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Digital Data Management - Biological Module

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[Slide 1]

Bringing Digital Data Management Training into Methods Courses for Anthropology

Biological Anthropology: Principles and Practices of Digital Data Management

George H. Perry
2016

[Slide 2]

Recommended citation:

Perry, George H. “Biological Anthropology: Principles and Practices of Digital Data Management.” In Bringing Digital Data Management Training into Methods Courses for Anthropology, edited by Blenda Femenías. Arlington, VA: American Anthropological Association, 2016.

http://www.americananthro.org/methods

© American Anthropological Association 2016

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Bringing Digital Data Management Training into Methods Courses for Anthropology is a set of five modules:

  1. General Principles and Practices of Digital Data Management
  2. Archaeology: Principles and Practices of Digital Data Management
  3. Biological Anthropology: Principles and Practices of Digital Data Management
  4. Cultural Anthropology: Principles and Practices of Digital Data Management
  5. Linguistic Anthropology: Principles and Practices of Digital Data Management

Project support: National Science Foundation, Workshop Grant 1529315; Jeffrey Mantz, Program Director, Cultural Anthropology

[Slide 3]

Organization

  1. Review of material from “General principles and practices” module
  2. Advantages for biological anthropology in data sharing
  3. Challenges for biological anthropology in data sharing
  4. Databases and considerations for various types of data
  5. Primary data compared to processed data
  6. Exercises
  7. References
  8. Acknowledgments

[Slide 4]

Review of material from “General principles and practices” module

[Slide 5]

Advantages for biological anthropology in data sharing

[Slide 6]

Biological anthropology as a data-rich discipline

A very partial list of biological anthropology data types:

Behavioral records, fossils, isotopic measurements, bones, hormone measurements, X-ray images, microscopic images, tissues, skeletal measurements, medical records, biomechanical models, cadavers, bioarchaeological assessments of age and sex, genetic/ genomic genotypes and sequences, volatile organic compound measurements, computed tomography images, geocoded sample information, environmental/ ecological data, food mechanical and nutritional properties, paleopathological differential diagnoses, histological data, energetics data

[Slide 7]

Biological anthropology data types

[Outside-class exercise: Discuss data types]

[Slide 8]

Data sharing and scientific impact

 

The graphic shows the results of an analysis based on 10,555 studies (from 2001– 2009) that generated gene expression microarray data. [credit] Piwowar and Vision 2013. Published under prevailing CC BY license: https://peerj.com/about/FAQ/license. DOI 10.7717/peerj.175/fig-1

 

[Slide 9]

Challenges for biological anthropology in data sharing

 

[Slide 10]

Challenges for biological anthropology in data sharing



 

Golden-crowned sifaka (Propithecus tattersalli) near Daraina, Madagascar.
[Credit] Photograph by George Perry

 

[Slide 11]

Challenges for biological anthropology in data sharing

[Slide 12]

Databases: Anthropological genetics/genomics

GenBank: A database maintained at National Institutes of Health (NIH) since 1982 for depositing determined nucleotide sequences of a gene/ genomic region for specified individuals and organisms.

[Image, Graph showing growth of GenBank, number of base pairs, 1/1982-1/2000.
[Credit] Wikimedia Commons. https://commons.wikimedia.org/wiki/File:Growth_of_Genbank.svg

[Slide 13]

Databases: Anthropological genetics/genomics

 

Reference sequences for the human genome and for other organisms, including archaic hominins such as Neandertals, are now available.

[Image, graph with pie chart] [caption] Exponential growth of the Sequence Read Archive, 2009–2013 [credit] Wikimedia Commons, by Ben Moore: https://commons.wikimedia.org/wiki/File:History_(and_predicted_future)_size_of_the_Sequence_Read_Archive.svg

 

[Slide 14]

Databases: Paleoanthropology/Skeletal biology

 

For some analyses, it is important to work with original fossil and skeletal material.

 


[Image, caption] Homo naledi mandible
[Credit] Wikimedia Commons, by Patrick Randolph-Quinney.
https://commons.wikimedia.org/wiki/File:Homo_naledi_mandible_2.jpg

 

[Slide 15]

Databases: Paleoanthropology/Skeletal biology

 

[Slide 16]

Databases: Paleoanthropology/Skeletal biology

[Image] 3D CT of bilateral mandible fracture
[Credit] Wikimedia Commons, by Coronation Dental Specialty Group,
http://commons.wikimedia.org/wiki/File:3D_CT_of_bilateral_mandible_fracture.jpg

[Optional exercise: 3D scanning and printing of skeletal elements]

 

[Slide 17]

Databases: Paleoanthropology/Skeletal biology

Resources available online:

[Slide 18]

Databases: Primatology

[Slide 19]

Primary data compared to processed data

[In-class exercise: Discussion of data collection and management]

[Slide 20]

In-class exercise: Discussion of data collection and management

  1. What types of data have you generated
  2. For each situation:
  3. Imagine that you returned to your data one year after collection.

