2 Session 2: Web based data archival

2.1 Best Practices: Data and Metadata

2.1.1 Learning Objectives

In this lesson, you will learn:

  • How to acheive practical reproducibility
  • Some best practices for data and metadata management

2.1.2 Best Practices: Overview

The data life cycle has 8 stages: Plan, Collect, Assure, Describe, Preserve, Discover, Integrate, and Analyze. In this section we will cover the following best practices that can help across all stages of the data life cycle:

  • Organizing Data
  • File Formats
  • Large Data Packages
  • Metadata
  • Data Identifiers
  • Provenance
  • Licensing and Distribution

Our goal, generally, is to operate within this lifecycle in ways that are FAIR

The FAIR principles are principles that help guide research so that it is more reusable. Organizing Data

We’ll spend an entire lesson later on that’s dedicated to organizing your data in a tidy and effective manner, but first, let’s focus on the benefits on having “clean” data and complete metadata.

  • Decreases errors from redundant updates
  • Enforces data integrity
  • Helps you and future researchers to handle large, complex datasets
  • Enables powerful search filtering

Much has been written on effective data management to enable reuse. The following two papers offer words of wisdom:

In brief, some of the best practices to follow are:

  • Have scripts for all data manipulation that start with the uncorrected raw data file and clean the data programmatically before analysis.
  • Design your tables to add rows, not columns. A column should be only one variable and a row should be only one observation.
  • Include header lines in your tables
  • Use non-proprietary file formats (ie, open source) with descriptive file names without spaces.

Non-proprietary file formats are essential to ensure that your data can still be machine readable long into the future. Open formats include text files and binary formats such as NetCDF.

Common switches:

  • Microsoft Excel (.xlsx) files - export to text (.txt) or comma separated values (.csv)
  • GIS files - export to ESRI shapefiles (.shp)
  • MATLAB/IDL - export to NetCDF

When you have or are going to generate large data packages (in the terabytes or larger), it’s important to establish a relationship with the data center early on. The data center can help come up with a strategy to tile data structures by subset, such as by spatial region, by temporal window, or by measured variable. They can also help with choosing an efficient tool to store the data (ie NetCDF or HDF), which is a compact data format that helps parallel read and write libraries of data. Metadata Guidelines

Metadata (data about data) is an important part of the data life cycle because it enables data reuse long after the original collection. Imagine that you’re writing your metadata for a typical researcher (who might even be you!) 30+ years from now - what will they need to understand what’s inside your data files?

The goal is to have enough information for the researcher to understand the data, interpret the data, and then re-use the data in another study.

Another way to think about it is to answer the following questions with the documentation:

  • What was measured?
  • Who measured it?
  • When was it measured?
  • Where was it measured?
  • How was it measured?
  • How is the data structured?
  • Why was the data collected?
  • Who should get credit for this data (researcher AND funding agency)?
  • How can this data be reused (licensing)?

Bibliographic Details

The details that will help your data be cited correctly are:

  • a global identifier like a digital object identifier (DOI);
  • a descriptive title that includes information about the topic, the geographic location, the dates, and if applicable, the scale of the data
  • a descriptive abstract that serves as a brief overview off the specific contents and purpose of the data package
  • funding information like the award number and the sponsor;
  • the people and organizations like the creator of the dataset (ie who should be cited), the person to contact about the dataset (if different than the creator), and the contributors to the dataset

Discovery Details

The details that will help your data be discovered correctly are:

  • the geospatial coverage of the data, including the field and laboratory sampling locations, place names and precise coordinates;
  • the temporal coverage of the data, including when the measurements were made and what time period (ie the calendar time or the geologic time) the measurements apply to;
  • the taxonomic coverage of the data, including what species were measured and what taxonomy standards and procedures were followed; as well as
  • any other contextual information as needed.

Interpretation Details

The details that will help your data be interpreted correctly are:

  • the collection methods for both field and laboratory data;
  • the full experimental and project design as well as how the data in the dataset fits into the overall project;
  • the processing methods for both field and laboratory samples IN FULL;
  • all sample quality control procedures;
  • the provenance information to support your analysis and modelling methods;
  • information about the hardware and software used to process your data, including the make, model, and version; and
  • the computing quality control procedures like any testing or code review.

Data Structure and Contents

Well constructed metadata also includes information about the data structure and contents. Everything needs a description: the data model, the data objects (like tables, images, matricies, spatial layers, etc), and the variables all need to be described so that there is no room for misinterpretation.

Variable information includes the definition of a variable, a standardized unit of measurement, definitions of any coded values (such as 0 = not collected), and any missing values (such as 999 = NA).

