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Data of "Pen mates' interactions, potential precursors of damaging behaviours, object manipulation, straw rooting, and primary activity: A detailed data set in undocked pigs under dietary protein restriction"
<p>Damaging behaviours, such as tail biting, are common problems in pig production, compromising animal welfare and causing economic losses. Detailed studies are impeded by the difficulty of directly observing these behaviours. Tail biting is a broader phenomenon that begins long before lesions manifest, and behavioural problems caused by various stressors present themselves weeks before they escalate to damaging behaviour, resulting in serious injuries. Therefore, detailed data on behaviours, which can be considered precursors of tail biting, such as oral and nasal manipulation of conspecifics, should be collected. The present data were collected in the course of a large study on the genetic potential of protein efficiency, in which the crude protein content in the diet was reduced to 80% of the recommendations. Dietary protein reduction is a promising way to reduce nitrogen emissions in pig manure, but its implications for animal welfare are not yet clear. Pigs differ phenotypically and genetically in their ability to utilise dietary proteins; therefore, there might be individual differences in how they cope with the protein reduction. Here, we present detailed data of focal observations of 95 pigs at an experimental farm with undocked tails that were fed a protein-reduced diet. Pigs were observed directly in their home pens for 5 min each on four different days. All actions directed towards objects in the pen, interactions with and confrontations among pen mates, and straw rooting behaviour and general activity were recorded. After the behavioural observations, wounds on different parts of the body and the cleanliness of the pigs were noted. The protein efficiency of 94 pigs was obtained. The data set comprises six tables. The first table contains information on the animals, including the identities of their parents, farrowing group, sex, and protein efficiency. The other data tables contain four 5-min observations of each pig on 10 object-manipulation behaviours; 150 interaction behaviours, including reactions; 14 confrontation behaviours and their outcomes and reactions; 10 mounting behaviours, including reactions; two rooting behaviours; seven basic behaviours; and an index of general activity. The observations took place under comparatively good housing conditions. Pigs were not tail-docked and were given fresh straw daily, <em>ad libitum</em> access to feed, floor space above the legal requirements (only a partially slatted floor), and daily cleaning of pens, and they were closely monitored for signs of damaging behaviour; all of these are favourable conditions as they limit stress and the risk of damaging behaviour. These data can be used to further explore the relationships of specific behaviours and phenomena and their association with protein efficiency. The ethogram can be used as a template for further observations. Practitioners could use the data to support pigs’ need for occupation, such as by providing sufficient straw.</p>
CornWeed Dataset: A dataset for training maize and weed object detectors for agricultural machines
<p>There are many datasets available for training object detectors in non agricultural domains such as Autonomous Driving but these datasets fail to generalize well enough to an agricultural use-case. This dataset contains 3574 hand labelled images with bounding boxes provided in both YOLO and COCO dataset format. Additionally there are 4981 unlabelled images for testing purposes. All the images are hand labelled with bounding boxes with several labellers and reviewed. The dataset contains two class IDs namely maize and weeds. The dataset also contains a crop-row instance but has not been used in the accompanying paper but could be interesting for other future work. The images have been recorded in two resolutions i.e. 720 x 1280 and 480 x 640 covering two crop rows and single crop row respectively. </p>
Phyloseq 16S v3v4 object accompanying paper: Host genotype affects endotoxin release in excreta of broilers at slaughter age
