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Although universities have extensive research and initiatives in place that align with the United Nations’ Sustainable Development Goals (SDGs), there is still a significant gap in documenting and assessing these efforts. This paper aims to discuss how academic libraries can apply their information management skills and open-access platforms, to facilitate the discoverability and retrieval of evidence on SDGs.

Introduced by a brief literature review on the role of libraries in contributing to the SDGs in general, the authors draw on their personal experiences as metadata specialists, participating in a project aimed at linking their university’s research output to the SDGs. A case study, from the University of Pretoria’s Veterinary Science Library, is used as an example to demonstrate the benefits of resourceful metadata in organising, communicating and raising awareness about the SDGs in the field of veterinary science.

Through practical examples and recommended workflows, this paper illustrates that metadata specialists are perfectly positioned to apply their information management skills and library platforms to facilitate the discoverability and retrieval of evidence on SDGs.

Although there are increasing reports on the contributions of libraries to support the successful implementation of the SDGs, limited information exists on the role of metadata specialists, as well as those with a practical focus.

The objective of this study is to determine how biophilic designs in study areas affect the productivity of students at the University of the Witwatersrand. The study also seeks to evaluate study areas at Wits in terms of biophilic design, determine whether biophilic design contributes to the preference of students and their study productivity.

This is a cross sectional study that used a mixed methodology. Five study areas were identified and evaluated in terms of biophilic design. At each study area, nine students participated in an open- and close-ended questionnaire regarding their perceptions on study areas and productivity.

The five study areas scored the following results according to the biophilic test: 29.09%, 34.55%, 36.36%, 80.00% and 85.45%, respectively. The students prefer to study in biophilic study areas as it prompts positive emotions and make them feel rejuvenated and energized. However, there are still students studying in the non-biophilic areas due to convenience or due to the biophilic areas that are noisy and lacks monitoring.

With the small sample size, the generalizability of the findings are limited, but does create a foundation for further research.

Universities can learn from the findings and benefit greatly from many biophilic study areas. This could also encourage architects and interior designers to include biophilic design more so in general buildings/rooms.

Study areas at the University of the Witwatersrand was evaluated in terms of biophilic design, while determining whether biophilic design contributes to the preference of students and study productivity.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

Evidence-based practice is a professional standard for occupational therapists, but limited time, resources and knowledge challenge its implementation. This study aims to identify what free evidence summary sources (FESS) can be found through a simple online search, related to child/youth interventions surrounding cerebral palsy (CP), autism spectrum disorder (ASD), developmental coordination disorder (DCD), mental health or attention-deficit/hyperactivity disorder (MH/ADHD). Evidence summaries share research in concise, time-efficient manners.

An internet-based scoping review was conducted between February 2022 and July 2022, using Google, and known evidence summary producers. Evidence summaries meeting the inclusion criteria were located and catalogued. Type of agency, target audiences, purpose and distribution of evidence summaries related to diagnosis were identified for each FESS.

Ten FESS were found, which produced 113 intervention-focused evidence summaries within the past 10 years. These FESS were aimed at a variety of target audiences: service providers, service users, parents/families, researchers and commissioners, and were produced primarily by non-profit/charity organisations (6 of 10) who were trying to fill a gap in evidence. Forty-eight evidence summaries were related to ASD, 34 to CP, 29 to MH/ADHD and two to DCD.

A catalogue of FESS that exist online was produced, to support evidence-based practice for paediatric occupational therapists with limited resources, and may support improved health promotion and informed decision-making for service users. No consistent framework for FESS evidence summaries exists at this time.

This paper highlights an assignment in a combination upper-division undergraduate and graduate civic leadership class at a Midwestern state comprehensive university. The three-part assignment challenges students’ critical thinking skills and research capabilities while simultaneously necessitating the exploration of contrasting viewpoints on contentious issues.

Intentionally exposing students to diverse perspectives in a controlled environment.

We posit that the severity and frequency of these issues can be mitigated through focused efforts.

Students are better prepared to engage in civil debate on controversial topics, which continuously divide our communities, after completing a class using this pedagogical strategy.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales.

This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020.

Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities.

This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.

This study aims to provide an overview of the dimension of stored collections displayed in visible storage and to indicate the main factors which hinder their accessibility.

This study is based on quantitative analysis: a survey was conducted through the offices of International Council of Museums and direct invitations to 2,558 museums located worldwide.

The study estimated 32% on average the share of stored collections displayed in visible storage. The analysis provides a picture of how many stored items are made accessible in visible storage across the continents, according to the collection’s type and size and the museums’ legal status. In addition, several aspects of visible storage are investigated to highlight whether or not it truly enables museums to achieve accessibility of their stored collections and which factors might hinder the accessibility. Amid them, the foremost factors involve the inadequacy of resources, such as the lack of staff (71%) and poor budget (68%). Because of it, museums are prone to setting up offsite storage (37%), often 16 km far from the city centre, thereby questioning the concept of accessibility itself.

One major limitation of this study is that it does not consider people’s standpoints. Therefore, the author recommends that future studies focus on what people opine on visible storage, such as their appreciation of the display format, the behind-the-scenes, their need for interpretation and the degree of satisfaction with their information needs, as well as their perception of the size of stored collections.

These findings suggest that museums could take action in areas whereby the data demonstrated weaknesses in terms of accessibility. For instance, museums could set up a shuttle service or arrange public transportation service to allow people to visit offsite storage. Additionally, financial accessibility might be achieved by not charging some groups (elderly, students, etc.).

The topic of stored collections and their accessibility has crucial social implications because not displaying collections triggers inequality amid social groups of excluded people and a small elite.

This study focuses on visible storage as a possible solution to enhance the accessibility of collections and indicates to what extent visible storage provides this accessibility. On the contrary, previous research did not estimate how much visible storage impacts the accessibility of stored collections.

This paper shares findings from a multi-year literacy professional development partnership between a school district and university (2014–2019). We share this case of a Literacy Cohort initiative as an example of cross-institutional professional development situated within several of NAPDS’ nine essentials, including professional learning and leading, boundary-spanning roles and reflection and innovation (NAPDS, 2021).

We asked, “In what ways did the Cohort initiative create conditions for community and collaboration in the service of meaningful literacy reforms?” Drawing on social design methodology (Gutiérrez & Vossoughi, 2010), we sought to generate and examine the educational change associated with this multi-year initiative. Our data set included programmatic data, interviews (N = 30) and artifacts of literacy teaching, learning and leading.

Our findings reflect the emphasis areas that are important to educators in the partnership: diversity by design, building relationships through collaboration and rooting literacy reforms in teacher leadership. Our discussion explores threads of reciprocity, simultaneous renewal and boundary-spanning leadership and their role in sustaining partnerships over time.

This paper contributes to our understanding of building and sustaining a cohort model of multi-year professional development through the voices, perspectives and experiences of teachers, faculty and district administrators.