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A tide prediction and tide height control system for laboratory mesocosm
This archive contains materials related to a tide prediction system and laboratory aquarium tide height control system. It includes a set of tide prediction libraries meant to run on Arduino microcontrollers. The libraries are based on data generated by the National Ocean and Atmospheric Administration's National Ocean Service, and were compiled by David Flater for the open source program XTide. The data from XTide were then adapted to generate the individual libraries for a variety of sites around the US mainland, Alaska, Hawaii, Puerto Rico and the Virgin Islands. We also provide a set of R scripts to generate new libraries for additional NOAA tide station sites that are not included in this repository. See the folder "Generate_new_site_libraries" in the archive for the scripts and description of the library generation process. Diagrams and parts lists for the mechanical portion of the tide control rack are provided as well. R code and raw data for the plant growth analysis are provided as well.
This archive contains materials related to a tide prediction system and laboratory aquarium tide height control system. It includes a set of tide prediction libraries meant to run on Arduino microcontrollers. The libraries are based on data generated by the National Ocean and Atmospheric Administration's National Ocean Service, and were compiled by David Flater for the open source program XTide. The data from XTide were then adapted to generate the individual libraries for a variety of sites around the US mainland, Alaska, Hawaii, Puerto Rico and the Virgin Islands. We also provide a set of R scripts to generate new libraries for additional NOAA tide station sites that are not included in this repository. See the folder "Generate_new_site_libraries" in the archive for the scripts and description of the library generation process. Diagrams and parts lists for the mechanical portion of the tide control rack are provided as well. R code and raw data for the plant growth analysis are provided as well.
Collection
Stanford Geospatial Center Teaching Data
This shapefile was created from the Clowns of America, International Membership Database (anonymized) obtained in 2007 from Clowns of America, International, for use in teaching. It was created by geocoding the ZipCode field of the original table, using OpenRefine and the Geonames.org PostalCodes API. Attributes include those from the original data table ('City', 'ZipCode', 'Clown_Name', and 'Country'), as well attributes added during the geocoding process ('admname1','adm1','adm2','placname','longitude','latitude') and an attribute 'Clown-Na_1' which represents the values in the 'Clown_Name' attribute field after a "Cluster and Edit" operation, performed in OpenRefine to collapse values so that "Co Co" or "Co-Co" both are clustered and edited to become "CoCo" for use in name frequency analysis.
This shapefile was created from the Clowns of America, International Membership Database (anonymized) obtained in 2007 from Clowns of America, International, for use in teaching. It was created by geocoding the ZipCode field of the original table, using OpenRefine and the Geonames.org PostalCodes API. Attributes include those from the original data table ('City', 'ZipCode', 'Clown_Name', and 'Country'), as well attributes added during the geocoding process ('admname1','adm1','adm2','placname','longitude','latitude') and an attribute 'Clown-Na_1' which represents the values in the 'Clown_Name' attribute field after a "Cluster and Edit" operation, performed in OpenRefine to collapse values so that "Co Co" or "Co-Co" both are clustered and edited to become "CoCo" for use in name frequency analysis.
Collection
Software and data produced by Baker Research Group
This page provides data and code to document the referenced paper, which examines four methods by which ground motions can be selected for dynamic seismic response analyses of engineered systems when the underlying seismic hazard is quantified via ground motion simulation rather than empirical ground motion prediction equations. Even with simulation-based seismic hazard, a ground motion selection process is still required in order to extract a small number of time series from the much larger set developed as part of the hazard calculation. Four specific methods are presented for ground motion selection from simulation-based seismic hazard analyses. One of the four methods provides a ‘benchmark’ result (i.e. using all simulated ground motions), enabling the consistency of the other three more efficient selection methods to be addressed.
This page provides data and code to document the referenced paper, which examines four methods by which ground motions can be selected for dynamic seismic response analyses of engineered systems when the underlying seismic hazard is quantified via ground motion simulation rather than empirical ground motion prediction equations. Even with simulation-based seismic hazard, a ground motion selection process is still required in order to extract a small number of time series from the much larger set developed as part of the hazard calculation. Four specific methods are presented for ground motion selection from simulation-based seismic hazard analyses. One of the four methods provides a ‘benchmark’ result (i.e. using all simulated ground motions), enabling the consistency of the other three more efficient selection methods to be addressed.
