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Data Management Planning: RESOURCES

Information on best practices and standards for data management planning.


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Additional Resources


  • Data Cleaning and Management Using OpenRefine 
    Course materials on using OpenRefine, a powerful tool for cleaning and transforming tabular data.

  • Data Cleaning and Management Using Python 
    A class on how to extract and manage data from text and HTML files, interface with the web, and perform search functions on large datasets using introductory Python techniques.

  • Extracting Text and Data from Files Using Optical Character Recognition (OCR) 
    An introduction to using OCR to transform images of documents into machine-readable text, including the use of pattern training.

  • Introduction to Jupyter Notebooks 
    This class is designed for first-time and longer-term users of Jupyter Notebooks, a workspace for writing code. The class focuses on using Notebooks to facilitate sharing and publishing of script workflows. It aims to provide users with knowledge about shortcuts, plugins, and best practices for maximizing re-usability and shareability of Notebook contents.

  • Intro to Git and GitHub 
    This workshop introduces the basic concepts of Git version control. Whether you're new to version control or just need an explanation of Git and GitHub, this two hour tutorial will help you understand the concepts of distributed version control. Get to know basic Git concepts and GitHub workflows through step-by-step lessons. We'll even rewrite a bit of history, and touch on how to undo (almost) anything with Git. This is a class for users who are comfortable with a command-line interface.

  • Managing a Personal Research Archive 
    A class on setting up and managing research materials; caring for digital files to enable collaboration, sharing, and re-use; and helpful software/digital tools for organizing personal research files.

  • Python for Harvesting Data on the Web​ 
    This session is an intermediate-to-advanced level class that offers some ideas for how to approach the following common data wrangling needs in research: 1) Obtain data and load it into a suitable data "container" for analysis, often via a web interface, especially an API, 2) parse the data retrieved via an API and turn it into a useful object for manipulation and analysis, and 3) perform some basic summary counts of records in a dataset and work up a quick visualization.

  • Research Project Management Using the Open Science Framework 
    An introduction to managing, annotating, organizing, archiving, and publishing research data using the Open Science Framework.

  • Writing a Data Management Plan​ 
    A class covering the basics of writing a successful data management plan for federal funding agencies such as the NEH, NSF, NIH, NASA, and others.


  • Citing & Being Cited: Code & Data Edition 
    A session on how to cite code and data, and how to enable your data and code to be cited by others.

  • Introduction to Gephi 
    A session from a class on the basics of using Gephi to visualize network data.

  • Introduction to Research Data Management 
    An introduction to the concepts and best practices of research data management.

  • Open Access Data and Connecting Data to Your Publications 
    A session devoted to tools and repositories that can help connect your publications with the corresponding data.

  • Reproducibility in Research 
    Have you heard about the reproducibility crisis in science (ex. in Nature and Economist)? Do you wonder how you could increase the reproducibility of your own work? This session will show you some hands-on, practical steps and tools that can help make your research reproducible in your field.


  • NYU Data Services 
    ​A joint service of New York University's Division of Libraries and Information Technology Services to support quantitative, qualitative, and geographical research at NYU. 

  • NYU Office of Sponsored Programs (OSP)
    OSP can help you identify appropriate potential sponsors, interpret guidelines, develop budgets, and fulfill application requirements. OSP also provides institutional sign off on proposal submissions, negotiates awards with sponsors and guides investigators in funded project administration.

  • NYU Policy on Retention of and Access to Research Data
    A policy that establishes University policy to assure that Research Data are appropriately recorded, archived for the required time, and available for review under the appropriate circumstances.

  • NYU Statement of Policy on Intellectual Property
    This Statement of Policy on Intellectual Property includes a Patent Policy, a Copyright Policy, and a Tangible Research Property Policy.

  • NYU Digital Library Technology Services (DLTS)
    DLTS processes, enables access to, and preserves digital materials that come from both the NYU community and from collaborating partner organizations. Methods include digitization, software development, research, project coordination, and the articulation of best practices.

  • NYU High Performance Computing (HPC)
    NYU Information Technology Services (ITS) supports high performance computing and networking for researchers and scholars.  It is home to several high performance clusters and high-speed networks equipped with a wide variety of research software packages.

  • NYU Faculty Digital Archive (FDA)
    The FDA is a place where full-time NYU faculty can deposit their work in digital form. FDA collections can be shared with the world, or restricted to selected people. The FDA is intended to be a highly visible repository of NYU faculty digital scholarship.


Creative Commons License
Original work in this LibGuide is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.