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14 results for “horizon scan”
Scanning the horizon for invasive plant threats using a data-driven approach
<p>This repository holds the data and code for the manuscript "Scanning the horizon for invasive plant threats using a data-driven approach". </p> <p><strong>Contents</strong></p> <ul> <li>code: descriptions below</li> <li>data: descriptions below</li> <li>intermediate-data: datasets produced by processing original data (see code) or produced through horizon scan process (descriptions below)</li> <li>fl-plants-horizon-scan.Rproj: RStudio project for running R scripts</li> </ul> <p> </p> <table> <thead> <tr> <th scope="col">code</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>gcw_processing.R</td> <td>R script to format data downloaded from the Global Compendium of Weeds</td> </tr> <tr> <td>native_introduced_ranges.R</td> <td>R script to create map of native and introduced ranges of taxa on the final list</td> </tr> <tr> <td>random_draws_plant_families.R</td> <td>R script to evaluate over- and underrepresentation of plant families in initial and final list</td> </tr> <tr> <td>review_process_comparison.R</td> <td>R script to evaluate differences in scores before and after peer-review and consensus-building</td> </tr> <tr> <td>scores_certainty_pathways.R</td> <td>R script to create figures of scores, certainty, and pathways for final list</td> </tr> <tr> <td>risk_scores_analys.R</td> <td>R script to evaluate final risk scores</td> </tr> <tr> <td>pathways_process.R</td> <td>R script to process pathways to introduction data</td> </tr> <tr> <td>taxa_list_processing.R</td> <td>R script to create list used for rapid risk assessments from an initial list</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>cab_list_full.csv</td> <td>list of potential invasive species to Florida generated by CABI Horizon Scan Tool on November 15, 2019</td> </tr> <tr> <td>GCW_full_list_020420.csv</td> <td>Global Compendium of Weeds downloaded on February 4, 2020</td> </tr> <tr> <td>PlantAtlasDataExport-20191211-194219.csv</td> <td>Atlas of Florida plants downloaded December 11, 2019</td> </tr> <tr> <td>Taxon_x_List_GloNAF_vanKleunenetal2018Ecology_121119.csv</td> <td>GloNAF 1.2 database downloaded December 11, 2019</td> </tr> <tr> <td>the-plant-list</td> <td>The Plant List Database downloaded August 3, 2021</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">intermediate-data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>federal_noxious_weed_list.csv</td> <td>manually formatted version of the USDA Federal Noxious Weed List downloaded March 16, 2020</td> </tr> <tr> <td>first_round_assessments_050120.csv</td> <td>rapid risk assessments for horizon scan pre-peer-review</td> </tr> <tr> <td>fl_prohibited_plants.csv</td> <td>manually compiled list of prohibited plants in Florida based on the Florida Noxious Weed List, Florida Prohibited Plants list, and Florida Invasive Species Council (all downloaded March 9, 2020)</td> </tr> <tr> <td>horizon_scan_plants_full_reviews_080321.csv</td> <td>rapid risk assessments for horizon scan post-peer-review and consensus-building</td> </tr> </tbody> </table> <p> </p>
Alien Futures Horizon Scanning dataset
<p>The data are the result of the Alien Futures Horizon Scanning project. They were collected through an open online survey (EnglishSurveyFinal.pdf) to poll specialists and stakeholders from around the world as to their opinion on the three most important issues that may affect the future global and local management of biological invasions in the next 20 to 50 years both globally and at their respective local working level.</p> <p>The dataset also contains the categorisation of these issues into topics conducted by the Alien Futures team and presented in:</p> <p>Dehnen-Schmutz, K., Boivin, T., Essl, F., Groom, Q. J., Harrison, L., Touza, J. M., Bayliss, H. (2018): Alien Futures: what is on the horizon for biological invasions?. <em>Diversity & Distributions </em>DOI:10.1111/ddi.12755</p> <p> </p>
Dataset of Horizon scanning to identify invasion risk of ornamental plants marketed in Spain
<p>Full dataset for the research entitled "Horizon scanning to identify invasion risk of ornamental plants marketed in Spain". We classified non-native species into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain, and their potential environmental and socioeconomic impacts.</p>
Teaching and learning in ecology: a horizon scan of emerging challenges and solutions
<p>We currently face significant, anthropogenic, global environmental challenges and therole of ecologists in mitigating these challenges is arguably more important than ever. Consequently there is an urgent need to recruit and train future generations of ecologists, both those whose main area is ecology, but also those involved in the geological, biological and environmental sciences. Here we present the results of a horizon scanning exercise that identified current and future challenges facing the teaching of ecology, through surveys of teachers, students and employers of ecologists. Key challenges identified were grouped in terms of the perspectives of three groups: students, for example the increasing disconnect between people and nature; teachers, for example the challenges associated with teaching the quantitative skills that are inherent to the study of ecology; and society, for example poor societal perceptions of the field of ecology. In addition to the challenges identified, we propose a number of solutions developed at a workshop by a team of ecology teaching experts, with supporting evidence of their potential to address many of the problems raised. These proposed solutions include developing living labs, teaching students to be ecological entrepreneurs and influencers, embedding skills-based learning and coding in the curriculum, an increased role for learned societies in teaching and learning, and using new technology to enhance fieldwork studies including virtual reality, artificial intelligence and realtime spoken language translation. Our findings are focused towards UK higher education, but they should be informative for students and teachers of a wide range of educational levels, policy makers and professional ecologists worldwide.</p>
Teaching and learning in ecology: a horizon scan of emerging challenges and solutions
Open the record for dataset details and reuse information.
