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Gridded maps of global population scaled to match the 2023 Wittgenstein Center (WIC) Population projections
<p>The gridded population data used for calculating exposed populations is based on the population projections from the original SSPs (KC and Lutz, 2017) which were subsequently gridded (Jones and O’Neill, 2016). These gridded projections were aggregated to 0.5 ° spatial resolution and then scaled to match the latest available projections for population in line with the updated SSPs, v3.0 (KC <em>et al.</em>, 2024). The scaling is done on a country basis for all countries included in the latest SSP projections. Countries, which are not included in these projections, remain unchanged. The scaling process is done on a country-level basis using the following step:<br> </p> <ol> <li>The total population for the original gridded data is calculated using the ISIMIP fractional country raster (Perrette, 2023), excluding border cells containing contributions from more than one country.</li> <li>The population of the fractional border cells is subtracted from the total population of the SSP population projections and the required scalar to match the population from the gridded data to the SSP population projections is calculated.</li> <li>This scalar is applied to all non-fractional cells. </li> </ol> <p>While it is possible to calculate the scalar for each country including the proportion of the population in the fractional border cells, this would require the scalar to also be applied to that proportion of the population in the border cells to match the overall population number for each country. Applying different scalars to the population proportions for each country in the same cell would, however, change the ratios of the population in the fractional border cells and subsequently lead to skewed results when reapplying the fractional country raster to the scaled data for the aggregation to country level. For small countries, where more population lives in fractional border cells than in non-border cells, and for countries that only consist of border cells with contributions from more than one country, all cells were used in the scaling process. <br><br>It should be noted that some smaller countries cannot be scaled properly and that the latest SSP population projections do not contain values for all countries. Since there has been no release of updated gridded population projections yet, the gridded population data created using this approach still provide the closest match to the latest SSP population projections currently available.</p>
Supplement information : Figures of the IAMM Project Report
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Dataset: A Non-Stationary Bias Adjustment Method for improving the Inter-annual Variability and Persistence of Projected Precipitation
<p>This dataset includes: (1) unbiased data using a novel non-stationary bias adjustment methodology specifically tailored for environmental variables exhibiting sporadic events characterized by substantial intensity variability; (2) the parameters of the non-stationary probability distributions using marinetools.temporal. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and employing a non-stationary theoretical and parametric quantile mapping to adjust associated biases.</p> <p>The dataset is part of the results obtained after the application of the methodology to daily precipitation projections from seven regional climatic models of the RCP 8.5 scenario spanning 2006-2100, alongside historical concurrent data from projections and observations spanning 1970-2005.</p> <p>Its efficacy is compared a widely used quantile mapping method, revealing notable differences in the performance of the methods concerning the distribution of events throughout the year and the behaviour of mean and extreme intensity values. The proposed method demonstrates promising potential in reducing uncertainty associated with systematic errors in inter-annual precipitation variability. This bears significance in evaluating hydrological responses and its associated impacts particularly in semi-arid mountainous basins.</p>
Future Projections and Life Cycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability
<p>The dataset presents the findings of the study "Future Projections and Lifecycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability". The data results are contained in the files "Results_data.xlsx" and "LCIs and LCA results.zip," while the "Figures data.xlsx" file includes the data needed for plotting. </p>
SAFRAN Present Forcing (Historical: 2004/2014) and Future projections (RCP4.5 and RCP8.5: 2041/2060).
