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Replication package for our paper entitled "Faster and Better Quantum Software Testing through Specification Reduction and Projective Measurements"
<p><br> -Experiment1<br> Contains the source files for experiment 1 and the study subjects.</p> <p> -Experiment2<br> Contains the source files for experiment 2 and a mutation generator.</p> <p> -results<br> Contains the results from experiment 1 and 2 and postprocessing files</p> <p> -RQ1,RQ2,RQ3<br> Contains source files for figures, tables and data for each research question<br> of the paper</p> <p><br>Please inspect the README.txt file for more details.</p>
Changing Outside Areas: North Vancouver Landscaping Projects
<p>Perfectly situated in the heart of British Columbia lies the city of North Vancouver. It blends urban living refinement with the splendor of nature. People adore their outside areas as extensions of their houses when they have views of the mountains and the sea. Here is the meeting point of functionality and tranquilly. North Vancouver Landscaping Services is a developing company that has emerged to enhance and maintain these outdoor areas.</p> <h1>Landscaping North Vancouver: Improving Outdoor Living</h1> <p>Apart from being a gardening company, <a href="https://goo.gl/maps/sYN36xktU7sxjX2p6" target="_blank" rel="noopener"><strong>North Vancouver Landscaping Installations</strong></a> also designs grounds that highlight the interests and lifestyles of the homeowners while blending in with the surrounding natural beauty. Small suburban yards to enormous estates, these services provide a variety of planning and installation choices to suit your requirements.</p> <p>Imagine meandering walkways in a superbly planned garden that beckon exploration. The vivid blooms would be twirling in the breeze. Carefully selecting and positioning every plant, stone, and feature to create the impression of a real world outside area is how North Vancouver Landscaping Installations does its magic.</p> <p>The will to be sustainable and to protect the environment is at the core of these works. Hardy and able to thrive in the local temperature, native plants are prized since they require less water and maintenance. Furthermore, modern techniques like collecting rainwater and putting up permeable flooring are applied to lessen environmental harm and boost eco-efficiency.</p> <p>North Vancouver Landscaping Installations transforms uninhabited areas into lovely, tranquil retreats. They can produce a chic modern landscape, a colourful floral paradise, or a comfortable patio refuge.</p> <h2>Keeping the Beauty: North Vancouver Property Maintenance</h2> <p>The first impression of North Vancouver's scenery is one of first beauty and excellent maintenance. Keeping these outside spaces in good condition and looking attractive all year round requires the use of property maintenance services in North Vancouver.</p> <p>Regular lawn care to seasonal clean-ups—these services provide a large selection of maintenance choices that may be tailored to each property's requirements. Experts in their trade address the issues brought on by North Vancouver's shifting weather by implementing strategies to withstand harsh winters, scorching summers, and all in between.</p> <p>More is done by <a href="https://www.helpfulhomeservices.ca/" target="_blank" rel="noopener"><strong>North Vancouver Property Maintenance</strong></a> services than merely maintaining grounds. They also put out effort to maintain their health and vigor over time. Using integrated pest management techniques lessens the need for dangerous chemicals, while using organic fertilizers strengthens plants and the soil. Through active maintenance, these services prolong the environmental life of outdoor spaces in addition to making them appear better.</p> <p>Owners of North Vancouver homes can rest assured that their outdoor investments are in capable care. Year-round, grounds are kept looking their best by the dependable care and attention to detail provided by property maintenance services.</p> <h2>North Vancouver Garden Renovation: Revitalising Outdoor Living Areas</h2> <p>Every now and then the outside needs to be renewed and updated in line with the seasons and tastes. Utilize North Vancouver Garden Renovation services to give outdated settings a fresh appearance and a professional touch.</p> <p>These services specialize in creating stunning focal pieces that delight and inspire from ancient or little used outdoor areas, whether they are given a complete makeover or a modest upgrade. Working with homeowners, designers carefully assess the existing layout, appearance, and utility to create and execute improvements that go above and beyond what was anticipated.</p> <p>Redesigning garden beds and installing stunning hardscapes to adding new elements like water features and outdoor kitchens, North Vancouver Garden Renovation services bring creativity and expertise to every project. What follows then? Outdoor spaces that excite, renew, and prepare you to fully enjoy.</p> <p>Moreover, these enhancements raise the value and comfort of houses in addition to their aesthetic appeal. Renovations to the yard done well not only improve the appearance of a house from the street but also expand outdoor living areas, which increases its value and facilitates sale.</p>
MS analysis of ([Hmim][OTf])3Zn - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents MS analysis of ([Hmim][OTf])3Zn.</p>
Transmission microscope photos of the structure of ionic liquid ([Hmim][OTf])3Zn - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents TEM analysis of ionic liquid ([Hmim][OTf])3Zn</p>
