Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs.

This proximal terrestrial high-throughput plant phenotyping data examples our early tri-metric field method, where a geo-referenced 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this development period, our Proximal Sensing Cart Mark1 (PSCM1) platform suspends a single cluster of sensors on a dual sliding vertical placement armature.

Experimental design and operational details of research conducted are contained in related published articles, however further description of the measured data signals as well as germane commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. We used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product being not only rugged and reliable but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120cm from a titanium dioxide white painted panel, a normalized unity value of 1.0 is set for each detector. To generate this dataset we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

This type of active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality, and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within each view. It does however, not represent a reflection of the plant material solely because it can contain additional features in view. Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are more easy to direct, and in our capture method we target a consistent sensor height that is 1m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically, while onboard the “black-box” device, than are other reflectance products which produce vegetation indices as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version. Fifteen collection events are presented.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost. Multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used, supplying data in a lined format, where variables are repeated for each sensor. This created a discrete data row for each individual sensor measurement instance.

We offer six high-throughput single pixel spectral colors, recorded at 530, 590, 670, 730, 780, and 800nm. The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including an increased noise).

Dual, or tandem approach, CropCircle paired sensor usage empowers additional vegetation index calculations, such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Improved Campbell Scientific data at 5Hz is presented for nine collection events, where thermal, ultrasonic displacement, and additional GPS metrics were recorded. Ultrasonic height metrics generated by the Honeywell sensor and present in this dataset, represent successful phenotypic recordings. The Honeywell ultrasonic displacement sensor has worked well in this application because of its 180Khz signal frequency that ranges 2m space. Air temperature is still a developing metric, a thermocouple wire junction (TC) placed in free air with a solar shade produced a low-confidence passive ambient air temperature.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

There is canopy thermal data from three view vantages. A nadir sensor view, and looking forward and backward down the plant row at a 30 degree angle off nadir. The high confidence Apogee Instruments SI-111 type infrared radiometer, non-contact thermometer, serial number 1022 was in a front position looking forward away from the platform, number 1023 with a nadir view was in middle position, and sensor number 1052 was in a rear position and looking back toward the platform frame. We have a long and successful history testing and benchmarking performance, and deploying Apogee Instruments infrared radiometers in field experimentation. They are biologically spectral window relevant sensors and return a fast update 0.2C accurate average surface temperature, derived from what is (geometrically weighted) in their field of view.

Data gaps do exist beyond null value -9999 designations, there are some instances when GPS signal was lost, or rarely on HS GeoScout logger error. GPS information may be missing at the start of data recording. However once the receiver supplies a signal the values will populate. Likewise there may be missing information at the end of a data collection, where the GPS signal was lost but sensors continue to record along with the data logger timestamping.

In the raw CS data, collections 1 through 7 are represented by only one table file, where the UTC from the GPS

The hurricane risk index is simply the product of the cumulative hurricane strikes per coastal county and the CDC Overall Social Vulnerability Index (SVI) for the given county. We normalize the hurricane strikes data to match the SVI data classification scheme (i.e., max value at 1); however, using the raw or normalized values of hurricane strikes has no impact on the spatial pattern of the risk index. Therefore, a risk index of value 1 indicates the county has the highest hurricane strikes of all the counties and is the most vulnerable county in the nation according to the SVI index. Because the analysis is over multiple states, we use the ‘United States’ SVI dataset at the county level. Values of the index are unevenly distributed so we classify intervals using the Jenks method and the first break at 0.08 is roughly equal to the median index value. For counties north of North Carolina, the low hurricane risk is most dependent on the low number of hurricane strikes. The vast majority of the counties fall in the lowest risk category and any in the second lowest category are there because of high social vulnerability.

This data set represents the average normalized atmospheric (wet) deposition, in kilograms, of Total Inorganic Nitrogen for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. Estimates of Total Inorganic Nitrogen deposition are based on National Atmospheric Deposition Program (NADP) measurements (B. Larsen, U.S. Geological Survey, written commun., 2007). De-trending methods applied to the year 2002 are described in Alexander and others, 2001. NADP site selection met the following criteria: stations must have records from 1995 to 2002 and have a minimum of 30 observations. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Overview

• The Naturalistic Neuroimaging Database (NNDb v2.0) contains datasets from 86 human participants doing the NIH Toolbox and then watching one of 10 full-length movies during functional magnetic resonance imaging (fMRI).The participants were all right-handed, native English speakers, with no history of neurological/psychiatric illnesses, with no hearing impairments, unimpaired or corrected vision and taking no medication. Each movie was stopped in 40-50 minute intervals or when participants asked for a break, resulting in 2-6 runs of BOLD-fMRI. A 10 minute high-resolution defaced T1-weighted anatomical MRI scan (MPRAGE) is also provided.
• The NNDb V2.0 is now on Neuroscout, a platform for fast and flexible re-analysis of (naturalistic) fMRI studies. See: https://neuroscout.org/

v2.0 Changes

• Overview
  • We have replaced our own preprocessing pipeline with that implemented in AFNI’s afni_proc.py, thus changing only the derivative files. This introduces a fix for an issue with our normalization (i.e., scaling) step and modernizes and standardizes the preprocessing applied to the NNDb derivative files. We have done a bit of testing and have found that results in both pipelines are quite similar in terms of the resulting spatial patterns of activity but with the benefit that the afni_proc.py results are 'cleaner' and statistically more robust.

