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- Creator:
- Vasudevan, Ram, Barto, Charles, Rosaen, Karl, Mehta, Rounak, Matthew, Johnson-Roberson, and Nittur Sridhar, Sharath
- Description:
- A dataset for computer vision training obtained from long running computer simulations
- Keyword:
- autonomous driving, simulation, Computer Vision and Pattern Recognition, deep learning, Computer Science, object detection, and Robotics
- Citation to related publication:
- M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen and R. Vasudevan, "Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 746-753. Available at https://arxiv.org/abs/1610.01983 and https://doi.org/10.1109/ICRA.2017.7989092
- Discipline:
- Engineering
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- Creator:
- Singh, Deepak
- Description:
- This includes data for all the plots and maps I created for my paper publication entitled "Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR".
- Discipline:
- Science
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- Creator:
- Kerber, Kevin A and Meurer, William J
- Description:
- Data set
- Keyword:
- dizziness
- Discipline:
- Health Sciences
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- Creator:
- Piette, John and Abbott, Patricia A
- Description:
- Contained within is a subset of the larger dataset collected in La Paz, Bolivia in 2014. This data contains the analytic dataset (cross-sectional/descriptive) that includs the PACIC, Morisky, PHQ8, AUDIT, and a subset of socidemographic characteristics for NCD patients in La Paz.
- Keyword:
- Global Health, Bolivia, and Non-communicable disease
- Citation to related publication:
- Abbott P, Banerjee T, Aruquipa Yujra AC, Xie B, Piette J (2018) Exploring chronic disease in Bolivia: A cross-sectional study in La Paz. PLOS ONE 13(2): e0189218. https://doi.org/10.1371/journal.pone.0189218
- Discipline:
- Health Sciences
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- Creator:
- Fries, Kevin J.
- Description:
- This data is in support of the WRR paper by Fries and Kerkez: Big Ship Data: Using Vessel Measurements to Improve Estimates of Temperature and Wind Speed on the Great Lakes Code is also provided
- Keyword:
- Gaussian process regression, Data integration, Wind speed, Water surface temperature, Air temperature, and student-friendly
- Citation to related publication:
- Fries, K., and B. Kerkez (2017), Big Ship Data: Using vessel measurements to improve estimates of temperature and wind speed on the Great Lakes, Water Resour. Res., 53, 3662–3679, http://doi.org/10.1002/2016WR020084.
- Discipline:
- Engineering
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- Creator:
- Keppel-Aleks, Gretchen and Liptak, Jessica
- Description:
- -CESM_bdrd _NEP_pulse_response_CO2.nc contains time series from the ‘FullyCoupled’ simulation -CESM_bdrcs_NEP_pulse_response_CO2.nc contains time series from the ‘NoRad’ simulation -CESM_bdrd_pftcon_NEP_pulse_response_CO2.nc contains data from the ‘NoLUC’ simulation -CESM_bdrd_Regional_Fluxes_NEP.nc contains NEP time series for each terrestrial source region from the FullyCoupled simulation - CESM_bdrcs_Regional_Fluxes_NEP.nc contains NEP time series for each terrestrial source region from the CESM ‘NoRad’ simulation - CESM_bdrd_pftcon_Regional_Fluxes_NEP.nc contains NEP time series for each terrestrial source region from the CESM ‘NoLUC’ simulation The 3-letter station IDs, latitudes, and longitudes of the sample locations are: ID Latitude (ºN) Longitude (ºE) 1. BRW 71.3 203.4 2. ZEP 78.9 11.9 3. SHM 52.7 174.1 4. THD 41.1 235.8 5. TAP 36.7 126.1 6. BMW 32.3 295.1 7. MLO 19.5 204.4 8. POCN15 15.0 215.0 9. ALT 82.5 297.5 10. BHD -41.4 174.9 11. EIC -27.2 250.6 12. GMI 13.4 144.7 13. HUN 47.0 16.7 14. IZO 28.3 343.5 15. LLN 23.5 120.9 16. NAT -5.8 324.7 17. WLG 36.3 100.9 18. HBA -75.6 333.8 19. BKT -0.20 100.3 20. UUM 44.5 111.1 21. CGO -40.7 144.5 22. SDZ 40.7 117.1 23. ASC -8.0 345.6 24. SEY -4.7 55.5 25. POCS20 -20.0 186.0 26. POCS35 -35.0 180.0 27. PSA -64.9 296.0 28. SYO -69.0 39.6 29. CHR 1.7 202.8 30. KEY 25.7 279.8 31. BAL 55.4 17.2 32. HPB 47.8 11.0 33. LMP 35.5 12.6 34. NMB -23.6 15.0 35. RPB 13.2 300.2 36. WIS 30.0 35.1 37. POCS10 -10.0 199.0 38. POCN10 10.0 211.0 39. MID 28.2 182.6 40. SMO -14.2 189.4 41. SPO -90.0 335.2 The terrestrial CO2 source region abbreviations are: 1. NBNA 2. SBNA 3. ETNA 4. WTNA 5. CNAM 6. AMZN 7. EASA 8. WESA 9. EURO 10. SAME 11. MDAF 12. AFRF 13. SOAF 14. EABA 15. WEBA 16. SOBA 17. CNAS 18. SEAS 19. EQAS 20. AUST 21. GNLD 22. ATCA
- Keyword:
- atmospheric CO2 annual cycle amplitude and CESM extended concentration pathway
- Citation to related publication:
- Hornick, T., Bach, L. T., Crawfurd, K. J., Spilling, K., Achterberg, E. P., Woodhouse, J. N., Schulz, K. G., Brussaard, C. P. D., Riebesell, U., & Grossart, H.-P. (2017). Ocean acidification impacts bacteria–phytoplankton coupling at low-nutrient conditions. Biogeosciences, 14(1), 1–15. https://doi.org/10.5194/bg-14-1-2017
- Discipline:
- Science
-
- Creator:
- James, David A.
