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1,506 results for “objects”
Logistics Transport Label Data - 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts'
<p>Example dataset described in ICMLA2019 Paper 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts' (Dörr, Brandt, Meyer, Pouls).</p>
Figure 2 in Objective record of Pterois russelii (Scorpaenidae: Pteroinae) from the Red Sea
Figure 2. – Relationship between supraocular tentacle length and standard length (mm) in Pterois russelii, showing ontogenetic change of the former.
Figure 1 in Objective record of Pterois russelii (Scorpaenidae: Pteroinae) from the Red Sea
Figure 1. – Fresh specimen of Pterois russelii from the Red Sea [SMF 35752 (KAU 14-168), 99.7 mm standard length, off Jizan, Saudi Arabia]. Photo by S. V. Bogorodsky.
3D super-resolution datasets associated with the paper "Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet"
<p>3D single-molecule super-resolution datasets corresponding to reconstructions shown in <em>Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet</em> by Saliba & Gagliano, Gustavsson et. al.</p>
Results files and R objects for Gilmore et al 2024 SNORD116 analysis
Open the record for dataset details and reuse information.
Object-Centric Event Log for Age of Empires Game Interactions
<p>The dataset contains object-centric event logs in the OCEL 2.0 ( <a href="https://www.ocel-standard.org/" target="_blank" rel="noopener">https://www.ocel-standard.org/ </a>) format.</p> <p>The event logs originate from 100,000 Age of Empires 2 matches. In this real-time strategy game, players control units (like villagers or archers) and build structures (like houses or lumber camps) to create an efficient economy and win against the other players. Players can control multiple units at once, and they can utilize game mechanics to automate parts of the process for them, so they must not trigger every event themselves. The beginning of the game focuses on building economic structures that are as efficient as possible. Normative process descriptions, so-called build orders, describe battle-tested interaction patterns for the beginning of the game, similar to chess openings.</p> <p>The large object-centric event log contains 1000 matches and has the following properties:</p> <table> <tbody> <tr> <td><strong>Property</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Objects</td> <td>361,935</td> </tr> <tr> <td>Object Types</td> <td>30</td> </tr> <tr> <td>Events</td> <td>2,372,505</td> </tr> <tr> <td>Event Types</td> <td>829</td> </tr> </tbody> </table> <p> </p> <p>The following table describes the most important object types:<br><br></p> <table> <tbody> <tr> <td><strong>Object Type or (Group of Object Types)</strong></td> <td><strong><span>Explanation</span> </strong></td> </tr> <tr> <td>Match</td> <td>The match represents the competition of two players.</td> </tr> <tr> <td>Player</td> <td>There is one object per player. They are connected to all events that involve player input.</td> </tr> <tr> <td>Session</td> <td>There is one session per player in a match. The session is connected to all events happening on the machine of a player. The events can involve the player directly or they can also be game logic-based events triggered by the game engine.</td> </tr> <tr> <td>Villager</td> <td>Worker units to gather resources and build infrastructure.</td> </tr> <tr> <td>Town Center</td> <td>Central buildings for villager production and resource drop-off. Capable of setting automated gather points to assign tasks for newly created villagers.</td> </tr> <tr> <td>(Resource Drop-Off Group)</td> <td>Includes Lumber Camps, Mining Camps, and Mills. These facilities not only serve as drop-off points but can automatically command workers to gather the corresponding resources upon build completion.</td> </tr> <tr> <td>Farms</td> <td>Agricultural units for a continuous food supply. Farms can sometimes be replenished automatically, depending on game settings or upgrades.</td> </tr> <tr> <td>(Military Buildings)</td> <td>Structures for training military units and producing siege weaponry. Capable of setting gather points to automate unit deployment.</td> </tr> <tr> <td>(Research Buildings)</td> <td> <p>Facilities dedicated to technological advancements and upgrades.