<p>This ready to load <strong>phyloseq</strong> R S4 object contains the ASV table, taxonomy table and sample metadata. This data was build using the DaDa2 (version 1.18.0) and phyloseq (version 1.34.0) R packages using the SILVA 138.1 nr99 database and raw MiSeq PE300 sequencing data deposited at NCBI-SRA under BioProject: PRJNA975731.</p> <p>F. Marcato, J.M.J. Rebel, S.K. Kar, I. Wouters, D. Schokker, A. Bossers, F. Harders, J.W. van Riel, M. Wolthuis-Fillerup and I.C. de Jong. </p> <p>The main goal of the current study was to investigate whether or not it was possible to modulate the fecal microbiome and thereby reducing endotoxin concentrations in the excreta of broiler chickens. An experiment was carried out with a 2 x 2 x 2 factorial arrangement including 3 factors, 1) genetic strain (fast-growing Ross 308 vs. slower-growing Hubbard JA757), 2) no vs. combined use of probiotics and prebiotics in the diet and drinking water, 3) early feeding at the hatchery vs. non early feeding. A total of 624 Ross 308 and 624 Hubbard JA757 day-old male broiler chickens were included until d 37 and d 51 of age, respectively. Broilers (26 chicks per pen) were housed in a total of 48 pens, and there were 6 replicate pens/treatment group. Pooled cloacal swabs (10 chickens per pen) for microbiome and endotoxin analyses were collected at a target body weight (BW) of 200 g, 1 kg and 2.5 kg.</p>
BeMAGIC_Synthesis of magnetoelectric nano-objects/nanoparticles
<p>BeMAGIC ITN (GA861145) Synthesis of magnetoelectric nano-objects/nanoparticles by template assisted electrodeposition, lithography and other wet chemistry. Results from UCAM, INRIM, AALTO, TUC, ETHZ and ICN2.</p>
Object Tracking on a Monopoly Game Board
<p><strong>Object Tracking on a Monopoly Game Board</strong></p> <p><strong>Author:</strong> Nathan Hoebeke</p> <p><strong>Supervisors:</strong> Maxim Van de Wynckel, Prof. Dr. Beat Signer</p> <p><strong>About</strong></p> <p>The goal of this dataset was to track game pieces on the physical game board of Monopoly. We make use of object classification where our training data consists of 100 pictures (taken at an angle) of the game board in order to classify the individual (moving) pieces. The training dataset was on the 9th of April 2023 and the test date recorded on the 7th of May 2023 using an iPhone 13 mini and iPhone 12.</p> <p>Two participants played a game of Monopoly and each individually took pictures of the current game state after every move. These images were then processed by our application to determine the location of pawns and other game pieces such as the red and green houses.</p> <p>Raw images are unprocessed but may have minor edits to ensure anonymisation of participants in the background. We used <a href="https://roboflow.com/">Roboflow</a> to label and train our dataset which is included in this repository.</p> <p>For more information about our processing and this dataset you can download the full Bachelor thesis here: <a href="https://wise.vub.ac.be/thesis/location-tracking-physical-game-board">https://wise.vub.ac.be/thesis/location-tracking-physical-game-board</a> (download link available after embargo at the end of the academic year)</p> <p>This dataset was published as part of the bachelor thesis: <em>Location Tracking on a Physical Game Board</em> for obtaining the degree of Bachelor in Computer Sciences at the <a href="https://vub.be">Vrije Universiteit Brussel</a>.</p> <p><strong>Data</strong></p> <table> <thead> <tr> <th>Data</th> <th>Pictures</th> <th>Device</th> </tr> </thead> <tbody> <tr> <td><strong>Training</strong></td> <td>213</td> <td>iPhone 13 mini</td> </tr> <tr> <td><strong>Test #1</strong></td> <td>102</td> <td>iPhone 12</td> </tr> <tr> <td><strong>Test #2</strong></td> <td>93</td> <td>iPhone 13 mini</td> </tr> </tbody> </table> <p><strong>Dataset contents</strong></p> <ul> <li><code>model</code>: Trained YOLOv5 model with labels. This dataset can also be found <a href="https://universe.roboflow.com/vub-vsmjx/boardgamedetection">here</a>.</li> <li><code>train</code>: Training data made by the author. <ul> <li><code>raw</code>: Raw pictures of the game board at various states. <ul> <li><code>GAME_2023-04-09_<HH>-<MM>-<SS>.jpg</code>: Images formatted based on the date and time when they were captured.</li> </ul> </li> <li><code>processed</code>: Processed pictures with perspective transformation applied. <ul> <li><code>canvas<X>.png</code>: (Pre)-processed image.</li> </ul> </li> </ul> </li> <li><code>test</code>: Test data made by indepedent participants. <ul> <li><code>participant_1</code>: Participant 1 data <ul> <li><code>raw</code>: Raw pictures of the game board taken by the parcipant after every move. <ul> <li><code>GAME_2023-05-07_<HH>-<MM>-<SS>.jpg</code>: Images formatted based on the date and time when they were captured.</li> </ul> </li> <li><code>processed</code>: Processed pictures with perspective transformation applied. Yellow rectangles are included when our own algorithm was able to determine the location. <ul> <li><code>canvas<X>.png</code>: Processed image.</li> </ul> </li> </ul> </li> <li><code>participant_2</code>: Participant 2 data <ul> <li><code>raw</code>: Raw pictures of the game board taken by the parcipant after every move. <ul> <li><code>GAME_2023-05-07_<HH>-<MM>-<SS>.jpg</code>: Images formatted based on the date and time when they were captured.</li> </ul> </li> </ul> </li> </ul> </li> <li><code>README.md</code>: Documentation and information about the dataset.</li> </ul> <p><strong>License</strong></p> <p>This license applies to the dataset for the game Monopoly. Any artwork or intellectual property from the game that is captured by this dataset is property of Hasbro, Inc.</p> <p>Copyright 2022-2023 Nathan Hoebeke, Beat Signer, Maxim Van de Wynckel, Vrije Universiteit Brussel</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the “Dataset”), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:</p> <p>The above copyright notice and this permission notice shall be included in all copies or substantial portions that make use of the Dataset.</p> <p>THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.</p>
