Data Lifecycle, Procedures

Data Management Plan (DMP)

PLAN OVERVIEW

Plan Title:

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Dataset or Type description:

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Plan Start Date:Click or tap to enter a date. Plan End Date:Click or tap to enter a date.

Data Governance Overview (Goals and Use of Data for this DMP):

E.g. Are Data Use Agreements required?

Data Governance Policies?

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Data Compliance Type(s):

E.g. PII, PHI, HIPAA, CJIS, etc.

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Tribal Consultation Agreement:

NA

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Data Security Requirement(s):

Include any Data Security policies or requirements.

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Data Privacy Requirement(s):

Include any Data Privacy policies or requirements.

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Data Sharing Requirement(s):

Include any Data Sharing policies or requirements.

Technology Requirement(s):

E.g. What are the technology requirements for this data set or type?

Are you using existing infrastructure?

Will this data set require new infrastructure or services?

Will a Data Storage Plan need to be included?

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Budget Requirement(s):

E.g. Include any costs for data management services.

Include any costs for additional technology requirements.

Include any costs for managing data during the project as well as after the project is complete.

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ROLES AND RESPONSIBILITIES

List staff/organizational roles in the following sections and their responsibilities for carrying out the data management plan. Include a description of time allocations, training requirements, and contributions of non-project staff, if available.

DMP Manager:

Person responsible for documenting changes, progress, & accomplishments for the DMP

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Data Owner(s):

People responsible for definitions, policy, and practice decisions about data within their area of responsibility. For business data, the individual may be called a business owner of the data.

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Data Steward(s):

A business leader and/or subject matter expert designated as accountable for:

a) the identification of operational and business intelligence data requirements within an assigned subject area

b) the quality of data names, business definitions, data integrity rules, and domain values within tan assigned subject area

c) compliance with regulatory requirements and conformance to internal data policies and data standards

d) application of appropriate security controls,

e) analyzing and improving data quality

f) identifying and resolving data related issues.

Data stewards are often categorized as executive data stewards, business data stewards, or coordinated data stewards.

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Additional Role(s):

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Other:

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COLLECTION AND CREATION

Description of Data to be Collected or Created:

Questions to consider:

What type, format and volume of data?

Do your chosen formats and software enable sharing and long-term access to the data?

Is this new data?

Is this existing data?

Will existing data be used to create new data?

Give a brief description of the data, including any existing data or third-party sources that will be used, in each case noting its content, type and coverage. Outline and justify your choice of format and consider the implications of data format and data volumes in terms of storage, backup and access.

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How Will Data be Collected or Created:

Questions to consider:

What standards or methodologies will you use?

How will data be captured, acquired or entered?

What quality assurance processes will you adopt?

Outline how the data will be collected/created and which community data standards (if any) will be used. Consider how the data will be organized during the project. Explain how the consistency and quality of data collection will be controlled and documented. This may include processes such as calibration, repeat samples or measurements, standardized data capture or recording, data entry validation, peer review of data or representation with controlled vocabularies.

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Data Types and Formats:

Describe the types and formats of the data. If you need to convert or migrate your data files from one format to another, be aware of the potential risk of the loss or corruption of your data and take appropriate steps to avoid/minimize it. Justify the use of format – is your chosen format open, non-proprietary, and in widespread use? Plan for creating sustainable file formats like .txt, .pdf, and .csv

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Data Volume:

Estimate how much data will be produced throughout the project, at what growth rate, and if the production rate will change during the project.

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Naming Convention(s):

Establishing Naming Conventions for your files, object / table names, variable / field names, shared datafile names, report names, etc.

and using them consistently will ensure maximum access to your data and records.

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Directory Structure(s):

Building Directory Structures refers to the method of classifying and organizing data sets to make them more useful

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Additional Information:

Questions to consider:

Do metadata standards need to be documented?

Does a Data Dictionary need to be created?

Create a Data dictionary or README files that define variables, measurement units, formats, and data types.

Ensure data quality and integrity during collection by creating training documentation that includes the data dictionary, establish shared understanding on what is collected or recorded and why, and document data decisions (ex. raw vs compound variables; age vs DOB).

Do you need documentation on code?

