In ISCN/SOCDRaHR2: Scripts to read in and harmonize different digital soil data archives
Purpose: This vignette describes the ISCN3 data contributions.
library(tidyverse)
library(SOCDRaH2)
library(ggplot2)
library(kableExtra)
dataDir <- '~/Desktop/datasets/ISCN'
library(ggmap)
library(kableExtra)
dataDir <- '~/Documents/Datasets/ISCN'
knitr::opts_chunk$set(eval=TRUE)
The ISCN database is a compilation of research and survey layer-level soil data and observations.
The most recent archive of ISCN3 is here https://portal-s.edirepository.org/nis/mapbrowse?scope=edi&identifier=360&revision=4
Setup
library(SOCDRaH2)
library(ggplot2)
dataDir <- 'Desktop/datasets/ISCN'
Accessing the data
Description of the tables and columns
Create a table with the table name, column name, and long description similar to what is on the EDI page.
New section for each data contribution
Include both a description of the data sets including counts of layers and sites, histograms and tables of variables, and site map.
List data contributions from largest to smallest.
ISCN is large and will take a minute to download and load.
ISCNv3.3 does not add any new data to ISCN3 however it correct several known data short comings.
ISCN3.ls <- ISCN3_3(data_dir = dataDir) #this will throw an NA coercion warning that you can ignore
ISCN profile (2-D) and layer (3-D) level data comes from the following contributing data sets.
profile_summary <- ISCN3.ls$profile %>%
select(dataset_name_sub, site_name) %>%
unique()%>%
group_by(dataset_name_sub) %>%
tally(name = 'profile_site_count')
layer_summary <- ISCN3.ls$layer %>%
select(dataset_name_sub, site_name) %>%
unique()%>%
group_by(dataset_name_sub) %>%
tally(name = 'layer_site_count')
table_summary <- ISCN3.ls$study %>%
select(dataset_name, `dataset_type (dataset_type)`, dataset_description) %>%
unique() %>%
right_join(profile_summary %>%
full_join(layer_summary, by = 'dataset_name_sub'), by=c('dataset_name' = 'dataset_name_sub'))
knitr::kable(table_summary %>%
arrange(layer_site_count),
caption = 'Site counts for each contributing dataset submision (dataname_sub) in the profile and layer data table.')%>%
kable_styling()
Data Contributions
University of Michigan Biological Station Forest Accelerated Succession ExperimenT (UMBS_FASET)
temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'UMBS_FASET')
The University of Michigan Biological Station Forest Accelerated Succession Experiment (UMBS_FASET) data set is uncited and unpublished.
study <- ISCN3.ls$study %>%
filter(dataset_name == "UMBS_FASET") %>% #pull the dataset of interest
select(where(~any(!is.na(.)))) %>% #take only columns with non-missing values
t() #flip the columns and rows
profile <- ISCN3.ls$profile %>%
filter(dataset_name_sub == "UMBS_FASET")%>%
select(where(~any(!is.na(.))))
layer <- ISCN3.ls$layer %>%
filter(dataset_name_sub == "UMBS_FASET") #%>%
select(where(~any(!is.na(.))))
locations <- profile %>%
select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`) %>%
bind_rows(layer %>%
select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`)) %>%
unique() %>%
filter(is.finite(`lat (dec. deg)` + `long (dec. deg)`))
#Check to see if there is different/new information in the layer table that is not in the profile
#profile_info_layer <- layer %>%
# select(any_of(setdiff(names(profile), c("layer_top (cm)", "layer_bot (cm)", "soc (g cm-2)")))) %>%
# unique()
#
#series <- profile_info_layer %>%
# select(profile_name, soil_taxon, soil_series) %>%
# unique() %>%
# anti_join(profile)
knitr::kable(study,
caption = 'Citations for the UMBS_FASET data set.') %>%
kable_styling()
This data set is located in the United States and has r nrow(layer) layer level observations and r nrow(profile) profile-level observations of r nrow(locations)` unique latitude-longitude location.
ggplot(locations) +
geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) +
geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) +
theme(axis.title = element_blank())
#remove data from the layer that appears in the profile that is not the top/bottom/soc
#select all numerical data, pivot and histogram
#select all factor data and tally
<<<<<<< HEAD
origin/KTB_dev
Fixed issues
Below is a list of know issues that were addressed between ISCN 3 and ISCN 3.3
- The UMBS FASET data set contains
r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "UMBS_FASET")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "UMBS_FASET")) profile-level information rows after cleaning.
