scholarly journals AgroFIMS: A Tool to Enable Digital Collection of Standards-Compliant FAIR Data

2021 ◽  
Vol 5 ◽  
Author(s):  
Medha Devare ◽  
Céline Aubert ◽  
Omar Eduardo Benites Alfaro ◽  
Ivan Omar Perez Masias ◽  
Marie-Angélique Laporte
Keyword(s):  
Data Science ◽  
Management Practices ◽  
Agricultural Sector ◽  
Agricultural Research ◽  
Large Data ◽  
Information Management System ◽  
Digital Data ◽  
Trial Management ◽  
Data Interoperability ◽  
The Creation

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing.

Building Australia’s Scalable Drone Cloud

2021 ◽  
Author(s):  
Jens Klump ◽  
Tim Brown ◽  
Rohan Clarke ◽  
Robert Glasgow ◽  
Steve Micklethwaite ◽  
...  
Keyword(s):  
Data Management ◽  
Best Practice ◽  
Management Practices ◽  
Agricultural Research ◽  
Terrestrial Ecosystem ◽  
Large Data ◽  
Sensor Data ◽  
Research Network ◽  
Ecosystem Research ◽  
Plant Phenomics

<p>Remotely Piloted Aircraft (RPA), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground- and satellite-based observations. Their versatility allows researchers to deliver near-real-time information for society.</p><p>Key to delivering RPA information is the capacity to enable researchers to systematically collect, process, manage and share RPA-borne sensor data. Importantly, this should allow vertical integration across scales and horizontal integration across different RPA deployments. However, as an emerging technology, the best practice and standards are still developing and the large data volumes collected during RPA missions can be challenging.</p><p>Australia’s Scalable Drone Cloud (ASDC) aims to coordinate and standardise how scientists from across earth, environmental and agricultural research manage, process and analyse data collected by RPA-borne sensors, by establishing best practices in managing 3D-geospatial data and aligned with the FAIR data principles.</p><p>The ASDC is building a cloud-native platform for research drone data management and analytics, driven by exemplar data management practices, data-processing pipelines, and search and discovery of drone data. The aim of the platform is to integrate sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools (including computer vision / deep learning techniques) to establish a national ecosystem for drone data management.</p><p>The ASDC is a partnership of the Monash Drone Discovery Platform, CSIRO and key National Collaborative Research Infrastructure (NCRIS) capabilities including the Australian Research Data Commons (ARDC), Australian Plant Phenomics Facility (APPF), Terrestrial Ecosystem Research Network (TERN), and AuScope.</p><p>This presentation outlines the roadmap and first proof-of-concept implementation of the ASDC.</p>

scholarly journals Scaling by Optimising: Modularisation of Data Curation Services in Growing Organisations

2021 ◽  
Vol 16 (1) ◽  
pp. 20
Author(s):  
Hagen Peukert
Keyword(s):  
Data Management ◽  
Data Science ◽  
Economies Of Scale ◽  
Management Practices ◽  
Management Science ◽  
Data Curation ◽  
Digital Data ◽  
Digital Management ◽  
Increasing Demand ◽  
The Cost

After a century of theorising and applying management practices, we are in the middle of entering a new stage in management science: digital management. The management of digital data submerges in traditional functions of management and, at the same time, continues to recreate viable solutions and conceptualisations in its established fields, e.g. research data management. Yet, one can observe bilateral synergies and mutual enrichment of traditional and data management practices in all fields. The paper at hand addresses a case in point, in which new and old management practices amalgamate to meet a steadily, in part characterised by leaps and bounds, increasing demand of data curation services in academic institutions. The idea of modularisation, as known from software engineering, is applied to data curation workflows so that economies of scale and scope can be used. While scaling refers to both management science and data science, optimising is understood in the traditional managerial sense, that is, with respect to the cost function. By means of a situation analysis describing how data curation services were applied from one department to the entire institution and an analysis of the factors of influence, a method of modularisation is outlined that converges to an optimal state of curation workflows.

TRENDS IN KNOWLEDGE PRODUCTION AND GENERATION IN THE AGRICULTURAL SECTOR: PERSPECTIVES OF NIGERIAN AGRICULTURAL RESEARCH INSTITUTES

Mousaion ◽  
2017 ◽  
Vol 34 (3) ◽  
pp. 123-145
Author(s):  
Kabiru Dahiru Abbas
Keyword(s):  
Tacit Knowledge ◽  
Knowledge Production ◽  
Crop Production ◽  
Management Practices ◽  
Explicit Knowledge ◽  
Agricultural Sector ◽  
Agricultural Research ◽  
Crop Improvement ◽  
Knowledge Generation ◽  
Research Institutes

Purpose - The paper is based on a study conducted to investigate the phenomenon of knowledge production and generation in agricultural sector, with particular focus on the Nigerian agricultural research institutes.Methodology - Qualitative and quantitative approaches known as mixed methods were used through survey design to collect data from the population of research scientists and directors of the institutes.Findings - The findings show that the knowledge produced by the institutes include: genetic improvement of varieties of cereals, crops, roots, tubers and barley; wheat, rice, soybeans, sugarcane, beniseed, millet; crop production, breeding, weed control, value-addition techniques, fertility of soil and mechanisation; crop improvement and management practices; generation of agricultural technologies and management practices;  pest management, agronomic practices and improved seeds; fish production and management practices. The study found that generations of explicit knowledge and tacit knowledge was high in the institutes: explicit knowledge generation was enhanced by the constant documentation of research findings and research reports, seminars, workshops and conference papers; while tacit knowledge generation was facilitated by knowledge sharing through formal and informal engagements such as review meetings, cropping scheme meetings and regular staff meetings.Research implications – Stimulate Nigeria to become self-sufficient in feeding its own people by investing in the agricultural knowledge production to drive research and innovation in the sector since knowledge production is a critical tool in innovation, research and development. Social implications – The study provides a deeper understanding of various phenomena pertaining to the knowledge production and generation in the agricultural sector which could serve as a basis for re-evaluation, re-strategising and re-focusing knowledge management practices in the research institutes. Originality/value - The originality of the study lies in its ability to investigate how concepts and variables from the Nonaka and Takeuchi (1995) and another three theories/models played out in the context of Nigerian agricultural research institutes. The study contributes to policy, theory, practice and society.

Statistical evaluation and forecasting of human resources support by specialists and workers of mass professions of agricultural organizations of the grainsheep-breeding zone of Stavropol region

2020 ◽  
pp. 58-75
Author(s):  
Alexey Gerasimov ◽  
Evgeny Gromov ◽  
Oksana Grigor'eva
Keyword(s):  
Agricultural Sector ◽  
Import Substitution ◽  
Educational Institutions ◽  
Industrial Complex ◽  
Stavropol Region ◽  
Breeding Zone ◽  
The Creation ◽  
Business Structures ◽  
Agroindustrial Complex ◽  
High Level

Improving the efficiency of agricultural production and the competitiveness of agricultural products is impossible without the creation of professional teams with a high level of productivity. The formation and development of the personnel potential of the agro-industrial complex comes to the fore in the light of ensuring the country’s food security and solving the problems of import substitution. The development of the industry relies more on the creation of a vertical education system, the development of rural territories, etc. Compilation of forecasts for the staffing of the agroindustrial complex will coordinate the efforts of educational institutions, business structures, and authorities in organizing the training and retraining of personnel for the agricultural sector.

scholarly journals Assessing Opportunities to Increase Yield and Profit in Rainfed Lowland Rice Systems in Indonesia

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 777
Author(s):  
Erythrina Erythrina ◽  
Arif Anshori ◽  
Charles Y. Bora ◽  
Dina O. Dewi ◽  
Martina S. Lestari ◽  
...  
Keyword(s):  
Best Management Practices ◽  
Management Practices ◽  
Nutrient Management ◽  
Agricultural Research ◽  
Rice Yield ◽  
Information Access ◽  
Water Reservoir ◽  
Dry Season ◽  
Rice Production ◽  
Yield Gap

In this study, we aimed to improve rice farmers’ productivity and profitability in rainfed lowlands through appropriate crop and nutrient management by closing the rice yield gap during the dry season in the rainfed lowlands of Indonesia. The Integrated Crop Management package, involving recommended practices (RP) from the Indonesian Agency for Agricultural Research and Development (IAARD), were compared to the farmers’ current practices at ten farmer-participatory demonstration plots across ten provinces of Indonesia in 2019. The farmers’ practices (FP) usually involved using old varieties in their remaining land and following their existing fertilizer management methods. The results indicate that improved varieties and nutrient best management practices in rice production, along with water reservoir infrastructure and information access, contribute to increasing the productivity and profitability of rice farming. The mean rice yield increased significantly with RP compared with FP by 1.9 t ha–1 (ranges between 1.476 to 2.344 t ha–1), and net returns increased, after deducting the cost of fertilizers and machinery used for irrigation supplements, by USD 656 ha–1 (ranges between USD 266.1 to 867.9 ha–1) per crop cycle. This represents an exploitable yield gap of 37%. Disaggregated by the wet climate of western Indonesia and eastern Indonesia’s dry climate, the RP increased rice productivity by 1.8 and 2.0 t ha–1, with an additional net return gain per cycle of USD 600 and 712 ha–1, respectively. These results suggest that there is considerable potential to increase the rice production output from lowland rainfed rice systems by increasing cropping intensity and productivity. Here, we lay out the potential for site-specific variety and nutrient management with appropriate crop and supplemental irrigation as an ICM package, reducing the yield gap and increasing farmers’ yield and income during the dry season in Indonesia’s rainfed-prone areas.

scholarly journals Givira ethela (Neumoegen and Dyar, 1893) (Lepidoptera: Cossidae), A Previously Unidentified Pest on Vitis vinifera (L.)

Insects ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 239
Author(s):  
Davide Scaccini ◽  
Enrico Ruzzier ◽  
Kent M. Daane
Keyword(s):  
Vitis Vinifera ◽  
Natural Enemies ◽  
Management Practices ◽  
Agricultural Sector ◽  
Central Valley ◽  
Vitis Vinifera L ◽  
Correct Identification ◽  
Pest Species ◽  
Monitoring Methods ◽  
Management Options

Grape cultivation is a billion-dollar agricultural sector in California, where invasive or novel pest species can disrupt management practices. We report herein on a new pest associated with California vineyards, the carpentermoth Givira ethela (Neumoegen and Dyar, 1893). Rather than an invasive species, G. ethela appears to be a newly recognized wood-boring pest of Vitis vinifera (L.) in regions of California’s Central Valley, where its initial occurrence has been dated back to, at least, the beginning of the 2000s. The habitus of adult, genitalia and pupa is illustrated. Givira ethela distribution in California is updated including published records and new data. Carpentermoth galleries seem to facilitate the access of Planococcus ficus Signoret, 1875 to vine sap and protection from natural enemies, environmental stresses, and pesticide treatments. Notes on pest status, life history, monitoring practices, natural enemies, and management options on grapes are also discussed. Tools for the Integrated Pest Management of G. ethela should include the correct identification of the insect and its damage, a full understanding of its biology and ecology, the application of monitoring methods, and the identification of economic thresholds and injury levels.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

scholarly journals Strategic knowledge management in brazilian organizations

2008 ◽  
Vol 1 (1) ◽  
pp. 65
Author(s):  
Rivadávia Correa Drummond de Alvarenga
Keyword(s):  
Knowledge Management ◽  
Organizational Context ◽  
Management Practices ◽  
Research Strategy ◽  
Validity And Reliability ◽  
Multiple Sources ◽  
Behavioral Issues ◽  
Units Of Analysis ◽  
The Creation ◽  
Constituent Elements

Investigates the theme known as “Knowledge Management” (KM) in three large Brazilian organizations trying to discuss its concepts, constituent elements, managerial approaches and tools, while aiming at leaving behind the purely terminological discussion, which is innocuous and naive. The basic presuppositions were two: (i) most of what it´s referred to or named KM is actually “Information Management” (IM) and IM is just one of the components of KM. KM is more than simply IM due to the fact that it includes and incorporates other concerns, such as the creation, use and sharing of information and knowledge in the organizational context; (ii) a conceptual model or map can be formulated based on three basic conceptions: (a) a strategic conception of information and knowledge, (b) the introduction of such strategy in the tactical and operational levels through the several managerial approaches and informaion technology tools and (c) the creation of an organizational space for knowledge. The main objective is to investigate and analyze the conceptions, motivations, practices and results of KM effectively implemented in three large Brazilian organizations. The qualitative research strategy used was the study of multiple cases with incorporated units of analysis and three criteria ere observed for the judgment of the quality of the research project: validity of the construct, external validity and reliability. Multiple sources of evidence were used and data analysis consisted of three flows of activities: data reduction, data displays and conclusion drawing/verification. The results confirmed the presuppositions and the fact that KM means a rethinking of management practices in the information ea. It was also identified that the main challenges facing organizations committed to KM have its focus on change management, cultural and behavioral issues and the creation of an enabling context that favors the creation, use and sharing of information and knowledge.

scholarly journals PLANNING OF THE HUMAN RESOURCES OF THE AGRARIAN SECTOR OF THE REGIONAL ECONOMY

2021 ◽  
pp. 23-32
Author(s):  
А.И. Клименко ◽  
М.А. Холодова
Keyword(s):  
Human Resource ◽  
Agricultural Production ◽  
Triple Helix ◽  
Large Scale ◽  
Agricultural Sector ◽  
Digital Technologies ◽  
State Regulation ◽  
Resource Potential ◽  
Variable Approach ◽  
The Creation