Establish documentation for code, scripts, and software (revise as the project continues). Include descriptive comments within code or scripts to explain what it’s doing. Record scripts for every stage of data processing and/or have a plan to document every manual action/change.

STORAGE AND MANAGEMENT

Many of the Storage and Management items may have been addressed during the planning step of the DMP. This Section can be used to add additional details or can link to a Data Storage Plan.

STORAGE

How Will Active Data be Stored?:

Review institutional storage options to better understand where to store data based on behavior, performance, and means of access.

Describe where the data will be stored during the course of active use. This may vary if you are working across multiple sites so explain each procedure.

A master copy of raw data should be retained, with further changes to subsequent versions well documented.

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Retention Considerations:

Consult applicable data retention policies to determine how long the data needs to be retained and/or preserved. Data should be located in appropriate storage locations, based on usage and function.

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Storage Cost:

Determine whether you will have sufficient storage throughout the process.

Will there be storage costs for additional services?

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DATA PROTECTION AND SECURITY

Data Security:

Ensure confidential and sensitive data is maintained.

Questions to consider:

Where will you store your data?

What are the risks to data security and how will these be managed?

How will you control access to keep the data secure?

How will you ensure collaborators access your data securely?

Will external media related to your research, such as paper lab notebooks, be kept secure in locked cabinets with access logs and a list of authorized users?

How will you protect the integrity of your data?

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Note: Data Compliance, Security and Privacy requirements may have been addressed in the Planning step. Additional information can be added here.

Also consider data security, particularly if your data is sensitive e.g., detailed personal data, politically sensitive information or trade secrets. Note the main risks and how these will be managed. Also note whether any institutional data security policies are in place.

Identify any formal standards that you will comply with, e.g., ISO 27001.

Data Compliance Type(s):

PII, PHI, HIPAA, CJIS, etc.

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Data Security Requirement(s):

Include any Data Security policies or requirements

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Data Privacy Requirement(s):

Include any Data Privacy policies or requirements.

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Data Permissions:

Detail all access permissions required for this data set/type.

Data Security Software:

How will you protect your hardware and software systems? (e.g. Anti-virus software, systematic plan for updating applications and OS, firewall, anti-intrusion software, restricted physical access).

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Data Security Hardware:

What are the security controls for the physical location(s) where your computers, servers, and data storage reside?

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DATA BACKUP AND RECOVERY

Data Backup:

Data backup plan information.

Questions to consider:

How will the data be backed up?

How often is data backed up?

Where are the backups stored (network drives, remote storage (Cloud)?

How many copies are being made (full or partial copies)?

Who will be responsible for data backup?

Does data backup include immutable copies?

How will you protect the identity of participants during data sharing (i.e. honest broker, anonymized data) according to any applicable rules (HIPAA, CJIS, etc.)?

How might managing identifiers affect the usability of the data set for secondary analysis?

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Data Recovery:

Data Recovery Plan information.

Questions to consider:

Who will be responsible for data recovery?

How will the data be recovered in the event of an incident/disaster?

Are Standard Operation Procedures (SOP) in place for recovering data?

What are the Recovery Point Objectives (RPO) for this data?

Recovery point objective (RPO) is defined as the maximum amount of data – as measured by time – that can be lost after a recovery from a disaster, failure, or comparable event before data loss will exceed what is acceptable to an organization.

What are the Recovery Time Objectives (RTO) for this data?

The Recovery Time Objective (RTO) is the maximum tolerable length of time that a computer, system, network or application can be down after a failure or disaster occurs.

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CURATION

Through ongoing curation, it is possible to make data assets discoverable for uses other than the original use and helps data analysts triage data they need for their research projects and control or margins of error. In short, the curation phase of the data lifecycle greatly improves the sharing and use of data assets, which is the next step in the data lifecycle.

Data Validation:

Review of the data set to validate the accuracy of the data.

Identify the data validation roles and responsibilities.

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Validating and Adding Metadata:

Data Linkage:

Use data linkage to bring data from different sources together about the same person or entity to create a new, richer dataset.

Identify the different data sources and describe the process of linking that data.

Identify the data linkage. roles and responsibilities.

