Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user

Pulsar Timing Constraints on the Fermi Massive Black-Hole Binary Blazar Population

Blazars are a sub-population of quasars whose jets are nearly aligned with the line-of-sight, which tend to exhibit multi-wavelength variability on a variety of timescales. Quasi-periodic variability on year-like timescales has been detected in a number of bright sources, and has been connected to the orbital motion of a putative massive black hole binary. If this were indeed the case, those blazar binaries would contribute to the nanohertz gravitational-wave stochastic background. We test the binary hypothesis for the blazar population observed by the \textit{Fermi} Gamma-Ray Space Telescope, which consists of BL Lacertae objects and flat-spectrum radio quasars. Using mock populations informed by the luminosity functions for BL Lacertae objects and flat-spectrum radio quasars with redshifts $z \le 2$, we calculate the expected gravitational wave background and compare it to recent pulsar timing array upper limits. The two are consistent only if a fraction $\lesssim 10^{-3}$ of blazars hosts a binary with orbital periods $<5$ years. We therefore conclude that binarity cannot significantly explain year-like quasi-periodicity in blazars.Comment: 5 pages, 4 figures, accepted by MNRAS Letter

Flipped Learning and Influential Factors: Case Analysis

Flipped learning has been a focus of research to explore potential learning environments that may positively affect student learning. The key issue is whether or how educators design such a learning environment, and what might be the factors that educators need to consider when designing a flipped learning environment. The first part of this study presents a critical review and analysis on factors identified from the literature that may influence the success of a flipped-learning case. 216 cases selected from current literature were analyzed on seven factors (Overall Design, Design of Information, Design of Technology Use, Active Learning, Motivation, Special Guidance, and Self-Regulated Learning) regarding their influence on the success of flipped learning experiences. Among them the first five factors were found to be significant and included in a prediction model. The second part of this study demonstrates an on-going case of flipped learning that reflects and examines the prediction model