NeoNet

_{Thomas Huet, Niccolò Mazzucco, Miriam Cubas Morera, Juan Gibaja, F. Xavier Oms, António Faustino Carvalho, Ana Catarina Basilio, Elías López-Romero}

NeoNet serves as a framework to study the transition from the Late Mesolithic to the Early Neolithic through spatio-temporal modeling of radiocarbon dates and by offering:

curated datasets
R functions and reference data
interactive app

Neonet radiocarbon datasets: "NeoNet Med" (North-Central Mediterranean, location) and "NeoNet Atl" (European South Atlantic river basin, location), here filtered by periods.

Studied periods

Studied periods are listed in the periods.tsv table:

period	period_full_name	color
EM	Early Mesolithic
MM	Middle Mesolithic
LM	Late Mesolithic
LMEN	Late Mesolithic/Early Neolithic
UM	Undefined Mesolithic
EN	Early Neolithic
EMN	Early/Middle Neolithic
MN	Middle Neolithic
LN	Late Neolithic
UN	Undefined Neolithic

NeoNet datasets

Med	Atl
id00140_doc_elencoc14.tsv	id00164_doc_elencoc14.tsv

These datasets are harvested by the c14bazAAR package, functions get_c14data("neonet") and get_c14data("neonetatl")

NeoNet app
_{^{mapping the Late Mesolithic/Early Neolithic transition}}

NeoNet app is an R Shiny application for mapping radiocarbon (C14). The application offers a mobile geographic window for date selection by location, various filters on chronology and date quality, a calibration window, and other tools to create a user-friendly interface supported by curated datasets of radiocarbon dates and archaeological contexts. NeoNet app is hosted on the server of the University of Pisa. This NeoNet app uses this radiocarbon dataset: https://doi.org/10.13131/archelogicadata-yb11-yb66 published as a data paper in the Journal of Open Archaeology Data and describe in this web document.

NeoNet app

NeoNet app dev

TODO

On export action (download button), export this metadata:
- max SD
- selected periods
- dataset (Med, Atl) DOI
- timestamp
- bibliographical reference

NeoNet functions
_{^{radiocarbon management}}

NeoNet functions enable the handling of radiocarbon dates sourced from the dataset or exported from the interactive app. Current functions cover:

data management
SPD
isochrones
Koppen climate classes
Harris matrices using the NeoNet-strati app.
reference data

Data

Reference data

standardised site names
outlier dates (raw | rendered)

Data preparation

Starting by running these neo_*() functions to manage a new XLSX dataset. Sourcing functions:

source("R/neo_subset.R")
source("R/neo_bib.R")
source("R/neo_matlife.R")
source("R/neo_calib.R")
source("R/neo_merge.R")
source("R/neo_html.R")
source("R/neo_datamiss.R")
source("R/neo_datasum.R")
source("R/neo_doi.R")

Read the new dataset and bibliographic file

data.c14 <- paste0(getwd(), "/inst/extdata/", "NeoNet_atl_ELR (1).xlsx")
df.bib <- paste0(getwd(), "/inst/extdata/", "NeoNet_atl_ELR.bib")

Cleaning the dataset and making it conform to the published NeoNet dataset

df.c14 <- openxlsx::read.xlsx(data.c14)
df.c14 <- neo_subset(df.c14,
                     rm.C14Age = TRUE,
                     rm.Spatial = FALSE,
                     rm.Period = FALSE)
df.c14 <- neo_calib(df.c14)
neo_doi(df.c14)

Prepare the dataset for the Shiny application by merging it with NeoNet Med, calculating materil life duration, and HTML popup layouts

df.c14 <- neo_merge(df.c14 = df.c14, 
                    data.bib = data.bib, 
                    merge.bib = F)
df.c14 <- neo_matlife(df.c14)
df.c14 <- neo_html(df.c14)