</p> </td> </tr> <tr> <td>(Military Units)</td> <td> <p>Units used for combat operations.</p> </td> </tr> </tbody> </table> <p> </p> <p>The following table describes the most important activities:</p> <p> </p> <table> <tbody> <tr> <td><strong>Event Type</strong></td> <td><strong><span>Explanation</span></strong></td> </tr> <tr> <td>Command Build [Structure]</td> <td>Issued by players to direct units to construct buildings.</td> </tr> <tr> <td>Start Build [Structure]</td> <td>Marks the beginning of the construction of a building by a designated group of villagers.</td> </tr> <tr> <td>Complete Build [Structure]</td> <td>Signals the completion of a building's construction, making the building operational and freeing up capacity of the constructing units.</td> </tr> <tr> <td>Gather [Resource]</td> <td>Represents the command to a unit to collect resources such as wood, stone, food, or gold.</td> </tr> <tr> <td>Command Research [Technology]</td> <td>Issued by players to initiate a research task in a research building.</td> </tr> <tr> <td>Start Research [Technology]</td> <td>Marks the beginning of the research process once resources arrived.</td> </tr> <tr> <td>Complete Research [Technology]</td> <td>Denotes the completion of a research task, unlocking new technologies or enhancements, and freeing up production capacity.</td> </tr> <tr> <td>Command Queue [Unit]</td> <td>Issued by players to add units to the production queue of a building.</td> </tr> <tr> <td>Start Production [Unit]</td> <td>Marks the beginning of unit production within a facility, as soon as there is capacity.</td> </tr> <tr> <td>Complete Queue [Unit]</td> <td>Signals the end of unit production, resulting in the deployment of a new unit and freeing up production capacity.</td> </tr> </tbody> </table> <p> </p> <p>The zip file contains filtered object-centric event logs that only contain 10 matches to explore the data set with faster loading time.</p>
EOL computer vision pipelines: Object Detection for Image Cropping: Aves
<p>Produced by an detection model pretrained on MS COCO 2017. Automatically crops images of birds (Aves) to square dimensions centered around animal(s). </p> <p>388,166 rows </p>
EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon
<p>Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>
EOL computer vision pipelines: Object Detection for Image Cropping: Chiroptera
<p>Produced by EOL Chiroptera Object Detection Model. Automatically crops images of bats (Chiroptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/chiroptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>17,401 rows</p>
EOL computer vision pipelines: Object Detection for Image Cropping: Lepidoptera
<p>Produced by EOL Lepidoptera Object Detection Model. Automatically crops images of butterflies and moths (Lepidoptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/lepidoptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>608,163 rows</p> <p> </p>
Data and Code Accompanying the Study "Partial Colexifications Reveal Directional Tendencies in Object Naming"
Open the record for dataset details and reuse information.
Supplementary Data: Data Imbalance in Drug Response Prediction: Multi-Objective Optimization Approach in Deep Learning Setting
Open the record for dataset details and reuse information.
Data set for "Understanding Older Adults' Needs: Psychosocial Wellbeing in Context of Perceived and Objective Built Environment"
<p>This entry contains datasets for the article "Understanding Older Adults' Needs: Psychosocial Wellbeing in Context of Perceived and Objective Built Environment".</p>
Linked collectors and determiners for: Vascular plants of the nature protected objects Kherson region (Ukraine).
Natural history specimen data linked to collectors and determiners held within, "Vascular plants of the nature protected objects Kherson region (Ukraine)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/96e39f42-6382-4996-a30b-28157186cec6">https://bionomia.net/dataset/96e39f42-6382-4996-a30b-28157186cec6</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/96e39f42-6382-4996-a30b-28157186cec6">https://gbif.org/dataset/96e39f42-6382-4996-a30b-28157186cec6</a>. Formatted as a Frictionless Data package.