ISCN SOC computation soil organic carbon calculations were removed and duplicate rows deleted.- Values in the
hzn column of the layer table were reassigned to an NA character from "?", a nonspecific character.
- Values in
country (country) column were reassigned to "United States" in the layer and profile tables because it was the known location of the collection state.
Future issues
- Citation practices were initially incompatible with CC-BY license for ISCN3; contacting data providers about modifying release for ISCN4
- Check and see if data is still being collected at this research site
Figrues
The soil data in this data set were sampled from Michigan (see figure below)
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>%
kable_styling()
The layer data shows
temp.outputs$layerTable%>%
kable_styling()
The profile data shows
temp.outputs$profileTable%>%
kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- ISCN SOC computations were removed to elminate gapfilled values.
- Layer level horizon information (`hzn`) were reassigned to `NA` from `"?"`.
- Layer and profile level country identifiers (`country (country)`) were reassigned to `"United States"` from an empty string.
- Citations were also updated.
A citation was pulled regarding the unpublished dataset and placed in the bibliography but should be revisted in ISCN 4.
Heckman lithosequence
datasetName <- 'Heckman lithosequence'
study <- ISCN3.ls$study %>%
filter(dataset_name == datasetName) %>%
select(where(~any(!is.na(.)))) %>%
t()
#comparison for pre ISCN soc stock correction
# dataset_profile_temp <- profile_raw %>%
# filter(dataset_name_sub == datasetName) %>%
# mutate(`country (country)` = 'United States') %>%
# standardCast()
profile <- ISCN3.ls$profile %>%
filter(dataset_name_sub == datasetName)%>%
select(where(~any(!is.na(.))))
#comparison before SOC correction
# dataset_layer_temp <- layer_raw %>%
# filter(dataset_name_sub == datasetName) %>%
# standardCast() %>%
# mutate(`country (country)` = 'United States')
layer <- ISCN3.ls$layer %>%
filter(dataset_name_sub == datasetName) %>%
## Heckman lithosequence
The Heckman lithosequence references @Heckman2009.
```r
study <- ISCN3.ls$study %>%
filter(dataset_name == "Heckman lithosequence") %>%
select(where(~any(!is.na(.)))) %>%
t()
profile <- ISCN3.ls$profile %>%
filter(dataset_name_sub == "Heckman lithosequence")%>%
select(where(~any(!is.na(.))))
layer <- ISCN3.ls$layer %>%
filter(dataset_name_sub == "Heckman lithosequence") %>%
select(where(~any(!is.na(.))))
locations <- profile %>%
select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`) %>%
bind_rows(layer %>%
select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`)) %>%
unique() %>%
filter(is.finite(`lat (dec. deg)` + `long (dec. deg)`))
#Check to see if there is different/new information in the layer table that is not in the profile
#profile_info_layer <- layer %>%
# select(any_of(setdiff(names(profile), c("layer_top (cm)", "layer_bot (cm)", "soc (g cm-2)")))) %>%
# unique()
#
#series <- profile_info_layer %>%
# select(profile_name, soil_taxon, soil_series) %>%
# unique() %>%
# anti_join(profile)
````
The `Heckman lithosequence` data set in ISCN3 contains `r nrow(dataset_layer)` layer-level information and `r nrow(dataset_profile)` profile-level information after cleaning for ISCN3.5.
In ISCN3 the country was not set to United states originally dispite the state being identified as Arizona, ISCN3.5 now identifies this as `'United States'`.
ISCN3 also filled in the soil carbon stocks where bulk density and organic carbon was provided, this was removed for ISCN3.5.