An annotated list of horizon scanned technologies with potential for application in alien species citizen science projects
<p><strong>Context</strong></p> <p>The contribution of volunteers in recording invasive alien species (IAS) has been fostered by technological developments such as social media, apps, low-cost sensors, search engines and predictive analytics. These technology developments, an increased attention to citizen science and a cultural change towards collaboration and openness in research within the policy agenda should increase the contribution of volunteer recording. Within the framework of the COST Action CA17122 <a href="https://www.ceh.ac.uk/our-science/projects/alien-csi"><em>Increasing Understanding of Alien Species through Citizen Science</em></a> (<a href="https://doi.org/10.3897/rio.4.e31412">Roy et al. 2018</a>) a group of researchers explored the value of emerging technologies for citizen science in the context of alien species, recognizing the contribution of volunteers and reviewing their potential to engage broad audiences, motivate volunteers, improve data collection, increase data quality etc.</p> <p><strong>Survey</strong></p> <p>The following criteria were then used to evaluate the potential of these technologies for alien species citizen science through a dedicated <a href="https://forms.gle/9GQJctnAbPLKyxDE7">survey</a>:</p> <p>● <em><strong>Audience</strong></em>: the technology can attract new target audiences for IAS citizen science and/or support more inclusivity in IAS citizen science (can overcome inequalities in participation, attract under-privileged audiences/those underrepresented in the scientific enterprise, allow participation of sensory/cognitive/otherwise impaired...)<br> ● <em><strong>Engagement </strong></em>with others: the technology supports better connections with other participants, helpful in building a community<br> ● <em><strong>Engagement via feedback</strong></em>: the technology increases the quality, amount or rate of feedback (including supporting learning) to participants<br> ● <strong><em>Application</em></strong>: the technology can be embedded in everyday life and therefore has the potential for wide, generic application</p> <p>● <em><strong>New data</strong></em>: the technology yields new types of data that would not be available without the technology (improved the detectability of IAS, new types of data, species interactions, new information sources)<br> ● <strong><em>Extends data</em></strong>: the technology expands the scope of data collection or analysis (e.g. better coverage spatially, temporally)<br> ● <strong><em>Improves data quality</em></strong>: the technology improves species ID, reduces uncertainty, improves validation<br> ● <strong><em>Improves the flow of data</em></strong>: the technology increases the speed of record transmission (e.g. for early warning)<br> ● <strong><em>Improves the curation of data</em></strong>: the technology itself allows for improved data curation (better metadata, sustainability and long term preservation data, open data, tracked provenance of data, FAIR data management, enable to better credit citizen scientists for their data contributions)</p> <p><strong>Dataset description</strong></p> <p>This dataset represents the list of technologies (in the broadest sense, including approaches) that were identified collectively by the experts as being relevant technologies in the framework of (alien species) citizen science. The dataset includes the following fields:</p> <ul> <li><em>Name</em>: name of the approach/technology</li> <li><em>Category</em>: broad categorisation of the approach/technology (Hardware and infrastructure, data collection and analysis tools, tools to improve user experience). If some approaches are combinations this is mentioned in description.</li> <li><em>Description</em>: a definition and/or description of the approach/technology</li> <li><em>Reference</em>: a reference on the approach/technology (e.g. paper, online reference), mostly with a doi</li> <li><em>Example</em>: an example of the approach/technology, mostly with reference to an (alien species) citizen science project that applied it</li> <li><em>Notes: </em>any further remarks</li> </ul>