Collection
Stanford Research Data
Included in this supplement is the R code used to calculate Pliocene changes in EAIS mass and global sea level as function of temperature change based on the LR04 benthic stack using Eq.'s 1 and 2 of Winnick and Caves (2015). We make this code available without restriction for any purpose as long as the original paper is properly cited.
Included in this supplement is the R code used to calculate Pliocene changes in EAIS mass and global sea level as function of temperature change based on the LR04 benthic stack using Eq.'s 1 and 2 of Winnick and Caves (2015). We make this code available without restriction for any purpose as long as the original paper is properly cited.
Collection
Stanford Research Data
The data package contains 10 anonymized datasets of scalp-recorded EEG in MATLAB (.mat) format. Each .mat file contains EEG data from one experimental subject. Data matrices have been preprocessed and are in the form used as input for classification. Dimensionality reduction/PCA has not been performed.
The data package contains 10 anonymized datasets of scalp-recorded EEG in MATLAB (.mat) format. Each .mat file contains EEG data from one experimental subject. Data matrices have been preprocessed and are in the form used as input for classification. Dimensionality reduction/PCA has not been performed.
Collection
VISTA Lab
This site houses sample data and code for the publication, Takemura, H., Wandell, B. A., and Pestilli, F. Ensemble tractography (under review). All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers.
This site houses sample data and code for the publication, Takemura, H., Wandell, B. A., and Pestilli, F. Ensemble tractography (under review). All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers.
Collection
Stanford Research Data
Comparison of magnetic characteristics of three major active regions before and after major flare.
Comparison of magnetic characteristics of three major active regions before and after major flare.
Database topics
Statistical and Numeric Data; Government Information: International and Foreign
Dataset
1 online resource. Digital: data file.
A single downloadable .csv file provides estimates on "violent deaths" from 2004 onwards. The violent deaths indicator combines national level statistics on homicide and data on fatalities occurred in armed conflict. The database covers more than 189 countries and territories and it is kept constantly updated. Estimates on violent deaths between 2007 and 2012 are at the core of the analysis presented in the third and latest edition of the Global Burden of Armed Violence, launched in May 2015. The database combines data from a wide range of sources that report the number of people died in violent events across both conflict and non-conflict settings. Typical sources are, among others, hospitals, mortuaries, police as well as those organizations that document casualties in areas affected by armed conflict.
A single downloadable .csv file provides estimates on "violent deaths" from 2004 onwards. The violent deaths indicator combines national level statistics on homicide and data on fatalities occurred in armed conflict. The database covers more than 189 countries and territories and it is kept constantly updated. Estimates on violent deaths between 2007 and 2012 are at the core of the analysis presented in the third and latest edition of the Global Burden of Armed Violence, launched in May 2015. The database combines data from a wide range of sources that report the number of people died in violent events across both conflict and non-conflict settings. Typical sources are, among others, hospitals, mortuaries, police as well as those organizations that document casualties in areas affected by armed conflict.
Dataset
1 online resource (1 data file, 1 text file (46 pages)) : color illustrations. Digital: data file; Excel. text file; PDF.
The NewSpace Global 2015 Investor Report examines over $10B in across over 400 investments in NewSpace. Over 250 investors and 100 companies are covered in the Report, including case studies of investment in Skybox Imaging, The Climate Corporation, and SpaceX. The DataDive contains the data at the foundation of the report, and the Analysis Guide contains "key takeaways" and conclusions based on the DataDive. Macros must be enabled to view the DataDive.
The NewSpace Global 2015 Investor Report examines over $10B in across over 400 investments in NewSpace. Over 250 investors and 100 companies are covered in the Report, including case studies of investment in Skybox Imaging, The Climate Corporation, and SpaceX. The DataDive contains the data at the foundation of the report, and the Analysis Guide contains "key takeaways" and conclusions based on the DataDive. Macros must be enabled to view the DataDive.
Collection
Stanford Research Data
Although safety standards have reduced fatal head trauma due to single severe head impacts, mild trauma from repeated head exposures may carry risks of long-term chronic changes in the brain’s function and structure. To study the physical sensitivities of the brain to mild head impacts, we developed the first dynamic model of the skull–brain based on in vivo MRI data. We showed that the motion of the brain can be described by a rigid-body with constrained kinematics. We further demonstrated that skull–brain dynamics can be approximated by an under-damped system with a low-frequency resonance at around 15 Hz. Furthermore, from our previous field measurements, we found that head motions in a variety of activities, including contact sports, show a primary frequency of less than 20 Hz. This implies that typical head exposures may drive the brain dangerously close to its mechanical resonance and lead to amplified brain–skull relative motions. Our results suggest a possible cause for mild brain trauma, which could occur due to repetitive low-acceleration head oscillations in a variety of recreational and occupational activities.
