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Digital Skill and Workforce Capacity

David CastleBlog post by David Castle (WDS Scientific Committee Member)

In July of this year, the Organization for Economic Cooperation and Development (OECD) released its report, Building Digital Workforce Capacity and Skills for Data Intensive Science. Commissioned by the OECD’s Global Science Forum (GSF) in 2019, this is the ninetieth report in the OECD’s series of Science, Technology and Innovation Policy Papers.

The main focus of the report is to understand the training needs of public sector research that is becoming digitized as scientific disciplines evolve, data management becomes more prevalent and rigorous, and open science continues to be a call to action and emerging practice. Digitization of research across all disciplines has also attracted digital infrastructure and cybersecurity investments. At the same time, however, digitization both drives research competitiveness in new directions for scientists and demands greater expertise in new competencies for research support personnel. Is everyone keeping up with the pace of change?

Venn Diagram of Roles and Responsibilities

Figure 1. Venn Diagram of Roles and Responsibilities
(Figure 3 from Building Digital Workforce Capacity and Skills for Data Intensive Science)
 

The answer to this question is mixed in three main ways. First, as the Venn diagram from the report visualizes it (Fig. 1), there are roles and responsibilities for researchers and support personnel working in data-intensive sciences that have functional titles, but where their competencies overlap. Using illustrative examples from several case studies shows that roles and competencies have been changing for some time. Second, because the mix of capacity and competencies is a moving target, the present challenge is to identify the skills needed as the composition of the research workforce changes. The third point is that training has been lagging behind the front wave of digitizing research, leaving skills gaps that may be ignored or go unnoticed.

Digital Workforce Capacity Maturity Model

Figure 2. Digital Workforce Capacity Maturity Model
(Figure 5 from Building Digital Workforce Capacity and Skills for Data Intensive Science)
 

The Expert Group convened by the OECD GSF, on which I served as a member, realizes that not every OECD member state, or non-members for that matter, has recognized the challenge of building workforce capacity and digital skills at the same pace, or with the same level of resource commitment. A ‘digital workforce capacity maturity model’ was developed to capture this diversity (Fig. 2). It serves as a rough indicator of what training is needed most urgently, according to where one lies on a spectrum of training depth.

Opportunities for Actors to Effect Change Across the Five Main Action Areas

Figure 3. Opportunities for Actors to Effect Change Across the Five Main Action Areas
(Table 2 from Building Digital Workforce Capacity and Skills for Data Intensive Science)
  

The report also offers insights, organized initially as a matrix (Fig. 3), into who might do what to provide training. The ‘who’ are the main actors: national and regional governments; research agencies and professional science associations; research institutes and infrastructures; and universities. The ‘what’ includes a wide array of initiatives: defining needs; provisioning of training and community building; career path rewards; and broader enablers. This is more fully fleshed out in many examples from around the world, describing some of the initiatives that have been undertaken to develop training.

Recommendations are made for the various actors, and the report takes special note of what can and should be done at research universities, and their associated libraries. The overall recommendation to OECD members is that policies recognizing and enabling both the need for workforce capacity growth and access to digital skills training must be embraced to maintain the competitiveness of national and internationally collaborative research, and thus achieving its highest goals.

The report was in its final stages of review and approval when the COVID-19 pandemic struck. As we observed in the conclusion of our Foreword, ’The COVID-19 pandemic highlights the importance and potential of data intensive science. All countries need to make digital skills and capacity for science a priority and they need to work together internationally to achieve this. To this end, the recommendations in this report are even more pertinent now than they were when they were first drafted in late 2019’. As we get nearer to the end of 2020, all indications are that the need to build workforce capacity and digital skills for data-intensive sciences has not only escalated, but now must address new realities, research priorities, urgent timelines for training, and challenge socioeconomic circumstances.

My Experiences Working at the WDS-ITO

Seiya TeradaBlog post by Seiya Terada (WDS-ITO Co-op Student)

I was very fortunate to have the opportunity to work as a co-op student at the World Data System – International Technology Office (WDS-ITO). The skills I developed and the experience that I gained from this 8-month work term were not something that I could learn in school, only from being in a professional working environment.

During my co-op term, I had opportunities to work on many projects, including creating websites, visualizations, presentation material, and much more. Some projects were more challenging than others, but I had lots of fun learning as I worked on them. The first big project I worked on was making a WDS Member visualization with Adobe After Effects. The visualization shows a globe that spins a full 360-degrees while highlighting the location of each WDS Member. This was my first time using After Effects, let alone making an animation-type visualization, so I had a hard time at first. I learned the basics of After Effects using online resources, then I learned to use more advanced features like masking, which I applied to the animation. The biggest struggle in making the animation was keeping the file size small, since it is to be used on the WDS-ITO website. This meant keeping the animation to a bare minimum, so that the file doesn’t get bloated.

