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  1. The KSU-UMD Dataset for Benchmarking for Audio Forensic Algorithms

    Work
    Title: The KSU-UMD Dataset for Benchmarking for Audio Forensic Algorithms
    Creator: Hafiz Malik and Muhammad Khurran Khan, King Saud University
    Description: Details of the microphone used for data collection, acoustic environment in which data was collected, and naming convention used are provided here. 1 - Microphones Used: The microphones used to collect this dataset belong to 7 different trademarks. Table (1) illustrates the number of used Mics of different trademarks and models. Table 1: Trademarks and models of Mics Mic Trademark Mic Model # of Mics Shure SM-58 3 Electro-Voice RE-20 2 Sennheiser MD-421 3 AKG C 451 2 AKG C 3000 B 2 Neumann KM184 2 Coles 4038 2 The t.bone MB88U 6 Total 22 2- Environment Description: A brief description of the 6 environments in which the dataset was collected is presented here: (i) Soundproof room: a small room (nearly 1.5m × 1.5m × 2m), which is closed and completely isolated. With an exception of a small window in the front side of the room which is made of glass, all the walls of the room are made of wood and covered by a layer of sponge from the inner side, and the floor is covered by carpet. (ii) Class room: standard class room (6m × 5m × 3m). (iii) Lab: small lab (4m × 4m × 3m). All the walls are made of glasses and the floor is covered by carpet. The lab contains 9 computers. (iv) Stairs: is in the second floor. The place of recording is 3m × 5m (v) Parking: is the college parking. (vi) Garden: is an open space outside the buildings. 3- Naming Convention: This set of rules were followed as a naming convention to give each file in the dataset a unique name: (i) The file name is 19 characters long, and consists of 5 sections separated by underscores. (ii) The first section is of 3 characters indicates the Microphone trademark. (iii) The second section of 4 characters indicates the microphone model as in table (2). (iv) The third section of 2 characters indicates a specific microphone within a set of microphones of the same trademark and model, since we have more than one microphone of the same trademark and model. (v) The fourth section of 2 characters indicates the environment, where Soundproof room --> 01 Class room --> 02 Lab --> 03 Stairs --> 04 Parking --> 05 Garden --> 06 (vi) The fifth section of 2 characters indicates the language, where Arabic --> 01 English --> 02 Chinese --> 03 Indonesian --> 04 (vii) The sixth section of 2 characters indicates the speaker. Table 2: Microphones Naming Criteria Original Mic Trademark and model --> Naming Convenient Shure SM-58 --> SHU_0058 Electro-Voice RE-20 --> ELE_0020 Sennheiser MD-421 --> SEN_0421 AKG C 451 --> AKG_0451 AKG C 3000 B --> AKG_3000 Neumann KM184 --> NEU_0184 Coles 4038 --> COL_4038 The t.bone MB88U --> TBO_0088 For example: SEN_0421_02_01_02_03 is an English file recorded by speaker number 3 in the soundproof room using microphone number 2 of Sennheiser MD-421
  2. Dizziness Scenario Randomized Intervention

    Work
    Title: Dizziness Scenario Randomized Intervention
    Creator: Meurer, William J and Kerber, Kevin A
    Description: Data set
  3. Abbott Piette Bolivia 2014 Dataset

    Work
    Title: Abbott Piette Bolivia 2014 Dataset
    Creator: Abbott Patricia A and Piette John
    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.
  4. Big Ship Data: Pre- and Post-Processed Spatiotemporal Data for 2006-2014 for Great Lakes Air Temperature, Dew Point, Surface Water Temperature, and Wind Speed

    Work
    Title: Big Ship Data: Pre- and Post-Processed Spatiotemporal Data for 2006-2014 for Great Lakes Air Temperature, Dew Point, Surface Water Temperature, and Wind Speed
    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
  5. Influence of invasive quagga mussels, phosphorus loads, and climate on spatial and temporal patterns of productivity in Lake Michigan: A biophysical modeling study

    Work
    Title: Influence of invasive quagga mussels, phosphorus loads, and climate on spatial and temporal patterns of productivity in Lake Michigan: A biophysical modeling study
    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
  6. 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
  7. 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.
  8. Deep Robot Optical Perception (DROP) Lab

    Title: Deep Robot Optical Perception (DROP) Lab
    Description: Datasets collected by DROP Lab.
  9. 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.
  10. Regional Climate Model Simulations

    Work
    Title: Regional Climate Model Simulations
    Creator: Steiner, A.L.
    Description: Land and water body surfaces play a critical role in hydroclimate by driving the transfer of moisture from the surface reservoir into the atmosphere. At the same time, atmospheric circulation transports moisture into and out of regions. To date, the hydroclimate impacts of the surface relative to large-scale transport and the variability over land and lake surfaces have not been quantified for the Great Lakes region despite known impacts of the lakes on the local climate. Using a Regional Climate Model (RegCM4) driven by three different boundary conditions, we simulate the hydroclimate of the Great Lakes region for a 23-year historical period. Observations and reanalysis products define land-lake-atmosphere feedbacks and are compared with the model. Reanalyses estimate up to 85% of the local precipitation is transported into the region from external sources. Seasonal RegCM4 precipitation biases reflect the biases in advective moisture flux, which depend on synoptic weather patterns such as the placement of troughs and ridges. In contrast with external sources, the land and lake surfaces account for over 30% of summer precipitation. However, RegCM4 underestimates the contribution of the land by 40% due to low evaporation rates as compared to the reanalyses. Observations at three flux towers indicate that evaporation and its drivers vary strongly by vegetation species, yet the lumped land cover description prescribed in the model neglects secondary species. Such species in the Great Lakes region have high evaporation potentials, and their absence in the model may account for the evaporation discrepancies. This highlights the need for greater complexity in the land cover classifications used in regional climate models to better capture land-atmosphere hydroclimate feedbacks. Over the lakes, one model member overestimates convective precipitation caused by enhanced evaporation under warm lake surface temperatures, highlighting the need for accurate representation of lake temperatures in the surface boundary condition. While external moisture sources dominate precipitation patterns, we conclude that the surface plays a substantial role in modifying regional hydroclimate.