SWMF is used to study the segmentation of SED plume into polar cap patches during the geomagnetic storm on Sep 7, 2017. The database includes the 3D output in the upper atmosphere from GITM, the 2D output from Ionospheric Electrodynamics (IE) and 3D output from BATSRUS. The output from GITM can be read with thermo_batch_new.pro. The output from IE can be opened with Spacepy at https://pythonhosted.org/SpacePy/. The output from BATSRUS can be opened with tecplot.
More details can be found in Readme.txt.
The project outputs summarize all the publications, talks, and codes we accomplished under this NSF funding. In the project, we develop methodologies to manage uncertainty in future electric power systems and quantify how uncertainty affects power system sustainability. and Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149653
We compared the response rates, cost, and the average income of participants pertaining to 6 different survey distribution methods used in an initial study about mobility-on-demand services. We used the data to identify survey and recruitment methods that are more effective in reaching hard-to-reach populations. All the raw data used for calculations and the calculations themselves can be found in the attached spreadsheets. and Initial analyses have identified in-person onsite recruitment as one of the better methods of reaching hard-to-reach populations, and is calling for continued work in improving research methods in the field of HCI.
Yan, X., Zhao, X., Han, Y., and Hentenryck, P. V. (2019). Mobility-on-demand versus fixed-route transit systems: an evaluation of traveler preferences in low-income communities. https://poverty.umich.edu/files/2019/02/Yan_et_al_WorkingPaper_Preference_for_mobility_on_demand.pdf , Atkinson, R., and Flint, J. Accessing Hidden and Hard-to-Reach Populations: Snowball Research Strategies. "Social Research Update" 33 (Jan 2001)., Buranyi, S. Rise of the racist robots: how ai is learning all our worst impulses, Aug 2017. Retrieved June 11, 2019 from https://www.theguardian.com/inequality/2017/aug/08/rise-of-the-racist-robots-how-ai-is-learning-all-our-worst-impulses., Dillahunt, T. R., Erete, S., Galusca, R., Israni, A., Nacu, D., and Sengers, P. Reflections on design methods for underserved communities. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (New York, NY, USA, 2017), CSCW ’17 Companion, ACM, pp. 409–413., Erete, S., Israni, A., and Dillahunt, T. An intersectional approach to designing in the margins. Interactions 25, 3 (Apr. 2018), 66–69., Foster, A. Concerning issue with driverless cars, Mar 2019. Retrieved June 11, 2019 from https://www.news.com.au/technology/innovation/motoring/on-the-road/driverless-cars-may-be-more-likely-to-hit-darkskinned-people-study-finds/news-story/b19959d01ef865f15bb336275b8903e8., Johnston, L. G., and Sabin, K. Sampling hard-to-reach populations with respondent driven sampling. Methodological Innovations Online 5, 2 (aug 2010), 38.1–48., Macaulay, A. C., Commanda, L. E., Freeman, W. L., Gibson, N., McCabe, M. L., Robbins, C. M., and Twohig, P. L. Participatory research maximises community and lay involvement. BMJ 319, 7212 (sep 1999), 774–778., Maestre, J. F., Eikey, E. V., Warner, M., Yarosh, S., Pater, J., Jacobs, M., Marcu, G., and Shih, P. C. Conducting research with stigmatized populations: Practices, challenges, and lessons learned. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (2018), ACM, pp. 385–392., Paterson, J. M., and Maker, Y. Why does artificial intelligence discriminate?, Jun 2019. Retrieved June 11, 2019 from https://pursuit.unimelb.edu.au/articles/why-does-artificial-intelligence-discriminate., Strohmayer, A., Laing, M., and Comber, R. Technologies and social justice outcomes in sex work charities: fighting stigma, saving lives. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (2017), ACM, pp. 3352–3364., and Sydor, A. Conducting research into hidden or hard-to-reach populations. Nurse researcher 20, 3 (2013).
Model simulations were conducted to investigate the role of soil moisture on the terrestrial carbon and water cycles. The data are composed of NetCDF files generated by the simulations that contain the data variables analyzed in the paper. and CLM5 Documentation - http://www.cesm.ucar.edu/models/cesm2/land/.
Files contain the atmospheric CO2 mole fraction responses to land flux type (HRcasa, HRcorpse, HRmimics) and land flux region (latband variable). Land flux regions are categorized as: Northern Hemisphere high latitudes (NHL; 61 to 90°N), midlatitudes (NML; 24 to 60°N), tropics (NT; 1 to 23°N), Southern Hemisphere tropics (ST; 0 to 23°S), and extratropics (SE; 24 to 90°S). See the README file for how these land flux region definitions relate to the file's latband variable. and To cite dataset: Basile, S., Lin, X., Keppel-Aleks, G. (2019). Simulated CO2 dataset using the atmospheric transport model GEOSChem v12.0.0: Response to regional land carbon fluxes [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/xjzc-xy05
Raw Rheology data in supplement to the 2019 Macromolecules publication: "Assessing the Range of Validity of Current Tube Models Through Analysis of a Comprehensive Set of Star-Linear 1,4-Polybutadiene Polymer Blends"
We used data collected from participants of the Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) study, which consists of three sequentially-enrolled birth cohorts of pregnant women. Research protocols of this study were approved by the Institutional Review Board at University of Michigan and the Mexico National Institute of Public Health. We obtained informed consent from mothers and informed assent from children prior to enrollment.
The dataset contains bulk sedimentary d15N, TOC, and TN data measured every 2 mm on the core SPR0901-03KC. Flood and turbidite layers are shaded with blue and orange in the files. and This work is supported by NSF OCE-1304327.
Wang, Y. , Hendy, I. L. and Thunell, R. (2019), Local and remote forcing of denitrification in the Northeast Pacific for the last 2000 years. Paleoceanography and Paleoclimatology. Accepted Author Manuscript. doi:10.1029/2019PA003577