Sound Data Management Training » History » Version 39
Steve Welburn, 2012-07-30 03:29 PM
1 | 5 | Steve Welburn | h1. WP1.2 Online Training Material |
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2 | 1 | Steve Welburn | |
3 | 7 | Steve Welburn | (back to [[Wiki]]) |
4 | 7 | Steve Welburn | |
5 | 9 | Steve Welburn | {{>toc}} |
6 | 9 | Steve Welburn | |
7 | 28 | Steve Welburn | h2. By Stage Of Research |
8 | 28 | Steve Welburn | |
9 | 34 | Steve Welburn | h3. Before Research - Planning Research Data Management |
10 | 28 | Steve Welburn | |
11 | 32 | Steve Welburn | It is likely that some form of data management plan will be required as part of a grant proposal. The 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 can be funded through the project. |
12 | 32 | Steve Welburn | |
13 | 32 | Steve Welburn | The main three questions the plan will cover are: |
14 | 1 | Steve Welburn | * What type of storage do you require ? |
15 | 34 | Steve Welburn | 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 ? |
16 | 1 | Steve Welburn | * How much storage do you require ? |
17 | 34 | Steve Welburn | Does it fit within the standard allocation for backed-up storage ? |
18 | 1 | Steve Welburn | * How long will you require the storage for ? |
19 | 35 | Steve Welburn | Is data being archived or published ? Does your funder require data publication ? |
20 | 32 | Steve Welburn | |
21 | 1 | Steve Welburn | Appropriate answers will relate to: the types of data you will be using and creating; available existing resources; funder requirements; and relevant policies (e.g. research group, institutional). |
22 | 35 | Steve Welburn | |
23 | 35 | Steve Welburn | Additional questions may include: |
24 | 35 | Steve Welburn | * What is the appropriate license under which to publish data ? |
25 | 38 | Steve Welburn | * Does your research data management plan comply with relevant legislation ? |
26 | 38 | Steve Welburn | e.g. Data Protection, Intellectual Property and Freedom of Information |
27 | 34 | Steve Welburn | |
28 | 34 | Steve Welburn | 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. |
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30 | 36 | Steve Welburn | In order to create an appropriate data management plan, it is necessary to consider data management requirements during and after the project. |
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32 | 28 | Steve Welburn | h3. During Research |
33 | 28 | Steve Welburn | |
34 | 31 | Steve Welburn | During the course of a piece of research, dat management is largely risk mitigation - it makes your research more robust and allows you to continue if something goes wrong. |
35 | 31 | Steve Welburn | |
36 | 31 | Steve Welburn | The two main areas to consider are: |
37 | 31 | Steve Welburn | * backing up research data - in case you lose, or corrupt, the main copy of your data; |
38 | 31 | Steve Welburn | * documenting data - in case you need to to return to it later. |
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40 | 37 | Steve Welburn | 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 resarch project. |
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42 | 28 | Steve Welburn | h3. At The End Of A Piece of Research |
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44 | 28 | Steve Welburn | (Includes on publication of a paper based on your research) |
45 | 28 | Steve Welburn | |
46 | 28 | Steve Welburn | * Archiving research data |
47 | 28 | Steve Welburn | * Publishing research data |
48 | 30 | Steve Welburn | * Reviewing the data management plan |
49 | 28 | Steve Welburn | |
50 | 39 | Steve Welburn | Publication of the results of your research will require: |
51 | 39 | Steve Welburn | * Summarising the results |
52 | 39 | Steve Welburn | * Publishing the paper |
53 | 39 | Steve Welburn | |
54 | 39 | Steve Welburn | Note that the EPSRC data management principles require sources of data to be referenced. |
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56 | 29 | Steve Welburn | h2. Researcher use cases |
57 | 1 | Steve Welburn | |
58 | 29 | Steve Welburn | h3. Quantitative research |
59 | 1 | Steve Welburn | |
60 | 1 | Steve Welburn | 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. |
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62 | 29 | Steve Welburn | There are two main types of quantitative research which we consider: |
