Data is an integral component of any research activity. The scientific community at ICRISAT and its partner organizations have been conducting numerous research activities and generating voluminous data and datasets for the past 41 years through institutional research activities and also through the CGIAR Research Programs (CRPs). This research data is very valuable, can be seen as long-term assets of ICRISAT and can be treated as a major International Public Good (IPG).
ICRISAT has developed various data management and sharing platforms for better pedigree management, breeding practice analysis, survey management, climate prediction activities and the like, for better data management and to maximize the benefits of these research data as long term assets of ICRISAT and the global scientific community.
Recognizing the value of research data, datasets and the need for better coordination of these data management platforms, ICRISAT established a Data Management Unit (DMU) in August 2010 to mainstream, support curation, manage research databases and make data available across the institute.However, a Data Management Policy is required at the institute level for effective implementation of DMU activities and to strengthen and support the data management practices across the institute.
1. Purpose of a Data Management Policy
The purpose of this policy is to promote research data management, curation, preservation, and data sharing across the institute as well as with the global community, while maintaining the stewardship of scientists for data generation, analysis and reporting.
2. Scope and Implementation of Data Management Policy
This policy covers data acquired, assembled or generated across on-going and new research projects at ICRISAT. The types of data include (refer data definition), but is not limited to, the data collected,generated or obtained during the course of, or as a result of, undertaking research.The policy applies to all scientists and research staff of ICRISAT, and is effective as of the date of approval.For the effective implementation and data management plan, this policy should be read in conjunction with the ICRISAT Data Management Implementation Guidelines, which may be updated from time to time to reflect current recommended practices. Each new project, the data management plan should be discussed with DMU and the Biometrics Unit before submitting to the concerned project management and DMU. (See Implementation Guidelines in Appendix).
3. Data Management Policy Statement
Data Management and Data Integrity: Scientists and research staff at ICRISAT shall abide by and support the data management practices across research programs. As there is an increasing requirement from the donors and funding agencies to make data available to the global scientific community for future research and for wider exploitation as IPGs, datasets generated across research programs should be managed and preserved in standard formats with proper metadata, while maintaining the data integrity, in order to share the data globally.
Data Ownership: All rights to data produced by ICRISAT’s scientists and research staff shall rest with ICRISAT; however the Project leader and Project Incharge should identify any restrictions applied by collaborating institutions.
Data generated by a project led by ICRISAT, or collected through ICRISAT research, shall be owned by ICRISAT. If the relevant work is undertaken in partnership, the data will be jointly owned by the participating institutes and may be made accessible through partner institution repositories, following any project guidelines on data.
Data Sharing: The Data Management Unit should work together with ICRISAT’s scientists and research staff to ensure that research results and datasets are shared with DMU for preservation (with sufficient bibliography and metadata etc), within 24 months from the completion date of data collection (subject to data confidentiality and copyrights).All data/data sets must be recorded in a central ICRISAT ‘inventory’ as soon as the data is collected to allow the Data Management Unit to check the correct metadata and track to ensure the data is made open access when the time is appropriate.
Data Authenticity: Every data source must have defined a single point of contact responsible for data authenticity. Data curation and quality checks shall be managed by scientists and research staff in coordination with the Biometric Unit. This curated data shall be further shared with the Data Management Unit.
Copyright and Open License: ICRISAT adopted an open access policy in May 2009; therefore same should be considered with respect to providing Open Access. Suitable open licenses shall be used in special conditions that recognize the legal rights to the data and encourages their use.
Data Repositories: Institutional repositories shall be used for data preservation and visualization to
enable easy access of researchers to research data. Other repositories can also be used if required (e.g,for further reach and as required by partners who jointly own the data).
Data Management Plan: A Data Management Plan should be prepared by every project to ensure implementation of the data management policy. Such a plan shall, in particular, outline a strategy and guidelines for effective research data management and for maximizing opportunities to make research data and datasets available with open access (See Implementation Guidelines in Appendix).
Data Preservation and Back-up: Preservation and backup of old data must be done once in 10 years for improved system performance and quick search of the research data. Datasets more than10 years old must be backed-up after separating unused, non-curated data and datasets.
Incentives and Recognition: Incentives and the development of professional expertise in all areas of data management shall be devised, adopted and promoted by the institute (See Implementation Guidelines in Appendix). However it should be insured that ‘Submission of data’ shall be a regular item on a researcher’s departure form given by HR, which has to be signed by the staff member’s project supervisor/s.
Research Data: Information that is collected, generated or obtained during the course of, or as a result of, undertaking research (which includes but is not limited to field and laboratory experiments, survey data, breeding data, video, audio and images, software, GIS and remote sensing data); and that is subsequently used by the researcher as a basis for analysis or drawing conclusions to develop, support or revise theories, practices and findings.
Primary Data: Raw data and other research data that are obtained directly from field or respondents.The data are generally captured through surveys, interviews, focus groups, or other direct interaction with individuals in the field.
Secondary Data: This refers to pre-existing data not gathered or collected for the current research project. Secondary data is usually that which has been collected by another organization or source, or data collected from the government publications.
Meta Data: Descriptive information about a particular data file, dataset including its source, citation, when and by whom it was collected etc.
Database: A collection of independent works, data or other materials, which are arranged in a systematic or methodical way and that are individually accessible by electronic or other means.
