Module 6 – Ocean Publishers and Funders

CIOOS Atlantic is the main organization behind this tutorial, whose work and funding are partially governed by Dalhousie University. It is a regional association of the Canadian Integrated Ocean Observing System. CIOOS is “a national online platform for sharing, discovering and accessing ocean data in Canada. Data that is integrated into CIOOS will be visible regionally and nationally. CIOOS currently consists of three Regional Associations (RAs).” The regional associations in question are CIOOS Atlantic which focuses on the Atlantic seaboard, CIOOS Pacific which focuses on the Pacific seaboard and Saint Lawrence Global Observatory (SLGO) which focuses on the Gulf of St. Lawrence and the Great Lakes.

International:
United Nation Organizations: The UN funds and controls a number of ocean-based organizations that operate on a global scale for the protection and betterment of the world’s oceans. Some of their repositories include OBIS

National:
In Canada, the Ocean Networks Canada Organization is based in BC and focuses on Canada’s 3 oceans and their associated coastlines.

Local:
The Mackenzie DataStream is just one of DataStreams regional partners, based around the Mackenzie River Basin in the Northwest Territories.

These are but a few ocean organizations that exist in Canada and the world.

Beyond just repositories, CIOOS has a consortium of partners within the ocean sector that all play important roles in helping us to excel at the work that we do!

CIOOS Atlantic Operational Partners

• OFI – The Ocean Frontier Institute conducts leading edge ocean science in the industry.
• Dalhousie University – This Halifax based university is Atlantic Canada’s primary research university.
• Ocean Tracking Network – A global organization that monitor’s the world’s aquatic animals.
• Memorial University – Newfoundland and Labrador’s only university, with a global capacity and reputation in leadership.
• Marine Institute – This research center is located on the edge of the Atlantic ocean and operates out of Memorial University, it is considered one of the leading institutes for ocean research.
• COINAtlantic – This non-governmental agency is located in Halifax, and dedicated to supporting coastal and Marine Spatial Planning (MSP) and decision-making through open data management and information sharing.

National Partners

• Ocean Networks Canada – Based at the University of Victoria, in BC, and monitors both the east and west coast.
• University of Victoria – One of Canada’s leading research universities, including ocean research.
• St. Lawrence Global Observatory – One of our partner organizations, another node of CIOOS. Geographic focus is the Gulf of Saint Lawrence, the Great Lakes and associated waterways.

Funding Partners

• Fisheries and Ocean Canada (DFO) – Federal department responsible for Canada’s fisheries and ocean resources.
• MEOPAR – Marine Environmental Observation, Prediction and Response Network links top marine researchers with applicable organizations and communities within the ocean sector.

Activity

Did any of those organizations interest you? If so, dig deeper into their philosophies and structures. Alternatively do a five-minute search for an ocean organization that is not listed here, to see which ones exist and what interesting different niches they might occupy. (Feel free to keep a record of your search engine entries and methods, as well as your results, if you want to, as there is a later module all about searching that you may want to use them for!)

For more information about the ocean sector in Canada see this Udemy tutorial.

Now that we know the who’s who of Ocean Publishers and Funders, let’s talk about what. Specifically what elements of data management we believe are most important to the various funders and publishers in the ocean sector.

Here we list some of the problems with data management that were presented in the earlier modules. Recall the data pitfalls video, as a few of these directly address some of the issues identified in that video. Mitigating these problems will make you invaluable to the previously discussed organizations!

Here are some of the most important elements to focus on:

• Active data management – Most data management, and DMPs are focused on what you will do with your data when the project is completed—will you submit to a repository, a journal or keep it at your institution? How long after your project will it be available for? And other such questions like that! Active data management however, ask what you’ll be doing with your data during the project. Who will handle the data during the project and make sure it’s backed up and up-to-date? How will it be stored during the project? Will the storage methods cost money, or are they free? Asking these questions and deciding on the answers before you even begin collecting data will do a lot to smooth out any potential bumps your project may have. Planning ahead of time for these elements means that there will be no scrambling later to figure it out.
• Data sharing – Data sharing will help you connect with other researchers and is one of the facets of the Tri-Agency’s mandates around data. There are many different places to share your data – open access repositories, data journals, or your institutions/organizations repositories. An important facet of data sharing is metadata. We’ll go into metadata in more detail in a later module, but metadata is the data about your data, and it helps machines to be able to find and parse the data you’ve shared, no matter what level of sharing you’ve done. It is a good idea to decide on a metadata schema before your project starts, while you are filling out your DMP.
• Coding – In this case we mean the process or activity of writing programs that can analyze or manipulate your datasets. Can be as simple as creating graphs from CSV files or as complex as creating a scraper for the raw data. While it is not necessary to be an expert coder to be a good data manager, even cursory knowledge of a coding language will serve well, especially when it comes to using data and understanding it. Python is a good versatile language, and R is common in data research for it’s utility in working with statistics. Learning a markup language like XML is also recommended, as understanding how webpages are built can help understand the way machines crawl the web and what they look for, allowing for better ideas about data sharing. Many markup languages also function structurally similarly to metadata schema. Spreadsheet analysis is also important to learn.
• Coming in with a data first mindset – Essentially all this means is coming into a project with context and intent for the data. A mistake some first time researchers make is collecting the data first and then realizing they need to store it somewhere, find a program to manipulate the data with, and find a journal to submit it to later. A data first mindset means visualizing all of the pieces of data of a project and the ways they interconnect, before starting the project.
• Using data creatively – to use data creatively means to study other dataset and raw data, recognize the way connections are made and how data is used by these organizations and individual researchers. (More on this in data literacy, the next module)
• Contextualizing data as geospatial/geographic. Geospatial means that we would consider the ocean in ways that both directly and indirectly reference a specific geographic area/location. Exploring this data helps create a more robust picture of the ocean and what it can do. Beyond just the study of bio-sciences and the creatures in the water, the location of the ocean itself becomes data. Even variables like salinity or temperature are affected by where on the planet that particular part of the ocean is.

As you can see most of the solutions involve making good data management plans! Luckily, you’re here, taking this tutorial, so you’re already on track.

Activity: thought experiment

In our very first module we talked about how anything could be data when given the proper context. If we take that concept and apply it to the ocean, there are many different kinds of data one could get. But no ocean data can be removed from it’s geospatial (geographic) component. Think of what could be ocean data and see if you can come up with any that isn’t geospatial data.