63 | 29 | Steve Welburn | * Testing new data using existing algorithms |
64 | 29 | Steve Welburn | * Using existing data, and algoritghms, to test a new algorithm. |
65 | 26 | Steve Welburn | |
66 | 1 | Steve Welburn | Data involved includes: |
67 | 1 | Steve Welburn | * Software for the algorithm (which can be hosted on "Sound Software":http://www.soundsoftware.ac.uk) |
68 | 1 | Steve Welburn | * An annotated dataset against which the algorithm can be tested |
69 | 1 | Steve Welburn | * Results of applying the new algorithm and competing algorithms to the dataset |
70 | 1 | Steve Welburn | * Documentation of the testing methodology |
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72 | 22 | Steve Welburn | 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. |
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74 | 23 | Steve Welburn | Also, if the testing is scripted, then the code used would be sufficient documentation during the research - readable documentation only being required at publication. |
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76 | 1 | Steve Welburn | If no suitable annotated dataset already exists, a new dataset may be created including: |
77 | 12 | Steve Welburn | * 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) |
78 | 11 | Steve Welburn | * Ground-truth annotations for the audio and the type of algorithm (e.g. chord sequences for chord estimation, onset times for onset detection) |
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80 | 25 | Steve Welburn | h3. Qualitative testing |
81 | 1 | Steve Welburn | |
82 | 27 | Steve Welburn | An example would be using interviews with performers to evaluate a new instrument design. |
83 | 1 | Steve Welburn | |
84 | 24 | Steve Welburn | Data involved may include: |
85 | 24 | Steve Welburn | * the interface design |
86 | 24 | Steve Welburn | * Captured audio from performances |
87 | 24 | Steve Welburn | * Recorded interviews with performers (possibly audio or video) |
88 | 24 | Steve Welburn | * Interview transcripts |
89 | 1 | Steve Welburn | |
90 | 24 | Steve Welburn | The research may also involve: |
91 | 1 | Steve Welburn | * Demographic details of participants |
92 | 1 | Steve Welburn | * Identifiable participants (Data Protection]) |
93 | 1 | Steve Welburn | * Release forms for people taking part |
94 | 1 | Steve Welburn | |
95 | 8 | Steve Welburn | and *will* involve: |
96 | 17 | Steve Welburn | * ethics-board approval |
97 | 17 | Steve Welburn | |
98 | 15 | Steve Welburn | h3. Primary Investigator (PI) |
99 | 14 | Steve Welburn | |
100 | 14 | Steve Welburn | The data management concerns of a PI will largely revolve around planning and appraisal of data management for research projects. |
101 | 14 | Steve Welburn | |
102 | 14 | Steve Welburn | Areas of interest may involve: |
103 | 14 | Steve Welburn | * legalities (Freedom of Information, Copyright and Data Protection) |
104 | 14 | Steve Welburn | * data management plan |
105 | 14 | Steve Welburn | ** covering the research council requirements |
106 | 14 | Steve Welburn | ** during the project |
107 | 14 | Steve Welburn | ** data archiving |
108 | 14 | Steve Welburn | ** data publication |
109 | 14 | Steve Welburn | * 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" |
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111 | 16 | Steve Welburn | 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. |
112 | 16 | Steve Welburn | |
113 | 19 | Steve Welburn | 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. |
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115 | 8 | Steve Welburn | h2. Overarching concerns |
116 | 8 | Steve Welburn | |
117 | 8 | Steve Welburn | Human participation - ethics, data protection |
118 | 8 | Steve Welburn | |
119 | 8 | Steve Welburn | Audio data - copyright |
120 | 10 | Steve Welburn | |
121 | 10 | Steve Welburn | Storage - where ? how ? SLA ? |
122 | 20 | Steve Welburn | |
123 | 21 | Steve Welburn | Short-term resilient storage for work-in-progress |
124 | 20 | Steve Welburn | |
125 | 21 | Steve Welburn | Long-term archival storage for research data outputs |
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127 | 1 | Steve Welburn | Curation of archived data - refreshing media and formats |
128 | 21 | Steve Welburn | |
129 | 21 | Steve Welburn | Drivers - FoI, RCUK |