Multiscale Spatial Patterns in Giant Dike Swarms Identified through Objective Feature Extraction Datasets
<p>S1 - Linked dike clusters for the Columbia River Flood Basalt group including the four identified subswarms: Chief Joseph, Monument, Ice Harbor, and Steens as compiled in Morriss et al., 2020. This dataset uses the a UTM Zone 11N projection (EPSG:26911).</p> <p>S2 - Linked dike clusters for the Deccan Traps including the four identified subswarms: Saurashtra, Narmada-Tapi, Central and Coastal. Due to their overlap Central and Coastal Swarms have been combined in this dataset into the Central Swarm. This dataset uses the a WGS 84 projection (EPSG:3857). </p> <p>S3 - Dike segment data for Spanish Peaks and Dike Mountain located in the Rio Grande Rift of Colorado. This dataset was digitized using QGIS based on the map by Johnson (1961). This dataset uses the a UTM Zone13N projection (EPSG:32613). The file includes the start, end points, and midpoints of the dikes; segment length; calculated $\rho$ and $\theta$ for the Hough Transform; the origin used for the Hough Transform which is different for each subswarm (xc,yc); dike rock type if known; and a unique identification calculated based on the start and endpoints. This dataset has been preprocessed to remove curving dikes and is the data set used to produce later products (Data set S4). </p> <p>S4 - Linked dike clusters for the Spanish Peaks and Dike Mountain. This dataset was produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. This dataset uses the a UTM Zone 13N projection (EPSG:32613). </p> <p> </p> <p> </p> <p>These datasets were produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. The datasets are in the format of a CSV file but can be read into GIS programs using Well Known Text (WKT) linestring. TThe file includes the start and end points of the average line in the cluster and it's mid points, cluster length and width (Xstart, Xend, Xmid, Ymid, in meters and UTM coordinates, Dike Cluster Width or R\_Width, Dike Cluster Length or R\_Length all in meters); calculated average $\rho$ and $\theta$ for the Hough Transform $\rho$ units measured in meters, $\theta$ units measured in degrees, unless otherwise stated); the origin used for the Hough Transform which is different for each subswarm ($xc$,$yc$, meters in UTM coordinates); average slope and intercept (AvgSlope, AvgIntercept meters); range and standard deviation for $\rho$ and $\theta$ for all objects in the cluster ($\rho$ units measured in meters, $\theta$ units measured in degrees); cluster size (Size); sum of segment lengths in a cluster (SegmentLSum, meters); whether the cluster crosses between negative and positive values (ClusterCrossesZero, boolean); overlap as calculated in the main text where the length of overlap is normalized by the sum of segment lengths in a cluster; maximum number of overlapping segments (nOverlapingSegments); twist angle which is the difference in angle betweeen the average cluster line and the average line formed by cluster midpoints (EnEchelonAngleDiff, degrees); the p-value for the midpoint line fit of the segments where $p<0.05$ is considered to be a significant fit (EEPValue); the maximum, median, and minimum segment nearest neighbors distances in the cluster which is calculated using the cartesian midpoints of each segment and normalized by the Cluster Length (MaxSegNNDist, MedianSegNNDist, MinSegNNDist); characterization of each cluster as filtered or not, filtered clusters are of size greater than $3$ and have a MaxSegNNDist of less than $0.5$ (TrustFilter, boolean); the date edited (Date\_Changed), and the clustering parameters used for each cluster (Rho\_Threshold in meters, Theta\_Threshold in degrees) and a unique identification calculated based on the start and endpoints (ClusterHash). </p>
3D reconstructions of semi-transparent submerged objects: Nanomia, Cystisoma, and validation object
