Sound Data Management Training

h2. During The Research

During the course of a piece of research, data management is largely risk mitigation - it makes your research more robust and allows you to continue if something goes wrong.

The two main areas to consider are:

* [[backing up]] research data - in case you lose, or corrupt, the main copy of your data;

* [[documenting data]] - in case you need to to return to it later.

In addition to the immediate benefits during research, applying good research data management practices makes it easier to manage your research data at the end of your research project.

We have identified three basic types of research projects, two quantitative (one based on new data, one based on a new algorithm) and one qualitative, and consider the data management techniques appropriate to those workflows. More complex research projects might require a combination of these techniques.

h3. Quantitative research - New Data

For this use case, the research workflow involves:

* creating a new dataset

* testing outputs of existing algorithms on the dataset

* publication of results

The new dataset might include:

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

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

Although the research is producing a single new dataset, the full set of research data involved includes:

* [[Managing Software As Data|software]] for the algorithms

* the new dataset

* identification of existing datasets against which results will be compared

* results of applying the algorithms to the dataset

* documentation of the testing methodology - e.g. method and algorithm parameters (including any default parameter values).

All of these should be [[documenting data|documented]] and [[backing up|backed up]].

Note that *if* existing algorithms have published results using the same existing datasets and methodology, then results should be directly comparable between the published results and the results for the new dataset. In this case, most of the methodology is already documented and only details specific to the new dataset need to be recorded separately.

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

h3. Quantitative research - New Algorithm

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.

Data involved includes:

* [[Managing Software As Data|software]] for the algorithm

* 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 to be recorded separately.

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

h3. Qualitative research

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

The workflow is:

* Gather data for the experiment (e.g. though interviews)

* Analyse data

* [[Publishing Research Data|Publish data]]

Data involved might include:

* the interface design

* Captured audio from performances

* Recorded interviews with performers (possibly audio or video)

* Interview transcripts

Survey participants and interviewees retain [[Copyright|copyright]] over their contributions unless they are specifically assigned to you! In order to have the freedom to publish the content a suitable rights waiver / transfer of copyright / clearance form / licence agreement should be signed. Or agreed on tape. Also, the people (or organisation) recording the event will have copyright on their materials... unless assigned/waived/licensed (e.g. video / photos / sound recordings). Most of this can be dealt with fairly informally for most research, but if you want to publish data then a more formal agreement is sensible. Rather than transferring copyright, an agreement to publish the (possibly edited) materials under a particular license might be appropriate.

Creators of materials (e.g. interviewees) always retain moral rights to their words: they have the right to be named as the author of their content; and they maintain the right to object to derogatory treatment of their material. Note that this means that in order to publish anonymised interviews, you should have an agreement that allows this.

If people are named in interviews (even if they're not the interviewee) then the [[Data Protection]] Act might be relevant.

The research might also involve:

* Demographic details of participants

* Identifiable participants ([[Data Protection]])

* Release forms for people taking part

and *is likely* to involve:

* [[Ethical Concerns|ethics-board approval]]