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95 results for “Logistics”
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
TAMPAR: Visual Tampering Detection for Parcels Logistics in Postal Supply Chains
<p>TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our paper and for visual samples our <a href="https://a-nau.github.io/tampar/">project page</a>. Features are: </p><ul><li>>900 annotated real-world images with >2,700 visible parcel side surfaces</li><li>6 different tampering types</li><li>6 different distortion strengths</li></ul><p>Relevant computer vision tasks:</p><ul><li>bounding box detection</li><li>classification</li><li>instance segmentation</li><li>keypoint estimation</li><li>tampering detection and classification</li></ul><p>If you use this resource for scientific research, please consider citing our WACV 2024 <a href="https://arxiv.org/abs/2311.03124">paper</a> <i>"TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains".</i></p>
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Context-Aware Activity Recognition in Logistics (CAARL) – A optical marker-based Motion Capture Dataset
<p><strong>CAARL </strong>is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances.</p> <p>In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end).</p> <p>The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request.</p> <p>CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper:</p> <ul> <li>Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: <a href="https://doi.org/10.5281/zenodo.3862782">10.5281/zenodo.3862782</a></li> <li>Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a></li> </ul> <p> </p> <p><strong>If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: <a href="https://doi.org/10.3390/s22010134">10.3390/s22010134</a></strong></p>
Sensor-based Pallet Activity Recognition in Logistics (SPARL Version 2) - A multi-modal Dataset
<p>SPARL is a freely accessible data set for sensor-based activity recognition of pallets in logistics. The data set consists of 20 recordings from three scenarios. A description of the scenarios can be found in the protocol file.</p> <p>Four different sensors were used simultaneously for all recordings:</p> <ul> <li>MSR Electronics MSR 145 <ul> <li>Sampling rate 50 Hz</li> </ul> </li> <li>MBIENTLAB MetaMotionS <ul> <li>Sampling rate 100 Hz</li> </ul> </li> <li>Kistler KiDaQ Module 5512A <ul> <li>Sampling rate 100 kHz</li> <li>the raw data is also downsampled to 5 kHz and 20 kHz for easier processing </li> </ul> </li> <li>Holybro Flightcontroller PX4FMU <ul> <li>The board uses two accelerometers and two gyroscopes, all with a sampling rate of 1000 Hz <ul> <li>Accelerometer 1: IvenSense MPU6000 </li> <li>Accelerometer 2: STMicroelectronics LSM303D </li> <li>Gyroscope 1: IvenSense MPU6000</li> <li>Gyroscope 2: STMicroelectronics L3GD20</li> </ul> </li> </ul> </li> </ul> <p>The recordings were accompanied by three logitech Mevo Start cameras, of which all recordings are included anonymously in the data set. </p> <p>The videos were annotated by one person in each frame. For this purpose, the annotation tool SARA was used, which can be found <a href="../records/8189341">here</a>. The JSON schema used for annotation is also included in the SPARL dataset. The R code used our evaluation can be found in <a title="https://github.com/bommert/WGTL24" href="https://github.com/bommert/WGTL24">GitHub</a>.</p> <p>If you have any questions about the dataset, please contact: sven.franke@tu-dortmund.de</p> <p><strong>If you use this dataset for research, please cite the following paper: “Data-driven, sensor-based taxonomy for environmental life cycle assessment of pallets”, Nr. 20 (2024): Logistics Journal: Proceedings, DOI: <a href="http://dx.doi.org/10.2195/lj_proc_franke_en_202410_01" target="_blank" rel="noopener">10.2195/lj_proc_franke_en_202410_01</a></strong></p>
Dataset for Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Kłodawski Michał, Jachimowski Roland, & Chamier-Gliszczyński Norbert, 2024. „Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs”. Energies 17: 1–24. https://doi.org/10.3390/en17050985 - published online: 2024-02-20, which discusses the application of simulation in solving the problem of the overhead crane energy consumption using different container loading strategies in Urban Logistics Hubs.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset.</li> <li>Data_Crane.xlsx: Contains the input data used in the model for estimating crane energy consumption.</li> <li>Results_01.csv: Contains output data - Simulation results of energy consumption, and total average energy recovery for each scenario.</li> <li>Results_02.csv: Contains output data - Simulation results - mean values from the results of all scenario replications.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
