TEMPLATE FOR A DATA MANAGEMENT PLAN
The following template can be used to develop a Data Management Plan to accompany a
research proposal. This template is based on the format suggested by the MRC. The notes (in
italics) provide further context and guidance for its completion. Text from submitted
applications is shown in red to provide an example of how sections can be addressed. Please
do not cut/paste directly from these examples. Text highlighted yellow provides
suggested text for University of Sheffield researchers to cut/paste.
If you opt NOT to use the template the topics listed in the template should be addressed if
requested by the funder.
You can also use the DMPonline tool to create a Data Management Plan. DMPonline outputs are
available a range of formats. Sign in with your institutional credentials by choosing 'University
of Sheffield' then login using MUSE. To create a new plan, select your research funder,
ensuring University of Sheffield is selected for institutional guidance and tick the box for DCC
guidance.
0. Proposal name
Exactly as in the proposal that the DMP 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.
Example: “This application encompasses experimental laboratory-based studies, a genomics
study and an experimental medicine study. The aim is to explore the role of xxx in respiratory
function and immunity from the genetic through to the biochemical and functional levels.”
Example: “This project will comprise three areas of study - in vitro, clinical cross-sectional and
in silico experiments - addressing the role of xxx in colorectal pathophysiology.”
Example: “This is a pre-clinical laboratory-based study using in vitro and in vivo
methodologies to model breast cancer metastasis in the skeleton.”
Example: “This project combines laboratory work, epidemiological and qualitative research
with an investigation into the cultural, socioeconomic, environmental and political factors that
influence both the prevalence of caries and the effectiveness of local oral healthcare
programmes.”
1.2
Types of data
Outline the types of research data that will be managed in the following terms: quantitative,
qualitative; generated from surveys, clinical measurements, models, interviews, medical
records, electronic health records, administrative records, genotypic data, images, audiovisual
data, tissue samples. Include the raw data arising directly from the research, the reduced
data derived from it, and published data.
Example: “We will collect qualitative and quantitative data from interviews, completion of
questionnaires, laboratory measurements of blood samples and the measurement of bone
mineral density.”
Example: “Qualitative and quantitative CT and MR imaging data will be recorded.
Demographic (age, sex), clinical measurements (WHO functional class, blood results, walk
test distance, lung function test data), imaging data (qualitative and quantitative cardiac and
pulmonary MRI and CT measurements), right heart catheter data (ASPIRE and National
Cohort datasets), genotypic data (National Cohort dataset) and outcome data (time to death
and deceased/alive) will be recorded.”
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Example: “Quantitative data generated from in vitro experiments will include data files from
plate readers, flow cytometry plots, light and fluorescent image files. Quantitative data of all
in vivo experiments will include uCT images, both raw and reconstructed, of mouse bones
(longitudinal and end of procedure) and images of sections. Sequence data will also be
generated from RNAseq experiments. Sample types will include human derived cells, murine
tissue samples and human patient tissue samples in addition to limited medical anonymised
data (disorder, age, sex, etc).”
1.3
Format and scale of the data
Outline and justify your choice of file formats, software used, number of records, databases,
sweeps, repetitions, etc. in terms that are meaningful in your field of research. Decisions may
be based on staff expertise, a preference for open formats, the standards accepted by data
centres or widespread usage within a given community. Do formats and software enable
sharing and long-term validity of data? Using standardised and interchangeable or open
lossless data formats ensures the long-term usability of data. See UKDS Guidance on
recommended formats.
Estimate the volume of data in MB/GB/TB and how this will grow to make sure any additional
storage and technical support required can be provided.
Example: “The majority of raw quantitative data will be stored in Microsoft Excel format with
statistical analysis performed in GraphPad Prism. Images of western blots and PCR gels will
be stored as jpegs. a slide scanning microscope, used for processed tissue sections, uses
proprietary Zen software but images are exported as jpegs. A Filemaker database is used to
store physiological data (and genotype) of animals used in experiments.
Colorimetric/fluometric data (ELISA/proliferation assays), quantitative PCR data are collected
using proprietary software but all software used can export data into sharable formats (.txt,
.xls, .jpeg, .png). Data volume will range from small <1 kB text files to image files exceeding
10 GB. The expected volume will total under 2 TB.”
Example: “A master Access spreadsheet (.mbd) will link to study-specific excel spreadsheets
(.xls) and will be backed up on a University server to maintain the long-term validity of the
data. The data will be in an anonymised format with data sharing procedures in place to
enable sharing of the data with other interested research groups. A master spreadsheet
linking to the patient identifiers will be stored on a password-protected NHS Trust computer.”
Example: “Quantitative biological data will be stored as excel and Graphpad PRISM files and
images as jpeg and pdf files. Optical, super-resolution images, uCT and multiphoton images
will be stored as tiff/jpeg files. Average amount of data for in vivo metastasis experiments will
be 20 MB and for in vivo experiments 4GB (Total across the project 37 GB). This project will
generate a large number of tissue samples from in vivo studies including primary (mammary
tumours), tibiae, femurs, serum, bone marrow, protein and RNA from 300 mice. These
samples will be stored in paraffin wax or at -80C. After all data have been collected surplus
material will be made available to other researchers via the SEARCHBreast initiative
(http://searchbreast.org).”
2. Data collection / generation
Justification for why new data collection or long term management is needed should be
included in the Case for Support. Use this section to focus on 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. Indicate how the data will be organised during the project, mentioning for
example naming conventions, version control and folder structures.
Example: “Data will be collected by specific software or handwritten (in indexed laboratory
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notebooks) and transferred into spreadsheets. Files will indicate the date of data acquisition,
an identifier for each experiment, replicate and/or time point and file version number. Files
will be stored in folders named by experiment, containing subfolders of original/raw and
edited/analysed data. When necessary for blinded analysis, independent investigators will
assign randomly generated codes for data/files and de-code these on completion of the
experimental analysis.”
Example: “Data analysis standard operating procedures (SOPs) have been developed for
generation of imaging data and all analysis will be carried out in accordance with SOP
documents. All data is electronic and will be stored in a central Access database, linking
together Excel spreadsheets.”
Example: “...The interviews will be conducted by trained post-doctoral researchers and
recorded on electronic recorders, transcribed and translated. Interview guides will be used to
direct the conversations and to enable the study team to obtain the information needed to
inform the project. All participants will be given a pseudonym will be used when referring
directly to their answers.”
Example: “.... Data collected by our partners in xxx will be securely transferred to the UK on
completion of the study and subsequently imported into the Access database.”
Example: “Data will be generated from the study protocols, mainly using the Hologic Scanner
software and GIMIAS based computer-assisted tool. The information collected from these
tools will be combined with anonymised patient extracts from hospital information systems
and expert assessments of the vertebral fracture assessment (VFA) images. All recruited
patients will be anonymised and a pseudo patient identifier eg. VERDICT_0001) will be
generated for each patient. All data collected as part of the study will be assigned to the
corresponding pseudo patient identifier. Patient demographics and clinical measurements will
be collected as Excel spreadsheets and published as records using the pseudo patient
identifier into the XNAT database. All imaging data will also be published as folders using the
same patient identifier in the XNAT database. On completion of any subsequent image
processing or expert-labelling step, the relevant data will be published using the same pseudo
patient identifier. The proposed project data heirarchy is as follows:
VERDICT_0001/
Demographics/...
Clinical measurements/...
Imaging/…
DXA/…
Spine radiograph/…
Assessments/…
VERDICT Tool/…
Experts/…”
2.2
Data quality and standards
Explain how the consistency and quality of data collection / generation will be controlled and
documented. What quality assurance processes will you adopt? This may include processes
such as calibration, repeat samples or measurements, standardised data capture or recording,
data entry validation, peer review of data or representation with controlled vocabularies.
Example: “Consistency and quality of the data collection will be tested on a monthly basis by
[the study data coordinator]. This will include scatter plots of the data and testing for
normality. Repeated sample testing will also be performed as described in the Case for
Support.”
Example: “Each in vitro experiment will be carried out at least four times using appropriate
positive and negative controls. Where feasible, technical replicates will also be carried out to
ensure consistency, robustness and data quality. Robustness of data from in vivo experiments
will be ensured through the use of littermate controls with cohort sizes as dictated by power
calculations. Acquisition parameters and experimental protocols will be standardised and
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cross-referenced to the raw data. Where analyses of immunostaining is required these will be
performed in a blinded manner and assessed by more than one person to generate a kappa
statistic.”
Example: “Data quality standards will be met through using the Hologic scanner, which is
calibrated at regular intervals in accordance with standard hospital practice. Data capture is
standardised using appropriate software, eg. Hologic scanner exporting DXA images and
GIMAS based computer-assisted tool generating VTK files. Data extraction from hospital
systems will be automated to minimise errors due to manual handling of data. Whee manual
entry is necessary, consistency will be maintained using peer review and cross checking of
results with existing measurements.”
Example: “... RNASeq data will first be checked for analysis suitability using Illumina Q scores
and the software package RNA-SeQC which generates a series of quality control metrics as an
HTML report and tab delimited files. It provides information on read counts (total, unique,
duplicate ends, rRNA reads, strands specificity), coverage (mean coverage; reads per base,
means of coefficient variation, coverage plots) as well as downsampling, GC bias and
correlation to reference expression profile. Reads will be processed and analysed using
established best practice tools such STARAlign (mapping), HTSeq/RSEM (gene and transcript
quantification) and DESeq2 (differential expression between two or more replicated groups).
Alternative methods that emerge during the project will also be considered if deemed to
significantly impact outputs.”
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.
Outline briefly how and where data will be stored, backed-up, managed and curated in the
short to medium term to ensure that data and metadata are stored securely for the lifetime
of the project. Specify any community agreed or other formal data standards used (with URL
references). [Enter data security standards in Section 4].
Note: Storing data on laptops, computer hard drives or external storage devices alone is not
recommended. The use of robust, managed storage with automatic backup is preferred by the
University and by funders. See UKDA guidance on data storage and backup. *All requests for
research data storage in the Faculty of Medicine, Dentistry and Health should be made to the
Faculty IT Hub in the first instance (med-it@sheffield.ac.uk). They will work with you to create
an appropriate folder structure and give access to authorised users.
“Data and definitive project documentation will be stored on centrally provisioned University of
Sheffield virtual servers and research storage infrastructure (
https://www.sheffield.ac.uk/cics/research) throughout the lifetime of the project. Both
Windows and Linux Virtual Servers with up to 10TB of storage are made available to research
projects. Access control is by authorised University computer account username and
password. Off-site access is facilitated by secure VPN connection authenticated by University
username and remote password. By default, two copies of data are kept across two physical
plant rooms, with a 28 day snapshot made of data and backed up securely offsite at least
daily. This service is maintained by the University’s Corporate Information and Computing
Services.
Storage of data on local hard drives and devices will be limited to xxxxxx. All mobile devices
e.g. laptops, tablets, mobile phones, external storage devices are encrypted as standard.
Google Drive may be used for more flexible collaborative working but only where non
personal-sensitive information is involved. Where Google Drive is used, copies of complete
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and definitive documents will be transferred to the main project repository on the University
research storage infrastructure.”
3.2
Metadata standards and data documentation
Describe plans for documenting, annotating and describing data so that research data are
usable by others than your own team. This may include information on the methodology used
to collect the data, analytical and procedural information, definitions of variables, units of
measurement, any assumptions made, the format and file type of the data. Include
description of documentation that will accompany the data to provide secondary users with
any necessary details to prevent misuse, misinterpretation or confusion, and will allow the
data to be read and interpreted in the future. Consider also how you will capture and create
the metadata and where it will be recorded e.g. in a database with links to each item, in a
‘readme’ text file, in file headers etc.
Researchers are strongly encouraged to use community standards to describe and structure
data, where these are in place. The DCC offers a catalogue of disciplinary metadata standards.