<p>These data were used to support the conclusions published in the article titled, "New method for rapid 3D reconstruction of semi-transparent underwater animals and structures", accepted for publication in Integrative Organismal Biology on May 9th, 2023.</p> <p>It focuses on three physical objects, two of which were live animals, collected under permit in the Monterey Bay NAtional MArine Sanctuary:</p> <ul> <li>A siphonophore of the species <em>Nanomia bijuga</em></li> <li>An amphipod of the family <em>Cystisoma</em></li> <li>A thin-walled plastic cylinder, for validation purposes.</li> </ul> <p>For each of these objects, we provide the original .cine file, as recorded by the Phantom 640S highspeed camera, and the derived high-quality .mov video file. We exported video frames as image stacks, included as ZIP files in this repository. We chose to either extract the red or green channel, or a balanced luminance value of each frame. Background subtraction was performed in some cases, by applying a minimum filter or median filter with a certain Z (time axis) extent, and subtracting this from the original frames. Furthermore, additional smoothing was performed in some cases as indicated by the filenames, in the form of a median filter with an X-by-Y-by-Z extent.</p> <p>3D Slicer software was used for segmentation, and bundled .mrb files are included, which have been tested with 3D Slicer version 5.0.3. Derived .STL or .PLY model files are included as well.</p>
Cortical topographic motifs emerge in a self-organized map of object space
<p>The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflects a smooth mapping of a unified representational space.</p>
Рис. 1. ПреΔпочтитеΛьное отношение паукообразных (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) к объектам питания по способу переΔвижения жертвы: I – Λетающие; II – прыгающие; III – бегающие; IV – хоΔящие; V – поΛзающие; VI – маΛопоΔвижные; VII – непоΔвижные. Fig. 1. Preferred attitude of arachnids (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) to food objects by the method of movement of pray: I – flying; II – jumping; III – running; IV – walking; V – crawling; VI – sluggish; VII – motionless. in Comparison of trophic spectra and hunting strategies of some large arachnids (Arachnida: Scorpiones, Solifugae, Aranei) in semi-desert biocenoses of Gobustan (Eastern Azerbaijan)
Рис. 1. ПреΔпочтитеΛьное отношение паукообразных (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) к объектам питания по способу переΔвижения жертвы: I – Λетающие; II – прыгающие; III – бегающие; IV – хоΔящие; V – поΛзающие; VI – маΛопоΔвижные; VII – непоΔвижные. Fig. 1. Preferred attitude of arachnids (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) to food objects by the method of movement of pray: I – flying; II – jumping; III – running; IV – walking; V – crawling; VI – sluggish; VII – motionless.
Supplementary materials for "Spanish lower and upper bounded change of state verbs: Focusing on transitive experiencer object verbs", published in Linguistics: An Interdisciplinary Journal of the Language Sciences
<p>Supplementary files are as follows:</p> <ul> <li><strong>R-code_Scalarity.Rmd</strong> contains the R-code in a markdown version of the statistical analysis of the study.</li> <li><strong>R-code_Scalarity.html</strong> displays the R-code_Scalarity.Rmd in an html format.</li> <li><strong>Results_Scalarity.csv</strong> contains the data of the study.</li> </ul> <p>For more details on the description of the data, see the publication: "Spanish lower and upper bounded change of state verbs: Focusing on transitive experiencer object verbs", Linguistics.</p>
Towards substitution of invasive telemetry: An integrated home cage concept for unobtrusive monitoring of objective physiological parameters in rodents - Minimal dataset
<p>Minimal dataset for unobtrusive monitoring of vital parameters in rodents.</p>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
ÖWF-OD - ÖWF Object Detection Dataset
<p>This dataset contains object detection annotations and quality metadata of historic film with scientific and educational content. The content is from the collection <a href="https://www.mediathek.at/wissenschaft-als-film/die-sammlung-des-oewf/">"Österreichische Bundesinstitut für den Wissenschaftlichen Film (ÖWF)"</a> of the <a href="https://www.mediathek.at">Austrian Mediathek</a>.</p> <p>This is a snapshot of the dataset's repository at https://github.com/TailoredMediaProject/OEWF_ObjectDetection</p>
Turfgrass Divot Dataset (Synthetic ) for divot detection object detection system
