The NASDAQ100 GA heatmap matrix using various round-turn trading rules.

The DJ30 GA heatmap matrix using various round-turn trading rules.

The solr-heatmap extension for CKAN aimed to provide a visualization of geospatial data stored within CKAN resources using heatmaps generated from Solr's spatial search capabilities. This extension likely allowed users to visually identify areas with a high concentration of data points based on geographical coordinates. This could potentially improve data discovery and provide insights into the distribution of geographically referenced datasets. Key Features (Inferred, based on likely functionality and naming): Heatmap Generation: Creates heatmaps directly from geospatial data stored within CKAN resources, visualizing density of datapoints. Solr Integration: Leverages Apache Solr's spatial search functionality to efficiently aggregate and process location data for heatmap generation. This suggests a dependency on a CKAN setup configured to use Solr for search indexing. Configurable Visualization Parameter: Provides configurable options for adjusting the heatmap appearance, such as color schemes, radius, and intensity, to optimize visualization based on the data. Technical Integration: Given its name, the solr-heatmap extension likely integrated with CKAN by adding a new view or visualization option for resources that contain geospatial data. It probably utilized CKAN's plugin architecture to extend the available viewers, adding a "heatmap" option. This component would then communicate with the Solr index to retrieve aggregated geospatial data and generate a dynamically rendered heatmap. Benefits & Impact (Inferred): While this extension is no longer maintained, implementing it may have significantly enhanced data visualization capabilities of CKAN, giving end-users an intuitive way to explore datasets that contain location information. Providing insights that may not be readily apparent through tabular data display. Important Note: This extension is no longer maintained as mentioned in the README. Future functionality can't be guaranteed.

A complete list of live websites using the cal-heatmap technology, compiled through global website indexing conducted by WebTechSurvey.

Heat map showing the general locations of pdf maps in Missouri for the Geology Map Index map.

RADARSAT-1, in operation from 1995 to 2013, is Canada's first earth observation satellite. Developed and operated by the Canadian Space Agency (CSA), it has provided essential information to government, scientists and commercial users.

Ultimately, the RADARSAT-1 mission generated the largest synthetic-aperture radar (SAR) data archive in the world. In April 2019, 36,000 images were made accessible through the Earth Observation Data Management System (eodms-sgdot.nrcan-rncan.gc.ca).

A heatmap of processed images was produced by the CSA and helps visualize the density of images available by mapped sector during the RADARSAT-1 mission.

A complete list of live websites using the Qa Heatmap Analytics technology, compiled through global website indexing conducted by WebTechSurvey.

The results of using VMA trading rules for DJ30 and NASDAQ100 indices from 2001 to 2020 (daily data).

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

Eldercare needs index heatmap determined by mean percentile ranks of block group level first order indices weighted by Landscan 90 meter population counts including: percent elder headed households and percent elder households in poverty.

Childcare needs index heatmap determined by mean percentile ranks of block group level first order indices weighted by Landscan 90 meter population counts including: percent single parent households and percent children under age five.

This web map is for use in the Joshua Tree National Park Climbing Management Plan Story Map. It features a heat map that visualizes estimates of climbing activity throughout the park, providing valuable insights for climbers and park management. This information is crucial for enhancing understanding and planning for climbing activities in the park. The map uses a color gradient to represent climbing activity levels. Blue indicates areas of less usage Red signifies areas of intense usageThe heat map is based on an analysis for four key characteristics that influence climbing activity:1.) Accessibility - Distance from parking areas to climbing routes2.) Route Difficulty - The range of difficulty levels available within the area3.) Popularity Rating - How frequently specific routes are climbed4.) Climbing Style - The types of climbing (e.g. trad, sport) that the location supportsThis estimated use heat map serves as a visual representation of climbing activity and does not indicate management areas or closures within the park. For any questions or further clarification on this web map or the underlying data, please contact the Joshua Tree National Park GIS team.The corresponding NPS DataStore on Integrated Resource Management Applications (IRMA) reference is Climbing Management Plan: Rock-Based Recreation in Joshua Tree National Park

Deze layer definition is automatisch gegenereerd, gebaseerd op de volgende metadata (provincie Zuid-Holland).