=======
```r
knitr::kable(study,
caption = 'Citations for the Heckman lithosequence data set.') %>%
kable_styling()
ISCN3_datareport
ggplot(locations) +
geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) +
geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) +
theme(axis.title = element_blank())
Figures
The soil data in this data set were sampled from Arizona (see figure below)
knitr::kable(study,
caption = 'Citations for the Heckman lithosequence data set.') %>%
kable_styling()
Changes from ISCN3 to ISCN3.3
The Heckman lithosequence data set in ISCN3 contains r nrow(dataset_layer) layer-level information and r nrow(dataset_profile) profile-level information after cleaning for ISCN3.5.
In ISCN3 the country was not set to United states originally dispite the state being identified as Arizona, ISCN3.5 now identifies this as 'United States'.
ISCN3 also filled in the soil carbon stocks where bulk density and organic carbon was provided, this was removed for ISCN3.5.
ggplot(locations) +
geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) +
geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) +
theme(axis.title = element_blank())
Figures
The soil data in this data set were sampled from Arizona (see figure below)
There are the following factors in the profile:
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>%
kable_styling()
The layer data shows
temp.outputs$layerTable%>%
kable_styling()
The profile data shows
temp.outputs$profileTable%>%
kable_styling()
The histograms for the data set show
knitr::kable(study,
caption = 'Citations for the Heckman lithosequence data set.') %>%
kable_styling()
This data set is located in the United States and has r nrow(layer) layer level observations and r nrow(profile) profile-level observations of r nrow(locations) unique latitude-longitude location.
ggplot(locations) +
geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) +
geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) +
theme(axis.title = element_blank())
#remove data from the layer that appears in the profile that is not the top/bottom/soc
#select all numerical data, pivot and histogram
#select all factor data and tally
Fixed issues
Below is a list of know issues that were addressed between ISCN 3 and ISCN 3.3
- The
Heckman lithosequence data set in ISCN3 contains r nrow(layer) layer-level information and r nrow(profile) profile-level information after cleaning for ISCN3.5.
- In ISCN3 the country was not set to United states originally despite the state being identified as Arizona, ISCN3.5 now identifies this as
'United States'.
- ISCN3 also filled in the soil carbon stocks where bulk density and organic carbon was provided, this was removed for ISCN3.5.
Future issues
Figrues
The soil data in this data set were sampled from Arizona (see figure below)
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>%
kable_styling()
The layer data shows
temp.outputs$layerTable%>%
kable_styling()
The profile data shows
temp.outputs$profileTable%>%
kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Myers Smith
#temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'Myers-Smith')
The Myers Smith data set is published and cited.
See @MyersSmithHarden2007 @MyersSmithMcGuire2007 for additional details
study <- ISCN3.ls$study %>%
select(dataset_name, citation) %>%
filter(dataset_name == "Myers-Smith")
knitr::kable(study,
caption = 'Citations for the Myers Smith data set.')%>%
kable_styling()
The Myers Smith data set contains r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "Myers-Smith")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "Myers-Smith")) profile-level information rows after cleaning.
Figrues
The soil data in this data set were sampled from Alaska (see figure below)
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>%
kable_styling()
The layer data shows
temp.outputs$layerTable%>%
kable_styling()
The profile data shows
temp.outputs$profileTable%>%
kableExtra::kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- ISCN SOC computations were removed to elminate gapfilled values.
- Citations were also updated.
Oak Ridge National Lab_Loblolly DWJ
temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'Oak Ridge National Lab_Loblolly_DWJ')
The Oak Ridge National Lab Loblolly DWJ data set is published and cited.
See @Parr2006 for additional details.
study <- ISCN3.ls$study %>%
select(dataset_name, citation) %>%
filter(dataset_name == "Oak Ridge National Lab_Loblolly_DWJ")
knitr::kable(study,
caption = 'Citations for the Oak Ridge National Lab Loblolly DWJ data set.')%>%
kable_styling()
The Oak Ridge National Lab Loblolly DWJ data set contains r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "Oak Ridge National Lab_Loblolly_DWJ")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "Oak Ridge National Lab_Loblolly_DWJ")) profile-level information rows after cleaning.
The data set does not contain profile level information therefore the profile information graphs are omitted.
The soil data in this data set were sampled from Tennessee (see figure below)
print(temp.outputs$layerMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>%
kable_styling()
The layer data shows
temp.outputs$layerTable%>%
kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- ISCN SOC computations were removed to elminate gapfilled values.
