species). Approximately 2. 6 million individual visit spectra are included in DR17; 399,505 sources have three or more visits (54%) and 35,009 sources (5%) have ten or more visits.
The final APOGEE survey map is shown in Figure  2 , where each circle represents a single "field" that is colorcoded by survey phase: APOGEE-1 (cyan), APOGEE-2N (blue), or APOGEE-2S (red). The difference in fieldof-view between APOGEE-N and APOGEE-S is visible by the size of the symbol, with each APOGEE-S field spanning 2. 8 deg 2 and APOGEE-N spanning 7 deg 2 (for the instrument descriptions, see  Wilson et al. 2019 ). Those fields with any new data in DR17 are encircled in black; new data can either be fields observed for the first time or fields receiving additional epochs. The irregular high Galactic latitude coverage is largely due to piggyback "co-observing" with MaNGA during dark time. Notably, these cooperative operations resulted in observations of an additional 162,817 targets, or 22% of the total DR17 targets (∼30% of targets in APOGEE-2), which is a comparable number of targets as were observed in all of APOGEE-1.
g. ,  Hubeny & Lanz 2017; Hubeny et al. 2021 ) was adopted for the primary analysis instead of the Turbospectrum code  (Alvarez & Plez 1998; Plez 2012 ) used in previous releases. This was not a straightforward choice because, while Synspec allows the NLTE levels to be used, it calculates the synthetic spectra under the assumption of plane parallel geometry, which becomes 157 https://github. com/sdss/apogee less valid for the largest giant stars. On the other hand, Turbospectrum can use spherical geometry, but does not accommodate NLTE populations to be specified. DR17 uses multiple sub-grids to span from T eff =3000 K (M dwarf) to T eff =20,000 K (BA), with log g ranges from 0 to 5 (3 to 5 for the BA grid). The full details of these grids and the reliability of the parameters as a function of stellar type are provided in J.
In comparison to DR16, the DR17 pipeline limits the span of the CCF for some stars to a velocity range around the mean radial velocity to ensure a more stable overall set of RV measurements; on the other hand the DR16 pipeline itself may fail on a larger number of individual visit spectra and thus not produce a full set of outputs. For the sources that have both good parameters and a complete CCF coverage for both DR16 and DR17, the widely resolved components of SBs are generally consistent with one another; close companions that have only small RV separations are not always identified in both datasets. For this reason, SBs that could be identified in both the DR16 and DR17 reductions are kept as separate entries in the catalog. Visual vetting was limited only to the data processed with the DR16 pipeline (e.
These targets are expected to make up the bright X-ray flux end of the eROSITA population. Much of the SDSS spectroscopy from the SPIDERS program was released as part of SDSS DR16  (Ahumada et al. 2020) , including the largest spectroscopic redshift catalogs of X-ray selected AGN and clusters  (Comparat et al. 2020b; Clerc et al. 2020; Kirkpatrick et al. 2021). Below, we highlight some recent pre-eROSITA SPI-DERS results that are based on the SDSS DR16 dataset. Then, in §7. 5 we present a new SPIDERS dataset (to be released as part of SDSS DR17 184  ), dedicated to the spectroscopic follow-up of X-ray sources discovered in early SRG/eROSITA performance verification observations. In §7. 5. 6 we discuss future plans for eROSITA followup in SDSS-V (also see §8).
The SDSS-RM sample includes 849 broad-line quasars with i PSF < 21. 7 and 0. 1 < z < 4. 5 without any constraints on quasar properties. The detailed sample properties are described in  Shen et al. (2019b). The primary science goal of SDSS-RM is to measure reverberation mapping lags of different broad emission   Homayouni et al. 2020) , where the lags are typically longer than one season in the observed frame. Lag measurements based on more extended light curves are reported in  Shen et al. (2019a) , and the analyzes of the final SDSS-RM dataset are underway. In addition to the main science goal of RM measurements, SDSS-RM also enables a diverse range of quasar science. Some notable examples are: measurements of continuum lags to constrain quasar accretion disk sizes  (Homayouni et al. 2019) ; constraints on the black hole mass -host galaxy correlations at z > 0.
