Digital literacy in practice: Developing an interactive and accessible open educational resource based on the SCONUL Seven Pillars of Information Literacy

As part of a review of the undergraduate and postgraduate curriculum at Leeds Metropolitan University, digital literacy was formally adopted as a graduate attribute in 2011. Libraries and Learning Innovation (LLI) have since been working on ways to improve the digital literacy of staff and students through a variety of means including promotion of Open Educational Resources (OER). This paper deals with one of those projects: the use of Xerte Online Toolkits (XOT) to create interactive resources which are supported by mobile devices. This ongoing project is truly collaborative, with members of academic staff and library staff (academic librarians, learning technologists and the repository developer) working together to create useful tools to support learning. The XOT project resulted from an audit by the university’s Open Educational Resources Group (led by LLI) which identified a need for mobile-friendly tutorials. From this, an interactive tutorial focussing on the SCONUL 7 Pillars of Information Literacy was developed. With the addition of new software to create interactive subject guides, the project aims to create more interactive resources to support students’ digital literacy

Core content modules at Leeds Metropolitan University

As part of Leeds Metropolitan University’s review of the postgraduate curriculum in 2012–13, Libraries and Learning Innovation (LLI) was asked to lead a project group to create two core content modules for use at Level 7 (Masters level) in Research Practice and Project Management. The rationale for choosing these two areas was the sheer number of modules in these subjects taught across a wide range of disciplines, each of which is currently designed and populated by individual course teams. The group consisted of representatives from the University’s Centre for Teaching and Learning, academic staff, learning technologists and academic librarians, and was chaired by the Associate Director of LLI, Wendy Luker

Practising What We Preach: Developing a Professional Reading Group for the Library Academic Support Team

This paper briefly considers the value of reading or journals clubs for the continuing professional development of library staff. It suggests that a reading club is not just a mechanism for keeping up to date with developments in the sector but can also be used to facilitate the development of a more robust and proactive culture where librarians perceive themselves increasingly as research active to inform and develop their own practice. It considers the Academic Support Team within Libraries and Learning Innovation (LLI) as an example

Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1. Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, has acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos contain valuable information for faunal and environmental monitoring, and are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2. In this study, we compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task in the context of an ichthyology class. Results were validated against counts obtained from a scientific expert. 3. All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4. As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques, as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development

Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1.Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2.We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert. 3.All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4.As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development

students_data

Contains all the data acquired by the group of students. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

algorithm_data

Contains all the data acquired using the computer vision algorithm. Details of the columns are as follows: Video: Name of the video in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

expert_data

Contains all the data acquired by the PhD student referred as the expert. Details of the columns are as follows: Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

crowd_data

Contains all the data acquired by Citizen Scientists using the Digital Fisher crowdsourcing platform. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group