- Layer and profile level country identifiers (`country (country)`) were reassigned to `"United States"` from an empty string.
- Layer level horizon information (`hzn`) were reassigned to `NA` from `"?"`.
- gapfilled bd with sampling were reasigned to "NA" based on method notes.
- Citations were also updated.
Oak Ridge National Lab TDE
temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'Oak Ridge National Lab_TDE')
The Oak Ridge National Lab TDE data set is published and cited.
See @Froberg2008, https://search.dataone.org/view/doi%3A10.3334%2FCDIAC%2FVRC.002, and
http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp078a/ndp078a.html for additional details
study <- ISCN3.ls$study %>%
select(dataset_name, citation) %>%
filter(dataset_name == "Oak Ridge National Lab_TDE")
knitr::kable(study,
caption = 'Citations for the Oak Ridge National Lab TDE data set.')%>%
kable_styling()
The Oak Ridge National Lab TDE data set contains r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "Oak Ridge National Lab_TDE")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "Oak Ridge National Lab_TDE")) profile-level information rows after cleaning.
The data set does not contain profile level information therefore the profile information graphs are omitted.
Figrues
The soil data in this data set were sampled from Tennessee (see figure below)
print(temp.outputs$layerMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>%
kable_styling()
The layer data shows
temp.outputs$layerTable%>%
kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- Empty columns were removed
- Cryptic `site_note` information in the profile level was also replaced with a more formal citation.
- Citations were also updated.
Citations and bibliography
Create a table with each dataset and citation, repeat the dataset name as needed for each citation.
#need to gap fill these or find the gap filled ones
study <- ISCN3.ls$study %>%
select(dataset_name, citation)
knitr::kable(study,
caption = 'Citations for each contributing dataset submision (dataname_sub).')%>%
kable_styling()
ISCN/SOCDRaHR2 documentation built on May 26, 2023, 6:44 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Purpose: This vignette describes the ISCN3 data contributions.
library(tidyverse) library(SOCDRaH2) library(ggplot2) library(kableExtra) dataDir <- '~/Desktop/datasets/ISCN' library(ggmap) library(kableExtra) dataDir <- '~/Documents/Datasets/ISCN' knitr::opts_chunk$set(eval=TRUE)
The ISCN database is a compilation of research and survey layer-level soil data and observations. The most recent archive of ISCN3 is here https://portal-s.edirepository.org/nis/mapbrowse?scope=edi&identifier=360&revision=4
Setup
library(SOCDRaH2) library(ggplot2) dataDir <- 'Desktop/datasets/ISCN'
Accessing the data
Description of the tables and columns
Create a table with the table name, column name, and long description similar to what is on the EDI page.
New section for each data contribution
Include both a description of the data sets including counts of layers and sites, histograms and tables of variables, and site map. List data contributions from largest to smallest.
ISCN is large and will take a minute to download and load. ISCNv3.3 does not add any new data to ISCN3 however it correct several known data short comings.
ISCN3.ls <- ISCN3_3(data_dir = dataDir) #this will throw an NA coercion warning that you can ignore
ISCN profile (2-D) and layer (3-D) level data comes from the following contributing data sets.
profile_summary <- ISCN3.ls$profile %>% select(dataset_name_sub, site_name) %>% unique()%>% group_by(dataset_name_sub) %>% tally(name = 'profile_site_count') layer_summary <- ISCN3.ls$layer %>% select(dataset_name_sub, site_name) %>% unique()%>% group_by(dataset_name_sub) %>% tally(name = 'layer_site_count') table_summary <- ISCN3.ls$study %>% select(dataset_name, `dataset_type (dataset_type)`, dataset_description) %>% unique() %>% right_join(profile_summary %>% full_join(layer_summary, by = 'dataset_name_sub'), by=c('dataset_name' = 'dataset_name_sub')) knitr::kable(table_summary %>% arrange(layer_site_count), caption = 'Site counts for each contributing dataset submision (dataname_sub) in the profile and layer data table.')%>% kable_styling()
Data Contributions
University of Michigan Biological Station Forest Accelerated Succession ExperimenT (UMBS_FASET)
temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'UMBS_FASET')
The University of Michigan Biological Station Forest Accelerated Succession Experiment (UMBS_FASET) data set is uncited and unpublished.
