We would be grateful for participation in a brief exercise that ask users to go through scenarios using the LD4PE website and Competency Index. Below is a series of prompts to follow. The assumption is that you would start at the LD4PE homepage: https://ld4pe.dublincore.org/

After completing each pair of prompts, there will be a short questionnaire.

Thank you for your participation. Naturally, you are welcome to provide feedback about the site, the Competency Index, and the resources through less formal channels. Contact information is available on our Participate page: https://ld4pe.dublincore.org/about/participate/

Open a separate browser window and begin at the LD4PE homepage: https://ld4pe.dublincore.org/

Prompts

A. You are a professor teaching an introductory course on Semantic Web and Linked Data concepts. One of the topics you wish to cover is the RDF Data Model. Specifically, you want to emphasize the differences between RDF and other data models.

Your task is comprised of two steps:
1. Browse through the Competency Index until you locate a competency which will meet the learning objective described above.
2. Choose an appropriate learning resource which has been aligned to that competency.

B. You work for a local government agency which is considering replacing a traditional relational database with a triple store (in order to better meet mandates for openness and transparency). However, some of your colleagues are wary of having to learn a “whole new language” to query data. It is your responsibility to gather information on what SPARQL is and how it is used.

Your task is comprised of two steps:
1. Browse through the Competency Index until you locate a competency which will meet the learning objective described above.
2. Choose an appropriate learning resource which has been aligned to that competency.

After completing prompts A & B, please proceed to a short questionnaire: https://www.surveymonkey.com/r/LD4PE

C. While researching SPARQL in Prompt B, you found that there are a handful of resources that describe the query language very well, and you want to save these resources in one place so that you can send a single link to some of your co-workers referring them to the resources. You notice that the website has a “Saved Sets” functionality, and decide to try it out.

Your task is comprised of three steps:
1. Create a new “Saved Set” and name it something meaningful you will remember later. Also add a description for additional context.
2. Add several SPARQL related resources to this Saved Set (one of these might be the resource you found for the previous prompt).
3. Make sure the Saved Set is “public”, so that your co-workers will be able to see it.

D. In this final prompt, you are again the professor from Prompt A, who was researching the RDF Data Model for your course on Semantic Web and Linked Data concepts. You quickly realize that it may be difficult for students to see the connections between the various components of Linked Data (RDF, SPARQL, OWL, the various serializations for publishing data). This means that presenting the concepts in the correct order will be very important. To help organize your thoughts and plan the curriculum for the course, you decide to use the websites’ “Learning Maps” functionality.

Your task is comprised of three steps:
1. Create a new “Learning Map” and name it something meaningful you will remember later. Also add a description for additional context.
2. Add several Competencies to the Learning Map. For example, you may want to start out with the RDF Data Model and then move on to a closely related concept, such as how to query data represented in RDF. The Learning Map functionality allows you to do this using the same skills you learned from Prompt A and Prompt B.
3. When you are done adding Competencies to your Learning Map, look at the order in which they appear. Can you think of a more logical way to group them? Try re-arranging the order of the Competencies in your Learning Map.

After completing prompts C & D, please proceed to a short questionnaire: https://www.surveymonkey.com/r/LD4PE2

Thank you for your participation. Naturally, you are welcome to provide feedback about the site, the Competency Index, and the resources through less formal channels. Contact information is available on our Participate page: https://ld4pe.dublincore.org/about/participate/

The index is organized hierarchically, going from broad topic clusters down to specific benchmarks:

Topic cluster
— Topic
—  — Competency: Tweet-length assertion of knowledge, skill, or habit of mind
—  —  — Benchmark: Action demonstrating accomplishment in related competencies

The current top level topic clusters are:
– Fundamentals of Resource Description Framework
– Fundamentals of Linked Data
– RDF vocabularies and application profiles
– Creating and transforming RDF Data
– Interacting with RDF Data
– Creating Linked Data applications

As with previous releases of the index, we are making the index available to the community as a Google Doc where we encourage comments, questions, and feedback about specific concepts of the index. Please help spread the word and help us solicit feedback from those that can contribute.

The Google Doc can be found here:
http://tinyurl.com/ld4pe-competencies-may14

For additional information on contacting or participating with the project, please see the Participate! page on the About Us section of this page.

The Competency Index is an important part of the project and is being used to organize learning resources in the Exploratorium. We are actively soliciting feedback for any aspect of the Index: broad topics to individual competencies and their benchmarks; missing concepts; current concepts that might not belong; and overall organization.

The Google Doc can be found here: https://docs.google.com/document/d/1i1k5kZLWncb3dJozb61SQiRS7hljUAox-gEXs4gHs7o/edit?usp=sharing

For additional information on participating and contacting the project, please see the Participate! page on the About Us section of this page.

2015-12-07. Throughout the month of December, the LD4PE project team will be doing a major update to the Competency Index for Linked Data including a phasing out of the prototype data used for development and demonstration and a phasing in of the Editorial Board-approved data and realignment of associated mappings. For more information on this December-long data transition, see the December 2015 Competencies Update.

The session is intended to be casual, conversational, and participatory. We look forward to meeting with Linked Data learners, practitioners, and instructors about their experiences learning and teaching Linked Data. What works? What’s hard? What should be prioritized? What’s overkill? What are your favorite resources?

