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95 results for “Logistics”
Dataset: Industrial Logistics Properties Trust (ILPT) 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.
Supporting data for: A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits
<p>The three following datasets are related to the article <em>A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits</em> (Santos, Guyomarc'h, Rmoutilova, & Bruzek, 2019):</p> <ul> <li><strong>data_refPELVIS_Santos2019AJPA.csv</strong>: is described as the "reference sample" of 592 ossa coxae in the article. This is the learning dataset available in the <a href="https://gitlab.com/f.santos/pelvis">PELVIS R package</a></li> <li><strong>data_518RightBones_Santos2019AJPA.csv</strong>: the dataset of 518 right ossa coxae used to discuss asymmetry and the impact of lateralization on the sex estimates produced by PELVIS</li> <li><strong>data_3D_Santos2019AJPA.csv</strong>: the virtual coxal data acquired through 99 CT-scan images</li> </ul>
SELIS platform for pan-European logistics applications datasets
<p>This publication comes as a result of the SELIS H2020 project (<a href="http://www.selisproject.eu">http://www.selisproject.eu</a>). The provided datasets are anonymized samples of the real data which describe some of the use cases we have encountered.</p> <p>In the interest of the Open Research Data Pilot, this publication (<a href="https://github.com/selisproject/selis-node-connectors">https://github.com/selisproject/selis-node-connectors</a>) provides a basic implementation of connectors used in real world applications for supply chain participants to connect and funnel their data to the SELIS Community Node (SCN). The prototype of the SELIS big data analytics and machine learning infrastructure is also openly available (<a href="https://github.com/selisproject/bda">https://github.com/selisproject/bda</a>).</p> <p>Along with the connectors, obfuscated/test data is also provided for each solution. The connectors all follow the same approach, which is parsing the data provided, transforming them into a SCN-compatible data exchange model and publishing them to the SCN under a specific configuration. The samples provided cover two use cases:</p> <p><strong>Adria Kombi Data set: </strong>This anonymized data set which includes 24 csv files describes a use case developed for the SELIS project. It involves the (as accurate as possible) estimation of time of arrival (ETA) for individual freight trains using historical data and live feeds.</p> <p><strong>SONAE Data set: </strong>This anonymized data set which includes 2 csv files describes a different use case developed for the same project. This specific use case involves the automatic generation of suggested order forecasts for a retailer (SONAE) given stock level data from different warehouses and information such as lead time (minimum time required from order till delivery) per product and supplier, delivery days etc.</p> <p> </p> <p><em>For a more detailed description of the data please refer to the README.md files included in the downloadable zipped directories.</em></p> <p> </p> <p> </p>
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 3 in Instances for "Sugarcane Harvest Logistics in Brazil"
Figure 3. - Linear regressions of morphometrics on total length (TL) for the eighteen Narcine bancrofti presenting normal pigmentation (black circles) as well as the single leucistic individual (open circle) included in this study. Abbreviations for measurements are as described in De Carvalho and Séret (2002).
Logistic Activity Recognition Challenge (LARa Version 03) – A Motion Capture and Inertial Measurement Dataset
<p><strong>LARa</strong><strong> Version 03</strong> is a freely accessible logistics-dataset for human activity recognition. In the “Innovationlab Hybrid Services in Logistics” at TU Dortmund University, two picking and one packing scenarios with 16 subjects were recorded using an optical marker-based Motion Capturing system (OMoCap), Inertial Measurement Units (IMUs), and an RGB camera. Each subject was recorded for one hour (960 minutes in total). All the given data have been labelled and categorised into eight activity classes and 19 binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221 unique attribute representations.</p> <p>The <strong>dataset was created according to the guideline</strong> of the following paper: “A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition”, PerCom, 2023 DOI: <a href="http://dx.doi.org/10.1109/PerComWorkshops56833.2023.10150401">10.1109/PerComWorkshops56833.2023.10150401</a></p> <p>The LARa Version 03 contains a <strong>new Annotation tool </strong>for OMoCap and RGB Videos, namely, the <strong>S</strong>equence <strong>A</strong>ttribute <strong>R</strong>etrieval <strong>A</strong>nnotator (<strong>SARA</strong>). SARA, developed and modified based on the LARa Version 02 annotation tool, includes desirable features and attempts to overcome limitations as found in the LARa annotation tool. Furthermore, few features were included based on the explorative study of previously developed annotation tools, see journal. In alignment with the LARa annotation tool, SARA focuses on OMoCap and video annotations. However, it is to be noted that SARA was not intended to be a video annotation tool with features such as subject tracking and multiple subject annotations. Here, the video is considered to be a supporting input to the OMoCap annotation. We would recommend other tools for pure video-based multiple-human activity annotation, including subject tracking, segmentation, and pose estimation. There are different ways of <strong>installing the annotation tool</strong>: Compiled binaries (executable files) for Windows and Mac can be directly downloaded from here. Python users can install the tool from https://pypi.org/project/annotation-tool/ (PyPi): “pip install annotation-tool”. For more information, please refer to the “Annotation Tool - Installation and User Manual”.</p> <p><strong>Upgrade:</strong></p> <ul> <li>Annotation tool (<strong>SARA</strong>) added (for Windows and MacOS, including an installation and user manual)</li> <li>Neural Networks updated (can be used with the annotation tool)</li> <li>OMoCap data: <ul> <li>Annotation errors corrected</li> <li>Annotations reformatted, fitting the SARA annotation tool</li> <li>“additional annotated data” extended</li> <li>“Markers_Exports” added</li> </ul> </li> <li>IMU data (MbientLab and MotionMiners Sensors) <ul> <li>Annotation errors corrected</li> </ul> </li> <li>README file (protocol) updated and extended</li> </ul> <p> </p> <p><strong>If you use this dataset for research, please cite the following paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes</strong><strong>”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a>.</strong></p> <p><strong>If you use the Mbientlab Networks, please cite the following paper: “From Human Pose to On-Body Devices for Human-Activity Recognition”, 25th International Conference on Pattern Recognition (ICPR), 2021, DOI: </strong><a href="https://doi.org/10.1109/ICPR48806.2021.9412283"><strong>10.1109/ICPR48806.2021.9412283</strong></a><strong>.</strong></p> <p>For any questions about the dataset, please contact Friedrich Niemann at friedrich.niemann@tu-dortmund.de.</p>
Vertebrate-habitat relationships: Logistic regression models predict probability of occurrence of bird and small mammal species in western Oregon
Logistic regression models predicting probability of occurrence of bird and of small-mammal species were produced using animal-habitat data sets from throughout western Oregon (Garman and Cole 1999 - Vertebrate Habitat Relationships Data Bank (VHRDB), Report to Coastal Landscape Analysis and Modeling Study). Regression coefficients, variables, and metrics related to model predictions are provided here under Entity 1, and in VHRDB as VERTLOGR.
Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach
All data are used in the following manuscript which is in prep Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach Calef et al.
STRIDE Project D5: Overcoming Barriers to Freight and Logistics Firm Collaboration with Urban Planning
<p>This dataset contains a list of Reddit posts and the corresponding threads for those posts resulting from targeted searches of four delivery-related subreddits conducted in Fall 2021. We used this dataset to understand driver practices in and views on delivering in urban areas and the challenges they face.</p> <p>It was downloaded using Reddit's API through the RedditExtractoR package for the R programming language. Re-use of this data is subject to Reddit API terms.</p>
Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified
<p>Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.</p>
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
<p>In this paper, the performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best-performing model for zero-inflated count data due to rare events.</p>
LORIS: a logistic regression-based immunotherapy-response score
<p>This is a repository of input data and code for reproducing the paper titled "LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic, and genomic features" by Chang et al. (Nature Cancer 2024).</p> <p>Briefly, in this work, Chang et al. developed a new clinical score called the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent and concise 6-feature logistic regression model. LORIS outperforms previous signatures in ICB response prediction and can identify responsive patients, even those with low tumor mutational burden or tumor PD-L1 expression. Importantly, LORIS consistently predicts both objective responses and short-term and long-term survival across multiple cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling more precise patient stratification across the board. As the method is accurate, interpretable, and only utilizes a few readily measurable features, it could help improve clinical decision-making practices in precision medicine to maximize patient benefit.</p>
Data related to feedstock supply, logistics and engineering work for Pilot 5 (Biosolvents) in WaysTUP! project
<p><span>The objectives of this dataset were to describe the feedstock supply, logistics and engineering work carried out for PILOT 5. A description of the design, construction and final start-up of the pilot is also included. </span><span>PILOT 5 is located in Athens (Greece). Feedstock material is source-separated biowaste provided by the Municipality of Vari-Voula-Vouliagmeni under the supervision of SUST. The necessary logistics for continuous and stable feedstock supply were arranged by SUST and verified by NTUA. </span><span>PILOT 5 is a pre-existing installation developed in the framework of the LIFE WASTE2BIO project (LIFE11 ENV/GR/000949 and</span><span> is installed in the premises of NTUA</span><span>. For the purposes of the WaysTUP!, this prototype plant was upgraded in order to meet the project’s needs. </span><span>It includes a dehydration unit, a bioconversion unit and a distillation unit for the recovery of the produced ethanol. The working volume of the bioreactors is 400L and up to</span><span> </span><span>80kg/d dried biowaste could be treated depending on the selected operational parameters. </span></p>
Cycle Logistics in Rio de Janeiro - Survey Data
<p>Data collected by the NGO Transporte Ativo (Brazil) from a survey of businesses that used cycle logistics (bicycles and tricycles for freight delivery) in nine commercial areas of Rio de Janeiro. Field researchers administered the survey in December 2014 and January 2015. Researchers canvassed every block of 1 square km in nine commercial centers of Rio to identify businesses that used cycle logistics.</p>
Figure 1 in Instances for "Sugarcane Harvest Logistics in Brazil"
Figure 1. - Capture location (black circle) of Narcine bancrofti examined in this study.
Figure 2 in Instances for "Sugarcane Harvest Logistics in Brazil"
Figure 2. - Leucistic Narcine bancrofti surrounded by conspecifics presenting normal pigmentation.
Understanding and Modeling Middle-Mile Logistics Automation
Open the record for dataset details and reuse information.
Logistics and Marine Operations datasets
<p>DTOceanPlus will accelerate the commercialisation of the Ocean Energy sector by developing and demonstrating an open-source suite of design tools for the selection, development, deployment, and assessment of ocean energy systems (including sub-systems, energy capture devices and arrays).</p> <p>To support the designs of the installation, maintenance, and decommissioning phases of ocean energy projects, generated by the Logistics and Marine Operations (LMO) module, catalogues of open data were produced as part of the DTOceanPlus project. These catalogues include datasets of vessels, port terminals, offshore equipment, but also operation-related datasets. These operational datasets in turn include recommended fleet selections for specific operations (e.g. towing), as well as operation and activity flowcharts. These have also been made publicly available under a Creative-Commons Attribution 4.0 International (CC BY 4.0) licence, to be used for other purposes.</p>
Isotonic Solution Administration Logistical Testing
ClinicalTrials.gov study NCT02345486. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Logistics of zoning, zoning for logistics: Toward healthy and equitable development for urban freight
Open the record for dataset details and reuse information.
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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.