Suggested minimum text: “Methods and SOPs will be stored electronically in Microsoft Word
documents (.doc) with the spreadsheets containing data”
Example: “...We have operational documents for qualitative and quantitative image analyses,
detailing the image acquisition and image analysis methods on Microsoft Word documents
(.doc) that are available to third parties.”
Example: “Methods used to generate and pre-process the data will be described and stored as
open file format. Metadata information regarding scanner settings, software settings, software
version and operator information are captured with each scan of a patient and stored as part
of XNAT database and can easily be exported to an open format.”
Example: “Data will be analysed graphically and statistically using Graphpad software, and
metadata will be presented using standard statistical inferences (mean and significance tests).
Files containing raw data will be labelled logically and stored in folders in a logical hierarchical
fashion. Explanation of methods used (experimental and analytical) will be stored alongside
the raw data in the same folders, in simple text documents. For genomic data, variant scores
will be callibrated according to GATK best practice and annotated using ANNOVAR.”
3.3
Data preservation strategy and standards
Outline your plans for long-term storage, preservation and planned retention period for the
research data. Include formal preservation standards, if any. Indicate which data may not be
retained (if any). Consider any additional resources needed to prepare data for deposit or
meet charges from data repositories if not using an established repository. Most research
funders expect data to be retained for a minimum of 10 years from the end of the project. For
data that by their nature cannot be re-measured, efforts should be made to retain them
indefinitely. See the DCC guide: How to appraise and select research data for curation.
Long term preservation and access may be best managed by using a specialist data
repository. See the Library RDM webpage on Data repositories. Look in re3data.org and at
Wellcome Trust - Data repositories and database resources to find an appropriate repository.
If no suitable repository is available you may deposit data in ORDA. Alternatively, data may be
retained in the University’s research storage infrastructure and registered in ORDA. This is
suitable if you need to regulate users through ‘Data sharing agreements’.
Suggested text in all cases: “Data will be archived in line with the University of Sheffield’s
Research Data Management Policy, which is a component of the University's Policy on Good
R&I Practices (the 'GRIP' Policy)”
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Where data is in paper format: “Data collected in paper form will be routinely digitised and the
paper form disposed of / stored for at least 10 years at our universities in secured areas.”
For data deposited in external data repositories: “Research data selected for long-term
preservation and sharing will be deposited in [name of repository/weblink]. The [name of
repository] is openly accessible and searchable and will guarantee preservation of these data
for ten years or more. Metadata records describing these data will be created in ORDA, the
University of Sheffield research data registry and repository”
Where some research data are being deposited in ORDA: “Data that are not deposited in
[name of repository/weblink] will be deposited in ORDA, a repository and registry of research
data produced at the University of Sheffield, which will preserve data for ten years or more.”
Where data is deposited in ORDA only: “Data selected for long-term preservation and sharing
will be deposited in ORDA, a repository and registry of research data produced at the
University of Sheffield, which will guarantee preservation for ten years or more.”
Where data is being retained locally, but not made ‘openly’ accessible:“Data selected for longterm preservation and sharing will be stored on centrally provisioned University of Sheffield
virtual servers and research storage infrastructure (https://www.sheffield.ac.uk/cics/research)
for at least ten years. Records of these data will be published in ORDA, a registry of research
data produced at the University of Sheffield.”
Example: “Laboratory notebooks will be stored by the University of Sheffield for an indefinite
period. Digital data will be stored for a minimum of 10 years after the completion of the study,
on University of Sheffield research storage infrastructure. All personal identifiable and study
data will also be kept for a minimum of 15 years by Sheffield Teaching Hospitals NHS
Foundation Trust in a secure off-site facility run by [name of management company].”
Example: “We plan to make processed image data available via our [xxxxx] server for at least
5 years after the end of this Programme. Raw image files for important experiments will also
be made available in this way. For other data, a single archive copy will be kept for 5 years
after the end of the Programme.”
Example: “Data will be retained for the recommended 20 year period as set out in MRC
guidance ‘Personal Information in Medical Research’ section 7. The quantitative data will be
stored using the simplest data standard format of comma separated values (CSV) and the
qualitative data will be stored as anonymised MS Word transcripts to ensure the best chance
of longevity. Once the storage period has expired, all data held by
[Department/School/Centre] will be destroyed according to [Department/School/Centre]
Information Governance procedures.”