[Slide 21]

Outside-class exercise: Data types

Objectives: Identify data types used in research as published in peer-reviewed articles, and evaluate current and future access to the data.

You may select an article from journals published by the American Anthropological Association, such as American Anthropologist, or others that meet criteria for peer-reviewed journals. (Consult your university library’s website for criteria.)

  1. Select two data types used in biological anthropology studies. You may choose from the list provided on Slide 6, or suggest additional types and have your instructor confirm your selection.
  2. Locate an article in a peer-reviewed journal in which each data type is used as the basis for analysis and interpretation.
  3. For each data type in each article:

[Slide 22]

Optional exercise: 3D scanning and printing for data about skeletal elements

Instructor notes: The guidelines provided are for small groups to do the exercise in two class periods, with the actual printing between the periods likely to be done outside class at the printer’s location. The exercise can also be done by individuals, and discussed afterward in class.

If the elements are available in your university’s collection or a nearby museum, students can scan objects before printing. Another option is to use data in an existing database as a basis for printing.

  1. Have a group discussion about hominin or non-human primate fossil or skeletal elements. These could be individual bones or cranial elements.
  2. Each student should choose 4 elements that would be valuable in biological anthropological research. You will examine, 3D scan and print, and compare the resulting printed objects.
  3. Discuss the value of 3D scanning and printing
  4. Decide on the 4 elements that the group will scan and print.
  5. Have the elements printed.
  6. In the next class, continue discussion with the specimens in hand.

3D print of human skull. [Credit] Photograph by Nevit Dilmen, Wikimedia Commons

 

[Slide 23]

References

Michener, William K. “Ten Simple Rules for Creating a Good Data Management Plan.” PLoS Comput Biol 11(10) (2016): e1004525. doi:10.1371/journal.pcbi.1004525

Mills, James A., et al. Archiving Primary Data: Solutions for Long-term Studies.” Trends in Ecology and Evolution 30(10) (2015): 581–89. DOI:10.1016/j.tree.2015.07.006

Piwowar, Heather A., and Todd J. Vision. “Data Reuse and the Open Data Citation Advantage.” PeerJ 1:e175 (2013). https://doi.org/10.7717/peerj.175

Reed, Denne, et al. “Digital Data Collection in Paleoanthropology. Evolutionary Anthropology 24(6) (2015): 238-49. DOI: 10.1002/evan.21466

Whitlock, Michael C., et al. “A Balanced Data Archiving Policy for Long-term Studies.” Trends in Ecology and Evolution 31(2) (2016): 84–85. DOI: 10.1016/j.tree.2015.12.001

Wilkinson, Mark D., et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (2016). doi:10.1038/sdata.2016.18

Web resources

Dryad Digital Repository. http://datadryad.org

Figshare. https://figshare.com

Forensic Anthropology Data Bank. https://fac.utk.edu/background/

GenBank. http://www.ncbi.nlm.nih.gov/genbank/

GitHub. https://github.com

MorphoSource. http://morphosource.org/

[Slide 24]

Acknowledgments

Modules: Writers, Arienne M. Dwyer, Blenda Femenías, Lindsay Lloyd-Smith, Kathryn Oths, George H. Perry; Editor, Blenda Femenías

Discussants:
Workshop One, February 12, 2016: Andrew Asher, Candace Greene, Lori Jahnke, Jared Lyle, Stephanie Simms
Workshop Two, May 13, 2016: Phillip Cash Cash, Jenny Cashman, Ricardo B. Contreras, Sara Gonzalez, Candace Greene, Christine Mallinson, Ricky Punzalan, Thurka Sangaramoorthy, Darlene Smucny, Natalie Underberg-Goode, Fatimah Williams Castro, Amber Wutich

American Anthropological Association:
Executive Director, Edward Liebow
Project Manager, Blenda Femenías
Research Assistant, Brittany Mistretta
Executive Assistant, Dexter Allen
Professional Fellow, Daniel Ginsberg
Web Services Administrator, Vernon Horn
Director, Publishing, Janine Chiappa McKenna



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