Although safety standards have reduced fatal head trauma due to single severe head impacts, mild trauma from repeated head exposures may carry risks of long-term chronic changes in the brain’s function and structure. To study the physical sensitivities of the brain to mild head impacts, we developed the first dynamic model of the skull–brain based on in vivo MRI data. We showed that the motion of the brain can be described by a rigid-body with constrained kinematics. We further demonstrated that skull–brain dynamics can be approximated by an under-damped system with a low-frequency resonance at around 15 Hz. Furthermore, from our previous field measurements, we found that head motions in a variety of activities, including contact sports, show a primary frequency of less than 20 Hz. This implies that typical head exposures may drive the brain dangerously close to its mechanical resonance and lead to amplified brain–skull relative motions. Our results suggest a possible cause for mild brain trauma, which could occur due to repetitive low-acceleration head oscillations in a variety of recreational and occupational activities.
Collection
Stanford Research Data
We develop comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) to reveal the composition and dynamics of specific long noncoding RNA- protein complexes (lncRNPs) in vivo. ChIRP-MS across four ncRNAs nominates protein interactors likely contributing to specific RNA functions. ChIRP-MS of snRNAs discovers an U1-specific link to 3’ RNA processing machinery. Xist, a key lncRNA for X- chromosome inactivation (XCI), interacts with 81 proteins in chromatin modification, nuclear matrix, and RNA remodeling pathways. Xist lncRNP is assembled in two steps coupled with transition from pluripotency to differentiation, and Xist lncRNP is nearly identical in random vs. imprinted XCI. HnrnpK participates in Xist-mediated gene silencing and histone modifications but not Xist localization. Drosophila Split ends homolog Spen interacts via the A-repeat domain of Xist and is further required for gene silencing. Thus, Xist lncRNA engages diverse protein complexes in a modular and developmentally controlled manner to coordinate chromatin spreading and silencing. The supplemental data contain the following: Table S1. Full list of enriched U1/U2 associated proteins and the peptide counts detected in each experiment. Table S2. Sequences of ChIRP probes for the indicated RNAs and Usp9x intron-smFISH probes used in this study. Table S3. Full list of enriched Xist RBPs and the peptide counts detected in each experiment (“-“ indicates respective negative controls). Table S4. Specific vs. non-specific components in Xist lncRNP. Columns C-F indicate the ranks of peptide abundances in each of the indicated ChIRP experiments. Xist-specific interactors (highlighted in yellow) are defined as proteins with Xist ChIRP-MS rank at least twice better than in ChIRP-MS of U1, U2, and 7SK. Known gene repressors are annotated in blue. Non-specific interactors (highlighted in red) are proteins that show rank ratio <2 in Xist ChIRP vs. U1, U2, or 7SK. Many in the latter set are retrieved by all four ncRNAs and should be interpreted with caution in future ChIRP-MS experiments. Table S5. Full list of siRNAs used in this study.
We develop comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) to reveal the composition and dynamics of specific long noncoding RNA- protein complexes (lncRNPs) in vivo. ChIRP-MS across four ncRNAs nominates protein interactors likely contributing to specific RNA functions. ChIRP-MS of snRNAs discovers an U1-specific link to 3’ RNA processing machinery. Xist, a key lncRNA for X- chromosome inactivation (XCI), interacts with 81 proteins in chromatin modification, nuclear matrix, and RNA remodeling pathways. Xist lncRNP is assembled in two steps coupled with transition from pluripotency to differentiation, and Xist lncRNP is nearly identical in random vs. imprinted XCI. HnrnpK participates in Xist-mediated gene silencing and histone modifications but not Xist localization. Drosophila Split ends homolog Spen interacts via the A-repeat domain of Xist and is further required for gene silencing. Thus, Xist lncRNA engages diverse protein complexes in a modular and developmentally controlled manner to coordinate chromatin spreading and silencing. The supplemental data contain the following: Table S1. Full list of enriched U1/U2 associated proteins and the peptide counts detected in each experiment. Table S2. Sequences of ChIRP probes for the indicated RNAs and Usp9x intron-smFISH probes used in this study. Table S3. Full list of enriched Xist RBPs and the peptide counts detected in each experiment (“-“ indicates respective negative controls). Table S4. Specific vs. non-specific components in Xist lncRNP. Columns C-F indicate the ranks of peptide abundances in each of the indicated ChIRP experiments. Xist-specific interactors (highlighted in yellow) are defined as proteins with Xist ChIRP-MS rank at least twice better than in ChIRP-MS of U1, U2, and 7SK. Known gene repressors are annotated in blue. Non-specific interactors (highlighted in red) are proteins that show rank ratio <2 in Xist ChIRP vs. U1, U2, or 7SK. Many in the latter set are retrieved by all four ncRNAs and should be interpreted with caution in future ChIRP-MS experiments. Table S5. Full list of siRNAs used in this study.