This will also be a chance for the community to learn and provide feedback about our methodology for identifying, describing, and evaluation learning resources for Linked Data. Finally, the session will provide an introduction to the Competency Index and provide information on how to the get involved with the project. The input of volunteers is essential is for making sure the Linked Data community continues to flourish.

Hope to see you there.

The process of searching the Web for resources which fit the broad definition of “teaching about Linked Data”, was very “hit or miss” at first; we simply did not know where to begin focusing on such a wide topic.  The only guidance we had was an early draft of the Competency Index, to which the learning objects we were discovering would ultimately be tied.  As the project advanced, individual conversations with various LD4PE project members, each of whom had their own particular areas of interest and expertise, were conducted to get their unique perspectives on what direction the Competency Index and resource discovery should take.  Some felt that we should include certain background competencies, such as familiarity with taxonomy building or a basic understanding of how relational databases (and the query languages used to access them) differ from triple stores.  Every suggestion made perfect sense, from the standpoint that all were somehow related to the (very) wide world of creating, publishing, or consuming Linked Data.

As our monthly Skype meetings continued, all involved in the project eventually reached the consensus that we were at risk of widening the scope of LD4PE to the extent that it would be unmanageable; we would not be able to deliver a finished project of the quality we all aspired to within the two-year period of our Institute of Museum and Library Services (IMLS) grant.  Finally, it was agreed upon that, at least for the next few months, we would focus in on one area of the Competency Index and resource discovery- SPARQL query language.

Moving ahead with a focus on SPARQL makes sense for two reasons:  1) Resource discovery efforts to this point had revealed that there existed a lot of learning objects for some Linked Data concepts, and a relative few for others.  A few months into the project, a heat map was made which demonstrated the relative abundance or scarcity of resources for each area of the draft Competency Index- and SPARQL stood out as the “hottest”; 2) SPARQL, along with the RDF data model and OWL, is commonly identified as one of the three “background technologies” which underpin Linked Data.  SPARQL is used to provide access to the triple stores that hold RDF data, as well as to assess the quality of those datasets and to maintain them.  It is the language which enable one to pull information from a dataset for use in Linked Data applications.  And, used in conjunction with RDFS and OWL, SPARQL can be used to perform reasoning- a quality which sets it (and Linked Data itself), above traditional, relational data.

The following “slices” from our draft Competency index show just how important SPARQL is, as it appears in several areas:

Topic Cluster: Searching and querying

• Topic:Discovery of RDF vocabularies and data sets
  • Competency:Monitors registries and referatories of RDF vocabularies, OWL ontologies and RDF  data stores
• Topic:Assessment of RDF vocabularies and data sets
• Topic:Anatomy of a simple SPARQL query
• Topic:Querying RDF data using SPARQL
  • Competency:Understands the SPARQL 1.1 query language, protocol, functions and operators
  • Competency:Uses query forms including ASK, SELECT, DESCRIBE, CONSTRUCT
  • Competency:  Uses query patterns including BGP, UNION, OPTIONAL, FILTER
  • Competency:Uses sequence modifiers including DISTINCT, REDUCED, ORDER BY, LIMIT, OFFSET

• Topic:Updating RDF with SPARQL 1.1
  • Competency: Performs data management using INSERT, DELETE, DELETE/INSERT
  • Competency: Performs graph management using LOAD, CLEAR, CREATE, DROP, COPY/MOVE/ADD

• Topic:Reasoning over RDF
  • Competency:Understands how reasoning and data integration can be achieved by utilizing domain knowledge embodied in RDFS and OWL
  • Competency:Utilizes the entailment regimes of RDFS and SPARQL 1.1 and understands their limitations
  • Competency: Understands OWL properties, property axioms, axioms and class constructions in reasoning

 Topic Cluster: Creating, publishing and manipulating RDF

• Topic: Creating and using SPARQL endpoints
  • Competency: Creates SPARQL endpoints for RDBMS
  • Competency: Uses SPARQL endpoints for RDBMS
  • Competency: Demonstrates knowledge of factors influencing whether to publish RDF or provide a SPARQL endpoint

We are excited to welcome you to the news blog for the Linked Data for Professional Educators (LD4PE) project. The LD4PE project is operated as part of the Dublin Core Education & Outreach Committee and is funded by the Institute of Museum and Library Services (IMLS).

From the highest level, the project seeks to identify, describe, and promote learning resources to assist in the teaching Linked Data. Most of the outputs from the effort will be captured in Exploring Linked Data. Exploring Linked Data will enable the indexing of learning resources according to the specific competencies, skills, and knowledge they address. Exploring Linked Data will continue to be supported by DCMI and its membership as part of DCMI’s larger education and outreach activities.

Participants in the project include:

• University of Washington, development lead for Exploring Linked Data and underlying technical and data infrastructure.

Additional technical and content contributions from:

• Access Innovations
• Dublin Core Metadata Initiative (DCMI)
• Elsevier
• Kent State University
• OCLC
• Sungkyunkwan University Institute of Information and Management
• Synaptica

We welcome participation from others and will be reaching out to the community through this blog, our wiki, in-person events, and other means to collect input and advice from practitioners and organizations during the course of the coming year. The project welcomes contributions in two main areas:

• Suggesting competency statements (assertions of what a learner of Linked Data should know or be able to do); and
• Identifying existing web-accessible learning resource from short recipe videos to large learning units that address one or more Linked Data competencies.

Please stay tuned here for more information on how to interact with the project.