The project I am particularly proud of and had the most fun working on was the website I made for the Research Metadata Schemas Working Group (WG) of the Research Data Alliance. The website hosts visualizations that are based on data from a survey conducted by the WG. As a software engineer undergrad, I was excited that I had a chance to build a website from scratch using my coding skills. I had never used HTML to build a website until this project, I had not even taken any courses on it at university, and so everything was new to me. I therefore had to learn HTML syntax as well as coding practices by using online resources before I started working on the website. I realized that building a sleek website from scratch with my current knowledge would have taken forever, so I decided to use a website template I found online to fill in my knowledge gaps, and tweaked it to fit to what I needed. The skills and experiences I gained from these projects are something I will never forget moving forward with my career.

Overall, I had a lot of fun working as a co-op student and it was a good experience. Although some of the projects were challenging, I was able to learn a lot and developed skills that I did not have before. The work environment was relaxed and easy to work in. I was also able to make a lot of unforgettable memories along the way thanks to the people I worked with. This whole experience will definitely help me with my career moving forward.

New OECD Report: Building Digital Workforce Capacity and Skills for Data-intensive Science

Building digital workforce capacity and skills for data-intensive scienceWe would like to bring your attention to the following report published by OECD and that may be of interest to the WDS community:

OECD (2020), “Building digital workforce capacity and skills for data-intensive science”, OECD Science, Technology and Industry Policy Papers, No. 90, OECD Publishing, Paris, https://doi.org/10.1787/e08aa3bb-en

This report was commissioned by the OECD Global Science Forum to identify: the skills needed for data-intensive science, the challenges for building sustainable capacity as these needs evolve, and the policy actions that can be taken by different actors to address these needs. The report includes policy recommendations for various actors and good practice examples to support these recommendations, and also notes the value of international cooperation in skills capacity efforts.

WDS International Technology Office Signs MoU with Canada's New Digital Research Infrastructure Organization

Karen.jpgBlog post by Karen Payne (WDS-ITO Associate Director)


You spoke. We listened. 

The WDS International Technology Office (WDS-ITO) was created to support Member Organizations of WDS as they develop their data repositories in the areas of data and metadata management, infrastructure, and interoperability. In order to respond most effectively to Member needs, last year the WDS-ITO, with the support of the WDS International Program Office, conducted a survey to evaluate your areas of interest and determine what types of projects you would like WDS to support. Our key finding was a list of potential WDS-ITO projects, ranked according to interest. You can read the report of the survey here. We discovered that the top two areas of interest were adding: 1) semantic markup to metadata and 2) harvestable metadata services. In response, the WDS-ITO has secured funds from Canada’s national New Digital Research Infrastructure Organization (NDRIO) to hire two fulltime staff members to work on these projects. The funding provides dedicated resources to develop collaborative partnerships among the WDS-ITO, its members, and relevant international and Canadian interest groups to increase availability and interoperability of metadata assets globally.

Over the next year, the WDS-ITO will be working with the Research Data Alliance Research Metadata Schemas Working Group (WG) to help provide repositories with guidance and tools to add Schema.org markup to metadata. As a first step, the WDS-ITO has prototyped an online visualization tool based on a survey of current practices in using schemas to describe research datasets. The tool shows how some communities have crosswalked common metadata terms to Schema.org properties, and can be useful to repositories that are interested in knowing how other repositories are utilizing Schema.org terms. It can also be used as consensus building for communities of practice that have not yet created a crosswalk between their metadata format of choice and Schema.org properties. We will continue to build on that tool, and provide other guidance to WDS Members to help make their metadata more ‘web friendly’ in the coming months.

Sankey DiagramFigure 1: A screenshot from the WDS-ITO prototype visualization tool showing crosswalks between Schema.org and common metadata standards.
Try it yourself at https://rd-alliance.github.io/Research-Metadata-Schemas-WG/

As part of our support for those groups interested in harvestable metadata, the WDS-ITO has created a WG of WDS Members who are interested in standing up harvestable metadata services. This WDS Harvestable Metadata Services (HMetS) WG is co-chaired by two members of the WDS Scientific Committee: Aude Chambodut, Director of the International Service of Geomagnetic Indices in Strasbourg (WDS Regular Member) and Juanle Wang, Director of the WDC for Renewable Resources and Environment in Beijing (WDS Regular Member). The HMetS WG is coordinated by Alicia Urquidi Diaz, the WDS-ITO’s first employee! To date, eight WDS Member Representatives have expressed interest in participating in the WG, and we welcome any other Members who would like to join.

This project is designed around three objectives:

  1. Documenting use cases, the current challenges faced by WDS Members who wish to create harvestable services. What is their current infrastructure?
  2. Helping develop implementation plans, written by Members to define a pathway to creating harvestable metadata services.
  3. A paper identifying lessons learned and guidance materials that can be used by the wider Research Data Management community

The HMetS WG will convene regular online meetings, and bring in presenters who can speak to some of the pathways and long-term benefits of creating harvestable metadata services.

Both of the above work packages will draw on the expertise of and synchronize with ongoing research data management activities in Canada, with the ultimate goal of opening up more metadata records to the international scientific community.

You can read the NDRIO funding announcement here in English and French.

