Neotoma Resources
The Neotoma Paleoecology
Database is a domain-specific data resource containing millions of
fossil records from around the globe, covering the last 5.4 million
years. The neotoma2 R package simplifies some of the data
structures and concepts to facilitate statistical analysis and
visualization. Users may wish to gain a deeper understanding of the
resource itself, or build more complex data objects and relationships.
For those users a partial list is provided here, including a table of
code examples focusing on different geographic regions, languages and
dataset types.
Resources
-
Neotoma Homepage
- The Neotoma homepage, with links to contacts, news and other tools and resources.
-
Neotoma Database
Manual
- Documentation for the database itself, with examples of SQL queries and descriptions of Neotoma tables.
-
Neotoma (JSON) API
- A tool to obtain data in JSON format directly through calls to Neotoma.
-
Neotoma GitHub
Organization
- Open code repositories to see how folks are using Neotoma and what kinds of projects we’re working on.
- Workshops and Code Examples (see the section below)
Neotoma Data Structure
Data in Neotoma is associated with sites, specific locations with lat/long coordinates. Within a site, there may be one or more collection units – locations at which samples are physically collected within the site. For example, an archaeological site may have one or more collection units, pits within a broader dig site; a pollen sampling site on a lake may have multiple collection units – core sites within the lake basin. Collection units may have higher resolution GPS locations, but are considered to be part of the broader site. Within a collection unit data is collected at various [analysis units] from which samples are obtained.
Because Neotoma is made up of a number of constituent databases (e.g., the Indo-Pacific Pollen Database, NANODe, FAUNMAP), a set of samples associated with a collection unit are assigned to a single dataset associated with a particular dataset type (e.g., pollen, diatom, vertebrate fauna) and constituent database.
Researchers often begin by searching for sites within a particular study area, whether that is defined by geographic or political boundaries. From there they interrogate the available datasets for their particular dataset type of interest. When they find records of interest, they will then often call for the data and associated chronologies.
The neotoma2 R package is intended to act as the
intermediary to support these research activities using the Neotoma
Paleoecology Database. Because R is not a relational database, we needed
to modify the data structures of the objects. To do this the package
uses a set of S4 objects to represent different elements within the
database.
neotoma2 R package, and the way the elements are
related to one another. Individual boxes represent the major classes
(sites, site, collectionunits, etc.). Each box then has a list of the
specific metadata contained within the class, and the variable type
(e.g., siteid: integer). Below these are the functions that can
be applied to the object (e.g., [[<-).It is important to note, here and elsewhere: Almost
everything you will interact with is a sites
object. A sites object is the general currency of
this package. sites may have more or less metadata
associated with them, but they are the primary object, and, as you can
see in the diagram above, they have the most functions associated with
them.
Package Requirements
The earlier neotoma package tried to use base R as much
as possible. The neotoma2 package now draws primarily on
dplyr and purrr packages from the
tidyverse, and on the sf spatial data package.
The choice to integrate tidyverse packages was made largely
because of the current ubiquity of the tidyverse in R
education.
Site Searches
The highest level object in Neotoma is the site. Sites have spatial coordinates and, in many cases, additional metadata related to lake parameters, or other site-specific properties.
Sites can be searched using the get_sites() function,
or, can be created using the set_site() function. A single
site object is a special object in R, that can be combined
with other sites into a sites object. A sites
object is effectively a list() of site objects
with special methods for printing, plotting and exporting
information.
Finding Sites
All sites in Neotoma have a unique numeric identifier. With the
neotoma2 package you can search for a site using the
get_sites() function by its unique site id
(siteid), by name (sitename), by altitude
(altmin, altmax), by geopolitical name
(gpid), location (loc) or age bounds.
If we’re looking for a site and we know its specific identifier, we
can use the simplest implementation of get_sites(). Here we
are searching for a site (Alexander Lake), where we know that the siteid
for the record in Neotoma is 24. We can get these siteids
using the Neotoma
Explorer web application, or if we have some familiarity with the
site records already.
# Search for site by a single numeric ID:
alex <- get_sites(24)
alex
#> siteid sitename lat long altitude
#> 24 Alexander Lake 53.33333 -60.58333 143
# Search for sites with multiple IDs using c():
multiple_sites <- get_sites(c(24, 47))
multiple_sites
#> siteid sitename lat long altitude
#> 24 Alexander Lake 53.33333 -60.58333 143
#> 47 Liberty 43.52000 -90.78000 305Once you search for a site, the neotoma2 R package makes
a call to the Neotoma Database, and returns a structured
sites object that contains metadata about the sites, and
some additional metadata about collection units and datasets at those
sites. This limited metadata helps speed up further searches, but is not
complete, for the purposes of analysis.
get_sites() call, is a sites object containing
two individual site objects. Each site object
contains a collunits object with some limited metadata. The
top site appears to have two collection units, while the
lower site has only a single collection unit. Each of the top two
collection units appear to contain two datasets, while the bottom site
has only the one collection unit with only one dataset.Searching for Sites by Name
Often we do not know the particular siteid. If we’re
looking for a site and we know its name or a part of its name, we can
search using the function with the sitename argument,
get_site(sitename = 'XXX'), where 'XXX' is the
site name. This does not support multiple text strings (i.e., you can’t
use c()).
alex <- get_sites(sitename = "Alexander Lake")
alex
#> siteid sitename lat long altitude
#> 24 Alexander Lake 53.33333 -60.58333 143Neotoma uses a Postgres Database to manage data. Postgres uses the
% sign as a general wildcard, so we can use the
% in the sitename argument operator to help us
find sites when we’re not sure the exact match. Note that the search is
case insensitive so a search for alex% or
Alex% will return the same results.
alex <- get_sites(sitename = 'Alex%')
alex
#> siteid sitename lat long altitude
#> 24 Alexander Lake 53.33333 -60.58333 143
#> 25 Alexis Lake 52.51667 -57.03333 200
#> 4478 Alexander [3CN117] 35.25000 -92.61667 88
#> 26226 Alexandra Lake 43.29030 -74.16966 354Searching for Sites by Age
There are several ways of searching for sites using age parameters. These are represented below:
ageof, ageyoung, ageold,
maxage and minage.We offer several methods of searching because different users have
different requirements. A user might be only interested in one specific
point in time in the past, for example the 8.2ka event. In this instance
they would search get_sites(ageof = 8200). They may want
sites with records that completely span a time period, for example the
Atlantic chronozone of the Holocene:
get_sites(ageyounger = 5000, ageolder = 8000). These sites
would have samples both within and outside the defined age range, so
that the user could track change into and out of the time period. A user
may also be interested in any record within a time bin, regardless of
whether the site spans that time zone or not. They would query
get_sites(minage = 5000, maxage = 8000).
We can see how these age bounds differ:
# Note, we are using the `all_data = TRUE` flag here to avoid the default limit of 25 records, discussed below.
# Because these queries are searching through every record they are slow and and are not
# run in knitting this vignette.
get_sites(ageof = 8200, all_data = TRUE) %>% length()
get_sites(ageyounger = 5000, ageolder = 8000, all_data = TRUE) %>% length()
get_sites(minage = 5000, maxage = 8000, all_data = TRUE) %>% length()It is possible to pass all parameters (ageof,
minage, maxage, ageyounger, . . .
), but it is likely that these will conflict and result in an empty set
of records. To avoid this, be aware of the relationships among these
search parameters, and how they might affect your search window.
Accessing sites metadata
Although the sites are structured using S4 objects (see
Hadley Wickham’s S4
documentation), we’ve added helper functions to make accessing
elements easier for users.
The alex object is composed of several smaller objects
of class site. We can call any individual site using
[[ ]], placing the index of the desired object between the
brackets. Then we can also call the particular variable we want using
the $ symbol.
alex <- get_sites(sitename = "Alexander Lake")
alex[[1]]$siteid
#> [1] 24The elements within a site are the same as the defined
columns within the Neotoma ndb.sites
table, with the exception of the collunits slot, which
contains the collection units and associated datasets that are found
within a site. You can see all the site slots using the
names() function. You can select individual elements of a
site, and you can assign values to these parameters:
names(alex[[1]])
#> [1] "siteid" "sitename" "geography" "altitude" "geopolitical"
#> [6] "area" "notes" "description" "collunits"
# Modify a value using $<- assignment:
alex[[1]]$area
#> [1] NA
alex[[1]]$area <- 100
alex[[1]]$area
#> [1] 100
# Modify a value using [<- assignment:
alex[[1]]["area"] <- 30
# alex[[1]][7] <- 30 This fails because the `Notes` field expects a character string.Using assignment, we can add information programmatically, for example, by working interactively with a digital elevation model or hydrographic data to obtain lake area measurements. Although not currently implemented, the goal is to support direct upload of updated information by users.
Creating a Site
As explained above, a site is the fundamental unit of
the Neotoma Database. If you are working with your own data, you might
want to create a site object to allow it to interact with
other data within Neotoma. You can create a site with the
set_site() function. It will ask you to provide important
information such as sitename, lat, and
long attributes.
my_site <- set_site(sitename = "My Lake",
geography = st_sf(a = 3, st_sfc(st_point(1:2))),
description = "my lake",
altitude = 30)
my_site
#> siteid sitename lat long altitude
#> e9cf20b7-bedc-40b0-a110-cfb4d36c0e87 My Lake 2 1 30If we have a set of sites that we are analyzing, we can add the new
site to the set of sites, either by appending it to the end, using
c(), or by replacing a particular element using
[[<-.
This method allows us to begin modifying site information for existing sites if we have updated knowledge about site properties.
# Add a new site that's been edited using set_site()
longer_alex <- c(alex, my_site)
# Or replace an element within the existing list of sites
# with the newly created site.
longer_alex[[2]] <- my_site
# Or append to the `sites` list with assignment:
longer_alex[[3]] <- my_siteWe can also use set_sites() as a tool to update the
metadata associated with an existing site object:
# Update a value within an existing `sites` object:
longer_alex[[3]] <- set_site(longer_alex[[3]],
altitude = 3000)
longer_alexDatasets
If you need to get to a deeper level of the sites object, you may
want to look at the get_datasets() function. You can use
get_datasets() using search parameters, or you can use it
on an existing sites object, such as our prior
alex dataset.
get_datasets() adds additional metadata to the
site objects, letting us know which
datasettypes are associated with a site, and the dataset
sample locations at the site.
get_datasets() provides more
complete metadata about a record, including the addition of
chronological information, and more complete metadata about the
datasets, compared to the get_sites() call, shown above.
The objects here are the same as above, but now have chronology
metadata, and contact metadata for the records. Note that there is still
no sample or taxonomic information about these records. This comes from
the get_downloads() function.Getting the datasets by id is the easiest call, you can also pass a
vector of IDs or, if you already have a sites object, you
can pass a sites object.
# Getting datasets by ID
my_datasets <- get_datasets(c(5, 10, 15, 20))
my_datasets
#> siteid sitename lat long altitude
#> 5 17/2 55.25000 -74.93333 335
#> 10 Site 1 (Cohen unpublished) 30.83000 -82.33000 37
#> 15 Aguilar -23.83333 -65.75000 4000
#> 20 Akuvaara 69.12326 27.67406 170You can also retrieve datasets by type directly from the API.
# Getting datasets by type
my_pollen_datasets <- get_datasets(datasettype = "pollen", limit = 25)
my_pollen_datasets
#> siteid sitename lat long altitude
#> 7 Three Pines Bog 47.00000 -80.11667 294
#> 8 Abalone Rocks Marsh 33.95639 -119.97667 0
#> 9 Adange 43.30556 41.33333 1750
#> 11 Konus Exposure, Adycha River 67.75000 135.58333 130
#> 12 Ageröds Mosse 55.93329 13.42559 58
#> 13 Aguas Calientes -23.08333 -67.40000 4210
#> 14 Aguas Calientes 2 -23.50000 -67.58333 4210
#> 15 Aguilar -23.83333 -65.75000 4000
#> 16 Ahlenmoor 53.69908 8.74688 2
#> 17 Ajata -18.25000 -69.20000 4700
#> 18 South Soefje Bog 29.60000 -97.51694 97
#> 19 Akulinin Exposure P1282 47.11667 138.55000 20
#> 20 Akuvaara 69.12326 27.67406 170
#> 21 Alazeya River Exposure, 8 m Terrace 68.50000 154.50000 40
#> 22 Alazeya River Exposure, 9 m Terrace 64.33333 154.50000 40
#> 24 Alexander Lake 53.33333 -60.58333 143
#> 25 Alexis Lake 52.51667 -57.03333 200
#> 27 Aliuk Pond 54.58333 -57.36667 25
#> 29 Lake Allie 44.80156 -94.55982 328
#> 30 Almora Lake 46.20611 -95.29361 437
#> 31 Alut Lake 60.13667 152.31278 480
#> 32 Amarete -15.23333 -68.98333 4000
#> 33 Amba River Exposure 596 43.31667 131.81667 5
#> 68 Amguema River Valley Exposure 1 67.75000 178.70000 175
#> 69 Amguema River Valley Exposure 2 67.66667 178.60000 87It can be computationally intensive to obtain the full set of records
for sites or datasets. By default the
limit for all queries is 25. The default
offset is 0. To capture all results we can use
the all_data = TRUE flag in our calls.
However, this is hard on the Neotoma servers. We tend
to prefer that users use all_data = TRUE once their
analytic workflow is mostly complete.
We can use that all_data = TRUE in R in the following
way:
allSites_dt <- get_sites(datasettype = "diatom")
allSites_dt_all <- get_sites(datasettype = "diatom", all_data = TRUE)
# Because we used the `all_data = TRUE` flag, there will be more sites
# in allSites_dt_all, because it represents all sites containing diatom datasets.
length(allSites_dt_all) > length(allSites_dt)Spatial Searches
You can get the coordinates to create a GeoJson bounding box from here, or you can use
pre-existing objects within R, for example, country-level data within
the spData package:
Accessing datasets by bounding box:
brazil <- '{"type": "Polygon",
"coordinates": [[
[-73.125, -9.102],
[-56.953, -33.138],
[-36.563, -7.711],
[-68.203, 13.923],
[-73.125, -9.102]
]]}'
# We can make the geojson a spatial object if we want to use the
# functionality of the `sf` package.
brazil_sf <- geojsonsf::geojson_sf(brazil)
brazil_datasets <- get_datasets(loc = brazil_sf)
brazil_datasets
#> siteid sitename lat long altitude
#> 32 Amarete -15.23333 -68.98333 4000
#> 211 Lago do Aquiri -3.16667 -44.98333 10
#> 323 Cala Conto -17.56667 -65.93333 2700
#> 347 Chacaltaya 1 -16.36667 -68.15000 4750
#> 348 Chacaltaya 2 -16.36667 -68.15000 4350
#> 523 Cumbre Unduavi -16.35000 -68.04167 4620
#> 532 Lagoa das Patas 0.26667 -66.68333 300
#> 843 Serra Campos Gerais -24.66667 -50.21667 1200
#> 1406 Katantica -14.80000 -69.18333 4820
#> 1994 Río Kaluyo -16.43333 -68.13333 4070
#> 2827 Wasa Mayu -17.53333 -65.81667 2720
#> 9685 Laguna Granja -13.26341 -63.71025 138
#> 9686 Laguna Orícore -13.34833 -63.52796 139
#> 11923 Lake Valencia 10.16576 -67.76755 410
#> 13964 Lake Chalalán -14.42868 -67.92125 330
#> 526 Lagoa da Curuça -0.76667 -47.85000 35
#> 1717 Monte Blanco -17.02500 -67.35000 4780
#> 2248 Lagoa Campestre de Salitre -19.00000 -46.76667 980
#> 11877 Lake Consuelo -13.95125 -68.99162 1333Now we have an object called brazil_datasets that
contains 19.
You can plot these findings!
plotLeaflet(brazil_datasets)Filtering Records
Sometimes we take a large number of records, do some analysis, and
then choose to select a subset. For example, we may want to select all
sites in a region, and then subset those by dataset type. If we want to
look at only the geochronological datasets from Brazil, we can start
with the set of records returned from our get_datasets()
query, and then use the filter function in
neotoma2 to select only those datasets that are
geochronologic:
brazil_dates <- neotoma2::filter(brazil_datasets,
datasettype == "geochronologic")
# or:
brazil_dates <- brazil_datasets %>%
neotoma2::filter(datasettype == "geochronologic")
# With boolean operators:
brazil_space <- brazil_datasets %>% neotoma2::filter(lat > -18 & lat < -16)The filter() function takes as the first argument, a
datasets object, followed by the criteria we want to use to filter.
Current supported criteria includes:
latlongelevdatasettype
You also need to make sure that you accompany any of these terms with
the following boolean operators: <, > or
==, !=. datasettype has to be of
type string, while the other terms must be numeric. If you need to
filter by the same argument, let’s say, you need to filter
“geochronologic” and “pollen data types, then you will also make use of
& and | operators.
