This page provides preliminary data analytics on various advanced reactor dockets. Note that ADAMS assigned multiple docket numbers to some reports. The results presented here should be interpreted with these limitations.
This page is updated Monday through Friday, hourly between 9AM ET and 5 PM ET. The last update was at 2026-01-08 18:17:53.965647 ET.
Pulling Advanced Reactor Docket Files
combined_dockets <- nrcadams::docket_codex |>
dplyr::pull(DocketNumber) |>
nrcadams::search_docket() |>
nrcadams::hyperlink_file_to_name() |>
dplyr::left_join(nrcadams::docket_codex) |>
dplyr::select(-c(Company, DocketNumber, `ML Number`)) |>
dplyr::filter(!is.na(Project)) |>
dplyr::mutate(Project = stringr::str_replace(Project, "Application", "App."))
docket_count <- combined_dockets |>
dplyr::count(Project, sort = TRUE)
docket_count |>
DT::datatable()Docket Plots
Bar Plots
There are too many document types coded by ADAMS to effectively show on a plot (i.e., > 180). As a result, the Type field is binned to facilitate a bar plot of various reactor applications.
library(ggplot2)
docket_types = combined_dockets |>
dplyr::mutate(
Project = factor(Project, levels = docket_count |> dplyr::pull(Project)),
) |>
nrcadams::simplify_type()
type_count = docket_types |>
dplyr::filter(NLWR) |>
dplyr::count(Type, sort = TRUE) |>
dplyr::arrange(n)
plt = docket_types |>
dplyr::mutate(Type = factor(Type, levels = type_count |> dplyr::pull(Type))) |>
ggplot() +
geom_bar(aes(y = Project, fill = Type)) +
theme_bw() +
scale_fill_brewer(palette="Set1") +
labs(
title = "Advanced Reactor ADAMS Documents",
subtitle = paste("as of", Sys.Date())
) +
xlab("Document Count")
plt |> plotly::ggplotly(tooltip = c("Type", "count"))The same data can also be plotted with the bars unstacked.
plt = docket_types |>
dplyr::mutate(Type = factor(Type, levels = type_count |> dplyr::pull(Type))) |>
ggplot() +
geom_bar(aes(y = Project, fill = Type), position = position_dodge()) +
theme_bw() +
scale_fill_brewer(palette="Set1") +
labs(
title = "Advanced Reactor ADAMS Documents",
subtitle = paste("as of", Sys.Date())
) +
xlab("Document Count")
plt |> plotly::ggplotly(tooltip = c("Type", "count"))Time Series
The plot below shows the number of documents submitted to a docket. Plot order is sorted from highest number of documents, NuScale 50 MWe Part 52 to lowest number of documents, USO SFR Owner Pre-Applicatoin.
Cumulative Plots
The cumulative number of documents on a given ADAMS docket is also presented as a function of document date. Due to the large number of plots, it is suggested that you use the interactive plot filter function. By clicking on a project name in the legend, you can remove that project from the plot to better focus on the remaining projects.
plot_min_date = "2020-01-01" |> lubridate::ymd()
grouped_dockets = combined_dockets |>
dplyr::group_by(Project) |>
dplyr::arrange(`Publish Date`) |>
dplyr::mutate(
Project = factor(Project, levels = docket_count |> dplyr::pull(Project)),
count = 1,
`Document Count` = cumsum(count),
`Publish Date` = `Publish Date` |> lubridate::as_date()
)
plt = grouped_dockets |>
dplyr::filter(`Publish Date` > plot_min_date) |>
ggplot() +
geom_step(aes(x = `Publish Date`, y = `Document Count`, color = Project)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(
title = "Advanced Reactor ADAMS Documents",
subtitle = paste("as of", Sys.Date())
) +
scale_y_continuous(
breaks= seq(
0,
max(grouped_dockets$`Document Count`),
by = 200
)
) +
scale_x_continuous(
breaks= seq(
min(plot_min_date),
max(grouped_dockets$`Publish Date` |> lubridate::as_date()) ,
by = "quarter"
)
)The data in linear scale:
plt |> plotly::ggplotly()The data in a y axis semi log scale:
plt = plt + scale_y_log10()
plt |> plotly::ggplotly()Cumulative Plots (By Document Type)
This section is a work in progress
Dockets can often be cluttered by affidavits, meeting notices, and
other documents that can obscure insights. Reclassifying these documents
using the simplify_type function and then removing the
Other type can help see what is really going on in a
docket. Warning, in order to see what is happening in each
docket, the y-axis is allowed to rescale to each docket. This can make
comparing dockets difficult.
