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  • Subjective Effect Reports of Food

    Creator: Schulte, Erica M
    Description: The data set supports a study investigating which foods may be most implicated in addictive-like eating by examining how nutritionally diverse foods relate to loss of control consumption and various subjective effect reports. Participants (n = 501) self-reported how likely they were to experience a loss of control over their consumption of 30 nutritionally diverse foods and rated each food on five subjective effect report questions that assess the abuse liability of substances (liking, pleasure, craving, averseness, intensity). Hierarchical cluster analytic techniques were used to examine how foods grouped together based on each question. Highly processed foods, with added fats and/or refined carbohydrates, clustered together and were associated with greater loss of control, liking, pleasure, and craving. The clusters yielded from the subjective effect reports assessing liking, pleasure, and craving were most similar to clusters formed based on loss of control over consumption, whereas the clusters yielded from averseness and intensity did not meaningfully differentiate food items. The associated study applies methodology used to assess the abuse liability of substances to understand whether foods may vary in their potential to be associated with addictive-like consumption. Highly processed foods (e.g., pizza, chocolate) appear to be most related to an indicator of addictive-like eating (loss of control) and several subjective effect reports (liking, pleasure, craving). Thus, these foods may be particularly reinforcing and capable of triggering an addictive-like response in some individuals. Future research is warranted to understand whether highly processed foods are related to these indicators of abuse liability at a similar magnitude as addictive substances. The data set is presented in both .sav format for use with SPSS software and in csv format.
  • Literature Search Strategies for "Substance Use Education in United States Schools of Pharmacy: A Systematic Review of the Literature"

    Creator: MacEachern, Mark P
    Description: The dataset represents the complete search strategies for all literature databases searched during the systematic review. The Endnote and Excel files of all citations considered for inclusion in the review are also included.
  • Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR

    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".
  • 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
  • Dizziness Scenario Randomized Intervention

    Creator: Meurer, William J and Kerber, Kevin A
    Description: Data set
  • 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.
  • 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
  • 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
  • Atmospheric CO2 time series derived from CESM NEP and GEOS-Chem pulse response CO2

    Creator: Keppel-Aleks, Gretchen and Liptak, Jessica
    Description: -CESM_bdrd contains time series from the ‘FullyCoupled’ simulation contains time series from the ‘NoRad’ simulation contains data from the ‘NoLUC’ simulation contains NEP time series for each terrestrial source region from the FullyCoupled simulation - contains NEP time series for each terrestrial source region from the CESM ‘NoRad’ simulation - 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

    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.