Sound Data Management Training¶
- Sound Data Management Training
Managing research data is basic good practice. It ensures your research data is available to complete the project, reducing risk in the project; and preserves your research for future use after the project is complete, increasing the impact of the project. In addition, good research data management will ensure that: you comply with funder and institutional requirements; and consider the ethical and legal implications related to your research data.
There are many counter-examples showing that poor research data management can result in lost research. Additionally, there are the success stories where good research data management has allowed research to continue after disasters.We consider three stages of a research project, and the appropriate research data management considerations for each of those stages. The stages are:
In addition, we consider the responsibilities of a Principal Investigator regarding data management.
These online materials are an output of the JISC-funded Sound Data Management Training (SoDaMaT) project.
Before The Research - Planning Research Data Management¶
A data management plan is an opportunity to think about the resources that will be required during the lifetime of the research project and to make sure that any necessary resources will be available for the project. In addition, it is likely that some form of data management plan will be required as part of a grant proposal.The main questions the plan will cover are:
- What type of storage do you require ?
Do you need a lot of local disk space to store copies of standard datasets ? Will you be creating data which should be deposited in a long-term archive, or published online ? How will you back up your data ?
- How much storage do you require ?
Does it fit within the standard allocation for backed-up storage ?
- How long will you require the storage for ?
Is data being archived or published ? Does your funder require data publication ?
- How will this storage be provided ?
- the types of data you will be using and creating;
- available existing data management resources;
- funder requirements;
- and relevant policies (e.g. research group, institutional).
- What is the appropriate license under which to publish data ?
- Are there any ethical concerns relating to data management e.g. identifiable participants ?
- Does your research data management plan comply with relevant legislation ?
e.g. Data Protection, Intellectual Property and Freedom of Information
A minimal data management plan for a project using standard C4DM/QMUL facilities could say:
During the project, data will be created locally on researchers machines and will be backed up to the QMUL network. Software will be managed through the code.soundsoftware.ac.uk site which provides a Mercurial version control system and issue tracking. At the end of the project, software will be published through soundsoftware and data will be published on the C4DM Research Data Repository.
For larger proposals, a more complete plan may be required. The Digital Curation Centre have an online tool (DMP Online) for creating data management plans which asks (many) questions related to RCUK principles and builds a long-form plan to match research council requirements.
It is important to review the data management plan during the project as it is likely that actual requirements will differ from initial estimates. Reviewing the data management plan against actual data use will allow you to assess whether additional resources are required before resourcing becomes a critical issue.
The Digital Curation Centre (DCC) provide DMP Online, a tool for creating data management plans. The tool can provide a data management questionnaire based on institutional and funder templates and produce a data management plan from the responses. Documents are available describing how to use DMP Online.
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.
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
- 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 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)
- 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).
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.
Quantitative research - New Algorithm¶
Data involved includes:
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.
- 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.
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
- Publish data
- the interface design
- Captured audio from performances
- Recorded interviews with performers (possibly audio or video)
- Interview transcripts
Survey participants and interviewees retain 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
At The End Of The Research¶
Whether you have finished a research project or simply completed an identifiable unit of research (e.g. published a paper based on your research), you should look at:
- Archiving research data
- Publishing research data
- Reviewing the data management plan (possibly for the project final report)
- Summarising the results
- Publishing a relevant sub-set of research data / summarised data to support your paper
- Publishing the paper
Note that the EPSRC data management principles require sources of data to be referenced.
The data management concerns of a PI will largely revolve around planning and appraisal of data management for research projects: to make sure that they conform with institutional policy and funder requirements; and to ensure that the data management needs of the research project are met.A data management plan (e.g. for use in a grant proposal) will show that you have considered:
- the costs of preserving your data;
- funder requirements for data preservation and publication;
- institutional data management policy
- and ethical issues surrounding data management (e.g. data relating to human participants).
- legalities (Freedom of Information, Copyright and Data Protection)
- covering the research council requirements
- data management 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.