Minor modifications (#5)

* Update datamanagementplan.md

* Update datamanagementplan.md

* Update ocapcare.md

* Update index.markdown

* Update schemas.md

* Update semantic_engine.md

docs/Data_Documentation/schemas.md

@@ -18,7 +18,7 @@ Better data schemas aid researchers in sharing data with the research community.
 {: .highlight }
 Documenting your data with a schema makes it more FAIR.
 
-Formalized, machine-readable schemas are very useful and can be expressed in a number of languages including LinkML, Overlays Capture Architecture (OCA), JSON Schema, XML Schema Definition, and JSON-LD. Different schema languages have different benefits but the biggest advantage of using any schema language to document a schema is that the schema is documented and in a machine-readable format.  Schemas also facilitiate application interface (API) development and exposes the structural information that enables users to query datasets directly.
+Formalized, machine-readable schemas are very useful and can be expressed in a number of languages including LinkML, Overlays Capture Architecture (OCA), JSON Schema, XML Schema Definition, and JSON-LD. Different schema languages have different benefits but the biggest advantage of using any schema language to document a schema is that the schema is documented and in a machine-readable format.  Schemas also facilitiate application programming interface (API) development and exposes the structural information that enables users to query datasets directly.
 
 With a machine-readable schema you can use it for many other tasks including data verification, data entry and data harmonization. For example, the [Semantic Engine](https://www.semanticengine.org) helps researchers write their own data schemas using the OCA schema language. The [Data Harmonizer](https://github.com/cidgoh/DataHarmonizer) uses custom LinkML schemas to let researchers edit and validate tabular data according to the LinkML schema.  
 

docs/Data_Documentation/semantic_engine.md

@@ -16,7 +16,7 @@ nav_order: 3
 
 All datasets have a schema, either implicit or explicit. The goal of the Semantic Engine is to take your knowledge of the data and document that explicitly using a schema. 
 
-To easily write machine-readable schemas in the OCA schema language, the organization Agri-food Data Canada from the University of Guelph has developed a friendly user-interface for documenting data and using the schemas that have been generated.
+To easily write machine-readable schemas in the OCA schema language, the organization Agri-food Data Canada (ADC) from the University of Guelph has developed a friendly user-interface for documenting data and using the schemas that have been generated.
 
 ## Using the Semantic Engine
 

docs/datamanagementplan.md

@@ -6,18 +6,18 @@ nav_order: 2
 
 # Data Management Plans
 
-All research projects for the Genome Canada's Climate-Smart Agriculture and Food Systems Initiative have created a Data Management Plan (DMP) using the [DMP Assistant of Portage](https://dmp-pgd.ca/).
+All Interdisciplinary Challenge Teams (ICTs) for the Genome Canada's Climate-Smart Agriculture and Food Systems Initiative have created a Data Management Plan (DMP) using the [DMP Assistant of the Digital Research Alliance of Canada (DRAC) ](https://dmp-pgd.ca/).
 
 {: .highlight }
 Share contents of your DMP with your team so all your project members know data management expectations.
 
-Data Management Plans are one of the foundations of good research data management (RDM), an international best practice, and increasingly required by institutions and funders, including the Canadian Tri-Agencies as outlined in their Research Data Management Policy. 
+Data Management Plans are one of the foundations of good research data management (RDM), an international best practice, and increasingly required by institutions and funders, including Canada's three major research funding agencies as outlined in their [Research Data Management Policy](https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy). 
 
 Data Management Plans contain important information for project participants such as how to name files, how and where to save data securely, what file formats to use, and what are relevant ethical and legal compliance considerations. 
 
 Be sure that all team members working in the project have the relevant information from the DMP, for example, that they know the file naming conventions. This information can be shared directly by making the DMP available, or it can be used to generate a lab and project data Standard Operating Procedure (SOP). The SOP acts like the syllabus for a course; it outlines the data essentials and expectations for the lab and/or project.
 
-Data Management Plans are living documents, they are expected to be updated and changed throughout the lifecycle of the project. The PDF versions of the DMP exported from Porage DMP Assistant contain the last modified date to help researchers identify which revision they are referencing.
+Data Management Plans are living documents, they are expected to be updated and changed throughout the lifecycle of the project. The PDF versions of the DMP exported from DRAC DMP Assistant contain the last modified date to help researchers identify which revision they are referencing.
 
 ## Samples of lab data SOPs
 

docs/index.markdown

@@ -8,7 +8,7 @@ nav_order: 1
 ---
 # Welcome to the Documentation Site
 
-Here the Data Hub is collecting resources that will be useful to Data Hub members. 
+This is where you'll find all the resources that will be useful to members of the DataHub. 
 
 If you would like to see more content hosted here let us know in the Data Hub Discussion board.
 

docs/ocapcare.md

@@ -4,7 +4,25 @@ title: OCAP and CARE
 nav_order: 8
 ---Re: Nouveau message Contact
 
-# OCAP and CARE Principles
+# OCAP Principles
+
+The First Nations principles of ownership, control, access, and possession – more commonly known as OCAP® – assert that First Nations have control over data collection processes, and that they own and control how this information can be used.
+[Find out more about OCAP principles](https://fnigc.ca/ocap-training/)
+
+
+# CARE Principles
+
+CARE stands for:
+Collective Benefit
+Authority to control
+Responsibility
+Ethics
+
+The current movement toward open data and open science does not fully engage with Indigenous Peoples rights and interests. Existing principles within the open data movement (e.g. FAIR: findable, accessible, interoperable, reusable) primarily focus on characteristics of data that will facilitate increased data sharing among entities while ignoring power differentials and historical contexts. The emphasis on greater data sharing alone creates a tension for Indigenous Peoples who are also asserting greater control over the application and use of Indigenous data and Indigenous Knowledge for collective benefit.
+
+This includes the right to create value from Indigenous data in ways that are grounded in Indigenous worldviews and realise opportunities within the knowledge economy. The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.
+
+(This text was obtained from the [CARE official site](https://www.gida-global.org/care))
 
 ## Resources