DOI: 10.7910/dvn/maigqe
title: Data and Inferences from a Collection of Pan Troglodytes Specimens from Rwanda, the Republic of Zaire (present-day Democratic Republic of the Congo), and Beyond

description: These data granularly explore the substance of collections data of Pan troglodytes (PT) specimens stored at the illustrious Field Museum of Natural History (FMNH) in Chicago, Illinois. Describing a specific dataset in the wake of thorough reviewing and analyzing, these collections reveal patterns that could lead to new research questions and hypotheses, calling for a deepening of our scientific understanding of the species' distribution and biology. Thematically, at its core, this data extraction and analysis emphasize the importance of studying chimpanzees, in light of their close genetic relationship(s) to human beings, in allowing for the unearthing of insights into human health and disease. In this work, Python code for data extraction is also provided, in describing extraction methodology and data categorization. Furthermore, this piece and data highlight some of the barriers associated with studying museum collections, concluding that studying PT collections data from the Field Museum can provide valuable insights that would inform conclusions in ecology, primate biology, biogeography, taxonomy, as well as anatomy, and physiology.
 

 The Field Museum of Natural History (FMNH), often referred to as the Field Museum, is an internationally-acclaimed, natural history museum located in one of the U.S.’s most populous cities, i.e., Chicago, Illinois. Named in honor of its primary benefactor as well as University of Chicago founding benefactor Marshall Field, the Museum is renowned for its exceptional scientific and educational programs, as well as its vast assortment of scientific specimens and artifacts. Internationally, the Museum’s zoological collections are regarded as one of the world’s most extensive collections, comprising millions of specimens preserved in dryness, fluids, or ice (e.g., to facilitate anatomical research and DNA analysis). Aiming to extract collections data to gather information on Pan troglodytes (PT), i.e., common chimpanzees, a search of museum records was conducted. The primary objective of this data extraction, and its subsequent analysis, entailed providing significant benefits, including a deeper understanding of the species’ taxonomy, ecology, and evolution. Additionally, surveying and analyzing PT collections aligns with uncovering patterns, which can foster the generation of novel research questions and hypotheses regarding primates and human beings. Furthermore, this work has the potential to document PT’s distribution and range, shedding light on PT interactions with other species and the environment. Fundamentally, therefore, studying PT collections data from a renowned institution like the Field Museum offers valuable insights into ecology, primate biology, biogeography, taxonomy, as well as anatomy and physiology —contributing incrementally to the advancement of scientific knowledge in these realms.

 Being close genetic relatives to human beings, chimpanzees allow for the harnessing of important insights into human health and disease. For instance, studying the immune system of chimpanzees has led to a new understanding of human immunology. Moreover, studying PT collections data offers the additional benefit of promoting greater cultural understanding. Chimpanzees bear cultural significance in various human societies, and exploring museum collections can therefore enhance people’s comprehension of the cultural significance of these animals. Despite these benefits, some barriers associated with studying museum collections exist. For instance, the FMNH notes that its web database does not represent the museum’s complete zoological holdings, and documentation for specimens may differ based on when and how they were collected, as well as how recently they were acquired. Furthermore, while steps are taken to ensure the accuracy of the information in the FMNH database, per FMNH, some of the collections data may contain inaccuracies. 

 Methodologically, based on the benefits of studying PT collections data, a search was conducted on the FMNH database to extract all available data regarding this primate species. The search criteria focus solely on the species’ name, resulting in 61 specimens with corresponding data. The extracted data, downloaded from the Museum in a comma-separated values (CSV) file format, were categorized by Occurrence ID, Institution Code, Collection Code, Catalog Number, Taxonomic Name, Phylum, Class, Order, Family, State/Province, County, Locality, DwC_Sex, Sex, Tissue Present, IRN, Prep Type, Date Collected, and PriLoan Status. Some columns from the original file, such as IRN, were duplicated and deleted, while other columns, such as Identified By, Type Status, Continent/Ocean, Preservation, Life Stage, Collection Methods, Available Data, Latitude, Longitude, Count, Birds/Mammals Parts, Measurement, and Measurement Value, contained empty cells.