grouped_project_type <- combined_dockets |>
nrcadams::simplify_type() |>
dplyr::filter(!Type == "Other") |>
dplyr::group_by(Project, Type) |>
dplyr::arrange(`Publish Date`) |>
dplyr::mutate(
Project = factor(Project, levels = docket_count |> dplyr::pull(Project)),
count = 1,
`Document Count` = cumsum(count),
`Publish Date` = `Publish Date` |> lubridate::as_date()
)
plt <- grouped_project_type |>
dplyr::filter(lubridate::as_date(`Publish Date`) > plot_min_date) |>
ggplot() +
geom_step(aes(x = `Publish Date`, y = `Document Count`, color = Type)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(
title = "Advanced Reactor ADAMS Documents",
subtitle = paste("as of", Sys.Date())
) +
scale_x_continuous(
breaks= seq(
min(plot_min_date),
max(grouped_project_type$`Publish Date` |> lubridate::as_date()) ,
by = "quarter"
)
) +
facet_grid(vars(Project), scales = "free_y")
plt |> plotly::ggplotly()Smoothed Interaction Rates
The daily document rate provides a metric to gauge the level of interactions between a project and the NRC at a given point in time. Unlikely the cumulative plots, this metric is memory-less and thus is not impacted by prior interactions. Due to the large number of plots, it is suggested that you use the interactive plot filter function. By clicking on a project name in the legend, you can remove that project from the plot to better focus on the remaining projects.
smoothed_grouped_dockets = combined_dockets |>
dplyr::arrange(`Publish Date`) |>
dplyr::mutate(
Project = factor(Project, levels = docket_count |> dplyr::pull(Project)),
count = 1,
`Day Published` = paste(
`Publish Date` |> lubridate::year(),
`Publish Date` |> lubridate::month(),
`Publish Date` |> lubridate::day(),
sep = '/'
) |> lubridate::ymd()
) |>
dplyr::group_by(`Day Published`, Project) |>
dplyr::summarise(`Daily Document Count` = sum(count)) |>
dplyr::group_by(Project) |>
tidyr::complete(
`Day Published` = seq.Date(
min(`Day Published`),
max(`Day Published`),
by = "day"
),
fill = list(`Daily Document Count` = 0)
) |>
dplyr::mutate(
`Smoothed Daily Document Rate` = zoo::rollmean(
`Daily Document Count`, k = 30, na.pad = TRUE, align = "center"
)
)
plt = smoothed_grouped_dockets |>
dplyr::filter(`Day Published` > plot_min_date) |>
ggplot() +
geom_step(
aes(x = `Day Published`, y = `Smoothed Daily Document Rate`, color = Project)
) +
theme_bw() +
labs(
title = "Advanced Reactor ADAMS Documents",
subtitle = paste("as of", Sys.Date())
) +
scale_y_continuous(
breaks= seq(
0,
max(smoothed_grouped_dockets$`Smoothed Daily Document Rate`, na.rm = TRUE),
by=1
)
) +
scale_x_date(
breaks= seq(
plot_min_date,
max(smoothed_grouped_dockets$`Day Published`),
by = "year")
)
plt |> plotly::ggplotly()Unfortunately, the data in this plot is a bit clustered so it is hard to pinpoint the trend of any given docket. Facet plots can separate out any given docket to more easily decipher trends.
facet_dockets = smoothed_grouped_dockets |>
dplyr::mutate(
Project = dplyr::case_when(
Project |> stringr::str_detect("Part 50") ~ Project |> stringr::str_replace(" Part 50", ""),
Project |> stringr::str_detect("Part 52") ~ Project |> stringr::str_replace(" Part 52", ""),
Project |> stringr::str_detect("Part 70") ~ Project |> stringr::str_replace(" Part 70", ""),
TRUE ~ Project |> stringr::str_replace(" Pre-App.", "")
)
) |>
tidyr::pivot_longer(
c(`Daily Document Count`, `Smoothed Daily Document Rate`),
names_to = "Plot Type",
values_to = "count"
) |>
dplyr::mutate(
`Plot Type` = dplyr::case_when(
`Plot Type` == "Daily Document Count" ~ "Raw Counts",
TRUE ~ "Smoothed"
),
count = count |> signif(1)
)
plt = facet_dockets |>
dplyr::filter(`Day Published` > plot_min_date) |>
ggplot() +
geom_step(
aes(x = `Day Published`, y = count, color = `Plot Type`)
) +
theme_bw() +
theme(legend.position="top") +
labs(
title = "Advanced Reactor ADAMS Documents",
subtitle = paste("as of", Sys.Date())
) +
scale_y_continuous(
breaks= seq(
0,
6,
by=1
),
limits = c(0,5.5)
) +
scale_x_date(
breaks= seq(
plot_min_date,
max(facet_dockets$`Day Published`),
by = "year")
) +
facet_grid(vars(Project)) +
ylab("Daily Document Count")
plt |> plotly::ggplotly()Docket Statistics
With the basic docket information parsed in previous sections, basic summary statistics can be computed to compare the state of various dockets at a glance.