Collection
Stanford Research Data
Birds improve vision by stabilizing head position with respect to the body, which is forced up and down during flapping flight. Stabilization is facilitated by compensatory motion of the sophisticated avian head-neck system. While relative head motion has been studied in stationary and walking birds, little is known about how birds accomplish head stabilization during flapping flight. To unravel this, we approximate the avian neck with a linear mass-spring-damper system for vertical displacements, analogous to proven head stabilization models for walking humans. We corroborated the model’s dimensionless natural frequency and damping ratios from high-speed video recordings of Whooper Swans (Cygnus cygnus) flying over a lake. The data show that flap-induced body oscillations can be passively attenuated through the neck by tuning neck stiffness and damping at time scales much slower than the wingbeat. We found that the passive model robustly attenuates large body oscillations, even in response to head mass and gust perturbations. Our proof of principle demonstrates that bio-inspired drones with flapping wings will record better images with a swan-inspired passive camera suspension.
Birds improve vision by stabilizing head position with respect to the body, which is forced up and down during flapping flight. Stabilization is facilitated by compensatory motion of the sophisticated avian head-neck system. While relative head motion has been studied in stationary and walking birds, little is known about how birds accomplish head stabilization during flapping flight. To unravel this, we approximate the avian neck with a linear mass-spring-damper system for vertical displacements, analogous to proven head stabilization models for walking humans. We corroborated the model’s dimensionless natural frequency and damping ratios from high-speed video recordings of Whooper Swans (Cygnus cygnus) flying over a lake. The data show that flap-induced body oscillations can be passively attenuated through the neck by tuning neck stiffness and damping at time scales much slower than the wingbeat. We found that the passive model robustly attenuates large body oscillations, even in response to head mass and gust perturbations. Our proof of principle demonstrates that bio-inspired drones with flapping wings will record better images with a swan-inspired passive camera suspension.
Database topics
Uncategorized
Dataset
1 online resource.
Public datasets related to transportation, land use, the economy, and the environment for the Bay Area.
Public datasets related to transportation, land use, the economy, and the environment for the Bay Area.
Collection
VISTA Lab
This site houses sample data and code for the publication, Takemura, H., Rokem, A., Winawer, J., Yeatman, J.D., Wandell, B. A., and Pestilli, F. A major human white-matter pathway between dorsal and ventral visual cortex. All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers.
This site houses sample data and code for the publication, Takemura, H., Rokem, A., Winawer, J., Yeatman, J.D., Wandell, B. A., and Pestilli, F. A major human white-matter pathway between dorsal and ventral visual cortex. All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers.
Collection
Stanford Research Data
In this supplement we include the data and R code necessary to reproduce our model fit for the eucalypt data from the paper, in two steps: First, constructData.R, which converts raw data into the processed data frames contained in allData.RData. Second, modeling.R, which loads allData.RData and fits the model that we fit in our article. There is a "lightweight version," biasCorrectionNoGrids.zip, which does not include the raw environmental grid data required to execute constructData.R, but does include allData.RData and modeling.R, so modeling.R can still be executed. See the readme file for more details. The data provided in this archive are described in Online Appendix C of Fithian et al. (2014). The presence-only species data are sourced from Atlas of Living Australia and Atlas of NSW Wildlife, Office of Environment and Heritage (OEH), both publicly available. The presence-absence data were downloaded from the Flora Survey Module of the Atlas of NSW Wildlife, Office of Environment and Heritage (OEH), and we thank them for permission to archive the data here. Any further use of these data should cite Fithian et al. (2014) and acknowledge the data sources.