4. Data security and confidentiality of potentially disclosive personal information
Complete this section only if your research data include personal data relating to human
participants in research. Information provided will be in line with your ethical review.
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 (ISO standard for data security) Note: Although The University of
Sheffield is not an accredited ISO 27001 institution, its information standards comply with
ISO/IEC 27001:2013, demonstrating strong and robust information security structure.
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“The University of Sheffield requires its users to adhere, as a minimum, to the following
security standards, http://www.shef.ac.uk/cics/policies/infosec and where necessary, for
example where patient data is involved, more secure system policies are defined.”
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. If your research data include personal data relating to human
participants in research, it is not sufficient to write not applicable under this heading. See
UKDS guidance on data security.
Example: “Clinical data in this project will be de-identified and obtained via application to the
[name of biobank]. All information is sorted in locked filing cabinets or password-encrypted
computers which are located in locked rooms.”
5. Data sharing and access
5.1
Suitability for sharing
Indicate whether the data you propose to collect (or existing data you propose to use) in the
study will be suitable for sharing. (“Yes” or “No”)
If “No,” indicate why they will not be suitable for sharing and then go to Section 6.
Example: “Data collected through this proposal (lab and genomic data) will be suitable to
share in an anonymised format for other interested researchers. Any non-anonymised data
which is patient-identifiable will not be shared unless explicit consent is provided by families.”
Example: “The Access database will be shareable as the data will be anonymised and in a
format suitable for preservation. The paper CRFs will not be shared to protect patient
confidentiality.”
5.2
Discovery by potential users of the research data
Indicate how potential new users can find out about your data and identify whether they could
be suitable for their research purposes, e.g. through basic discovery metadata (i.e. the title,
author, subjects, keywords and publisher) being readily available on the study website, or in
other databases or catalogues. Also, indicate whether your policy or approach to data sharing
is (or will be) published on your study website (or by other means).
Most research funders recommend the use of established data repositories, community
databases and related initiatives to aid data preservation, sharing and reuse. Identify any that
will be entrusted with storing, curating and/or sharing data from your study. An international
list of data repositories is available via Databib or Re3data. See also the list maintained by
Wellcome.
Suggested text in all cases: “Records of datasets will be published in ORDA, the University of
Sheffield’s registry of research data produced at the University, which will issue DataCite DOIs
for registered datasets and promote discovery.”
Example: “At the end of the project all published models will be uploaded onto the [name of
database and link to repository] and remain available via Github. Additional datasets that we
believe may be of future value to the research community (for instance, those collected using
specialist equipments such as [xxxxx], that may not be widely available) will be selected for
longer term preservation and sharing and deposited in ORDA.”
Example: “All data will be archived on the University research storage infrastructure for 10
years after the end of the project. Gene expression profiles will also be deposited in the [name
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of database and link to repository]. These data will be MIAME compliant and allow subsequent
meta-analyses to be performed using the data.”
5.3
Governance of access
The methods used to share data will be dependent on a number of factors such as the type,
size, complexity and sensitivity of data. Identify who makes or will make the decision on
whether to supply research data to a potential new user. For population health and patientbased research, indicate how independent oversight of data access and sharing works (or will
work) in compliance with MRC policy. Indicate whether the research data will be deposited in
and available from an identified community database, repository, archive or other
infrastructure established to curate and share data.
Suggested text for use when data will not be placed in a repository: “The lead PI and project
team [including collaborators if applicable] will review applications to access experimental
data and make the decision on whether to supply research data to potential applicants. Data
will then be released on a case by case basis.”
Additional text [optional]: “Data will be made available through shared research platforms
[insert examples relevant to project] with the relevant permissions in place.”
5.4
The study team’s exclusive use of the data
Data (with accompanying metadata) should be shared in a timely fashion. It is generally
expected that timely release would be no later than publication of the main findings and
should be in-line with established best practice in the field. Research funders typically allow
embargoes in line with practice in the field, but expect these to be outlined up-front and
justified. MRC’s requirement is for timely data sharing, with the understanding that a limited,
defined period of exclusive use of data for primary research is reasonable according to the
nature and value of the data, and that this restriction on sharing should be based on simple,
clear principles.
