Open Access and Open Data at CGIAR: Challenges and Solutions

CGIAR is a global research partnership of 15 geographically and scientifically diverse Centers dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resource management. The Centers are charged with accelerating innovation to tackle challenges at a variety of scales from the local to the global. This requires data and other research outputs to be findable, accessible, interoperable, and reusable – that is, open via FAIR principles, and inter-linked where relevant. CGIAR Centers have made strong progress in implementing publication and data repositories; however, many of these still represent silos whose contents are not generally easily discoverable or inter-linked (e.g., agronomic trial data with socioeconomic or adoption data in the same geographies). In the absence of such interoperability-mediated discovery, “open” is of limited utility. The overall goal is for CGIAR’s trove of research data and associated information to be indexed and interlinked through a demand-driven cyberinfrastructure for agriculture, ensuring that research outputs are discoverable by humans and machines, and reusable via appropriate licensing to enhance innovation, uptake and impact. There are challenges to achieving this goal, not only across CGIAR, but for the agricultural domain in general. Among the foremost hurdles is that “open” tends to remain an unfunded mandate, making it difficult to operationalize effectively. Further, there is still significant concern on the part of scientists about making data open – largely centered around issues of trust, time, and quality – resulting in repositories frequently exposing metadata rather than the data sets themselves. While the ability to find metadata about resources qualifies as improvement, it continues to impose barriers to data access, discoverability, integration, and analysis, without which complex challenges to global agriculture development cannot be effectively addressed. CGIAR is addressing the urgent need to create a data sharing culture and enabling environment for Open Access and Open Data (OA/OD) that includes projects planning for OA/OD and allocating funds to support it, in parallel with the technical infrastructure mentioned above. While the technology necessary to enable FAIR outputs exists, achieving success implies data provider and consumer trust and buy-in, agreement and adherence to interoperability standards and/or mapping across varied approaches, and compliance with guidelines (including those on citation and licensing governing content reuse). Agricultural institutions, including CGIAR, are only now beginning to address these issues systematically, to agree on and adopt standards-based systems and processes, and to build cross-walks across differing schemas. Through its Open Access and Open Data initiative funded by the Bill and Melinda Gates Foundation, and via plans for an ambitious Big Data and ICT Platform , CGIAR is developing technical and cultural approaches that will enable research content to be consistently and seamlessly discovered, interlinked, and analyzed across its Centers. This paper describes the strategy used to identify the specific contexts and challenges faced by Centers in building an infrastructure and culture for OA/OD across CGIAR, with the ultimate goal of achieving greater impact in agricultural research for development

Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market

The songbird trade has been identified as a major threat to wild populations, and the bird market has now expanded to online platforms. The study explored the use of machine learning models as a monitoring framework; developed models for taxa identification; applied the best model to understand the current market situation (taxa composition, asking price, and location); and conducted a survey to understand the profile of sellers. The authors found that the machine learning models produced a high level of accuracy in distinguishing relevant ads and identified the songbirds’ taxa. The Support Vector Machine (SVM) was selected as the best model and was used to predict the ad population. The model identified 284,118 songbirds from 247 taxa that were listed online from April 2020 to September 2021. The authors also found that 6.2% of ads listed threatened taxa based on the IUCN Red List. The survey results suggested that songbird sellers are mostly hobbyists or breeders looking for extra income from selling birds. As current studies of the songbird market are mostly conducted offline in the bird markets, transactions by non-bird traders or among hobbyists in the online market are remain underreported. Therefore, monitoring needs to be extended to the online market and to our knowledge, currently there is no applied system or platform is identified for monitoring online songbird market. The result from this study can help fill this gap. Information from the monitoring of the songbird online market in this study may assist stakeholders in formulating corrective action based on the current market situation

A stocktaking of knowledge products on peatlands, fires and haze in Southeast Asia, 1990 to 2020

One key activity of MAHFSA is to stocktake existing knowledge products and develop and deploy the knowledge products related to peatlands and fires in Southeast Asia. The stocktake analysis synthesises existing knowledge products by categorising them into five thematic areas policies, tenure, economics, best practices, and monitoring. It also classified knowledge products based on geographical location, focusing on the country and regional levels. Moreover, the study categorises the knowledge products based on the elements of integrated fire management, from prevention, preparedness, suppression, and recovery. By applying text and co-occurrence analyses, the study highlights salient topics of the knowledge products related to peatlands in Southeast Asia. The result shows that thematic areas and knowledge product types vary between ASEAN member states