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published: 2018-01-11
 
Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evidence supporting causality of the Training Quality causal model. Includes bibliographic information, context-specific text from the reference, and three weighted values; (M1) credibility of reference, (2) causality determined by the author, and (3) analysts confidence level. (M1, M2, and M3) Weight metadata are based on probability language from <a href="https://www.ipcc.ch/ipccreports/tar/vol4/english/index.htm" style="text-decoration: none" >Intergovernmental Panel on Climate Change (IPCC), Climate Change 2001: Synthesis Report</a>. The language can be found in the “Summary for Policymakers” section, in the PDF format. Weight Metadata: LowerBound_Probability, UpperBound_Probability, Qualitative Language 0.99, 1, Virtually Certain 0.9, 0.99, Very Likely 0.66, 0.9, Likely 0.33, 0.66, Medium Likelihood 0.1, 0.33, Unlikely 0.01, 0.1, Very Unlikely 0, 0.01, Extremely Unlikely
keywords: Data-Theoretic; Training; Organization; Probabilistic Risk Assessment; Training Quality; Causal Model; DT-BASE; Bayesian Belief Network; Bayesian Network; Theory-Building
published: 2017-06-16
 
Table S1. Pollen types identified in the BCI and PNSL pollen rain data sets. Pollen types were identified to species when possible and assigned a life form based on descriptions provided in Croat, T.B. (1978). Taxa from BCI and PNSL were assigned a 1 if present in forest census data or a 0 if absent. The relative representation of each taxon has been provided for each extended record and by dry and wet season representation respectively. CA loadings are provided for axes 1 and 2 (Fig. 1).
keywords: pollen; identifications; abundance; data; BCI; PNSL; Panama
published: 2016-06-23
 
This dataset contains hourly traffic estimates (speeds) for individual links of the New York City road network for the years 2010-2013, estimated from New York City Taxis.
keywords: traffic estimates; traffic conditions; New York City
published: 2017-10-11
 
The International Registry of Reproductive Pathology Database is part of pioneering work done by Dr. Kenneth McEntee to comprehensively document thousands of disease cases studies. His large and comprehensive collection of case reports and physical samples was complimented by development of the International Registry of Reproductive Pathology Database in the 1980s. The original FoxPro Database files and a migrated access version were completed by the College of Veterinary Medicine in 2016. Access CSV files were completed by the University of Illinois Library in 2017.
keywords: Animal Pathology; Databases; Veterinary Medicine
published: 2017-11-15
 
Monthly water withdrawal records (total pumpage and per-capita consumption) for the City of Austin, Texas (2000-2014). Data were provided by Austin Water Utility.
keywords: Water use; Water conservation
published: 2017-11-14
 
