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Benchmarks for the Urban-PLUMBER model evaluation project Phase 1 (AU-Preston)
<p><strong>AU-Preston benchmarks for the Urban-PLUMBER project</strong></p><p>These timeseries data are associated with the study:</p><blockquote><p>Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results</p></blockquote><p>doi: <a href="https://doi.org/10.1002/qj.4589">https://doi.org/10.1002/qj.4589</a></p><p>These are empirical models of various complexity used as benchmarks to evaluate land surface models in the project. The benchmarking process follows the methods in the PLUMBER project (Best et al., 2015).</p><p><strong>Benchmarks</strong></p><p>Physically based</p><ol><li><strong>Manabe_1T</strong>: A simple 'slab and bucket' model (Fig. 1a) based on physical principles (i.e. conservation of energy, mass and momentum). The impervious (built) fraction is simulated using a one-tile slab scheme (Best, 2005). For the pervious fraction a simple representation allows precipitation to fill a store which overflows when full, and otherwise freely evaporate (Manabe, 1969). At each timestep, the impervious and pervious tile outputs are calculated and aggregated with a weighted mean.</li></ol><p>Empirical (out-of-sample)</p><ol><li><strong>REG1-SWdown</strong>: Linear regression with one variable (SWdown, e.g. Fig. 1b) is used separately to predict SWup, LWup, Qh, Qle, and Qtau. At night predicted values are constant where SWdown = 0.</li><li><strong>REG2-SWdown-Tair</strong>: Two-variable (SWdown and Tair) linear regression (e.g. Fig. 5c) provides some information at night and more generally for variables closely dependent on temperature (e.g. Lwup and Qh).</li><li><strong>KM3-SWdown-Tair-RH</strong>: Following PLUMBER's conceptual arguments, three predictor variables (SWdown, Tair and RH data) are split into three (low, medium and high) to create 3^3=27 groups, for which independent regressions are trained. K-means clustering is used to determine the training data groups (e.g. Fig. 1d). To use this piecewise regression benchmark, at each time step the input data's proximity to one of the 27 cluster centroids is determined to select the regression to apply. This benchmark equates to PLUMBER's EMP3KM27 (Best et al., 2015), which based on common metrics outperformed all their participating land surface models when predicting sensible and latent heat fluxes across 20 sites.</li></ol><p>Empirical (in-sample)</p><ol><li><strong>KM3-IS-SWdown-Tair-RH</strong>: This follows the KM3-SWdown-Tair-RH method, but trained with in-sample data only (i.e. AU-Preston). This should outperform an out-of-sample model because of the reuse of the forcing data, but performance is expected to degrade if applied to dissimilar conditions (i.e. another site).</li><li><strong>KM4-IS-SWdown-Tair-RH-Wind</strong>: The k-means approach is applied again, but with an additional variable (wind speed), which increases the clusters to 81 (3^4) given the above rationale. Wind speed provides information that helps to predict turbulent heat and momentum fluxes.</li></ol><p><strong>Authors</strong></p><p>Emperical benchmarks were developed by Mathew Lipson: <a href="https://orcid.org/0000-0001-5322-1796">https://orcid.org/0000-0001-5322-1796</a></p><p>The physical benchmark was developed by Martin Best: <a href="https://orcid.org/0000-0003-4468-876X">https://orcid.org/0000-0003-4468-876X</a></p><p>Co-authors for the associated manuscript are:</p><p>Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, Seung-Bu Park, Michael Roth, Robert Schoetter, Andres Simon, Gert-Jan Steeneveld, Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, Chenghao Wang, Zhi-Hua Wang</p><p><strong>References</strong></p><p>Observational data</p><p>Lipson, M., Grimmond, S., Best, M., Chow, W., Christen, A., Chrysoulakis, N., Coutts, A., Crawford, B., Earl, S., Evans, J., Fortuniak, K., Heusinkveld, B. G., Hong, J.-W., Hong, J., Järvi, L., Jo, S., Kim, Y.-H., Kotthaus, S., Lee, K., Masson, V., McFadden, J. P., Michels, O., Pawlak, W., Roth, M., Sugawara, H., Tapper, N., Velasco, E., and Ward, H. C.: Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project, <a href="https://doi.org/10.5281/zenodo.7104984">https://doi.org/10.5281/zenodo.7104984</a>, 2022.</p><p>Benchmark references</p><p>Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., Hurk, B. J. J. van den, Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.: The Plumbing of Land Surface Models: Benchmarking Model Performance, Journal of Hydrometeorology, 16, 1425–1442, <a href="https://doi.org/10.1175/JHM-D-14-0158.1">https://doi.org/10.1175/JHM-D-14-0158.1</a>, 2015.</p><p>Best, M. J.: Representing urban areas within operational numerical weather prediction models, Boundary-Layer Meteorol, 114, 91–109, <a href="https://doi.org/10.1007/s10546-004-4834-5">https://doi.org/10.1007/s10546-004-4834-5</a>, 2005.</p><p>Manabe, S.: CLIMATE AND THE OCEAN CIRCULATION: I. THE ATMOSPHERIC CIRCULATION AND THE HYDROLOGY OF THE EARTH'S SURFACE, Monthly Weather Review, 97, 739–774, <a href="https://doi.org/10.1175/1520-0493(1969)097">https://doi.org/10.1175/1520-0493(1969)097</a><0739:CATOC>2.3.CO;2, 1969.</p>
STRIDE Project - Enabling the Shared Mobility Revolution - dataset