Data Quality: Accuracy, completeness, authenticity and validity of the data.
Data Repository: Storage space that serves as a place for data backup, data preservation and archiving of research results and information objects.
Data Users: Person/Organization who uses the research data. (Example: Researchers, partners, donors,policy makers, general public)
Open Access: Immediate, unrestricted and free online access by any user worldwide to data and datasets, and unrestricted re-use (which could be restricted to non-commercial use and/or granted subject to appropriate licenses), subject to proper attribution.
Copyright: Copyright is a type of intellectual property that is based on a person’s creative skill and labor.It allows the copyright owner to control the material they produce and to manage how others deal with their material (such as copying) and to prevent others from using protected material without permission or attribution (unless an exception applies).
Dispute Resolution: Dispute resolution will be handled by the respective project management/ project leader, in coordination with the IP office and Data Management Unit. Unresolved issues may be elevated to the Global Leader-KSI and respective Research Program Director/Regional Director for resolution, in consultation with the Deputy Director General-Research, if needed.
Data Usage: The data can be downloaded, used, shared and redistributed for research, academic training and related non-profit purposes, free of charge, and ICRISAT encourages wide usage of data.However, users must acknowledge ICRISAT as the source of the data wherever ICRISAT’s data is used.Explicit prior written approval from ICRISAT is required for any commercial use or redistribution for commercial purposes.
Data interoperability: The process or technique that allows easy sharing of data between different organizations across different platforms is called data interoperability. For practical implications, ICRISAT will have an interoperable institutional repository with a common metadata framework.
5. Data Management Roles and Accountabilities
The following are roles and responsibilities to ensure effective implementation of ICRISAT’s data
management policy in a timely and comprehensive manner. The Biometrics Unit and Data Management
Unit will coordinate and support overall data management activities across research programs at
Data Management Unit:
-Manage and provide open access to research data and datasets created by the scientific community at ICRISAT.
-Data aggregation, preservation, management and sharing through suitable data repositories and data management platforms as requested by the projects.
-Develop a comprehensive, practical and user-friendly suite of work-flows and protocols that facilitate research data storage, access and sharing.
-Create awareness among all ICRISAT scientists and research staff on the importance of data management, and sensitize them on data management policy and guidelines.
-Organize need based capacity building programs for scientists and research support staff on data conservation, preservation, methods and techniques, data and metadata standards.
-Develop protocols for proper data cleaning and curation and ensure institute-wide implementation by effectively coordinating with scientists, research staff and DMU.
-Coordinate with scientists and research staff in use of user-friendly data management software/tools to automate Data Collection from the field in collaboration with DMU.
-Provide curated quality research data and summaries to Data Management Unit in coordination with the data producer from the respective research program for preservation and sharing.
-Organizing capacity building programs for scientists and research support staff in use of a userfriendly suite of data management software/tools for trial design, electronic data capture and data management in coordination with DMU.
Scientists and Research Staff:
-Ensure that a pre-defined standard format for data collection is used in order to minimize the effort and duration for data curation and cleaning.
-Research staff should maintain adequate metadata to facilitate identification and effective reuse of research data where appropriate.
-Provide information on the data and datasets to be shared and preserved through DMU, subject to partner agreements.
-Inform the project team about the policy guidelines, and ensure that the team follows assigned roles and responsibilities.
-Define and designate a single point of contact responsible for data authenticity, reliability and data sharing with the Data Management Unit.
-Communicate the project requirements in terms of data collection and other related requirements of the Data Management Unit to help design and develop a standard format for data collection across the project
-Identify ownership of research data, subject to any contractual agreement between ICRISAT and collaborating institutions.
-Coordinate effectively with the third party/field staff and other participating partners responsible for field data collection, and ensure data authenticity, responsibility and timely collection of data to avoid any resource risk.
-Ensure successful adoption of this policy.
-Share best practices and success stories with partners and other CGIAR Centers with regard to the data management policy.
ICRISAT makes no warranties for the accuracy, correctness, completeness and quality of the data provided. ICRISAT also makes no warranties that the data provided will not infringe any copyright or related intellectual property right protection. All content and information related to data is subject to change and non-binding. Further, ICRISAT accepts no liability to any consequence resulting in relation to the data use. The recipient assumes full responsibility for complying with all relevant national
regulations and rules as to the usage of the data.
This policy will be reviewed annually to ensure that it keeps pace with the scientific requirements and best data management practices up to date. ICRISAT could also share the learning’s and inputs from the implementation of the data management policy with other partners and member institutes.
Note: Implementation Guidelines
The purpose of the Implementation Guidelines is to provide additional information, guidance and illustrations with regard to the ICRISAT Data Management Policy, to facilitate understanding of the Policy, guide its interpretation and ensure its effective implementation at an operational level. The aim is to provide a document that will aid research staff and other stakeholders in implementing rigorous Data Management practices for the benefit of the global public.
The following information can be obtained from the implementation guideline as a separate annexure:
1) Information on purpose, objective, scope, timing and implementation, rational to data management and transition period
2) Types of Data
3) Elaborated information on data management policy statements
4) Data Management Plan
5) Definitions of data and types of data with respect to data management
6) Roles and responsibility of each individual with respect to data management
7) Data Management Protocol
8) Metadata standards