dataesr/scientific-paragraphs-categorization huggingface dataset:
Other Ancillary Programs We refer the reader to the DR13 paper  (Albareti et al. 2017)  for the most complete list of MaNGA ancillary programs 14. These approved programmes make use of ∼5% of MaNGA bundles, and provide in Table  4  an updated list of the number of bundles available in each documented sample. There are three new ancillary programs for DR15. These provide observations in fields of IC 342, M31 as well as data for a selection of SN1a Hosts (MANGA TARGET3 target bits 20, 21 and 26, respectively). The IC342 program will uniformly mosaic the disk of IC 342 using 61 plates, following an initial pilot program of 3 plates that target individual HII regions across the disk. This galaxy serves as a local reference with 30 pc resolution that can inform our understanding of the unresolved physics in the ∼ 1 kpc resolution main MaNGA survey.
Spectral Modeling In DR15 there are new releases for both the FIREFLY  (Goddard et al. 2017a ) and Pipe3D  (Sánchez et al. 2016 ) stellar population modeling and emission line analysis VACs. Full details of both can be found in the DR14 paper (and references therein), so we give only updates specific to this DR15 release version below. For DR15 Pipe3D version 2. 4. 3 was run over the MaNGA DR15 dataset. The main difference with respect to version 2. 1. 2 (used in DR14) besides the number of analyzed galaxies, was to solve a bug in derivation of the equivalent widths of the analyzed emission lines. The current Pipe3D VAC provides two different types of data products: (1) A catalog comprising 94 different parameters measured for each of 4660 galaxies (all galaxies in MaNGA cubes for which Pipe3D was able to derive the main stellar population, emission line and kinematics properties); and (2) a set of 4660 datacubes manga.
HI-MaNGA -HI 21cm Follow-up for MaNGA The first data release of "HI-MaNGA", the HI followup project for MaNGA is provided as a VAC in DR15. This follow-up program is presented in  Masters et al. (2018)  and is the result of single dish radio 21cm HI observations of MaNGA galaxies using the Robert C. Byrd Green Bank Telescope (GBT). The depth of this observing is aimed to be similar to the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) blind HI survey  (Haynes et al. 2018 ) which covers some of the MaNGA footprint (see Figure  2 ) with a goal of enabling studies to use HI data from both surveys. In this first release, data are provided for 331 MaNGA galaxies observed in the 2016 GBT observing seasons under project code AGBT16A 95. Total HI masses, and line widths (measured with five different common techniques) are provided for all detections, while HI mass upper limits (assuming a line width of 200km/s) are provided for non-detections.
HI-MaNGA has observed an additional ∼ 1800 MaNGA galaxies in the 2017 observing season (under project code AGBT17A 12); these data will be released in a future VAC. The sky distribution of all MaNGA galaxies observed by this program is shown in Figure  2. The environment in which a galaxy resides plays an important role in its formation and evolution. Galaxies evolve as a result of intrinsic processes (i. e. their nature -this includes processes such as internal secular evolution, feedback of various kinds etc), but they are also exposed to the influences of their local and large-scale environments (i. e. how they are "nurtured"). We present a the Galaxy Environment for MaNGA Value-Added Catalog (GEMA-VAC) which provides a quantification of the the local and large-scale environments of all MaNGA galaxies in DR15.
MaNGA Spectroscopic Redshifts We present a Value-Added Catalog that contains the best-fit spectroscopic redshift and corresponding model flux for each MaNGA spectrum that has sufficient signalto-noise. We provide the mean of the spectroscopic redshifts sampled within the inner high signal-to-noise region of the MaNGA galaxies, which can be compared to the single-valued NSA catalog redshift. Since the MaNGA instrument uses the BOSS spectrograph, our results are derived by iterative application of the BOSS pipeline's spec1d software  (Bolton et al. 2012 ) to precisely measure the redshift using the higher signal-tonoise measurements as a prior to the algorithm, which allows us determine good redshifts on spectra that would not otherwise have sufficient signal-to-noise to result in a good redshift.
(2015). For example, the work demonstrates some improvement in Comparison of SDSS/APOGEE DR13 and DR14 Values to Optical Results  Jönsson et al. (2018)  evaluates the ASPCAP performance for both the DR13 and DR14 APOGEE datasets with 160,000 and 270,000 stars, respectively. A comparison of the ASPCAP-derived stellar parameters and abundances is done to analogous values inferred from optical spectra and analysis with a subset of several hundred stars. For most elements,  Jönsson et al. (2018)  find that the DR14 ASPCAP results have systematic differences with the comparison samples of less than 0. 05 dex (median) and random differences of less than 0. 15 dex (standard deviation). These departures are attributed to a combination of the uncertainties in both the comparison samples as well as the ASPCAP-analysis.
