diff_months: 16

Data management Plan Assignment

Download Solution Now
Added on: 2022-10-28 05:53:59
Order Code: 472845
Question Task Id: 0
  • Country :

    United Kingdom


Data Management Plan

0. Proposal name

Exactly as in the proposal that the data management plan accompanies

1. Description of the data

1.1       Type of study                                                                  

Up to three lines of text that summarise the type of study (or studies) for which the data are being collected.

1.2       Types of data

Types of data to be managed in the following terms: quantitative, qualitative; generated from surveys, clinical measurements, interviews, medical records, electronic health records, administrative records, genotypic data, images, tissue samples...

1.3       Format and scale of the data

File formats, software used, number of records, databases, sweeps, repetitions… (in terms that are meaningful in your field). Do formats and software enable sharing and long-term validity of data?

2. Data collection / generation

Focus in this template on the good practice and standards for ensuring new data are of high quality and processing is well documented.

2.1       Methodologies for data collection / generation

How the data will be collected/generated and which community data standards (if any) will be used at this stage.

2.2       Data quality and standards

How consistency and quality of data collection / generation will be controlled and documented, through processes of calibration, repeat samples or measurements, standardised data capture or recording, data entry validation, peer review of data or representation with controlled vocabularies.

3. Data management, documentation and curation

Keep this section concise and accessible to readers who are not data-management experts. Focus on principles, systems and major standards. Focus on the main kind(s) of study data. Give brief examples and avoid long lists.

3.1       Managing, storing and curating data.

Briefly describe how data will be stored, backed-up, managed and curated in the short to medium term. Specify any community agreed or other formal data standards used (with URL references). [Enter data security standards in Section 4].

3.2       Metadata standards and data documentation

What metadata is produced about the data generated from the research/innovation? For example, descriptions of data that enable research/innovation data to be used by others outside of your own team. This may include documenting the methods used to generate the data, analytical and procedural information, capturing instrument metadata alongside data, documenting provenance of data and their coding, detailed descriptions for variables, records, etc.

3.3       Data preservation strategy and standards

Plans and place for long-term storage, preservation and planned retention period for the data. Formal preservation standards, if any. Indicate which data may not be retained (if any).

4. Data security and confidentiality of potentially disclosive information

This section MUST be completed if your data includes personal data relating to human participants. For other research/innovation, the safeguarding and security of data should also be considered. Information provided will be in line with your ethical review. Please note this section concerns protecting the data, not the patients.

4.1       Formal information/data security standards

Identify formal information standards with which your study is or will be compliant. An example is ISO 27001. If your organisation is ISO compliant, please state the registration number.

4.2       Main risks to data security

All personal data has an element of risk. Summarise the main risks to the confidentiality and security of information related to human participants, the level of risk and how these risks will be managed. Cover the main processes or facilities for storage and processing of personal data, data access, with controls put in place and any auditing of user compliance with consent and security conditions. It is not sufficient to write not applicable under this heading.

5. Data sharing and access

Identify any data repository (-ies) that are, or will be, entrusted with storing, curating and/or sharing data from your study, where they exist for particular disciplinary domains or data types.  


5.1       Suitability for sharing

Is the data you propose to collect (or existing data you propose to use) in the study suitable for sharing?  If yes, briefly state why it is suitable.

If No, indicate why the data will not be suitable for sharing and then go to Section 6.

5.2       Discovery by potential users of the research/innovation data

Indicate how potential new users (outside of your organisation) can find out about your data and identify whether it could be suitable for their research/innovation purposes, e.g. through summary information (metadata) being readily available on the study website or in databases or catalogues. How widely accessible is this depository?

Indicate whether your policy or approach to data sharing is (or will be) published on your study website (or by other means).

5.3       Governance of access

Identify who makes or will make the decision on whether to supply the data to a potential new user.

Indicate whether the data will be deposited in and available from an identified community database, repository, archive or other infrastructure established to curate and share data.

5.4       The study team’s exclusive use of the data

Scientific advancement is aided by timely data sharing, although a limited, defined period of exclusive use of data for primary research/innovation may be reasonable according to the nature and value of the data, and that this restriction on sharing should be based on simple, clear principlesWhat are the timescale/dependencies for when data will be accessible to others outside of your team? Summarize the principles of your current/intended policy.

5.5       Restrictions or delays to sharing, with planned actions to limit such restrictions

Restriction to data sharing may be due to participant confidentiality, consent agreements or intellectual property rights. Strategies to limit restrictions may include data being anonymised or aggregated; gaining participant consent for data sharing; gaining copyright permissions. For prospective studies, consent procedures should include provision for data sharing to maximise the value of the data for wider research/innovation use, while providing adequate safeguards for participants. As part of the consent process, proposed procedures for data sharing should be set out clearly and current and potential future risks associated with this explained to participants.

5.6       Regulation of responsibilities of users

Indicate whether external users are (will be) bound by data sharing agreements, setting out their main responsibilities.

6.         Responsibilities

Apart from the PI, who is responsible at your organisation/within your consortia for:

·         study-wide data management

·         metadata creation,

·         data security

·         quality assurance of data.

7. Relevant institutional, departmental or study policies on data sharing and data security

Please complete, where such policies are (i) relevant to your study, and (ii) are in the public domain, e.g. accessible through the internet. For example, policies for the University of a cohort study you are using in your study.

Add any other policies that are relevant


Data Management Policy & Procedures

Data Security Policy

Data Sharing Policy

Institutional Information Policy



8. Author of this Data Management Plan (Name)





  • Uploaded By : Katthy Wills
  • Posted on : October 28th, 2022
  • Downloads : 0
  • Views : 136

Download Solution Now

Can't find what you're looking for?

Whatsapp Tap to ChatGet instant assistance

Choose a Plan


80 USD
  • All in Gold, plus:
  • 30-minute live one-to-one session with an expert
    • Understanding Marking Rubric
    • Understanding task requirements
    • Structuring & Formatting
    • Referencing & Citing


30 50 USD
  • Get the Full Used Solution
    (Solution is already submitted and 100% plagiarised.
    Can only be used for reference purposes)
Save 33%


20 USD
  • Journals
  • Peer-Reviewed Articles
  • Books
  • Various other Data Sources – ProQuest, Informit, Scopus, Academic Search Complete, EBSCO, Exerpta Medica Database, and more