In this supplement we include the data and R code necessary to reproduce our model fit for the eucalypt data from the paper, in two steps: First, constructData.R, which converts raw data into the processed data frames contained in allData.RData. Second, modeling.R, which loads allData.RData and fits the model that we fit in our article. There is a "lightweight version," biasCorrectionNoGrids.zip, which does not include the raw environmental grid data required to execute constructData.R, but does include allData.RData and modeling.R, so modeling.R can still be executed. See the readme file for more details. The data provided in this archive are described in Online Appendix C of Fithian et al. (2014). The presence-only species data are sourced from Atlas of Living Australia and Atlas of NSW Wildlife, Office of Environment and Heritage (OEH), both publicly available. The presence-absence data were downloaded from the Flora Survey Module of the Atlas of NSW Wildlife, Office of Environment and Heritage (OEH), and we thank them for permission to archive the data here. Any further use of these data should cite Fithian et al. (2014) and acknowledge the data sources.
Collection
Multimedia Files for Digital Image Processing Class at Stanford
Visual information plays an important role in almost all areas of our life. Today, much of this information is represented and processed digitally. Digital image processing is ubiquitous, with applications ranging from television to tomography, from photography to printing, from robotics to remote sensing. EE368/CS232 is a graduate-level introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications. We expect to cover topics such as point operations, color processing, image thresholding/segmentation, morphological image processing, image filtering and deconvolution, eigenimages, noise reduction and restoration, scale-space techniques, feature extraction and recognition, image registration, and image matching. Lectures will be complemented by computer exercises where students develop their own image processing algorithms. For the term project, students will have the option of designing and implementing image processing algorithms on an Android mobile device.
Visual information plays an important role in almost all areas of our life. Today, much of this information is represented and processed digitally. Digital image processing is ubiquitous, with applications ranging from television to tomography, from photography to printing, from robotics to remote sensing. EE368/CS232 is a graduate-level introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications. We expect to cover topics such as point operations, color processing, image thresholding/segmentation, morphological image processing, image filtering and deconvolution, eigenimages, noise reduction and restoration, scale-space techniques, feature extraction and recognition, image registration, and image matching. Lectures will be complemented by computer exercises where students develop their own image processing algorithms. For the term project, students will have the option of designing and implementing image processing algorithms on an Android mobile device.
Collection
Multimedia Files for Digital Image Processing Class at Stanford
Visual information plays an important role in almost all areas of our life. Today, much of this information is represented and processed digitally. Digital image processing is ubiquitous, with applications ranging from television to tomography, from photography to printing, from robotics to remote sensing. EE368/CS232 is a graduate-level introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications. We expect to cover topics such as point operations, color processing, image thresholding/segmentation, morphological image processing, image filtering and deconvolution, eigenimages, noise reduction and restoration, scale-space techniques, feature extraction and recognition, image registration, and image matching. Lectures will be complemented by computer exercises where students develop their own image processing algorithms. For the term project, students will have the option of designing and implementing image processing algorithms on an Android mobile device.
Visual information plays an important role in almost all areas of our life. Today, much of this information is represented and processed digitally. Digital image processing is ubiquitous, with applications ranging from television to tomography, from photography to printing, from robotics to remote sensing. EE368/CS232 is a graduate-level introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications. We expect to cover topics such as point operations, color processing, image thresholding/segmentation, morphological image processing, image filtering and deconvolution, eigenimages, noise reduction and restoration, scale-space techniques, feature extraction and recognition, image registration, and image matching. Lectures will be complemented by computer exercises where students develop their own image processing algorithms. For the term project, students will have the option of designing and implementing image processing algorithms on an Android mobile device.
Collection
Stanford Research Data
In this code supplement we offer a Matlab software library that includes: - A function that calculates the optimal shrinkage coefficient in known or unknown noise level. - Scripts that generate each of the figures in this paper. - A script that generates figures similar to Figure 7, comparing AMSE to MSE in various situations.
In this code supplement we offer a Matlab software library that includes: - A function that calculates the optimal shrinkage coefficient in known or unknown noise level. - Scripts that generate each of the figures in this paper. - A script that generates figures similar to Figure 7, comparing AMSE to MSE in various situations.
Collection
Software and data produced by Baker Research Group
Example code and spreadsheets to illustrate the calculations from the paper "Efficient analytical fragility function fitting using dynamic structural analysis."
Example code and spreadsheets to illustrate the calculations from the paper "Efficient analytical fragility function fitting using dynamic structural analysis."
Collection
Software and data produced by Baker Research Group
Example code to illustrate the calculations from the paper "Ground-motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization."
Example code to illustrate the calculations from the paper "Ground-motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization."