Chapter 1 Before a Project

1.1 Project Management

Introduction

While not directly related to data management, poor project management can cripple your ability to keep track of data, produce new data, and complete your interdisciplinary project in a timely manner. Good communication & project planning from the very beginning is critical for an interdisciplinary & highly collaborative project - as several teams will be bringing their own workflows & communication styles to the table. For interdisciplinary projects in particular, team members assumptions regarding how a project should be managed may not align with the other team members.

Without clearly defined goals & expectations, new pieces and components could be added to the project that exceed the original workload. It can be difficult to know when working with other disciplines if a new component is necessary and expected - or if it’s an extraneous addition. It may also be possible that certain disciplines expect different outputs, and have differing expectations of what particular outputs mean (i.e., publication). All of these concerns can lead to a chaotic working environment and slower progress.

Questions to Consider

  • What are the expected final outputs of this project? Where and when will they be published?
    • What will you need to create for the project to be considered successful?
    • How will you share these creations with others?
  • What are the project deadlines? What does each team or member contribute to the expected outputs?
  • How will communications be set up between team members? How will documents and information be shared?
    • How frequently will group members need to communicate (e.g., daily, monthly)?
    • What tools best suit your communication needs (e.g., Google Suite, Slack, Teams)?

1.2 Sharing among the group after the project

Sharing among the group after the project

Planning about who will be in charge of which components of project data is vital to the project’s success. In interdisciplinary and collaborative projects, it is easy for data to be lost, not obtained, or left out of discussion or analysis because data was not properly shared between relevant researchers. Lack of data sharing can contribute to inaccurate conclusions and delays.

Questions to Consider

  • What data is each group collecting? What data needs to be shared? How and when will data be shared?
    • What file types will be used? Will other group members be able to access them with appropriate permissions?
    • What will you use to make files available to other group members? Will your communications tool work as a file sharing network?
    • Will multiple people need to work on the same file? If so, how will this happen and what software will you use?
  • How will the data be explained? How much does each group member have to understand the whole dataset?
    • What explanation or context would you need to give a particular group member so they can understand the data shared and do something with it?

1.3 Choosing from a variety of tools/methods

Introduction

Many researchers have domain specific tools to accomplish tasks within interdisciplinary projects. In addition, there are a variety of data management tools/software, and it is highly likely that an interdisciplinary team will have varying skill sets and familiarity with these tools. Being clear on which tools are used and how they are selected will help mitigate difficulties in unifying and working with data. Factors such as how long it takes to learn a new tool, its availability to everyone on the project, and compatibility with other tools can affect decisions to use them.

Questions to Consider

  • What software does your team use to collect, analyze, and visualize data? What are the costs of these tools? How much time will it take to learn these tools?
  • How much time will it take to move data from one tool to another? Will tools be used by multiple teams/disciplines, and if so what is the learning curve for their usage?
    • Do other group members need to learn and understand specific software?

1.4 Where will the data be stored? Who has access?

Introduction

Effective data storage is a critical component for any successful research project, and its importance is only magnified with the volume and diversity of data that is typically involved with an interdisciplinary and highly collaborative research projects. Researcher choices about data storage, including where it can be stored, who has access, and in what format, depend on discipline and available resources. The group should lay out which pieces of data need to be shared and accessible by group members and collaborators - and find a platform that can allow for such sharing if it is needed. Additional training, secure storage, and/or access restrictions may be required for data containing sensitive information.

Questions to Consider

  • Is data sensitive? Do you need approval from another academic unit (e.g. IRB, Export Control, Research Compliance)? What servers will the data be stored on? Does the data need to be stored in a secure location?
    • Is your data reasonably identifiable to certain individuals? Based on the data and other openly available information could you guess who individuals are?
  • Does everyone on the project need access to a particular dataset? Only some of them? Which team members will have access to which files?
    • Do you need special authorization or training to use a dataset, such as the (CITI) Human Subjects Research Basic Course or special human subject training?
    • Will it be overwhelming if every group member has access to all of the data? Can data access be compartmentalized for each team or members?
    • What data needs to be shared among all group members?