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  • 1
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    Using and Distributing Spaceflight Data: The Johnson Space Center Life Sciences Data Archive (1995)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: Life sciences data collected before, during and after spaceflight are valuable and often irreplaceable. The Johnson Space Center Life is hard to find, and much of the data (e.g. Sciences Data Archive has been designed to provide researchers, engineers, managers and educators interactive access to information about and data from human spaceflight experiments. The archive system consists of a Data Acquisition System, Database Management System, CD-ROM Mastering System and Catalog Information System (CIS). The catalog information system is the heart of the archive. The CIS provides detailed experiment descriptions (both written and as QuickTime movies), hardware descriptions, hardware images, documents, and data. An initial evaluation of the archive at a scientific meeting showed that 88% of those who evaluated the catalog want to use the system when completed. The majority of the evaluators found the archive flexible, satisfying and easy to use. We conclude that the data archive effectively provides key life sciences data to interested users.
    Keywords: Documentation and Information Science
    Type: NASA-TM-111626 , NAS 1.15: 111626 , AIAA Paper 95-1045 , Life Sciences and Space Medicine; Apr 03, 1995 - Apr 05, 1995; Houston, TX; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 2
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    Magnetic anomalies characterization: Deep learning and explainability (2022)
    Elsevier
    Details
    Publication Date: 2024-01-08
    Description: In recent years, deep learning methods have shown great promise in the field of geophysics, especially for seismic interpretation. However, there is very little information with regard to its application in the field of magnetic methods. Our research introduces the use of convolutional neural networks for the characterization of magnetic anomalies. The models developed allow the localization of magnetic dipoles, including counting the number of dipoles, their geographical position, and the prediction of their parameters (magnetic moment, depth, and declination). To go even further, we applied visualization tools to understand our model's predictions and its working principle. The Grad-CAM tool improved prediction performance by identifying several layers that had no influence on the prediction and the t-SNE tool confirmed the strong capacity of our model to differentiate between different parameter combinations. Then, we tested our model with real data to establish its limitations and application domain. Results demonstrate that our model detects dipolar anomalies in a real magnetic map even after learning from a synthetic database with a lower complexity, which indicates a significant generalization capability. We also noticed that it is unable to identify dipole anomalies of shapes and sizes different from those considered for the creation of the synthetic database. Finally, the perspectives for this work consist of creating a more complex database to approach the complexity traditionally observed in magnetic maps, using real data from multiple acquisition campaigns, and other applications with alternative geophysical methods.
    Type: Article , PeerReviewed
    Format: text
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    PAPER (OCEANREP)
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