Hybrid (online and in-person) workshop on organizing securing, and sharing ethnographic field data, led for graduate and undergraduate students at UC Berkeley, spring 2022.

1. Organizing and Securing
Ethnographic Field Materials
Celia Emmelhainz
UC Berkeley Anthropology Library, Apr. 2022

2.  Organizing and labeling your field materials
 Safeguarding and anonymization
 Levels of informed consent
 Using standardized vocabularies
 Whether and how to share field materials

3. At your table or in the Zoom chat, please share:
- Your name
- Your sub/field of study
- What you hope to learn today

4. Observational data are “the most
important to preserve, because these
data are the least replicable”
- Christine L. Borgman (2015)

8. Essay 62, Mullingar, New Urban Living Collection, from the Irish Qualitative Archive

9. In groups / In the Chat:

10. Actions that allow you to store,
preserve and share/reuse field
materials throughout the research
lifecycle.

11. All researchers: “should be prepared to place their data
in fully cleaned and documented form in a data
archive or library within one year after the
expiration of an award.
Before an award is made, investigators will be asked
to specify in writing where they plan to deposit their
data set” in a two-page data management plan.
- NSF guide for social and economic sciences at nsf.gov/sbe/ses/common/archive.jsp

12. Breakout Groups:
What are your concerns with organizing, curating,
and sharing ethnographic data?
What are potential benefits to organizing, curating,
and sharing ethnographic data?

13. Fears in sharing data…
Criticism of your methods, analysis, or results
Exposure or re-identification of sensitive data
Political or legal ramifications
Getting “scooped” on analysis
Low benefits, high costs

14. Reduces burden on highly studied communities
Reduces costs to new and international researchers
Lets others build on your unpublished raw material
Allows collaboration across field sites
Increases accountability for methods and ethics
Helps promote your work
Benefits of sharing selected data

15. WHAT’S IN A DATA MANAGEMENT PLAN?
What types of data will I collect?
What formats will I store them in?
Who will access and manage this data?
What methods will I use to process the data?
Where will data be stored during and after research?
How will you give context to and document your project?
How will data be shared or archived for future users?

16. Help with DMPs
Don’t do it alone! Think through data ethics with
experts; email: researchdata@berkeley.edu.

18. Type
 Text
 Tabular
 Image
Format
 Word document (.docx)
 PDF
 Handwritten
 Excel spreadsheet (xls)
 Comma-delimited text (csv)
 Google spreadsheet
 .JPG, .PNG, etc.

20. We experimented with digitizing a portion
of Elizabeth Colson’s field notes on “the
standard” of the time, a 3.5” floppy disk…
Now we have a shoebox of 3.5” disks with
files saved in 1990s proprietary software.
We could find technicians to free those
files from their fossilized form, but it would
require determination, time, and funding…
- Lisa Cliggett, 2013

21. .rtf for text (not Word!)
.csv for spreadsheets (not Excel!)
.xml for databases (not Access!)
.mpeg for video
.mp3 for audio
tiff or jpeg 2000 for images
See library.stanford.edu/research/data-management-services/data-best-practices/best-
practices-file-formats for full list

24.  Uniquely identify the text or image
 Include date, version, author
 Add pseudonym, topic, or location (for context)
 Dates in YYYY-MM-DD, to sort by date
 Leading zeros (001, 010, 100), to sort by number
 Avoid special characters like & , ( ) ! ‘ ? - + /
2015-05-18_Ulaanhus_Interview_001_CE_v3.txt
is better than notes1.txt

25. Fire, flood, earthquake
Hard drive failure
Campus server failures
Theft of your computer
Format out of date and can’t be
opened
Accidental deletion
Hacking by foreign
governments/ransomware
Loss of institutional
commitment
Can’t afford software updates
Ways to Lose Your Collected Materials:
CC
image
by
Sharyn
Morrow
on
Flickr
CC
image
by
momboleum
on
Flickr
Slide credit: DataONE Education Module 1.

