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67 results for “prioritisation”
WCRP Baseline Variables - MIP Prioritisation raw data
<p>Supplementary material for the publication: Juckes et al. (2024) Baseline Climate Variables for Earth System Modelling, accepted in GMD. Preprint: https://doi.org/10.5194/egusphere-2024-2363. </p> <p>This data summarises the WCRP Baseline Variables list, and includes the raw data from throughout the prioritisation process.</p>
S36 | UBAPMT | Prioritised PMT/vPvM substances in the REACH registration database
<p><strong>Prioritised PMT/vPvM substances in the REACH registration database</strong></p> <p>This is the 2022 update (first update) of the UBA list of prioritised persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) substances in the REACH registration database. All substances are registered under REACH (EC No 1907/2006) and meet the <a href="https://www.umweltbundesamt.de/publikationen/protecting-the-sources-of-our-drinking-water-the">PMT/vPvM criteria as proposed by UBA in 2019</a>. Compared to the first version from 2019, this 2022 update of the UBA list adds new substances and improved the PMT/vPvM assessment. This UBA list is published as UBA TEXTE xxx /2022. It is indicated if a substance would also meet the less stringent PMT/vPvM criteria as published by the European Commission (EC) in September 2021, which are currently under discussion for inclusion in the Classification, Labelling and Packaging (<a href="https://echa.europa.eu/guidance-documents/guidance-on-clp">CLP</a>) regulation (EC No 1272/2008).</p> <p><em>Reference: </em>Hans Peter H Arp, Sarah E Hale, Ivo Schliebner and Michael Neumann (2022). Prioritised PMT/vPvM substances in the REACH registration database, Texte | XXX/2022, edited by Michael Neumann and Ivo Schliebner, IV 2.3 Chemicals, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804 xxx pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0 and Report No. (to be announced)</p> <p><em>Previous version:</em></p> <p>The first version of this UBA list from 2019 was published as a <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">technical note (UBA TEXTE 126/2019)</a>.</p> <p><em>Reference: </em>Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN:1862-4804, 131 pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG.</p> <p>This collection is associated with list S36 UBAPMT on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>).</p>
Data from the National Prioritisation of Australian plant species after the 2019-2020 bushfires
<p>Data for 26,062 native Australian plant species assessed against ten post-fire recovery criteria. Details of criteria and methods available in Gallagher, R. V. (2020) <em>National prioritisation of Australian plants affected by the 2019–2020 bushfire season.</em> Report to the Commonwealth Dartement of Agriculture, Water and Environment. https://www.environment.gov.au/system/files/pages/289205b6-83c5-480c-9a7d-3fdf3cde2f68/files/final-national-prioritisation-australian-plants-affected-2019-2020-bushfire-season.pdf </p>
Datapackage for national high-resolution conservation prioritisation of boreal forests
<p>This data package concerns the following work:</p> <p>Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen,<br> National high-resolution conservation prioritisation of boreal forests,<br> Forest Ecology and Management, Volume 541, 2023, 121079<br> ISSN 0378-1127</p> <p><a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>The overall objective of the work was to develop spatial prioritizations that can assist the forest conservation programme METSO (The Finnish Government 2008; 2014) to make well-informed decisions about acquisition of forests for protection. The results are also aimed to be useful for other actors interested in forest conservation or biodiversity friendly forest management. We focused the prioritization on the most threatened forest types and areas that display some or many elements of natural forests: more than one and preferably more than two tree species, forest structure that present else than even age structure or a history of clear-cut harvesting, and the amount of dead wood that exceed the volume of dead tree material in managed forests. From the perspective of connectivity, these areas should be situated close (varying from metres to a few kilometres) to other valuable forest areas. These kinds of forest areas represent the most threatened forest types and forest species in Finland (Hyvärinen et al. 2019; Kontula and Raunio 2019).</p> <p>This data package includes 3 folders (see details of the data in the article):</p> <p>1) Data folder<br> a) DWP features: the 20 input data layers of the modelled biodiversity surrogate: dead wood potential. These are combinations of 4 tree species and 5 forest site type classes. Not that these are not normalized.<br> (1) bir = birch, obl = other broad leaved tree, st = forest site type<br> b) Other data layers:<br> i) condition_layer.img where the magnitude of the penalty is defined<br> ii) ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not<br> iii) WRSCR04_PA.img layer consists of permanently protected areas cut form weighted range size corrected richness output layer from analysis version 4 to execute the positive interaction between the forests and permanently protected areas.<br> iv) similarity matrix</p> <p>2) Input folder<br> a) example setup files for analysis version 7 (hierarchical analysis where permanently protected areas are forced to highest priorities, including information on dead wood potential of the forest stands, penalties followed by the forest management, connectivity within the forests, observations of red-listed forest species, and connectivity to forest key habitats and permanently protected areas)<br> i) .spp file for list of input features for the analysis<br> ii) .dat file for the analysis settings<br> iii) .bat file to run the analysis in command line<br> iv) conditionlayer.txt to define the used condition file in the analysis<br> v) groups file to define the use of the condition layer<br> vi) interact file to define the interactions between feature layers in connectivity calculations</p> <p>3) Output folder<br> a) includes folder for each analysis version. Each folder includes<br> i) rank file in .img format which is the actual spatial priority ranking result<br> ii) wrscr file which describes the weighted range size corrected richness of all input features<br> iii) curves file: the performance of each input feature within the cell removal<br> iv) jpg picture of the result</p> <p>The package DOES NOT include sensitive data. For species observations, ask for Finnish Biodiversity Info Facility https://laji.fi/en. For forest key habitats (small forest patches protected by the Forest Act, that are classified as “habitats of special importance to safeguard the biodiversity of forests”) on state owned land and land owned by companies, ask the data providers and owners.</p> <p>See Moilanen et al. (2014) for more technical information on the input and output files.</p> <p><br> Overview of the data</p> <p>The resolution of the spatial data is 96 m x 96 m. The study area covered the forested land area in Finland, excluding the autonomous Åland Islands.</p> <p>The data on forest stands are from year 2015, the forest management year 2017, and protected area network early winter 2018. See details of the data extraction in the article, Appendix A.</p> <p>The main source of biodiversity information were the modelled dead wood potential (DWP) indices. The DWP is an estimation of the potential of a stand for hosting dead wood dependent species. The potential is increased when the stand can be expected to produce more dead wood and more varied dead wood in terms of size and tree species composition. The modelling is based on forest growth and increase of dead wood calculated with Motti forest simulator 3.3 (Salminen et al., 2005; Hynynen et al., 2014; Hynynen et al., 2015) for 168 combinations of seven tree species, six forest site types, and four vegetation zones. See detailed information on the dead wood potential modelling in doi:10.3390/f11090913 (Mikkonen et al. 2020, Modeling of Dead Wood Potential Based on Tree Stand Data)</p> <p>The DWP was calculated for each stand or pixel based on the forest data (Finnish Forest Centre 2015; Metsähallitus 2015; Metsähallitus Parks & Wildlife Finland and Centres for Economic Development Transport and the Environment 2015; Natural Resources Institute Finland 2015b; 2015a): tree species and tree stock quantities (mean diameter at breast height and volume), soil fertility (Cajander, 1926), and location. In the DWP modelling the size information was combined with stand volume and forest site type. Eventually, the data were compiled to 20 input layers. See detailed information on the pre-processing of the input-data in the Appendix B.</p> <p>Spatial conservation prioritizations were made with the Zonation software 4.0 (Moilanen et al. 2005; Moilanen et al. 2009; Moilanen et al. 2011). With multiple analysis versions, the greatest interest is on those areas that repeatedly receive high ranks – these areas are important from all perspectives included in analysis.</p> <p>The ecological model of conservation value included seven analysis versions that start from a local perspective and then evolve towards regional and national levels (following Lehtomäki et al. 2009). Each new analysis version included everything that had been included in the previous simpler versions. The versions are 1) local estimation of the conservation potential of the forests based on tree stock alone, 2) local estimation with additional information about forest management and drainage, 3) landscape level (not local but not regional either) estimation with internal forest connectivity, 4) landscape level estimation with additional information about observations of red-listed forest species, 5) landscape-level estimation with added short distance connectivity to key forest habitats, 6) regional estimation with added long distance connectivity to permanently protected areas, and 7) regional estimation of the most appropriate addition to the present conservation network.</p> <p>These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning.</p> <p><br> Literature</p> <p>Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI.</p> <p>Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. (Eds.). 2019. The 2019 Red List of Finnish Species. Helsinki, Ministry of the Environment & Finnish Environment Institute. 704 p.</p> <p>Kontula, T. & Raunio, A. (Eds.). 2019. Threatened Habitat Types in Finland 2018. Red List of Habitats – Results and Basis for Assessment. Helsinki, Finnish Environment Institute and Ministry of the Environment. The Finnish Environment 2/2019. 254 p. http://urn.fi/URN:ISBN:978-952-11-5110-1<br> http://hdl.handle.net/10138/308426.</p> <p>Lehtomäki, J., Tomppo, E., Kuokkanen, P., Hanski, I. & Moilanen, A. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management 258(11): 2439-2449.</p> <p>Metsähallitus. 2015. [dataset] SutiGIS 2015. Forestry resource and planning system for Metsähallitus Forestry Ltd and Protected Area Biotope Information System; biotope, and tree stock data on state-owned conservation areas, for Metsähallitus Parks & Wildlife Finland.</p> <p>Metsähallitus Parks & Wildlife Finland & Centres for Economic Development Transport and the Environment. 2015. [dataset] SutiGIS 2015: Protected area biotope information system, biotope and tree stock data on private conservation areas.</p> <p>Mikkonen, N., Leikola, N., Lehtomäki, J., Halme, P. & Moilanen, A. 2023. National high-resolution conservation prioritisation of boreal forests. Forest Ecology and Management, Volume 541. <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A. & Tanhuanpää, T. 2020. Modeling of Dead Wood Potential Based on Tree Stand Data. Forests 11(913): 21.</p> <p>Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B. & Thomas, C. D. 2005. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proceedings of the Royal Society B-Biological Sciences 272(1575): 1885-1891.</p> <p>Moilanen, A., Kujala, H. & Leathwick, J. 2009. The Zonation framework and software for conservation prioritization. In: Moilanen, A., Wilson, K. A. & Possingham, H. P. (Eds.). Spatial conservation prioritization - Quantitative Methods & Computational tools. New York, Oxford University Press Inc. p. 196-210.</p> <p>Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393.</p> <p>Moilanen, A., Pouzols, F. M., Meller, L., Veach, V., Arponen, A., Leppänen, J. & Kujala, H. 2014. Zonation - Spatial conservation planning methods and software. Version 4. User Manual. 4. Helsinki, C-BIG Conservation Biology, Informatics Group, Department of Biosciences, University of Helsinki, Finland. 290 p.</p> <p>Natural Resources Institute Finland. 2015a. [dataset] Segmented multi-source national forest inventory data of Finland: estimates of mean diameter at breast height for tree species based on National Forest Inventory 2013. Unpublished. Date of datacut 19.8.2015.</p> <p>Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0.</p> <p>The Finnish Government. 2008. Decision-in-Principle of The Finnish Government on the Forest Biodiversity Programme for Southern Finland for years 2008-2016 (in Finnish). 13.</p> <p>The Finnish Government. 2014. Decision-in-Principle of the Finnish Government on extension of the Forest Biodiversity Programme for Southern Finland (METSO) for years 2014-2025. 18.</p> <p> </p>
Supplementary Material - Maritime Cargo Prioritisation during a prolonged pandemic lockdown using an integrated TOPSIS-Knapsack technique
<p>Supplementary Material - Maritime Cargo Prioritisation during a prolonged pandemic lockdown using an integrated TOPSIS-Knapsack technique: A Case Study on Small Island Developing States – the Rodrigues Island</p> <p>Results and Sensitivity analysis</p>
Accessibility Rank: A Machine Learning Approach for Prioritising Accessibility User Feedback
