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Living on the Data Fringes: Open Science Goes Beyond Open Data

Fri, 2021-03-26 09:56

open science umbrellaReflecting on the immense amount of data openly and freely available online, especially on COVID-19, I wanted to write a blog post about the value and opportunities available to researchers related to open data. But as I began to write I thought about the other aspects of ‘openeness’ and realized there is so much more to write about than just open data. A recent blog post published by the SEA region of NNLM during love data week, 23 things about open data, completely covers the open data piece and I have nothing to add there. In addition, you may want to check out the very comprehensive list of COVID-19 open-access data and computational resources compiled by the Office of Data Science Strategy.

However, I think there are other aspects of open science at a broader level that could use some additional explanation and examples. The Carpentries, a non-profit organization, provides open and free coding and data science training opportunities through three programs, Data Carpentry, Software Carpentry, and Library Carpentry. Their lessons are all available online for self-directed learning or you can participate in training opportunities near you. Open Science can also entail open and participatory data collection through citizen science research activities like SciStarter. Open science initiatives and scientists often rely on open-source software and tools such as Zotero for collaborating on citation collection, Open Refine, Phyton, and R studio for data collection and manipulation, as well as many other visualization and data applications so that data can be easily shared and manipulated. Open Science also entails open collaboration for doing research that integrates tools for storing and sharing open science projects through the full research cycle such as the Open Science Framework (OSF). Open repositories can provide an infrastructure and space for collecting, archiving and preserving open data and provide identifiers for data collections when the research is finally published. And last but not least, is the emerging number of opportunities for publishing open research such as journals and books. Although many publishers require the author to pay publications fees for making research open to other researchers, there are many quality and open research examples available.

Even as I have been research open science and open scholarship I have found some open textbooks about open science I would like to recommend such as the Open Data Handbook, Open: The Philosophy and Practices that are Revolutionizing Education and Science, Issues in Open Research Data, and international perspectives in the Social Dynamics of Open Data. The Foster Open Science website in the EU offers some interesting paths into open science based on what you are interested in doing So to get started, jump into the open culture at any of these different open points to learn more about open data, how to find and manipulate open data, and how to share and publish in open formats.

Open Sicence Umbrella Image: Flicker

The post Living on the Data Fringes: Open Science Goes Beyond Open Data first appeared on MidContinental Region News.
Categories: Data Science

Living on the Data Fringes: Making Sense of Competencies

Wed, 2021-01-27 17:19

Organizations are striving to become data driven as they realize that data is becoming a mission critical topic. Finding, wrangling, analyzing, and managing data can require advanced computation skill sets. However, not everyone wants to be a data scientist, data analyst, or what the EDC Oceans of Data Institute calls a ‘data practitioner’. All members in an organization, no matter what their job, should have an appreciation of the scope of data, an awareness of how data is used in the organization, as well as, some basic data literacy skills. Striving to become data savvy should not be limited to those with science jobs who work with data on a daily basis. It is a competency that is also found in areas such as business, the social sciences, and even the humanities. We all need to understand how data is integrated into our lives. It would be helpful for someone diagnosed with a serious disease and researching at a public library to know how to interpret statistics about treatment and care options. Students in k-12 and up to the college level need to know how to find and interpret data as they conduct research in their classes. Humanities researchers might need to know how to do text mining to analyze a text corpus. But how do we know if we are data literate or competent in data skills? One way is to look at data competency models and find areas that you can relate to your daily work situation. What do you already know and/or do already? What competency areas do you need or want to learn more about?

Competence wordcloudLet’s start with the definition of a competency. A competency is a collection of knowledge, skills and behaviors that together demonstrate effective work performance in a particular area. It is a visible application of knowledge and behaviors. For example there may be managerial/leadership competencies, technical competencies, or functional competencies related to particular disciplines or job tasks. A competency does not equal a skill (although both are action-oriented). A skill may be one of the components of a competency, or several skills may be part of one competency. This competency concept is also important in k-12 and higher education where it is called competency-based education (CBE).

Now, thinking about data competencies, I believe the best approach to using competencies is to look at a variety of competency models or frameworks to better understand the expectations for knowledge, skills and behaviors related to your work position or desired job.  Many of the published competency models overlap, and you may be surprised to learn that your professional organization may have already established some data competencies for your role or position. Different competency models or frameworks might also overlap, so try looking outside of your specific area for more general competency areas such as research, communication, and collaboration. Use the competencies you find to guide personalized data professional development. What competency areas are you comfortable with? Which ones do you need to learn more about? You can combine the competency components that best fit your particular work situation, and use that as guide a build a professional development plan. The competencies can be a way to focus your learning and reflect on how to level up your data knowledge and skills.

Here are a few models to review and reflect on and find focus areas for your professional development:

So enjoy reflecting on your librarian role and how you can expand your competencies! It will help you articulate your strengths and skills and make a better case for your impact on the library and community.

Image: Gerd Altmann from Pixabay 

The post Living on the Data Fringes: Making Sense of Competencies first appeared on MidContinental Region News.
Categories: Data Science