Export the merged dataset

write.table(df.c14, "C:/Rprojects/neonet/R/app-dev/c14_dataset_med_x_atl.tsv",
            sep = "\t",
            row.names = FALSE)

Data report

Calculate basic statistics

neo_datasum(df.c14)

Calculate basic statistics: missing data

neo_datamiss(df.c14)

Missing data (empty cells)

Data alignment

Retrieve dates coming from other databases (with the c14bazAAR R package) and mapped to be compliant with the Neonet format (..., LM, EN, ...) and functions, using neo_dbs_parse(), a mapping table (XLSX) created with neo_dbs_create_ref(), and neo_dbs_align():

source("R/neo_dbs_parse.R")
source("R/neo_dbs_align.R")

# spatial and chronological limits
where <- sf::st_read("https://raw.githubusercontent.com/zoometh/neonet/main/doc/talks/2024-simep/roi.geojson", quiet = TRUE)
when <- c(-9000, -4000) # in cal BC

# parameters
l.dbs <- c("calpal", "medafricarbon", "agrichange", "bda", "calpal", "radon", "katsianis") 
col.c14baz <- c("sourcedb", "site", "labnr", "c14age", "c14std", "period", "culture", "lon", "lat")

# collect the dates form different DBs, standardize the cultural period layout, filter on 'when' and 'where'
df <- neo_dbs_parse(l.dbs = l.dbs,
                    chr.interval.uncalBC = when, 
                    roi = where,
                    col.c14baz = col.c14baz)
df.c14 <- neo_dbs_align(df,
                        mapping.file = "C:/Rprojects/neonet/doc/ref_table_per.xlsx")

Alignments are done using the mapping file ref_table_per.xlsx, a default parameter in neo_dbs_align().

Mapping file

The XLSX file ref_table_per.xlsx has been created using:

df <- neo_dbs_parse(l.dbs = l.dbs,
                    chr.interval.uncalBC = when, 
                    roi = where,
                    col.c14baz = col.c14baz)

# create the mapping file
neo_dbs_create_ref(df.all.res = df,
                   root.path = "C:/Rprojects/neonet/results",
                   outFile = "df_ref_per.xlsx")

This mapping file ref_table_per.xlsx is a reference table to map cultural assessment coming from external DBs, collected throug c14bazAAR, to the Neonet one (..., LM, EN, ...) format. This XLSX file has to be updated manually by specialists.

Equivalences between third part databases cultural assessment (columns 'period' and 'culture') and Neonet classes (column 'class')

For example:

df.c14 <- neo_dbs_align(df,
                        mapping.file = "C:/Rprojects/neonet/doc/ref_table_per.xlsx")
head(df.c14)

Gives a dataframe where all fields have been renamed to be parsed with the Neonet functions. Among this mapping the column 'Period' with, for example, MM Middle Mesolithic) maps the bda period = Mésolithique 1 and culture = Capsien ancien or Capsien typique (columns 'db_period' and 'db_culture'):

sourcedb	SiteName	LabCode	C14Age	C14SD	db_period	db_culture	Period	lon	lat
bda	Mechta el Arbi	Poz-92231	6600	80	Mésolithique 1	Capsien ancien	MM	6.130900	36.09940
bda	Mechta el Arbi	Poz-92232	6250	130	Mésolithique 1	Capsien ancien	MM	6.130900	36.09940
bda	Mechta el Arbi	Poz-92230	3500	120	Mésolithique 1	Capsien ancien	MM	6.130900	36.09940
bda	Kef Zoura D	UOC-2925	7787	48	Mésolithique 1	Capsien typique	MM	7.682121	35.04205
bda	Bortal Fakher	L-240A	6930	200	Mésolithique 1	Capsien typique	MM	8.176087	34.35480
bda	Relilaï (B)	Gif-1714	7760	180	Mésolithique 1	Capsien typique	MM	7.694694	35.04480

Outlier dates

To filter aberrant dates, a combination of different function allow to retrieve the current information (once mapped to the Neonet layout) of these potential outliers and their original information (from their source database).

