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:

1. curated datasets
2. interactive app
3. R functions

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

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.

Data

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: ```R 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](https://github.com/zoometh/neonet/blob/main/inst/extdata/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. ```R 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 Spain 36.57873 lon shortref 1 -6.226273 Cantillo-Duarte et al. 2010 ``` The list of aberrant dates, for example [c14_aberrant_dates.tsv](https://github.com/zoometh/neonet/blob/main/inst/extdata/c14_aberrant_dates.tsv), is used to discard some dates with the `neo_dbs_rm_date()` function ```R 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](https://github.com/zoometh/neonet/blob/main/doc/run_outliers.R) #### Wrong coordinates Site coordinates can be wrong, for example for the Jordian site of Sabha ([Xronos](https://xronos.ch/sites/12960) | [Google maps](https://maps.app.goo.gl/r4GEuFyzwysAwLe89)). Curated coordinates are listed in the [c14_corrected_coordinates.tsv](https://github.com/zoometh/neonet/blob/main/inst/extdata/c14_corrected_coordinates.tsv) file and handle by the `neo_dbs_coord_dates()` function. ### 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. ```R 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](https://zoometh.github.io/neonet/doc/#export_dates). For example [neonet-data-2023-09-24.geojson](https://github.com/zoometh/neonet/blob/main/results/neonet-data-2023-09-24.geojson), see also: [isochrones](https://github.com/zoometh/neonet?tab=readme-ov-file#isochrones) ```R neo_spd(df.c14 = "https://raw.githubusercontent.com/zoometh/neonet/main/results/neonet-data-2023-09-24.geojson", export = T) ``` ### Isochrones Create a map with isochrone contours to model the spread of Neolithic. The file [neonet-data-2023-09-24.geojson](https://github.com/zoometh/neonet/blob/main/results/neonet-data-2023-09-24.geojson) is an export from the NeoNet app (see "export dates" in the [web document](https://zoometh.github.io/neonet/doc/#export_dates)). 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. ```R 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](https://andrewcparnell.github.io/Bchron/) R package)

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

#### Earliest Neolithic and Latest Paleolithic The same function can be used symetrically: instead of plotting the earliest dates of the Neolithic, one can also plot the latest dates of the Paleolithic ```R 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 app integrates Koppen Climate Classification (KCC) for 6,000 BP to 10,000 BP created with the R [pastclim](https://github.com/EvolEcolGroup/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. ```R # 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](https://raw.githubusercontent.com/zoometh/neonet/main/doc/data/clim/koppen_11k.tif)| | |koppen_10k [download](https://raw.githubusercontent.com/zoometh/neonet/main/doc/data/clim/koppen_10k.tif)| | |koppen_9k [download](https://raw.githubusercontent.com/zoometh/neonet/main/doc/data/clim/koppen_9k.tif)| | |koppen_8k [download](https://raw.githubusercontent.com/zoometh/neonet/main/doc/data/clim/koppen_8k.tif)| | |koppen_7k [download](https://raw.githubusercontent.com/zoometh/neonet/main/doc/data/clim/koppen_7k.tif)| | |koppen_6k [download](https://raw.githubusercontent.com/zoometh/neonet/main/doc/data/clim/koppen_6k.tif)| Past Koppen Climate Classification calculated in Kyears BP with the pastclim R package ### Classes The Koppen Climate Classes are listed [here](https://github.com/zoometh/neonet/blob/main/inst/extdata/koppen.tsv) | code | num | value | hexa color | 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 |  | ### 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.). The `neo_kcc_map()` creates a KCC map with a layer of dates above ```R 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

Or: ```R ``` 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 ```R 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

```mermaid flowchart TD A[NeoNet dataset] --is read by--> B{{neonet-strati}}:::neonetshiny; B --edit
site stratigraphy--> B; B --export
site stratigraphy
file--> C[Roc du Dourgne_2023-07-30.csv]; C --is read by--> D{{neonet_strat}}:::neonetfunct; D --export --> E[maps
charts
listings
...]; classDef neonetfunct fill:#e3c071; classDef neonetshiny fill:#71e37c; ```

Strati app and strati analysis overall Workflow

### App interface The app is composed by different panels: a site to be recorded ([Site Stratigraphy panel](https://github.com/zoometh/neonet?tab=readme-ov-file#site-stratigraphy-panel)), and the complete dataset ([All sites panel](https://github.com/zoometh/neonet?tab=readme-ov-file#all-sites-panel)). A site name is copied from [All sites panel](https://github.com/zoometh/neonet?tab=readme-ov-file#all-sites-panel) to [Site Stratigraphy panel](https://github.com/zoometh/neonet?tab=readme-ov-file#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](https://github.com/zoometh/neonet#harris-matrix)) #### Harris Matrix The output CSV file exported by [NeoNet-strati](https://github.com/zoometh/neonet#neonet-strati) can be read by the `neo_strat()` function. For example, ploting the `C14Age` and the `PhaseCode`. ```R 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](https://zoometh.github.io/neonet/doc/#3_data_panel)) ```R 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:

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](https://xronos.ch/), the radiocarbon aggregator [C14bazAAR](https://github.com/ropensci/c14bazAAR), and many others, see the [open-archaeo](https://open-archaeo.info/tags/radiocarbon-dating-calibration-and-sequencing/) site ## Documentation * NeoNet app [web document](https://zoometh.github.io/neonet/doc/) * [Contribution rules](https://github.com/zoometh/neonet/blob/master/github/CONTRIBUTING.md) * [NeoNet package license](https://github.com/zoometh/neonet/blob/master/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 * SIMEP - GitHub: https://zoometh.github.io/neonet/doc/talks/2024-simep/pres