pXRF investigation of copper objects from the Congo Basin (Museum of Anthropology and Archaeology (MAA), University of Cambridge)
<p>This dataset contains the data related to the pXRF analyses conducted on copper objects from the Congo Basin curated at the Museum of Anthropology and Archaeology (MAA), University of Cambridge in December 2020. The study has been done in the framework of the H2020-MSCA-IF-2019 Project Number 890896 ‘An Archaeology of Exchange Networks in Central Africa. The Cases of the Copperbelt and Niari Basin Copper Deposits’ (ArCAN).</p> <p><a href="http://cordis.europa.eu/project/id/890896">https://cordis.europa.eu/project/id/890896</a></p> <p>All information about the dataset can be found in the readme file.</p>
Theoretical Models of Ultra-Cool Objects (Brown Dwarfs and Free-Floating Planets) Including Water Latent Heat Effect: Thermal Structure, Spectra, and Photometry
<p><strong>OVERVIEW</strong><br> Data presented here are from the work of Tang et al. (2021), which present a one-dimensional (vertical) atmospheric structure model for ultra-cool objects<br> that includes moist adiabatic convection (water latent heat release effect).<br> Model grids across a range of effective temperatures (350, 300, 250, 200 K), metallicities ([M/H] of 0.0, 0.5, 0.7, 1.5),<br> and gravities (logg of 4.0, 4.5, 4.7, 5.0) that mimic the atmosphere condition of Y-class brown dwarf and Free-Floating Planets.</p> <p><strong>ATMOSPHERIC THERMAL STRUCTURE</strong><br> The pressure-temperature profile are saved under the ./pT_profiles/, which have two folders:</p> <ol> <li>pT_profile_dry_adiabat: for dry adiabatic treatment.</li> <li>pT_profile_moist_adiabat: for moist adiabatic treatment.</li> </ol> <p>File name of each compressed files gives the effective temperature (Teff, in Kelvin), gravity (in MKS), and metallicity (in [M/H]) information as for example:<br> t200g100nc_m0.0.cmp.gz --> Teff of 200K, gravity as 100 m/s2, and [M/H]=0.0.</p> <p>The table formate of the pressure-temperature profile, i.e., the .cmp files, is made to work with the [<a href="https://natashabatalha.github.io/picaso/">PICASO software</a>](https://natashabatalha.github.io/picaso/).<br> Each .cmp file contains</p> <ul> <li>Column 01 (x) : atmosphere pressure level</li> <li>Column 02 (pressure) : atmosphere pressure in bar</li> <li>Column 03 (DEN) : atmosphere density in cm^{-3}</li> <li>Column 04 (temperature) : atmosphere temperature in kelvin</li> <li>Column 05-14 : molecule mixing ratios for H2, He, CH4, H2O, NH3, CO, PH3, H2S, N2, CO2</li> <li>Column 15 (MU) : mean molecular weight in grams/mole</li> </ul> <p><strong>SPECTRA</strong><br> Model spectra are saved under the ./spectra/ with wavelength ranging from 1 to 30 micron.<br> File name of each compressed file gives the effective temperature (Teff, in Kelvin), gravity (in MKS), metallicity (in [M/H]), adiabatic treatment, and<br> the resolving power information as for example:<br> sp_t200_g100_m0.0_dry_R3000.csv.gz --> Teff of 200K, gravity as 100 m/s2, [M/H]=0.0, with dry adiabatic treatment, and R=3000.<br> Each spetrum .cv file contains two columns:</p> <ul> <li>Column 01 wavelength [micron]</li> <li>Column 02 Flux [W/m2/micron], the spectral fluxes are given at the top of the atmosphere, taken to be 1 Jupiter radius.</li> </ul> <p><strong>PHOTOMETRY</strong><br> Photometry data derived from the spectra at the top of the atmosphere (assuming one Jupiter radius) are given in the syn_photometry.csv.<br> Photometry filters are from the Mauna Kea Observatory (MKO) photometry system, the Wide-field Infrared Survey Explorer (WISE), and the<br> Spitzer Space Telescope. Filter profiles and Vega magnitude zero points are from the [<a href="http://svo2.cab.inta-csic.es/theory/fps/">SVO Filter Profile Service</a>](http://svo2.cab.inta-csic.es/theory/fps/)</p> <ul> <li>Column 01 (gravity) , gravity in MKS</li> <li>Column 02 (teff) , effective temperature in Kelvin</li> <li>Column 03 ([M/H]) , metallicity</li> <li>Column 04-12 , photometry magnitude for IRAC36, IRAC45, W1, W2, GiminiM, MKO_J, MKO_H, MKO_K, MKO_Lp all in Vega system.</li> <li>Column 13 (adiabatic), adiabatic treatment; D: dry adiabatic, M: moist adiabatic</li> </ul> <p><strong>CREDITS</strong><br> Please cite Tang et al. (2021, Astrophysical Journal, in press.)<br> [<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract">ADS link</a>](https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract) if you used data here in your research.</p>
NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>NSNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>BHNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
A deep learning dataset for underwater object detection of tropical freshwater fish species in northern Australia