The eighteenth data release of the SDSS (DR18), which will include the first SDSS-V data, mostly targeting and other information, is currently anticipated for 2022. SDSS-V will also build on the rich educational legacy established by previous SDSS generations. This will include continuing to distribute as many SDSS plug plates to schools and science centers as possible, and broadening the reach of the SDSS Voyages suite of educational activities. SDSS's large online datasets are an ideal resource for supporting hands-on activities using real archival data, at a wide range of ability levels, and have the potential to act as a gateway for numerous data science and analysis topics. After twenty-one years of Sloan Digital Sky Surveys, the data released by SDSS-IV in DR17 has made significant contributions to our understanding of the MW, galaxy evolution, and the Universe as a whole, and it will continue to enable new research for years to come.
Marvin Visualization and Analysis Tools Marvin (Cherinka et al. 2019)  was developed as the tool for streamlined access to the MaNGA data, optimized for overcoming the challenges of searching, accessing, and visualizing the complexity of the MaNGA dataset. Besides patches and internal optimizations, the DR17 updates to Marvin include several enhancements such as querying targets by MaNGA quality and target bitmasks and values, full support for installation on Windows machines, as well as updates to the web interface. The Marvin Web Galaxy Page (Figure7) now includes data quality indicators for the DAP Maps, as well as toggle-able features for the spectrum display. For how to use the web or Python tools, see the Marvin documentation 172. See the Marvin Changelog for a complete list of what has changed since the last released version.
ACKNOWLEDGEMENTS Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is www. sdss. org. SDSS-IV is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Center for Astrophysics -Harvard & Smithsonian ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Korean Participation Group ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University ,  New York University, University of Notre Dame, Observatário Nacional / MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University , and  Yale University.
Agreement. This research has made use of  NASA 's Astrophysics Data System. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. The original description of the SIMBAD service was published in Wenger et al. (2000). This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published Ochsenbein et al. (2000). This research makes use of data from the  Green Bank Observatory. The  Green Bank Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. This research made use of astropy, a communitydeveloped core python (http://www. python. org)package for Astronomy (Robitaille et al.
5. 3. 3 Marvin Visualization and Analysis Tools Marvin (Cherinka et al. 2019)  was developed as the tool for streamlined access to the MaNGA data, optimized for overcoming the challenges of searching, accessing, and visualizing the complexity of the MaNGA dataset. Besides patches and internal optimizations, the DR17 updates to Marvin include several enhancements such as querying targets by MaNGA quality and target bitmasks and values, full support for installation on Windows machines, as well as updates to the web interface. The Marvin Web Galaxy Page (Figure7) now includes data quality indicators for the DAP Maps, as well as toggle-able features for the spectrum display. For how to use the web or Python tools, see the Marvin documentation 172. See the Marvin Changelog for a complete list of what has changed since the last released version.
Data Sea Surface temperature (1850-2019). Sea Surface Temperature (SST, °C) from 1850 to 2019 originated from the COBE SST2 1°× 1°gridded dataset  74  , https://psl. noaa. gov/data/gridded/data. cobe2. html. SST data were interpolated on a 0. 25°l atitude × 0. 25°longitude grid on a monthly scale from 1850 to 2019. Bathymetry. Bathymetry (m) came from GEBCO Bathymetric Compilation Group 2019 (The GEBCO_2019 Grid-a continuous terrain model of the global oceans and land). Data are provided by the British Oceanographic Data Centre, National Oceanography Centre, NERC, UK. doi:10/c33m. (https://www. bodc. ac. uk/data/published_data_library/catalogue/10. 5285/836f016a-33be-6ddc-e053-6c86abc0788e/). These data were interpolated on a 0. 25°latitude × 0. 25°l ongitude grid. Biological data. Daily mass concentration of chlorophyll-a in seawater (mg/m 3 ) originated from the Glob Colour project (http://www.
The daily simulations of four different models (i. e. CNRM-ESM2-1, GFDL-ESM4, IPSL-CM6A-LR, and UKESM1-0-LL) covering the time period 1850-2014 (historical simulation) and 2015-2100 (future projections for the two SSPs scenarios) were used. All the data were interpolated on a 0. 25°by 0. 25°regular grid. Key references (i. e. DOI and dataset version) are provided in Supplementary Text 1. Long-term changes in modelled SSB were based on these data (including modelled daily chlorophyll data). The FishClim model. Let K t be the maximum standardised Spawning Stock Biomass (mdSSB hereafter) that can be reached by a fish stock at time t for a given environmental regime φ t. X t+1 , standardised SSB (dSSB hereafter) at time t+1 was calculated from dSSB at time t as follows: X tþ1 ¼ X t þ rX t 1 À X t K t À αX t ð1Þ α is the fishing intensity that varies between 0 (i.
