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opencc-by-4.0Nov 2022View details →

Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study

The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials.  The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level.  Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with  a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen. The Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site.  A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion.  Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks.  Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune. Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. The monitoring consisted of: <ul> <li> topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </li> <li> aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </li> <li> sediment sampling and grain size analysis.  </li> </ul> Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot. This dataset consists of data related to monitoring activities.

An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona

The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets. How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a> This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions. <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is : VV_new_perNN_LSTWW where: <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1

Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Creatures case studies (data)

This data set contains case studies of creative practices that aim to produce transformations towards sustainability identified during the Creatures project

TRANSIT long-distance multimodal trips model results - a Spanish case study

The files downloaded present the results per scenario considered obtained with the agent-based model developed for the assessment of the Intermodal Timetable Synchronisation solution proposed in the scope of the TRANSIT project (<a href="https://www.transit-h2020.eu/">https://www.transit-h2020.eu/</a>). An agent-based modelling framework called <a href="https://github.com/StefanoPenazzi/jtap/tree/main">J-TAP</a> has been developed and put at work to implement a Spanish long-distance multimodal trips model. The enhanced version of J-TAP used in this work can be found on a <a href="https://github.com/NommonSolutionsAndTechnologies/jtap">github repository</a>. The case study is focused on modelling the long-distance travel patterns of the residents in the Valencia (Spain) area. The destinations considered include the whole of Spain. The period under study is a full year from March 2019 to February 2020 (both inclusive).The files are structured in three different scenarios: <ol> <li>CS01 - Baseline. The current state of the network is considered and the actual long-distance travel patterns are obtained.</li> <li>CS02 - HSR connection with Madrid-Barajas airport. The long-distance travel patterns are modelled with hard measures, the high-speed rail is connected to Madrid-Barajas airport.</li> <li>CS03 - HSR connection with Madrid-Barajas airport and timetable synchronisation. The effects of the timetable synchronisation are modelled.</li> </ol> Each scenario includes the following files: <ul> <li>ctapModelParameters. A folder containing all the information extracted from the <a href="https://neo4j.com/product/graph-data-science/?utm_program=emea-prospecting&utm_source=google&utm_medium=cpc&utm_campaign=emea-search-offers&utm_adgroup=dynamic&utm_content=dynamic&utm_placement=&utm_network=g&gclid=Cj0KCQiAwJWdBhCYARIsAJc4idAo4CEi9lU8TXwmBym8MNHpEIZHPBs3x_4phxbu76y1XKbYlFoZCjIaAiGhEALw_wcB">neo4j</a> graph database created to model the multimodal network and the agents. J-TAP contains packages that simplify network creation in the graph database. This information is stored in .json files (e.g., "Os2DsTravelCostParameter.json" contains the generalised cost for each OD pair and transport mode, "AttractivenessParameter.json" contains the attractiveness by destination, activity, time of the year and agent, etc.). The solver included in the J-TAP framework uses this information to calculate the agents plans.</li> <li>population.json. The result of the J-TAP optimisation. It includes the fitness value for each agent plan evaluated during the J-TAP execution. The best plan for each agent is selected as the plan performed by the agent. An agent plan includes: <ul> <li>activities - Sequence of activities.</li> <li>locations - Sequence of locations</li> <li>ts - Initial time of the activity</li> <li>te - Final time of the activity</li> </ul> </li> <li>LinkTimeFlow.csv. It is obtained after processing the previous file. It contains the number of agents using each link in the network (i.e., road, rail, air and cross links) in each time interval. The first column represents the link id and the rest of columns indicates the number of agents in each interval.</li> </ul> The J-TAP simulation framework is explained in detail in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D5.1_Modelling_Framework_v02.00.00.pdf">D5.1. TRANSIT Modelling and Simulation Framework</a> and the complete description of the case studies and scenarios tested is included in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D6.1_Assessment_of_Intermodal_Concepts_00.02.00.pdf">D6.1. Impact Assessment of New Intermodal Concepts and Passenger Information Services: Conclusions and Recommendations</a>. Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us.

Golden Agents - Processes of Creativity (case study)

We can observe a 'rise' of or 'renewed interest' in the illustrated book in the second half of 17th century and the beginning of the 18th century. New genres and techniques and an increasing competition in the book market lead to new collaborations between authors, printers, and illustrators. It would be interesting to track these producers who collaborated on illustrated book projects and trace whether these commercial collaborations were grounded or resulted in social and/or religious interactions. For this, we are bringing together several datasets in the Golden Agents infrastructure that each contain information on the production of books, or on social relations between actors involved in the production process. Aim of case study: <ul> <li>Linking & querying different datasets to find overarching patterns on creative industries in the Dutch Republic</li> </ul> Overarching Research Question: <ul> <li>How can we map innovation in cultural production?</li> <li>What is the relation between different sorts (bonding & bridging) of creative and social interactions?</li> </ul> This release (v0.9) marks the state of the data and case study at the end of the Golden Agents project (2016-2022).

opencc-by-4.0Apr 2022View details →

COALMOD-World 2.0 data, results, figures for: Stranded Assets in the Coal Export Industry? The Case of the Australian Galilee Basin

This dataset contains all COALMOD-World 2.0 data for Hauenstein et al. (2023): New coal mines in the Australian Galilee Basin are not economically viable and are prone to become stranded assets (doi.org/10.1016/j.oneear.2023.07.005). With the input data files and the GAMS scenario file the model (https://github.com/chauenstein/COALMOD-World_v2.0) can be run to reproduce the model results. Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV2

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV1

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 3 FOV2

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 3 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV3

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV3) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 1 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 1 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONROL CASE 3 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (CONTROL CASE 3 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 2 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 2 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 1 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 1 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 4 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 4 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV3

Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1 FOV3) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 1 FOV3

Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 1 FOV3) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 3 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 3 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 2 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 2 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.