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144 results for “graph data”
Models and data for "Modelling and controlling a Segway using bond graphs"
<p>The files here contain models and data for the technical report "Modelling and controlling a Segway using bond graphs".</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Aedes Sensed Data Graph
<p>The presented graph is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The graph is part of Indoor Monitoring Framework sensed data (temperature and humidity) developed for Living Prototype Exhibition at Aedes Forum in Berlin for a period of 35 days. </p>
User Unfairness Mitigation by Graph Data Augmentation in Recommendation
<p>Dataset for the paper submission `User Unfairness Mitigation by Graph Data Augmentation in Recommendation`.</p> <p>The included datasets are: MovieLens-1M, Last.FM 1K.</p> <p> </p>
Dataset of A User-driven Hybrid Neuro-symbolic Approach for Knowledge Graph Creation from Relational Data
<p>This dataset contains the following:</p> <p>1. achieved percentage values of the generated RML rules using LXS and manually</p> <p>2. basic information about the example used and with which creation type users started</p> <p>3. all answers of users to the User Experience Questionnaire</p> <p>4. Answers to the structured part of the user interview</p>
Data and code from: PathVGAE: A path-based variational graph autoencoder framework for ranking centrality in road networks
Open the record for dataset details and reuse information.
Data from: On the use of random graphs in analysing resource utilization in urban systems
Open the record for dataset details and reuse information.
Data from: Knowledge graphs for seismic data and metadata
Open the record for dataset details and reuse information.
Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Beyond 2022 Knowledge Graph Sample Data
<p>This dataset contains a CSV file and an RDF Turtle file. Both files contain information on a few people mentioned in the Irish Exchequer Payments 1270-1326, a book written by Connolly, P and published by the Irish Manuscripts Commission in 1998. A historian transcribed those people in a CSV file, subsequently transformed into RDF using an R2RML mapping. This dataset contains the records and the output of a handful of people transcribed in this way. This dataset illustrates how the Beyond 2022 project avails of CIDOC-CRM to populate its knowledge graph.</p> <p>Beyond 2022 is funded by the Government of Ireland, through the Department of Culture, Heritage and the Gaeltacht, under the Project Ireland 2040 framework. The project is also partially supported by the ADAPT Centre for Digital Content Technology under the SFI Research Centres Programme (Grant 13/RC/2106).</p>
CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure (video)
Full video presentation of the paper: CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure.<br><br>Appears in Session 5 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Supplementary data – OSH knowledge graph (OSH-KG)
<div> <div> <div> <p>Additional materials for the paper "Obtaining Occupational Safety and Health Insights with a Knowledge Base" that introduces the Occupational Safety and Health Knowledge Graph (OSH-KG).</p> <h2>Content</h2> <ul> <li><code>ontology.tar.gz</code> – the OSH-KG ontology. The archive contains both the individual ontology files and a merged version.</li> <li><code>queries.tar.gz</code> – SPARQL queries used in the paper, along with their results.</li> <li><code>rules.tar.gz</code> – SHACL-AF reasoning rules used in the paper.</li> <li><code>assist.tar.gz</code> – vocabulary, reasoning rules, and queries for integrating OSH-KG with data from the <a href="https://assist-iot.eu/">ASSIST-IoT project</a>.</li> <li><code>*.nq.gz</code> / <code>*.nt.gz</code> – individual named graphs of OSH-KG in the N-Quads format.</li> <li><code>*.jelly.gz</code> – individual named graphs of OSH-KG in the <a href="https://w3id.org/jelly">Jelly format</a>.</li> <li><code>code.tar.gz</code> – code in Python (Jupyter notebooks) used to generate the vocabularies and RDF translations of Eurostat data.</li> </ul> <h2>License</h2> <p>Please see LICENSE.md for details. Different resources in this repository have different licenses, but are generally freely licensed.</p> </div> </div> </div>
Data of decomposition, topology, and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes
<p>Data of decomposition, topology and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes</p>
Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Results of Multi-Perspective Concept Drift Detection on BPIC17 data using Event Knowledge Graphs
<p>Extensive results of multi-perspective concept drift detection performed on the BPI Challenge 2017 dataset retrieved using the related implementation from Github: <a href="https://github.com/multi-dimensional-process-mining/ekg-bpic17-concept-drift-detection-multi-perspective">https://github.com/multi-dimensional-process-mining/ekg-bpic17-concept-drift-detection-multi-perspective</a></p> <p> </p>
African Red Slip Ware Digital (ARS3D) - Graph-Data
<p>Characteristic of the North African bowls, plates, and jugs are their pictorial decorations applied mainly by appliqués and stamps. As mass-produced image carriers and everyday objects, the ARS spread throughout the empire.</p> <p>The range of motifs includes mythological scenes as well as scenes from the Old and New Testament, circus, arena and hunting scenes as well as fish and plant motifs. The appliqués-decorated pottery thus provides insights into Late Antique imagination and its changes, as well as into the economic history of the period between the 3rd and 5th centuries AD in North Africa.</p> <p>Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.</p> <p>Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.</p> <p>The developed ontology uses CIDOC CRM and various extensions. The data mainly consists of objects (E24), on which e.g. applications (features, E25) can be found. These objects can be described semantically, e.g. by the shape or time epoch. Features are described with observations, e.g. a human type man standing and wearing a beard. These observations lead to interpretations whose arguments can be observations.</p>
Extra Data for "Analyzing Phylogenetic Trees with a Tree Lattice Coordinate System and a Graph Polynomial"
<p>This dataset includes extra data generated or used in the experiments of the paper "Analyzing Phylogenetic Trees with a Tree Lattice Coordinate System and a Graph Polynomial".</p>
Code, data and supplementary material from: Analyzing phylogenetic trees with a tree lattice coordinate system and a graph polynomial
<p>This dataset includes code, used and generated data for the experiments in the paper Analyzing Phylogenetic Trees with a Tree Lattice Coordinate System and a Graph Polynomial, and the supplementary material of the paper. The paper introduces representation and comparison methods for rooted unlabelled phylogenetic trees based on a tree lattice that serves as a coordinate system for rooted binary trees with branch lengths and a graph polynomial that fully characterizes tree shapes. The methods provide fast, accurate, likelihood-free, distance-based appraoches to analyzing phylogenetic trees and allow analysis for large trees with more than a thousand taxa. </p> <p>The dataset has three sections. The first section is the implementation of the introduced methods for phylogenetic tree analysis as an R package, inlcuding the source code and the binary of the package. This packege provides basic functions for tree analysis and can be used or edited for related analysis. The secoond section includes all code for analyses performed in the the paper, and all the data used and generated in the analyses. The third section contains the supplementary material for the paper, which includes supplememntary results and figures. For details, please refer to the paper and the README file.</p>
Planar Graph Classifier - Result Data
<p>CSV file containing loss and accuracy of the Planar Graph Classifier evaluation on the test set.</p>
Data for paper "Graph Deep Learning Model for Mapping Mineral Prospectivity"
<p>Four prospecting information, namely, NE- and NW- trending faults, Agno Batholithic pluton margins, and porphyry intrusive contacts for mineral prospectivity mapping in Baguio district, Philippines.</p>
ScienceDex guides
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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.