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Data and code for the Reclamation S&T1845 Yuma project
<p>Data and code for the Reclamation S&T1845 Yuma project</p>
New results of the LIFE project: characterization of the A5Ib-II supergiant 19 Aur and the newest sample of magnetic stars
<p>The Large Impact of magnetic Fields on the Evolution of hot stars (LIFE) project is focused on detecting magnetic fields in bright evolved OBA giants and supergiants to investigate the evolution of these stars from their main sequence (MS) forms into their late post-MS stages. We have previously reported that the longitudinal surface magnetic field of these objects may be very weak, but the field configuration resembles those of main sequence hot stars. Here we present the newest results of the survey from spectropolarimetric observations using the ESPaDOnS instrument of the Canada–France–Hawaii Telescope. These results include the first characterization of a truly evolved OBA supergiant, the A5Ib-II supergiant 19 Aur, updates to the project’s detection rates, and newly discovered magnetic stars.</p>
Loon Project Database
<p>Data collected since 1993 in Oneida County, Wisconsin, on the breeding and territorial behavior of the common loon, Gavia immer. This study population is marked with USGS metal bands and colored leg bands for individual identification. It includes many individuals banded as chicks, whose life histories are known thoroughly. Data are collected from April through August of most years on this migratory species. Data are in the form of a MS-Access file, with many individual "tables" containing data on banding, breeding, dispersal and other life-history information. </p>
Co-benefits of forest carbon projects in Southeast Asia
<p>This dataset contains the output rasters produced to determine the climate mitigation potential, crop pollination service for pollinator-dependent agriculture, quantity of nitrogen retained and Key Biodiversity Areas conserved at the various carbon price points, namely USD 1, 2, 3, 4, 5, 10, 15, 20, 25, 50 and 100 t<sup>-1</sup> CO<sub>2</sub>e across Southeast Asia. All data are masked to forest areas determined across the region at 1 km resolution, based on a cylindrical equal area projection.</p> <p>We also determined locations across the region where forest protections would deliver multiple benefits through a spatial overlay. Areas identified to contribute any level of co-benefit were coded 1 to 3, indicating the number of co-benefits that could be attained in addition to climate mitigation from avoided forest loss, assessed for each price point.</p> <p>Further details of the datasets can be found in Sarira et. al. </p> <p>For further questions or issues with the datasets, please contact Tasya Vadya Sarira at <a href="mailto:tasya.sarira@adelaide.edu.au">tasya.sarira@adelaide.edu.au</a>.</p>
FIGURE 24. Cloeodes melanotarsus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 24. Cloeodes melanotarsus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 20. Cloeodes lucifer, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 20. Cloeodes lucifer, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 28. Cloeodes xyrognathos, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 28. Cloeodes xyrognathos, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 7. Cloeodes aiuruoca, nymph. a. Fore femur, apex projection. b. Mid femur, apex projection. c. Hind femur, apex projection. d. Fore leg, anterior surface. e in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 7. Cloeodes aiuruoca, nymph. a. Fore femur, apex projection. b. Mid femur, apex projection. c. Hind femur, apex projection. d. Fore leg, anterior surface. e. Detail of setae on dorsal margin. f. Apex of fore tibia. g. Posterior margin of segment IV. h. Gill IV. i. Detail of gill margin. j. Paraproct (v.v.).
FIGURE 9. Cloeodes amantykyra, nymph. a. Fore femur, apex projection. b. Mid femur, apex projection. c. Hind femur, apex projection. d. Fore leg, anterior surface. e in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 9. Cloeodes amantykyra, nymph. a. Fore femur, apex projection. b. Mid femur, apex projection. c. Hind femur, apex projection. d. Fore leg, anterior surface. e. Detail of setae on dorsal margin. f. Apex of fore tibia. g. Posterior margin of segment IV. h. Gill IV. i. Detail of gill margin. j. Paraproct (v.v.).
FIGURE 16. Cloeodes guara, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 16. Cloeodes guara, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 12. Cloeodes atlanticus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 12. Cloeodes atlanticus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 26. Cloeodes tracheatus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 26. Cloeodes tracheatus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 22. Cloeodes magnus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 22. Cloeodes magnus, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
FIGURE 14. Cloeodes boldrinii, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 14. Cloeodes boldrinii, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (d.v.).
FIGURE 18. Cloeodes ioachimi, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c in Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges
FIGURE 18. Cloeodes ioachimi, nymph. a. Fore leg, anterior surface. b. Fore femur, apex projection. c. Apex of fore tibia. d. Posterior margin of segment IV. e. Gill IV. f. Paraproct (v.v.).
Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
<p><strong>Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"</strong><br> This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.</p> <p><strong>Structure</strong><br> The structure of the replication kit is as follows:</p> <ul> <li><strong>additional_visualizations</strong>: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li><strong>data_analysis</strong>: contains python scripts that we used to analyze our raw data</li> <li><strong>data_collection_tools</strong>: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li><strong>mongodb_no_authors</strong>: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> <p><br> <strong>Additional Visualizations</strong><br> We provide two additional visualizations for each project:<br> 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set)<br> 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set)</p> <p>For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).</p> <p>Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.</p> <p><br> <strong>Analysis scripts</strong><br> Requirements:<br> - python3.5<br> - tabulate<br> - scipy<br> - seaborn<br> - mongoengine<br> - pycoshark<br> - pandas<br> - matplotlib</p> <p>Both python files contain all code for the statistical analysis we performed.</p> <p><strong>Data Collection Tools</strong><br> We provide all data collection tools that we have implemented and used throughout our paper:</p> <ul> <li><strong>BugFixClassifier</strong>: Used to classify our defects.</li> <li><strong>comfort-core</strong>: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.</li> <li><strong>comfort-jacoco-listner</strong>: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li><strong>jSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java).<strong> </strong></li> <li><strong>pycoSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li><strong>tools-changedistiller</strong>: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li><strong>vcsSHARK</strong>: Used to collect data from the VCSs of the projects.</li> </ul> <p> </p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-10]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC / Barnard catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Training beyond borders: A WPA pilot project for child and adolescent mental health course in Pakistan
<p><strong>Purpose</strong>: The World Psychiatric Association (WPA) strives to close the gap between expertise in high-income countries (HICs) and low and middle-income countries (LMICs). WPA volunteering programme is a project framework wherein area-specific experts from HICs volunteer to support local experts in the LMICs in training their own professionals. This paper reports on the outcome of a collaborative project on Child and Adolescent Mental Health (CAMH) training in Pakistan.</p> <p><strong>Materials and methods</strong>: Thirty participants from twenty-three different institutions across Pakistan were selected on the basis of their interest in CAMH. Two volunteers (ST and MH) met online with the Pakistani representatives (NI and QUH) to develop a programme. They also regularly met online between the sessions to evaluate adjustments in order to increase the appropriateness of the offered content according to participant feedback and suggestions.</p> <p><strong>Results</strong>: The pre- and post-course evaluation revealed statistically significant improvement in knowledge and skills gained in different topics covered in the course (P-value<0.001) as well as improvement in confidence in applying the theoretical understanding of CAMH problems in their daily clinical practice (P-value<0.001)</p> <p><strong>Conclusions</strong>: The WPA volunteering programme is a valuable strategy to deliver effective and much-needed training in low-resource settings. Long-distance learning via live online sessions is effective and worthwhile, especially when they provide opportunities for participants to learn from interactive sessions.</p>
Coastal dataset including exposure and vulnerability layers, Deliverable 3.1 - ECFAS Project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p><p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.</p><p><i><strong>Description of the containing files inside the Dataset.</strong></i></p><p>The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.</p><p>Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.</p><p>* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina</p><p>* Malta was added to the dataset</p><p><strong>Copernicus Land Monitoring Service:</strong></p><p><strong>Coastal LU/LC</strong></p><p>Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones</p><p><strong>EU-Hydro - Coastline</strong></p><p>Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline</p><p><strong>Natura 2000</strong></p><p>Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation</p><p><strong>European Settlement Map</strong></p><p>Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe</p><p><strong>Imperviousness Density</strong></p><p>Resolution 10m; The percentage of sealed area</p><p><strong>Impervious Built-up</strong></p><p>Resolution 10m; The part of the sealed surfaces where buildings can be found</p><p><strong>Grassland 2018</strong></p><p>Resolution 10m; A binary grassland/non-grassland product</p><p><strong>Tree Cover Density 2018</strong></p><p>Resolution 10m; Level of tree cover density in a range from 0-100%</p><p> </p><p><strong>Joint Research Center:</strong></p><p><strong>Global Human Settlement Population Grid</strong><br><strong>GHS-POP)</strong></p><p>Resolution 250m; Residential population estimates for target year 2015</p><p><strong>GHS settlement model layer</strong><br><strong>(GHS-SMOD)</strong></p><p>Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities</p><p><strong>GHS-BUILT</strong></p><p>Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018</p><p><strong>ENACT 2011 Population Grid</strong></p><p><strong>(ENACT-POP R2020A)</strong></p><p>Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations</p><p><strong>JRC Open Power Plants Database (JRC-PPDB-OPEN)</strong></p><p>Europe's open power plant database</p><p><strong>GHS functional urban areas</strong><br><strong>(GHS-FUA R2019A)</strong></p><p>Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)</p><p><strong>GHS Urban Centre Database</strong><br><strong>(GHS-UCDB R2019A)</strong></p><p>Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface</p><p> </p><p><strong>Additional Data:</strong></p><p><strong>Open Street Map (OSM)</strong></p><p>BF, Transportation Network, Utilities Network, Places of Interest</p><p><strong>CEMS</strong></p><p>Data from Rapid Mapping activations in Europe</p><p><strong>GeoNames</strong></p><p>Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.</p><p><strong>Global Administrative Areas</strong></p><p>Administrative areas of all countries, at all levels of sub-division</p><p><strong>NUTS3 Population Age/Sex Group</strong></p><p>Eurostat population by age and sex statistics interescted with the NUTS3 Units</p><p><strong>FLOPROS</strong></p><p>A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales</p><p> </p><p><i><strong>Disclaimer:</strong></i></p><p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p><p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p>
BIOnTop project components and information flow
<p>The project components pictured in a diagram. BIOnTOP project will deliver recyclable-by-design cost competitive packaging solutions that can be mechanically recycled, industrially/home composted or even suitable for anaerobic digestion.</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.