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Are you reading an article or link that you think other CDI members might be interested in?

Maybe you only finished reading the title and abstract, but want to share your find before it disappears into your browser history.

Feel free to share your own papers!

You can use this thread to post your links. Yet another CDI learning experiment!


  1. Thanks to CDI member Richie Erickson for sharing this overview related to the FY16 and FY17 CDI projects on high-throughput computing!

    Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor

    Erickson RA, Fienen MN, McCalla SG, Weiser EL, Bower ML, Knudson JM, et al. (2018) Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor. PLoS Comput Biol 14(10): e1006468.

    Author summary:

    Computational biology often requires processing large amounts of data, running many simulations, or other computationally intensive tasks. In this hybrid primer/tutorial, we describe how high-throughput computing (HTC) can be used to solve these problems. First, we present an overview of high-throughput computing. Second, we describe how to break jobs down so that they can run with HTC. Third, we describe how to use HTCondor software as a method for HTC. Fourth, we describe how HTCondor may be applied to other situations and a series of online tutorials.

  2. Future Water Priorities for the Nation

    Directions for the U.S. Geological Survey Water Mission Area (2018)

    National Academies of Sciences, Engineering, and Medicine. 2018. Future Water Priorities for the Nation: Directions for the U.S. Geological Survey Water Mission Area. Washington, DC: The National Academies Press.

  3. Geological Surveys Database project

    The Geological Surveys Database project is a collaborative effort between the American Geosciences Institute and U.S. state geological surveys to help increase the discoverability and use of geological survey publications.

    The Geological Surveys Database includes publications from the state geological surveys and factsheets from the U.S. Geological Survey.

  4. Machine learning for ecosystem services

    Willcock, S., Martínez-López, J., Hooftman, D.A., Bagstad, K.J., Balbi, S., Marzo, A., Prato, C., Sciandrello, S., Signorello, G., Voigt, B. and Villa, F., 2018. Machine learning for ecosystem services. Ecosystem Services.

    Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it.

  5. Towards globally customizable ecosystem service models

    Martínez-López, J., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B., Athanasiadis, I., Pascual, M., Willcock, S. and Villa, F., 2018. Towards globally customizable ecosystem service models. Science of The Total Environment.

    We demonstrate 5 cloud-based ARIES models that can run on global or customized data.

    Community-level data and model sharing can advance progress in ES modeling.

    ARIES was discussed at the February 2018 CDI Monthly Meeting

  6. Best Paper at the International Digital Curation Conference 2018, by CDI member Wade Bishop

    Measuring FAIR Principles to Inform Fitness for Use

    Bishop, B.W., and Hank, C., 2018, Measuring FAIR Principles to Inform Fitness for Use, 13th International Digital Curation Conference, 19-22 February 2018, accessed online October 18 at

    From the abstract:

    ...This paper’s purpose is to present a method for qualitatively measuring the FAIR principles through operationalizing findability, accessibility, interoperability, and reusability from a re-user’s perspective. The findings may inform assessments that could also be used to develop situationally-relevant fitness for use frameworks.

    Attached here: bishop_hank_idcc18final20jan2018.pdf