Sound Data Management Training

h1. WP1.2 Online Training Material

(back to [[Wiki]])

{{>toc}}

h2. By Stage Of Research

h3. Before Research 

* Planning research data management

h3. During Research

* Backing up research data

* Documenting data

h3. At The End Of A Piece of Research

(Includes on publication of a paper based on your research)

* Archiving research data

* Publishing research data

h2. C4DM Researcher use cases

h3. Quantitative testing - machine testing

bq. A common use-case in C4DM research is to run a newly-developed analysis algorithm on a set of audio examples and evaluate the algorithm by comparing its output with that of a human annotator. Results are then compared with published results using the same input data to determine whether the newly proposed approach makes any improvement on the state of the art.

Two main cases:

* New data, existing algorithms

* Existing data, new algorithm

Data involved includes:

* Software for the algorithm (which can be hosted on "Sound Software":http://www.soundsoftware.ac.uk)

* An annotated dataset against which the algorithm can be tested

* Results of applying the new algorithm and competing algorithms to the dataset

* Documentation of the testing methodology

Note that *if* other algorithms have published results using the same dataset and methodology, then results should be directly comparable between the published results and the results for the new algorithm. In this case, most of the methodology is already documented and only details specific to the new algorithm (e.g. parameters) need separately recording.

Also, if the testing is scripted, then the code used would be sufficient documentation during the research - readable documentation only being required at publication.

If no suitable annotated dataset already exists, a new dataset may be created including:

* Selection of underlying (audio) data (the actual audio may be in the dataset or the dataset may reference material - e.g. for [[Copyright|copyright]] reasons)

* Ground-truth annotations for the audio and the type of algorithm (e.g. chord sequences for chord estimation, onset times for onset detection)

h3. Qualitative testing

An example would be using interviews with performers to evaluate a new instrument design.

Data involved may include:

* the interface design

* Captured audio from performances

* Recorded interviews with performers (possibly audio or video)

* Interview transcripts

The research may also involve:

* Demographic details of participants

* Identifiable participants (Data Protection])

* Release forms for people taking part

and *will* involve:

* ethics-board approval

h3. Publication

Additionally, publication of results will require:

* Summarising the results

* Publishing the paper

Note that the EPSRC data management principles require sources of data to be referenced.

h3. Primary Investigator (PI)

The data management concerns of a PI will largely revolve around planning and appraisal of data management for research projects.

Areas of interest may involve:

* legalities (Freedom of Information, Copyright and Data Protection)

* data management plan

** covering the research council requirements

** during the project

** data archiving

** data publication

* After the project is completed, an appraisal of how the data was managed should be carried out as part of the project's "lessons learned"

Data management training should provide an overview of all the above, and keep PIs informed of any changes in the above that affect data management requirements.

The DCC DMP Online tool provides a series of questions which allow the user to build a data management plan which will match research council requirements.

h2. Overarching concerns

Human participation - ethics, data protection

Audio data - copyright

Storage - where ? how ? SLA ?

Short-term resilient storage for work-in-progress

Long-term archival storage for research data outputs

Curation of archived data - refreshing media and formats

Drivers - FoI, RCUK