<p>SAFRAN forcing refers to a set of meteorological data used to model the climate, covering both historical periods (2004-2014) and future projections under two greenhouse gas concentration scenarios (RCP4.5 and RCP8.5) for the period 2041-2060. Each dataset includes key parameters for climate analysis: cloud cover (NB), humidity (QA), direct radiation (RD), infrared radiation (RI), precipitation (RR), visible radiation (RV), snow cover (SS), temperature (TA), and wind strength (UA). These parameters are measured on a grid with variable resolution, ranging from 8 km in plains to 1 km in mountains and irrigated agricultural lands. This data is essential for understanding current and future atmospheric dynamics in the context of climate change.</p>
Knowledge needed for large-scale landscape restoration projects: scientific and experiential. 13.2B Session, Society for Ecological Restoration Conference 2024, Room Hurt, 16:30 -18:00, Thursday, 29th August
<p><strong>Disclaimer: </strong>The DOI of this video does not apply to the content of the slides of the contributors' presentations during this session. The authors of each communication maintain their copyright on the content of the presentations' slides.</p> <p><strong>The creators' roles were as follows:</strong> I. V. A., chair, session coordinator, and presenter; B. S., session coordinator; E. P. M., session coordinator, and presenter; H. J., presenter; F. S., presenter; W. E., presenter; G. M., presenter; and P. I., presenter.</p> <p><strong>Session description</strong></p> <p>This session aimed to explore the critical capacities and functions relevant to the large-scale ecosystem restoration (LER) economy and point out the complementarity of scientific and experiential knowledge and the key role of case studies in increasing this resilience. The restoration economy can be defined as “the market consisting of a network of businesses, investors, consumers, and government initiatives engaging in or driving the economic activity related to ecological restoration.” Its segment dealing with LER can be delimited by the large complexity of the system related to the natural, legislative, institutional, funding, project structure, and organizational network to implement the project, as well as of the economic sectors involved in implementing the projects. Success or failure depends on the interplay between the variables describing the environment of the project, its structure, and its functioning. For success, the uptake of scientific information is as important as the experiential (tacit) knowledge of practitioners, stakeholders, and policymakers. Scientific knowledge can be delivered to the LER economy by structures (models) and data (variables and measurements/estimations of their values). Still, in practice, it is often not a limiting resource. Experiential knowledge can be transferred to some extent by narratives (stories, opinions, examples in common language). The role of case studies for LER is to provide hints about what worked and did not work in other situations and, on this basis, to prepare the adaptive management and increase the resilience of each new project. The session provided opportunities for scientists to interact with practitioners about the problem of LER adaptive management and illustrate the issues with examples of scientific knowledge transfer to current or potential projects and case studies. The session was conceived as crosscutting the missions of the LER and European sections of the Society of Ecological Restoration. It is relevant for the development of the restoration economy in the EU through the implementation of the future European Nature Restoration Law, its effectiveness, and its efficiency.</p> <p> </p> <p><strong>Session report</strong></p> <p><em><strong>Three key elements emerged from the session:</strong></em></p> <p>· Even though there is a widespread need for this kind of knowledge in all phases of project and program cycle development, the role of experiential knowledge/ know-how in ecological restoration is not explicitly treated.</p> <p>· The proprietary know-how can be included in portfolios of patents for technologies and services needed in landscape restoration, which are open-access or undisclosed by practitioners. These types are functionally complementary in the restoration industry and appropriate for specific circumstances.</p> <p>· One can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects.</p> <p> </p> <p><em><strong>Narrative of the session:</strong></em></p> <p>The session showed exciting complementarity between various restoration project phases and provided solid arguments about the importance of experiential knowledge [1]. In the first communication, Jeoelen showed us social science research about what factors positively and negatively control cooperation in the restoration ecosystem of individuals, private organizations, NGOs, and public institutions. Then, she provided information about guidelines for practitioners about how to build trustful relations. She was asked what solutions one has when there are people in the team with undesirable traits, and the answer was to avoid such persons from the very beginning or, if one needs that in the team for other qualities, to discuss with them explicitly the problems. The second speaker, Sebastian, contributed with a complex process-based socio-ecological model presentation and its use in decision-making. His question was about the possibility of including details of cost-benefit analysis in the models of stakeholders' decisions, and the answer was that, in principle, it is possible. However, one must also consider the model's optimal complexity in terms of incertitude propagation. In the third presentation, Pal and Erik presented the practical approach to peatland restoration in catchments from Norway. In their context, it seemed feasible because of data available for hydrological modeling from other state monitoring systems and the fact that most restoration projects occurred on public land.