26. Reducing risk:
Master vs. Access copies

28. University of Southampton, School of Electronics & Computer Science 2005
NOT ALL
IN THE
SAME
PLACE!
www.theregister.co.uk/2005/10/31/south_research_fire

29. Breakout Groups:
How are you organizing and backing up now?
What’s one step you can take to improve your
backup or file storage system?

31. The policeman asked me: “Are you responsible for this?”
“Excuse me?” I said, not quite understanding…
“You know the girls?”
“Some of them.”
“And the people who are teaching them?”
“They are all the subject of my academic research project.”
“Good,” he said. He nodded and wrote.
He finally asked me if I had any questions for him.
“Is this interview a normal procedure for Americans…?”
“No,” he said, matter-of-factly, “It is only for you.”
“Why me?”
“Your topic is interesting to us.”
- Kristen Ghodsee, anthropologist in Bulgaria (2011, p. 180).

32. Anonymize
before coding
Record
changes in log
Redact
statements
that could
cause harm
www.data-archive.ac.uk/create-manage/consent-ethics/anonymisation

33. Example A: So my first workplace was X which was
about X minutes from my home in X. My best colleagues
from day one were X, X and X and in fact, I am still very
good friends with X to this day. X lives in the same parish
still with her husband X and their X X.
Example B: So my first workplace was [name] which was
about 20 minutes from my home in Norwich. My best
colleagues from day one were Andy, Julie and Louise and
in fact, I am still very good friends with Julie to this day.
She lives in the same parish still with her husband Owen
and their son Ryan.
Anonymization of qualitative materials from
www.ukdataservice.ac.uk/manage-data/legal-ethical/anonymisation/qualitative.aspx

34. In general:
Replace direct identifiers (name, address,
birthdate) with new or general name (Friend2).
Generalize or split up indirect identifiers, which
can ID someone when combined
(race/profession/location).
Consider redacting sensitive topics.
Redact sensitive locations, of e.g. endangered
species, burial sites, sacred places.

35. Questions to Ask Yourself
 Why am I collecting this in the first place?
 Is it worth the risks to my interlocutors?
 Can I collect with less identifying information?
 Can I better encrypt this information (in
transmission/storage)?
 What can I remove or break up to minimize risk?
 Can I put this in a restricted archive with embargos or
secondary IRBs required for access?

36. Think about how you apply for IRB:
Sample language: “De-identified data from this study
may be shared with the research community…
We will remove or code personal information likely to
identify you before files are shared. . .
Despite these measures, we cannot guarantee
anonymity of your personal data.”
qdr.syr.edu/qdr-blog/participant-protection-informed-consent-and-data-sharing

37. Breakout groups:
How have you handled sensitive data at this point?
What do you see as potential opportunities and risks?

39. CC
image
by
acordova
on
Flickr

40. ● Who wrote these notes? What biases do they have?
● Why was this project undertaken? Where and when?
● What methods were used? How was consent handled?
● What limitations or gaps should I expect?
● How was this coded? Is there a hard-to-interpret code?
● Which software lets me view the video or coded text?
● If I use this, do I have rights to share my results?

41. DATA
DETAILS
Anthro. materials are selectively collected in the field
You, the PI, rapidly start to forget details, dates, people, context
The how and why of your project fade or shift over time.
Theft, disaster,
obsolete formats =
instant loss of data
You end the project, switch careers, or
retire. More mental context is lost.
Notebook or files found
without context, thrown
out = full data loss
TIME (From Michener et. al. 1997)
Adapted from DataONE slide

42. Principal investigator. Celia Emmelhainz, Kent State University
Title: Research and Information use Among Social Science Faculty and Students in Kazakhstan.
Project description. Study of how faculty and students in Central Asia undertake research in the university setting.
General ethnographic data on gender, workplace, and culture in Kazakhstan was also collected.
Sample and sampling procedures. Snowball sample of faculty, students, and librarians at Nazarbayev University; a
related questionnaire uses a snowball sample of government librarians at....
Substantive, temporal, and geographic coverage. Libraries, 2011-2014, Astana, Kazakhstan.
Unit(s) of analysis/observation. Individual librarians, faculty, and students.
Variables. N/A, qualitative research. Topics covered with students include major, department, steps in research,
hard/easy information to locate, professors’ assistance, attendance at library instruction, resourced used at the
library. Topics covered with faculty include learning to research, current projects, process for finding literature,
resources used at the library, impact of region on research, projects assigned to students, teaching information
literacy to students, databases used.
Technical information on files. .rtf and .doc for text, .jpg for images, .mp4 for video.
Data collection instruments. Partial interview questionnaire: 1. Can you tell me about your studies? Which year are
you? In which department? Prompt: why did you choose that department/major? 2. Have you done any[ . . . ]
Coding instrument. Interviews were coded in Atlas.ti and on paper for themes. Emerging themes included student
challenges in accessing and interpreting information, the need for training on critical thinking and citing materials…
Project Metadata: Data Documentation Initiative Format

