USAIN/CBHL 2024 Biennial Meeting

Evaluating the reporting quality of systematic reviews and meta-analyses in agricultural publications
Chao Cai (Purdue University Libraries and School of Information Studies)
Jane Yatcilla (Purdue University Libraries and School of Information Studies)
Justyna Czader (Purdue University School of Languages and Cultures)

Systematic reviews, as a methodology that comprehensively synthesizes current research results, provide the highest level of evidence that could guide the practices in medicine, engineering, as well as agriculture. However, the quality of the reporting of published systematic review articles significantly impacted the biases and reproducibility of these articles, thus impact the quality of the evidence provided in these systematic reviews. In recent years, systematic reviews, especially systematic reviews with meta-analyses gained popularity in agricultural research. It is crucial to ensure that published systematic reviews and meta-analyses fulfill the reporting standards, so that they could be used as appropriate evidence to guide agricultural practices. we conducted an umbrella review to examine the current reporting quality of systematic reviews and meta-analyses in agricultural research. We searched and screened all systematic reviews and/or meta-analyses published before December 2023 in the Web of Science Core Collection under the “Agriculture” research area. We conducted a bibliometric analysis to demonstrate the publication trend, topics, authors, and affiliations of these publications. To evaluate the reporting quality, we took a random subset of these publications, and coded the quality metrics of them, including if an article reported a protocol, full search strategies, risk of bias assessment, etc. Our results showed that most of the recently published systematic reviews and meta-analyses did poorly in reporting important information. Our results suggested that training on how to conduct and report a systematic review project properly, is needed among agricultural researchers, as well as journal editors and reviewers in agricultural research.

Search Hedges to Improve Discovery of Animal Welfare Citations
Elizabeth Tobey (National Agricultural Library)
The "3Rs" of animal research refers to the replacement, reduction, and refinement of animals in biomedical research. The Animal Welfare Act requires that researchers consider alternatives to painful and distressful procedures that lab animals may undergo. One way to comply with this regulation is to conduct a literature search for the 3Rs (i.e., animal use alternatives) when planning a study using animals. However, 3Rs articles can be difficult to find in bibliographic databases due to inadequate indexing, searcher skill and knowledge gaps.

Our library unit developed search hedges to improve the discoverability of 3Rs literature. This could lead to increased adoption and implementation of 3Rs methods. Search hedges are premade or predetermined search strings. Individuals, including non-librarians, can copy and paste hedges directly into a database to retrieve citations on a specific topic. We have worked in collaboration with other librarians to create, test, revise, and publish animal welfare search hedges. In 2022, we published search hedges designed to retrieve citations on 15 species of domestic animals commonly used in research. Throughout 2023, we developed and tested new hedges focused on non-animal models (replacement), environmental enrichment for mice (refinement), and pain/anesthesia (refinement). In addition, our unit also published two hedges on alternatives to oral gavage for rodents and social housing of laboratory animal species. We plan to assess hedge use by measuring page views and through user feedback.

In this presentation, we will give an overview of our two-year process of creating animal welfare hedges. We will discuss the challenges and questions encountered when developing new hedges. We will also discuss our ongoing efforts to establish best practices for testing hedges. We hope that sharing our experience will empower other agricultural libraries to create their own hedges.

Adapting Evidence-Based Review Methodology for Data Infrastructure Using a Climate Smart Agriculture Search 
Stephanie Ritchie (USDA National Agricultural Library)

Most data included in evidence-based reviews is reported in journal articles, study reports, and other text-based, narrative documentation. A key component of the systematic review process is data extraction, a manual process to identify and collect data for synthesis across studies. Some hypothesize, if relevant datasets, rather than documents, could be identified using a standardized evidence-based review process, the labor-intensive manual extraction process might be omitted.

For our study, we demonstrate the use of evidence-based review methods for collecting climate-smart agricultural datasets. We employ a standardized process to identify relevant data repositories that aggregate or host climate related agricultural datasets. For the resulting repositories, we evaluate search infrastructure and other characteristics to determine capabilities for search. We further assess the use of systematic review methodology practices following search and identify the gaps in data repository infrastructure to support these methods. We will share lessons learned from utilizing evidence-based review methodologies for discovering climate-smart agricultural data and convey the need for addressing these within the scientific community as well as within data repositories, to extend the usefulness of data repositories as an information source.