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Managing Data

Learning Objectives

After this lesson, you should be able to:

  • 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?

Most scientific work centers on generating new data or working with existing data, which means researchers spend a lot of time dealing with it. Ensuring that data are effectively organized, shared, and preserved is critical to making your science impactful, efficient, and open.

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.

Activity

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? - Think about the sources (observational, experimental, simulated, compiled/derived) - Also consider the formats (tabular, sequence, database, image, etc.) 2. What is the scale of your data?

Tip

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

  1. What is your strategy for storing and backing up your data?
  2. What is your strategy for verifying the integrity of your data? (i.e. verifying that your data has not be altered)
  3. What is your strategy for searching your data?
  4. 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.

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

Observational

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

Experimental

  • 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

Simulation

  • 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

Tip

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.

lifecycle

Image from Strasser et al.

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.

Warning

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.

Plan

  • 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

Collect

  • 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

Assure

  • 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.

Describe:

  • 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.

Preserve

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 FAIRSharing.org
  • 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.

Discover

Integrate

  • 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

Analyze

  • 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

FAIR Data

Learning Objectives

  • Recall the meaning of FAIR
  • Understand why FAIR is a collection of principles (rather than rules)
  • Use self-assessments to evaluate the FAIRness of your data

FAIR Principles

In 2016, the FAIR Guiding Principles for scientific data management and stewardship were published in Scientific Data. Read it.

Findable

  • F1. (meta)data are assigned a globally unique and persistent identifier
  • F2. data are described with rich metadata (defined by R1 below)
  • F3. metadata clearly and explicitly include the identifier of the data it describes
  • F4. (meta)data are registered or indexed in a searchable resource

Accessible

  • A1. (meta)data are retrievable by their identifier using a standardized communications protocol
  • A1.1 the protocol is open, free, and universally implementable
  • A1.2 the protocol allows for an authentication and authorization procedure, where necessary
  • A2. metadata are accessible, even when the data are no longer available

Interoperable

  • I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
  • I2. (meta)data use vocabularies that follow FAIR principles
  • I3. (meta)data include qualified references to other (meta)data

Reusable

  • R1. meta(data) are richly described with a plurality of accurate and relevant attributes
  • R1.1. (meta)data are released with a clear and accessible data usage license
  • R1.2. (meta)data are associated with detailed provenance
  • R1.3. (meta)data meet domain-relevant community standard

Tip

Open vs. Public vs. FAIR:

FAIR does not demand that data be open: See one definition of open: http://opendefinition.org/

Tip

Why Principles?

FAIR is a collection of principles. Ultimately, different communities within different scientific disciplines must work to interpret and implement these principles. Because technologies change quickly, focusing on the desired end result allows FAIR to be applied to a variety of situations now and in the foreseeable future.

CARE Principles

The CARE Principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event "Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop," 8 November 2018, Gaborone, Botswana.

Collective Benefit

  • C1. For inclusive development and innovation
  • C2. For improved governance and citizen engagement
  • C3. For equitable outcomes

Authority to Control

  • A1. Recognizing rights and interests
  • A2. Data for governance
  • A3. Governance of data

Responsibility

  • R1. For positive relationships
  • R2. For expanding capability and capacity
  • R3. For Indigenous languages and worldviews

Ethics

  • E1. For minimizing harm and maximizing benefit
  • E2. For justice
  • E3. For future use

FAIR - TLC

Traceable, Licensed, and Connected

How to get to FAIR?

This is a question that only you can answer, that is because it depends on (among other things)

  1. Your scientific discipline: Your datatypes and existing standards for what constitutes acceptable data management will vary.
  2. The extent to which your scientific community has implemented FAIR: Some disciplines have significant guidelines on FAIR, while others have not addressed the subject in any concerted way.
  3. Your level of technical skills: Some approaches to implementing FAIR may require technical skills you may not yet feel comfortable with.

While a lot is up to you, the first step is to evaluate how FAIR you think your data are:

Exercise

Thinking about a dataset you work with, complete the ARDC FAIR assessment.

References and Resources

https://www.nature.com/articles/sdata201618

Data Management Plans

Learning Objectives

  • Describe the purpose of a data management plan
  • Describe the important elements of a data management plan
  • Use a self-assessment to design a data management plan

"A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. [1] The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future."

(Source: https://en.wikipedia.org/wiki/Data_management_plan)

Example DMP

Why bother with a DMP?

How would you answer?

Do you have a data management plan? If so, how do you use it?

"Those who fail to plan, plan to fail"

Returning to the assertion that data (and its value) is at the foundation of your science, working without a data management plan should be considered scientific misconduct.

Those are strong words. And while we might have an intuition of the boundaries of research ethics - data mismanagement seems more like an annoyance than misconduct. However, if your mismanagement leads to error in your research data, or the inability to make publicly-funded research open to the public, these are serious consequences. Increasingly, funders realize this.

Stick:

Carrot:

  • Make your life easier
  • Planning for you project makes it run more smoothly
  • Avoid surprise costs

Elements of a good DMP

  • Information about data & data format(s)

    • data types
    • data sources
    • analysis methods
    • formats
    • QA/QC
    • version control
    • data life cycle

  • Metadata content and format(s)

    • format
    • standards

  • Policies for access, sharing, and re-use

    • funder obligations
    • ethical and privacy issues (data justice)
    • intellectual property, copyright, citation
    • timeline for releases

  • Long-term storage, data management, and preservation

    • which data to preserve
    • which archive/repository

  • Budget(PAPPG)

    • each of the above elements cost time/money
    • Personnel time for data preparation, management, documentation, and preservation (including time)
    • Hardware and/or software for data management, back up, security, documentation, and preservation (including time)
    • Publication/archiving costs (including time)

Not only what, but who (roles).

Extra challenges for collaborative projects.

Machine actionable DMPs

  • DMPs describe research methods that will evolve over the course of a project
  • to be a useful tool for researchers and others, the content must be updated to capture the methods that are employed and the data that are produced

maDMP

(Source: https://doi.org/10.1371/journal.pcbi.1006750.g002)

Tools for DMPs

Exercise

Thinking about a dataset you work with, complete the Data Stewardship Wizard.

References and Resources

Self Assessment

What is a Data Management Plan?

Important: A data management plan (DMP) is now required aspect of publicly funded research.

DMPs are short, formal, documents outlining what types of data will be used, and what will be done with the data both during and after a research project concludes.

True or False: When science project funding ends, the data should end with it

False

Data live on after a project ends.

Ensuring that data have a full lifecycle where they can be (re)hosted and made available after a project ends is critical to open science and reproducible research

Maybe

Sometimes destroying data is part of the life cycle of data - this may be required if data are sensitive and could be used unethically in the future, beyond the control of the original investigator team.

True or False: FAIR and CARE data principles are the same

False

The CARE principles were created in order to help guide and answer when and how applying FAIR data principles to soverign indigenous-controlled data should be done and when it should not.

Last update: 2023-03-06