<p>This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia. </p> <p>Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the <a href="https://www.dcceew.gov.au/science-research/supervising-scientist">Supervising Scientists</a> annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). More information can be found <a href="https://www.dcceew.gov.au/sites/default/files/documents/ss-atr-2020-21.pdf">here</a>.</p> <ul> <li>All images are in .jpg format and are 1920x1080 in dimension.</li> <li>Bounding box annotations are in COCO format. </li> </ul> <p>Two .zip files are included:</p> <ul> <li><a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-CompactModel.zip">202210-KakaduFishAI-CompactModel.zip</a>: includes compact model weights in tensorflow format (.pb) trained using Azure's Custom Vision platform. This model is suitable for edge devices due to its reduced size. Code is provided to use the compact model for inferencing. </li> <li> <a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-TrainingData.zip">202210-KakaduFishAI-TrainingData.zip</a>: includes all images and one COCO (.json) file with annotations. </li> </ul> <p>Fish taxa include: </p> <ol> <li><em>Ambassis agrammus</em></li> <li><em>Ambassis macleayi</em></li> <li><em>Amniataba percoides</em></li> <li><em>Craterocephalus stercusmuscarum</em></li> <li><em>Denariusa bandata</em></li> <li><em>Glossamia aprion</em></li> <li><em>Glossogobius</em> spp.</li> <li><em>Hephaestus fuliginosus</em></li> <li><em>Lates calcarifer</em></li> <li><em>Leiopotherapon unicolor</em></li> <li><em>Liza ordensis</em></li> <li><em>Megalops cyprinoides</em></li> <li><em>Melanotaenia nigrans</em></li> <li><em>Melanotaenia splendida inornata</em></li> <li><em>Mogurnda mogurnda</em></li> <li><em>Nemetalosa erebi</em></li> <li><em>Neoarius</em> spp.</li> <li><em>Neosilurus</em> spp.</li> <li><em>Oxyeleotris</em> spp.</li> <li><em>Scleropages jardinii</em></li> <li><em>Strongylura kreffti</em></li> <li><em>Syncomistes butleri</em></li> <li><em>Toxotes chatareus</em></li> </ol> <p>If you use this data for your own deep learning project we'd love to hear about how you used this dataset: andrew.jansen@environment.gov.au.</p>
Object-based image analysis for monitoring plant invasions, can we use an open-source solution?
<p><strong>Introduction</strong></p> <p>This is a practical exercise testing possibilities of open-source solutions (FOSS) for object-based image analysis (OBIA) to monitor plant invasion using unoccupied aerial system (UAS, drone).</p> <p>The material is accompanying a chapter <strong><em>Müllerová, J. et al. (2023). Vegetation mapping and monitoring by unoccupied aerial systems – current state and perspectives. In: Manfreda, S. et Eyal B.D. (eds). Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. Elsevier.</em></strong></p> <p>The material is meant for readers to run the workflow and detect invasion of giant hogweed on the UAS data themselves testing different FOSS solutions.</p> <p> </p> <p><strong>Data</strong></p> <p>• a subset of UAS-borne data (consumer camera) collected in Czech Republic during the flowering of a noxious invasive plant species giant hogweed (<em>Heracleum mantegazzianum</em>)</p> <p>• training dataset</p> <p>• eCognition rulebase (proprietary OBIA software)</p> <p>• a script for SegOptim package implemented in R</p> <p> </p> <p><strong>Description</strong></p> <p>The use case represents a simple application of OBIA approach based on the SegOptim package implemented in R.</p> <p>Four bands (RGBN) UAS image subset are available, capturing the central area of a heavily invaded location (CZ) by giant hogweed (Heracleum mantegazzianum). Thanks to the proper image timing, the invasive species is clearly observable as white objects (in RGB) representing the various stage of the blossom. Considering the complex shape of the flower heads, detection based on image segmentation outperforms pixel-based classification (Müllerová et al., 2017). Simple segmentation of input imagery is performed (for simplicity only the spectral bands are considered both for segmentation and feature space definition, however additional features such as vegetation indices or textural measures may be included), followed by supervised classification using training data. Finally, a visual comparison of result detection both from proprietary (eCognition) and open-source (SegOptim) implementation is provided, confirming comparable results.</p> <p>Based on #github("joaofgoncalves/SegOptim")</p> <p> </p> <p><strong>References</strong></p> <p>Gonçalves, J., Pôças, I., Marcos, B., Mücher, C. A., & Honrado, J. P. (2019). SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data. <em>International Journal of Applied Earth Observation and Geoinformation</em>, <em>76</em>, 218-230.</p> <p>Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M. & Pyšek, P. (2017b). Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring. Frontiers in Plant Science 8:1–13.</p> <p>Accompanying material for</p> <p>Müllerová, J. et al. (2023). Vegetation mapping and monitoring by unoccupied aerial systems – current state and perspectives. In: Manfreda, S. et Eyal B.D. (eds). Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. Elsevier</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.