Research Data

Permanent URI for this collection

https://hdl.handle.net/10217/172830

The Research Data collection contains the research data produced by scholars at CSU that has been made available in Mountain Scholar through 2022. This collection has a particular focus on the natural sciences, featuring the Shortgrass Steppe - Long Term Ecological Research (SGS-LTER) collection and a number of datasets from the Natural Resource Ecology Lab (NREL) and the Department of Atmospheric Science. By using these files, users agree to the CSU Libraries' Research Data Terms of Use.

CSU now offers data publishing through an institutional membership in Dryad. For more information about how to publish your data to meet journal and funder requirements, please see our Dryad information page.

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Recent Submissions

Now showing 1 - 5 of 310
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    Library Web Site Usage Statistics: Digital Collections, 2003-2017
    (Colorado State University. Libraries, 2023) Vogl, Greg; Paschal, Dawn; Rettig, Patty; Hunter, Nancy; Lopez-Terrill, Vicky; Meyer, Linda; Wilde, Michelle; Lunde, Diane; Ball Wicklund, Amy
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    Open Access
    Dataset associated with the Colorado Agriculture Bibliography NEH & USAIN Project
    (Colorado State University. Libraries, 2023) Level, Allison V.; Standish, Sierra
    As family farms disappear from the landscape, most Americans do not have contact with the agricultural roots of the country. More than ever, the historical literature that helps in telling the story of American farming needs protection. The Preserving the History of United States Agriculture and Rural Life Project, administered by Cornell University, calls for states to identify and list important agricultural literature for preservation purposes. This paper discusses the Colorado project and highlights the scope and identification of materials and the organization of a Web-searchable bibliographic database. Because Colorado began to participate after approximately half of the states had completed bibliographies, staff could access already-tested methodologies for scope descriptions, subject headings, and other "how-to" processes. By using the lessons learned by others, staff were able to expand the scope, capture extra records, and design a nuanced Web site as a portal to the bibliography.
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    Dataset associated with "Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects"
    (Colorado State University. Libraries, 2022) Yang, C. Kevin; Chiu, Christine; Marshak, Alexander; Feingold, Graham; Várnai, Tamás; Wen, Guoyong; Yamaguchi, Takanobu; van Leeuwen, Peter Jan
    There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100–500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01+5%AOD, and the mean bias is approximately –2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.
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    MODIS Monthly Fog and Low Cloud Cover Rasters 2000-2022
    (Colorado State University. Libraries, 2022) Werner, Zackary; Choi, Christopher Tsz Hin; Winter, Anna; Vorster, Anthony G.; Berger, Anika; O'Shea, Kristen; Evangelista, Paul; Woodward, Brian
    The MODIS Monthly Fog and Low Cloud Cover Rasters 2000-2022 dataset contains fog and low cloud cover (FLCC) observations summarized into days per month along the California and Southern Oregon Coast from 2000-2022. This dataset accompanies the publication https://doi.org/10.1016/j.rsase.2022.100832, which describes the methodology for creating this dataset. The dataset can also be viewed through a Google Earth Engine web application https://christopherchoi98.users.earthengine.app/view/modis-fog-detection-app.
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    Channel delineation datasets associated with "River channel response to invasive plant treatment across the American Southwest"
    (Colorado State University. Libraries, 2022) Wieting, Celeste; Friedman, Jonathan; Rathburn, Sara L.
    Invasive riparian plants were introduced to the American Southwest in the early 19th century and contributed to regional trends of decreasing river channel width and migration rate in the 20th century. More recently efforts to remove invasive riparian vegetation (IRV) have been widespread, especially since 1990. To what extent has IRV treatment reversed the earlier trend of channel narrowing and reduced dynamism? In this study, paired treated and untreated reaches at 15 sites along 13 rivers were compared before and after IRV treatment using repeat aerial imagery to assess long-term (~10 year) channel change due to treatment on a regional scale across the Southwest U.S. We found that IRV treatment significantly increased channel width and floodplain destruction. Treated reaches had higher floodplain destruction than untreated reaches at 14 of 15 sites, and IRV treatment increased the rate of floodplain destruction by a median factor of 1.9. The effect of treatment increased with the stream power of the largest flow over the study period. Resolving observations of channel change into separate measures of floodplain destruction and formation provided more information on underlying processes than simple measurements of channel width and centerline migration rate. Restoration practitioners who perform IRV treatment projects often focus on wildlife or vegetation response; however, geomorphic processes should be considered in restoration planning because they drive aquatic habitat and vegetation dynamics, and because of the potential for damage to downstream infrastructure. Depending on the restoration goal, management practices can be used to enhance or minimize the increase in channel dynamism caused by IRV removal.