</p> <p>Several examples demonstrated the approach's success in a catchment near Oslo, where the public's interest in recreation is high, and there is support for restoration. The question from the audience related their work to the first presentation of the session topic, namely human resources and trust building with stakeholders. In their context, the usual interactions and roundtables with stakeholders before the start of the projects, as well as continuous formal and informal communication in the implementation phase, were enough to build the needed social capital for a successful restoration outcome. In the first online presentation, Martina provided details about non-formal education projects in marine areas that need restoration ecology. The target group was children, but the projects also engaged adults and environmental professionals from the local socio-ecological systems, thus propagating the informed trust building to the whole community. Her details pointed out the experiential approach in education. Iulia from WWF Romania underlined the importance of case studies in communicating restoration's positive and negative aspects to practitioners. She touched on critical points of experiential knowledge related to the genuine national-scale social interest in restoration, despite the significant interest of local users and local authorities and even the existence of funding for restoration. The lack of cooperation between institutions is a limiting factor for up-scaling the restoration to the full Lower Danube River scale. Even in these conditions, WWF-Romania managed to implement many restoration projects on the Danube River and built a solid relationship with the local people by putting an equal accent on nature and people in the design and implementation phase of the projects. Iulia was asked what they do when there are problems with environmental management (such as illegal garbage disposal) that are not formally relevant to the restoration project but occur in the communities they work with. The answer was that they adopted a proactive and solution-oriented approach and started discussions with the people to identify potential solutions and even develop new projects in cooperation that targeted those problems. Finally, Virgil referred to the audience to the content of this supplementary material, as available on the WhatsApp group of the conference in its preliminary form, and communicated about the cross-cutting session 13.0 from Friday as a framework for further discussion of the issues and disentangling opportunities and challenges in landscape restoration. His presentation showed the relationship between scientific knowledge and experiential one (know-how) in the technology readiness level system. It illustrated this notion by developing a TRL6 environmental service for the cumulative impact assessment of multiple active management actions in contaminated river basins, including restoration of buffer zones and control of mining point sources by remediation projects [2].</p> <p> </p> <p><em><strong>Operational concept of experiential knowledge</strong>:</em></p> <p>· Experiential knowledge is undiscursive knowledge needed for practical activities, particularly ecological restoration.</p> <p>· Related terms: know-how, tacit knowledge.</p> <p>· We acquire it by working with experienced people, and the chains of transfer lead to practical traditions.</p> <p>· It can be transferred to some extent by narratives, films, etc, formally communicated by case studies in professional societies, but personal interactions are decisive for the successful transfer.</p> <p>· It complements scientific knowledge, forming the knowledge capital needed for ecological restoration.</p> <p>· It can be proprietary, non-proprietary, disclosed, and undisclosed.</p> <p>· Its availability geographically and in specific phases of the restoration programs and project cycles limits the success of the restoration.</p> <p>· Due to its complexity, large-scale ecosystem/landscape restoration is highly sensitive to the impact of a lack of experiential knowledge.</p> <p>· Experiential knowledge is needed at the first level in all phases of the program and project cycles and in all themes associated with landscape restoration (at general objectives formulation, implementation/monitoring, financing, and supporting the local economies). Appendix 1 of the full report [1] shows a potential distribution of the contributions to landscape restoration sessions and workshops by these themes, referring in several cases to experiential knowledge).</p> <p>· An extra level of experiential knowledge is needed to successfully transfer first-level know-how/ experiential knowledge.</p> <p>· Despite its unstructured and informal/unprocedural reality, one can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects. This complements the challenge of hypotheses testing at scientific standards of active measures in restoring ecological networks (large-scale ecosystems, landscapes).</p> <p><em><strong>Notes</strong></em></p> <p>[1] For the place of the contributions of this session in the general landscape of the conference, one can download the full report here: <a href="https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/">https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/</a></p> <p>[2] The pdf of his presentation is available for download at <a href="https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/">https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/</a></p> <p><strong>Acknowledgments</strong></p> <p>We thank the organizers of the SERE 2024 conference and the technical team involved in broadcasting and registering this video. We also thank to the persons from the audience who contributed with questions and comments.</p>
