8 research outputs found

    The SpacePy space science package at 12 years

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    For over a decade, the SpacePy project has contributed open-source solutions for the production and analysis of heliophysics data and simulation results. Here we introduce SpacePy's functionality for the scientific user and present relevant design principles. We examine recent advances and the future of SpacePy in the broader scientific Python ecosystem, concluding with some of the work that has used SpacePy.Comment: 14 pages, 3 figures, accepted for publication in Frontiers in Astronomy and Space Science

    Determining the significance of associations between two series of discrete events : bootstrap methods /

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    We review and develop techniques to determine associations between series of discrete events. The bootstrap, a nonparametric statistical method, allows the determination of the significance of associations with minimal assumptions about the underlying processes. We find the key requirement for this method: one of the series must be widely spaced in time to guarantee the theoretical applicability of the bootstrap. If this condition is met, the calculated significance passes a reasonableness test. We conclude with some potential future extensions and caveats on the applicability of these methods. The techniques presented have been implemented in a Python-based software toolkit

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    A Survey of Interplanetary Small Flux Ropes at Mercury

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    Interplanetary magnetic flux ropes with durations from a few minutes to a few hours have been termed small flux ropes (SFRs). We have built a comprehensive catalog of SFRs at Mercury using magnetometer data from the orbital phase of the MESSENGER mission (2011–2015). In the absence of solar wind plasma measurements, we developed strict identification criteria for SFRs in the magnetometer observations, including force-free field fits for each flux rope. We identified a total of 48 events that met our strict criteria, with events ranging in duration from 2.5 minutes to 4 hr. Using superposed epoch analysis, we obtained the generic SFR magnetic field profile at Mercury. Due to its eccentric orbit, Mercury\u27s heliospheric distance varies between 0.31 and 0.47 au, a range of ∼0.16 au. This distance is potentially large enough for the SFRs to undergo measurable changes due to distance. Thus, we split the data into two distance bins to look for such changes. We found that the average SFR profile is more symmetric farther from the Sun, in line with the idea that SFRs form closer to the Sun and undergo a relaxation process in the solar wind. Based on this result, as well as the SFR durations and the magnetic field strength fall-off with heliocentric distance, we infer that the SFRs observed at Mercury are expanding as they propagate with the solar wind. We also determined that the SFR occurrence frequency is nearly four times as high at Mercury as for similarly detected events at 1 au. Most interestingly, we found two SFR populations in our data set, one likely generated in a quasi-periodic formation process near the heliospheric current sheet, and the other formed away from the current sheet in isolated events

    HelioSwarm: A Multipoint, Multiscale Mission to Characterize Turbulence

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    HelioSwarm (HS), a NASA MidEx multispacecraft observatory with inter-spacecraft separations covering MHD and ion scales, will measurement the structure and dynamics of magnetized turbulence. This paper surveys turbulent systems that constitute compelling areas of heliophysics research and describes how HS will measure energy transport across scales in turbulent plasmas throughout the universe

    Update on the Worsening Particle Radiation Environment Observed by CRaTER and Implications for Future Human Deep-Space Exploration

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    Over the last decade, the solar wind has exhibited low densities and magnetic field strengths, representing anomalous states that have never been observed during the space age. As discussed by Schwadron, Blake, et al. (2014, https://doi.org/10.1002/2014SW001084), the cycle 23–24 solar activity led to the longest solar minimum in more than 80 years and continued into the “mini” solar maximum of cycle 24. During this weak activity, we observed galactic cosmic ray fluxes that exceeded theERobserved small solar energetic particle events. Here we provide an update to the Schwadron, Blake, et al. (2014, https://doi.org/10.1002/2014SW001084) observations from the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) on the Lunar Reconnaissance Orbiter. The Schwadron, Blake, et al. (2014, https://doi.org/10.1002/2014SW001084) study examined the evolution of the interplanetary magnetic field and utilized a previously published study by Goelzer et al. (2013, https://doi.org/10.1002/2013JA019404) projecting out the interplanetary magnetic field strength based on the evolution of sunspots as a proxy for the rate that the Sun releases coronal mass ejections. This led to a projection of dose rates from galactic cosmic rays on the lunar surface, which suggested a ∼20% increase of dose rates from one solar minimum to the next and indicated that the radiation environment in space may be a worsening factor important for consideration in future planning of human space exploration. We compare the predictions of Schwadron, Blake, et al. (2014, https://doi.org/10.1002/2014SW001084) with the actual dose rates observed by CRaTER in the last 4 years. The observed dose rates exceed the predictions by ∼10%, showing that the radiation environment is worsening more rapidly than previously estimated. Much of this increase is attributable to relatively low-energy ions, which can be effectively shielded. Despite the continued paucity of solar activity, one of the hardest solar events in almost a decade occurred in September 2017 after more than a year of all-clear periods. These particle radiation conditions present important issues that must be carefully studied and accounted for in the planning and design of future missions (to the Moon, Mars, asteroids, and beyond)

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    No full text
    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press
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