Search Constraints
1 - 3 of 3
Number of results to display per page
Search Results
-
- Description:
- The mechanisms governing tree drought mortality and recovery remain a subject of inquiry and active debate given their role in the terrestrial carbon cycle and their concomitant impact on climate change. Counter-intuitively, many trees do not die during the drought itself. Indeed, observations globally have documented that trees often grow for several years after drought before mortality. A combination of meta-analysis and tree physiological models demonstrate that optimal carbon allocation after drought explains observed patterns of delayed tree mortality and provides a predictive recovery framework. Specifically, post-drought, trees attempt to repair water transport tissue and achieve positive carbon balance through regrowing drought-damaged xylem. Further, the number of years of xylem regrowth required to recover function increases with tree size, explaining why drought mortality increases with size. These results indicate that tree resilience to drought-kill may increase in the future, provided that CO2 fertilization facilitates more rapid xylem regrowth.
- Keyword:
- drought, optimality theory, vegetation model, CO2 fertilization, hydraulic-carbon coupling, and carbon metabolism
- Subject:
- droughts and vegetation
- Creator:
- Medvigy, D., Anderegg, W. R. L., Detto, M., Bartlett, M. K., Pacala, S. W., Schwalm, C., Schahher, B., and Trugman, Anna T.
- Owner:
- BRIAN MCBRIDE
- Language:
- English
- Date Uploaded:
- 07/10/2019
- Date Modified:
- 04/17/2024
- Date Created:
- Spring 2018
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S5N29V4F
-
- Description:
- Objective: In 2018, the Network of the National Libraries of Medicine (NNLM) launched a national sponsorship program to support U.S. public library staff in completing the Medical Library Association’s (MLA) Consumer Health Information Specialization (CHIS). The primary objective of this research project was to determine if completion of the sponsored specialization was successful in improving public library staff ability to provide consumer health information and whether it resulted in new services, programming, or outreach activities at public libraries. Secondary objectives of this research were to determine motivation for and benefits of the specialization and to determine the impact on sponsorship on obtaining and continuing the specialization. Methods: To evaluate the sponsorship program, we developed and administered a 16-question online survey via REDCap in August 2019 to 224 public library staff that were sponsored during the first year of the program. We measured confidence and competence in providing consumer health information using questions aligned with the eight Core Competencies for Providing Consumer Health Information Services [1]. Additionally, the survey included questions about new consumer health information activities at public libraries, public library staff motivation to obtain the specialization, and whether it led to immediate career gains. To determine the overall value of the NNLM sponsorship, we measured whether funding made it more likely for participants to complete or continue the specialization. Results: Overall, 136 participants (61%) responded to the survey. Our findings indicated that the program was a success: over 80% of participants reported an increase in core consumer health competencies, with a statistically significant improvement in mean competency scores after completing the specialization. Ninety percent of participants have continued their engagement with NNLM, and over half offered new health information programs and services at their public library. All respondents indicated that completing the specialization met their expectations, but few reported immediate career gains. While over half of participants planned to renew the specialization or obtain the more advanced, Level II specialization, 72% indicated they would not continue without the NNLM sponsorship. Conclusion: Findings indicate that NNLM sponsorship of the CHIS specialization was successful in increasing the ability of public library staff to provide health information to their community. and This dataset represents the de-identified raw results of a 16-question, online survey (via REDCap) collected in August 2019 to 224 public library staff who were sponsored for a Consumer Health Information Specialization (CHIS). The purpose of the study was to determine whether the sponsorship program had an impact on public library staff to provide consumer health information.
- Subject:
- Interprofessional Relations, Information Services, Professional Competence, Librarians / education, Libraries, Libraries, Medical , Consumer Health Information, and Humans
- Creator:
- Lake, Erica, Wolfe, Susan M, Knapp, Molly , Spatz, Michele, and Kiscaden, Elizabeth
- Owner:
- Molly Knapp
- Based Near Label Tesim:
- United States, , United States
- Language:
- English
- Date Uploaded:
- 11/12/2020
- Date Modified:
- 02/05/2021
- Date Created:
- August 2019
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D1DAY2QQQ
-
- Description:
- Weather-related research often requires synthesizing vast amounts of data that need archival solutions that are both economical and viable during and past the lifetime of the project. Public cloud computing services (e.g., from Amazon, Microsoft, or Google) or private clouds managed by research institutions are providing object data storage systems appropriate for long-term archives of such large geophysical data sets. , Current Status: Our research group no longer needs to maintain archives of High Resolution Rapid Refresh (HRRR) model output at the University of Utah since complete publicly-accessible archives of HRRR model output are now available from the Google Cloud Platform and Amazon Web Services (AWS) as part of the NOAA Open Data Program. Google and AWS store the HRRR model output in GRIB2 format, a file type that efficiently stores hundreds of two-dimensional variable fields for a single valid time. Despite the highly compressible nature of GRIB2 files, they are often on the order of several hundred MB each, making high-volume input/output applications challenging due to the memory and compute resources needed to parse these files. With support from the Amazon Sustainability Data Initiative, our group is now creating and maintaining HRRR model output in an optimized format, Zarr, in a publicly-accessible S3 bucket- hrrrzarr. HRRR-Zarr contains sets for each model run of analysis and forecast files sectioned into 96 small chunks for every variable. The structure of the HRRR-Zarr files are designed to allow users the flexibility to access only the data they need through selecting subdomains and parameters of interest without the overhead that comes from accessing numerous GRIB2 files. , and History: This effort began in 2015 to illustrate the use of a private cloud object store developed by the Center for High Performance Computing (CHPC) at the University of Utah. We began archiving thousands of two-dimensional gridded fields (each one containing over 1.9 million values over the contiguous United States) from the High-Resolution Rapid Refresh (HRRR) data assimilation and forecast modeling system. The archive has been used for retrospective analyses of meteorological conditions during high-impact weather events, assessing the accuracy of the HRRR forecasts, and providing initial and boundary conditions for research simulations. The archive has been accessible interactively and through automated download procedures for researchers at other institutions that can be tailored by the user to extract individual two-dimensional grids from within the highly compressed files. Over a thousand users have voluntarily registered to use the HRRR archive at the University of Utah. Our archive has grown to over 130 Tbytes of model output but we no longer need to continue that effort since the GRIB2 files are available now via Google and AWS. As mentioned above, we now provide much of the same information in an alternative format that is appropriate particularly for machine-learning applications.
- Keyword:
- data assimilation, Zarr, weather, forecasts, high resolution rapid refresh, and numerical weather prediction
- Subject:
- atmospheric science
- Creator:
- Horel, John and Blaylock, Brian
- Contributor:
- University of Utah Center for High Performance Computing, NOAA Earth Systems Research Laboratory, Amazon Open Data Program, and NOAA Environmental Modeling Center
- Depositor:
- BRIAN MCBRIDE
- Owner:
- JOHN HOREL
- Based Near Label Tesim:
- Alaska, Alaska, United States and United States, , United States
- Language:
- binary and English
- Date Uploaded:
- 07/10/2019
- Date Modified:
- 04/18/2024
- Date Created:
- 2015-04-18 to 2019-07-10
- License:
- CC BY – Allows others to use and share your data, even commercially, with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://dx.doi.org/10.7278/S5JQ0Z5B