Assessment of quality of commercially available some selected edible oils accessed in Ethiopia

Tolesa, Leta Deressa; Chala, Tolesa Fita; Abdi, Gemechu Fikadu; Geleta, Tekalign Kejela

doi:10.21203/rs.3.rs-1443082/v1

Cited by 75 publications

(92 citation statements)

References 12 publications

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“…If you like this vision, join us! [See our contact page (Polson and Barnum 2022)]. Our field would be more on the same page, making it easier to answer our questions about the Universe.…”

Section: Discussionmentioning

confidence: 99%

Making an executable paper with the Python in Heliophysics Community to foster open science and improve reproducibility

Polson

Ringuette

Rastäetter

et al. 2023

Front. Astron. Space Sci.

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We share the story of how we made this paper, the first executable paper in Heliophysics, through cross-disciplinary collaboration to highlight the benefits of our process. Executable papers are interactive documents that put a publication’s text inline with the code used in the research in a containerized environment with the data and dependencies needed to run the code. This approach enables readers to reproduce every step taken to arrive at the publication’s conclusions and to easily build upon and extend the work—all important components of open science. Open science is, broadly speaking, transparent and accessible knowledge that is shared and developed through collaborative networks. In this work, we present an adaptable workflow to compare magnetosphere models to spacecraft observations. It is one example of many other workflows that can be developed through collaborations between software developers and scientists in a move towards open science. Most of the authors are members of the Python in Heliophysics Community (PyHC), an international, multi-organizational community that serves as a knowledge base for performing Heliophysics research in the Python programming language. PyHC promotes the executable paper format as a supplemental tool to improve the reproducibility of publications and support open science. A key takeaway is that our collaboration made such a complex task an easy feat in the end. Additionally, the executable version of our paper makes it trivial for others to reproduce our work, and it gives them a better launching point to extend it. These facts underscore the success of our approach. In highlighting this new open science approach, we hope to be an example to our field and encourage this way of doing science.

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“…If you like this vision, join us! [See our contact page (Polson and Barnum 2022)]. Our field would be more on the same page, making it easier to answer our questions about the Universe.…”

Section: Discussionmentioning

confidence: 99%

Making an executable paper with the Python in Heliophysics Community to foster open science and improve reproducibility

Polson

Ringuette

Rastäetter

et al. 2023

Front. Astron. Space Sci.

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show abstract

“…This has been also possible thanks to recently created ad hoc web services. Applications range from recognition of the plot boundaries [15] to those providing interoperability between drones and satellite data [16], up to decision support systems (DSS) with customizable packages [17].…”

Section: A Help From Above: the Use Of Satellite Imagery In Agriculturementioning

confidence: 99%

Bibliometric and Social Network Analysis on the Use of Satellite Imagery in Agriculture: An Entropy-Based Approach

Dainelli

Saracco

2023

Agronomy

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Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, “resolution”, “Landsat”, “yield”, “wheat” and “multispectral” are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors.

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“…The prediction of canga cover in a single image using the GPU took approximately 35 minutes. The model was coded in the R language (R Core Team 2016) with the RStudio interface to Keras (Chollet et al 2015, Allaire & Chollet 2016) and a Tensor Flow backend (Abadi et al 2016).…”

Section: F Precision Recall Precision Recallmentioning

confidence: 99%

Mapping threatened canga ecosystems in the Brazilian savanna using U-Net deep learning segmentation and Sentinel-2 images: a first step toward conservation planning

Pereira¹,

Wagner²,

Kamino³

et al. 2023

Biota Neotrop.

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Canga ecosystems are iron-rich habitats and pose a challenge for conservation and environmental governance in Brazil. They support high levels of biodiversity and endemism and, at the same time, have suffered intense losses and degradation due to large-scale iron ore mining. The Peixe Bravo River Valley in the Brazilian savanna is one of the last natural canga areas that has yet to face the irreversible impacts of mining. However, there are vast gaps in data on the vegetation cover, location, spatial distribution, and area of occurrence of this ecosystem. Therefore, more information is needed on the appropriate scale, without which it is difficult to establish conservation planning and strategies to prevent, mitigate or compensate for impacts on canga ecosystems. In this study, we provide the first map of canga ecosystems in Brazil using the U-Net deep learning model and Sentinel-2 images. In addition, we estimate the degree of direct threat faced by ecosystems due to the spatial overlap of the mapped cangas and the location of mining concession areas for iron ore exploitation. The deep learning algorithm identified and segmented 762 canga patches (overall accuracy of 98.5%) in an area of 30,000 ha in the Peixe Bravo River Valley, demonstrating the high predictive power of the mapping approach. We conclude that the direct threat to canga ecosystems is high since 99.6% of the observed canga patches are included in mining concession areas. We also highlight that the knowledge acquired about the distribution of cangas through the application of an effective method of artificial intelligence and the use of open-source satellite images is especially important for supporting conservation strategies and environmental public policies.

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Assessment of quality of commercially available some selected edible oils accessed in Ethiopia

Cited by 75 publications

References 12 publications

Making an executable paper with the Python in Heliophysics Community to foster open science and improve reproducibility

Making an executable paper with the Python in Heliophysics Community to foster open science and improve reproducibility

Bibliometric and Social Network Analysis on the Use of Satellite Imagery in Agriculture: An Entropy-Based Approach

Mapping threatened canga ecosystems in the Brazilian savanna using U-Net deep learning segmentation and Sentinel-2 images: a first step toward conservation planning

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