Here the dates 260 (circled in red), 396 and 345 seem aberrant

To check the info of the date numbered 260, do:

source("R/neo_find_date.R")
source("R/neo_dbs_info_date.R")

abber.date <- neo_find_date(df = isochr$data, print.it = FALSE, idf.dates = 260)
ad <- neo_dbs_info_date(df.c14 = df.c14, LabCode = abber.date$labcode)

Gives:

[1] "Reads a 'sf' dataframe"
[1] "Layout for 'aberrant dates' format:"
neonetatl	Sac-1676	El Retamar	-6263	EN	None	None

Where neonetatl is the source DB, Sac-1676 is the labnum, El Retamar the site name, -6263 the weighted mean (cal BC), EN the period (Early Neolithic), None the culture (no data) and the second None a place holder for a description explaining why this date has been discarted. If the archaeological documentation shows that date Sac-1676 (aka 260) is wrong (mixed assemblage, possibly coming from another layer, etc.). The last line neonetatl Sac-1676 El Retamar -6263 EN None None can be pasted as it into the c14_aberrant_dates.tsv file. More info can be found for this date, reusing the ad dataframe. Running the following functions helps to contextualize the date.

source("R/neo_dbs_info_date_src.R")

neo_dbs_info_date_src(db = ad$sourcedb,
                      LabCode = ad$LabCode)

Gives:

   sourcedb sourcedb_version    labnr c14age c14std       site    feature period material species country      lat
1 neonetatl       2023-12-08 Sac-1676   7400    100 El Retamar Conchero 6     EN    shell    <NA>   Spain 36.57873
        lon                    shortref
1 -6.226273 Cantillo-Duarte et al. 2010

The list of aberrant dates, for example c14_aberrant_dates.tsv, is used to discard some dates with the neo_dbs_rm_date() function

source("R/neo_dbs_rm_date.R")

df_filtered <- neo_dbs_rm_date(df.c14)

By default, dates having a dash prefix in their sourcedb column will be skipped (ex: -radon)

Screenshot of the 'c14_to_remove.tsv' table

A template script to run this operation: run_outliers.R

Wrong coordinates

Site coordinates can be wrong, for example for the Jordian site of Sabha (Xronos | Google maps). Curated coordinates are listed in the c14_corrected_coordinates.tsv file and handle by the neo_dbs_coord_dates() function.

Correct Site names

When aggregate different databases, using ce c14bazAAR package, two same sites can have different site names (ex: Pollera and Grotta della Pollera). To avoid these mismatches, the function neo_dbs_sitename_dates() uses the mapping table c14_corrected_sitenames.tsv

source("R/neo_dbs_sitename_dates.R")
df.c14 <- neo_dbs_sitename_dates(df.c14)

SPD plot

Plot the summed probabilty densities (SPD) of the two datasets, once df.c14 calculated. The function neo_spd() calls neo_spdplot(). The latter has been adapted from rcarbon::plot.stackCalSPD.R, to fetch NeoNet default period colors.

library(rcarbon)

source("R/neo_spd.R")
source("R/neo_spdplot.R")

neo_spd(df.c14 = df.c14)

NeoNet dataset SPD with default period colors

neo_spd() can be run on a GeoJSON file exported from the NeoNet app (see "export dates" in the web document. For example neonet-data-2023-09-24.geojson, see also: isochrones

neo_spd(df.c14 = "https://raw.githubusercontent.com/zoometh/neonet/main/results/neonet-data-2023-09-24.geojson",
        export = T)

In the same way, SPD can be done for KCC

source("R/neo_spd.R")
source("R/neo_spdplot.R")
source("R/neo_kcc_extract.R")
kcc.file <- c("koppen_6k.tif", "koppen_7k.tif", "koppen_8k.tif",
              "koppen_9k.tif", "koppen_10k.tif", "koppen_11k.tif")
df_cc <- neo_kcc_extract(df.c14 = df_filtered,
                         kcc.file = kcc.file)
neo_spd(df.c14 = df_cc, color.on = "kcc")

Isochrones

Create a map with isochrone contours to model the spread of Neolithic using the neo_isochr() function.

