WEBVTT

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In Part 3 of this course,
we describe how to prepare and share your

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qualitative research data.

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Depending on the nature of the data,
there are different approaches to sharing

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it.

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The first approach is to share data in an
open repository.

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Open repositories provide public access
to data and promote transparency,

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collaboration, and unrestricted use.

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The second approach is to archive the
data in a repository or archive with

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controlled access.

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This limits access to specific users or
groups,

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which is necessary when data privacy,
confidentiality, or security is a concern.

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When sharing data in an open repository,
you must first anonymize the data to

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protect the confidentiality of the
research participants,

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as we discussed in Part 1.

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The next step is to select an open data
repository for storing and sharing your

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data, such as Dataverse NO,
which offers a safe infrastructure for

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open research data.

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The data then need to be prepared in
accordance with the repositories'

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guidelines.

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This involves providing documentation
such as metadata, consent forms,

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interview guides, data files,
and a so-called README file.

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These documents provide important context
for the data and facilitate its reuse by

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other researchers.

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Once the data are anonymized and prepared
according to the repositories' guidelines,

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the dataset can be published and made
publicly accessible in the repository.

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Anonymizing data involves removing
personal information,

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and this can be challenging.

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It must be impossible to re-identify
individuals,

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and the process of removing identifying
characteristics must be irreversible.

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In some cases,
it can be challenging to ascertain that

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the data are completely anonymous.

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A participant's identity can potentially
be determined by linking multiple pieces

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of seemingly harmless data such as age,
gender and occupation.

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The Norwegian Data Protection Authority
has a guide for anonymization.

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You can also get help from research
administration here at the university.

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To anonymize data,
you must first locate any data that can

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potentially identify a participant.

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Who else might know that information?

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How would they know it?

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What other records might contain that
information?

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You must then decide whether to keep or
change or remove any identifying data.

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We have some examples of how to do this
in the next slides.

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And finally,
you should assess the validity of the

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anonymization by checking with your
research colleagues or the participants

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themselves and/or with research
administrators.

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Here are some examples of how to keep,
change or remove potentially identifiable

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data.

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You can keep data if if it is not very
specific.

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For example, I live in Norway.

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You can change data by blurring.

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For example,
you can delete the name of a specific

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school and replace it with a school when
it is not possible to blur.

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You can delete data by using empty
brackets as in the last example here.

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Here are more examples of blurring data.

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To protect privacy and confidentiality,
certain details in the data can be

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generalized.

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For example,
the year 1995 can be changed to more than

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25 years ago.

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Mother can be replaced with family member.
Age 22 can be represented as 18 to 25.

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Diabetes can be expressed as a physical
health condition.

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I work at INN can be altered to I work at
a university.

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These changes ensure that personal data
details are sufficiently anonymized while

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maintaining the overall context of the
information.

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After anonymizing our data,
we need to find an appropriate data

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repository.

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Researchers are free to choose an open
repository for their data as long as the

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repository is trustworthy.

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You can select either a generic data
repository such as DataverseNO or a

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subject-specific data repository.

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Dataverse NO is INN's open generic
repository for research data.

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It's also used by several other
universities and research institutions in

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Norway.

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On the library's webpages are links to
Dataverse NO and to a detailed

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description of how to create a dataset.

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An example of a subject specific data
repository is Trolling,

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Which publishes mainly qualitative data
on language and linguistics based on text

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analysis and interviews.

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Each repository has its own deposit guide.

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Here we have the DataverseNO

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basic requirements that should be
provided for each dataset.

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These are a README file which provides a
description of the methods used to gather

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the data and the context of the research,
an information letter and consent form

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containing the information that has been
provided to research objects,

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and the consent form where informants
consent to participate in the research

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and to share anonymized data from the
research. Data files,

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including interview guides,
transcriptions, field notes, photos, etc.,

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must be in preferred file formats.

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Guidelines on preferred file formats can
be found in DataverseNO.

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NO other file formats such as Excel or
SPSS files can be uploaded in addition to

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files in preferred file formats.

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When you have anonymized your data and
converted your files into preferred file

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formats,
you can follow these steps to upload your

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data and files into DataverseNO.

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77147a9d-8e4b-4a1c-9456-844a3f2a3ffb-0
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You'll need to first register as a user
using this link.

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Once registered,
you should provide a description of

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yourself and your research,
and provide metadata to help others

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understand the context and content of
your data.