If you use this dataset, please cite the IJRR data paper (bibtex is below). We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an IMU, and a GPS device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The data is divided into subsets, which can be downloaded individually. Video previews are available on Youtube: https://www.youtube.com/channel/UCOU9e7xxqmL_s4QX6jsGZSw The information below can also be found in the README files provided in the 527 dataset and each of its subsets. The purpose of this document is to assist researchers in using this dataset. Images ====== Raw --- The raw images are stored in the cam0 and cam1 directories in bmp format. They are bayered images that need to be debayered and undistorted before they are used. The camera parameters for these images can be found in camchain-imucam.yaml. Note that the camera intrinsics describe a 1600x1200 resolution image, so the focal length and center pixel coordinates must be scaled by 0.5 before they are used. The distortion coefficients remain the same even for the scaled images. The camera to imu tranformation matrix is also in this file. cam0/ refers to the left camera, and cam1/ refers to the right camera. Rectified --------- Stereo rectified, undistorted, row-aligned, debayered images are stored in the rectified/ directory in the same way as the raw images except that they are in png format. The params.yaml file contains the projection and rotation matrices necessary to use these images. The resolution of these parameters do not need to be scaled as is necessary for the raw images. params.yml ---------- The stereo rectification parameters. R0,R1,P0,P1, and Q correspond to the outputs of the OpenCV stereoRectify function except that 1s and 2s are replaced by 0s and 1s, respectively. R0: The rectifying rotation matrix of the left camera. R1: The rectifying rotation matrix of the right camera. P0: The projection matrix of the left camera. P1: The projection matrix of the right camera. Q: Disparity to depth mapping matrix T_cam_imu: Transformation matrix for a point in the IMU frame to the left camera frame. camchain-imucam.yaml -------------------- The camera intrinsic and extrinsic parameters and the camera to IMU transformation usable with the raw images. T_cam_imu: Transformation matrix for a point in the IMU frame to the camera frame. distortion_coeffs: lens distortion coefficients using the radial tangential model. intrinsics: focal length x, focal length y, principal point x, principal point y resolution: resolution of calibration. Scale the intrinsics for use with the raw 800x600 images. The distortion coefficients do not change when the image is scaled. T_cn_cnm1: Transformation matrix from the right camera to the left camera. Sensors ------- Here, each message in name.csv is described ###rawimus### time # GPS time in seconds message name # rawimus acceleration_z # m/s^2 IMU uses right-forward-up coordinates -acceleration_y # m/s^2 acceleration_x # m/s^2 angular_rate_z # rad/s IMU uses right-forward-up coordinates -angular_rate_y # rad/s angular_rate_x # rad/s ###IMG### time # GPS time in seconds message name # IMG left image filename right image filename ###inspvas### time # GPS time in seconds message name # inspvas latitude longitude altitude # ellipsoidal height WGS84 in meters north velocity # m/s east velocity # m/s up velocity # m/s roll # right hand rotation about y axis in degrees pitch # right hand rotation about x axis in degrees azimuth # left hand rotation about z axis in degrees clockwise from north ###inscovs### time # GPS time in seconds message name # inscovs position covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz m^2 attitude covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz deg^2 velocity covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz (m/s)^2 ###bestutm### time # GPS time in seconds message name # bestutm utm zone # numerical zone utm character # alphabetical zone northing # m easting # m height # m above mean sea level Camera logs ----------- The files name.cam0 and name.cam1 are text files that correspond to cameras 0 and 1, respectively. The columns are defined by: unused: The first column is all 1s and can be ignored. software frame number: This number increments at the end of every iteration of the software loop. camera frame number: This number is generated by the camera and increments each time the shutter is triggered. The software and camera frame numbers do not have to start at the same value, but if the difference between the initial and final values is not the same, it suggests that frames may have been dropped. camera timestamp: This is the cameras internal timestamp of the frame capture in units of 100 milliseconds. PC timestamp: This is the PC time of arrival of the image. name.kml -------- The kml file is a mapping file that can be read by software such as Google Earth. It contains the recorded GPS trajectory. name.unicsv ----------- This is a csv file of the GPS trajectory in UTM coordinates that can be read by gpsbabel, software for manipulating GPS paths. @article{doi:10.1177/0278364917751842, author = {Martin Miller and Soon-Jo Chung and Seth Hutchinson}, title ={The Visual–Inertial Canoe Dataset}, journal = {The International Journal of Robotics Research}, volume = {37}, number = {1}, pages = {13-20}, year = {2018}, doi = {10.1177/0278364917751842}, URL = {https://doi.org/10.1177/0278364917751842}, eprint = {https://doi.org/10.1177/0278364917751842} }
keywords: slam;sangamon;river;illinois;canoe;gps;imu;stereo;monocular;vision;inertial
published: 2017-10-10
 
This dataset contains ground motion data for Newmark Structural Engineering Laboratory (NSEL) Report Series 048, "Modification of ground motions for use in Central North America: Southern Illinois surface ground motions for structural analysis". The data are 20 individual ground motion time history records developed at each of the 10 sites (for a total of 200 ground motions). These accompanying ground motions are developed following the detailed procedure presented in Kozak et al. [2017].
keywords: earthquake engineering; ground motion records; southern Illinois seismic hazard; dynamic structural analysis; conditional mean spectrum
published: 2017-09-28
 
