DRAFT - Data Management and Access Plan Template

Data Management Plan Attributes

NB. These two tables should be one table with six columns. Formatting constraints here prevented this.

Review Number	Action/Update	Approved by
Sign Off	Agreement sign off between the Data and Innovation Team and Grantees	Name
1
2

Approval Date	Next Review Date
Date	Date

Definitions

Term	Definition
Add the term/acronym/abbreviation	Add the definition for the term/acronym/abbreviation

Relevant Policies

Policy Name	Link	Notes
Name the relevant policy	Provide a link to the named policy	Provide any notes that could be useful. This might include referencing sections of the policy or providing details on how to engage with it

The Data Management and Access Plan

SECTION ONE - STRATEGY

1. Outline who will take responsibility for different data management tasks and ensure roles and responsibilities are clearly defined and communicated. (Resourcing)

2. Outline how you will communicate the importance of FAIR data sharing for the investment? (Culture)

3. Outline how the investment will adhere to data sharing and privacy regulations while also providing FAIR data outcomes. (Support)

4. Outline how you will build trust and resolve differences between members in order to align approaches to FAIR data standards and policies. (Culture)

5. Outline how FAIR data management practices are integrated into daily workflows? (Support)

6. Outline how individuals can be supported by communities of practice in order to close knowledge gaps. (Culture)

7. Outline the guidance materials and training programmes for FAIR implementation in your project (Support)

8. Estimate the costs associated with data management and sharing, including data hosting, data data catalogue, reference data management, data modelling, retention and archiving (beyond investment completion) (Technical)

9. Specify who will take responsibility for different data management tasks and ensure roles and responsibilities are clearly defined and communicated. (Resourcing)

10. Briefly outline any conflicts between internal data policies and external regulatory, ethical, or privacy requirements and how you will resolve those conflicts. (Policy)

SECTION TWO - IMPLEMENTATION

The following questions will provide an overview of data to be collected, generated, or reused in your project, and the practical implementation of how to manage that data. You can answer these questions in any format you like (SMART style objective, a small amount of copy, or bullet points etc) as long as consideration has been given to each point, and it remains understandable at a later review.

Data Summary

1. Provide a concise summary of the objectives and reasons for data collection or generation within the project. This should align with the project’s goals and objectives and could mention specific research questions or hypotheses that the data will address. (Technical)

2. Specify the types of data to collect, generate or use, such as experimental measurements, survey responses, observational data, images, etc and specify which of these data are third party (if any). (Technical)

3. For each data type, specify the format (e.g. CSV for tabular data, TIFF for images, json, geojson) for storage and for sharing (if different from storing). Note whether this format is proprietary or not and if so, provide a justification. (Technical)

4. For the types of data listed as already existing (i.e. they are being (re)used), note the source and explain how that data will be integrated with the new data and note which permissions are needed to use existing data. (Resourcing and Technical)

5. For data collected, or derived from collection, describe where the data will come from for each type (e.g. sensor readings, interviews, simulations, field observations or a mix) and a brief overview of the methodology. (Resourcing and Technical)

6. Provide an estimate of the total volume of data you expect to generate or collect in gigabytes or terabytes and give a brief description of the infrastructure in place to store it. (Technical)

7. Identify which of the project data contain personal or sensitive information and specify what the information is which makes it personal, sensitive or combined. (Policy)

8. Identify and explain who might benefit from the data and the potential for reuse beyond the project lifecycle. (Culture)

Making Data Findable

1. Outline how data will be made discoverable by stakeholders during the project and internally, afterwards. If there is a plan to publish the data openly, after the project, outline how it will be made discoverable for that, too. (Policy and Culture)

2. Describe the use of persistent identifiers for your data types. These unique ID’s ensure datasets can be reliably found overtime and avoid confusion and duplication. (Policy and Technical)

3. Describe the practice of naming conventions for datasets within this project. (Policy and Technical)

4. Specify the metadata that will be captured to describe the data. (Technical)

5. Describe how the data will be documented, and in what detail and format it will be provided. (Resourcing, Policy and Culture)

Making Data Accessible

1. Outline how project data will be made accessible, if data is not to be made open, provide reasoning as to why. Aim to make data openly accessible unless there are specific reasons to restrict access such as personal or sensitive information. (Policy)

2. Are there any embargo periods applicable to project data, and if so, provide the details and a rationale explaining why it is necessary. (Policy)

3. Describe the strategies and infrastructure in place to preserve data beyond the project lifecycle. (Technical)

Making Data Interoperable

1. Outline what software and tools are required to support the interoperability of the project data? (Technical)

2. Where it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies, if so, outline your approach. (Technical)

3. Describe the end to end infrastructure in place to support data management (e.g., institutional repositories, cloud storage, data analysis tools). It might help to refer to the data lifecycle diagram in the recipe and address each step in turn. (Technical)