Genealogical Forest Files for Simons Genome Diversity Project
<p>Genealogical forest files for the 1000 Genomes phase 3 autosomes. Converted using <a href="https://github.com/lukashuebner/gfkit">gfkit</a> from the <code>.trees</code> files available <a href="../records/3052359">here</a>.</p>
Genealogical Forest Files for Thousand Genome Project
<p>Genealogical forest files for the 1000 Genomes phase 3 autosomes. Converted using <a href="https://github.com/lukashuebner/gfkit">gfkit</a> from the <code>.trees</code> files available <a href="../records/3051855">here</a>.</p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Shear wave velocity profiles from Italian Seismic Microzonation project
<p>The dataset SMDB contains the shear wave velocity profiles from Italian Seismic Microzonation project (14,897 profiles).</p> <p>The records are:</p> <p>-survey id;</p> <p>-survey latitude and longitude in UTM33N coordinates;</p> <p>-survey type;</p> <p>-depth in meters;</p> <p>-shear wave velocity value (Vs) in m/s;</p> <p>-seismic microzonation (SM) cluster id.</p> <p>The file percentile_sigma_ln_vs.csv contain sigma lnvs in depth for the three percentiles 16,50,84.</p>
CORE Project - Task 5.2 Survey Data
<p><span>The datasets represents the raw data collected from the citizens surveys launched as part of the project's task 5.2, a study investigating the positive and negative aspects of the safety culture in different disaster scenarios, regions, and groups using multiple perspectives. </span></p>
Data_Pezzoli_Master_project_2023_10-2024_05
<div> <div> <div> <p><span>During the Master’s project the composition of sputtered FeGd multilayers was obtained to obtain a skyrmion lattice.<br></span></p> <p><span>The multilayers were characterized with Magnet-Optical Kerr Effect (MOKE) and Magnetic Force Microscopy (MFM)</span></p> </div> </div> </div>
Dataset for the DECCAR-INTERACT project
<p>This dataset contains data about living trees and deadwood collected at the Hyytiälä Forest Research Station (Finland) during the sampling campaing in 2021. The campaign was part of the project "<span>(DECCAR) Relationship analysis between deadwood decay rate, microbial biodiversity and climate change mitigation in boreal forests" under the INTERACT funding.</span></p> <p><span>Project granted for Hyytiälä Forest Research Station (SMEAR II) (Finlandia). Grant Agreement n. 730938.</span></p> <p>The dataset is composed by an Excel file with 4 different tabs:</p> <ul> <li><strong>Living Trees:</strong> Reporting dendrometric data for living trees </li> <li><strong>Standing dead trees:</strong> Reporting data of standing dead trees (deadwood component)</li> <li><strong>Stumps:</strong> Reporting data of stumps (deadwood component)</li> <li><strong>Lying deadwood:</strong> Reporting data of lying dead trees (deadwood component)</li> <li><strong>Legend:</strong> Reporting the name of the old growth forests and the number of plots, with the abbreviation used in the dataset</li> </ul>
Fathi Camel Microbiome Project (FCMP) Fecal Metagenome-assembled Genomes (MAGs)
<p>The Fathi Camel Microbiome Project (FCMP) aims to characterize the diversity and phenotypic associations of the dromedary camel microbiome. The gut microbiome of N = 55 camels was deeply sequenced via dropped stool. The raw reads, after QC, were assembled and binned into metagenome-assembled genomes (MAGs). We include here a collection of 3165 medium-quality or higher prokaryotic MAGs by MiMAG-like criteria (completeness >= 50%, contamination <= 5%). </p>
COMP 7882 project data
<p>Here is the dataset and MATLAB code for analysis experinment data</p>
EyCon project Photographs and Metadata
<p>EyCon project</p> <p>Nearly 130,000 JPEG images and pages are included in the EyCon dataset, covering armed conflicts between the 1890s and 1918. Thousands of digitized images and metadata from numerous partner institutions were complemented by a corpus of previously unpublished and little-accessible documents. As part of the enrichment of the existing metadata, visual and multimodal similarity studies as well as the detection of humans and animals were performed. In order to include descriptive metadata in the XML format of the dataset, the document captions were processed to identify and extract named entities. Identifying and publishing graphic images from the EyCon dataset posed challenges due to the limitations of automated tools in detecting violence, necessitating human expertise and contextual understanding to accurately classify historically sensitive material. Datasets are presented both in an online digital collection and on a Zenodo repository that can be downloaded and reused.</p> <p><a href="https://eycon.hypotheses.org/" target="_blank" rel="noopener">Presentation of the project</a></p> <p><a href="https://eycon.huma-num.fr/s/en/page/accueil" target="_blank" rel="noopener">The database published online as a digital library</a></p>
General video about the PROMISCES project - Preventing Recalcitrant Organic Mobile Industrial chemicalS for Circular Economy in the Soil sediment water system
<p>General video presentation about the PROMISCES project.</p>
Data for the project 'Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial'
<p>Data for the project 'Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial' including four complete datasets with all variables collected in this project and a corresponding README file ('README-File_Data_Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial.txt'). The latter provides (1) general information, (2) sharing and access information, (3) data and file overview, (4) methodological information, and (5) data-specific information.</p>
HAEOLUS Project Fuel Cell Data.