XES Logistics And Transportation Dataset - Small (~1 Day)
<p><strong>Vienna Tram Line 71 (days 2022-12-06 to 2022-12-07): Delays, Weather, Trafic, Construction Sites</strong></p> <p>This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through <a href="http://cpee.org">https://cpee.org</a>.</p> <p>The data set contains raw data and refined and aggregated data in the XES SensorStream format <a href="https://arxiv.org/abs/2206.11392">https://arxiv.org/abs/2206.11392</a>.</p> <p>The dataset contains data from the following sources:</p> <ol> <li>Tram line delay data & construction sites in the vicinity of the tram line stops: <a href="https://digitales.wien.gv.at/open-data/">https://digitales.wien.gv.at/open-data/</a>. All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis).</li> <li>Traffic data in the vicinity of the tram line stops: <a href="https://developer.tomtom.com/store/maps-api/">https://developer.tomtom.com/store/maps-api/</a>. We use data gathered through the TomTom "Freemium" plan, which is covered by <a href="https://developer.tomtom.com/terms-and-conditions/">https://developer.tomtom.com/terms-and-conditions/</a>. To the best of our knowledge the gathered traffic flow data can be freely distributed and used for analysis purposes, as TomTom invites researchers (and business) to explore new applications.</li> <li>Weather data: <a href="https://openweathermap.org/">https://openweathermap.org/</a>. We collect data based on the "Free" plan, which is covered by a CC BY-SA 4.0 license (https://openweathermap.org/full-price#licenses).</li> </ol> <p> </p>
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50 in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50% of females reach functional sexual maturity (CL50).
F I G U R E 2 Fitted logistic curves with 95 in Circadian and seasonal flight activity differences between the sexes of the biocontrol agent Eadya daenerys (Hymenoptera: Braconidae) and the impact of host size on adult emergence
F I G U R E 2 Fitted logistic curves with 95% confidence intervals for the effect of Paropsisterna agricola beetle prepupal weight (mg) for three post-beetle prepupal outcomes (dead beetle prepupa, beetle or E. daenerys wasp).
Digitalisation to improve automated agro-export logistics: Comprehensive bibliometric analysis
<p><strong>Introduction/objective</strong>: Digitalization in logistics transcended in the search for continuous improvement of good process optimization. This study aims to know the effectiveness of digitization implemented by companies to improve the automated logistics of cross-border trade in the agricultural sector.</p> <p><strong>Methodology</strong>: A bibliometric analysis was generated, exploring the evolution of the state of the art through Scopus, WOS and Dimensions databases, in order to select relevant empirical studies on digitization and automated logistics, using quality criteria and the application of the Prisma 2020 flowchart.</p> <p><strong>Results:</strong> Since 2017, there were signs of increased interest from researchers, highlighting authors such as Zoubek, Kumar and Ghobakhloo. This review provided insight into how digitization contributes to cost and time optimization in the logistics chain. Designing public policies allows a better integration of technology, such as IoT and AI. It identified 3 important blocks that have contributed to the effectiveness of digitization in automated logistics, they refer to “Impact of digitization on logistics efficiency and supply chain”, “Technology integration and automation in cross-border logistics” and “Governance, policy and social considerations in logistics digitization”.</p> <p><strong>Conclusions</strong>: Digitalization has been a fundamental element to improve logistics and make it autonomous within cross-border trade, allowing technology to get involved, integrating digital technologies such as artificial intelligence (AI), which reduced obstacles affecting the supply chain.</p>
Toward consumer-centric sustainability development model: A reverse logistics multi-criteria decision-making analysis datasets
<p>Toward consumer-centric sustainability development model: A reverse logistics multi-criteria decision-making analysis datasets</p>
OpenPack: Public multi-modal dataset for packaging work recognition in logistics domain