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Submit important files in your dataset,
including a README file which provides a

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detailed description of your research and
data so that others can interpret and

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00:06:35.916 --> 00:06:36.440
reuse it.

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Include other relevant data files such as
the consent form as well.

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After compiling and organising all
necessary information and files,

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submit your dataset for review.

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The curators can then review the
submission to see if it complies with the

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deposit guidelines.

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Here you can see metadata for a dataset.

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Metadata includes the DOI of the dataset,
a brief description, title, author,

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00:07:06.338 --> 00:07:09.960
subject area, keywords,
related publications, etc.

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It's the metadata that makes the dataset
searchable so that others can find it.

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A detailed description in the metadata is
very important for qualitative data,

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where the context of the research is
essential for interpreting the data.

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00:07:27.160 --> 00:07:30.604
When you share data,
you need to assign a license to your

dca7d12b-349e-4570-b743-272d7dff37d0-1
00:07:30.604 --> 00:07:31.080
dataset.

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This license explains how others can use
your data.

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When reusing other researchers' data,
it is crucial to check the license and

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00:07:40.220 --> 00:07:41.000
terms of use.

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00:07:41.760 --> 00:07:45.321
Researchers can, for example,
choose a Creative Commons license,

77fd6578-9ed2-4853-9f49-5d6016b6028d-1
00:07:45.321 --> 00:07:49.870
which is a standardized way to grant the
public permission to use others' creative

77fd6578-9ed2-4853-9f49-5d6016b6028d-2
00:07:49.870 --> 00:07:51.240
work under copyright law.

09de427d-4b3e-439e-a7ed-fe04d998b8e5-0
00:07:52.320 --> 00:07:56.640
This helps others to know how they can
use, share, and build upon the data.

2b4909f0-a612-4c46-a7e8-be0f52d85f55-0
00:07:57.840 --> 00:08:00.980
CC0 is a is commonly used for research
data,

2b4909f0-a612-4c46-a7e8-be0f52d85f55-1
00:08:00.980 --> 00:08:04.120
and it is the default choice in
DataverseNO.

ce514812-4b55-490e-9397-6e9a3afae3f7-0
00:08:04.920 --> 00:08:09.460
This license is an increasingly accepted
method for enabling data reuse with a

ce514812-4b55-490e-9397-6e9a3afae3f7-1
00:08:09.460 --> 00:08:10.840
minimum of restrictions.

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00:08:11.760 --> 00:08:14.670
However,
those who use your research data still

134b0258-8fe6-45ff-b5b8-3ea3796f0d3f-1
00:08:14.670 --> 00:08:18.066
need to cite you,
and when you use someone else's data,

134b0258-8fe6-45ff-b5b8-3ea3796f0d3f-2
00:08:18.066 --> 00:08:19.400
you need to cite them.

34f6fc0f-1132-4316-833b-2fdd8f132269-0
00:08:20.360 --> 00:08:24.248
Researchers can alternatively select
other types of licenses with more

34f6fc0f-1132-4316-833b-2fdd8f132269-1
00:08:24.248 --> 00:08:24.960
restrictions.

583a74cc-410e-424c-80d7-0f3c5c3e815d-0
00:08:26.880 --> 00:08:30.276
INN's Open repository for research data,
DataverseNO,

583a74cc-410e-424c-80d7-0f3c5c3e815d-1
00:08:30.276 --> 00:08:34.679
is also used by several other
universities and university colleges in

583a74cc-410e-424c-80d7-0f3c5c3e815d-2
00:08:34.679 --> 00:08:35.119
Norway.

d837f47e-286a-4d61-99d6-69940d66ec54-0
00:08:35.920 --> 00:08:40.975
On the library's webpages are links to
DataverseNO and to a detailed description

d837f47e-286a-4d61-99d6-69940d66ec54-1
00:08:40.975 --> 00:08:43.160
of how to how to prepare a dataset.

0a71c459-d4f1-4404-9fa4-9070fb281474-0
00:08:43.960 --> 00:08:46.250
When you've submitted a dataset for
review,

0a71c459-d4f1-4404-9fa4-9070fb281474-1
00:08:46.250 --> 00:08:48.904
it will be automatically sent to us to be
curated,

0a71c459-d4f1-4404-9fa4-9070fb281474-2
00:08:48.904 --> 00:08:52.600
and you'll hear from us if there are any
changes that need to be made.

cf8f4e2b-9691-42e9-a0ae-264c5d9563ff-0
00:08:53.280 --> 00:08:56.960
You can contact us if you have questions
about publishing your research data.

