Guidance on Preparing a Data Management Plan Office of Sponsored Programs – Boise State University

Motivation for Data Sharing Plan Many federal funding agencies, including NIH and most recently NSF, are requiring that grant applications contain data management plans for projects involving data collection.

National Institutes of Health The Final NIH Statement on Sharing Research Data was published in the NIH Guide on February 26, 2003. “Starting with the October 1, 2003 receipt date, investigators submitting an NIH application seeking $500,000 or more in direct costs in any single year are expected to include a plan for data sharing or state why data sharing is not possible. “ Not part of peer review No later than the main findings from the final data set are accepted for publication

National Science Foundation The National Science Foundation has released a new requirement for proposal submissions regarding the management of data generated using NSF support. Starting in January, 2011, all proposals must include a data management plan (DMP). Up to 2 pages Plan will be reviewed What data are generated by your research? What is your plan for managing the data?

Elements of a Data Management Plan Description Recommended? Data description Provide a brief description of the information to be gathered; the nature, scope and scale of the data that will be generated or collected. Highly recommended. Generic Example 1: This project will produce public-use nationally representative survey data for the United States covering Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. Data description is important to include because it will help reviewers understand the characteristics of the data, their relationship to existing data, and any disclosure risks that may apply.

Elements of a Data Management Plan Description Recommended? Data description Provide a brief description of the information to be gathered; the nature, scope and scale of the data that will be generated or collected. Highly recommended. Generic Example 2: This project will generate data designed to study the prevalence and correlates of DSM III-R psychiatric disorders and patterns and correlates of service utilization for these disorders in a nationally representative sample of over 8000 respondents. The sensitive nature of these data will require that the data be released through a restricted use contract. The second example describes a data collection that is also nationally representative, but also mentions the sensitive nature of the data and the need for using a restricted-use data agreement to disseminate the data.

Elements of a Data Management Plan Description Recommended? Metadata A description of the metadata to be provided along with the generated data, and a discussion of the metadata standards used. Highly recommended. Generic Example 1: Metadata will be tagged in XML using the Data Documentation Initiative (DDI) format. The codebook will contain information on study design, sampling methodology, fieldwork, variable-level detail, and all information necessary for a secondary analyst to use the data accurately and effectively. Why this is important Good descriptive metadata are essential to effective data use. Metadata are often the only form of communication between the secondary analyst and the data producer, so they must be comprehensive and provide all of the needed information for accurate analysis. Structured or tagged metadata, like the XML format of the Data Documentation Initiative (DDI) standard, are optimal because the XML offers flexibility in display and is also preservation-ready and machine-actionable. The first example references this ideal.

Elements of a Data Management Plan Description Recommended? Existing data A survey of existing data relevant to the project and a discussion of whether and how these data will be integrated. Generic Example 1: Few datasets exist that focus on this population in the United States and how their attitudes toward assimilation differ from those of others. The primary resource on this population, [give dataset title here], is inadequate because... More optional elements… When the data are unique.

Elements of a Data Management Plan Description Recommended? Existing data A survey of existing data relevant to the project and a discussion of whether and how these data will be integrated. Generic Example 2: Data have been collected on this topic previously (for example: [add example(s)]). The data collected as part of this project reflect the current time period and historical context. It is possible that several of these datasets, including the data collected here, could be combined to better understand how social processes have unfolded over time. When the data relate to past data that have been collected.

Elements of a Data Management Plan Description Recommended? Data organization How the data will be managed during the project, with information about version control, naming conventions, etc. Generic Example 1: Data will be stored in a CVS system and checked in and out for purposes of versioning. Variables will use a standardized naming convention consisting of a prefix, root, suffix system. Separate files will be managed for the two kinds of records produced: one file for respondents and another file for children with merging routines specified. Why this is important It is important to describe situations in which research data are in some way atypical with respect to how they will be organized. For example, some data collections are dynamically changing and version control is central to how the data will be used and understood by the scientific community.

Elements of a Data Management Plan Description Recommended? Quality Assurance Procedures for ensuring data quality during the project. Example 1: Quality assurance measures will comply with the standards, guidelines, and procedures established by the World Health Organization. Why this is important Producing data of high quality is essential to the advancement of science, and every effort should be taken to be transparent with respect to data quality measures undertaken across the data life cycle.

Elements of a Data Management Plan Description Recommended? Quality Assurance Procedures for ensuring data quality during the project. Example 2: For quantitative data files, the [repository] ensures that missing data codes are defined, that actual data values fall within the range of expected values and that the data are free from wild codes. Processed data files are reviewed by a supervisory staff member before release.

Elements of a Data Management Plan Description Recommended? Responsibility Names of the individuals responsible for data management in the research project. Example 1: The project will assign a qualified data manager certified in disclosure risk management to act as steward for the data while they are being collected, processed, and analyzed. Why this is important Typically data are owned by the institution awarded a Federal grant and the principal investigator oversees the research data (collection and management of data) throughout the project period. It is important to describe any atypical circumstances. For example, if there is more than one principal investigator the division of responsibilities for the data should be described.