<p><strong>OpenPack</strong> is an open-access logistics dataset for human activity recognition, which contains human movement and package information from 16 subjects in four scenarios. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing. The package information includes the size and number of items included in each packaging job. </p> <p>In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. 12 workers with previous packaging experience and 4 without experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the different scenarios, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while wearing 4 ATR IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the hand terminal used to register product, packet, and address label codes as well as package details that can be useful to relate operations to specific packages.</p> <p>The 4 different scenarios include; sequential packing, worker-decided sequence changes, pre-ordered item packing, and time-sensitive stressors. Each of the subjects performed 20 packing jobs in 5 work sessions for a total of 100 packing jobs. <strong>53+</strong> hours of packaging operations have been labeled into 10 global operation classes and 16 sub-action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations. </p> <p>You can find information on how to use this dataset at: <a href="https://open-pack.github.io/">https://open-pack.github.io/</a>. For details on how this dataset was collected please check the following publication "OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments" <a href="https://doi.ieeecomputersociety.org/10.1109/PerCom59722.2024.10494448">10.1109/PerCom59722.2024.10494448</a>.</p> <p> </p> <p><strong>Full Dataset</strong></p> <p>In this repository, the data and label files are contained in separate files for each worker. Each worker's file contains; IMU, E4, 2d keypoint, 3d keypoint, annotation, and system-related<em> </em>data<em>.</em></p> <p><em><strong>Preprocessed Dataset (IMU with operation and action Labels)</strong></em></p> <p>We have received many comments that it was difficult to combine multiple workers' IMU and annotation data. Therefore, we have created several CSV files containing the four IMU's sensor data and the operation labels in a single file. These files are now included as "imu-with-operation-action-labels.zip". </p> <p><em><strong>Preprocessed Dataset (Kinect 2D and 3D keypoint data with operation and action Labels)</strong></em></p> <p>We have received several requests for a preprocessed dataset containing only specific types of keypoint data with its assigned operation and action labels. Two new preprocessed files have been added for 2D and 3D keypoint data extracted from the frontal view Kinect camera. These files are:</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-2d-kpt-with-operation-action-labels.zip</a>", and</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-3d-kpt-with-operation-action-labels.zip</a>".</p> <p> </p> <p>Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release <strong>[1.1.0]</strong> was uploaded on 24/04/2024. </p> <p><strong><em>Changes LOG:</em></strong></p> <ul> <li>v1.0.0: Add tutorial preprocessed dataset for IMU data with operation labels.</li> <li>v1.1.0: Update preprocessed datasets. (Include Kinect 2d and 3d keypoint data with Operation and action labels)</li> </ul> <p> </p> <p><strong>We hosted an activity recognition competition using this dataset (OpenPack v0.3.x) awarded at a PerCom 2023 Workshop! The task was very simple: Recognize 10 work operations from the OpenPack dataset. You can refer to this website for coding materials relevant to this dataset. </strong><a href="https://open-pack.github.io/challenge2022"><strong>https://open-pack.github.io/challenge2022</strong></a></p>
Fig. 2 in Does the Mean Individual Biomass (MIB) of carabids as a bioindicator of forest succession follow a logistic function? - Examples from Western German beech and Polish Scots pine forests
Fig. 2. Logistic regression curve – Relationship between age of the Polish Scots pine stands (years) and mean individual biomass of carabids (mg)
Fig. 1 in Does the Mean Individual Biomass (MIB) of carabids as a bioindicator of forest succession follow a logistic function? - Examples from Western German beech and Polish Scots pine forests
Fig. 1. Logistic regression curve – Relationship between age of the Western German beech stands (years) and mean individual biomass of carabids (mg)
Dataset: Covenant Logistics Group, Inc. (CVLG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: MingZhu Logistics Holdings Limited (YGMZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Universal Logistics Holdings, Inc. (ULH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Pangaea Logistics Solutions, Ltd. (PANL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Proficient Auto Logistics, Inc. (PAL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Jayud Global Logistics Limited (JYD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.