study <- ISCN3.ls$study %>% filter(dataset_name == "UMBS_FASET") %>% #pull the dataset of interest select(where(~any(!is.na(.)))) %>% #take only columns with non-missing values t() #flip the columns and rows profile <- ISCN3.ls$profile %>% filter(dataset_name_sub == "UMBS_FASET")%>% select(where(~any(!is.na(.)))) layer <- ISCN3.ls$layer %>% filter(dataset_name_sub == "UMBS_FASET") #%>% select(where(~any(!is.na(.)))) locations <- profile %>% select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`) %>% bind_rows(layer %>% select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`)) %>% unique() %>% filter(is.finite(`lat (dec. deg)` + `long (dec. deg)`)) #Check to see if there is different/new information in the layer table that is not in the profile #profile_info_layer <- layer %>% # select(any_of(setdiff(names(profile), c("layer_top (cm)", "layer_bot (cm)", "soc (g cm-2)")))) %>% # unique() # #series <- profile_info_layer %>% # select(profile_name, soil_taxon, soil_series) %>% # unique() %>% # anti_join(profile)
knitr::kable(study, caption = 'Citations for the UMBS_FASET data set.') %>% kable_styling()
This data set is located in the United States and has r nrow(layer) layer level observations and r nrow(profile) profile-level observations of r nrow(locations)` unique latitude-longitude location.
ggplot(locations) + geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) + geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) + theme(axis.title = element_blank()) #remove data from the layer that appears in the profile that is not the top/bottom/soc #select all numerical data, pivot and histogram #select all factor data and tally
<<<<<<< HEAD
origin/KTB_dev
Fixed issues
Below is a list of know issues that were addressed between ISCN 3 and ISCN 3.3
- The UMBS FASET data set contains
r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "UMBS_FASET"))layer-level rows andr nrow(filter(ISCN3.ls$profile, dataset_name_sub == "UMBS_FASET"))profile-level information rows after cleaning. ISCN SOC computationsoil organic carbon calculations were removed and duplicate rows deleted.- Values in the
hzncolumn of thelayertable were reassigned to an NA character from "?", a nonspecific character. - Values in
country (country)column were reassigned to "United States" in thelayerandprofiletables because it was the known location of the collection state.
Future issues
- Citation practices were initially incompatible with CC-BY license for ISCN3; contacting data providers about modifying release for ISCN4
- Check and see if data is still being collected at this research site
Figrues
The soil data in this data set were sampled from Michigan (see figure below)
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>% kable_styling()
The layer data shows
temp.outputs$layerTable%>% kable_styling()
The profile data shows
temp.outputs$profileTable%>% kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- ISCN SOC computations were removed to elminate gapfilled values. - Layer level horizon information (`hzn`) were reassigned to `NA` from `"?"`. - Layer and profile level country identifiers (`country (country)`) were reassigned to `"United States"` from an empty string. - Citations were also updated.
A citation was pulled regarding the unpublished dataset and placed in the bibliography but should be revisted in ISCN 4.