Example

The file neonet-data-2023-09-24.geojson is an export from the NeoNet app (see "export dates" in the web document). This GeoJSON file can be curated in a GIS (ex: removing aberrant dates) before running the following functions (neo_isochr, neo_spd, etc.).

Screen capture of the NeoNet app before the export of the `neonet-data-2023-09-24.geojson` file: Early Neolithic (EN) dates only having a SD ≤ 50, and calBC interval between -7000 and -3000

The output is a map with isochrones calculated on the median of calibrated dates.

library(rcarbon)

source("R/neo_isochr.R")
source("R/neo_spd.R")
source("R/neo_calib.R")

neo_isochr(df.c14 = "https://raw.githubusercontent.com/zoometh/neonet/main/results/neonet-data-2023-09-24.geojson", 
           show.lbl = FALSE)

Where neo_calib() calculate the cal BC min and max (i.e, calibrates), and the medidan (with 'intcal20' and the Bchron R package)

Output map from the `neonet-data-2023-09-24.geojson`

Interpolation

Isochrones are created from the interpolation map:

source("R/neo_isochr_inter_map.R")
inter.map <- neo_isochr_inter_map(isochr$inter)
inter.map
## Not Run
# ggplot2::ggsave(paste0(root.path, "img/", "isochrones-barriere-Italy-EN-inter-map.png"), inter.map, width = 7, height = 7)

Weighted medians interpolated

EN and LM

The same function can be used symetrically: instead of plotting the earliest dates of the Neolithic (EN), one can also plot the latest dates of the Paleolithic (or Late Mesolithic, LM)

myc14data <- "https://raw.githubusercontent.com/zoometh/neonet/main/results/1_AOI_France_E-W.geojson"
neo_isochr(df.c14 = myc14data, mapname = "France_EW")
neo_isochr(df.c14 = myc14data, mapname = "France_EW", selected.per = c("LM"))

Output maps from the `1_AOI_France_E-W.geojson`: earliest Neolithic dates and latest Paleolithic dates

Koppen

KCC, Koppen Climate Classification, Koeppen Climate Classification

The Neonet framework and online app integrate Koppen Climate Classification (KCC) for 6,000 BP to 10,000 BP.

Classes

Koppen classification uses five broad classes:

code	climate	colors
A*	Tropical
B*	Arid
C*	Temperate
D*	Cold
E*	Polar

And, there are 30 basic classes

code	num	value	hexa color
Af	1	Tropical, rainforest	0000FF
Am	2	Tropical, monsoon	0078FF
Aw	3	Tropical, savannah	46AAF
BWh	4	Arid, desert, hot	FF0000
BWk	5	Arid, desert, cold	FF9696
BSh	6	Arid, steppe, hot	F5A500
BSk	7	Arid, steppe, cold	FFDC64
Csa	8	Temperate, dry summer, hot summer	FFFF00
Csb	9	Temperate, dry summer, warm summer	C8C800
Csc	10	Temperate, dry summer, cold summer	969600
Cwa	11	Temperate, dry winter, hot summer	96FF96
Cwb	12	Temperate, dry winter, warm summer	64C864
Cwc	13	Temperate, dry winter, cold summer	329632
Cfa	14	Temperate, no dry season, hot summer	C8FF50
Cfb	15	Temperate, no dry season, warm summer	64FF50
Cfc	16	Temperate, no dry season, cold summer	32C800
Dsa	17	Cold, dry summer, hot summer	FF00FF
Dsb	18	Cold, dry summer, warm summer	C800C8
Dsc	19	Cold, dry summer, cold summer	963296
Dsd	20	Cold, dry summer, very cold winter	966496
Dwa	21	Cold, dry winter, hot summer	AAAF
Dwb	22	Cold, dry winter, warm summer	5A78DC
Dwc	23	Cold, dry winter, cold summer	4B50B4
Dwd	24	Cold, dry winter, very cold winter	320087
Dfa	25	Cold, no dry season, hot summer	00FFFF
Dfb	26	Cold, no dry season, warm summer	37C8FF
Dfc	27	Cold, no dry season, cold summer	007D7D
Dfd	28	Cold, no dry season, very cold winter	00465F
ET	29	Polar, tundra	B2B2B2
EF	30	Polar, frost	666666