Sample and Taxonomic data
Once we have the set of records we wish to examine, we then want to
recover the actual sample data. This will provide us with information
about the kinds of elements found at the site, within the dataset, their
sample ages, and their counts or measurements. To do this we use the
get_downloads() call. Note, as before, we are returning a
sites objects, but this time with the most complete
metadata.
get_downloads() returns a
sites object, but one that contains dataset objects with
filled samples slots. The samples slot is
often very large relative to the other metadata associated with
sites, and so it is commonly held back until a direct
request is provided. Helper functions at the sites level
can pull out sample data once get_downloads()
has been called.Assuming we continue with our example from Brazil, we want to extract records from the country, filter to only pollen records with samples covering the last 10,000 years, and then look at the relative frequency of taxa across sites. We might do something like this:
brazil <- '{"type": "Polygon",
"coordinates": [[
[-73.125, -9.102],
[-56.953, -33.138],
[-36.563, -7.711],
[-68.203, 13.923],
[-73.125, -9.102]
]]}'
# We can make the geojson a spatial object if we want to use the
# functionality of the `sf` package.
brazil_sf <- geojsonsf::geojson_sf(brazil)
brazil_records <- get_datasets(loc = brazil_sf) %>%
neotoma2::filter(datasettype == "pollen" & age_range_young <= 1000 & age_range_old >= 10000) %>%
get_downloads(verbose = FALSE)
#> [1] "data/downloads?datasetid=356,537,547,872,2069,18100,21537"
count_by_site <- samples(brazil_records) %>%
dplyr::filter(elementtype == "pollen" & units == "NISP") %>%
group_by(siteid, variablename) %>%
summarise(n = n()) %>%
group_by(variablename) %>%
summarise(n = n()) %>%
arrange(desc(n))
#> `summarise()` has grouped output by 'siteid'. You can override using the
#> `.groups` argument.In this code chunk we define the bounding polygon for our sites,
filter by time and dataset type, and then return the full records for
those sites. We get a sites object with dataset and sample
information (because we used get_downloads()). We execute
the samples() function to extract all the samples from the
sites objects, and then filter the resulting
data.frame to pull only pollen (a pollen dataset may
contain spores and other elements that are not, strictly speaking,
pollen) that are counted using the number of identified specimens (or
NISP). We then group_by() the unique site identifiers
(siteid) and the taxa (variablename) to get a
count of the number of times each taxon appears in each site. We then
want to summarize() to a higher level, just trying to
understand how many sites each taxon appears in. After that we
arrange() so that the records show the most common taxa
first in the resulting variable count_by_site.
Publications
Many Neotoma records have publications associated with them. The
publication object (and the publications
collection) provide the opportunity to do this. The publication
table in Neotoma contains an extensive number of fields. The methods for
publications in the neotoma2 package provide
us with tools to retrieve publication data from Neotoma, to set and
manipulate publication data locally, and to retrieve publication data
from external sources (e.g., using a DOI).
get_publications() from Neotoma
The most simple case is a search for a publication based on one or more publication IDs. Most people do not know the unique publication ID of individual articles, but this provides a simple method to highlight the way Neotoma retrieves and presents publication information.
Get Publication By ID
We can use a single publication ID or multiple IDs. In either case
the API returns the publication(s) and creates a new
publications object (which consists of multiple individual
publications).
one <- get_publications(12)
two <- get_publications(c(12, 14))From there we can then then subset and extract elements from the list
using the standard [[ format. For example:
two[[2]]
#> publicationid
#> 1 14
#> citation
#> 1 Baker, R.G., R.S. Rhodes II, D.P. Schwert, A.C. Ashworth, T.J. Frest, G.R. Hallberg, and J.A. Janssens. 1986. A full- glacial biota from southeastern Iowa, USA. Journal of Quaternary Science 1(2):91-107.
#> doi
#> 1 <NA>Will return the second publication in the list, corresponding to the
publication with publicationid 14 in this case.
Get Publication using Search
We can also use search elements to search for publications. The
get_publication method uses the Neotoma API to search for
publications. We can search using the following properties:
publicationiddatasetidsiteidfamilynamepubtypeyearsearchlimitoffset
michPubs <- get_publications(search = "Michigan", limit = 2)This results in a set of 2 publications from Neotoma, equal to the
limit. If the number of matching publications is less than
the limit then the length() will be smaller.
Text matching in Neotoma is approximate, meaning it is a measure of the overall similarity between the search string and the set of article titles. This means that using a nonsense string may still return results results:
noise <- get_publications(search = "Canada Banada Nanada", limit = 5)This returns a result set of length 5.
