Skip to Content

Search Constraints

Search Results

  1. Atmospheric CO2 time series derived from CESM NEP and GEOS-Chem pulse response CO2

    Work
    Title: Atmospheric CO2 time series derived from CESM NEP and GEOS-Chem pulse response CO2
    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
  2. European Folk Costumes Excel Spreadsheet and Access Database

    Work
    Title: European Folk Costumes Excel Spreadsheet and Access Database
    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.
  3. Deep Robot Optical Perception (DROP) Lab

    Title: Deep Robot Optical Perception (DROP) Lab
    Description: Datasets collected by DROP Lab.
  4. Equilibrium configurations of hard polygons near the melting transition

    Work
    Title: Equilibrium configurations of hard polygons near the melting transition
    Creator: Joshua A. Anderson, James Antonaglia, Jaime A. Millan, Michael Engel, and Sharon C. Glotzer
    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.
  5. Semantic-Based Document Retrieval Using Spatial Distributions of Concepts

    Work
    Title: Semantic-Based Document Retrieval Using Spatial Distributions of Concepts
    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.
  6. A Video-Based Intervention to Improve Belt Fit

    Work
    Title: A Video-Based Intervention to Improve Belt Fit
    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.
  7. Neighborhood effects : Information and Education Environment

    Work
    Title: Neighborhood effects : Information and Education Environment
    Creator: Okullo, Dolorence, Goodspeed, Robert, Veinot, Tiffany C, Clarke, Phillipa J., Data Driven Detroit, Reddy, Shruthi, and Gomez-Lopez, Iris N.
    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.
  8. Simulation Parameters used in the Study titled "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method"

    Work
    Title: Simulation Parameters used in the Study titled "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method"
    Creator: Huang, Wenjun
    Description: We provide the parameters used in Umbrella Sampling simulations reported in our study "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method", namely the set positions and spring constants for each window in simulations. Two tables are provided. Table 1 lists the names of the peptides and their corresponding sequences. Table 2 lists the parameters. The abstract of our work is the following: We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with Weighted Histogram Analysis Method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse-grained (CG) force field (MARTINI) is less prone to significant error induced by biased-sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3-4 residue peptide segments while leaving the remain-ing peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and relia-bly estimate the binding free energy between an arbitrary peptide and an MHC class II molecule.
  9. Growth factor signaling to mTORC1 by amino acid–laden macropinosomes

    Work
    Title: Growth factor signaling to mTORC1 by amino acid–laden macropinosomes
    Creator: Yoshida, Sei and Swanson, Joel A
    Description: The rapid activation of the mechanistic target of rapamycin complex-1 (mTORC1) by growth factors is increased by extracellular amino acids through yet-undefined mechanisms of amino acid transfer into endolysosomes. Because the endocytic process of macropinocytosis concentrates extracellular solutes into endolysosomes and is increased in cells stimulated by growth factors or tumor-promoting phorbol esters, we analyzed its role in amino acid–dependent activation of mTORC1. Here, we show that growth factor-dependent activation of mTORC1 by amino acids, but not glucose, requires macropinocytosis. In murine bone marrow–derived macrophages and murine embryonic fibroblasts stimulated with their cognate growth factors or with phorbol myristate acetate, activation of mTORC1 required an Akt-independent vesicular pathway of amino acid delivery into endolysosomes, mediated by the actin cytoskeleton. Macropinocytosis delivered small, fluorescent fluid-phase solutes into endolysosomes sufficiently fast to explain growth factor–mediated signaling by amino acids. Therefore, the amino acid–laden macropinosome is an essential and discrete unit of growth factor receptor signaling to mTORC1
  10. Neighborhood Effects: Food Environment

    Work
    Title: Neighborhood Effects: Food Environment
    Creator: Veinot, Tiffany C., Goodspeed, Robert, Data Driven Detroit, Okullo, Dolorence, Yan, Xiang (Jacob), and Gomez-Lopez, Iris N.
    Description: The food environment is: 1) The physical presence of food that affects a person’s diet; 2) A person’s proximity to food store locations; 3) The distribution of food stores, food service, and any physical entity by which food may be obtained; or 4) A connected system that allows access to food. (Source: https://www.cdc.gov/healthyplaces/healthtopics/healthyfood/general.htm) Data included here concern: 1) Food access; and 2) Liquor access. Spatial Coverage for most data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area, Michigan, USA. See exception for grocery store data below.