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Data Management Overview


  • Recognize data as the foundation of open science and be able to describe the “life cycle of data”
  • Use self-assessments to evaluate your current data management practices
  • Cite tools and resources to improve your data management practices
  • Know the biggest challenge to effective data management

Why should you care about data management?

Data is the raw currency of open science. Most work centers on creating data or adding to its value. The goal of this section is to highlight how you can make your data more valuable by managing it more effectively and with less effort.

How would you answer?

If you give your data to a colleague who has not been involved with your project, would they be able to make sense of it? Would they be able to use it properly?

If you come back to your own data in five years, will you be able to make sense of it? Will you be able to use it properly?

When you are ready to publish a paper, is it easy to find all the correct versions of all the data you used and present them in a comprehensible manner?

Data management skills produce self-describing datasets that:

Data Self-assessment

Part I: Basic questions

Here are some questions about how you manage and work with data. We will complete some more formal assessments later, but for now let’s see where you are.


In small groups, discuss the following questions. You will be provided with a space for documenting our shared answers.

  1. What are the two or three data types that you most frequently work with?

  2. What is the scale of your data?


    We often talk about the scale of data using the "Three V's":

    • Volume: Size of the data (MBs, GBs, TBs); can also include how many files (e.g dozens of big files, or millions of small ones)
    • Velocity: How quickly are these data produced and analyzed? A lot coming in a single batch infrequently, or, a constant small amount of data that must be rapidly analyzed?
    • Variety: How many different data types (raw files? databases?)

    A forth V (Veracity) captures the need to make decisions about data processing (i.e., separating low- and high-quality data)

  3. What is your strategy for storing and backing up your data?

  4. What is your strategy for verifying the integrity of your data? (i.e. verifying that your data has not be altered)

  5. What is your strategy for searching your data?

  6. What is your strategy for sharing (and getting credit for) your data? (i.e. How will do you share with your community/clients? How is that sharing documented? How do you evaluate the impact of data shared? )

Data Management Basics

Let’s learn a little more about data so that we can evaluate your self- assessment responses.


The Data Life Cycle

Data management is the set of practices that allow researchers to effectively and efficiently handle data throughout the data life cycle. Although typically shown as a circle (below) the actually life cycle of any data item may follow a different path, with branches and internal loops. Being aware of your data’s future helps you plan how to best manage them.


Image from Strasser et al..

Data Types

Different types of data require different management practices. What are some data types and sources you might use in your work? (Adapted from DMP Tool Data management general guidance)

Data Types

  • Text: field or laboratory notes, survey responses
  • Numeric: tables, counts, measurements
  • Audiovisual: images, sound recordings, video
  • Models, computer code
  • Discipline-specific: FASTA in biology, FITS in astronomy, CIF in chemistry
  • Instrument-specific: equipment outputs

Data Sources


  • Captured in real-time, typically outside the lab
  • Usually irreplaceable and therefore the most important to safeguard
  • Examples: Sensor readings, telemetry, survey results, images


  • Typically generated in the lab or under controlled conditions
  • Often reproducible, but can be expensive or time-consuming
  • Examples: gene sequences, chromatograms, magnetic field readings


  • Machine generated from test models
  • Likely to be reproducible if the model and inputs are preserved
  • Examples: climate models, economic models

Derived / Compiled

  • Generated from existing datasets
  • Reproducible, but can be very expensive and time-consuming
  • Examples: text and data mining, compiled database, 3D models

Part II: Data-specific questions

Here are some questions about how you manage and work with data. We will complete some more formal assessments later, but for now let’s see where you are.


In small groups, discuss the following questions. You will be provided with a space for documenting our shared answers.

  1. Which of the following data types (above) do you frequently work with?
  2. Do you follow specific recommendations for managing the data you work with? If not, do you know if guidelines exist?

Best practices for the data life cycle

The most important thing to remember about data management is that you are not alone. There are data management experts within your discipline, many of them not farther away than your university library system. In addition, there are many organizations (including many cited below) which work to solve.


The biggest challenge to effective data management

The biggest challenge to data management making it an afterthought.

Unfortunately, poor data management doesn’t have a high upfront cost. You can do substantial work before realizing you are in trouble. Like a swimmer in rip current, by the time you realize you are in trouble, you may already be close to drowning.

The solution? Make data management the first thing you consider when starting a research project. It also needs to be a policy you institute right away for your research group.

Here are some excellent steps to consider. The summary below is adapted from the excellent DataONE best practices primer.


  • Describe the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime
  • A good plan considers each of the stages below


  • Have a plan for data organization in place before collecting data
  • Collect and store observation metadata at the same time you collect the metadata
  • Take advantage of machine generated metadata


  • Record any conditions during collection that might affect the quality of the data
  • Distinguish estimated values from measured values
  • Double check any data entered by hand
  • Perform statistical and graphical summaries (e.g., max/min, average, range) to check for questionable or impossible values.
  • Mark data quality, outliers, missing values, etc.


  • Comprehensive data documentation (i.e. metadata) is the key to future understanding of data. Without a thorough description of the context of the data, the context in which they were collected, the measurements that were made, and the quality of the data, it is unlikely that the data can be easily discovered, understood, or effectively used.

  • Organize your data for publication. Before you can describe your data, you must decide how to organize them. This should be planned before hand, so that data organization is a minimal task at the time of publication.

  • Thoroughly describe the dataset (e.g., name of dataset, list of files, date(s) created or modified, related datasets) including the people and organizations involved in data collection (e.g., authors, affiliations, sponsor). Also include:

    • An ORCID (obtain one if you don’t have one).
    • The scientific context (reason for collecting the data, how they were collected, equipment and software used to generate the data, conditions during data collection, spatial and temporal resolution)
    • The data themselves
      • How each measurement was produced
      • Units
      • Format
      • Quality assurance activities
      • Precision, accuracy, and uncertainty

Metadata standards and ontologies are invaluable for supporting data reuse. Metadata standards tell you:

  • Which metadata attributes to include
  • How to format your metadata
  • What values are allowable for different attributes

Some metadata standards you may want to consider:

Ontologies provide standardization for metadata values:

The CyVerse Data Commons supports good data description through:

  • Metadata templates
  • Bulk metadata upload
  • Automatic collection of analysis parameters, inputs, and outputs in the DE.


In general, data must be preserved in an appropriate long-term archive (i.e. data center). Here are some examples:

  • Sequence data should go to a national repository, frequently NCBI
  • Identify data with value – it may not be necessary to preserve all data from a project
  • The CyVerse Data Commons provides a place to publish and preserve data that was generated on or can be used in CyVerse, where no other repository exists.
  • See lists of repositories at
  • See lists of repositories at Data Dryad
  • Github repos can get DOIs through Zenodo
  • Be aware of licensing and other intellectual property issues
    • Repositories will require some kind of license, often the least restrictive (see for example Creative Commons
    • Repositories are unlikely to enforce reuse restrictions, even if you apply them.



  • Data integration is a lot of work
  • Standards and ontologies are key to future data integration
  • Know the data before you integrate them
  • Don’t trust that two columns with the same header are the same data
  • Properly cite the data you reuse!
  • Use DOIs (Digital Object Identifiers) wherever possible


  • Follow open science principles for reproducible analyses (CyVerse, RStudio, notebooks, IDEs)
  • State your hypotheses and analysis workflow before collecting data. Tools like Open Science Framework (OSF) allow you to make this public.
  • Record all software, parameters, inputs, etc.

References and Resources

DataOne best practices

Center for Open Science

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