This returns the (Neotoma) ID, the citation and the publication DOI
(if that is stored in Neotoma). We can get the first publication using
the standard [[ nomenclature:
two[[1]]
#> publicationid
#> 1 12
#> citation
#> 1 King, J.E. 1973. Late Pleistocene palynology and biogeography of the western Missouri Ozarks. Ecological Monographs 43:539-565.
#> doi
#> 1 <NA>The output will look similar to the output for two
above, however you will see that only a single result will be returned
and the class (for a single publication) will be of type
publication (as opposed to publications).
We can select an array of publication objects using the
[[ method, either as a sequence (1:10, or as a
numeric vector (c(1, 2, 3))):
# Select publications with Neotoma Publication IDs 1 - 10.
pubArray <- get_publications(1:10)
# Select the first five publications:
subPub <- pubArray[[1:5]]
subPub
#> publicationid
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> citation
#> 1 International Organization for Standardization. 2006. Codes for the representation of names of countries and their subdivisions — Part 1: Country codes. ISO 3166-1, Geneva, Switzerland.
#> 2 United States Central Intelligence Agency. 2004. CIA World Factbook. Washington, DC, USA.
#> 3 Davis, M.B. 1978. Climatic interpretation of pollen in Quaternary sediments. Pages 35-51 in D. Walker and J.C. Guppy, editors. Biology and Quaternary environments. Australian Academy of Science, Canberra City, Australia.
#> 4 Nanavati, W., C. Whitlock, V. Outes, and G. Villarosa. 2011. Diatom-inferred wind activity at Lac du Sommet, southern Québec, Canada: A multiproxy paleoclimate reconstruction based on diatoms, chironomids, and pollen for the past 9500 years. The Holocene 21(6):925-938. http://hol.sagepub.com/content/21/6/925.full.pdf+html. [DOI: 10.1177/0959683611400199]
#> 5 Janssen, C.R. 1968. Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota. Canadian Journal of Botany 46(11):1397-1410. [DOI: 10.1139/b68-190]
#> doi
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 10.1177/0959683611400199
#> 5 10.1139/b68-190Create (or Import) New Publications
Just as we can use the set_sites() function to set new
site information, we can also create new publication information using
set_publications(). With set_publications()
you can enter as much or as little of the article metadata as you’d
like, but it’s designed (in part) to use the CrossRef API to return
information from a DOI.
new_pub <- set_publications(
articletitle = "Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota",
journal = "Canadian Journal of Botany",
volume = 46)A publication has a large number of slots that can be
defined. These may be left blank, they may be set directly after the
publication is defined:
new_pub@pages <- "1397-1410"Workshops and Code Examples
- 2022 International AL/IPA Meeting; Bariloche, Argentina
-
English
Language Simple Workflow
- Topics: Simple search, climate gradients, stratigraphic plotting
- Spatial Domain: South America
- Dataset Types: Diatoms
-
Spanish
Language Simple Workflow
- Topics: Simple search, climate gradients, stratigraphic plotting
- Spatial Domain: South America
- Dataset Types: Diatoms
-
English
Language Complex Workflow
- Topics: Chronology building, Bchron
- Spatial Domain: South America
- Dataset Types: Diatoms
-
Spanish
Language Complex Workflow
- Topics: Chronology building, Bchron
- Spatial Domain: South America
- Dataset Types: Diatoms
-
English
Language Simple Workflow
- 2022 European Pollen Database Meeting; Prague, Czech Republic
-
English
Language Simple Workflow
- Topics: Simple search, climate gradients, stratigraphic plotting, taxonomic harmonization
- Spatial Domain: Europe/Czech Republic
- Dataset Types: Pollen
-
English
Language Complex Workflow
- Topics: Chronology building, Bchron
- Spatial Domain: Europe/Czech Republic
- Dataset Types: Pollen
-
English
Language Simple Workflow
- 2022 American Quaternary Association Meeting
-
English
Language Simple Workflow
- Topics: Simple search, climate gradients, stratigraphic plotting
- Spatial Domain: North America
- Dataset Types: Pollen
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English
Language Complex Workflow
- Topics: Chronologies
- Spatial Domain: North America
- Dataset Types: Pollen
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English
Language Simple Workflow
- Neotoma-charcoal Workshop, Göttingen, Germany. Authors: Petr Kuneš
& Thomas Giesecke
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English
Language Workflow
- Topics: Simple Search, PCA, DCA, Charcoal/Pollen Correlation
- Spatial Domain: Global/Czech Republic
- Dataset Types: Pollen, Charcoal
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English
Language Workflow