 An alternative method for producing the CSV file using Python entails using the following lines of code, encompassing:

import requests
from time import sleep
import pandas as pd
from lxml import etree
from bs4 import BeautifulSoup

with open("input.txt", "r") as f:
 url = f.readline().strip()

headers = {
 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
 'Accept-Language': 'en-US,en;q=0.9',
 'Cache-Control': 'max-age=0',
 'Connection': 'keep-alive',
 'If-None-Match': '"1677430844-1"',
 'Sec-Fetch-Dest': 'document',
 'Sec-Fetch-Mode': 'navigate',
 'Sec-Fetch-Site': 'none',
 'Sec-Fetch-User': '?1',
 'Upgrade-Insecure-Requests': '1',
 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36',
 'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Google Chrome";v="110"',
 'sec-ch-ua-mobile': '?0',
 'sec-ch-ua-platform': '"Windows"'
}

response = requests.get(url, headers=headers)
response_text = response.text
soup_bs4 = BeautifulSoup(response_text, 'html.parser')
tree = etree.HTML(response_text)

records_divs = tree.xpath('//div[contains(@class,"views-row views-row")]')
print(len(records_divs))

records = [ { 'irn': record_div.find('span', text='IRN: ').find_next().text, 'irn_link': record_div.find('span', text='IRN: ').find_next('a')['href'],
 'catalog_subset': record_div.find('span', text='Catalog Subset: ').find_next(default='No Entry Available.').text,
 'higher_classification': record_div.find('span', text='Higher Classification: ').find_next(default='No Entry Available.').text,
 'catalog_number': record_div.find('span', text='Catalog number: ').find_next(default='No Entry Available.').text,
 'taxonomic_name': record_div.find('span', text='Taxonomic Name: ').find_next(default='No Entry Available.').text,
 'dwc_locality': record_div.find('span', text='DwC Locality: ').find_next(default='No Entry Available.').text,
 'collector_field_number': record_div.find('span', text='Collector Field Number: ').find_next(default='No Entry Available.').text,
 'collection_date': record_div.find('

 To yield the conclusions below, the current uploaded data were formatted by removing the duplicated IRN column as well as an empty column lacking a label or any data. For improved readability, the Date Collected column was reformatted to display days, months, and years. And despite lacking useful data, the columns with labels were retained.

 Based on the available information, the following conclusions can be drawn about the collection specimens:

(1) The specimens with a Country of Collection label originate from Zaire, specifically Kivu and Ituri County, as well as Rwanda.

(2) Each specimen has a unique Occurrence ID, IRN, Catalog Number, Field Number, Collector/Field entry, Country, Locality, Prep Type, and Date Collected.

(3) Only two specimens with a PriLoan Status indication were found and are identifiable by IRN 2541663 and 2546396.

(4) All specimens share the same Institution Code, Taxonomic Name (Pan troglodytes), Phylum (Chordata), Class (Mammalia), Order (Primates), and Family (Hominidae).

(5) The specimens are from different catalog subsets, suggesting that they were acquired from various collections or sources.

(6) Most specimens are identified as either male or female, but some lack sex information.

(7) Tissue is available for only two specimens, i.e., those with IRN 2626187 and 2678884.

(8) Some specimens have coordinates, while others do not.

(9) The specimens were obtained from various locations, including zoos and unidentified locations.

(10) Those with collection dates were obtained on different dates between 1924 and 1982.

(11) Most specimens were collected as skulls or skeletons, though some include tissue samples and skin.

(12) The specimens were obtained from different individuals, as evidenced by the distinct Collector/Field entries.

(13) Specimens collected from Paris, Ringling Brothers Circus, and Ward’s Natural Science Establishment are among those with well-known, and perhaps interesting, Collector/Field entries.

(14) Image files are available for some specimens.

 Given the above conclusions, studying collections data from renowned institutions like the Field Museum can offer valuable insights into PT anatomy, physiology, ecology, and even history. The data can be employed to advance scientific knowledge and support medical research. Although studying museum collections faces some systemic barriers, the benefits of such a practice outweigh the difficulties. The conclusions drawn from the information on the FMNH-collected PT specimens are that PT specimens are limitedly diverse, originating from Zaire (present-day Democratic Republic of the Congo) and Rwanda, and possess unique occurrence IDs, catalog numbers, preparation types, and collection dates. More importantly, tissue is presently available for two PT specimens, and most specimens were collected as skulls or skeletons. Given these findings and others, it becomes clear that studying primate collections data can contribute importantly toward more thoroughly understanding primates.