The Partnerships for Data Innovations (PDI): Facilitating data stewardship and catalyzing research engagement in the digital age

Harmel, R. Daren; Kleinman, Peter J. A.; Eve, Marlen; Ippolito, James A.; Beebout, Sarah; Delgado, Jorge A.; Vandenberg, Bruce; Buser, Michael D.

doi:10.1002/ael2.20055

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Both the public open data and private data from apps are aggregated in raw form into the farmdata data lake. University spin-offs and other approved providers can interact with the platform by contributing tools and by developing complementary services using data from the platform, thereby increasing its impact for farmers and other stakeholders [94].…”

Section: Discussionmentioning

confidence: 99%

A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers

Borrero

Mariscal

2022

Agriculture

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New players are entering the new and important digital data market for agriculture, increasing power asymmetries and reinforcing their competitive advantages. Although the farmer remains at the heart of agricultural data collection, to date, only a few farmers participate in data platforms. Despite this, more and more decision support systems (DSSs) tools are used in agriculture, and digital platforms as data aggregators could be useful technologies for helping farmers make better decisions. However, as these systems develop, the efficiency of these platforms becomes more challenging (sharing, ownership, governance, and transparency). In this paper, we conduct a case study for an accessible and scalable digital data platform that is focused on adding value to smallholders. The case study research is based on meta-governance theory and multidimensional multilayered digital platform architecture, to determine platform governance and a data development model for the Andalusian (Spain) fruit and vegetable sector. With the information obtained from the agents of this sector, a digital platform called farmdata was designed, which connects to several regional and national, and public and private databases, aggregating data and providing tools for decision making. Results from the interviews reflect the farmer’s interests in participating in a centralized cloud data platform, preferably one that is managed by a university, but also with attention being paid toward security and transparency, as well as providing added value. As for future directions, we propose further research on how the benefits should be distributed among end users, as well as for the study of a distributed model through blockchain.

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Section: Discussionmentioning

confidence: 99%

A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers

Borrero

Mariscal

2022

Agriculture

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“…With the tools that are linked and updated within the research data environment, PDI allows new ways for scientists to interact with their data in a living, working data environment. It is a "network of networks" where researchers can interact virtually across locations and disciplines, thus increasing the impact for scientists, collaborators, customers, and stakeholders [11]. In the next section, case studies currently using PDI will be evaluated.…”

Section: Decision Support Informatics Platformmentioning

confidence: 99%

Linking and Sharing Technology: Partnerships for Data Innovations for Management of Agricultural Big Data

Kharel

Ashworth

Owens

2022

Data

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Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

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“…rough the real-time monitoring of the warehouse location, the overall cargo utilization rate and cargo turnover speed are improved, and the detectable grid management of the cargo space further improves the intelligence of warehouse management. Containers and cargo spaces can present corresponding intelligent and physical properties, facilitating unified data management [19].…”

Section: Based On the Demand Analysis Of Intelligent Warehousementioning

confidence: 99%

Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning

Zhang¹,

Zhou²,

Jin³

et al. 2022

Computational Intelligence and Neuroscience

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This study applies the Internet of things information-aware technology to the process of electronic market warehousing and logistics management, effectively perceives warehouse electronic product logistics information, and improves the real-time perception of electronic product logistics information and the efficiency of electronic product storage logistics management. This study first analyzes the needs of the intelligent electronic market warehouse logistics management system and then builds the IoT architecture of the intelligent warehouse logistics assembly logistics management system for electronic warehouses based on machine learning algorithms, which solves the problems that exist in the current workshop electronic market warehouse logistics management. Then, the principle of RFID technology is introduced. The accuracy of RFID tag estimation is analyzed by the PEPC tag estimation algorithm. It is concluded that the PEPC tag estimation algorithm reduces the tag estimation error and improves the accuracy of tag estimation. Finally, an intelligent warehousing logistics management system based on IoT RFID technology is established. The test results show that the system can meet the requirements of intelligent warehousing function in the electronic market, which will greatly improve the warehousing efficiency of electronic products.

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The Partnerships for Data Innovations (PDI): Facilitating data stewardship and catalyzing research engagement in the digital age

Cited by 5 publications

References 10 publications

A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers

A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers

Linking and Sharing Technology: Partnerships for Data Innovations for Management of Agricultural Big Data

Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning

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