The Koppen Climate Classes (KCC) are listed here

Koppen functions

Koppen functions are designed not only for the Neonet dataset, but also for all radiocarbon dataset respecting the minimum data stracture (site, labcode, x, y, etc.).

Create KCC maps

The app integrates Koppen Climate Classification (KCC) for 6,000 BP to 10,000 BP created with the R pastclim package and the neo_kcc_create() function.

The Koppen Climate Classification calculated for 8,000 BP (8k) with the pastclim R package and hosted on a GeoServer

Neonet functions help to blend pastclim KCC and radiocarbon dates.

# install the packages pastclim and terra
devtools::install_github("EvolEcolGroup/pastclim", ref="dev")
library(pastclim)
library(terra)

# set paths and create maps
outDir <- "C:/Rprojects/neonet/doc/data/clim/"
pastclim::set_data_path(path_to_nc = outDir)

source("R/neo_kcc_create.R")
neo_kcc_create()

Creates these KCC GeoTiffs:

KCC are created as GeoTiffs using the R pastclim package

Geotiff image	description
	koppen_11k download
	koppen_10k download
	koppen_9k download
	koppen_8k download
	koppen_7k download
	koppen_6k download

Past Koppen Climate Classification calculated in Kyears BP with the pastclim R package

Extract

This set of functions creates an interactive dataset of df.c14 dates and the climates to which they belong.

kcc.file <- c("koppen_6k.tif", "koppen_7k.tif", "koppen_8k.tif",
              "koppen_9k.tif", "koppen_10k.tif", "koppen_11k.tif")
source("R/neo_kcc_extract.R")
df_kcc <- neo_kcc_extract(df.c14 = df.c14, kcc.file = kcc.file)
source("R/neo_kcc_extract_longformat.R")
df_kcc_long <- neo_kcc_extract_longformat(df_kcc)
source("R/neo_dbs_info_dates_datatable.R")
dt.out <- neo_dbs_info_dates_datatable(df.c14 = df_kcc_long,
                                       fields = c("SiteName", "code", "Period", "median", "map", "LabCode", "db_period", "db_culture", "sourcedb", "X", "Y", "color"),
                                       font.size = "16pt")
## Not Run
# htmlwidgets::saveWidget(dt.out, "C:/Rprojects/neonet/doc/talks/2024-simep/img/dates_kcc.html")

Map

The neo_kcc_map() creates a KCC map with a layer of dates above

source("R/config.R") # default variables: column names mapping, colors, etc.

df <- c14bazAAR::get_c14data("neonet")
df <- sf::st_as_sf(df, coords = c("lon", "lat"), crs = 4326)
neo_kcc_map(df.c14 = df,
            kcc = "C:/Rprojects/neonet/doc/data/clim/koppen_7k.tif",
            export = TRUE,
            fileOut = "neonet_kcc.png" )

Gives:

