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2,015 results for “context”
Synth-Yard-MCMOT-1 – A synthetic multi camera multi object dataset in the context of yard logistics
<p>The dataset "Synth-Yard-MCMOT-1" is a novel image dataset for multi-camera multi-object tracking (MCMOT). It is the first of its kind to be generated in a virtual environment with the main focus on the tracking of trucks in yard logistics environments. The dataset consists of a total of 12,008 images generated by eight different cameras. The images contain 44,232 bounding boxes and segmentation masks and 52 individual tracks. Additionally, we provide a ninth camera, which is used to generate unified ground-truth information for the whole scene from an orthographic, top-down perspective comparable to a bird’s eye or map-view. The purpose of this dataset is to provide yard management systems with relevant data, which can be employed when aiming to determine the exact position of a truck and specifically identifying which gateway or designated parking spot it is located in. <br>Additionally in this dataset we added the annotations in the COCO-style format as well as the raw image-data.<br>Initial benchmarks for single-camera tracking demonstrate a mean IDF1 score of 0.96 and a mean MOTA score of 0.94, laying the baseline for computing world coordinates via MCMOT.<br><br><span>This work is part of the project “Silicon Economy Logistics </span><span>Ecosystem” which is funded by the German Federal Ministry </span><span>of Transport and Digital Infrastructure. <br></span><span>This research has partly been funded by the Federal Ministry </span><span>of Education and Research of Germany and the state of North-</span><span>Rhine Westphalia as part of the Lamarr-Institute for Machine </span><span>Learning and Artificial Intelligence. <br></span><span>We would like to thank our colleagues Julian Hinxlage, </span><span>Oleg Belov, Habib Kilic, Nicole Wagner-Hanl, Leona Cerimi, </span><span>Antonia Ponikarov and Nico Lindemann for their support.</span></p> <p><span>The code to create this dataset can be found here: </span><span>https://git.openlogisticsfoundation.org/silicon-economy/base/ml-toolbox/</span><span>synth-data-mcmot</span></p> <div> <div> </div> <div> </div> </div> <p> </p>
Motor context dominates output from Purkinje cell functional regions during reflexive visuomotor behaviors
<p>Example electrophysiology datasets</p> <p>Variables are stored as .mat (MATLAB) and electrophysiological traces as .h5 files</p>
Dataset for 'Context-dependent dispersal mutualisms mediate coexistence of unequal competitors in a metapopulation'
<div> <div>This repo contains code and data for manuscript titled 'Context-dependent dispersal mutualisms mediate coexistence of unequal competitors in a metapopulation'.</div> <div>Mutualisms are context-dependent interactions, with their costs and benefits fluctuating in space and time. Due to their impact on species growth, mutualisms can mediate the coexistence of competing species. One such context-dependent mutualism is endozoochory, where frugivores acquire nutrition by predating on fruits while dispersing their seeds via excretion. The presence of costs and benefits in these mutualisms at different spatial scales and of different currencies (i.e. local predation vs global dispersal) necessitates developing mechanistic models to understand their dynamics. Here, we study how frugivory could engender coexistence between two competing plant species across connected habitats. By combining local population dynamics with ideas from metapopulation theory, we decipher the conditions under which dispersive mutualisms engender competitive coexistence across habitats. We find that frugivore dispersal, fruit consumption rates, and degree of specialisation all contribute to shaping the coexistence of competing fruiting plant species across patches.</div> </div>
Coral: a dual context-aware basecaller for nanopore direct RNA sequencing - DRS test dataset
<p>Raw RNA sequencing data from Oxford Nanopore. The dataset is comprised of 100k randomly selected reads.</p> <p>Zebrafish's original data is from ENA accession ERX9536682. </p>
Data from 'Global distribution and contexts of interactions between humans and non-human primates: a systematic review'
<p>[OLD VERSION - This material is not suitable for use]</p> <p>The dataset presented here accompanies the systematic review titled "Global distribution and contexts of interactions between humans and primates: a systematic review", published in the journal <em>People and Nature</em>. This dataset includes key information from the reviewed articles, such as the titles, authors, journals of publication, publication year, geographic area of the study (coutnry and continent), type of human and non-human primate interaction described, and additional relevant variables extracted for analysis (e.g., contexts of the contacts, uses of social sciences in the studies, diseases studied at the human and non-human primate interface). </p>
Data from: A Tournaisian (earliest Carboniferous) conglomerate-preserved non-marine faunal assemblage and its environmental and sedimentological context
A conglomerate bed from the Tournaisian Ballagan Formation of Scotland preserves a rich array of vertebrate and other non-marine fossils providing an insight into the wider ecosystem and palaeoenvironment that existed during this pivotal stage of Earth history. It challenges hypotheses of a long-lasting post-extinction trough following the end-Devonian extinction event. The fauna recovered includes a wide size range of tetrapods, rhizodonts and dipnoans, from tiny juveniles or small-bodied taxa up to large adults, and more than one taxon of each group is likely. Some fauna, such as actinopterygians and chondrichthyans, are rare as macrofauna but are better represented in the microfossil assemblage. The fauna provides evidence of the largest Carboniferous lungfish ever found. The specimens are preserved in a localised, poorly-sorted conglomerate which was deposited in the deepest part of a river channel, the youngest of a group of channels. In addition to the fossils (micro- and macro-), the conglomerate includes locally-derived clasts of palaeosols and other distinctive elements of the surrounding floodplains. Charcoal fragments represent stem and possible trunk tissue from arborescent pteridosperms. Preservation of the fossils indicates some aerial exposure prior to transport, with abrasion from rolling. The findings presented here contrast with other published trends in vertebrate size that are used to interpret a reduction in maximum sizes during the Tournaisian. The richness of the fauna runs counter to the assumption of a depauperate non-marine fauna following the end-Devonian Hangenberg event, and charcoal content highlights the occurrence of fire, with the requisite levels of atmospheric oxygen during that stage.
