Conceptualizing Digital Transformation for digital curation
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Title of Series | ||
| Number of Parts | 30 | |
| Author | ||
| License | CC Attribution - NonCommercial 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/58591 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
| |
| Keywords |
6
00:36
Computer animation
06:14
Computer animation
08:51
Computer animationDiagram
10:52
Computer animation
12:42
Computer animation
Transcript: English(auto-generated)
00:02
Hello, thank you for being here. I'm Seokjung Lim at KISTI in South Korea. The title of my presentation is Conceptualizing Data Transformation for Data Curation. What I'm going to talk about is data transformation.
00:23
I will divide my presentation into introduction, methods, findings, practices, and conclusion. So let's start with a brief overview of the previously reviewed data transformation concept.
00:46
Philip O'Reilly et cetera reviews the papers since 2010 and the key concepts for a new definition of data transformation as strategy, process, business models, paradigm shift.
01:06
Their characteristics of data transformation are radical, disruptive, evolutionary, continuous, and complex. The research summarized some keywords from the new definition of data transformation by O'Reilly's research.
01:31
I want to say that these keywords are key concepts of data transformation. They are evolutionary process, data capabilities, data technologies, business
01:46
models, operational processes, and customer experiences, and value creation. Data transformation in scientific research.
02:05
The rise of the data-driven research paradigm. In 2009, Hay and Tency and Toh research. Thousands of years, experimental or empirical science.
02:22
Hundreds of years ago, theoretical science. In decades, computational science. Now, current, data-intensive science. Data transformation of the R&D design pace, exploring vast literature in a short time with AI technology to derive necessary facts.
02:47
Data transformation of the R&D stage. Data twin technology in production development. Virtual reality, augmented reality in the medical field. Virtual reality, hackathon, and prototyping using 3D printing.
03:14
The background and research questions for studying this study are as follows. Why is the concept of data transformation necessary in the field of science and technology information management?
03:28
This is because we believe that data creation activities in the field of science and technology information management require data improvement through data capabilities,
03:41
organizational structure change, process automation, new value creation, and innovative changes in user experience. And our question is how to approach to introduce data transformation in existing data curation.
04:02
The concept of transformation exists in the existing data curation model as well. However, the concept of transform is focused on the activity of converting and preserving the file format of data, so it does not fully accommodate the concept of data transformation in a broad sense.
04:28
Therefore, it was necessary to conceptualize it in the data curation model to accommodate the definition and characteristics of data transformation, mentioned before.
04:44
The purpose of this study is to redefine the concept of data transformation in the field of STI management within the data curation lifecycle model. The methods. This study was designed using mixed methods approach and included.
05:03
The first systematic literature investigation is categorized into academic field and practical field. The second we analyzed several creation models being used in the field. Finally, we received advice on the concept design through surveys and interviews with experts.
05:28
The target models for searching the concept of transformation. They are targets. Data One Data Lifecycle, UK Data Archive Data Curation,
05:41
DDI Lifecycle, Data Curation Network, University of Oxford Research Data Management Model. This is the creation lifecycle model, just the creation lifecycle model. This is the creation lifecycle model and just the creation lifecycle model.
06:01
As the diagram, the concepts related to transformation in the traditional creation model are surveyed. Concepts related to transformation in the traditional creation lifecycle model.
06:23
There are lots of vocabularies. Integrate, analyze, transport, processing, document processing, file conversion and preservation, computation, visualization and curation.
06:47
Now we design the concept of data transformation in the new data creation model. Four conceptual elements of data transformation is cleansing, transmutation, transformation, innovation.
07:08
The first cleansing is the process of filtering and reforming data from a data set or objects. Transportation includes conversion between physical objects to digital data and digital data to physical object.
07:33
It is a concept that can be used within a physical platform and a digital platform.
07:41
It means from data to object or from object to data. The third transformation is the completion of data transportation and data curation activities within a digital platform, after which the data is transferred to another platform.
08:03
It means from digital platform to another platform. Next, innovation is where digital transport data constitutes AI or user experience
08:20
as an entity identified and connected to other digital data or physical objects. Next, innovation, it is included value creation and innovation are achieved through user experience.
08:54
This is the schematic of digital transformation concept. The concept of digital transformation is the purification of physical objects and digital data.
09:07
The application of digital capabilities and technologies inside digital platforms are three-step XYG transmutation process. Data transfer is transformation between platforms.
09:26
And finally, innovation is user experience creates value as innovative data. The conceptual elements of digital transformation can be compared from a
09:42
data perspective to traditional terms such as data information, knowledge, and wisdom. This figure can describe various practical data processes and creation activities from the perspective of digital transformation.
10:03
This figure is a diagram in its simplest form. This rectangle is the platform. A, B, C, D, E are the data. And XYG represents the three-step transmutation. And migrating data from data D to data E means moving from one platform to another.
10:27
Finally, data E shows how innovation is created user experience.
10:43
This means that the concept of digital transformation is applied to the digital curation model.
11:05
This research can be seen as a research activity at the conceptualization stage. The last conclusion.
11:23
This study designed a conceptual model to accommodate the core elements of the existing digital transformation concept. Evolutionary process, digital capabilities, digital technology. Business model and operational processing, customer experience and value creation.
11:47
And also considering SDI and data creation and technology disseminate and policy data and enhance, manage and evaluate.
12:01
In conclusion, this study conceptualized the digital transformation as cleansing, transmutation, transformation, and innovation in terms of digital curation.
12:25
This represents the addition of digital transformation elements to the revision of the QSTI digital curation model. I look forward to your interest and critical review of your concept.
12:48
Thank you.
Recommendations
Series of 8 media