          SamenvattingWoonmilieu typering volgens Rosetta. Vlakkenindeling o.b.v. buurten en wegen, zijnde bouwblokken omsloten door doorgaande wegen dan wel buurtgrenzen. De woonmilieus zijn bepaald o.b.v. de grote woonomgevingstest (2018) en geëxtrapoleerd voor heel Zuid-Holland. In de grote woonomgevingstest van 2018 konden geënquêteerden zelf opgeven in welk woonmilieu hun leefgebied viel. In deze rapportage zijn de kenmerken per woonmilieu aangegeven: https://www.spring-co.nl/wp-content/uploads/2018/12/DGOT-rapportage-bijlagen.pdf.
          Dataset aangemaakt: 2020-05-01
          Dataset gepubliceerd: 2020-05-13
          Dataset laatst bijgewerkt: 2020-05-13
          Update frequentie: Onbekend
          Downloads, views en links
          Bezoek de metadata-pagina voor de kaartlaag- en dataset services.
          Open metadata 

          Algemene beschrijving herkomstExtrapolatie is gebaseerd op de volgende kenmerken: 

• Gemiddelde pandhoogte (o.b.v. BAG en AHN3)
• Gemiddelde bouwjaar (BAG)
• Oppervlakte van woonblok
• Gemiddelde functiemenging (o.b.v. BAG functiemenging heatmap)
• Gemiddelde Inwoners (o.b.v. inwoners heatmap, inwoners van CBS vierkantstatistieken)
• Gemiddelde werknemers (o.b.v. werknemers heatmap, werknemers o.b.v. bedrijvenregister)
• Adressendichtheid (BAG)
• Gemiddelde FSI (BAG)
• Gemiddelde GSI (BAG)
• Gemiddeld aantal verblijfsobjecten (adressen) per pand (BAG)
• Gemiddelde WOZ (o.b.v. steekproef WOZ loket)
• Percentage werkgebied (BAG)
• Percentage kassengebied (Kassenbestand PZH)
• Verhouding woningcorporaties/particulier (BAG en Overmorgen corporatiebestand)

Dataset and scripts for the manuscript: "Individual, but not population asymmetries, are modulated by social environment and genotype in Drosophila melanogaster". We upload:

1.Data. This directory contains the raw tracking data used for the analysis and the calibration files required by Flytracker for each strain of the Drosophila Genetic Reference Panel (RAL-69, RAL-136, RAL-338, RAL-535, RAL-796).
The format of the dataset consists in the output of tracking the (x,y) position and related features (e.g. velocity, distance) of flies in a circular arena using the software Flytracker - http://www.vision.caltech.edu/Tools/FlyTracker/ - For each video we have a trak.mat file and a feat.mat file. For instance, for a video in which we tracked the DGRP line RAL-136 (d338), with a pair of males (mm), trial 3 (t5) we had as outputs d338mmt5joined-feat.mat and d338mmt5joined-track.mat . You can find the description of the output here: http://www.vision.caltech.edu/Tools/FlyTracker/documentation.html

2.Load_data _and_circling_scripts. This directory contains a MATLAB file with the instructions on how to load datasets and how to compute the circling index.

3.Heatmap. This directory contains the instructions (readme_heatmap.txt) and scripts to (a) fix a bug in the feat files ouput by Flytracker using the calibration files and the script feat_compute_revised.m (b) plot the heatmap of the position and distance between partner flies in a dyad.

"We believe that by accounting for the inherent uncertainty in the system during each measurement, the relationship between cause and effect can be assessed more accurately, potentially reducing the duration of research."

Short description

This dataset was created as part of a research project investigating the efficiency and learning mechanisms of a Bayesian adaptive search algorithm supported by the Imprecision Entropy Indicator (IEI) as a novel method. It includes detailed statistical results, posterior probability values, and the weighted averages of IEI across multiple simulations aimed at target localization within a defined spatial environment. Control experiments, including random search, random walk, and genetic algorithm-based approaches, were also performed to benchmark the system's performance and validate its reliability.

The task involved locating a target area centered at (100; 100) within a radius of 10 units (Research_area.png), inside a circular search space with a radius of 100 units. The search process continued until 1,000 successful target hits were achieved.

To benchmark the algorithm's performance and validate its reliability, control experiments were conducted using alternative search strategies, including random search, random walk, and genetic algorithm-based approaches. These control datasets serve as baselines, enabling comprehensive comparisons of efficiency, randomness, and convergence behavior across search methods, thereby demonstrating the effectiveness of our novel approach.

Uploaded files

The first dataset contains the average IEI values, generated by randomly simulating 300 x 1 hits for 10 bins per quadrant (4 quadrants in total) using the Python programming language, and calculating the corresponding IEI values. This resulted in a total of 4 x 10 x 300 x 1 = 12,000 data points. The summary of the IEI values by quadrant and bin is provided in the file results_1_300.csv. The calculation of IEI values for averages is based on likelihood, using an absolute difference-based approach for the likelihood probability computation. IEI_Likelihood_Based_Data.zip

The weighted IEI average values for likelihood calculation (Bayes formula) are provided in the file Weighted_IEI_Average_08_01_2025.xlsx

This dataset contains the results of a simulated target search experiment using Bayesian posterior updates and Imprecision Entropy Indicators (IEI). Each row represents a hit during the search process, including metrics such as Shannon entropy (H), Gini index (G), average distance, angular deviation, and calculated IEI values. The dataset also includes bin-specific posterior probability updates and likelihood calculations for each iteration. The simulation explores adaptive learning and posterior penalization strategies to optimize the search efficiency. Our Bayesian adaptive searching system source code (search algorithm, 1000 target searches): IEI_Self_Learning_08_01_2025.pyThis dataset contains the results of 1,000 iterations of a successful target search simulation. The simulation runs until the target is successfully located for each iteration. The dataset includes further three main outputs: a) Results files (results{iteration_number}.csv): Details of each hit during the search process, including entropy measures, Gini index, average distance and angle, Imprecision Entropy Indicators (IEI), coordinates, and the bin number of the hit. b) Posterior updates (Pbin_all_steps_{iter_number}.csv): Tracks the posterior probability updates for all bins during the search process acrosations multiple steps. c) Likelihoodanalysis(likelihood_analysis_{iteration_number}.csv): Contains the calculated likelihood values for each bin at every step, based on the difference between the measured IEI and pre-defined IE bin averages. IEI_Self_Learning_08_01_2025.py

Based on the mentioned Python source code (see point 3, Bayesian adaptive searching method with IEI values), we performed 1,000 successful target searches, and the outputs were saved in the:Self_learning_model_test_output.zip file.

Bayesian Search (IEI) from different quadrant. This dataset contains the results of Bayesian adaptive target search simulations, including various outputs that represent the performance and analysis of the search algorithm. The dataset includes: a) Heatmaps (Heatmap_I_Quadrant, Heatmap_II_Quadrant, Heatmap_III_Quadrant, Heatmap_IV_Quadrant): These heatmaps represent the search results and the paths taken from each quadrant during the simulations. They indicate how frequently the system selected each bin during the search process. b) Posterior Distributions (All_posteriors, Probability_distribution_posteriors_values, CDF_posteriors_values): Generated based on posterior values, these files track the posterior probability updates, including cumulative distribution functions (CDF) and probability distributions. c) Macro Summary (summary_csv_macro): This file aggregates metrics and key statistics from the simulation. It summarizes the results from the individual results.csv files. d) Heatmap Searching Method Documentation (Bayesian_Heatmap_Searching_Method_05_12_2024): This document visualizes the search algorithm's path, showing how frequently each bin was selected during the 1,000 successful target searches. e) One-Way ANOVA Analysis (Anova_analyze_dataset, One_way_Anova_analysis_results): This includes the database and SPSS calculations used to examine whether the starting quadrant influences the number of search steps required. The analysis was conducted at a 5% significance level, followed by a Games-Howell post hoc test [43] to identify which target-surrounding quadrants differed significantly in terms of the number of search steps. Results were saved in the Self_learning_model_test_results.zip

