# data[].attributes.body
Is there any type of feedback that we can get once finished with the DMP? If any, who provides it?

If not, can we see the common types of errors that should be avoided?

# data[].attributes.headline
Collecting and processing experimental data

# data[].attributes.subject
Collecting and processing experimental data

# data[].attributes.body
How do you ensure the ethical aspect and the quality of the data when you apply experimental methods?
Is there any special requirements about experimental data when we make the DMP?

# data[].attributes.body
Other legislations

# data[].attributes.body
- raw data, processed, coded, analyzed and archived
- different questions about quality

# data[].attributes.body
Our group discussed how we put in the consent form that other people will have access to my data. My supervisors, my colleagues, my interpreters, the people transcribing  my interview from some agency, and perhaps, if the research is open source, other people too

# data[].attributes.headline
Different (cultural) contexts and languages might affect the quality. Also working alone being the only one checking the data. What can be done is trying to have "second opinions" on coding. Using text analysis tools or other automatic tools might also be helpful

# data[].attributes.subject
Different (cultural) contexts and languages might affect the quality. Also working alone being the only one checking the data. What can be done is trying to have "second opinions" on coding. Using text analysis tools or other automatic tools might also be helpful

# data[].attributes.headline
Potential problems with data management: permissions issues, reliability of data (conducting interviews in pairs), transcription (including private data), language issues (translation) 

# data[].attributes.subject
Potential problems with data management: permissions issues, reliability of data (conducting interviews in pairs), transcription (including private data), language issues (translation) 

# data[].attributes.body
- critical source analysis, contextualization
- consistency: using similar archival funds

# data[].attributes.headline
What should you take into consideration when you're using already existing material, e.g. legal documents? In terms of the accuracy of the data? 

# data[].attributes.subject
What should you take into consideration when you're using already existing material, e.g. legal documents? In terms of the accuracy of the data? 

# data[].attributes.body
How do you ensure the quality of the data and minimise the risks of data accuracy when it comes to social media feeds? As data on social media can be changed any time (either edited or deleted), how should this be taken into consideration? (I will be doing content analysis in closed groups on Facebook)

# data[].attributes.body
- concealed observation: ethical issues: consent
- on the other hand, if observer's identity is known, it affects the data

# data[].attributes.body
- self-reflection to acknowledge own biases; objectivity is not possible, but we can be open about our stances and limitations

# data[].attributes.body
- intercoder reliability, quality of the coding protocol, coding instructions

# data[].attributes.body
- questions, who interviews, sampling, trust, background of the interviewer, how well one knows the topic, how serious the interviewee is

# data[].attributes.body
Discuss in your group how the data collection, analysis and processing methods used may affect the quality of the data and how you will minimise the risks related to data accuracy?