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10 results for “level of detail”
Agile detail levels
<p>Following <a href="https://en.wikipedia.org/wiki/INVEST_(mnemonic)">INVEST principles (by Bill Wake)</a> allows you to write user stories at different levels of detail. User stories should be scaleable with a certain scope. However, it may helps to group stories under a bigger meaning (Epic, Theme, or Activity). Scalable content means to be negotiable over time by all parties involved. The most detailed level describes the decisions made, all tasks a user story is considered to be done, and the specific acceptance criteria the realized story have to meet. </p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
ConFiRMa dataset_01: simulation of CRM characterization tests with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of characterization tests performed on Composite Reinforced Mortar samples with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Characterization of Textile Reinforced Mortar: state of the art and detailed modelling with a free open source finite element code" (<a href="https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240">https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240</a>).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p> <p> </p>
ConFiRMa dataset_02: simulation of tests on CRM strengthened masonry elements with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on masonry elements strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Masonry elements strengthened through Textile-Reinforced Mortar:application of the detailed level modelling with a free open-source Finite-Element code".</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p>
Benchmark Hypergraphs and Detailed Experimental Results of "k-way Hypergraph Partitioning via n-Level Recursive Bisection"
<p>This dataset contains hypergraphs derived from three benchmark sets: The<br /> ISPD98 VLSI Circuit Benchmark Suite [1], the University of Florida Sparse Matrix Collec-<br /> tion [2] and the international SAT Competition 2014 [3]. From the latter, we randomly selected<br /> 100 instances from the application track and converted them into hypergraphs as follows:<br /> Each boolean variable (and its complement) is mapped to one vertex and each clause constitutes<br /> a net [41]. The Sparse Matrix Collection is organized into 172 groups and each group contains<br /> matrices of different application areas. From each group, we choose one matrix for each appli-<br /> cation area that has between 10 000 and 10 000 000 columns. In case multiple matrices fulfill<br /> our criteria, we randomly select one. In total, we include 192 matrices, which are translated into<br /> hypergraphs using the row-net model, i.e. each row is treated as a net and each column as<br /> a vertex. Empty rows are discarded. Both vertices and nets have unit weight. Together with the<br /> 18 ISPD98 VLSI instances , a total of 310 hypergraphs constitute our benchmark set. 4 Each of<br /> these hypergraphs is partitioned into k ∈ {2, 4, 8, 16, 32, 64, 128} blocks with ε = 0.03. For each<br /> value of k, a k-way partition is considered to be one test instance, resulting in a total of 2170<br /> instances.</p> <p>See the README for further information on the different files contained in this dataset.</p> <p>[1 ]C. J. Alpert. The ISPD98 Circuit Benchmark Suite. In Proc. of the 1998 Int. Symp. on Physical Design, ISPD ’98, pages 80–85, New York, 1998. ACM.<br /> [2] T. A. Davis and Y. Hu. The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw.,38(1):1:1–1:25, 2011.<br /> [3] A. Belov, D. Diepold, M. Heule, and M. Järvisalo. The SAT Competition 2014. http://www.satcompetition.org/2014/, 2014.</p>
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19.
Deaths and work related occupational injuries detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019
<p>Ensuring social data's reliability is essential in accurately evaluating social and economic impacts across geographical locations, economic sectors and stakeholder categories. Yet, the MRIO model utilized in our research (EXIOBASE) was hindered by out-of-date or significantly proxy fatality statistics, causing potential inaccuracies in our findings. We have comprehensively revised EXIOBASE fatality data to address this shortcoming, incorporating detailed, nation-specific, and up-to-date data. The update includes work-related fatal occupational injuries as well as fatalities associated with occupational exposure to a variety of 17 hazardous substances and conditions such as asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulfuric acid, trichloroethylene, asthmagens, particulate matter, gases and fumes, noise and ergonomic factors. Our methodological process is built on three pillars: data acquisition, raw data processing, and computation of fatal injuries by country, gender, year, and EXIOBASE economic sector.</p> <p>Data were sourced from the World Health Organization (WHO) (Pega et al., 2021) and Eurostat databases (Publications Office of the European Union, 2013). The WHO data was carefully screened based on specific criteria such as age above 15 years, gender, and fatal injuries only. Eurostat data provided granular information on work-related fatalities, classified by economic activities in the European Community (or NACE Rev.2 (Eurostat, 2008)). The WHO provided aggregate fatality data for 2010 and 2016. The strategy for allocating these deaths across Eurostat categories depended on the countries' geographical location, with different methods applied to European and non-European nations.