NOTES
1. Before beginning this tutorial, take this brief questionnaire to get a
template that is customized for your dataset.
2. Important companion information for this template is available
online:
a. data contribution template: a guide to its structure and variable
names
b. glossary of terms for variables with controlled vocabularies
*Inside the template file, the top row of each worksheet contains
internal database names for the variables and must not be removed.
The second row has common names for the variables; these are not
important for automatic processing. You may delete any columns that
you don’t end up using from the template. Finally, depending on how
you customize your template, the variables in this example template
may not be located in the same columns as in your template.
TO GET STARTED
1. Open your customized template file and select the ‘layer’
worksheet.
2. Follow this example dataset, which uses data from 12 samples
that are the individually collected soil layers from 3 soil profiles
at the University of Michigan Biological Station. (sample = layer)
3. If at any time you require help with this process, or would like to
provide feedback, contact ISCN Support.
Did you choose the right variables for %C and BD?
Note the difference between columns G&I.
1.
Hide all but the most basic columns involved in initial data entry (i.e., sample
ID’s, layer depths and horizon names, %C, bulk density)
2.
For each sample, enter your own unique ID in the layer_number column.
3.
Enter the depth values for the top and bottom of each layer. We prefer layers
from complete profiles, where the top of the O horizon = 0 cm. In this example,
there are no depth values for the O nor A horizons because these were removed
as intact monoliths before sampling the underlying mineral soil increments by
depth. Later slides will demonstrate how to compute total profile C contents for
these profiles, which due to their lack of depth measurements on the O and A
horizons must be treated as special cases and computed manually. (The
database automatically sums layer SOC contents up to their full-profile values
for profiles that are complete and have fully contiguous layers)
4.
For each sample (layer), enter the %C and bulk density (you should have
selected the appropriate type of %C and bulk density for your data during the
template customization questionnaire).
5.
Enter horizon designations if appropriate. In this example, O+A horizons were
collected as monoliths and then separated by horizon, so their horizon
designations are known and straightforward. However, mineral soil layers were
sampled by depth. Because in this soil the 0-10 cm and 10-20 increments mostly
correspond to E- and B-horizons, respectively, these increments are given
entries in ‘Horizon Designation Other.’ Finally, although it was sampled by
depth, the 20-40 cm increment universally corresponds to a B-horizon in this
soil, so this is entered as a proper Horizon Designation. Note that for soils where
depth increments do not reliably correspond to genetic horizons, it is
inappropriate to enter anything in the ‘Horizon Designation Other’ column.
1.
In column N, calculate the SOC content of each layer for
which you wish to do so. If you have not performed
these computations before submitting your template,
the database will automatically compute layer SOC
contents from your reported %C, bulk density, and layer
thickness data.
2.
In this dataset, Oi, Oe and A horizon SOC contents were
manually calculated from the %C and mass density (g
cm-2) of the material making up each horizon. This will
be the case for similar sampling strategies where forest
floors/O-horizons are sampled in the field with
templates of known area, then separated by horizon,
weighed and analyzed for %C in the lab.
3.
For the first two profiles in this dataset, the SOC
contents of the 0-10, 10-20, and 20-40 cm layers were
manually computed, so that it will be easier to add the
SOC contents of these mineral layers to the manually
computed SOC contents of their superadjacent O and A
horizons when filling out the profile worksheet (more
on that later). Note that for the third profile in this
example (FASET.F60m.5), in which O and A horizons
were not sampled, the 0-10 and 10-20 cm SOC contents
were left blank. Therefore, the database will
automatically compute the 0-20 cm SOC content for
this profile, and we will have to identify this profile as
one that lacks data on surficial horizons (the O and A) in
a subsequent step.
1.
2.
3.
4.
5.
6.
Unhide columns and enter additional information about the samples.
In this example, text was entered into the ‘layer_note’ column describing why the 0-10 and 10-20 cm increments were given ‘Horizon Designation Other’ names
rather than proper horizon designations.
In the ‘c_method’ column, it is noted that the combustion analysis for c_tot was done on the oven-dried, root free fine earth fraction.
In the bd_method column, it is noted that the bulk density of each depth increment corresponds to the oven-dried, root free fine earth mass divided by the
volume of the cored increment. This verifies the selection of bd_samp as the appropriate bulk density statistic for these data. (since O and A horizons were
measured for mass density but not bulk density, there are no entries for O- or A-horizons in either the bd_samp or bd_method columns)
In the ccon_layer_method column, each layer that has a manually computed SOC content contains a description of those computations.
%N, C and N isotopes were entered for each sample in the appropriate columns.
A descriptive name for the sample
Your lab ID# for the sample
1.
In column C, assign layer names to the samples you have been reporting thus far
using your own internal laboratory IDs (which are in column D, layer_number). In
this example, layer_name and layer_number are the same; however, many
contributors will have simple numeric values for layer_number that should be
translated into more descriptive layer_names for entry into the NSCN database.
2.
In this example, each layer_name describes the following about the origin of each
sample: which of two landscape-level experimental treatments the sample came
from (REF vs. FASET), which plot (Q5, C3, F60m), which profile (10 were taken in
each plot), and what depth within the profile (Oe, A, 10 cm, 10-20 cm, etc.)
3.
In column B, assign names to the soil profiles that contain each of the layers you
have reported. Note that the profile name is repeated for each layer within the
profile, since the layers are individually sampled components of the profile.
4.