This dataset contains randomly generated sequences of bin selections (1-40) from a control search algorithm (random search) used to benchmark the performance of Bayesian-based methods. The process iteratively generates random numbers until a stopping condition is met (reaching target bins 1, 11, 21, or 31). This dataset serves as a baseline for analyzing the efficiency, randomness, and convergence of non-adaptive search strategies. The dataset includes the following: a) The Python source code of the random search algorithm. b) A file (summary_random_search.csv) containing the results of 1000 successful target hits. c) A heatmap visualizing the frequency of search steps for each bin, providing insight into the distribution of steps across the bins. Random_search.zip

This dataset contains the results of a random walk search algorithm, designed as a control mechanism to benchmark adaptive search strategies (Bayesian-based methods). The random walk operates within a defined space of 40 bins, where each bin has a set of neighboring bins. The search begins from a randomly chosen starting bin and proceeds iteratively, moving to a randomly selected neighboring bin, until one of the stopping conditions is met (bins 1, 11, 21, or 31). The dataset provides detailed records of 1,000 random walk iterations, with the following key components: a) Individual Iteration Results: Each iteration's search path is saved in a separate CSV file (random_walk_results_.csv), listing the sequence of steps taken and the corresponding bin at each step. b) Summary File: A combined summary of all iterations is available in random_walk_results_summary.csv, which aggregates the step-by-step data for all 1,000 random walks. c) Heatmap Visualization: A heatmap file is included to illustrate the frequency distribution of steps across bins, highlighting the relative visit frequencies of each bin during the random walks. d) Python Source Code: The Python script used to generate the random walk dataset is provided, allowing reproducibility and customization for further experiments. Random_walk.zip

This dataset contains the results of a genetic search algorithm implemented as a control method to benchmark adaptive Bayesian-based search strategies. The algorithm operates in a 40-bin search space with predefined target bins (1, 11, 21, 31) and evolves solutions through random initialization, selection, crossover, and mutation over 1000 successful runs. Dataset Components: a) Run Results: Individual run data is stored in separate files (genetic_algorithm_run_.csv), detailing: Generation: The generation number. Fitness: The fitness score of the solution. Steps: The path length in bins. Solution: The sequence of bins visited. b) Summary File: summary.csv consolidates the best solutions from all runs, including their fitness scores, path lengths, and sequences. c) All Steps File: summary_all_steps.csv records all bins visited during the runs for distribution analysis. d) A heatmap was also generated for the genetic search algorithm, illustrating the frequency of bins chosen during the search process as a representation of the search pathways.Genetic_search_algorithm.zip

Technical Information

The dataset files have been compressed into a standard ZIP archive using Total Commander (version 9.50). The ZIP format ensures compatibility across various operating systems and tools.

The XLSX files were created using Microsoft Excel Standard 2019 (Version 1808, Build 10416.20027)

The Python program was developed using Visual Studio Code (Version 1.96.2, user setup), with the following environment details: Commit fabd6a6b30b49f79a7aba0f2ad9df9b399473380f, built on 2024-12-19. The Electron version is 32.6, and the runtime environment includes Chromium 128.0.6263.186, Node.js 20.18.1, and V8 12.8.374.38-electron.0. The operating system is Windows NT x64 10.0.19045.