The neonet dataset over the KCC 7k

To assess what were the climates classes that where inhabited in the past, during the Late Mesolithic (LM) and Middle Mesolithic (MM) based on previous dates

df.c14 <- neo_calib(df.c14)
df.c14 <- sf::st_as_sf(df.c14, coords = c("lon", "lat"), crs = 4326)
kcc.file <- c("koppen_6k.tif", "koppen_7k.tif", "koppen_8k.tif",
              "koppen_9k.tif", "koppen_10k.tif", "koppen_11k.tif")
df_cc <- neo_kcc_extract(df.c14 = df.c14, kcc.file = kcc.file)
col.req <- gsub(pattern = ".tif", "", kcc.file)
neo_kcc_plotbar(df_cc = df_cc, 
                kcc.file = c("koppen_8k.tif", "koppen_9k.tif"),
                col.req = col.req,
                selected.per = c("EN"),
                title = "Neolithic: transition btw 7,000 and 6,000 BC",
                legend.show = FALSE)

Gives:

KCC occupied during the EN between 7,000 and 6,000 BC (9 ka and 8 ka BP) with counts of sites belonging to these time slices

The neo_kcc_extract() function collects the KCC values (climates) of each date.

NeoNet strati

NeoNet-strati is an online R Shiny interactive app to record the stratigraphy of NeoNet's archaeological sites in an editable dataframe based on LabCode identifiers.

NeoNet-strati app

flowchart TD
    A[NeoNet dataset] --is read by--> B{{<a href='https://trainingidn.shinyapps.io/neonet-strati'>neonet-strati</a>}}:::neonetshiny;
    B --edit<br>site stratigraphy--> B;
    B --export<br>site stratigraphy<br>file--> C[<a href='https://github.com/historical-time/data-samples/blob/main/neonet/Roc%20du%20Dourgne_2023-07-30.csv'>Roc du Dourgne_2023-07-30.csv</a>];
    C --is read by--> D{{<a href='https://github.com/historical-time/caa23/blob/main/neonet/functions/neo_strat.R'>neonet_strat</a>}}:::neonetfunct;
    D --export --> E[maps<br>charts<br>listings<br>...];
    classDef neonetfunct fill:#e3c071;
    classDef neonetshiny fill:#71e37c;

Loading

Strati app and strati analysis overall Workflow

App interface

The app is composed by different panels: a site to be recorded (Site Stratigraphy panel), and the complete dataset (All sites panel). A site name is copied from All sites panel to Site Stratigraphy panel.

Site Stratigraphy panel

Plot a selected site in an editable table to record its stratigraphical relationships.

Panel "Site Stratigraphy" editable dataframe. By default the app opens on "Pokrovnik"

All sites panel

Show the complete NeoNet dataset. A site can be selected by searching it in the selection search bar (top-right) and copying its name (Site Name column). Here Roc du Dourgne, highlighted in blue.

Panel "All sites". Selection of the "Roc du Dourgne" site

"Roc du Dourgne" site sorted on its "PhaseCode"

The stratigraphical relations can be added into the "After" column, and thereafter exported in CSV

"Roc du Dourgne" stratgraphical relationships (column "After") after edition

For example, "Roc du Dourgne" relationships are:

LabCode	After	Period	PhaseCode	C14Age	C14SD
MC-1101	MC-1102	EN	C5	5050	100
MC-1102	MC-1103	EN	C5	6170	100
MC-1103	MC-1105	EN	C6	5100	80
MC-1104	MC-1105	EN	C6	6470	100
MC-1105	MC-1107	EN	C6	5550	80
MC-1107		LM	C7	6850	100
MC-781		EN	C6	5000	170
MC-782		LM	Layer 7	5770	170

The first row can be read as: "the layer containing radiocarbon date MC-1101 comes after the layer containing radiocarbon date MC-1102".

Once the site stratigraphy recorded, save the changes by downloading the dataset pressing the button (top-left) as a CSV file. The output file name is the site name and the current date (ex: Roc du Dourgne_2024-04-12.csv).