Heckman lithosequence
datasetName <- 'Heckman lithosequence' study <- ISCN3.ls$study %>% filter(dataset_name == datasetName) %>% select(where(~any(!is.na(.)))) %>% t() #comparison for pre ISCN soc stock correction # dataset_profile_temp <- profile_raw %>% # filter(dataset_name_sub == datasetName) %>% # mutate(`country (country)` = 'United States') %>% # standardCast() profile <- ISCN3.ls$profile %>% filter(dataset_name_sub == datasetName)%>% select(where(~any(!is.na(.)))) #comparison before SOC correction # dataset_layer_temp <- layer_raw %>% # filter(dataset_name_sub == datasetName) %>% # standardCast() %>% # mutate(`country (country)` = 'United States') layer <- ISCN3.ls$layer %>% filter(dataset_name_sub == datasetName) %>% ## Heckman lithosequence The Heckman lithosequence references @Heckman2009. ```r study <- ISCN3.ls$study %>% filter(dataset_name == "Heckman lithosequence") %>% select(where(~any(!is.na(.)))) %>% t() profile <- ISCN3.ls$profile %>% filter(dataset_name_sub == "Heckman lithosequence")%>% select(where(~any(!is.na(.)))) layer <- ISCN3.ls$layer %>% filter(dataset_name_sub == "Heckman lithosequence") %>% select(where(~any(!is.na(.)))) locations <- profile %>% select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`) %>% bind_rows(layer %>% select(`lat (dec. deg)`, `long (dec. deg)`, `datum (datum)`)) %>% unique() %>% filter(is.finite(`lat (dec. deg)` + `long (dec. deg)`)) #Check to see if there is different/new information in the layer table that is not in the profile #profile_info_layer <- layer %>% # select(any_of(setdiff(names(profile), c("layer_top (cm)", "layer_bot (cm)", "soc (g cm-2)")))) %>% # unique() # #series <- profile_info_layer %>% # select(profile_name, soil_taxon, soil_series) %>% # unique() %>% # anti_join(profile) ```` The `Heckman lithosequence` data set in ISCN3 contains `r nrow(dataset_layer)` layer-level information and `r nrow(dataset_profile)` profile-level information after cleaning for ISCN3.5. In ISCN3 the country was not set to United states originally dispite the state being identified as Arizona, ISCN3.5 now identifies this as `'United States'`. ISCN3 also filled in the soil carbon stocks where bulk density and organic carbon was provided, this was removed for ISCN3.5. ======= ```r knitr::kable(study, caption = 'Citations for the Heckman lithosequence data set.') %>% kable_styling()
ISCN3_datareport
ggplot(locations) + geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) + geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) + theme(axis.title = element_blank())
Figures
The soil data in this data set were sampled from Arizona (see figure below)
knitr::kable(study, caption = 'Citations for the Heckman lithosequence data set.') %>% kable_styling()
Changes from ISCN3 to ISCN3.3
The Heckman lithosequence data set in ISCN3 contains r nrow(dataset_layer) layer-level information and r nrow(dataset_profile) profile-level information after cleaning for ISCN3.5.
In ISCN3 the country was not set to United states originally dispite the state being identified as Arizona, ISCN3.5 now identifies this as 'United States'.
ISCN3 also filled in the soil carbon stocks where bulk density and organic carbon was provided, this was removed for ISCN3.5.
ggplot(locations) + geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) + geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) + theme(axis.title = element_blank())
Figures
The soil data in this data set were sampled from Arizona (see figure below)
There are the following factors in the profile:
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>% kable_styling()
The layer data shows
temp.outputs$layerTable%>% kable_styling()
The profile data shows
temp.outputs$profileTable%>% kable_styling()
The histograms for the data set show
knitr::kable(study, caption = 'Citations for the Heckman lithosequence data set.') %>% kable_styling()
This data set is located in the United States and has r nrow(layer) layer level observations and r nrow(profile) profile-level observations of r nrow(locations) unique latitude-longitude location.
ggplot(locations) + geom_polygon(data = map_data('world'), aes(x=long, y = lat, group = group)) + geom_point(aes(x=`long (dec. deg)`, y = `lat (dec. deg)`), color = 'red', size = 3) + theme(axis.title = element_blank()) #remove data from the layer that appears in the profile that is not the top/bottom/soc #select all numerical data, pivot and histogram #select all factor data and tally
Fixed issues
Below is a list of know issues that were addressed between ISCN 3 and ISCN 3.3
- The
Heckman lithosequencedata set in ISCN3 containsr nrow(layer)layer-level information andr nrow(profile)profile-level information after cleaning for ISCN3.5. - In ISCN3 the country was not set to United states originally despite the state being identified as Arizona, ISCN3.5 now identifies this as
'United States'. - ISCN3 also filled in the soil carbon stocks where bulk density and organic carbon was provided, this was removed for ISCN3.5.