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Documenting, Cataloging and Contextualizing Data:

Describe the types of documentation that will accompany the data to help secondary users to understand and reuse it. This should at least include basic details that will help people to find the data, including who created or contributed to the data, its title, date of creation and under what conditions it can be accessed.

Documentation may also include details on the methodology used, analytical and procedural information, definitions of variables, vocabularies, units of measurement, any assumptions made, and the format and file type of the data. Consider how you will capture this information and where it will be recorded. Wherever possible you should identify and use existing community standards.

Questions to consider:

What information is needed for the data to be to be read and interpreted in the future?

How will you capture / create this documentation and metadata?

What metadata standards will you use and why?

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Other:

SHARING AND USE

During this phase, users access and modify data as needed and carry out other data-related operations, such as collaboration, business intelligence, advanced analytics or visualization. Data usage can also result in additional data being created, which must then be stored and perhaps further processed. In effect, this phase is what enables authorized users to be able to do their jobs. It would be the responsibility of data stewards to maximize the sharing and use of data assets for mission goals, while still protecting those assets from inappropriate access or use. Some data may need to be sterilized or access strictly controlled. Policies and procedures should be associated with the sharing and use of any restricted data assets.

How Will Data be Shared?:

Questions to consider:

How will potential users find out about your data?

With whom will you share the data, and under what conditions?

Will you share data via a repository, handle requests directly or use another mechanism?

When will you make the data available?

Will you pursue getting a persistent identifier for your data?

Consider where, how, and to whom data with acknowledged long-term value should be made available. The methods used to share data will be dependent on a number of factors such as the type, size, complexity and sensitivity of data. If possible, mention earlier examples to show a track record of effective data sharing. Consider how people might acknowledge the reuse of your data.

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Data Sharing Restrictions:

Questions to consider:

What action will you take to overcome or minimize restrictions?

For how long do you need exclusive use of the data and why?

Outline any expected difficulties in sharing data with acknowledged long-term value, along with causes and possible measures to overcome these. Restrictions may be due to confidentiality, lack of consent agreements or IPR, for example.

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Data Sharing or Use Agreement(s):

Questions to consider:

Will a data sharing or use agreement (or equivalent) be required?

Consider whether a non-disclosure agreement would give sufficient protection for confidential data.

ARCHIVE/DISPOSAL

At some point, data will reach the end of its useful life which might include a combination of statutory mandates, record retention policies and agency business needs. Most agencies have data retention policies and procedures that tell data platforms how long data can and should be held before it is either archived—that is, moved off of immediately accessible media or applications and into storage--or disposed of—that is, permanently destroyed. Policies and procedures should also be in place for any type of data restoration for re-use.

Data Retention Requirement(s):

Add any data retention requirements that apply to this data set/type.

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Data Retention Policies:

Add any data retention policies that apply to this data set/type.

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Data Hygiene:

Questions to consider:

Are there data hygiene practices in place?

Do new data hygiene practices and procedures need to be created for this data set/type?

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Data Appraisal:

A small percentage of data and related records might be identified for permanent storage as a part of the historical record of a discipline or agency, or as intellectual property.

Question to consider during data appraisal:

What data must be retained/destroyed for contractual, legal, or regulatory purposes?

How long will the data be retained and preserved?

Who will be responsible for the data appraisal?

Will data need to be restored for reuse?

If used, are there any restrictions on the reuse of third-party data?

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Archiving Data:

Permanent retention, or archiving, is often a significant investment for an agency, as it implies ongoing migration of electronic formats and storage costs, as well as care, maintenance, and access services for the records in perpetuity.

Question to consider:

Where will data be archived?

How much storage space is necessary to house the archived data?

Who is responsible for archiving the data?

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Data Restoration Policies:

Add any policies required for data restoration and re-use.

Restoration of Archived Data:

Question to consider:

Who is responsible for restoring the archived data??

How long will it take to restore data from archive?

What procedures are in place to restore data?

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Data Disposal:

Knowing which data and records are safe to dispose of can be complicated to determine.

Questions to consider:

Who will determine what data can be permanently destroyed?

What methods are required to permanently destroy the data?

Are there procedures in place for data disposal?

Who is responsible for the data disposal?

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