Strati analysis

The file exported from the NeoNet strati app can be read by the neo_strati() function (see Harris Matrix)

Harris Matrix

The output CSV file exported by NeoNet-strati can be read by the neo_strat() function. For example, ploting the C14Age and the PhaseCode.

neo_strat(inData = 'https://raw.githubusercontent.com/historical-time/data-samples/main/neonet/Roc du Dourgne_2023-07-30.csv',
          outLabel = c("C14Age"))
neo_strat(inData = 'https://raw.githubusercontent.com/historical-time/data-samples/main/neonet/Roc du Dourgne_2023-07-30.csv',
          outLabel = c("PhaseCode"))

Gives:

"Roc du Dourgne" stratgraphical relationships using LabCode identifiers, ordered on the "LabCode" column, displaying the C14Age (left) and the LabCode (right)

Changing the outLabel to Period allows to color on periods using the default period colors (see the web document)

neo_strat(inData = 'https://raw.githubusercontent.com/historical-time/data-samples/main/neonet/Roc du Dourgne_2023-07-30.csv',
          outLabel = c("Period"))

Gives:

"Roc du Dourgne" stratgraphical relationships using LabCode identifiers, ordered on the "LabCode" column

Leafrog alignment

Using neo_leapfrog(DT = T) to merge dataframe from NeoNet and Leapfrog on common C14 LabCode values: https://historical-time.github.io/caa23/neonet/results/NN_and_LF.html

Screen capture of [NN_and_LF.html](https://historical-time.github.io/caa23/neonet/results/NN_and_LF.html)

Sibling projects

There are mainy IT-based projects dealing with radiocarbon dates, such as the comprehensive platform Xronos, the radiocarbon aggregator C14bazAAR, and many others, see the open-archaeo site

Documentation

NeoNet app web document
Contribution rules
NeoNet package license

Communication

Big Historical Data Conference
- Shiny server (with the app embeded): http://shinyserver.cfs.unipi.it:3838/neonet/bhdc
- GitHub (without the app embeded): https://zoometh.github.io/neonet/doc/talks/2023-bhdc
Neonet workshop
- GitHub: https://zoometh.github.io/neonet/doc/talks/2024-neonet-ws/pres
Oxford Seminar Series
- GitHub: https://zoometh.github.io/neonet/doc/talks/2024-oxford/pres
SIMEP
- GitHub: https://zoometh.github.io/neonet/doc/talks/2024-simep/pres
ASD-CSIC
- GitHub: https://zoometh.github.io/neonet/doc/talks/2024-asd/pres

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README.md

README.md

NeoNet

NeoNet datasets

NeoNet app
_{^{mapping the Late Mesolithic/Early Neolithic transition}}

TODO

NeoNet functions
_{^{radiocarbon management}}

Data

Reference data

Data preparation

Data report

Data alignment

Mapping file

Outlier dates

Wrong coordinates

Correct Site names

SPD plot

Isochrones

Example

Interpolation

EN and LM

Koppen

Classes

Koppen functions

Create KCC maps

Extract

Map

NeoNet strati

App interface

Site Stratigraphy panel

All sites panel

Strati analysis

Harris Matrix

Leafrog alignment

Sibling projects

Documentation

Communication

Files

README.md

Latest commit

History

README.md

File metadata and controls

NeoNet

NeoNet datasets

NeoNet app mapping the Late Mesolithic/Early Neolithic transition

TODO

NeoNet functions radiocarbon management

Data

Reference data

Data preparation

Data report

Data alignment

Mapping file

Outlier dates

Wrong coordinates

Correct Site names

SPD plot

Isochrones

Example

Interpolation

EN and LM

Koppen

Classes

Koppen functions

Create KCC maps

Extract

Map

NeoNet strati

App interface

Site Stratigraphy panel

All sites panel

Strati analysis

Harris Matrix

Leafrog alignment

Sibling projects

Documentation

Communication

NeoNet app
_{^{mapping the Late Mesolithic/Early Neolithic transition}}

NeoNet functions
_{^{radiocarbon management}}