Not only is this information helpful to you and any other researcher in the future using your data, but it is also helpful to search engines. The semantics of your dataset are crucial to ensure your data is both discoverable by others and interoperable (that is, reusable).

For example, if you were to search for the character string carbon dioxide flux in the general search box at the Arctic Data Center, not all relevant results will be shown due to varying vocabulary conventions (ie, CO2 flux instead of carbon dioxide flux) across disciplines — only datasets containing the exact words carbon dioxide flux are returned. With correct semantic annotation of the variables, your dataset that includes information about carbon dioxide flux but that calls it CO2 flux WOULD be included in that search.

Demonstrates a typical search for “carbon dioxide flux,” yielding 20 datasets. (right) Illustrates an annotated search for “carbon dioxide flux,” yielding 29 datasets. Note that if you were to interact with the site and explore the results of the figure on the right, the dataset in red of Figure 3 will not appear in the typical search for “carbon dioxide flux.”

Rights and Attribution

Correctly assigning a way for your datasets to be cited and reused is the last piece of a complete metadata document. This section sets the scientific rights and expectations for the future on your data, like:

  • the citation format to be used when giving credit for the data;
  • the attribution expectations for the dataset;
  • the reuse rights, which describe who may use the data and for what purpose;
  • the redistribution rights, which describe who may copy and redistribute the metadata and the data; and
  • the legal terms and conditions like how the data are licensed for reuse.

So, how do you organize all this information? There are a number of metadata standards (think, templates) that you could use, including the Ecological Metadata Language (EML), Geospatial Metadata Standards like ISO 19115 and ISO 19139, the Biological Data Profile (BDP), Dublin Core, Darwin Core, PREMIS, the Metadata Encoding and Transmission Standard (METS), and the list goes on and on. The Arctic Data Center runs on EML.

2.1.3 Data Identifiers

Many journals require a DOI - a digital object identifier - be assigned to the published data before the paper can be accepted for publication. The reason for that is so that the data can easily be found and easily linked to.

At the Arctic Data Center, we assign a DOI to each published dataset. But, sometimes datasets need to be updated. Each version of a dataset published with the Arctic Data Center has a unique identifier associated with it. Researchers should cite the exact version of the dataset that they used in their analysis, even if there is a newer version of the dataset available. When there is a newer version available, that will be clearly marked on the original dataset page with a yellow banner indicating as such.

Having the data identified in this manner allows us to accurately track the dataset usage metrics. The Arctic Data Center tracks the number of citations, the number of downloads, and the number of views of each dataset in the catalog.

We filter out most views by internet bots and repeat views within a small time window in order to make these metrics COUNTER compliant. COUNTER is a standard that libraries and repositories use to provide users with consistent, credible, and comparable usage data.

2.1.4 Data Citation

Researchers should get in the habit of citing the data that they use - even if it’s their own data! - in each publication that uses that data. The Arctic Data Center has taken multiple steps towards providing data citation information for all datasets we hold in our catalog, including a feature enabling dataset owners to directly register citations to their datasets.

We recently implemented this “Register Citation” feature to allow researchers to register known citations to their datasets. Researchers may register a citation for any occasions where they know a certain publication uses or refers to a certain dataset, and the citation will be viewable on the dataset profile within 24 hours.

To register a citation, navigate to the dataset using the DOI and click on the citations tab. Once there, this dialog box will pop up and you’ll be able to register the citation with us. Click that button and you’ll see a very simple form asking for the DOI of the paper and if the paper CITES the dataset (that is, the dataset is explicitly identified or linked to somewhere in the text or references) or USES the dataset (that is, uses the dataset but doesn’t formally cite it).

We encourage you to make this part of your workflow, and for you to let your colleagues know about it too!

2.1.5 Provanance & Preserving Computational Workflows

While the Arctic Data Center, Knowledge Network for Biocomplexity, and similar repositories do focus on preserving data, we really set our sights much more broadly on preserving entire computational workflows that are instrumental to advances in science. A computational workflow represents the sequence of computational tasks that are performed from raw data acquisition through data quality control, integration, analysis, modeling, and visualization.

In addition, these workflows are often not executed all at once, but rather are divided into multiple workflows, earch with its own purpose. For example, a data acquistion and cleaning workflow often creates a derived and integrated data product that is then picked up and used by multiple downstream analytical workflows that produce specific scientific findings. These workflows can each be archived as distinct data packages, with the output of the first workflow becoming the input of the second and subsequent workflows.

In an effort to make data more reproducible, datasets also support provenance tracking. With provenance tracking, users of the Arctic Data Center can see exactly what datasets led to what product, using the particular script or workflow that the researcher used.