<p>The dataset provided below has been synthetically created using Blender. A fundamental analysis on this data was conducted utilizing the YOLO V3 object detection technique to identify divots or areas of damage.</p> <p>Used for paper: </p> <p>Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection</p> <p>https://github.com/stevefoy/Turfgrass-Divot-Object-Detection</p> <pre><span>@inproceedings</span>{<span>IMVIP2024</span>, <span>author</span> = <span><span>{</span>Stephen Foy and Simon McLoughlin<span>}</span></span>, <span>title</span> = <span><span>{</span>Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection<span>}</span></span>, <span>booktitle</span> = <span><span>{</span>Irish Machine Vision and Image Processing Conference (IMVIP)<span>}</span></span>, <span>year</span> = <span><span>{</span>2024<span>}</span></span> }</pre> <p><strong>Contents of the Zip File</strong>:</p> <ul> <li> <p><strong>synthDivot_416x416 Folder</strong>:</p> <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Corresponding masks for each image</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> <li> <p><strong>synthDivot_608x608 Folder</strong>:</p> <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> </ul> <p> </p> <p> </p>
Order Management Object-centric Event Log in OCEL 2.0 Standard
<p><strong>General Description</strong></p> <p>This process describes the management of customer orders within a company, comprising both the registration and payment of incoming orders, as well as the process of packing and shipping these orders. For these tasks, our company deploys staff in their sales, warehousing, and shipment departments.</p> <p>This is an artificial event log according to the <a href="https://www.ocel-standard.org/">OCEL 2.0 Standard</a> simulated using CPN-Tools. Both the CPN and the SQLite can be downloaded. The simulation is an extension of the <a href="https://www.ocel-standard.org/beta/event-logs/simulations/legacy-logs">order management log</a> in the former OCEL standard.</p> <p><strong>Process Overview</strong></p> <p>At our company, <strong>customers</strong> place <strong>orders</strong> <em>(place order)</em> for different <strong>products</strong> in varying amounts. Each product type has a price and a weight. In the current market situation, there is an inflation that irregularly leads to an increase of prices. These price rises have a negative impact on customers’ purchasing power, i.e., on order volumes.</p> <p>When a customer places an order, this order is assigned to an <strong>employee</strong> of our company’s sales department. To foster customer satisfaction, our company has a single-face-to-customer policy. This means that per customer there is one primary sales representative who ought to render all services related to that customer. If that first representative is unavailable, a second sales representative should take care of the order. Should this employee be also unavailable, the order has to be managed by another employee. The tasks of sales employees comprise the registration <em>(confirm order)</em> as well as payment processing <em>(payment reminder, pay order)</em>.</p> <p>In parallel to this, the shipment of goods is prepared. For this, the stock of our company is checked by an employee of the warehousing department for the availability of the ordered <strong>items</strong>. If necessary, the warehouser reorders the item <em>(item out of stock, reorder item)</em>. Items ready for shipment are collected <em>(pick item)</em> for the placement into <strong>packages</strong> that are addressed to single customers. Here, it may happen that a package content relates to multiple orders, and order volumes are distributed over multiple packages.</p> <p>After all items allocated to a package have been picked, the package is compiled by a warehousing employee <em>(create package)</em>. Later on, this package is picked up by a shipment employee for transport <em>(send package)</em>. According to another policy, a warehousing employee should provide assistance to the shipment employee in loading the package. However, oftentimes shippers act contrary to that policy and load packages alone or together with a second shipment employee.</p> <p>Finally, the package is shipped. Deliveries may fail repeatedly <em>(failed delivery)</em> until successful delivery <em>(package delivered)</em>.</p> <p>The figure below depicts the process in a simplified manner, using an informal process notation to describe the control-flow and the involved object types. A formal description is given along with the artifacts in the next section.</p> <p>Further information can be found at: <a href="https://www.ocel-standard.org/event-logs/simulations/order-management/">https://www.ocel-standard.org/event-logs/simulations/order-management/</a></p> <p><strong>General Properties </strong></p> <p>An overview of log properties is given below.</p> <table> <thead> <tr> <th>Property</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Event Types</td> <td>11</td> </tr> <tr> <td>Object Types</td> <td>6</td> </tr> <tr> <td>Events</td> <td>21008</td> </tr> <tr> <td>Objects</td> <td>10840</td> </tr> </tbody> </table> <p><strong>Control-Flow Behavior </strong></p> <p>The behavior of the log is described by a <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/full-ocpn.svg">respective object-centric Petri net</a>. Also, individual object types exhibit behavior that can be described by simpler Petri nets. See below.