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1,838 results for “location”
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
A Hybrid Feature Location Technique for Re-engineering Single Systems into Software Product Lines
<p>The dataset used for evaluating the hybrid feature location technique presented in the paper: "A Hybrid Feature Location Technique for Re-engineering Single Systems into Software Product Lines". This enables reproducibility, evaluation, and comparison of our study.</p> <p>_________________________________________________________________________________________________________</p> <p>Folder "Dataset" contains for each subject system used:</p> <p>(i) the artificial variants and their configurations;</p> <p>(ii) the ECCO repository containing the traces;</p> <p>(iii) the ground truth and composed variants;</p> <p>(iv) the metrics results.</p> <p>_________________________________________________________________________________________________________</p> <p>Folder "Scenarios" contains for each subject system used:</p> <p>(i) the videos recorded from exercising features on GUI.</p>
Dataset for the paper "Slavic morphosyntax is primarily determined by its geographic location and contact configuration", Scando-Slavica Journal
<p>This is the raw dataset for the paper "Slavic morphosyntax is primarily determined by its geographic location and contact configuration", Scando-Slavica</p>
KaKiOS-16: a probabilistic, non-linear, absolute location catalog of the 1981-2011 Southern California seismicity
<p>This is the KaKiOS-16 earthquake catalog for southern California. We locate the southern California seismicity using the state-of-the-art probabilistic and nonlinear method NonLinLoc. We use only the P wavepicks to avoid introducing the velocity-model and picking-time errors of the S phase, which is harder to detect and thus less constrained. Using a subset of the best locatable earthquakes, we conduct a joint inversion using the VELEST software to obtain a minimum 1D velocity model and station corrections. We use the NonLinLoc method with this 1D velocity model and the inferred model uncertainties to obtain realistic location distributions for each event.<br> </p>
Data archive for the journal article: "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites"
<p>Data archive accompanying the peer-reviewed journal article "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites". In January 2021 this article was accepted for publication in the journal <em>Atmospheric Measurement </em><em>Techniques</em>. Data are uploaded in the form of Igor 8.0 graphics source files (.pxp) and data exported to Excel spreadsheet (.xlsx).</p>
GBIF: US bird locations for sightings in 2013
<p>A tab delimited file containing the coordinates rounded to 1 decimal place for U.S. bird observations for each day of 2013. Dataset holds data available through GBIF.org on 29th December 2014. </p> <p>Data schema: day,month,year,latitude,longitude,numberOfRecords</p>
LiDAR-derived forest structure data and predictions of the locations of old-growth forests for Central Finland.
<p><strong>INTRO</strong><br> This archive contains data and analysis code for the Biodiversity Map -project conducted by Open Knowledge Finland (http://fi.okfn.org/projects/biodiversity-map/)</p> <p><strong>LICENCE</strong><br> The files listed below are all released to the public domain under a CC0 public domain dedication (https://creativecommons.org/publicdomain/zero/1.0/)</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em><strong>FILE 1:</strong></em> background.zip<br> Inside the archive is a comma-separated file "background.csv" containing LiDAR-derived forest structure variables for 2/3 of Central Finland. These were derived from 3 raster data sets describing forest canopy maximum height (mh), forest canopy cover (cc) and lidar return intensity (in). The rasters had resolutions of 6 metres, 6 metres and 2 metres, respectfully. An 18 m resolution grid was then used to aggregate the rasters into average, minimum and maximum values + standard deviations of the original variables. The original LiDAR data was made available by the National Land Survey of Finland.</p> <p><br> <em><strong>FILE 2:</strong></em> conservation.lambdas<br> This file contains fitted parameters for the maxent model. For more information, check maxent documentation at https://www.cs.princeton.edu/~schapire/maxent/</p> <p><strong><em>FILE 3:</em></strong> conserved_swd.csv<br> Forest structure variables at 18 meter resolution for old-growth conservation areas in Central Finland. A subset of background.csv. This file still has a header, the variables are the same as in background.csv</p> <p><em><strong>FILE 4:</strong></em> grass_create_forest_rasters_from_las.sh<br> A shell script used to convert LiDAR files to raster maps of forest structure with GRASS 7.</p> <p><em><strong>FILE 5:</strong></em> lidar_coverage.png<br> A map showing the extent of LiDAR data available for Central Finland when we did the analyses.</p> <p><em><strong>FILE 6:</strong></em> maxent_model_run_product.sh<br> A shell script used to fit the maximum entropy model to predict the locations of conservation-area-like forests in Central Finland.</p> <p><em><strong>FILE 7:</strong></em> projection_product.csv<br> The results of the maxent model in a comma separated file. The first row has the variable names: x,y,product_fit. x and y are coordinates in the CRS ETRS-TM35FIN (EPSG:3067). product_fit is "the probablility that this 18*18 meter grid cell is old-growth conservation area".</p> <p><em><strong>FILE 8:</strong></em> README<br> A file with a description of the dataset in human-readable form.</p> <p><strong>VALIDATION FILES</strong><br> The data in these files was collected to validate the results of the aforementioned maxent model. The data were collected in a hierarchical sampling scheme: six randomly determinded unintersecting 9 km * 9 km landscape windows were chosen for sampling. From each window, three samples were taken. One sample from conservation areas, one sample from the "best" 10 % of forests as determined by the maxent model excluding conservation areas and one random sample. Not all windows contained conservation areas, and not all areas were accessible (islands, for example). In addition a few areas were skipped due to time constraints.</p> <p>The sampled points are identified by their lanscape window (suuralue), their sample (otos) and their sample number (mittauspiste).</p> <p><em><strong>FILE 9:</strong></em> validation_felled.csv<br> A comma separated list of those points that were not measured because they were felled.</p> <p><em><strong>FILE 10:</strong></em> validation_gps_results_2016-09-07.csv<br> A list of gps coordinates for all the sample points. product_fit is the value of the geographically closest prediction from the maxent model described above.</p> <p><em><strong>FILE 11:</strong></em> validation_lying_deadwood_transects_2016-08-30.csv<br> A comma separated file with data from deadwood transects. From each validation point, three 30 m long transects were made with 120 degree angles between them, and all lying deadwood more than 2 cm in diameter were measured. For some validation points, there were geographical obstructions which prevented the full 90 m of transect being surveyed, this is also recorded in the data. Each row holds measurements from one lying trunk.<br> </p> <p><em><strong>FILE 12:</strong></em> validation_relascope_2016-08-30.csv<br> Relascope measurements from the validation points. Each row is measurements for one species from one validation point. Dead and alive trees are counted separately.<br> </p> <p><strong>MORE INFORMATION</strong></p> <p>For more in-depth descritions of the files, read the file named README.<br> For some auxilliary files and information, check our old hackathon repository on github: https://github.com/Koalha/bdm_hackathon</p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</p>
Level A Pan Europe Locations observation stations, E-HYPE 2.5
Shapefile showing the positions of gauging stations adjusted to the delineation and routing of E-HYPEv2.5 Original data source: GRDC-Europe, EWA stations, ES, baltex_stns, EWA. Tools for repurposing: WHIST. Spatial resolution: Locations. Data format: Shapefile containing the following information: SUBID Subbasin where station is positioned; DATA_SOURC=Original data source from where observations can be ordered; STATION_NO= ID of the gauging station; STATION=Name of station; X_EHYPEv2.5= x coordinate in decimal degrees adjusted for E-HYPEv2.5 delineation and routing; Y_EHYPEv2.5= y coordinate in decimal degrees adjusted for E-HYPEv2.5 delineation and routing. The dataset, Subbasin(EHYPE2pt5).zip (shapefile with subbasin polygons) can be linked with the data.
Documentation of a Pyu inscription (PYU023) located on the Phaya Taung hill in the grounds of the monastery of Tondaw village, 30 miles from Sandoway town, Arakan
<p>This data set includes photographs (.jpg), RTIs (.ptm) and related files documenting a Pyu inscription (inventory number PYU023) located on the Phaya Taung hill in the grounds of the monastery of Tondaw village, 30 miles from Sandoway town, Arakan. The photographer was James Miles or Archeovision, working on behalf of the Pyu epigraphy sub-project (PI, Nathan W. Hill of SOAS University of London) of the ERC synergy grant "Beyond Boundaries: Religion, Region, Language and the State" (Identifier: ASIA 609823) in collaboration with the project "From Vijayapuri to Sriksetra? The Beginnings of Buddhist Exchange across the Bay of Bengal as Witnessed by Inscriptions from Andhra Pradesh and Myanmar" (PI Arlo Giffiths of the EFEO) funded by The Robert H. N. Ho Family Foundation.</p>
How Developers Locate Performance Bugs — Supplementary Material
<p><strong>Abstract:</strong></p> <p><em>Background:</em> Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues.</p> <p><em>Method:</em> We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view.</p> <p><em>Results:</em> We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs.</p> <p><em>Conclusion:</em> Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.</p> <p><strong>Dataset:</strong></p> <ol> <li> <p><strong>Tutorial:</strong> We provide the slides (PDF) and the video (MP4) we used in the tutorial phase of our study.</p> </li> <li> <p><strong>Locating Bugs:</strong> We also provide supplementary material for each research question. We provide the advices we prepared for each bug in case a team got stuck (PDF); the questions we asked after each bug fixing session can be found on the introduction slides (PDF).</p> <ul> <li> <p><strong>RQ1:</strong> Navigating and Understanding</p> <ul> <li> <p><strong>RQ1.1:</strong> <em>How was information from the profiling tool or other parts of the IDE used to locate the performance bug?</em> Cross-case analysis (in German) (XLSX+ODS)</p> </li> <li> <p><strong>RQ1.2:</strong> <em>Is the in-situ visualization of the profiling data beneficial compared to a traditional list representation?</em> Cross-case analysis (in German) (XLSX+ODS)</p> </li> <li> <p><strong>RQ1.3:</strong> <em>What navigation strategies do developers pursue to locate a specific performance bug?</em> Interaction logs (TXT), Navigation visualizations (SVG), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)</p> </li> </ul> </li> <li> <p><strong>RQ2:</strong> Understanding and Communicating</p> <ul> <li> <p><strong>RQ2.1:</strong> <em>How do developers communicate with each other when locating a performance bug?</em> Coding (XLSX+ODS), Sketches (PDF), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)</p> </li> <li> <p><strong>RQ2.2:</strong> <em>Could sketches help to understand and communicate a performance bug?</em> Coding (XLSX+ODS), Sketches (PDF), Cross-case analysis (in German) (XLSX+ODS), Sketching videos for Bug 3 (MP4, without audio because of confidentiality)</p> </li> </ul> </li> </ul> </li> <li> <p><strong>Questionnaire:</strong> The questionnaire that the participants filled out at the end of the study can be found here (PDF).</p> </li> </ol>
Beaver territory locations in Flanders 2014-2022
<p>Data compiled by the Agency For Nature and Forest (ANB) on beaver territories in Flanders (Belgium) as reported by various persons and organisations. A first version of this dataset was compiled over the years 2014-2019. This second version also includes the situation up to 2022. The dataset contains geographical coordinates (WGS84) for each territory, the year in which the territory was first reported and the years thereafter in which this territory was occupied.</p>
Supplementary material to: Russian verbal aspect and the activation of event knowledge: Processing typical and atypical location adverbials in perfective and imperfective sentences
<p>Supplementary material for a self-paced reading experiment:</p> <ol> <li>Material: contains the verbal stimuli (experimental and filler sentences)</li> <li>raw_data.zip: E-Prime output for all 50 participants (tab-separated txt-files)</li> <li>spr_aspect_respinf.txt: Basic information on the participants; tab-separated txt-file</li> <li>spr_aspect_preprocessing_subm2_fin.R: data preprocessing and calculation of correct responses per speaker in R; R script<br>Input: <br>- Files from the "raw_data"-folder<br>- spr_aspect_respinf.txt<br>Output: <br>- spr_aspect_respinf_corr_resp.txt: same as spr_aspect_respinf.txt + number of correct responses per participant; tab-separated txt-file<br>- spr_aspect_all.txt: relevant data from all participants in one file; tab-separated txt-file<br>- spr_aspect_all_without-outlier.txt: same as spr_aspect_all.txt, but outliers set to NA; tab-separated txt-file</li> <li>spr_aspect_figures-stats_subm2_fin.R: plots figures and calculates statistics (descriptive statistics and GLMM) in R, R script<br>Input: <br>- spr_aspect_all_without-outlier.txt<br>Output:<br>- spr_aspect_avg.txt: Mean RT and SD per condition; tab-separated txt-file<br>- Figures</li> </ol>
Figure 10. Geographic location map for Pepsis cerberus, P in The Pepsis menechma Lepeletier (Hymenoptera: Pompilidae: Pepsinae) taxonomic and nomenclatural problem
Figure 10. Geographic location map for Pepsis cerberus, P. elegans, and P. novitia in the Nearctic Region (based on Brimley 1936; Hurd 1952; Krombein 1952; Johnston 2000; Bond and Opell 2002; Vardy 2005; Leavengood et al. 2011; Bond and Godwin 2013; Hamilton et al. 2016; Norden 2017; Godwin and Bond 2021; Durand, pers. comm.; BugGuide.net; flickr.com; iNaturalist.org; gbif.org; SCAN; and specimen records from 36 insect collections as listed in Materials and Methods). Black lines represent range limits of potential host spider genera. Solid black line represents geographic limit of Ummidia (Halonoproctidae) species (Godwin and Bond 2021). Dashed black line represents geographic limit of Aphonopelma (Theraphosidae) species (Hamilton et al. 2016). Dotted black line represents geographic limit of Eucteniza (Euctenizidae) species (Bond and Godwin 2013). Dash-dotted black line represents geographic limit of Entychides Simon (Euctenizidae) species (Bond and Opell 2002).