Figure 1 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 1 - A Sir William Kirby, date and age uncertain B Robert Grant in 1852, aged 59.
Figure 8 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 8 - Report of the meeting discussing trinomial nomenclature.
Figure 7 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 7 - A Elliot Coues B Joel A. Allen
Figure 3 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 3 - Early draft of the Stricklandian Code with handwritten comments by Darwin and Ogilby.
Figure 2 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 2 - Hugh Edwin Strickland A age 26 B aged 42, when he died.
Figure 9 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 9 - Announcement of Sherborn's plans for his hugely ambitious project.
Figure 5 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 5 - John Edward Gray, the chief Keeper of Zoology at the British Museum (Bloomsbury).
Figure 6 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 6 - Charles Darwin and a plate from his work on barnacles.
Figure 4 from: McOuat G (2016) Naming and Necessity: Sherborn's Context in the 19th Century. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 57–69. https://doi.org/10.3897/zookeys.550.7399
Figure 4 - Stricklandian Code – discussion of the philosophy of language.
Source Data for the publication: Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in Acute Promyelocytic Leukemia
<p>Source Data for the publication: Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in Acute Promyelocytic Leukemia</p>
"Understanding leisure constraints in tourism context: An investigation of Russian tourists' constraints in participating in recreational fishing in Finland", SPSS dataset
<p>SPSS Dataset for journal article titled ""Understanding leisure constraints in tourism context: An investigation of Russian tourists' constraints in participating in recreational fishing in Finland", SPSS dataset".<br> <br> <strong>Article abstract</strong><br> Research on leisure constraints brings insight on consumer preferences and benefits tourism service providers and developers. Understanding why people seek or avoid destination activities, such as recreational fishing, helps to develop strategies to address these constraints and to modulate participation. This case study utilizes the conceptual model of leisure constraints and extends leisure constraints research to an international setting, by focusing on Russian tourists’ recreational fishing activities in Finland. It also explores the customers’ views on the role of water quality in fishing activity. A panel data survey with 457 Russian tourists visiting Finland has showed that leisure constraints vary across recreational fishing participants, non-participants and potential participants. While Russian tourists, who have been fishing in Finland, barely express any constraints, intrapersonal and structural constraints related to knowledge about Finnish fishing environment and information about service providers received the highest ratings among non-participants and potential participants. The language barrier has become the most highly rated constraint across all groups. While females are generally more constrained than males, a closer look to gender differences among non-participants and potential participants shows that males are more constrained across several statements. This case study contributes to the leisure constraints literature, particularly applying the constraints framework to the context of international tourism and to water quality.</p>
[Dataset] Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context
<p>Repository mining of bug fixes from version control systems like GitHub is a challenging problem as far as the precision of the bug context is concerned before and after the fix. Coupled with this, identification of the type of the bug fix goes a long way towards creating high quality datasets that can be used for several downstream tasks. However, existing bug fix datasets suffer from the following limitations that dilute the data quality. Firstly, they do not focus on multilingual projects in their entirety given that most open-source projects are now multilingual. Secondly, the granularity of the bug fixes are considered only at the function/method level without specifying line/statement level information. Thirdly, bug fixes lying within the scope of a source file but outside any of its constituent functions have not been examined. In this paper, we propose a solution to overcome the aforementioned limitations by introducing a novel and extensive dataset named Minecraft. With a size of 28.8GB (considering 416 GitHub projects encompassing programming languages such as C, C++, Java, and Python, 2.2M commits, 3.29M bug-fix pairs), Minecraft surpasses the existing datasets by 4-fold enlargement in terms of data availability. We believe Minecraft would serve as a valuable resource for various stakeholders in the software development and research communities, empowering them to improve software quality, develop innovative bug detection and auto-fix techniques, and<br> advance the field of software engineering.<br> </p>
PHD Thesis: Graph Set Data Mining - Clustering and Pattern Mining in the Context of Cheminformatics - Evaluation Data
<p>Evaluation data for the PHD thesis:</p> <p>Graph Set Data Mining<br> Clustering and Pattern Mining in the Context of Cheminformatics</p> <p>zur Erlangung des Grades eines<br> Doktors der Naturwissenschaften<br> der Technischen Universität Dortmund<br> an der Fakultät für Informatik</p> <p>von</p> <p>TIll Schäfer</p>
Couple & Family Contexts
ClinicalTrials.gov study NCT01631721. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.