</p> <table> <thead> </thead> <tbody> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/orders-ocpn.svg">orders</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/customers-ocpn.svg">customers</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/items-ocpn.svg">items</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/employees-ocpn.svg">employees</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/packages-ocpn.svg">packages</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/products-ocpn.svg">products</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/order-management/images/full-ocpn.svg"><strong>Full object-centric Petri net</strong></a></td> </tr> </tbody> </table> <p><strong>Object Relationships </strong></p> <p>The company pursues the "one-face-to-the-customer" policy, in which every customer has a dedicated sales representative as well as a deputy (secondary representative). These relationships are described in the log.</p> <table> <thead> <tr> <th>Source Object Type</th> <th>Target Object Type</th> <th>Qualifier</th> </tr> </thead> <tbody> <tr> <td>employees</td> <td>customers</td> <td>primarySalesRep</td> </tr> <tr> <td>employees</td> <td>customers</td> <td>secondarySalesRep</td> </tr> </tbody> </table> <p>Additionally, object-to-object relations can emerge at executions of specific activities:</p> <table> <thead> <tr> <th>Activity</th> <th>Source Object Type</th> <th>Target Object Type</th> <th>Qualifier</th> </tr> </thead> <tbody> <tr> <td>create package</td> <td>package</td> <td>employee</td> <td>packed by</td> </tr> <tr> <td>send package</td> <td>package</td> <td>employee</td> <td>forwarded by</td> </tr> <tr> <td>send package</td> <td>package</td> <td>employee</td> <td>shipped by</td> </tr> </tbody> </table> <p><strong>Simulation Model </strong></p> <p>The CPN used to create this event log can also be downloaded.To obtain simulated data, extract the linked ZIP file and play out the CPN therein, e.g., by using <a href="https://cpntools.org/">CPN Tools</a>.</p> <p>The play-out produces CSV files according to the schema of OCEL2.0. The provided jupyter notebook can be used to convert these files to an SQLite dump.</p> <p>For a technical documentation of the simulation model, please open the attached CPN with CPN Tools and see the annotations therein.</p> <p><strong>Acknowledgements</strong></p> <p>Funded under the Excellence Strategy of the Federal Government and the Länder<em>. </em>We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p>
Angular GitHub Commits Object-centric Event Log
<p><strong>Overview</strong></p> <p>This real-world object-centric event log in the OCEL 2.0 standard contains an extraction of the commit information from the <a href="https://github.com/angular/angular">GitHub repository</a> used to developed the <a href="https://www.angular.io/">Angular</a> platform. A single code commit in the repository is abstracted to one event in the log. The dataset contains essential information for each commit, such as the timestamp and the contributor's details. Crucially, commit information is connected to two classes of objects: the file(s) affected by the commit, and the branch(es) in the repository containing the commit.</p> <p><strong>Description</strong></p> <p>GitHub, a popular platform for developers offering the functionalities of the Git versioning system, allows to record single modifications to software projects by contributors; such modifications are grouped in units called <strong>commits</strong>. Commits contain all details of the edits operated on a group of files in the projects. Therefore, all commits of a project constitute a ledger, that allows to rewind or fast-forward all contributions in the project.</p> <p>Commits in a project are arranged in <strong>branches</strong>, which form a tree-like structure. A contributor may create a new branch, essentially a copy of the project, in order to commit modifications safely. Once the contributor is satisfied with the edits, they may <strong>merge</strong> their new branch back into the pre-existing branch (realized by applying the modifications of all the new commits sequentially, and then solving the conflicts that may arise).</p> <p>This log contains an extraction of the commit information of the <a href="https://www.angular.io/">Angular</a> project on <a href="https://github.com/angular/angular">GitHub</a>. The abstraction level is such that every commit corresponds to an event in the log.