4. Indicate which domain specific data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? (Technical)

Domain Vocabulary Table

Standard	Used?	Description	Reference
Agronomy Ontology		Describes agronomic practices, pest management, and soil fertility.	Agronomy
AgrO CGIAR Agronomy Ontology		Ontology for standardising terms in agronomic research and field trials from CGIAR	Agronomy Field Trials
MIAPPE		Reconciling data from plant phenotyping experiments	Crop Breeding
BrAPI		Standardises access to plant breeding data.	Data Standardisation in Crop Breeding
EnvO		Describes environmental conditions like temperature, humidity, and rainfall.	Environment
AGROVOC		Multilingual agricultural vocabulary covering crops, diseases, techniques.	General Agriculture
CAB Thesaurus		Controlled vocabulary for agriculture, forestry, horticulture, soil science, entomology, mycology, parasitology, veterinary medicine, nutrition, rural studies.	Life Sciences and Social Sciences
Sequence Ontology		Features and attributes of biological sequence.​	Sequences
Gene Ontology		Molecular entities focused on ‘small’ chemical compounds.	Genes
ChEBI Ontology		Molecular functions, biological processes, cellular components.​	Biochemical Entities
Protein Ontology		Protein-related entities.	Proteins
Cell Ontology		Cell types in animals	Cell
Cell Behaviour Ontology		Multi-cell computational models.	Cell
BRENDA		Tissues, cell types and enzyme sources.	Tissues and Enzymes
Plant Ontology		Plant anatomy, morphology, growth and development and phenotypic traits.	Anatomy
Anatomical Entity Ontology		Ontology of anatomical structures.	Anatomy
NCBI Taxonomy		Organisms’ classification and nomenclature.	Species
MycoBank		Mycological nomenclatural novelties.	Species
EPPO		Pest-specific information	Species
Plant Experimental Conditions Ontology		Growth conditions used in plant experiments.	Environment
Crop Ontology		Species-specific phenotypic plant traits.	Plant Phenotype
Phenotype and Trait Ontology		Phenotypic qualities.	Plant and Animal Phenotypes
Small Scale Fisheries and Aquaculture Ontology		Fisheries and aquaculture (under development)	Fisheries and Aquaculture
Animal Trait Ontology for Livestock		Phenotypes of livestock in their environment.	Animals and Livestock
Food Ontology		Food, fodder and food processes.	Food and Nutrition
Compositional Dietary Nutritional Ontology		Nutritional attributes contributing to human diet.	Food and Nutrition
SEONT		Ontology for agricultural household surveys.	Socioeconomics
CG Core Metadata Schema		Minimum set of elements applicable for data and publication annotation and curation across CGIAR.	CGIAR Metadata Elements
Embrace Data and Methods Ontology		Comprehensive ontology for bioscientific data analysis and data management.	Bioscience Data Collection and Analysis
National Agricultural Library Thesaurus		Online vocabulary tools English and Spanish to select controlled vocabulary terms for subject indexing of AGRICOLA, PubAg and other databases.	General Agriculture
AgMES		Description, resource discovery, interoperability and data exchange of different types of information relevant to food production, nutrition and rural development	Agricultural Metadata Elements Set
AGRIS		A metadata standard drawing on Dublin Core and AgMES	Agricultural Metadata Standard

1. Indicate which domain agnostic standard vocabularies for all data types present in your data set to allow interdisciplinary interoperability? (Technical)

General Vocabulary and Standards Table

Used?	Standard	Description	Reference
	Comparative Data Analysis Ontology	Formalisation of concepts and relations relevant to evolutionary comparative analysis.	Data Analysis
	GAZ	Represents places through their names using an ontological approach to promote semantic coherence.	Geographical
	Geonames	Covers all countries and contains over eleven million place names.	Geographical
	Research Organisation Registry	Registry of open, sustainable, usable, and unique identifiers for every research organisation in the world.	Domain Agnostic
	Units of Measurement Ontology	Ontology of units of measurements.	Domain Agnostic
	Variation Ontology	Ontology for standardised, systematic description of effects, consequences and mechanisms of variations.	Domain Agnostic
	VIVO Ontology	Open-source software and an ontology for representing scholarship.	Domain Agnostic
	Dublin Core	Metadata standard for describing various digital resources.	Domain Agnostic
	DCAT	Data Catalog Vocabulary, used for describing datasets and data catalogues.	Domain Agnostic
	PROV-O	Ontology for provenance information of datasets and resources.	Domain Agnostic
	RDF	Resource Description Framework, a standard for data interchange on the web.	Domain Agnostic
	CEDAR	Describes the format of the generic metadata artifacts	Metadata Standards

Making Data Reusable

1. Specify the licences under which data will be made available (e.g., Creative Commons, GPL) and what conditions or restrictions that has on data reuse. Also specify where you will make the guidance for this available. (Technical and Policy)

2. Indicate how long datasets will remain reusable. (Policy and Culture)

3. Outline the actions that will be taken to ensure data quality. (Policy)

FAIR Assessments

1. Describe how you will track the project's FAIR potential maturity level throughout the investment. (Policy and Technical)

2. Describe how you will monitor the FAIRness of the project’s data assets through the use of the FAIR Data Assessment Tool. (Policy and Technical)

Data Security

1. Outline measures in place for data protection throughout the data lifecycle. (Technical, Policy and Culture)

2. Describe the planned use of access control for project data. (Technical and Policy)

3. Detail the backup and recovery procedures in place. (Policy and Technical)

4. Outline your approach to managing risk for your sensitive and personal data. More guidance on this can be found in the 'Resources' section of the recipe. (Policy)

Ethics

1. Outline any ethical issues that have been identified and need managing throughout the duration of the investment. (Policy)

2. Describe how informed consent will be obtained if applicable to this project. (Policy)

3. Outline the approach to anonymising data where applicable. (Policy and Culture)

Roles and Responsibilities

1. Outline any skill gaps within the team related to the data lifecycle and how this will be addressed. (Resourcing and Support)

Data Sharing

1. Briefly outline your data sharing agreement (if applicable) and any recipients of it, alongside any restrictions, constraints or challenges observed. (Policy)

Compliance and Support

1. Describe how you will monitor compliance with the Data Management and Access Plan throughout the investment. (Policy)

2. Outline the mechanisms for enforcing the Data Management and Access Plan including support and training for those who need it. (Policy)