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- Creator:
- Yang, Bing and Wittkopp, Patricia J
- Description:
- Datafiles and code described in accompanying MS, currently in review
- Keyword:
- regulatory network evolution
- Citation to related publication:
- Discipline:
- Science
- Title:
- Data and code files for Bing and Wittkopp MBE submission 11/22/16
-
- Creator:
- Title, Pascal O. and Bemmels, Jordan B.
- Description:
- The ENVIREM dataset v1.0 is a set of 16 climatic and 2 topographic variables that can be used in modeling species' distributions. The strengths of this dataset include their close ties to ecological processes, and their availability at a global scale, at several spatial resolutions, and for several time periods. The underlying temperature and precipitation data that went into their construction comes from the WorldClim dataset ( www.worldclim.org), and the solar radiation data comes from the Consortium for Spatial Information ( www.cgiar-csi.org). The data are compatible with and expand the set of variables from WorldClim v1.4 ( www.worldclim.org). For more information, please visit the project website: envirem.github.io
- Keyword:
- raster, species distribution modeling, and bioclimatic
- Citation to related publication:
- Title, P. O. and Bemmels, J. B. (2018), ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41: 291-307. http://doi.org/10.1111/ecog.02880
- Discipline:
- Science
- Title:
- ENVIREM: ENVIronmental Rasters for Ecological Modeling version 1.0
-
- Creator:
- Larson, Ronald G., Wen, Fei, Huang, Wenjun, and Huang, Ming
- 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.
- Keyword:
- Molecular Dynamics, Binding Free Energy, Protein, MHC, and Coarse-Grained
- Citation to related publication:
- M. Huang, W. Huang, F. Wen, R. G. Larson. J. Comput. Chem. 2017, 38, 2007–2019. https://doi.org/10.1002/jcc.24845
- Discipline:
- Engineering and Science
- 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:
- Gomez-Lopez, Iris N., Goodspeed, Robert, Reddy, Shruthi, Clarke, Phillipa J., Okullo, Dolorence, Veinot, Tiffany C, and Data Driven Detroit
- 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.
- Keyword:
- Residential Broadband Data Adoption Rates, Census tract level, Broadband Internet Access and Speed, Colleges and Universities, Public Libraries, Spatial Measures, and Schools
- Citation to related publication:
- Discipline:
- Science, Health Sciences, and Social Sciences
- Title:
- Neighborhood effects : Information and Education Environment
-
- 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:
- 10.5194/bg-14-1-2017
- Discipline:
- Science
- Title:
- Atmospheric CO2 time series derived from CESM NEP and GEOS-Chem pulse response CO2
-
- 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
- Keyword:
- Zooplankton, Model, Hydrodynamic model, Dreissenid mussels, Quagga mussel, Phytoplankton, and Lake Michigan
- Citation to related publication:
- To access videos streaming of these files go: http://hdl.handle.net/2027.42/136202
- Discipline:
- Science
- 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:
- Malik, Hafiz and Khan, Muhammad Khurran, 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
- Keyword:
- audio forensic, multimedia forensics, microphone identification, tamper detection, splicing detection, and codec identification
- Citation to related publication:
- http://dx.doi.org/10.1080/00450618.2017.1296186
- Discipline:
- Government, Politics and Law, Engineering, and Science
- Title:
- The KSU-UMD Dataset for Benchmarking for Audio Forensic Algorithms
-
- 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".
- Citation to related publication:
- Discipline:
- Science
- Title:
- Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR
-
- Creator:
- Blesh, Jennifer
- Description:
- This dataset contains three data files used in: Blesh, J. 2017. Functional traits in cover crop mixtures: biological nitrogen fixation and multifunctionality. Journal of Applied Ecology. There are also three corresponding metadata files. The file “Ecosystem_functions_soil_species.csv” contains data organized by farm, treatment, replicate block, and species combining the fall and spring sampling time points. These data include aboveground biomass, nitrogen and carbon content, and biological nitrogen fixation for the plant species. The dataset also includes measured soil characteristics for each farm site. The file “Ecosystem_functions_soil_treatment.csv” contains data organized by farm, treatment, and replicate block for the fall and spring sampling time points combined. These data include aboveground biomass, nitrogen and carbon content, and biological nitrogen fixation aggregated by treatment. The dataset also includes measured soil characteristics for each farm site. The file “Traits_unstandardized.csv” contains individual plant trait data, a subset of which were used to calculate an index of functional diversity after they were standardized to have zero mean and unit variance. These data are organized by farm, treatment, replicate block, and species. The corresponding metadata files: “Ecosystem_functions_soil_species_metadata.csv”, “Ecosystem_functions_soil_treatment_metadata.csv”, and “Traits_unstandardized_metadata.csv” provide a detailed description of all variables in each dataset and any abbreviations used. Note: On Dec 19th 2017, the format of the files was changed to csv to aid preservation. The following information was added to the three metadata files: the name of the data file the metadata refers to, an explanation as to the meaning of blank cells in the data file, a full citation to the paper where the author describes her findings and contact information for the author.
- Keyword:
- agroecology, biological nitrogen fixation, functional diversity, and cover crop
- Citation to related publication:
- Blesh J. Functional traits in cover crop mixtures: Biological nitrogen fixation and multifunctionality. J Appl Ecol. 2018;55:38–48. https://doi.org/10.1111/1365-2664.13011
- Discipline:
- Science
- Title:
- Data from: Functional traits in cover crop mixtures: biological nitrogen fixation and multifunctionality
-
- Creator:
- Steiner, Allison and Bryan, Alex
- Description:
- Included are RegCM simulations driven by three different types of boundary conditions 1. ERA - present day only (1979-2005) 2. GFDL - present day (1978-2005) and future (2041-2065) 3. HadGEM - present day (1978-2005) and future (2041-2065) Each directory has three files with monthly averaged values: ATM: includes 4D (t,z,y,x) atmospheric fields (pressure, winds, temperature, specific humidity, cloud water) and some 3D fields (t,y,x) precipitation, soil temperature, soil water SRF: includes 3D (t,y,x) surface variables (surface pressure, 10m winds, drag coefficient, surface temperature, 2m air temperature, soil moisture, precipitation, runoff, snow, sensible heat flux, latent heat flux, surface radiation components (SW, LW), PBL height, albedo, sunshine duration) RAD: includes 4D radiative transfer variables (SW and LW heating, TOA fluxes, cloud fraction, ice water content) clm_h0 files: CLM land surface files, includes canopy variables, surface fluxes, soil moisture by layers, etc. "
- Keyword:
- climate
- Citation to related publication:
- Bryan, A. M., A. L. Steiner, and D. J. Posselt (2015), Regional modeling of surface-atmosphere interactions and their impact on Great Lakes hydroclimate, J. Geophys. Res. Atmos., 120, 1044–1064. https://doi.org/10.1002/2014JD022316
- Discipline:
- Science
- Title:
- Regional Climate Model simulations