</p> <p>For each event, the following information is recorded:</p> <ul> <li>a unique identifier (<strong>hash</strong>)</li> <li>the author's timestamp of the commit (includes timezone information)</li> <li>an <strong>activity label</strong>: the Angular project conforms to the <a href="https://www.conventionalcommits.org/">Conventional Commits</a> initiative, which mandates commit messages containing an initial identifier. This helps to reconstruct a clean activity notion. Some of the labels have been cleaned by hand (for instance, in case of typos)</li> <li>the message of the commit</li> <li>the contributor's name</li> <li>the contributor's email (<strong>resource</strong>)</li> <li>a <strong>merge</strong> flag; <strong>True</strong> if the commit is a merge, <strong>False</strong> otherwise</li> <li>information related to the <strong>files</strong> edited by the commit (in case of renames, we track the new name)</li> <li>information related to the <strong>branches</strong> in which the commit appears</li> </ul> <p>Files and branches are two distinct object types in this log. Note that a commit might not be associated to any file. Conversely, a commit always appears in at least one branch.</p> <p>This event log has been extracted with the help of <a href="https://github.com/ishepard/pydriller">PyDriller</a>.</p> <p><strong>Properties</strong></p> <p>This event log has the following properties:</p> <table> <tbody> <tr> <td><strong>Property</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Events</td> <td>27847</td> </tr> <tr> <td>Activity Labels</td> <td>67</td> </tr> <tr> <td>Object Types</td> <td>2</td> </tr> <tr> <td>Objects (files)</td> <td>35392</td> </tr> <tr> <td>Objects (branches)</td> <td>119</td> </tr> </tbody> </table> <p><strong>Get started</strong></p> <p>Download the dataset, and position it in the folder of your Python script or console.</p> <p><em>pip install pm4py</em></p> <p>To manipulate object-centric logs programmatically, use the functionality of the <em>ocel</em> package <a href="https://pm4py.fit.fraunhofer.de/static/assets/api/2.7.5.1/api.html#object-centric-process-mining-pm4py-ocel">in the PM4Py library</a>. Additionally, check out the <a href="https://www.ocel-standard.org/beta/tool-support/overview/">tool support</a> for object-centric event logs!</p> <p><em>from pm4py import ocel</em></p> <p><strong>Acknowledgements</strong></p> <p>We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p>
Dataset (Pareto fronts and sets) for the Multi-objective pathfinding problem
<p>The dataset was used in several publications. In contains global Pareto fronts and sets for various instances of the problem.</p>
Reaching and grasping objects in depth for people with stereovision deficits and healthy controls.
<p>Decades of research into hand-object interaction and manipulation skills has yielded fundamental insights with applications in robotics and motor learning. Nevertheless, integrating visual function (especially binocular function, important to perceive depth) into this equation is crucial, forming a triangle between vision, reaching, and object manipulation.</p> <p> </p> <p>The ReGraD dataset provides kinematic data during hand-object interaction in monocular and binocular conditions at different depths and monocular/binocular conditions. It comprises two sub-datasets: ReGraD A (two measurements) can determine its test-retest reliability, whilst ReGraD B (one measurement) can characterize individuals with and without visual disorders. ReGraD includes 35 controls and 3 patients with amblyopia aged 6 to 35.</p> <p> </p> <p>The ReGraD dataset may aid to (1) gain insights into hand-object interaction under various eye conditions and depths, (2) assess reliability and reproducibility and (3) examine the effects of groups (control vs. patients) and age, among others. The ReGraD dataset contains raw data that can also be used to develop algorithms for data segmentation and data interpolation in the kinematic field.</p>
Figure 6 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 6. – Histological changes in testis development of Engraulis encrasicolus at the different reproductive maturity stages. A: Stage I, represented only by spermatogonia cells, inactive spermatogenesis, the number of the fields concerned in the biometric analysis N = 236. B: Stage II, active spermatogenesis with primary spermatogenium multiplication and tubular size increase, the number of the fields concerned in the biometric analysis N = 241. C: Stage III, active spermatogenesis, spermatozoa are concentrated in the light of the seminiferous tubules, the number of the fields concerned in the biometric analysis N = 125. D: Stage IV, spermiation, the number of the fields concerned in the biometric analysis N = 93. E: Stage V, seminiferous tubules are empty, spermatozoa are eliminated by release or resorption, the number of the fields concerned in the biometric analysis N = 988.
Figure 4 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 4. – Mean values of the gonadosomatic index (GSI, represented in percentage) in Engraulis encrasicolus male collected in the Gulf of Bejaia from October 2007 to May 2008. Data are represented as Mean ± SEM. (values indicates the number of fishes).
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.