[Instructions in italics and between square brackets. Remove them from the final document. The material in this document may be integrated into a data management plan or used as a stand-alone agreement. There may be some overlap between the Internal Data Sharing Plan and the Data Management Plan. Remove or add text in this document as necessary].
Rationale: This data sharing plan contains the expectations that will govern internal data sharing during the duration of the project. By internal data sharing we understand the sharing of datasets generated or compiled during a project among the members of the project while the project is ongoing.
Rationale: bad data management practices increase the chances of conflict among the members of a research project. All projects should have a data management plan, regardless of the size of the project, and regardless of the funding source of the project.
This agreement is between all the members of the project [insert project title]. Throughout this document we will refer to it as the Project.
The data management plan for the Project can be found [complete].
[if there are datasets that will be managed differently with respect to internal data sharing, describe them here so that we can refer to them throughout this document. Each section of this document should refer to each of the dataset groups outlined below. Feel free to refer to the DMP for a description of the dataset groups if that makes sense.]
Rationale: Data management takes time and effort. In order to not oversee any important data management action, it should be clear to all the members of the team who is responsible for each of them.
[adapt the definition of each of the roles for the Project. These roles are defined so that this document will not need to be adapted every time that there are changes within the Project team. These definitions should reflect as accurately as possible the roles in the project. For example, if the project will have Postdocs but not technicians, rename the Researcher role to Postdoc. For example, if there are going to be two kinds of students (field students and lab students) that will have different data management roles, these should be outlined here. For example, if the project is going to have a data manager, outline the role here].
Principal Investigator (PI): as designated by the funder. If there is no funder or the funder does not designate the principal investigator, it will be a faculty member.
Faculty Investigator: a faculty member who actively perform research on all or a part of the research project. They may provide active mentorship to students.
Team member: they contribute to the scientific development or execution of a study in a substantive, measurable way (research/postdoctoral fellows, technicians, associates and consultants).
Student: member of the project pursing a degree and performs a degree-related research under the supervision of faculty member (principle investigator). Undergraduate, master, PhD or others.
[adapt the definition of each of these responsibilities to the Project. Add more or remove if necessary. Decide who (which role) is going to be responsible for each of these].
DMP Implementation: responsible for ensuring Data Management Plan and the Internal Data Sharing Plan move from planning into implementation; ensure that any practices, responsibilities, policies outlined in the plan are followed; ensure that new members of the Project will receive data management training; responsible for maintaining the Data Management Plan and the Internal Data Sharing Plan up to date, and making sure that all members of the Project understand and are prepared to apply the changes.
Responsible Parties: [complete with one of the roles defined above].
Access control: responsible for regulating access to data based on the roles of the authorized user, whether from the project or not. Access is the ability to perform a specific task, such as view, create, or modify a file. Responsible for granting access to data by members outside of the project when requested during the duration of the project.
Responsible Parties: [complete with one of the roles defined above].
Protection of sensitive and protected data: responsible for complying with applicable laws and regulations, institutional policies, and ethical principles governing the conduct of human subjects research, sensitive and protected data.
Responsible Parties: [complete with one of the roles defined above].
Software creation and maintenance: responsible for the creation, design, and installation of a software products (e.g. code writing) and maintenance of the system (software update, error correction, enhancement of existing features).
Responsible Parties: [complete with one of the roles defined above].
Instrumentation maintenance: responsible for conducting tasks related to instruments such as installation, calibration, testing, and performing maintenance of instrumentation equipment.
Responsible Parties: [complete with one of the roles defined above].
Data collection/ data generation: responsible for data collection and creation (research, locate, identify, and measure), data entry, information processing (transcribing and manipulation), data generation (prototyping, models, and database).
Responsible Parties: [complete with one of the roles defined above].
Data organization: responsible for maintaining the data in an organized data structure so that it is easy to find (i.e. folder structure, file naming conventions). Responsible for saving the data in the appropriate formats.
Responsible Parties: [complete with one of the roles defined above].
Metadata generation: responsible for generating metadata (data description), documentation, using the metadata standards or templates specified in the Data Management Plan.
Responsible Parties: [complete with one of the roles defined above].
Quality control: responsible for performing quality assurance and quality control. It involves testing, reviewing, cleansing of data, calibration, correcting errors, data remediation, and documentation of quality control on the data points.
Responsible Parties: [complete with one of the roles defined above].
Data analysis: responsible of various activities related to data analysis such as examining, analyzing, sorting, aggregating, transforming, modeling, visualizing, validating, presenting, to answer research questions.
Responsible Parties: [complete with one of the roles defined above].
Archiving and preservation: responsible for assuring archiving and storage, preservation and access to data (and associated metadata) long term (e.g. in a repository or managed internally).
Responsible Parties: [complete with one of the roles defined above].