- Description:
- An Excel spreadsheet listing the information recorded on each of 18,686 costume designs can be viewed, downloaded, and explored. All the usual Excel sorting possibilities are available, and in addition a useful filter has been installed. For example, to find the number of designs that are Frieze Type #1, go to the top of the frieze type 2 column (column AS), click on the drop-down arrow and unselect every option box except True (i.e. True should be turned on, all other choices turned off). Then in the lower left corner, one reads “1111 of 18686 records found”. Much more sophisticated exploration can be carried out by downloading the rich and flexible Access Database. The terms used for this database were described in detail in three sections of Deep Blue paper associated with this project. The database can be downloaded and explored. HOW TO USE THE ACCESS DATABASE 1. Click on the Create Cohort and View Math Trait Data button, and select your cohort by clicking on the features of interest (for example: Apron and Blouse). Note: Depending on how you exited on your previous visit to the database, there may be items to clear up before creating the cohorts. a) (Usually unnecessary) Click on the small box near the top left corner to allow connection to Access. b) (Usually unnecessary) If an undesired window blocks part of the screen, click near the top of this window to minimize it. c) Make certain under Further Filtering that all four Exclude boxes are checked to get rid of stripes and circles, and circular buttons, and the D1 that is trivially associated with shoes. 2. Click on Filter Records to Form the Cohort button. Note the # of designs, # of pieces, and # of costumes beside Recalculate. 3. Click on Calculate Average Math Trait Frequency of Cohort button, and select the symmetry types of interest (for example: D1 and D2) . 4. To view the Stage 1 table, click on Create Stage 1 table. To edit and print this table, click on Create Excel (after table has been created). The same process works for Stages 2, 3.and 4 tables. 5. To view the matrix listing the math category impact numbers, move over to a button on the right side and click on View Matrix of Math Category Impact Numbers. To edit and print this matrix, click on Create Excel, use the Excel table as usual.
- Keyword:
- Group Theory, European regional costume, Symmetry, Ethnomathematics, European folk costume, and Classification of designs
- Citation to related publication:
- James, D. A., James, A. V., & Root, M. J. (2017). Symmetry in European folk costumes. Ann Arbor: University of Michigan. Retrieved from the Deep Blue institutional repository website: http://hdl.handle.net/2027.42/136161
- Discipline:
- Other
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- Creator:
- Engel, Michael, Glotzer, Sharon C., Anderson, Joshua A., Antonaglia, James, and Millan, Jaime A.
- Description:
- This dataset was generated for our work "Shape and symmetry determine two-dimensional melting transitions of hard regular polygons". The dataset includes simulation results for 13 different polygons (equilateral triangles through regular tetradecagons and the 4-fold pentille) at a variety of packing fractions near the isotropic fluid to solid phase transition. Each trajectory contains the final 4 frames of each simulation run we conducted at system sizes of over one million particles. For each shape, there is a JSON file that describes the vertices of the polygon and a number of simulation trajectory files in GSD ( https://bitbucket.org/glotzer/gsd) format. The trajectory files contain the positions and orientations of all the polygons at each frame, along with the simulation box size. The trajectory file names identify the packing fraction of that simulation run.
- Citation to related publication:
- Anderson, J.A., Antonaglia, J., Millan, J.A., Engel, M., Glotzer, S.C., 2017. Shape and Symmetry Determine Two-Dimensional Melting Transitions of Hard Regular Polygons. Phys. Rev. X 7, 021001. https://doi.org/10.1103/PhysRevX.7.021001
- Discipline:
- Engineering
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- Creator:
- Steiner, A.L. and Kawecki, S.
- Description:
- Kansas City, MO emissions can affect a severe weather system by altering the number of CCN, which drives changes in the hydrometeor development. The hydrometeor changes affect cold pool strength, size, and propagation which ultimately determine the strength of the squall line that crosses Kansas City, MO.
- Keyword:
- Great Plains, aerosols, mesoscale convective systems, and weather
- Citation to related publication:
- Kawecki, S., G.M. Henebry, and A.L. Steiner, 2016: Effects of Urban Plume Aerosols on a Mesoscale Convective System. J. Atmos. Sci., 73, 4641–4660, https://doi.org/10.1175/JAS-D-16-0084.1
- Discipline:
- Science
-
- Creator:
- Grosky, William I. and Ruas, Terry L.
- Description:
- This dataset was used for a proof-of-concept of fixed lexical chain approach for semantic information retrieval.
- Keyword:
- fixed lexical chains
- Citation to related publication:
- Ruas, T. L., & Grosky, W. I. (2017). Exploring and expanding the use of lexical chains in information retrieval. Ann Arbor: University of Michigan. Retrieved from the Deep Blue institutional repository website: http://dx.doi.org/10.3998/2027.42/136659
- Discipline:
- Engineering