This is the dataset used in the Journal of Ecology publication of the same name. It is a site by species matrix of species relative abundances. The file BH.veg.data.csv contains a site by species matrix of species relative abundance (percent cover across all sampling quadrats within site). Data under the heading Year refers to sampling periods. Year 1 refers to the first set of samples taken between 1997 and 2000, Year 2 refers to the second set taken between 2002 and 2005, Year 3 refers to the third set taken between 2007 and 2010, and Year 4 refers to the fourth set taken between 2012 and 2015. All sites met Critical Trends Assessment Program (CTAP) size criteria of being at least 2 ha in size with a minimum of 500 m2 of suitable sampling area. The data in file BH.site.location.csv contains Public Land Survey System ranges and townships in which specific sites were located. All sites were located within the U.S. state of Illinois. More information about this dataset: Interested parties can request data from the Critical Trends Assessment Program, which was the source for the data on the wetlands in this study. More information on the program and data requests can be obtained by visiting the program webpage. Critical Trends Assessment Program, Illinois Natural History Survey. http://wwx.inhs.illinois.edu/research/ctap/
keywords: biodiversity; biotic homogenization; invasive species; Phalaris arundinacea; plant population and community dynamics; similarity index; wetlands
published: 2017-09-26
 
This file contains the supplemental appendix for the article "Farmer Preferences for Agricultural Soil Carbon Sequestration Schemes" published in Applied Economic Policy and Perspectives (accepted 2017).
keywords: appendix; carbon sequestration; tillage; choice experiment
published: 2017-09-08
 
Transport and MFM data of brickwork artificial spin ice composed of permalloy are included, which are reproductions of the data in an article named "Magnetic response of brickwork artificial spin ice". Transport data represent magnetic response of connected brickwork artificial spin ice, and MFM data represent how both connected and disconnected brickwork artificial spin ice react to external magnetic fields. SEM images of typical samples are included, where individual nanowire leg (island) is approximately 660 nm long and 140 nm wide with a 40 nm thickness. For the transport, each sample was measured in a longitudinal and a transverse geometry. Red curves are the 2500 Oe to -2500 Oe sweeps and the blue curves are -2500 Oe to 2500 Oe sweeps. Transport measurements were taken by using a standard 4-wire technique. Each plot was saved in pdf format.
keywords: Magnetotransport
published: 2017-09-06
 
Spire angle data for sinistral whelks of the family Busyconidae. Data focuses on spire angles, with some data on total shell length. Locality information is present for all modern specimens.
keywords: lightning whelk; sinistral whelk; spire angle; sourcing; Busycon; Cahokia; Spiro
published: 2017-07-29
 
This dataset contains the PartMC-MOSAIC simulations used in the article “Plume-exit modeling to determine cloud condensation nuclei activity of aerosols from residential biofuel combustion”. The data is organized as a set of folders, each folder representing a different scenario modeled. Each folder contains a series of NetCDF files, which are the output of the PartMC-MOSAIC simulation. They contain information on particle and gas properties, both of the biofuel burning plume and background. Input files for PartMC-MOSAIC are also included. This dataset was used during the open review process at Atmospheric Chemistry and Physics (ACP) and supports both the discussion paper and final article.
keywords: CCN; cloud condensation nuclei; activation; supersaturation; biofuel
published: 2017-06-16
 
Table S2. Raw pollen counts and climatic data for each seasonal sampling period. Climatic data reflects the average daily conditions observed over the duration samples were collected (˚C/day, mm/day, MJ/m2/day). Lycopodium counts and counts for each pollen taxon reflect the aggregated pollen sum from four sampling heights.
keywords: pollen; count; climate; data; BCI; PNSL; Panama
published: 2017-06-16
 
Table S3. Mean slope response for each predictive model used in the ecoinformatic analysis. Mean responses are provided for each seasonal and annual pollen data set analyzed from BCI and PNSL and are summarized by life form. Calculated p-values are provided for each model.
keywords: pollen; response; climate; ecoinformatics; BCI; PNSL; Panama
published: 2017-06-15
 
Datasets used in the study, "Optimal completion of incomplete gene trees in polynomial time using OCTAL," presented at WABI 2017.
keywords: phylogenomics; missing data; coalescent-based species tree estimation; gene trees
published: 2017-05-31
 