• Normalization

  • Emily Finn and Clare Grall at Dartmouth and Rick Reynolds and Paul Taylor at AFNI, discovered and showed us that the normalization procedure we used for the derivative files was less than ideal for timeseries runs of varying lengths. Specifically, the 3dDetrend flag -normalize makes 'the sum-of-squares equal to 1'. We had not thought through that an implication of this is that the resulting normalized timeseries amplitudes will be affected by run length, increasing as run length decreases (and maybe this should go in 3dDetrend’s help text). To demonstrate this, I wrote a version of 3dDetrend’s -normalize for R so you can see for yourselves by running the following code:

  # Generate a resting state (rs) timeseries (ts)
  # Install / load package to make fake fMRI ts
  # install.packages("neuRosim")
  library(neuRosim)
  # Generate a ts
  ts.rs <- simTSrestingstate(nscan=2000, TR=1, SNR=1)
  # 3dDetrend -normalize
  # R command version for 3dDetrend -normalize -polort 0 which normalizes by making "the sum-of-squares equal to 1"
  # Do for the full timeseries
  ts.normalised.long <- (ts.rs-mean(ts.rs))/sqrt(sum((ts.rs-mean(ts.rs))^2));
  # Do this again for a shorter version of the same timeseries
  ts.shorter.length <- length(ts.normalised.long)/4
  ts.normalised.short <- (ts.rs[1:ts.shorter.length]- mean(ts.rs[1:ts.shorter.length]))/sqrt(sum((ts.rs[1:ts.shorter.length]- mean(ts.rs[1:ts.shorter.length]))^2));
  # By looking at the summaries, it can be seen that the median values become  larger
  summary(ts.normalised.long)
  summary(ts.normalised.short)
  # Plot results for the long and short ts
  # Truncate the longer ts for plotting only
  ts.normalised.long.made.shorter <- ts.normalised.long[1:ts.shorter.length]
  # Give the plot a title
  title <- "3dDetrend -normalize for long (blue) and short (red) timeseries";
  plot(x=0, y=0, main=title, xlab="", ylab="", xaxs='i', xlim=c(1,length(ts.normalised.short)), ylim=c(min(ts.normalised.short),max(ts.normalised.short)));
  # Add zero line
  lines(x=c(-1,ts.shorter.length), y=rep(0,2), col='grey');
  # 3dDetrend -normalize -polort 0 for long timeseries
  lines(ts.normalised.long.made.shorter, col='blue');
  # 3dDetrend -normalize -polort 0 for short timeseries
  lines(ts.normalised.short, col='red');
  

• Standardization/modernization

  • The above individuals also encouraged us to implement the afni_proc.py script over our own pipeline. It introduces at least three additional improvements: First, we now use Bob’s @SSwarper to align our anatomical files with an MNI template (now MNI152_2009_template_SSW.nii.gz) and this, in turn, integrates nicely into the afni_proc.py pipeline. This seems to result in a generally better or more consistent alignment, though this is only a qualitative observation. Second, all the transformations / interpolations and detrending are now done in fewers steps compared to our pipeline. This is preferable because, e.g., there is less chance of inadvertently reintroducing noise back into the timeseries (see Lindquist, Geuter, Wager, & Caffo 2019). Finally, many groups are advocating using tools like fMRIPrep or afni_proc.py to increase standardization of analyses practices in our neuroimaging community. This presumably results in less error, less heterogeneity and more interpretability of results across studies. Along these lines, the quality control (‘QC’) html pages generated by afni_proc.py are a real help in assessing data quality and almost a joy to use.