Современная трансформация сельскохозяйственного производства, обусловленная переходом к цифровым технологиям и масштабным обновлением материальнотехнической базы, влечет за собой необходимость разработки научно обоснованной методики планирования потребности аграрного производства в рабочей силе и ее адаптации к новым вызовам. Статья посвящена разработке методики планирования кадрового потенциала аграрного сектора экономики в условиях цифровых технологий. Разработан алгоритм планирования кадровой потребности отрасли на среднесрочную перспективу. Ключевыми показателями плановой работы по определению потребности в кадровом обеспечении согласно методике должны выступить контрольные цифры приема в образовательные учреждения и создание условий по обеспечению трудоустройства выпускников. С целью обоснования прогноза ежегодной дополнительной потребности аграрного сектора экономики в кадрах предложен расчетно-аналитический инструментарий с применением технологий стратегического форсайтинга, который позволяет в условиях формирования аграрной экономики инновационного типа сформировать новую парадигму прогнозирования кадровой потребности для сельскохозяйственного производства, демонстрирующую переход от сценарного (вариативного) подхода к подходу «тройная спираль». Подход «тройная спираль» не только позволит прогнозировать качественную динамику кадрового потенциала сельского хозяйства, учитывающую изменение условий труда, тенденции масштабной модернизации производственных фондов, применение современных технологий, достигнув максимального соответствия между ресурсным потенциалом отрасли и ее ежегодными кадровыми потребностями, но и будет способствовать созданию системы объективного государственного регулирования общего профессионального и дополнительного аграрного образования на региональном уровне. Практическая значимость исследования заключается в разработке организационно-экономического механизма государственного регулирования вопроса кадрового обеспечения на основе проектных методов управления в сельском хозяйстве, позволяющего сбалансировать ситуацию на аграрном рынке труда. The modern transformation of agricultural production, due to the transition to digital technologies and large-scale modernization of the material and technical base, entails the need to develop a scientifically based methodology for planning the needs of agricultural production in the labor force and its adaptation to new challenges. The article is devoted to the development of a methodology for planning the personnel potential of the agricultural sector of the economy in the context of digital technologies. An algorithm for planning the personnel needs of the industry for the medium-term perspective has been developed. The key indicators of the planned work to determine the need for personnel support according to the methodology should be the control figures for admission to educational institutions and the creation of conditions for ensuring the employment of graduates. In order to justify the forecast of the annual additional demand for personnel in the agricultural sector of the economy, a calculation and analytical tool is proposed with the use of strategic foresight technologies, which allows, in the conditions of the formation of an innovative agricultural economy, to form a new paradigm for predicting the personnel need for agricultural production, demonstrating the transition from a scenario (variable) approach to the «triple helix» approach. The «Triple Helix» approach will not only predict the qualitative dynamics of the human resource potential of agriculture, taking into account changes in working conditions, trends in large-scale modernization of production assets, the use of modern technologies, achieving maximum compliance between the resource potential of the industry and its annual human resource needs, but will also contribute to the creation of a system of objective state regulation of general professional and additional agricultural education at the regional level. The practical significance of the study lies in the development of an organizational and economic mechanism for state regulation of the issue of labor security on the basis of project management methods in agriculture, which allows to balance the situation in the agricultural labor market.

scholarly journals Shared Data Science Infrastructure for Genomics Data

2018 ◽  
Author(s):  
Hamid Bagher ◽  
Usha Muppiral ◽  
Andrew J Severin ◽  
Hridesh Rajan
Keyword(s):  
Data Science ◽  
Gene Annotation ◽  
Large Data ◽  
Biological Data ◽  
Genomic Research ◽  
Data Repository ◽  
Small Data ◽  
Data Repositories ◽  
Shared Data ◽  
Genome Assemblies

AbstractBackgroundCreating a computational infrastructure to analyze the wealth of information contained in data repositories that scales well is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared Data Science Infrastructures like Boa can be used to more efficiently process and parse data contained in large data repositories. The main features of Boa are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories.ResultsHere, we present an implementation of Boa for Genomic research (BoaG) on a relatively small data repository: RefSeq’s 97,716 annotation (GFF) and assembly (FASTA) files and metadata. We used BoaG to query the entire RefSeq dataset and gain insight into the RefSeq genome assemblies and gene model annotations and show that assembly quality using the same assembler varies depending on species.ConclusionsIn order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, BoaG, can provide greater access to researchers to efficiently explore data in ways previously not possible for anyone but the most well funded research groups. We demonstrate the efficiency of BoaG to explore the RefSeq database of genome assemblies and annotations to identify interesting features of gene annotation as a proof of concept for much larger datasets.

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