In column A, provide a site name. For large sites (>a few sq km2) that are sampled
via a network of permanent plots, the individual plots can be considered distinct
‘sites’ if their lat/longs are known and the plots are distributed in such a way that
site-level variables are not consistent from plot to plot (e.g., if plots occur on
different landforms, or different land cover types). Conversely, if the site is large
and relatively homogenous, or if the plots were distributed across the range of
landscape-level variation without quantifying the unique landform/cover
attributes of each plot, then the plots are best treated as representing a single
site. The latter is the case of this example dataset, because the study from UMBS
covers a large (~100 ha) but relatively homogenous area, and the plots are laid
out in a way to average across what spatial heterogeneity does exist.
Furthermore, lat/long is not known for the individual plots or profiles within the
broader site.
Parentheses indicate this is a variable with a controlled vocabulary
1.
2.
3.
4.
5.
6.
7.
Switch to the ‘profile’ worksheet of the template file and enter the three profile names you created in the previous slide. Enter information about the profiles
such as the sampling date and its level of accuracy, the dominant plant genera at the location of the profile, as well as soil series name and taxonomy.
Enter the profile total SOC content (column F) for any profiles for which you manually computed this quantity. If you are leaving this calculation to the database,
do not enter a value in column F. Recall from slide #3 that the first two profiles in this dataset are special cases, which required manually computing the SOC
contents of surficial layers of unknown depth/thickness and then adding these values to the computed SOC contents of the underlying mineral layers. However,
the third profile consists entirely of mineral soil layers with measured depths, so the SOC content for this profile has been left blank and will be automatically
computed by the database.
It is necessary to identify the completeness of all profiles in column G. Although they required special handling, the first two profiles represent the whole soil
(organic + mineral layers). The third profile is more straightforward in the sense of having contiguous layers with measured depths, but is nonetheless an
incomplete (mineral-only) profile. Note that profiles which exclusively consist of organic layers (e.g., histosols, or a later submission of O-horizons for
FASET.F60m.5), should be given an entry of ‘org’ in column G.
For profiles with manually computed SOC contents, enter the depth to the bottom of the profile. Briefly describe the methods used to compute the SOC contents
of these profiles in column I.
Enter soil series name and taxonomic information of each profile. In this example all profiles are Rubicon sands.
In the ‘Profile Notes’ column, enter important information about the profiles. In this example it is noted that the first two profiles include O and A horizons in
their SOC content computations, but not in the determination of their total profile depths. Similarly, it is noted that the third profile does not include
superadjacent O or A horizons.
Enter sampling information in columns M, N, O (methods, names of individuals involved)
1.
Switch to the ‘site’ worksheet of the template file and enter site name(s), latitude and longitude. Lat/long to 5 decimal degrees is preferred, but for large
homogenous sites less accuracy is acceptable. Also, for older site visits (i.e., before GPS units with meter-level accuracy), highly precise measurements may not
exist; simply provide the best value you can and some estimate of the location accuracy in column F. Location accuracy is a value in meters for which the true site
origin has a 95% or greater probability of being located. In the case of this example dataset, there is a 95% chance that all three profiles from this site are located
within 800 m of the reported site origin. Enter the latitude/longitude datum; if the datum column is left blank or deleted it is assumed to be WGS84.
2.
Enter additional geographic, geomorphic and ecologic information about the site: elevation, landscape/landform, vegetation age, climate, etc. For a site with
significant spatial variation in any of these properties, it is better to simply leave this information blank than to mis-characterize any of the profiles within the site.
Also, remember that users with the ability to accurately sample with high spatial resolution (5 dec. degrees) may create distinct site names for groups of closely
situated profiles with similar geomorphic/ecologic properties.
3.
Additional information about the site or complementary measurements conducted there should be entered in columns marked ‘C Flux Notes’ and ‘Site Notes’.
1.
If any of your site(s) include soil profiles that have been visited by
known disturbances, switch to the ‘disturbance’ sheet of the
template and enter the name(s) of the site(s) containing these
profiles.
2.
Your replies to the template customization questionnaire should
have eliminated all disturbance columns except those relevant to
the disturbances or treatments that have acted upon your soil
profiles. If there are extra columns or columns for which
information is not known, these may be deleted.
3.
Enter the date (and date qualifier/accuracy) when each
disturbance or treatment was initiated. If the disturbance was
ongoing, indicate this in the disturbance notes column rather
than by adjusting the date qualifier.
4.
In the disturbance notes column, enter additional data about the
disturbance.
1.
2.
3.
4.
Go to the metadata worksheet of the template and enter your name, contact info, and additional names, such as colleagues, other individuals who have
oversight of the data, or other people who may by chance report the same data (possible within large collaborative studies). Add a dataset description.
If data correspond to a publication, enter the citation. If multiple citations are provided, identify which ones correspond to which components of the dataset.
This is how data users will identify and cite your work when using your data from the database.
If data are unpublished, provide an acknowledgment that data users will include in any products they prepare that utilize your data. Provide guidelines
for the acknowledgment usage, especially if you intend to move unpublished data out of embargo immediately after submitting to the ISCN.
Enter information about methods of sample collection, analysis, and data treatment (gap fills or special cases).
This is the end of the tutorial. Login to the NSCN webpage and go here to upload your finished template. If you do not yet have an NSCN login, sign up
for one here. If you have forgotten your login information, contact ISCN support.
Thank you for sharing your data with the ISCN!