5d00a382-3a27-46d1-8c75-59ca56f49b16-0
00:08:58.360 --> 00:09:02.464
We've now described how to share your
data in open repositories,

5d00a382-3a27-46d1-8c75-59ca56f49b16-1
00:09:02.464 --> 00:09:07.200
but it's also possible to share it in
repositories with controlled access.

2adced96-5eb0-42b1-b10a-312f23eb88d9-0
00:09:09.000 --> 00:09:13.900
If data is not anonymized and you have
permission from the research subjects to

2adced96-5eb0-42b1-b10a-312f23eb88d9-1
00:09:13.900 --> 00:09:17.636
archive and share it with other
researchers in a secure way,

2adced96-5eb0-42b1-b10a-312f23eb88d9-2
00:09:17.636 --> 00:09:20.760
you can use data repositories with access
control.

6a980c48-c22b-4444-acdf-c152ed4c9b15-0
00:09:21.160 --> 00:09:23.800
For example, in Norway, we have SIKT.

e0d407f9-7172-49f1-824b-b09f3d61822c-0
00:09:24.280 --> 00:09:28.262
If you share your data in SIKT,
the metadata will be searchable in the

e0d407f9-7172-49f1-824b-b09f3d61822c-1
00:09:28.262 --> 00:09:28.880
SurveyBank.

a8168793-5fea-43cf-ab60-86f38e69be92-0
00:09:29.520 --> 00:09:33.018
Researchers must submit a data processing
agreement to SIKT,

a8168793-5fea-43cf-ab60-86f38e69be92-1
00:09:33.018 --> 00:09:37.550
including information about how long the
data can be archived and who can gain

a8168793-5fea-43cf-ab60-86f38e69be92-2
00:09:37.550 --> 00:09:38.640
access to the data.

db6c90b2-6bb5-4f60-ac7a-a1bdf272dbb7-0
00:09:39.320 --> 00:09:43.461
If you're going to deposit your data in
repositories with access control,

db6c90b2-6bb5-4f60-ac7a-a1bdf272dbb7-1
00:09:43.461 --> 00:09:47.770
we recommend using SIKT instead of
international data archives because there

db6c90b2-6bb5-4f60-ac7a-a1bdf272dbb7-2
00:09:47.770 --> 00:09:51.800
are differences in the laws and
regulations regarding data in different

db6c90b2-6bb5-4f60-ac7a-a1bdf272dbb7-3
00:09:51.800 --> 00:09:52.360
countries.

4f6c47ee-8833-4238-977c-0896d8c4eaea-0
00:09:54.720 --> 00:09:58.319
We have now discussed the definition of
qualitative research data,

4f6c47ee-8833-4238-977c-0896d8c4eaea-1
00:09:58.319 --> 00:10:00.200
its' importance, and some examples.

55aeff04-71fa-4d79-87d6-f5fcd05081d7-0
00:10:00.640 --> 00:10:05.045
We've described what to think about
before collecting data and how to prepare

55aeff04-71fa-4d79-87d6-f5fcd05081d7-1
00:10:05.045 --> 00:10:09.000
and share your data in either open or
access-controlled repositories.

fbf2a1ac-1cc5-4ebf-b189-128ce9e8c1fa-0
00:10:10.400 --> 00:10:15.205
Our university library has useful
webpages on research data both in

fbf2a1ac-1cc5-4ebf-b189-128ce9e8c1fa-1
00:10:15.205 --> 00:10:16.760
Norwegian and English.

549ebbd2-7585-4067-b3d0-c442b5541798-0
00:10:16.760 --> 00:10:22.800
Here you can also find our university's
guidelines for research data.

eaf6f6f6-2060-4cfd-8e67-31fb74c95a26-0
00:10:22.800 --> 00:10:27.615
Here we recommend useful resources that
are relevant for research data and

eaf6f6f6-2060-4cfd-8e67-31fb74c95a26-1
00:10:27.615 --> 00:10:31.147
anonymization,
and these are the references we used in

eaf6f6f6-2060-4cfd-8e67-31fb74c95a26-2
00:10:31.147 --> 00:10:32.560
preparing this course.

644bbf48-3b0c-4261-b34d-e8f54c4b8e35-0
00:10:33.920 --> 00:10:36.760
Thank you for listening and good luck
with your research.