The statistical analysis included in this dataset was partially conducted using IBM SPSS Statistics, Version 29.0.1.0

The CSV files in this dataset were created following European standards, using a semicolon (;) as the delimiter instead of a comma, encoded in UTF-8 to ensure compatibility with a wide

Data and results from the Imageomics Workflow. These include data files from the Fish-AIR repository (https://fishair.org/) for purposes of reproducibility and outputs from the application-specific imageomics workflow contained in the Minnow_Segmented_Traits repository (https://github.com/hdr-bgnn/Minnow_Segmented_Traits).

Fish-AIR: This is the dataset downloaded from Fish-AIR, filtering for Cyprinidae and the Great Lakes Invasive Network (GLIN) from the Illinois Natural History Survey (INHS) dataset. These files contain information about fish images, fish image quality, and path for downloading the images. The data download ARK ID is dtspz368c00q. (2023-04-05). The following files are unaltered from the Fish-AIR download. We use the following files:

extendedImageMetadata.csv: A CSV file containing information about each image file. It has the following columns: ARKID, fileNameAsDelivered, format, createDate, metadataDate, size, width, height, license, publisher, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.

imageQualityMetadata.csv: A CSV file containing information about the quality of each image. It has the following columns: ARKID, license, publisher, ownerInstitutionCode, createDate, metadataDate, specimenQuantity, containsScaleBar, containsLabel, accessionNumberValidity, containsBarcode, containsColorBar, nonSpecimenObjects, partsOverlapping, specimenAngle, specimenView, specimenCurved, partsMissing, allPartsVisible, partsFolded, brightness, uniformBackground, onFocus, colorIssue, quality, resourceCreationTechnique. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.

multimedia.csv: A CSV file containing information about image downloads. It has the following columns: ARKID, parentARKID, accessURI, createDate, modifyDate, fileNameAsDelivered, format, scientificName, genus, family, batchARKID, batchName, license, source, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.

meta.xml: A XML file with the metadata about the column indices and URIs for each file contained in the original downloaded zip file. This file is used in the fish-air.R script to extract the indices for column headers.

The outputs from the Minnow_Segmented_Traits workflow are:

sampling.df.seg.csv: Table with tallies of the sampling of image data per species during the data cleaning and data analysis. This is used in Table S1 in Balk et al.

presence.absence.matrix.csv: The Presence-Absence matrix from segmentation, not cleaned. This is the result of the combined outputs from the presence.json files created by the rule “create_morphological_analysis”. The cleaned version of this matrix is shown as Table S3 in Balk et al.

heatmap.avg.blob.png and heatmap.sd.blob.png: Heatmaps of average area of biggest blob per trait (heatmap.avg.blob.png) and standard deviation of area of biggest blob per trait (heatmap.sd.blob.png). These images are also in Figure S3 of Balk et al.

minnow.filtered.from.iqm.csv: Filtered fish image data set after filtering (see methods in Balk et al. for filter categories).

burress.minnow.sp.filtered.from.iqm.csv: Fish image data set after filtering and selecting species from Burress et al. 2017.

These are species-level heatmaps of bacterial abundances across all 4 timepoints of the study, and at each sampling location. These are to aid in the visualization of seeing the different microbial populations from stream, to sewage, to treated water.

Rules used for the resolution of name for a given range index from a Definition file, based on the mapping configuration.

Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the land surface temperature (heatmaps) of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets.

There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health.

The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.

- 2013-2017
- 2014-2018
- 2015-2019
- 2016-2020
- 2017-2021
- 2018-2022
- 2019-2023

The input data used in this dataset come from Landsat 8 downloaded from Earth Explorer (usgs.gov).

The format of this dataset is organized in two ZIP format files:

- LANDSAT_8_L2SP_000000-milan_LST_peak.zip

- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip

- LANDSAT_8_L2SP_000000-athens_LST_peak.zip

Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.

The values of the images are in degree Celsius and nodata value is -9999.