Dataset includes maternal antigen treatment and early-life antigen treatment for male zebra finches. Also includes data on beak coloration, measures of song complexity for each male, and female responses to treated males. Male beak color and song metadata: * MATID= Maternal Identity * MATTRT=Maternal antigen treatment prior to egg laying (KLH=keyhole limpet hemocyanin, LPS= lipopolysaccharide, PBS=phosphate buffered saline) * YGTRT= Young antigen treatment post-hatch (KLH=keyhole limpet hemocyanin, LPS= lipopolysaccharide, PBS=phosphate buffered saline)) * NESTBANDNUM= Nestling band number * Haptoglobin=haptoglobin levels at day 28 (mg/ml) * Mean TE= Mean number of total elements in that male's song * TE (z)= Z-transformed total elements * Mean UE=Mean number of unique elements in the song * UE (z)= z-transformed unique elements * mean phrases= Mean number of song phrases * Phrases (z)= z-transformed song phrases * Mean D= Mean song duration in seconds * D (z)=z-transformed song duration * B2 standard=beak brightness standardized so that lower values reflect less bright beaks * B2 (z)=z-transformed brightness * S1R standard= beak saturation at high wavelengths standardized so that lower values reflect less red beaks * S1R (z)=z-transformed S1R * S1U standard= beak saturation at low wavelengths standardized so that lower values reflect less red beaks * S1U (z)=z-transformed S1U * H4B standard= beak hue standardized so that lower values reflect less red beaks * H4B (z)=z-transformed H4B Female choice metadata: * Control Bird=PBS denotes that all control males received phosphate buffered saline * Treatment Bird= Treatment the male received (keyhole limpet hemocyanin (KLH) or lipopolysaccharide (LPS)) * Beak Wipes Control=# of beak wipes the female performed when on the control male side * Beak Wipes Treatment=# of beak wipes the female performed when on the "treatment male" side * Hops Control=# of hops female performed when on the control male side * Hops Treatment=# of hops female performed when on the treatment male side * Time Spent Near Control=amount of time (sec) female spent on the control male side * Time Spent Near Treatment=amount of time (sec) the female spent on the treatment male side
keywords: early-life; stress; immune response; phenotypic correlation; sexual signal; zebra finch;birdsongs; acoustic signals; beak coloration; mate selection
published: 2017-05-01
 
Indianapolis Int'l Airport to Urbana: Sampling Rate: 2 Hz Total Travel Time: 5901534 ms or 98.4 minutes Number of Data Points: 11805 Distance Traveled: 124 miles via I-74 Device used: Samsung Galaxy S6 Date Recorded: 2016-11-27 Parameters Recorded: * ACCELEROMETER X (m/s²) * ACCELEROMETER Y (m/s²) * ACCELEROMETER Z (m/s²) * GRAVITY X (m/s²) * GRAVITY Y (m/s²) * GRAVITY Z (m/s²) * LINEAR ACCELERATION X (m/s²) * LINEAR ACCELERATION Y (m/s²) * LINEAR ACCELERATION Z (m/s²) * GYROSCOPE X (rad/s) * GYROSCOPE Y (rad/s) * GYROSCOPE Z (rad/s) * LIGHT (lux) * MAGNETIC FIELD X (microT) * MAGNETIC FIELD Y (microT) * MAGNETIC FIELD Z (microT) * ORIENTATION Z (azimuth °) * ORIENTATION X (pitch °) * ORIENTATION Y (roll °) * PROXIMITY (i) * ATMOSPHERIC PRESSURE (hPa) * SOUND LEVEL (dB) * LOCATION Latitude * LOCATION Longitude * LOCATION Altitude (m) * LOCATION Altitude-google (m) * LOCATION Altitude-atmospheric pressure (m) * LOCATION Speed (kph) * LOCATION Accuracy (m) * LOCATION ORIENTATION (°) * Satellites in range * GPS NMEA * Time since start in ms * Current time in YYYY-MO-DD HH-MI-SS_SSS format Quality Notes: There are some things to note about the quality of this data set that you may want to consider while doing preprocessing. This dataset was taken continuously as a single trip, no stop was made for gas along the way making this a very long continuous dataset. It starts in the parking lot of the Indianapolis International Airport and continues directly towards a gas station on Lincoln Avenue in Urbana, IL. There are a couple parts of the trip where the phones orientation had to be changed because my navigation cut out. These times are easy to account for based on Orientation X/Y/Z change. I would also advise cutting out the first couple hundred points or the points leading up to highway speed. The phone was mounted in the cupholder in the front seat of the car.
keywords: smartphone; sensor; driving; accelerometer; gyroscope; magnetometer; gps; nmea; barometer; satellite
published: 2017-03-08
 