<p>This folder contains all the relevant data used (input and output) in the context of the STRIDE Project "Enabling the Shared Mobility Revolution" for the microscopic simulation analysis of curb environments.</p>
EU FAR Project: animation presentation
<p>Animation presentation of the EU FAR project, English version.</p>
Using genomics to guide seed-sourcing at the right taxonomical level for ecological restoration projects: the complex case of Carex bigelowii s.lat. in Norway
<p>There is a growing demand for ecological restoration using suitable seeds following international standards or national legal demands for local seed-sourcing. However, before selecting the appropriate geographic origin of seeds, it is vital to explore taxonomic complexity related to the focal taxa. We used ddRAD-seq to screen genomic diversity within <i>Carex bigelowii</i> s.lat. focussing on Norway. This species complex is considered a candidate for seeding, but presents considerable morphological, ecological, and genetic variation. The genetic structure of 132 individuals of <i>C</i>. <i>bigelowii</i> s.lat., including <i>C</i>. <i>nigra</i> as an outgroup, was explored using ordinations, clustering analyses, and a genetic barrier algorithm. Two highly divergent clusters were evident, supporting the recognition of two taxonomic units '<i>C</i>. <i>dacica</i>' and 'subsp. <i>bigelowii</i>'. Previously defined seed-sourcing regions for <i>C</i>. <i>bigelowii</i> s.lat. did not consider the known taxonomic complexity, and therefore interpreted the overall genetic structure as seed-sourcing regions, not taxa. We estimated genetic neighbourhood sizes within each taxon to be 100-150 km and 300 km, respectively, indicating species-specific delimitations of local seed-sourcing regions. Frequent hybrids, local genetic distinctiveness, and suggested ecotypes add complexity to the discussed seed-sourcing regions. Our results show how genomic screening of diversity and structure in a species complex can alleviate the taxonomic impediment, inform practical questions and legal requirements related to seed-sourcing, and together with traditional taxonomic work provide necessary information for a sound management of biodiversity.</p>
Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
<p>Tropical savannas are characterised by high primary productivity and high fire frequency, such that much of the carbon captured by vegetation is rapidly returned to the atmosphere. Hence, there have been suggestions that management-driven reductions in savanna fire frequency and/or severity could significantly reduce greenhouse gas emissions and sequester carbon in tree biomass. However, a key knowledge gap is the extent to which savanna tree biomass will respond to modest shifts in fire regimes due to plausible, large-scale management interventions. Here, we: (1) characterise relationships between the frequency and severity of fires and key demographic rates of savanna trees, based on long-term observations in vegetation monitoring plots across northern Australia; (2) use these relationships to develop a process-explicit demographic model describing the effects of fire on savanna tree populations; and (3) use the demographic model to address the question: to what extent is it feasible, through the strategic application of prescribed burning, to increase tree biomass in Australian tropical savannas? Our long-term tree monitoring dataset included observations of 12,344 tagged trees in 236 plots, monitored for between 3 and 24 years. Analysis of this dataset showed that frequent high-severity fires significantly reduced savanna tree recruitment, survival and growth. Our demographic model suggested that: (1) despite the negative effects of frequent high-severity fires on demographic rates, savanna tree biomass appears to be suppressed by only a relatively small amount by contemporary fire regimes, characterised by a mix of low- to high-severity fires; and (2) plausible, management-driven reductions in the frequency of high-severity fires are likely to lead to increases in tree biomass of about 11.0 t DM ha<sup>–1</sup> (95% confidence interval: -1.2–20.8) over a century. Accounting for this increase in carbon storage could generate significant carbon credits, worth on average three times those generated annually by current greenhouse gas (methane and nitrous oxide) abatement projects, and has the potential to significantly increase the economic viability of fire/carbon projects, thereby promoting ecologically sustainable management of tropical savannas in Australia and elsewhere. This growing industry has the potential to bring much-needed economic activity to savanna landscapes, without compromising important natural and cultural values.</p>
A map showing the locations of instruments used during the Convective Storm Initiation Project (CSIP) pilot
The CSIP pilot project was conducted in July 2004 centred around the Chilbolton radar facility where there is a 25 metre radar dish with two of the most sophisticated meteorological radars in the world. The pilot project utilised many of the instruments and facilities from the NCAS Universities' Facility for Atmospheric Measurement (UFAM) including a Cessna aircraft, wind profiler, Doppler lidar, sodar and automatic weather stations and scientists from the universities of Aberystwyth, Leeds, Reading, Salford, Essex, Bath and UMIST as well also from the Met Office.