43. Sample individual file:

44. Session: “Cooking Shashlik,” June 2014, Haileybury Astana, men
cooking lamb kebabs for a school staff party in Kazakhstan.
Project: Ethnography of Libraries in Kazakhstan, semi-structured
interviews and observation.
Content: Food preparation, unscripted, researcher in observation
mode, controlled (workplace) environment, end-of-year teacher
celebration, face to face, in Russian language.
Actors: “Dauren,” young Kazakh, fanning meat on skewers,
“Oleg,” Russian male from Kazakhstan, turning skewers, “Berik,”
older Kazakh man observing.
Conversation is in Russian. Ethnographer, Celia Emmelhainz,
is off-screen recording with a handheld camera.
Resources: Transcript available at [link].
References: [link to other sessions, related publications.]
Object Metadata: IDMI format for video

45. Standardizing
language:

46. From Emmelhainz 2014, Controlled Vocabularies for Anthropological Datasets

49. Someone else:
- Hosts, maintains, and backs up your field materials
- Provides access according to a deposit agreement
- Takes care of your materials after you’re gone
- Allows others to search for and cite your work
- Provides a permanent ID/stable location for your data

50. Thinking about archiving
• Restrict or embargo
• Add to consent form
• Secondary IRBs and Data
Use Agreements
• Fieldnotes will

52.  Data not available elsewhere
 Project in line with collecting policies/priorities
 Data are well-documented
 Ownership is clear
 Privacy and confidentiality of subjects is protected
 Data are in a format that facilitates ease of use and
preservation
www.dcc.ac.uk/events/research-data-management-forum-rdmf/rdmf13-preparing-data-deposit

55. Clip from EVIA: https://media.eviada.org/eviadasb/displaysegment.html?id=9-S0452

57.  Alternative to western push for “open access”
 Allows culturally appropriate digital access for
indigenous communities.
 Can return control to community
 Each community (college class,
neighborhood) can have different access.
 Cultural protocols determine whether e.g.
men or elders can view a given item.
https://sustainableheritagenetwork.org/digital-heritage/mukurtu-cms-communities-cultural-protocols-and-categories-0

58. TK Family TK Seasonal TK Women Restricted
http://localcontexts.org/tk-labels/

59. Dataset Description
Reuse Potential
Methods
Overview/Context

60. Becker, Kara; Khan, Sameer ud Dowla; Zimman, Lal,
2016, "Reed Linguistics Gender and Language Project",
Harvard Dataverse, V1,
http://dx.doi.org/10.7910/DVN/M1EKER
Creator Year
Dataset
title
Repository Version Identifier

61. A “digital identifier of an object” (DOI) can allow you to
track physical or digital research materials, including data
or resulting publications.
Graphic from www.dcc.ac.uk/resources/how-guides

62. John L. Campbell
Assistant Professor & Senior Researcher
Forest Ecosystems & Society
Oregon State University, Corvallis, OR
OR
John L. Campbell?
Forest Research Ecologist
Center for Research on Ecosystem Change
US Forest Service, Durham, NC
Sources: Oregon State College of Forestry, Center for Research on Ecosystem Change, USDA Forest Service

63. Suggestion: Sign up for ORCiD
 Register at https://orcid.org/
 Give your ORCiD to publishers to clarify
your identity vs. others with a similar
name, or connect your work under
multiple names in one profile.
Open Researcher & Contributor ID