seconds_per_year = 365 * 24 *60 *60
smoothed_grouped_dockets |>
dplyr::group_by(Project) |>
dplyr::summarise(
`First Date` = min(`Day Published`[`Daily Document Count` > 0]),
`Last Date` = max(`Day Published`[`Daily Document Count` > 0]),
`Total` = sum(`Daily Document Count`),
`Years` = round(lubridate::interval(`First Date`, `Last Date`) |> lubridate::int_length() / seconds_per_year, digits = 2),
`Yearly Rate` = round(Total / Years, digits = 2)
) |>
DT::datatable()Reports
This section is updated once a day and presents the Topical Reports, Technical Reports, Environmental Reports, and Safety Evaluations available on new reactor dockets. The last update was at 2026-01-08 23:19:14.33748. The following dockets are searched:
- MSRR Pre-Application
- MSRR Part 50
- Shine Part 50
- Shine Recycling Pre-Application
- Xe-100 Pre-Application
- TRISO-X Fab. Part 70
- Long Mott Pre-Application
- Long Mott Part 50
- AP300
- Natrium Pre-Application
- Natrium Part 50
- USO SFR Owner Pre-Applicatoin
- UIUC HTGR Pre-Application
- eVinci Pre-Application
- IMSR Pre-Application
- BWRX-300 Pre-Application
- SMR-300 Pre-Application
- NuScale 50 MWe Part 52
- NuScale 77 MWe Pre-Application
- NuScale 77 MWe Part 52
- Carbon Free Power Project
- KP Pre-Application
- Hermes Part 50
- Hermes 2 Part 50
- Hermes 2 Unit 2 Part 50
- Oklo Reactor Pre-Application
- Oklo Fuel Cycle Pre-Application
- Aurora Part 52
- Aalo-1 Pre-Application
- RELLIS ESP Pre-Application
- Natura Pre-Application
- BWRX-300 Clinch River Pre-Application
- BWRX-300 Clinch River Part 50
Topical Reports
The following table summarizes tagged Topical Reports.
Topicals = combined_dockets |>
nrcadams::remove_noisy_files("Topical Report")
Topicals |>
DT::datatable(
caption = "Topical Reports",
filter = list(position = 'top', clear = TRUE, plain = FALSE),
escape = FALSE
)Technical Paper
The following table summarizes tagged Technical Reports.
Technicals = combined_dockets |>
nrcadams::remove_noisy_files("Technical")
Technicals |>
DT::datatable(
caption = "Technical Paper",
filter = list(position = 'top', clear = TRUE, plain = FALSE),
escape = FALSE
)Environmental Reports
The following table summarizes tagged Environmental Reports.
Environmental = combined_dockets |>
nrcadams::remove_noisy_files("Environmental")
Environmental |>
DT::datatable(
caption = "Environmental Reports",
filter = list(position = 'top', clear = TRUE, plain = FALSE),
escape = FALSE
)Safety Analysis Reports
The following table summarizes tagged Safety Analysis Reports.
Safety_Analysis = combined_dockets |>
nrcadams::remove_noisy_files("Safety Analysis")
Safety_Analysis |>
DT::datatable(
caption = "Safety Analysis Documents",
filter = list(position = 'top', clear = TRUE, plain = FALSE),
escape = FALSE
)Safety Evaluations Reports
The following table summarizes tagged Safety Evaluations Reports.
Safety_Evaluation = combined_dockets |>
nrcadams::remove_noisy_files("Safety Evaluation")
Safety_Evaluation |>
DT::datatable(
caption = "Safety Evaluation Documents",
filter = list(position = 'top', clear = TRUE, plain = FALSE),
escape = FALSE
)