<p>This repository serves as a comprehensive collection of datasets, code scripts, and associated data used in my master's research conducted at the University of Auckland on accessibility-related reviews. The research findings and methodology are described in detail in our paper titled "Accessibility Rank: A Machine Learning Approach for Prioritising Accessibility User Feedback". By making these resources openly available, we aim to foster collaboration, reproducibility, and advancement in the field of accessibility research. Researchers and developers can leverage these datasets, associated data, and code scripts to gain insights, validate findings, and explore novel approaches to addressing accessibility challenges.</p> <p>We encourage users to refer to our paper for a comprehensive understanding of our research methodology, experimental setup, and results. Proper attribution and citation of our paper are appreciated when utilizing any part of this repository in further research or publications.</p>
Data from: Conservation prioritisation of genomic diversity to inform management of a declining mammal species
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Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
<p>Spatial assessments of Ecosystem Services (ES) are increasingly used in environmental management and spatial planning, but rarely provide information on the accuracy of predictions. Uncertainty estimates are essential to allow for confidence in the quality and credibility of ES assessments to enable informed decision-making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle-based method for ES modelling by accounting for model variability, data coverage, and uncertainty in thresholds and parameters. A sensitivity analysis was applied on ES models for marine bivalves (<em>Austrovenus stutchburyi</em> and <em>Paphies australis</em>) and their contribution to <em>Food provision, Water quality regulation, Nitrogen removal,</em> and <em>Sediment stabilisation</em>.<em> </em>ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi-plots and multiple ES through spatial prioritisation using Zonation. Results showed spatially explicit differences in uncertainty patterns for ES and between species. <em>Food</em><em> provision</em> had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 10% and top 5% area that allowed for a consistently better representation of ES when accounting for uncertainty. The spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid informed decision-making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency. </p>
Data on plant health stakeholder priorities for tests and general prioritisation framework
<p>Data collected in the framework of work package 4 of the Valitest project. They correspond to a qualitative assessment of plant health stakeholder requirements, have been collected using online surveys supplemented by desk-based research, as well as impact assessments</p>
Prioritising GitHub Priority Labels - Data Set and Software
<p>This is the data set and software produced for the paper <em>Prioritising GitHub Priority Labels</em>, J. Caddy and C. Treude.</p> <p>The CSV file contains a manually categorised set of GitHub issue labels that are priority-related. They have been ranked and normalised into three values; "High", "Medium", and "Low" priorities. These labels have been gathered from the 5000 most-starred repositories on GitHub as of 2022-06-01.</p> <p>The Python script makes use of this data set as an example, and will retrieve the highest priority issues from all of the repositories contributed to by the author specified.</p> <p>Run the python script from the same directory as the CSV file, providing the username you wish to see the highest priority issues for as the first command line argument. Supply your GitHub Personal Access Token either at the prompt so it's not displayed, or as the second command line argument.</p>
South East Queensland estuaries restoration prioritisation for fish data
<p>Data used to prioritise restoration actions in estuaries in southeast Queensland, Australia</p>
Conservation prioritisation through genomic reconstruction of demographic histories applied to two endangered suids in the Malay Archipelago