• New afni_proc.py command line

  • The following is the afni_proc.py command line that we used to generate blurred and censored timeseries files. The afni_proc.py tool comes with extensive help and examples. As such, you can quickly understand our preprocessing decisions by scrutinising the below. Specifically, the following command is most similar to Example 11 for ‘Resting state analysis’ in the help file (see https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html): afni_proc.py \ -subj_id "$sub_id_name_1" \ -blocks despike tshift align tlrc volreg mask blur scale regress \ -radial_correlate_blocks tcat volreg \ -copy_anat anatomical_warped/anatSS.1.nii.gz \ -anat_has_skull no \ -anat_follower anat_w_skull anat anatomical_warped/anatU.1.nii.gz \ -anat_follower_ROI aaseg anat freesurfer/SUMA/aparc.a2009s+aseg.nii.gz \ -anat_follower_ROI aeseg epi freesurfer/SUMA/aparc.a2009s+aseg.nii.gz \ -anat_follower_ROI fsvent epi freesurfer/SUMA/fs_ap_latvent.nii.gz \ -anat_follower_ROI fswm epi freesurfer/SUMA/fs_ap_wm.nii.gz \ -anat_follower_ROI fsgm epi freesurfer/SUMA/fs_ap_gm.nii.gz \ -anat_follower_erode fsvent fswm \ -dsets media_?.nii.gz \ -tcat_remove_first_trs 8 \ -tshift_opts_ts -tpattern alt+z2 \ -align_opts_aea -cost lpc+ZZ -giant_move -check_flip \ -tlrc_base "$basedset" \ -tlrc_NL_warp \ -tlrc_NL_warped_dsets \ anatomical_warped/anatQQ.1.nii.gz \ anatomical_warped/anatQQ.1.aff12.1D \ anatomical_warped/anatQQ.1_WARP.nii.gz \ -volreg_align_to MIN_OUTLIER \ -volreg_post_vr_allin yes \ -volreg_pvra_base_index MIN_OUTLIER \ -volreg_align_e2a \ -volreg_tlrc_warp \ -mask_opts_automask -clfrac 0.10 \ -mask_epi_anat yes \ -blur_to_fwhm -blur_size $blur \ -regress_motion_per_run \ -regress_ROI_PC fsvent 3 \ -regress_ROI_PC_per_run fsvent \ -regress_make_corr_vols aeseg fsvent \ -regress_anaticor_fast \ -regress_anaticor_label fswm \ -regress_censor_motion 0.3 \ -regress_censor_outliers 0.1 \ -regress_apply_mot_types demean deriv \ -regress_est_blur_epits \ -regress_est_blur_errts \ -regress_run_clustsim no \ -regress_polort 2 \ -regress_bandpass 0.01 1 \ -html_review_style pythonic We used similar command lines to generate ‘blurred and not censored’ and the ‘not blurred and not censored’ timeseries files (described more fully below). We will provide the code used to make all derivative files available on our github site (https://github.com/lab-lab/nndb).

  We made one choice above that is different enough from our original pipeline that it is worth mentioning here. Specifically, we have quite long runs, with the average being ~40 minutes but this number can be variable (thus leading to the above issue with 3dDetrend’s -normalise). A discussion on the AFNI message board with one of our team (starting here, https://afni.nimh.nih.gov/afni/community/board/read.php?1,165243,165256#msg-165256), led to the suggestion that '-regress_polort 2' with '-regress_bandpass 0.01 1' be used for long runs. We had previously used only a variable polort with the suggested 1 + int(D/150) approach. Our new polort 2 + bandpass approach has the added benefit of working well with afni_proc.py.

  Which timeseries file you use is up to you but I have been encouraged by Rick and Paul to include a sort of PSA about this. In Paul’s own words: * Blurred data should not be used for ROI-based analyses (and potentially not for ICA? I am not certain about standard practice). * Unblurred data for ISC might be pretty noisy for voxelwise analyses, since blurring should effectively boost the SNR of active regions (and even good alignment won't be perfect everywhere). * For uncensored data, one should be concerned about motion effects being left in the data (e.g., spikes in the data). * For censored data: * Performing ISC requires the users to unionize the censoring patterns during the correlation calculation. * If wanting to calculate power spectra or spectral parameters like ALFF/fALFF/RSFA etc. (which some people might do for naturalistic tasks still), then standard FT-based methods can't be used because sampling is no longer uniform. Instead, people could use something like 3dLombScargle+3dAmpToRSFC, which calculates power spectra (and RSFC params) based on a generalization of the FT that can handle non-uniform sampling, as long as the censoring pattern is mostly random and, say, only up to about 10-15% of the data. In sum, think very carefully about which files you use. If you find you need a file we have not provided, we can happily generate different versions of the timeseries upon request and can generally do so in a week or less.

• Effect on results

  • From numerous tests on our own analyses, we have qualitatively found that results using our old vs the new afni_proc.py preprocessing pipeline do not change all that much in terms of general spatial patterns. There is, however, an

This data set represents the average normalized atmospheric (wet) deposition, in kilograms, of Nitrate (NO3) for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. Estimates of NO3 deposition are based on National Atmospheric Deposition Program (NADP) measurements (B. Larsen, U.S. Geological Survey, written commun., 2007). De-trending methods applied to the year 2002 are described in Alexander and others, 2001. NADP site selection met the following criteria: stations must have records from 1995 to 2002 and have a minimum of 30 observations. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

This data set represents the average normalized atmospheric (wet) deposition, in kilograms, of Ammonium (NH4) for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. Estimates of NH4 deposition are based on National Atmospheric Deposition Program (NADP) measurements (B. Larsen, U.S. Geological Survey, written commun., 2007). De-trending methods applied to the year 2002 are described in Alexander and others, 2001. NADP site selection met the following criteria: stations must have records from 1995 to 2002 and have a minimum of 30 observations. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.