Hydrogen Projects Database. October 2021
<p><span>Overview</span></p> <p><span>This dataset covers all projects worldwide that have been commissioned since 2000 for the production of hydrogen for energy or climate change mitigation purposes, i.e. their objective is either to reduce the emissions associated with the production of hydrogen for existing applications, or the introduction of hydrogen as an energy carrier or industrial feedstock in new applications where it is not currently widely used.,and has the potential to be a low-carbon<span> </span>technology option. Projects in planning or construction are also included.</span></p> <p><span>Projects are categorised by the production technology (electrolysis; fossil fuels with carbon capture, utilisation and storage; other technologies), the hydrogen-based fuel produced (hydrogen, methanol, ammonia, methane, synthetic hydrocarbons) and the use of the of th fuel produced</span><span>. </span></p> <p> </p> <p><span>Data sources </span></p> <p><span>This database is an update from the fisrt database published along with the report "The future of hydrogen. Seizing today’s opportunities".</span></p> <p><span>Data collection and processing by IEA. IEA want to acknowledge the generous support from the IEA Hydrogen Technology Collaboration Programme and other different stakeholders that kindly provided information for the preparation of the datbase.</span></p> <p><span>We encourage and welcome contributions from all stakeholders where additions and changes are required. Please contact hydrogen@iea.org<span> </span></span></p> <p><span><span> </span></span></p> <p><span>IEA (2020). All rights reserved</span></p> <p> </p> <p><span>Please note that this publication is subject to specific restrictions that limit its use and distribution. The terms and conditions are available online at </span><span><a>www.iea.org/t&c/</a></span></p> <p><span> </span></p> <p><span>You could also add the following if you would like, although the terms and conditions also explain our preferred citation format. </span></p> <p><span> </span></p> <p><span>This database may be cited as follows: </span></p> <p><span> </span></p> <p><span>Source: IEA (2021), Hydrogen Projects Database, </span><span><a>https://www.iea.org/reports/hydrogen-projects-database</a></span><span>. All rights reserved. </span></p>
Verveci project
<p><span>Data set on the validation of the measurement tool on Organisational Resilience.</span></p>
Enhanced Accounting for Item Cost Variability in AASHTOWare Project Software Dataset
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Dataset used as inputs for the D-rex project
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MULTIPLIERS_WP3/4_Science learning project on Vaccination_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Vaccination</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers, and OSC members (</span><span>Pseudo-/Anonymised)</span></li> <li><span>Summary</span><span> of transcripts from group interviews with students (</span><span>Pseudo-/Anonymised)</span></li> </ul>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 9: Operational and damaged scenarios in regular waves
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 9. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
"Coastal clean-up project": Pepsico and WWF-Pakistan's mission to clean Karachi's Beaches
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
Annex C_Prioritisation of project proposals
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Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs.</p> <p>The dataset includes:</p> <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> <p>1) base-case model and base-case+Noise in one project,</p> <p>2) 30Hz model,</p> <p>3) no_thin_units model,</p> <p>4) no_karst model,</p> <p>5) perfect_illumination model.</p> <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> <p>1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project,</p> <p>2) model with 10x thicker Kapp Starostin layer in the overburden model.</p> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Research project data and analysis
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Global and regional projections of the economic burden of Asthma: A value of statistical life approach
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Introduction to the TOPS SCHOOL Project: NLU Fall 2024 Student Cohort
<p>In this video, Juan Martinez and Kytt MacManus give an overview of the TOPS SCHOOL project to the fall 2024 NLU student cohort. They provide an overview of the project, <a href="https://ciesin-geospatial.github.io/TOPSTSCHOOL/" target="_blank" rel="noopener">website</a>, <a href="https://github.com/ciesin-geospatial/TOPSTSCHOOL" target="_blank" rel="noopener">GitHub</a>, and tasks. You can watch the video below or find a link in the 'Additional details' section.</p>
project logo
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Data compilation for project Ground vertical displacement and its relationship with injection and extraction of fluids in North Louisiana
<p>This repository contains the database for the project "Ground vertical displacement and its relationship with injection and extraction of fluids in North Louisiana". This is the third chapter of the PhD thesis of Hurtado-Pulido</p> <p>Authors: Hurtado-Pulido C., Ebinger, C.</p> <p>The datasets used in this project were compiled and curated by Hurtado-Pulido from different sources for her PhD dissertation. <br>Earthquakes in the ISLA dataset were collected during the ISLA project (2019-2022), which was led by Ebinger (the Project's PI).</p> <p>If you use this dataset or part of it please also cite the original references listed in the READme document.</p> <p>Please address any questions about this dataset to Hurtado-Pulido.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.