<p><strong>Aim</strong>: The biodiversity of the Malay Archipelago is the product of the region's rich biogeographical history with periods of island connectivity and isolation during the Pleistocene glacial cycles. Here, the case of two endemic suid species, the Javan (<em>Sus verrucosus</em>) and Bawean (<em>S. blouchi</em>) warty pigs, was used to illustrate how biogeographic processes and recent anthropogenic pressures can shape demographic histories with significant implications for species conservation.</p> <p><strong>Location</strong>: Malay Archipelago, with focus on Bawean and Java.</p> <p><strong>Methods</strong>: We employed genome-wide single nucleotide polymorphisms from the Porcine SNP60 v2 BeadChip to assess interspecific genetic differentiation, to estimate divergence times, and to perform demographic model selection.</p> <p><strong>Results</strong>: In contrast to the hypothesis of recent divergence during the last glacial maximum, <em>S. blouchi</em> was found to have diverged from <em>S. verrucosus</em> at least 166k years ago following a founder event. The contemporary <em>S. blouchi</em> population was characterised by a recent bottleneck that reduced the effective population size to less than 20. The genomic assessment supports the single species status of <em>S. blouchi</em>, as was previously proposed based on morphometrics. The demographic history of <em>S. verrucosus</em> showed evidence of secondary contact with the sympatric banded pig (<em>S. scrofa vittatus</em>) that colonised Java 70k years ago.</p> <p><strong>Main</strong> <strong>conclusions</strong>: While the Javan and Bawean warty pigs have persisted throughout the Pleistocene climatic oscillations, contemporary pressures from human activities threaten their survival and immediate action should be taken to grant legal protection to both <em>S. verrucosus</em> and <em>S. blouchi</em>. This study highlighted the use of demographic history modelling using genomic data to identify evolutionary significant units and inform conservation.</p>
Data and R scripts from: Using conservation genetics to prioritise management options for an endangered songbird
<p>Genetic data can be highly informative for answering questions relevant to practical conservation efforts but remain one of the most neglected aspects of species recovery plans. Framing genetic questions with reference to practical and tractable conservation objectives can help bypass this limitation of the application of genetics in conservation. Using a single-nucleotide polymorphism dataset from reduced-representation sequencing (DArTSeq), we conducted a genetic assessment of remnant populations of the endangered forty-spotted pardalote (<em>Pardalotus</em> <em>quadragintus</em>), a songbird endemic to Tasmania, Australia. Our objectives were to inform strategies for conservation of genetic diversity in the species and estimate effective population sizes and patterns of inter-population movement to identify management units relevant to population conservation and habitat restoration. We show population genetic structure and identify two small populations on mainland Tasmania as 'satellites' of larger Bruny Island populations connected by migration. Our data identify management units for conservation objectives relating to genetic diversity and habitat restoration. Although our results do not indicate the immediate need to genetically manage populations, the small effective population sizes we estimated for some populations indicate that they are vulnerable to genetic drift, highlighting the urgent need to implement habitat restoration to increase population size and to conduct genetic monitoring. We discuss how our genetic assessment can be used to inform management interventions for the forty-spotted pardalote, and show that by assessing contemporary genetic aspects, valuable information for conservation planning and decision-making can be produced to guide actions that account for genetic diversity and increase chances of recovery in species of conservation concern.</p>
Association Between Geriatric Frailty and Medication Related Problems in the Emergency Department to Help Clinical Pharmacists Prioritise Patients
ClinicalTrials.gov study NCT07282379. IPD Sharing: YES. Countries: 1. Publications: 6.
Using predictive models to identify kelp refuges in marine protected areas for management prioritisation
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Data from: Densities of the endangered Large blue butterfly Phengaris arion vary by 100-fold in restored conservation grasslands, providing a tool to prioritise future introductions
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Conservation prioritisation through genomic reconstruction of demographic histories applied to two endangered suids in the Malay Archipelago
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Data and code for: Acoustic monitoring enables multi-taxa conservation assessment and prioritisation over large scales and for rare and cryptic species
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Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
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Data and R scripts from: Using conservation genetics to prioritise management options for an endangered songbird
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.