Full transcript
0:00Welcome to Fair Squared Data Management.
0:03Get ready to start your AIdriven data
0:05management journey with Frontiers. Tasks
0:08that once took months of manual work
0:10will now take just minutes. To get
0:13started, you need a loop account to
0:15connect via Frontiers submission system.
0:18Methodology document describing how your
0:20data was collected, processed, or
0:23validated. A structured, tidy data
0:26table. Ready to transform your data
0:28sharing? Once you've signed in, Clara,
0:32our AI data steward, will prepare your
0:35project space. Customize the page to
0:38suit your preferences. Clara will give
0:40you prompts as you go. Use the chat box
0:43to collaborate with her directly. She
0:46can even change language preferences for
0:48you. Simply drag and drop your files to
0:51get started and let Clara guide you
0:53through composing, curating, and
0:55visualizing your data. On this data
0:58table example, you can see that each row
1:01is one observation. Each column is one
1:04clear variable, and each cell only
1:06contains one value. She will generate a
1:10title, author list, keywords, and
1:12initial summary for you to review. Once
1:15you are happy with the information
1:17generated and have included all relevant
1:19data sets, you can begin data curation.
1:23Your method section is now being
1:25extracted and organized from your
1:26uploaded documents. Clara will prompt
1:29you when it's ready. You can edit any of
1:32the sections and CL will review these
1:34for you and help you to refine.
1:38Next, a data dictionary is created to
1:40describe each field in your data set.
1:43This is an important step for ensuring
1:45your data package provides common
1:47understanding of data by defining its
1:49structure, relationships, and
1:51attributes.
1:53This eliminates ambiguity and ensures
1:56data is interpreted consistently by both
1:58humans and machines.
2:01Clara will tell you what to look out for
2:03during this step. Now, the fun part.
2:06Let's start visualizing your data. Clara
2:09will make recommendations on the best
2:11ways to visualize. And if you agree, ask
2:14her to go ahead and generate those
2:16figures for you to review. You can
2:18explore the figures and ask her for
2:20adjustments any time. Select which
2:23figures you would like to include in
2:24your Fair Square data article. Now you
2:27can work together to prepare your
2:29article for submission to a Frontiers
2:31journal. Clara will take you through
2:33each step of the article curation from
2:36abstract introduction and methods to the
2:39data overview which comes with a fair
2:42squared certification recognizing that
2:44your data not only meets the fair
2:46principles of findability,
2:48accessibility, interoperability and
2:50reusability but is also AI ready and
2:54responsibly reusable.
2:56Review each step and ensure the data
2:59overview clearly describes contents,
3:01structure, and key features of your data
3:04set. If you need to make any additions,
3:06just ask Clara. Now you can start seeing
3:09your article come to life. Review the
3:12displayed visualizations that you chose
3:14earlier in the process along with their
3:16described observations.
3:19Data Explorer is an interactive portal
3:21created uniquely from your data set and
3:24article which will be published
3:26alongside your data article. Take a
3:28moment to explore its features. Dive
3:31deeper into the data set, playing with
3:33the filters at the top before you move
3:36on to a final review of the interactive
3:38Fairsquare data portal.
3:40And your article is now ready for
3:42submission to a Frontier's journal.
3:45Together we are reshaping discovery and
3:48turning your data sets into practical
3:50infrastructure for real world impact.
3:54This is science unleashed.