Supplementary material 2 from: Kenis M, Agboyi LK, Adu-Acheampong R, Ansong M, Arthur S, Attipoe PT, Baba A-SM, Beseh P, Clottey VA, Combey R, Dzomeku I, Eddy-Doh MA, Fening KO, Frimpong-Anin K, Hevi W, Lekete-Lawson E, Nboyine JA, Ohene-Mensah G, Oppong-Mensah B, Nuamah HSA, van der Puije G, Mulema J (2022) Horizon scanning for prioritising invasive alien species with potential to threaten agriculture and biodiversity in Ghana. NeoBiota 71: 129-148. https://doi.org/10.3897/neobiota.71.72577
Risk scores for potential invasive alien plant pests in Ghana
Supplementary material 1 from: Kenis M, Agboyi LK, Adu-Acheampong R, Ansong M, Arthur S, Attipoe PT, Baba A-SM, Beseh P, Clottey VA, Combey R, Dzomeku I, Eddy-Doh MA, Fening KO, Frimpong-Anin K, Hevi W, Lekete-Lawson E, Nboyine JA, Ohene-Mensah G, Oppong-Mensah B, Nuamah HSA, van der Puije G, Mulema J (2022) Horizon scanning for prioritising invasive alien species with potential to threaten agriculture and biodiversity in Ghana. NeoBiota 71: 129-148. https://doi.org/10.3897/neobiota.71.72577
Guidelines for horizon scanning for plant pests potentially threatening Ghana
Supplementary material 3 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Table S2
Supplementary material 2 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Table S1
Supplementary material 4 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Table S3
Supplementary material 5 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Table S4
Supplementary material 1 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Methods S1
NIBIO_MLS: a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)
<h1>General description</h1> <p>This dataset consists of a ML-ready labelled mobile laser scanning (MLS) point cloud dataset including 16 manually labelled forest plots (approx. 250 m2) for forest panoptic segmentation and thus including both semantic and instance labels. The data was collected using a Geoslam Horizon RT and processed using Geoslam Hub.</p> <h1>Labels</h1> <p>The data were then labelled into the following semantic classes (<em>label</em>):</p> <ul> <li>1= ground</li> <li>2= vegetation: these include both branches, leaves, and low vegetation</li> <li>3= lying deadwood</li> <li>4= stems</li> </ul> <p>In addition for each tree, a unique tree identifier (<em>treeID</em>) was also assigned to each point.</p> <h1>Data split</h1> <p>Each plot was split into train (50%), validation (25%), and test (25%) sets by dividing the circular plot into four slices, out of which the first two were used for training, the third for validation, and the fourth for test. </p> <p>Thus the users might play around with merging the train and validation dataset as they prefer. These two sets can be used during model training, hyperparameter tuning, and model selection. However, the test set should be kept as an independent set to be used for benchmarking against the values reported in the two studies indicated below. </p> <h1>Citation</h1> <p>To cite this datasets and for a more detailed description use:</p> <p>Wielgosz, M., Puliti, S., Xiang, B., Schindler, K. and Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. <em>Remote Sensing of Environment; </em></p> <h2>Other studies using these data</h2> <p>Wielgosz, M., Puliti, S., Wilkes, P. and Astrup, R., 2023. Point2Tree (P2T)—Framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest. <em>Remote Sensing</em>, <em>15</em>(15), p.3737; available <a href="https://www.mdpi.com/2072-4292/15/15/3737" target="_blank" rel="noopener">here</a></p> <h1>Funding</h1> <p>This work is part of the Center for Research-based Innovation SmartForest: Bringing Industry 4.0 to<br>the Norwegian forest sector (NFR SFI project no. 309671, smartforest.no).</p> <h1>⚖️ Licensing</h1> <p>📄 Please refer to the specific licenses below for details on how the data can be used.</p> <h4>🔑 Key Licensing Principles:</h4> <ul> <li>✅ You may access, use, and share the dataset and models freely.</li> <li>🔄 Any derivative works (e.g., trained models, code for training, or prediction tools) must also be made publicly available under the same licensing terms.</li> <li>🌍 These licenses promote <strong>collaboration</strong> and <strong>transparency</strong>, ensuring that research using this dataset benefits the broader scientific and open-source community 🙌</li> </ul>
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