Photographs from 2005 Convective Storm Initiation Project (CSIP)
Photographs from 2005 Convective Storm Initiation Project (CSIP)
AFFIRMO Project Video Presentation
<p>This video presentation aims at illustrating the AFFIRMO project and present the project team. It was presented for the first time during the EuGMS Congress 2022 in late September 2022. </p>
Marine heatwave projection output as in Hayashida et al. (2020)
<p><strong>There are two files</strong></p> <p><em>grid_spec_simple.nc</em> contains the grid information for OFAM3 (e.g. longitude, latitude, cell area).</p> <p><em>mhwout_1982-2018_OFAM_cmip5.nc</em> contains the MHW metrics over 1982-2050 simulated by OFAM3:</p> <ol> <li>It’s a 4D array [3, 69, 1200, 3600], where:</li> <li>The first dimension refers to the MHW metric (count, total_days, total_icum). ‘count’ is the frequency (number of MHW events in a year), ‘total_days’ is the annual MHW days, and ‘total_icum’ is the total cumulative intensity (degree days). To calculate mean MHW intensity (as in Hayashida et al. (2020)), you need to divide ‘total_icum’ by ‘total_days’.</li> <li>The second dimension is the years from 1982-2050.</li> <li>The third and fourth dimensions are the latitude and longitude grid points.</li> </ol> <p><strong>Reference </strong></p> <p>Hayashida, H., Matear, R.J., Strutton, P.G. <em>et al.</em> Insights into projected changes in marine heatwaves from a high-resolution ocean circulation model. <em>Nat Commun</em> <strong>11</strong>, 4352 (2020). https://doi.org/10.1038/s41467-020-18241-x</p>
Project Ô Overview video
<p>Project Ô shows how local, targeted water treatment technologies can help improve global water management, allowing current and future generations to benefit from this shared resource.</p> <p>--</p> <p>In January 2022, it was decided that a brand new video would be produced to include an updated overview of the project. A new script was written to explain how Project Ô supports a circular water economy. The key message was that, while there are 4 demosites and 6 solutions, Project Ô technologies can be replicated and applied much more widely.</p> <p>The IMI team had several meetings with partners from each demosite to fully understand how each of the technologies worked. The script was revised multiple times to ensure that the information was correct, clear and concise.</p> <p>The production of this video included pre-production (script-writing, storyboards), production (designing and animating infographics, motion graphics, recording voice-over), and post-production (compiling, video and audio editing, visual effects, rendering).</p>
Project Ô Media Assets | Illustrations
<p>In line with the DoA’s statement that in T8.5 External Communications, ‘Dissemination material for non-expert communities will be produced,’ new illustrative media, photos, visuals and other communication materials were created by IMI. This included creating custom-designed illustrations to communicate key project ideas and establishing a consortium-accessible media repository. This repository is part of the project legacy and can be used by other EC-funded projects promoting a circular water economy. All visuals planned for the website were developed such that they boost communication on the website, but also act as individual shareable items across social media and other communication channels.</p>
"RESEARCH & PROJECTS Achievement"-Additional Proof Materials
<p>Additional proof materials for Zongyue ZHANG's achievements regarding research & academic projects he worked on during his Master's education at PSL Research University and Huazhong University of Science & Technology and during various training programs he participated in different labs and institutions in China and Europe.</p>
Block-level population projections in Washington state under different SSPs from 2020 to 2040
<p>This shapefile contains block-level population projections in Washington state under different Shared Socioeconomic Pathways (SSPs) for every decade from 2020 to 2040. </p> <p>CONTENTS:</p> <p>Pop_proj_block.7z</p> <ul> <li>contains block-level rural/urban/total population projections under SSP2, SSP3, and SSP5 for 2020, 2030, and 2040. In the shape file, each population projection is stored in one column, with the first two character of the column name representing SSPs ("S2": SSP2; "S3":SSP3; "S5":SSP5), the third character indicating population type ("R": rural; "U": urban; "T": total), and the year information is attached in the end. For example, "S2R2040" represents rural population projection for year 2040 under SSP2.</li> </ul> <p>README.txt</p> <ul> <li>Illustrates population projection downscaling methods.</li> </ul>