</p> <p>For European nations, fluctuations in fatality numbers within a NACE Rev.2 sector mirrored the changes registered by Eurostat. For non-European countries, fatality figures were proportionally allocated across economic sectors split according to the NACE Rev.2 classification, reflecting the workforce size associated with each economic sector. Due to the scarcity of data for nations within Asia, America, or Africa, we adopted a regional approach, computing fatality ratios over each NACE Rev.2 category for each region by integrating data for available countries over a reference year. For 2010 and 2016, the aggregate fatality figures for nations within these three zones were established. Due to the temporal proximity of both reference years, we postulated a linear trend in the fatality count between these two years. The number of fatalities for a specific country, year, and per NACE Rev.2 activity was then calculated by applying the previously mentioned fatality ratio to the total number of deaths for that nation. Last, we applied the European annual ratios to their total mortality figures for the few countries that could not be classified as European or belonging to one of the aforementioned zones.</p> <p>The result is a comprehensive database that includes the number of fatalities (expressed in the number of deaths for work-related fatal occupational injuries and in Disability-adjusted life years (DALYs), for fatalities associated with occupational exposure to a specific risk factor), detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019, providing insights into work-related fatal injuries across different health effects and geographical regions.</p> <p> </p> <p><strong>Nomenclature</strong></p> <p>Archives:</p> <ul> <li><em>Concordance_ISIC_Exiobase.xlsx : </em>Concordance between the International Standard Industrial Classification (ISIC) and the exiobase sectors</li> <li><em>Concordance_ISO3_EXIO3.xlsx - </em>Concordance between the ISO3 code and the Exiobase regions</li> <li><em>Workforce_by_ISO3.csv - </em>Number of active persons per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)</li> <li><em>Workforce_by_EXIO3.csv - </em>Number of active persons per Exiobase region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)</li> <li><em>Death_ISO3.csv - </em>Number of death per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li><em>Death_EXIO3.csv - </em>Number of death per Exiobase Region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li><em>Death_EXIO3_region_exiobase_sector.csv - </em>Number of death per Exiobase Region (EXIO3 code), exiobase sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li>Injuries_ISO3.zip - Archive of DALY per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)</li> <li>Injuries_EXIO3.zip - Archive of DALY per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)</li> <li><em>Workforce_EXIO3_sector_exiobase.xlsx - </em>Number of active persons per Exiobase region (EXIO3 code), per exiobase sector, Sex, Year (from 1991 to 2021).<br>This file is available here : Berthet, Etienne and Lavalley, Julien and Anquetil-Deck, Candy and Ballesteros, Fernanda and Stadler, Konstantin and Soytas, Ugur and Hauschild, Michael and Laurent, Alexis, Assessing the Social and Environmental Impacts of Critical Mineral Supply Chains for the Energy Transition in Europe. Available at SSRN: <a href="https://ssrn.com/abstract=4610350" target="_blank" rel="noopener noreferrer">https://ssrn.com/abstract=4610350</a> or <a href="http://dx.doi.org/10.2139/ssrn.4610350" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.2139/ssrn.4610350</a> </li> </ul> <p><strong>Content of Injuries_*.zip:</strong></p> <ul> <li>arsenic_*.csv</li> <li>asbestos_*.csv</li> <li>asthmagens_*.csv</li> <li>benzene_*.csv</li> <li>beryllium_*.csv</li> <li>cadmium_*.csv</li> <li>chromium_*.csv</li> <li>diesel_*.csv</li> <li>ergonomic_*.csv</li> <li>formaldehyde_*.csv</li> <li>gases_*.csv</li> <li>nickel_*.csv</li> <li>noise_*.csv</li> <li>polycyclic_*.csv</li> <li>silica_*.csv</li> <li>sulfuric_*.csv</li> <li>trichloro_*.csv</li> </ul>
Run-time Performance Measurements to "Instanced Rendering of Parameterized 3D Glyphs with Adaptive Level-of-Detail using three.js"
Open the record for dataset details and reuse information.
The sampling information and detail levels of individual SVOCs along the Arctic Expedition Cruises in the Northern Hemisphere and Antarctic Expedition Cruise in the Southern Cruise
<p>This dataset comprises samples collected from the Marine Atmospheric Boundary Layer (MABL) during 3 research expeditions: the 2016 summer Arctic research cruise (74 surface seawater samples), the 2017 summer Arctic research cruise (16 atmospheric and 16 surface seawater samples), and the 2016-2017 Antarctic research cruise (40 atmospheric and 34 surface seawater samples). The sampling locations encompassed various regions, including the East China Sea to Northwest Pacific transect, the Arctic Ocean region, the East China Sea to India Ocean transect, the South Ocean, and the Antarctic offshore region. The samples were analyzed for 39 semi-volatile organic compounds (SVOCs), which included polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), and current-use pesticides (CUPs). All samples were collected using High-volume solid phase extraction and high throughput in situ ultrasound analysis.</p> <p>This dataset is the Supporting Information of the research article: “Global Geographical Redistribution and Source Dynamics of SVOCs in the Marine Air Boundary Layer”, Zheng et al., 2023. The unauthorized use of this database is prohibited.</p>
State Anxiety Levels of Pregnant Women to Inform About Fetus With Ultrasound Images During Detailed Ultrasound.
ClinicalTrials.gov study NCT05960357. IPD Sharing: NO. Countries: 1. Publications: 0.
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.