Analyses Mapping of maximum standardised SSB. mdSSB is close to the "dynamic B 0 " approach; B 0 is the SSB in the absence of fishing (generally expressed in tonnes)  51  whereas mdSSB is the SSB in the absence of fishing standardised between 0 and 1 and assessed from the knowledge of the niche of the species. We first assessed mdSSB in the North-east Atlantic (around UK) at a spatial resolution of 0. 25°l atitude × 0. 25°longitude on a daily basis from 1850 to 2019. For this analysis, FishClim was run on monthly COBE SST (1850-2019), mean bathymetry and a climatology of daily mass concentration of chlorophyll-a in seawater from the Glob Colour project (see Data section). We then calculated an annual average based on the main seasonal productive period around UK, i. e. from March to October  90.
Acknowledgements The authors acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modelling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. This work has been partially financially supported by  Université du Littoral Côte d'Opale, France  as part of the  IFSEA Graduate School , and by the  CPER IDEAL.
IV. SIMULATION RESULTS To test our calibration method, simulations are carried out by constructing start and stop fine TDCs in MATLAB. Each simulated TDC has 256 delay elements and a total delay of 5 ns. Furthermore, the time distribution profile of the simulated TDCs is similar to that of a real TDC implemented on a Cyclone V FPGA kit following the methodology explained in  [6]. Firstly, in order to build the LUTs of the bin-by-bin calibration and the calibration tables of the matrix calibration, a code density test is performed by simulating 10 7 random events, with a start and stop arrival times uniformly distributed over the FSR of the start and stop TDCs. Next, to evaluate and compare the calibration methods, a new code density test is simulated with the same number of events. The start signal of these simulated events uniformly covers the FSR of the start fine TDC, whereas the stop signal arrives after the start signal with a random delay from 0 to 5 ns.
For plasma physics applications, we use the regression feature of machine learning as we are interested by the prediction of physical quantities. For the results presented here, we have used the open-access machine-learning platform called TensorFlow  [24]  which is based on neural networks. We have precisely used the Python deep-learning API (Application Programming Interface) called Keras (see Keras website  [25] ). The regression algorithm which we have used is based on the ADAM optimizer (see Keras optimizers  [26] ). Note that there exist other machine learning platforms such as the open-access, Python-based Sickit-learn platform  [27].
Results and Discussion To illustrate the results of the application of the regression algorithm which we have adopted from the TensorFlow ML platform to our synthetized Hα/Dα line spectra, we show in Figure  7  the isotopic ratio (in percentages) as predicted by the ML algorithm versus the real isotopic ratio as used in the preprocessing code to generate the Hα/Dα line profiles. The loss function of the DL algorithm for our application is shown on Figure  8  as a function of the number of iterations or epochs. To obtain results corresponding to more realistic situations, we have added Gaussian noise to the synthetized Hα/Dα line profiles. The deep learning algorithm values are scattered around the black solid line representing the  =  function. We found a mean error of 7. 6% and a median error of 2.
Conclusions In this paper, we have demonstrated the proof-of-principle of a new technique to determine the isotopic ratios of hydrogen isotopes of interest for magnetic fusion research. In this technique, we combined simple spectral characteristics of the Balmer-α line emitted by hydrogen-deuterium gas mixtures with a deep learning regression algorithm adopted from the open-access machine-learning platform TensorFlow with Keras as the Python API. The demonstration was based on results obtained with Hα/Dα line profiles which we have computed varying a number of parameters such as the strength of the magnetic-filed B, the temperatures and fractions of the cold and warm populations of neutrals, reflecting the recycling mechanisms in tokamak divertors, and the concentration of hydrogen (in the range 1-25%).
The SuperIso program can be downloaded at http://superiso. in2p3. fr. Details on the installation and the description of the various subroutines can be found in Ref. [4]  or in the manual which can be downloaded from the SuperIso website http://superiso. in2p3. fr/superiso4. 1. pdf. In what follows we first list the observables included in SuperIso, in section 3 we briefly review the recently added kaon observables, in section 4 we show the implications of rare -decays and we conclude in section 5.