WMO Polar Prediction Project: Year Of Polar Prediction Animation
<p>This 5 minute animated video</p> <p>The Year of Polar Prediction (YOPP) - from research to improved environmental safety. This video explains the background, methods and aims of the Year of Polar Prediction. www.polarprediction.net Thank you to the translators: Marta Terrado (Barcelona Supercomputing Center) for the Spanish Subtitles, Luisa Cristini (Alfred Wegener Institute) for the Italian Subtitles, Juhyeong Han (World Meteorological Organization) for the Korean Subtitles, Matthieu Chevallier (Météo France) for the French Subtitles.</p>
UWB-IODA project: Datasets associated to WP3
<p>Measured IR-UWB data files corresponding to WP3 of the UWB-IODA project. The detailed description of the experimental setup and associated signal processing algorithms is provided at https://hal.archives-ouvertes.fr/hal-03582174.</p>
Materials Project Dataset
<p>Structure-formation energy pairs from February 2022 version of MP database.</p>
Data set for EMPIR project MeDDII paper bmt-2022-0039
<p>Data set for EMPIR project MeDDII paper https://doi.org/10.1515/bmt-2022-0039</p> <p>In-line measurements of the physical and thermodynamic properties of single and multicomponent liquids</p>
Data for: Ecological interactions mediate projected loss of kelp biomass under climate change
<p>This dataset contains the data used to define a stacked species distribution model (SDM) for kelp (<em>Ecklonia radiata</em>) and urchins (<em>Centrostephanus rodgersii</em>) in eastern Australia. The spatial extent of this study encompasses the eastern coast of Australia, between 28.0–37.5°S. Within this region, <em>E. radiata</em> is the only laminarian kelp species, while <em>C. rodgersii </em>is the dominant urchin species, with ecological interactions between these two species creating a patchwork of kelp forests and urchin barrens on shallow reefs.</p>
Evaluations of the workshops 1, 2 and 3 in the participatory process of the project KlimaRhön
<p>The dataset contains the evaluation answers of the participating stakeholders in the digital workshops 1, 2 and 3 of the participatory process of the project KlimaRhön. The evaluations of workshops 1, 2 and 3 were realized with SoSci Survey (Leiner, 2019) and were shared with the stakeholders via www.soscisurvey.de. It is the original dataset, which is why it is in German, and only personal information of the stakeholders were deleted. The evaluation was done anonymously but the stakeholders had the possibility to indicate their organisation and name in the evaluation of workshop 1, which was deleted to ensure anonymity and data protection.</p> <p>The evaluation of workshop 3 contains statements to reveal the cultural theory type of the stakeholders. The statements follow van der Wal et al. (2014).</p> <p>Leiner, D. J. (2019). SoSci Survey (Version 3.2.12, Version 3.2.23 and Version 3.2.30) [Computer software]. Available at https://www.soscisurvey.de</p> <p>van der Wal, Merel; De Kraker, Joop; Offermans, Astrid; Kroeze, Carolien; Kirschner, Paul A. & van Ittersum, Martin (2014): Measuring social learning in participatory approaches to natural resource management. In: Environmental Policy and Governance, 24 (1): 1-15.</p>
NRP "Cultural capital as a resource for the sustainable development of Latvia / CARD" Cultural Indicators Sub-project Dataset
<p>The aim of this research is to define what kind of key challenges and implemented solutions regarding the artist status are common in Europe, and how to develop a systematic approach for artist recognition and support in the Baltic context. Using qualitative research methodology (desk research, focus group discussions) the authors analyze the current theoretical and empirical discussion in the European Union member states and especially in the Baltic countries, highlighting different practices and experiences in defining the status and recognition of the artist, and support systems that adjust for specific needs in different countries.</p> <p>Full dataset contains tabular data (published here) and an audio recording of the focus group discussion, which is available under restricted access due to containing sensitive information (please contact luize.daksa@lka.edu.lv for further inquiries).</p>
ScienceDex guides
Understand access before you commit
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.