6 below). From data taken for the RQS SDSS-IV program to date, we expect RQS to add another recent epoch of spectroscopy for ∼16000 SDSS quasars, sampling across a broad range of properties including redshift, luminosity, and quasar subclass type. 6. 4. MaNGA MaNGA will continue to take observations for the next two years of SDSS-IV operations. The time trade with eBOSS has slowed the rate of observations during 2018; however, it will provide an overall increase in the total observing time allocation for MaNGA by 8%. The projected final survey footprint, assuming we continue nominal survey operations through July 2020, is shown for two different expectations for weather at the telescope and overlaid on other relevant surveys in Figure  2. We expect to exceed our original goal of 10,000 galaxies slightly under nominal weather conditions.
The SAS Team is currently implementing a cephbased archival quality object storage system  (Weil et al. 2006 ) similar to that used by organizations specializing in big data (e. g. Google and Amazon) providing complete internal redundancy, support for geographical distribution, internal failure detection and self recovery, and inexpensive backup in cloud-based big data object storage systems. • Science Archive Database: The census of what is contained on the SAS is managed through a 19 Funded by a dedicated grant from the Sloan Foundation Python system with a database which records the hundreds of millions of file paths, file sizes, and file verification checksums. This system is currently being re-implemented to allow a more seamless and high speed data access. • Migrating the SDSS Software Repository to GitHub: The SDSS subversion software repository, currently served along-side the SAS, will be replaced by repositories copied into a GitHub organization (https://github.
com/sdss), with GitHub Teams created to manage repository access control, with public release of software including open source licensing, starting with DR15 (e. g. Marvin and the underlying code "Marvin's Brain"). • SDSS Software Framework Development: The Data and Operations Teams are currently designing a new software framework to provide Python-based tools, including improved data access, database access, data model documentation and machine-readability. • SDSS Software Containers: Portable images of SDSS systems have been developed and implemented on docker hub, and are currently used at NERSC for the Science Archive Mirror and JHU for Science Archive Webapp development (e. g. Marvin at JHU). The data team is now looking at developing a new wave of such virtual machines to replicate the experience of working on an SDSS computer at the University of Utah.
In short, we want to adopt a philosophy of going from "working to working" versions as we modernize the site. We list below the specific changes we are currently working on. • Upgrade Technology: First and foremost, we are upgrading the web technology underneath the SkyServer. This includes everything from the version of HTML and CSS that it was originally written in, to the way that the SkyServer website code is logically organized. We are going toward the MVC (model-view-controller) paradigm that modern websites use to produce modular, reusable and robust web applications. • Portability: The SkyServer has been a Microsoft Windows application developed with the. NET framework all along. This has made it very difficult to port even the front end to any other platform. Now, with the availability of.
This allows new pages and functionality to be added to Voyages much more easily than SkyServer. As part of the SkyServer modernization, we are migrating all the SkyServer student projects to Voyages and using the same CMS (WordPress) for SkyServer too. We are also integrating Voyages further with SkyServer so that it uses the SkyServer API to run queries on the SDSS data. • Streamline CAS ⇔ SAS Interface: There are hooks currently between SkyServer/Voyages and the Science Archive Server, but they are awkward at best. The SAS API has recently been upgraded, and the points of access to SAS data that currently exist in SkyServer and Voyages will be updated to use the proper SAS API calls. These steps will help ensure the availability of SDSS data to astronomers for years to come, and long beyond the current funded plans for SDSS-IV and SDSS-V.
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, Harvard-Smithsonian Center for Astrophysics ,  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.
This publication uses data generated via the Zooniverse. orgplatform, development of which is funded by generous support, including a  Global Impact Award  from  Google , and by a grant from the  Alfred P. Sloan Foundation. This research was made possible through the use of the  AAVSO Photometric All-Sky Survey (APASS) , funded by the  Robert Martin Ayers Sciences Fund. The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
Normalization Process Theory (NPT) was used to frame the interview guides and analysis. NPT is an empirically-derived theory, developed by sociologists, that uses four concepts to understand the process of routinising new practices. Results: Staff interviews took place across eight National Health Service trusts at a time of widespread restructuring in smoking cessation services. Principally interviewees worked in maternity (n = 63) and smoking cessation services (n = 35). Five main themes, identified inductively, influenced the implementation: 1) initial preparedness of the organisations; 2) staff training; 3) managing partnership working; 4) resources; 5) review and planning for sustainability. Conclusions: NPT was used to show that the babyClear© package was acceptable to staff in a range of organisations.