Springboard Blog Post on the TRUST Principles

We would like to point you to the following article, published on 8 June 2020 on the Springboard blog of the Springer Nature Group, and which we believe is of direct interest to the WDS community:

• Future-proofing research data – it’s a question of TRUST

In this blog post, Varsha Khodiyar (Data Curation Manager, Research Data and New Product Development) describes why Springer Nature has endorsed the TRUST Principles and their importance to data management within the research community.

For more information on the TRUST Principles and how your organization can endorse them, please see our news article.

Knowledge Service for Disaster Risk Reduction: A Practice Using Big Data Technology

Juanle WangBlog post by Juanle Wang (WDS Scientific Committee Member)

Under the dual influences of global climate change and human activities, the frequency and the intensity of natural disasters have been growing in recent years, and resulting in increasingly serious disaster losses. Disaster Risk Reduction (DRR) is thus a common and urgent global challenge. Driven by the United Nations Educational, Scientific and Cultural Organization’s (UNESCO’s) DRR mission, the DRR Knowledge Service (DRRKS) System was founded under the UNESCO International Knowledge Centre for Engineering Sciences and Technology. The remit of the System is to formulate global disaster metadata standards; build global disaster metadata database; integrate global or regional disaster data; establish disaster knowledge services; carry out disaster prevention education, training, and technology promotion; and form comprehensive technology and service capabilities [1].

The DRRKS System has established 16 online knowledge applications, as shown on their homepage, to mine, analyze, and visualize disaster information based on Big Data resources. In this blog post, I would like to briefly introduce two cases that are supported by Big Data technologies in remote sensing and social media mining.

Case 1: Land Degradation and Restoration Monitoring in Mongolia Using Remote Sensing [2]

Land degradation is an important environmental problem facing the world. ‘Land Degradation Neutrality’ is one of the core indicators of Goal 15 (Life on Land) of the United Nations Sustainable Development Goals. Mongolia is one of the areas of the world that is most affected by desertification. It is therefore of great importance to accurately comprehend the state of desertification in Mongolia to (1) prevent its further advance, (2) control desertification risks, and (3) guarantee ecological security and sustainable social development. To this end, fine resolution (30-m) land cover datasets of Mongolia were obtained by using an object-oriented method, and the land degradation and restoration patterns during 1990–2010 and 2010–2015 analyzed (Fig.1). For the past 25 years, the trend of land change in Mongolia has been dominated by land degradation. However, this land degradation was accompanied by ongoing restoration of some land areas in Mongolia, and the capacity for land restoration is gradually improving. The northwestern and northeastern parts of Mongolia have shown the most significant land restoration; namely, the areas having relatively sufficient water resources.

Figure 1: Typical regions of land degradation and land restoration between 1995–2010 in Mongolia. (a) 1990–2010 (land degradation), (b) 1990–2010 (land restoration)

Figure 1: Typical regions of land degradation and land restoration between 1995–2010 in Mongolia.
(a) 1990–2010 (land degradation), (b) 1990–2010 (land restoration)

Case 2: Public Sentiment Analysis of COVID-19 Events in China Using Social Media

Similar to Twitter, SINA microblog is a social media channel in which Chinese people regularly post their opinions. These types of social media indicate the public’s changing thoughts and emotions rapidly and frequently during an epidemic (now pandemic) such as the Novel Coronavirus Disease (COVID-19). The DRRKS team analyzed the temporal and spatial changes to microblogs referencing the (then) epidemic, and gathered the main topics being discussed by the public according to data from SINA microblog. Through the permitted data Application Programming Interface of the SINA Microblog, original messages have been collected since 00:00 on 9 January 2020 containing the keywords “coronavirus” and “pneumonia”. The following information has been extracted: timestamp (i.e., the time when the message was posted), text (the message posted by a user), and location information. The DRRKS team have then analyzed the Microblog messages related to the Coronavirus outbreak in terms of space and time. Temporal changes over one-hour and one-day intervals, and spatial distribution at provincial levels, have been investigated through a kernel density estimation using ArcGIS to identify hotspots of public opinion. The spatial and temporal distribution of public opinion in China during the early stages of the epidemic has been discovered and is available in a DRRKS online application. For example, Figure 2 shows the distribution of help and donation hot spots from 9 January to 10 February. 

Figure 2: Distribution of help and donation hot spots according to microblogs in China (9 January to 10 February 2020)

Figure 2: Distribution of help and donation hot spots according to microblogs in China
(9 January to 10 February 2020)

Reference

[1] Juanle Wang, Kun Bu, Fei Yang, Yuelei Yuan, Yujie Wang, Xuehua Han, Haishuo Wei. Disaster Risk Reduction Knowledge Service: A Paradigm Shift from Disaster Data Towards Knowledge Services, Pure and Applied Geophysics. (2020) 177:135-148
[2] Juanle Wang, Haishuo Wei, Kai Cheng, Altansukh Ochir, Davaadorj Davaasuren, Pengfei Li, Faith Ka Shun Chan, Elbegjargal Nasanbat. Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia, Environmental Development, 2020.