This is a useful tool to make data more compliant with the FAIR principles. In addition to making data more reproducible, it is also useful for building on the work of others; you can produce similar visualizations for another location, for example, using the same code.

RMarkdown itself can be used as a provenance tool, as well - by starting with the raw data and cleaning it programmatically, rather than manually, you preserve the steps that you went through and your workflow is reproducible.

2.2 Data Documentation and Publishing

2.2.1 Learning Objectives

In this lesson, you will learn:

  • About open data archives
  • What science metadata is and how it can be used
  • How data and code can be documented and published in open data archives

2.2.2 Data sharing and preservation

2.2.3 Data repositories: built for data (and code)

  • GitHub is not an archival location
  • Dedicated data repositories: KNB, Arctic Data Center, Zenodo, FigShare
    • Rich metadata
    • Archival in their mission
  • Data papers, e.g., Scientific Data
  • List of data repositories: http://re3data.org

2.2.4 Metadata

Metadata are documentation describing the content, context, and structure of data to enable future interpretation and reuse of the data. Generally, metadata describe who collected the data, what data were collected, when and where it was collected, and why it was collected.

For consistency, metadata are typically structured following metadata content standards such as the Ecological Metadata Language (EML). For example, here’s an excerpt of the metadata for a sockeye salmon data set:

<?xml version="1.0" encoding="UTF-8"?>
<eml:eml packageId="df35d.442.6" system="knb" 
        <title>Improving Preseason Forecasts of Sockeye Salmon Runs through 
            Salmon Smolt Monitoring in Kenai River, Alaska: 2005 - 2007</title>
        <creator id="1385594069457">
            <organizationName>Alaska Department of Fish and Game</organizationName>
            <positionName>Fishery Biologist</positionName>
            <phone phonetype="voice">(907)260-2911</phone>

That same metadata document can be converted to HTML format and displayed in a much more readable form on the web: https://knb.ecoinformatics.org/#view/doi:10.5063/F1F18WN4

And as you can see, the whole data set or its components can be downloaded and reused.

Also note that the repository tracks how many times each file has been downloaded, which gives great feedback to researchers on the activity for their published data.

2.2.5 Structure of a data package

Note that the data set above lists a collection of files that are contained within the data set. We define a data package as a scientifically useful collection of data and metadata that a researcher wants to preserve. Sometimes a data package represents all of the data from a particular experiment, while at other times it might be all of the data from a grant, or on a topic, or associated with a paper. Whatever the extent, we define a data package as having one or more data files, software files, and other scientific products such as graphs and images, all tied together with a descriptive metadata document.

These data repositories all assign a unique identifier to every version of every data file, similarly to how it works with source code commits in GitHub. Those identifiers usually take one of two forms. A DOI identifier is often assigned to the metadata and becomes the publicly citable identifier for the package. Each of the other files gets an internal identifier, often a UUID that is globally unique. In the example above, the package can be cited with the DOI doi:10.5063/F1F18WN4.

2.2.6 DataONE Federation

DataONE is a federation of dozens of data repositories that work together to make their systems interoperable and to provide a single unified search system that spans the repositories. DataONE aims to make it simpler for researchers to publish data to one of its member repositories, and then to discover and download that data for reuse in synthetic analyses.

DataONE can be searched on the web (https://search.dataone.org/), which effectively allows a single search to find data form the dozens of members of DataONE, rather than visiting each of the currently 43 repositories one at a time.

Publishing data from the web

Each data repository tends to have its own mechanism for submitting data and providing metadata. With repositories like the KNB Data Repository and the Arctic Data Center, we provide some easy to use web forms for editing and submitting a data package. Let’s walk through a web submission to see what you might expect.

Download the data to be used for the tutorial

I’ve already uploaded the test data package, and so you can access the data here:

Grab both CSV files, and the R script, and store them in a convenient folder.

Login via ORCID

We will walk through web submission on https://demo.nceas.ucsb.edu, and start by logging in with an ORCID account. ORCID provides a common account for sharing scholarly data, so if you don’t have one, you can create one when you are redirected to ORCID from the Sign In button.

When you sign in, you will be redirected to orcid.org, where you can either provide your existing ORCID credentials, or create a new account. ORCID provides multiple ways to login, including using your email address, institutional logins from many universities, and logins from social media account providers. Choose the one that is best suited to your use as a scholarly record, such as your university or agency login.

Create and submit the data set

After signing in, you can access the data submission form using the Submit button. Once on the form, upload your data files and follow the prompts to provide the required metadata. Required sections are listed with a red asterisk.