Identifying Interaction Location in SuperCDMS Detectors
<p>The Super Cryogenic Dark Matter Search (SuperCDMS) experiment uses silicon and germanium particle detectors operated at temperatures of ∼ 30 mK to search for Weakly Interacting Massive Particles (WIMPs), which are candidate dark matter particles that interact weakly with nuclei in the detectors. In operating these detectors, it is required not only to measure the energy of the interaction between the WIMP and the nuclei, but also to reconstruct where the interaction occurred, as the location can be used to separate background interactions from signal and to correct for variations with the location of the energy response.</p> <p>In this project, we, as a team from the University of Minnesota, aim to address the problem of accurately reconstructing the locations of interactions in the SuperCDMS detectors using machine learning methods.</p> <p>The dataset we provided here includes interactions at thirteen different locations from test data taken at the University of Minnesota. For each interaction, a set of parameters was extracted from the signals from each of the five sensors. These parameters represent information known to be sensitive to interaction location, including the relative timing between pulses in different channels, and features like the pulse shape. The relative amplitudes of the pulses are also relevant but due to instabilities in amplification during the test, this data is not included. The parameters included for each interaction are described in our <a href="https://github.com/FAIR-UMN/FAIR-UMN-CDMS/blob/main/doc/FAIR%20Document%20-%20Identifying%20Interaction%20Location%20in%20SuperCDMS%20Detectors.pdf" target="_blank" rel="nofollow noopener noreferrer">project document</a>.</p> <p>For more details, feel free to check our Github page: <a href="https://fair-umn.github.io/FAIR-UMN-CDMS/" target="_blank" rel="nofollow noopener noreferrer">https://fair-umn.github.io/FAIR-UMN-CDMS/</a></p>
Fig. 2 in Daphnia Cucullata Sars, 1862 (Crustacea: Cladocera) Distribution And Location In Composition Of Zooplankton Cenosis In Lake Dridzis
Fig. 2. Redundancy analysis (RDA) ordination plot for zooplankton abundance from Lake Dridzis during the sampling period of May to September 2011. Abbreviations: ORP- Oxidation-reduction potential; NTU- Turbidity.
Fig. 1 in Daphnia Cucullata Sars, 1862 (Crustacea: Cladocera) Distribution And Location In Composition Of Zooplankton Cenosis In Lake Dridzis
Fig. 1. Redundancy analysis (RDA) ordination plot for zooplankton abundance from Lake Dridzis during the sampling period of May to September 2010. Abbreviations: ORP- Oxidation-reduction potential; NTU- Turbidity.
Quick scan 'Locations for highest-potential greenhouse development in the world'
<p><br>Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses.</p> <p>This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown.</p> <p>This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge.</p> <p>The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. </p> <p>Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.<br>Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.<br>Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt.</p> <p>Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:<br>• Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.<br>• Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.<br>• Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico.</p> <p>The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below. </p>
Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston
<p>Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments.</p>
Figure 2. Location photographs. A in Before the freeze: otoliths from the Eocene of Seymour Island, Antarctica, reveal dominance of gadiform fishes (Teleostei)
Figure 2. Location photographs. A, aerial view of IAA 1/90, 'Ungulate site', 64Ǫ14,04.67ĮĮS,56Ǫ 39,56.38ĮĮ W, marked by asterisk; B, panoramic view of site IAA 1/90 with 'Natica horizon' marked by asterisks; C, Argentine-Swedish field party collecting fossils at IAA 2/95, 'Marsupial site', 64Ǫ13,58ĮĮS,56Ǫ39,06ĮĮ W); D, panoramic view of site IAA 2/95 with Cockburn Island in background; E, 'Natica horizon' near site IAA 2/95 showing lens-like character of the beds. Photographs by F. Degrange (A, D), T. Mors (B) and J. Hagstrom (C, E).
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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.