Future issues
Figrues
The soil data in this data set were sampled from Arizona (see figure below)
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>% kable_styling()
The layer data shows
temp.outputs$layerTable%>% kable_styling()
The profile data shows
temp.outputs$profileTable%>% kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Myers Smith
#temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'Myers-Smith')
The Myers Smith data set is published and cited. See @MyersSmithHarden2007 @MyersSmithMcGuire2007 for additional details
study <- ISCN3.ls$study %>% select(dataset_name, citation) %>% filter(dataset_name == "Myers-Smith") knitr::kable(study, caption = 'Citations for the Myers Smith data set.')%>% kable_styling()
The Myers Smith data set contains r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "Myers-Smith")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "Myers-Smith")) profile-level information rows after cleaning.
Figrues
The soil data in this data set were sampled from Alaska (see figure below)
print(temp.outputs$layerMap)
The profile level map shows
print(temp.outputs$profileMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>% kable_styling()
The layer data shows
temp.outputs$layerTable%>% kable_styling()
The profile data shows
temp.outputs$profileTable%>% kableExtra::kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- ISCN SOC computations were removed to elminate gapfilled values. - Citations were also updated.
Oak Ridge National Lab_Loblolly DWJ
temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'Oak Ridge National Lab_Loblolly_DWJ')
The Oak Ridge National Lab Loblolly DWJ data set is published and cited. See @Parr2006 for additional details.
study <- ISCN3.ls$study %>% select(dataset_name, citation) %>% filter(dataset_name == "Oak Ridge National Lab_Loblolly_DWJ") knitr::kable(study, caption = 'Citations for the Oak Ridge National Lab Loblolly DWJ data set.')%>% kable_styling()
The Oak Ridge National Lab Loblolly DWJ data set contains r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "Oak Ridge National Lab_Loblolly_DWJ")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "Oak Ridge National Lab_Loblolly_DWJ")) profile-level information rows after cleaning.
The data set does not contain profile level information therefore the profile information graphs are omitted.
The soil data in this data set were sampled from Tennessee (see figure below)
print(temp.outputs$layerMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>% kable_styling()
The layer data shows
temp.outputs$layerTable%>% kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- ISCN SOC computations were removed to elminate gapfilled values. - Layer and profile level country identifiers (`country (country)`) were reassigned to `"United States"` from an empty string. - Layer level horizon information (`hzn`) were reassigned to `NA` from `"?"`. - gapfilled bd with sampling were reasigned to "NA" based on method notes. - Citations were also updated.
Oak Ridge National Lab TDE
temp.outputs <- plotISCN3(ISCN3 = ISCN3.ls, 'Oak Ridge National Lab_TDE')
The Oak Ridge National Lab TDE data set is published and cited. See @Froberg2008, https://search.dataone.org/view/doi%3A10.3334%2FCDIAC%2FVRC.002, and http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp078a/ndp078a.html for additional details
study <- ISCN3.ls$study %>% select(dataset_name, citation) %>% filter(dataset_name == "Oak Ridge National Lab_TDE") knitr::kable(study, caption = 'Citations for the Oak Ridge National Lab TDE data set.')%>% kable_styling()
The Oak Ridge National Lab TDE data set contains r nrow(filter(ISCN3.ls$layer, dataset_name_sub == "Oak Ridge National Lab_TDE")) layer-level rows and r nrow(filter(ISCN3.ls$profile, dataset_name_sub == "Oak Ridge National Lab_TDE")) profile-level information rows after cleaning.
The data set does not contain profile level information therefore the profile information graphs are omitted.
Figrues
The soil data in this data set were sampled from Tennessee (see figure below)
print(temp.outputs$layerMap)
A summary of the data set and its contacts are shown below.
temp.outputs$summaryStudyTable%>% kable_styling()
The layer data shows
temp.outputs$layerTable%>% kable_styling()
The histograms for the data set show
print(temp.outputs$profileValueHistograms)
The layer depth plots show
print(temp.outputs$depthValuePlots)
Changes from ISCN3 to ISCN3.3
- Empty columns were removed - Cryptic `site_note` information in the profile level was also replaced with a more formal citation. - Citations were also updated.
Citations and bibliography
Create a table with each dataset and citation, repeat the dataset name as needed for each citation.
#need to gap fill these or find the gap filled ones study <- ISCN3.ls$study %>% select(dataset_name, citation) knitr::kable(study, caption = 'Citations for each contributing dataset submision (dataname_sub).')%>% kable_styling()
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