Click Add Files to choose the data files for your package

You can select multiple files at a time to efficiently upload many files.

The files will upload showing a progress indicator. You can continue editing metadata while they upload.

Enter Overview information

This includes a descriptive title, abstract, and keywords.

The title is the first way a potential user will get information about your dataset. It should be descriptive but succinct, lack acronyms, and include some indication of the temporal and geospatial coverage of the data.

The abstract should be sufficently descriptive for a general scientific audience to understand your dataset at a high level. It should provide an overview of the scientific context/project/hypotheses, how this data package fits into the larger context, a synopsis of the experimental or sampling design, and a summary of the data contents.

Keywords, while not required, can help increase the searchability of your dataset, particularly if they come from a semantically defined keyword thesaurus.

Optionally, you can also enter funding information, including a funding number, which can help link multiple datasets collected under the same grant.

Selecting a distribution license - either CC-0 or CC-BY is required.

People Information

Information about the people associated with the dataset is essential to provide credit through citation and to help people understand who made contributions to the product. Enter information for the following people:

  • Creators - all the people who should be in the citation for the dataset
  • Contacts - one is required, but defaults to the first Creator if omitted
  • Principal Investigators
  • and any other that are relevant

For each, please strive to provide their ORCID identifier, which helps link this dataset to their other scholarly works. Temporal Information

Add the temporal coverage of the data, which represents the time period to which data apply.

Location Information

The geospatial location that the data were collected is critical for discovery and interpretation of the data. Coordinates are entered in decimal degrees, and be sure to use negative values for West longitudes. The editor allows you to enter multiple locations, which you should do if you had noncontiguous sampling locations. This is particularly important if your sites are separated by large distances, so that a spatial search will be more precise.

Note that, if you miss fields that are required, they will be highlighted in red to draw your attention. In this case, for the description, provide a comma-separated place name, ordered from the local to global. For example:

  • Mission Canyon, Santa Barbara, California, USA


Methods are critical to accurate interpretation and reuse of your data. The editor allows you to add multiple different methods sections, include details of sampling methods, experimental design, quality assurance procedures, and computational techniques and software. Please be complete with your methods sections, as they are fundamentally important to reuse of the data.

Save a first version with Submit

When finished, click the Submit Dataset button at the bottom.
If there are errors or missing fields, they will be highlighted.
Correct those, and then try submitting again. When you are successful, you should see a green banner with a link to the current dataset view. Click the X to close that banner, if you want to continue editing metadata.


File and variable level metadata

The final major section of metadata concerns the structure and contents of your data files. In this case, provide the names and descriptions of the data contained in each file, as well as details of their internal structure.

For example, for data tables, you’ll need the name, label, and definition of each variable in your file. Click the Describe button to access a dialog to enter this information.

The Attributes tab is where you enter variable (aka attribute) information. In the case of tabular data (such as csv files) each column is an attribute, thus there should be one attribute defined for every column in your dataset. Attribute metadata includes:

  • variable name (for programs)
  • variable label (for display)

  • variable definition (be specific)
  • type of measurement

  • units & code definitions

You’ll need to add these definitions for every variable (column) in the file. When done, click Done.

Now the list of data files will show a green checkbox indicating that you have full described that file’s internal structure. Proceed with the other CSV files, and then click Submit Dataset to save all of these changes.

Note that attribute information is not relevant for data files that do not contain variables, such as the R script in this example. Other examples of data files that might not need attributes are images, pdfs, and non-tabular text documents (such as readme files). The yellow circle in the editor indicates that attributes have not been filled out for a data file, and serves as a warning that they might be needed, depending on the file.

After you get the green success message, you can visit your dataset and review all of the information that you provided. If you find any errors, simply click Edit again to make changes.

Add workflow provenance

Understanding the relationships between files in a package is critically important, especially as the number of files grows. Raw data are transformed and integrated to produce derived data, that are often then used in analysis and visualization code to produce final outputs. In DataONE, we support structured descriptions of these relationships, so one can see the flow of data from raw data to derived to outputs.

You add provenance by navigating to the data table descriptions, and selecting the Add buttons to link the data and scripts that were used in your computational workflow. On the left side, select the Add circle to add an input data source to the filteredSpecies.csv file. This starts building the provenance graph to explain the origin and history of each data object.

The linkage to the source dataset should appear.

Then you can add the link to the source code that handled the conversion between the data files by clicking on Add arrow and selecting the R script:

Select the R script and click “Done.”

The diagram now shows the relationships among the data files and the R script, so click Submit to save another version of the package.

Et voilà! A beatifully preserved data package!