Study design The process evaluation, uses a qualitative methodology, based on existing MRC guidance  [31, 32] , that recommended combining quantitative and qualitative methods for evaluating complex interventions, including natural experiments. By using qualitative methods to consider the context and individual perceptions, the guidance suggests, the study's explanatory power of causality and variation and accuracy of interpretation will be increased, improving knowledge of the intervention package's transferability and reducing bias  [31]. Data were collected between January 2014 -January 2015 and followed implementation as it occurred. The process was recorded by SJ through field notes, observation and semi-structured individual and group interviews. The study itself had several components and included data from pregnant smokers, midwives, trainers, stop smoking service staff, managers and community clinicians.
The data from pregnant smokers focused on acceptability of the intervention to women and will be reported elsewhere. NPT informed the interview schedule questions and was used as a framework for data analysis. The Teesside team, JS, SH and SJ, who conducted the qualitative data collection and analysis have backgrounds in public health and nursing. They had no role in the development or implementation of the intervention package; VA-S and MWh gave feedback on the planned pathway of care regarding targeting barriers and facilitators identified by Beenstock et al. [22]  and Fresh supported the implementation.
Participant selection Sampling Training sessions were observed that took place after ethical approval was gained. Where there was more than one similar session, they were chosen to include different trainers and attendees from a variety of organisations. Interviews took place in services after the intervention package was fully implemented. Data saturation was achieved in some services, but not others, due to delay in implementation; however data saturation was achieved in the dataset overall  [33]. Inclusion criteria were: managers of services, trained service delivery personnel, trainers and a representative from Fresh (Table  4 ). All staff who met the inclusion criteria were invited to take part (no incentives were given) and those who agreed, participated (n = 103).
Interviews were guided by topic schedules informed by NPT (Additional file 2). One hundred and three participants took part overall. Individual face-to-face interviews, were conducted with 32 participants, including 13 SSS staff, 14 maternity staff, all three trainers, a representative from Fresh and a pharmacist. Sixteen paired interviews also took place with 32 staff. Thirty-seven participants contributed to ten group interviews, which comprised 3-5 participants, allowing for participant preference. Additionally, one participant exchanged emails with the researcher and one participant took part in a telephone interview. Due to delay in introducing the RPT, the longitudinal aspect was modified. Data were collected on completion of implementation or end of data collection period, whichever was sooner.
Short interviews were generally in areas where there was less enthusiasm for the implementation. Data were collected in all eight Trusts when organisations were in the early stages of training and implementation. Conversations were digitally recorded unless the participant requested otherwise (n = 2), whereupon notes were taken. Data collection continued until every organisation and staff role had participated and no new issues were arising from coding the data  [33]. Recordings were transcribed verbatim. Transcripts were returned for member checking on request and anonymised before analysis was undertaken.
The relevant data were mapped across then grouped to create the themes presented here (Additional file 3: Table  S5 ). Although NPT informed each of the stages of this study, we have taken a pragmatic decision to present the findings as these practical themes. We have focused on the requirements for normalization in a complex environment, rather than theoretical concepts, to make the findings easily accessible to all. Transcripts from trainers, the representative from Fresh and field notes were used to check the data e. g. for factual accuracy, system details and consistency between researcher observations and staff reports of the training. NVivo 10 software was used to manage data analysis.
Some service structures were reported to act as barriers to these processes (e. g. sending SSPS calls which hid the caller's number). To monitor progress and enable 'reflexive monitoring' , participants said that a data management system, operating to a high standard, was required. Participants explained that these information systems were required to capture data on the implementation and delivery of the intervention, enter data at source, create efficient and effective intra and inter agency feedback loops, allow for performance management, promote continuity of care and enable payments to providers. QuitManager© (a data management software package)  [36]  was recommended by iPiP. QuitManager© does allow you to have that robust data; we can examine it on an individual level, across the team level and break it down by attendance, break it down by opt-out, break it down even by setting quit dates, you know.