Rationale: Most of the data management responsibilities outlined in Section II require a lot of time and effort. Often, datasets are shared within members of the same project and the use of these datasets improves or makes possible scholarly outcomes such as publications of articles, book chapters, presentations in conferences, proceedings, etc. It is necessary to have a common understanding on how to acknowledge the role of data managers, data creators, data analysts in the research process. These roles may not be appropriate as manuscript authors, but there are many other options. Acknowledging these roles is not a legal matter (no law requires it), but it is an ethical one. Responsible conduct of research involves acknowledging other people’s roles in managing data. Acknowledging the roles may also have an impact of the careers of researchers involved.
[Decide what are the procedures that you will follow to acknowledge data management roles, and if there are any preferred methods. This template lists the options in order: options that follow best practices are noted at the beginning, while practices that we discourage are noted at the end. We use here “data management” as a general term but consider changing it for more specific roles. For example, you may want to consider offering co-authorship to the researchers involved in data collection and data quality control as authors in data publications, and adding the researchers involved in instrumentation maintenance in the acknowledgements].
All members of the Project involved in roles related to data management will be acknowledged in some way. Specifically:
Members of the Project that were involved in data management [change to a more specific role] will be offered co-authorship to papers that make use of their data. Co-authorship will require participation in the interpretation of the data, writing, or critical review of the manuscript, approval of the final manuscript. [if the group defines authorship using a specific set of criteria, include a link to these criteria here. A few examples of current definitions of authorship can be found in https://publicationethics.org/resources/discussion-documents/what-constitutes-authorship-june-2014]. The offer for co-authorship may be accepted or declined.
Datasets will be published separately from the research in a repository or as an article in a data journal [change if there are more discipline specific options]. Members of the Project with a significant data management contribution will be listed as co-authors in the data publication. Every member of the Project that makes use of the published datasets will cite the dataset and list it in the reference list in their publications.
When possible, publications will be made in journals that use the CRediT authorship taxonomy (http://docs.casrai.org/CRediT) or similar. The roles of each of the members of the Project involved in data management will be documented using the appropriate roles.
Members of the Project involved in data management [change to a more specific role] will be acknowledged in the acknowledgment section in papers and publications. 󠄀
Members of the Project involved in data management [change to a more specific role] will be listed as co-authors in publications. [this option is not recommended, as the authors of publications should participate in the writing or critical review of the piece of scholarship. For a discussion about definitions of authorship see https://publicationethics.org/resources/discussion-documents/what-constitutes-authorship-june-2014 ].
Members of the Project involved in data management won’t be acknowledged in any way.
Rationale: researchers normally do not notify each other when a process (task) is complete or if they move from one role to another. Setting expectations about how and when datasets will be shared internally will minimize conflict during the project.
Datasets will be shared internally [specify when researchers are expected to share their datasets. Some examples: as soon as possible after the data is collected/at the end of the sampling season/6 months after it is collected/on January of each year/when a researcher of the Project requests it].
Datasets will be shared internally with [who? Some examples: all the members of the team/members of the team approved by the IRB/the data manager of the project/the researcher who requested the dataset].
Datasets will be shared internally in _______ format [is there an expected format? For example: excel, or csv, or spss, or…].
Datasets will be shared internally [at which quality level? For example: after a quality control level has been assigned to each point following the schema in X / after following the protocol X for quality control/at any quality control level, as long as the documentation clarifies the quality control procedures that have been followed /only if all the data points have been subject to the whole quality control process outlined in X].
Datasets will be shared internally accompanied of [which documentation? For example: a readme file outlining at least the methods followed for data collection, the quality control procedures that have been followed, and a data dictionary/documentation using the template X/documentation using the metadata template X].
When a member of the Project uses a dataset shared by another member of the team [how will the use be notified? For example: a courtesy e-mail will be sent to the contact person/no notification will be needed at this stage/the member of the Project using the shared data will write his/her name in a log].
When a new version of a dataset is generated, it will be notified to the other members of the Project that may want to use the dataset by [for example: sending a general e-mail to the whole group/documenting in the documentation file the new version and sending individual e-mails to the members of the team that are known to be using the dataset].
Datasets will be shared internally by [how are the datasets going to be delivered? For example: by e-mail/by depositing them in Box/Google drive/external hard drive/shared drive/website].
The measures for establishing safeguards to protect the confidentiality of the data among the researchers in this Project are [Complete. Feel free to refer to the DMP or IRB for detailed description of the confidentiality measures]. This helps prevent unauthorized use or access to the data and limiting access to data for authorized staff.
[Include other workflow details that will be useful if necessary. For example, there may be details in the data management plan that can be outlined or detailed here. For example, when will the datasets be made publicly available? Who will decide when to make the dataset available if there are several researchers working with them?].
This internal data sharing plan is effective from ________ through ______.
This plan may be amended, modified, or updated. All members of the Project should discuss the amendments modifications or updates and reach consensus over the new version of the Plan. Such consensus will be formalized by signing the plan by each of the Project members.
This Internal Data Sharing Plan will be given to all new Project members. Questions or concerns should be addressed.