<p>Dataset from a Fuel Cell test in the HAEOLUS European Project <a href="https://haeolus.eu/" target="_blank" rel="noopener">https://haeolus.eu/.</a></p> <p> </p>
Dataset and code for paper 'Qu'allons devenir des pamphlets: Een digitale analyse van de politieke pamfletten tijdens de Brabantse Omwenteling (1789-1790)' (thesis project)
<p>This folder contains:</p> <ol> <li>The final version of my dissertation (<a href="../api/records/11488783/draft/files/Thesis_Oliver_Bogaerts.pdf/content" target="_blank" rel="noopener noreferrer">Thesis_Oliver_Bogaerts.pdf</a>)</li> <li>A ZIP file with the transcripts (.txt) of all pamphlets that were used for this project (<a href="../api/records/11488783/draft/files/Transcripts_thesis_Oliver_Bogaerts.zip/content" target="_blank" rel="noopener noreferrer">Transcripts_thesis_Oliver_Bogaerts.zip</a>)</li> <li>An Excel file containing metadata about the pamphlets (<a href="../api/records/11488783/draft/files/Thesis_Oliver_Bogaerts.xlsx/content" target="_blank" rel="noopener noreferrer">Thesis_Oliver_Bogaerts.xlsx</a>)</li> <li>A Jupyter notebook with code I used to develop a text classification model and multiple visualisation techniques (<a href="../api/records/11488783/draft/files/Thesis_Oliver_Bogaerts.ipynb/content" target="_blank" rel="noopener noreferrer">Thesis_Oliver_Bogaerts.ipynb</a>)</li> <li>A text file with all French stopwords that were removed during preprocessing (<a href="../api/records/11488783/draft/files/stopwords-fr.txt/content" target="_blank" rel="noopener noreferrer">stopwords-fr.txt</a>)</li> </ol>
ARCHIMEDES Project's Animated Video 'ARCHIMEDES: Pioneering a Sustainable Future'
<div> <p><span>The ARCHIMEDES project, a leading initiative in sustainable innovation, has launched an animated video titled "ARCHIMEDES: Pioneering a Sustainable Future." This video offers a detailed look into the project’s groundbreaking efforts to integrate sustainability with advanced technology across various sectors.</span></p> </div> <div> </div> <div> </div> <div> <p><span>ARCHIMEDES stands at the intersection of innovation and sustainability, aiming to revolutionize the future of mobility, energy, and safety within the framework of Society 5.0. The project focuses on developing state-of-the-art components, models, and methodologies to significantly improve the efficiency and lifespan of propulsion systems, power components, and energy storage devices.</span></p> </div> <div> </div> <div> </div> <div> <p><span>The video highlights how the ARCHIMEDES project spans multiple key industries:</span></p> </div> <div> <ul> <li> <p><span><strong>Automotive:</strong> Pioneering new propulsion systems to make vehicles more efficient and environmentally friendly.</span></p> </li> <li> <p><span><strong>Aviation:</strong> Enhancing energy efficiency and sustainability in the aerospace sector.</span></p> </li> <li> <p><span><strong>Industry:</strong> Transforming industrial processes to be more sustainable and cost-effective.</span></p> </li> </ul> </div> <div> </div> <div> </div> <div> <p><span>The ARCHIMEDES project is committed to creating a sustainable future by leveraging the latest innovations and technologies. By adopting an interdisciplinary approach, the project aims to be at the forefront of advancements in mobility, energy, and industrial processes. The vision is to not only meet current demands but also to anticipate and address future challenges.</span></p> </div> <div> </div> <div> </div> <div> <p><span>The newly released animated video provides a comprehensive overview of how ARCHIMEDES is leading the way toward a more sustainable future. The project invites viewers to join in this transformative journey.</span></p> </div>
Future sea level rise in northwest Mexico is projected to decrease the distribution and habitat quality of the endangered Calidris canutus roselaari (Red Knot)
<p>Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of <em>Calidris canutus roselaari </em>(Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for <em>C. c. roselaari</em>. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of <em>C. c. roselaari</em> are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of <em>C. c. roselaari</em> (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR.</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.