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11 results for “data management policy”
IPBES Data Management Tutorials - Session 2.1: Introduction to the data management policy
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. </p> <p>This session,<em> Introduction to the data management policy</em><em>, </em>defines what a data management policy is and why it is important.</p>
IPBES Data Management Tutorials - Session 2.4: Implementation of the data management policy
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session on the<em> Implementation of the data management policy </em>provides a brief overview of the contents of the following chapters and how it all works together to improve the transparency and credibility of IPBES. </p>
IPBES Data Management Tutorials - Session 2.2: Why a data management policy for IPBES and how it concerns experts
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management policy </em>chapter provides an introduction of the IPBES data and knowledge management policy. It discusses why IPBES has a data and knowledge management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session, <em>Why a data management policy for IPBES and how it concerns experts, </em>provides background on what IPBES wants to achieve with its data and knowledge management policy and how it impacts experts within IPBES. The session now has a supplement which covers the development of the policy from version 1.0 to 2.0. </p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p>
Data from: Finding the best management policy to eradicate invasive species from spatial ecological networks with simultaneous actions
1. Spatial management of invasive species is more likely to be successful when multiple locations are treated simultaneously. However, selecting the best locations to act is difficult due to the many options available at any time. 2. We design a near-optimal policy for applying multiple actions simultaneously for faster invasive species control within a network. Our method uses a recent optimisation tool, the Graph-based Markov decision process (GMDP). Since the policy can be difficult to interpret, we extracted a simpler policy using classification trees. We applied our approach to the eradication of invasive mosquitofish (Gambusia holbrooki) from the habitat of the red-finned blue-eye (Scaturiginichthys vermeilipinnis), a critically endangered fish with a global population that is restricted to seven artesian springs in Queensland, Australia. 3. The policy returned by the GMDP was to manage springs occupied by mosquitofish and their connected neighbours, unless the neighbours were occupied by red-finned blue-eyes. 4. Simultaneous management resulted in rapid declines in simulated mosquitofish occupancy even if eradication effectiveness was low; however the cost of simultaneous eradication was high and sustained eradication effort was necessary to maintain low mosquitofish occupancy. 5. Synthesis and applications. Our paper finds a near-optimal, multi-action control policy to remove an invasive species from a multi-species spatial network. We introduce the Graph-based Markov decision process (GMDP) and apply it to a real case study – eradication of invasive mosquitofish from the habitat of the red-finned blue-eye. We find that the GMDP can generate policies for networks with extremely large state spaces, however it works best when nodes have fewer than five neighbours. We conclude that simultaneous eradications are effective for rapid control of invasive species; however, managers should consider the cost and time required for an effective eradication program.
Ecosystem management policy implications based on Tonga main tuna species catch data 2002–2018
<p>From 2002 to 2018, the Tongan Long Line fishery collected and compiled catch data (limited to presence records) for Albacore, Bigeye, Skipjack, and Yellowfin. The Tonga Ministry of Fishery and the South Pacific Community (SPC) Office in New Caledonia provided this data. To ensure adherence to regulations, at least two fisheries offices cross-verified the entire fish catch. The data consists of daily fishing positions (latitude and longitude) and date (day, month, and year), organized in a 1^0 spatial grid. Our study utilized catch per unit effort (CPUE), a standardized metric indicating fishing efficiency and effort, calculated by dividing the weight of the catch in metric tons by the number of hooks deployed per fishing record. To align with the temporal scales of the predictor variables, we aggregated the CPUE data into monthly and annual datasets using Microsoft Excel.</p>
Data from: Finding the best management policy to eradicate invasive species from spatial ecological networks with simultaneous actions
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Ecosystem management policy implications based on Tonga main tuna species catch data 2002–2018
Open the record for dataset details and reuse information.
Manuscript code & data: Foster et al. (2020) "Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy"
<p>Data to reproduce results presented in Foster, T., Mieno, T. and Brozovic, N. (2020). <em>Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy.</em> Water Resources Research. In Review. Files provide include:</p> <ul> <li>"ReviewMetadata_FosterWRR_2020.xlsx" - data needed to reproduce meta-analysis presented in the paper</li> <li>"MeasurementErrorCode_FosterWRR_2020.m" and "Foster2018_ProductionFunction.mat" - Matlab code and data needed to reproduce welfare loss analysis presented in the paper.</li> </ul>
Data from "Cooperative Management of Ecosystem Services: Coalition Formation, Landscape Structure and Policies"
<p>Simulated data generated to perform the simulations of the paper "Bareille, F., Zavalloni, M., Raggi, M., & Viaggi, D. (2021). Cooperative management of ecosystem services: coalition formation, landscape structure and policies. Environmental and Resource Economics, 79(2), 323-356."</p>
Figure 2 from: Neylon C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773. https://doi.org/10.3897/rio.3.e21773
Figure 2 - The Cultural Science model of Hartley and Potts (2014).The co-creation of culture and group in the context of an external environment.
Figure 1 from: Neylon C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773. https://doi.org/10.3897/rio.3.e21773
Figure 1 - The Institutional Analysis and Design framework adapted from (Ostrom 2005).
ScienceDex guides
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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.