Elements of a Data Management Plan Description Recommended? Selection and retention periods A description of how data will be selected for archiving, how long the data will be held, and plans for eventual transition or termination of the data collection in the future. Example 1: Our project will generate a large volume of data, some of which may not be appropriate for sharing since it involves a small sample that is not representative. The investigators will work with staff of the [repository] to determine what to archive and how long the deposited data should be retained. Why this is important Not all data need to be preserved in perpetuity, so thinking through the proper retention period for the data is important, in particular when there are reasons the data will not be preserved permanently.

Elements of a Data Management Plan Description Recommended? Audience Describe the audience of users for the data. Example 1: The data to be produced will be of interest to demographers studying family formation practices in early adulthood across different racial and ethnic groups. In addition to the research community, we expect these data will be used by practioners and policymakers. Why this is important The audience for the data may influence how the data are managed and shared—for example, when audiences beyond the academic community may use the research data.

Elements of a Data Management Plan Description Recommended? Access and sharing Indicate how you intend to archive and share your data and why you have chosen that particular option. Highly recommended. This Element of the Data Management Plan will be covered by the Institutional template section The next element – access and sharing – is important to include in a DMP to specify how the data will be accessed and shared. Sharing data helps to advance science and to maximize the research investment. When data are shared through an archive, research productivity increases and many times the number of publications result as opposed to when data are not shared.

Elements of a Data Management Plan Description Recommended? Intellectual property rights Entities or persons who will hold the intellectual property rights to the data, and how IP will be protected if necessary. Any copyright constraints (e.g., copyrighted data collection instruments) should be noted. Highly recommended. This section of Data Management Plan with be covered by the Institutional template portion In order to disseminate data, archives need a clear statement from the data producer of who owns the data. The principal investigator's university is usually considered to be the copyright holder for data the PI generates.

Elements of a Data Management Plan Description Recommended? Ethics and privacy A discussion of how informed consent will be handled and how privacy will be protected, including any exceptional arrangements that might be needed to protect participant confidentiality, and other ethical issues that may arise. Highly recommended. This section of Data Management Plan with be covered by the Institutional template portion Protection of human subjects is a fundamental tenet of research and an important ethical obligation for everyone involved in research projects. Disclosure of identities when privacy has been promised could result in lower participation rates and a negative impact on science. One of the most important considerations is that whenever possible – researchers collecting data should not include langue in the informed consent that prohibits data sharing. The first example makes clear how to write this into a DMP.

Elements of a Data Management Plan Description Recommended? Format Formats in which the data will be generated, maintained, and made available, including a justification for the procedural and archival appropriateness of those formats. Highly recommended. This Portion of the Data Management Plan will be covered by the institutional template portion of the document Why this is important Depositing data and documentation in formats preferred for archiving can make the processing and release of data faster and more efficient. Preservation formats should be platform-independent and non-proprietary to ensure that they will be usable in the future.

Elements of a Data Management Plan Description Recommended? Archiving and preservation The procedures in place or envisioned for long-term archiving and preservation of the data, including succession plans for the data should the expected archiving entity go out of existence. Highly recommended. This section of Data Management Plan with be covered by the Institutional template portion Why this is important Digital data need to be actively managed over time to ensure that they will always be available and usable. This is important in order to preserve and protect our investment in science. Preservation of digital information is widely considered to require more constant and ongoing attention than preservation of other media. Depositing data resources with a trusted digital archive can ensure that they are curated and handled according to good practices in digital preservation.

Elements of a Data Management Plan Description Recommended? Storage and backup Storage methods and backup procedures for the data, including the physical and cyber resources and facilities that will be used for the effective preservation and storage of the research data. Highly recommended. This section of Data Management Plan with be covered by the Institutional template portion Why this is important Digital data are fragile and best practice for protecting them is to store multiple copies in multiple locations.

Elements of a Data Management Plan Description Recommended? Security A description of technical and procedural protections for information, including confidential information, and how permissions, restrictions, and embargoes will be enforced. This section of Data Management Plan with be covered by the Institutional template portion Why this is important Security for digital information is important over the data life cycle. Raw research data may include direct identifiers or links to direct identifiers and should be well-protected during collection, cleaning, and editing. Processed data may or may not contain disclosure risk and should be secured in keeping with the level of disclosure risk inherent in the data. Secure work and storage environments may include access restrictions (e.g., passwords), encryption, power supply backup, and virus and intruder protection.

Elements of a Data Management Plan Description Recommended? Budget The costs of preparing data and documentation for archiving and how these costs will be paid. Requests for funding may be included. This section of Data Management Plan with be covered by the Institutional template portion Why this is important How will the costs for creating data and documentation suitable for archiving be paid?