Strengths and limitations Strengths This is the first study to explore how an intervention, babyClear©, that has proven effective at decreasing smoking in pregnancy, was implemented in practice; and the first to use NPT to do this. A range of methods of data collection, enabling triangulation of data sources and analyses, were used. Large samples of participants from all NHS trusts in the region and all staff types and grades who were involved in designing, commissioning and delivering the intervention participated. Therefore all relevant professional perspectives are included in the data. Thematic saturation for each set of interviews was achieved and a sample of training sessions observed. Research was underpinned using NPT, ensuring theoretical rigour in both data collection and analyses.
Implications for policy and practice To sustain the core intervention -CO monitoring, opt-out referral and follow-up -there is a requirement to identify and understand, in what circumstances elements of the babyClear© package operate, and which can be tailored to local circumstances and resources, without destroying the integrity of the original model. Local contexts were very different and there is evidence that some flexibility and tailoring is helpful in a 'real world' setting. Understanding of the process of normalization of the babyClear© package can assist practitioners in implementing best practice guidelines more effectively and at scale.
Conclusions NPT was used to show that babyClear© offers an evidence-informed, intervention package that is acceptable to staff and which can be implemented effectively. It is predicated on producing individual behaviour change in pregnant women who smoke, but does this, in large part through reorganising and standardising maternity and SSPS systems and upskilling staff. Re-structuring those systems requires staff to understand the essence of the new intervention package (coherence), to believe in it and find it congruent with their other work and professional standards (cognitive participation), to set up and maintain practical adjustments to their work practices (collective action) and be reflective about adapting themselves and the work context as new challenges are encountered (reflexive monitoring).
However, graph theory is often applied in an off-the-shelf manner and this type of approach is limited for explaining the time-series nature of the fMRI measurements. In this context the present study aims to describe the fMRI-related functional associations between the nodes in the brain network, also known as regions of interest (ROIs), while properly taking time into account. We follow recent works that employed dynamic models of the brain activity to link SC and FC  [14]. A great variety of network designs has been explored to combine experimental data in various levels of detail  [15, 16, 17, 18, 19, 20]. These dynamic models typically involve a connectivity matrix that describes how the activity propagates in the network. This should be contrasted with another active line of research focusing on the 'dynamic FC' that relies on sliding time windows to capture the statistical (functional) dependences between ROIs on the timescale of a minute  [21].
After recapitulating important points about the MOU-EC estimation from BOLD signals  [18] , we present in Methods the formalism to analyze complex network dynamics with linear coupling  [37]. It is based on a core graph-like measure for the interactions between brain regions across time, "dynamic" communicability, which serve as the basis for multivariate descriptors. We illustrate the framework using resting-state fMRI and diffusion MRI of the ARCHI database acquired by the European project Connect  [45, 46]  and available from the Human Brain Project Neuroinformatics platform. In particular, we show how communicability provides a finer and more general description of the network dynamics -by capturing the propagation of the BOLD signals throughout the whole network via EC-than classical analysis based on FC or SC.
Acquisition of resting-state fMRI time series and structural connectomes The analyses were applied to the ARCHI database  [45, 46]   The T1-weighted structural MRI data was processed using Freesurfer 5. 3 software  [47] , which performed cortical tissue (gray and white matters) segmentation, and built surface meshes for both pial interface and grey/white interface of each hemisphere. Surface alignment was performed to an inter-subject template, allowing the projection of the Desikan atlas on these anatomical meshes  [48]. The fMRI final activity was obtained by averaging the BOLD signals projected on each mesh vertex over each ROI of the Desikan atlas, see Table  1  below. Individual SC matrices were built using Diffusion weight imaging (DWI) following the processing steps of the Connectomist-2.
0 software  [49] : artifact correction, geometrical distortion correction, analytical Q-ball model  [50] , streamline probabilistic fiber tracking inside an improved T1-based brain mask  [51]  using 27 seeds per voxel at the T1 image resolution and propagation step size of 0. 4 mm. Unreliable short fibers under 30 mm were removed, then region-to-region SC matrices could be built by counting fibers connecting regions of a given atlas  [52]. From the individual SC matrices, we calculated a generic SC matrix by averaging over all subjects. The Constellation parcellation is a refinement from the Freesurfer parcellation based on the DTI data. It subdivides each Freesurfer ROI according to the structural connectivity profile of each ROI point such as to obtain 3 to 5 Constellation ROIs with increased SC homogeneity  [52].