Suggested text in all cases: “The project group (including collaborators) will have exclusive
use of the data until the main research findings are published or patent applications have
been filed [if potentially relevant to project]” and/or “...or for a period of x months/years.”
Additional text [optional]: “Following publication, data will be made available on request or
shared through the relevant research platforms.”
5.5
Restrictions or delays to sharing, with planned actions to limit such
restrictions
Outline any expected difficulties in data sharing, along with causes and possible measures to
overcome these. Restriction to data sharing may be due to participant confidentiality, consent
agreements or IPR. 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 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 research
participants.
If no restrictions are foreseen: “At present we do not foresee any delays in data sharing
following publication of the main research findings.”
For patient-based studies: “Patients will be made aware of our data sharing procedures at the
time of consent.”
Additional text [optional]: “Delays in sharing data may arise through a delayed ability to
analyse or publish the research findings.” and/or “Delays in sharing data may arise due to IPR
and if this is a factor, advice will be sought from the University’s Research & Innovation
Services.”
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5.6
Regulation of responsibilities of users
Indicate whether external users are (will be) bound by data sharing agreements, licenses or
end-user agreements setting out their main responsibilities. If so, set out the terms and key
responsibilities to be followed. Note how access will be controlled, for example by the use of
specialist services. A data enclave provides a controlled secure environment in which eligible
researchers can perform analyses using restricted data resources. Where a managed access
process is required, the procedure should be clearly described and transparent.
“External users will be bound by data sharing agreements as specified by the [name of
funder] Data Sharing Policy.”
[where an external collaborator is involved] “Data sharing agreements will be put in place with
[name of collaborator], who will be a primary re-user of data”
“The University of Sheffield’s Good Research and Innovation Practice (GRIP) Policy follows
RCUK principles for data sharing (http://www.rcuk.ac.uk/research/datapolicy/)”
6.
Responsibilities
Specify who, alongside the PI, is responsible for ensuring the study-wide data management,
as well as for specific roles such as data capture, metadata production, data quality, storage
and backup, data archiving & data sharing. For collaborative projects you should explain the
co-ordination of data management responsibilities across partners. See UKDS guidance on
data management roles and responsibilities.
Example: “In addition to the PI [name], the data capture and data management will be
supported by the Co-applicant [name] who will oversee the [xxxx] aspects of the project.”
Example: “Each member of the research team will be responsible for the management of data
relevant to this study according to guidelines set by the study protocol.”
Example: “The data co-ordinator (50% FTE) will be responsible on a day-to-day basis for
ensuring the study-wide data management, data security and quality assurance of data. The
PI will have overall responsibility for ensuring that the data management plan is adhered to.”
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. accessibly through the internet.
Add any others that are relevant. Some of the information you give in the remainder of the
DMP will be determined by the content of other policies. If so, point/link to them here.
Policy
Data
Manageme
nt Policy &
Procedures
Data
Security
Policy
Data
Sharing
Policy
Institutiona
l
URL or Reference
University of Sheffield Research Data Management Policy
http://www.shef.ac.uk/polopoly_fs/1.553350!/file/GRIPPolicyextractRDM.pdf
University of Sheffield Data protection policy:
http://www.shef.ac.uk/cics/policies/infosecpolicy
The study will adhere to the MRC and RCUK principles
http://www.mrc.ac.uk/research/policies-and-guidance-for-researchers/datasharing/
University of Sheffield Good Research and Innovation Practice (GRIP) Policy
http://www.sheffield.ac.uk/polopoly_fs/1.356709!/file/GRIPPolicySenateapprove
d.pdf
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Informatio
n Policy
University of Sheffield Data protection policy:
http://www.shef.ac.uk/cics/policies/infosecpolicy
Other:
Other
8. Author of this Data Management Plan (Name) and, if different to that of the Principal
Investigator, their telephone & email contact details
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