This dataset includes early embryogenesis and post-embryonic development of Soybean cyst nematode.
keywords: Soybean cyst nematode; Embryogenesis; Post-embryonic development
published: 2017-03-07
 
This is a sample 5 minute video of an E coli bacterium swimming in a microfluidic chamber as well as some supplementary code files to be used with the Matlab code available at https://github.com/dfraebel/CellTracking
published: 2017-03-02
 
This data was collected between 2004 and 2010 at White River National Wildlife Refuge (WRNWR) and Saint Francis National Forest (SF). It was collected as part of two master’s and one PhD project at Arkansas State University USA studying Swainson’s Warbler habitat use, survival, and body condition.
keywords: Swainson’s Warbler; Limnothlypis swainsonii; flooding; natural disturbance; apparent survival; body condition
published: 2017-02-28
 
Leesburg, VA to Indianapolis, Indiana: Sampling Rate: 0.1 Hz Total Travel Time: 31100007 ms or 518 minutes or 8.6 hours Distance Traveled: 570 miles via I-70 Number of Data Points: 3112 Device used: Samsung Galaxy S4 Date Recorded: 2017-01-15 Parameters Recorded: * ACCELEROMETER X (m/s²) * ACCELEROMETER Y (m/s²) * ACCELEROMETER Z (m/s²) * GRAVITY X (m/s²) * GRAVITY Y (m/s²) * GRAVITY Z (m/s²) * LINEAR ACCELERATION X (m/s²) * LINEAR ACCELERATION Y (m/s²) * LINEAR ACCELERATION Z (m/s²) * GYROSCOPE X (rad/s) * GYROSCOPE Y (rad/s) * GYROSCOPE Z (rad/s) * LIGHT (lux) * MAGNETIC FIELD X (microT) * MAGNETIC FIELD Y (microT) * MAGNETIC FIELD Z (microT) * ORIENTATION Z (azimuth °) * ORIENTATION X (pitch °) * ORIENTATION Y (roll °) * PROXIMITY (i) * ATMOSPHERIC PRESSURE (hPa) * Relative Humidity (%) * Temperature (F) * SOUND LEVEL (dB) * LOCATION Latitude * LOCATION Longitude * LOCATION Altitude (m) * LOCATION Altitude-google (m) * LOCATION Altitude-atmospheric pressure (m) * LOCATION Speed (kph) * LOCATION Accuracy (m) * LOCATION ORIENTATION (°) * Satellites in range * GPS NMEA * Time since start in ms * Current time in YYYY-MO-DD HH-MI-SS_SSS format Quality Notes: There are some things to note about the quality of this data set that you may want to consider while doing preprocessing. This dataset was taken continuously but had multiple stops to refuel (without the data recording ceasing). This can be removed by parsing out all data that has a speed of 0. The mount for this dataset was fairly stable (as can be seen by the consistent orientation angle throughout the dataset). It was mounted tightly between two seats in the back of the vehicle. Unfortunately, the frequency for this dataset was set fairly low at one per ten seconds.
keywords: smartphone; sensor; driving; accelerometer; gyroscope; magnetometer; gps; nmea; barometer; satellite; temperature; humidity
published: 2017-02-23
 
GBS data from diverse sorghum lines. Project funded by DOE, ARPA-E, and startup funds to PJ Brown.
published: 2017-02-21
 
GBS data from biparental sorghum populations provided by Dr. Bill Rooney, TAMU. Data produced and analyzed by Pradeep Hirannaiah to study recombination in sorghum. Funding for this study was provided by the Sorghum Checkoff.