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  • Influence of invasive quagga mussels, phosphorus loads, and climate on spatial and temporal patterns of productivity in Lake Michigan: A biophysical modeling study

    Work
    Creator: Rowe, Mark D.
    Description: Animation files show the 12-month “baseline” simulations for 2000, 2005, and 2010 (see Table 1 of the paper cited above). temp_1_animation.wmv: Surface temperature Chl_1_animation.wmv: Surface chlorophyll-a PO4_1_animation.wmv: Surface total dissolved phosphorus Detritus_1_animation.wmv: Surface detritus concentration (particulate organic carbon, excluding phytoplankton and zooplankton). Zooplankton_1_animation.wmv: Surface zooplankton carbon concentration MRATION_1_animation.wmv: Rate of food assimilated by mussels, according to the product f_a F_A P in Equation 2, expressed as mg phytoplankton carbon per mg mussel biomass carbon per day × 100%. BIO_M_1_animation.wmv: Simulated mussel biomass in mg ash-free-dry-mass m^-2
  • Atmospheric CO2 time series derived from CESM NEP and GEOS-Chem pulse response CO2

    Work
    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
  • European Folk Costumes Excel Spreadsheet and Access Database

    Work
    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.
  • Deep Robot Optical Perception (DROP) Lab

    Description: Datasets collected by DROP Lab.
  • Equilibrium configurations of hard polygons near the melting transition

    Work
    Creator: Sharon C. Glotzer, James Antonaglia, Michael Engel, Joshua A. Anderson, and Jaime A. Millan
    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.
  • Regional Climate Model Simulations

    Work
    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.
  • Semantic-Based Document Retrieval Using Spatial Distributions of Concepts

    Work
    Creator: Ruas, Terry L. and Grosky, William I.
    Description: This dataset was used for a proof-of-concept of fixed lexical chain approach for semantic information retrieval.
  • Three-Dimensional Body Shape Manikins of Young Children for Child Restraint Design

    Work
    Creator: Jones, Monica L.H.
    Description: These manikins represent body shape models for children weighing 9 to 23 kg in a seated posture relevant to child restraint design. The design of child restraints is guided in part by anthropometric data describing the distributions of body dimensions of children. However, three-dimensional body shape data have not been available for children younger than three years of age. These manikins will be useful for assessing child accommodation in restraints. The SBSM can also provide guidance for the development of anthropomorphic test devices and computational models of child occupants. The sampled manikins were predicted for a range of torso length and body weight dimensions. The SBSM model was exercised for two torso lengths and nine body weights to obtain 18 body shapes. The 3D shape models can be downloaded in a standard mesh format (PLY). Each body shape is accompanied by predicted landmark locations and standard anthropometric variables.
  • A Video-Based Intervention to Improve Belt Fit

    Work
    Creator: Jones, Monica L.H.
    Description: This study evaluated the performance of a video-based intervention for improving the belt fit obtained by drivers. Previous laboratory studies have demonstrated that some drivers position their seat belts suboptimally. Specifically, the lap portion of the belt may be higher and farther forward relative to the pelvis than best practice, and the shoulder portion of the belt may be outboard or inboard of mid-shoulder. A video was developed to present the most important aspects of belt fit best practices, with emphasis on the lap belt. The video demonstrated how a seat belt should be routed with respect to an individual’s anatomy to ensure a proper fit. The three key belt fit concepts conveyed in the video were: 1) Lap belt low on hips, touching the thighs. 2) Shoulder belt crossing middle of collarbone. 3) Belt snug, as close to bones as possible. Additional context about the ability to achieve to good belt fit, such as opening a heavy coat or adjusting the height adjusters on the B-pillar behind the windows, were also presented.
  • Neighborhood effects : Information and Education Environment

    Work
    Creator: Veinot, Tiffany C, Data Driven Detroit, Gomez-Lopez, Iris N., Clarke, Phillipa J., Goodspeed, Robert, Okullo, Dolorence, and Reddy, Shruthi
    Description: The information and education environment refers to: 1) the presence of information infrastructures such as broadband Internet access and public libraries in a location; 2) a person’s proximity to information infrastructures and sources; 3) the distribution of information infrastructures, sources and in a specific location; and 4) exposure to specific messages (information content) within a specific location. Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.